import { typeOf } from '@ember/utils';
import { A } from '@ember/array';
import Mixin from '@ember/object/mixin';
import { camelize } from '@ember/string';
import { set, get } from '@ember/object';
import { warn } from '@ember/debug';
/**
## Using Embedded Records
`DS.EmbeddedRecordsMixin` supports serializing embedded records.
To set up embedded records, include the mixin when extending a serializer,
then define and configure embedded (model) relationships.
Note that embedded records will serialize with the serializer for their model instead of the serializer in which they are defined.
Below is an example of a per-type serializer (`post` type).
```app/serializers/post.js
import DS from 'ember-data';
export default DS.RESTSerializer.extend(DS.EmbeddedRecordsMixin, {
attrs: {
author: { embedded: 'always' },
comments: { serialize: 'ids' }
}
});
```
Note that this use of `{ embedded: 'always' }` is unrelated to
the `{ embedded: 'always' }` that is defined as an option on `DS.attr` as part of
defining a model while working with the `ActiveModelSerializer`. Nevertheless,
using `{ embedded: 'always' }` as an option to `DS.attr` is not a valid way to setup
embedded records.
The `attrs` option for a resource `{ embedded: 'always' }` is shorthand for:
```js
{
serialize: 'records',
deserialize: 'records'
}
```
### Configuring Attrs
A resource's `attrs` option may be set to use `ids`, `records` or false for the
`serialize` and `deserialize` settings.
The `attrs` property can be set on the `ApplicationSerializer` or a per-type
serializer.
In the case where embedded JSON is expected while extracting a payload (reading)
the setting is `deserialize: 'records'`, there is no need to use `ids` when
extracting as that is the default behavior without this mixin if you are using
the vanilla `EmbeddedRecordsMixin`. Likewise, to embed JSON in the payload while
serializing `serialize: 'records'` is the setting to use. There is an option of
not embedding JSON in the serialized payload by using `serialize: 'ids'`. If you
do not want the relationship sent at all, you can use `serialize: false`.
### EmbeddedRecordsMixin defaults
If you do not overwrite `attrs` for a specific relationship, the `EmbeddedRecordsMixin`
will behave in the following way:
BelongsTo: `{ serialize: 'id', deserialize: 'id' }`
HasMany: `{ serialize: false, deserialize: 'ids' }`
### Model Relationships
Embedded records must have a model defined to be extracted and serialized. Note that
when defining any relationships on your model such as `belongsTo` and `hasMany`, you
should not both specify `async: true` and also indicate through the serializer's
`attrs` attribute that the related model should be embedded for deserialization.
If a model is declared embedded for deserialization (`embedded: 'always'` or `deserialize: 'records'`),
then do not use `async: true`.
To successfully extract and serialize embedded records the model relationships
must be setup correcty. See the
[defining relationships](/guides/models/defining-models/#toc_defining-relationships)
section of the **Defining Models** guide page.
Records without an `id` property are not considered embedded records, model
instances must have an `id` property to be used with Ember Data.
### Example JSON payloads, Models and Serializers
**When customizing a serializer it is important to grok what the customizations
are. Please read the docs for the methods this mixin provides, in case you need
to modify it to fit your specific needs.**
For example review the docs for each method of this mixin:
* [normalize](/api/data/classes/DS.EmbeddedRecordsMixin.html#method_normalize)
* [serializeBelongsTo](/api/data/classes/DS.EmbeddedRecordsMixin.html#method_serializeBelongsTo)
* [serializeHasMany](/api/data/classes/DS.EmbeddedRecordsMixin.html#method_serializeHasMany)
@class EmbeddedRecordsMixin
@namespace DS
*/
export default Mixin.create({
/**
Normalize the record and recursively normalize/extract all the embedded records
while pushing them into the store as they are encountered
A payload with an attr configured for embedded records needs to be extracted:
```js
{
"post": {
"id": "1"
"title": "Rails is omakase",
"comments": [{
"id": "1",
"body": "Rails is unagi"
}, {
"id": "2",
"body": "Omakase O_o"
}]
}
}
```
@method normalize
@param {DS.Model} typeClass
@param {Object} hash to be normalized
@param {String} prop the hash has been referenced by
@return {Object} the normalized hash
**/
normalize(typeClass, hash, prop) {
let normalizedHash = this._super(typeClass, hash, prop);
return this._extractEmbeddedRecords(this, this.store, typeClass, normalizedHash);
},
keyForRelationship(key, typeClass, method) {
if ((method === 'serialize' && this.hasSerializeRecordsOption(key)) ||
(method === 'deserialize' && this.hasDeserializeRecordsOption(key))) {
return this.keyForAttribute(key, method);
} else {
return this._super(key, typeClass, method) || key;
}
},
/**
Serialize `belongsTo` relationship when it is configured as an embedded object.
This example of an author model belongs to a post model:
```js
Post = DS.Model.extend({
title: DS.attr('string'),
body: DS.attr('string'),
author: DS.belongsTo('author')
});
Author = DS.Model.extend({
name: DS.attr('string'),
post: DS.belongsTo('post')
});
```
Use a custom (type) serializer for the post model to configure embedded author
```app/serializers/post.js
import DS from 'ember-data';
export default DS.RESTSerializer.extend(DS.EmbeddedRecordsMixin, {
attrs: {
author: { embedded: 'always' }
}
})
```
A payload with an attribute configured for embedded records can serialize
the records together under the root attribute's payload:
```js
{
"post": {
"id": "1"
"title": "Rails is omakase",
"author": {
"id": "2"
"name": "dhh"
}
}
}
```
@method serializeBelongsTo
@param {DS.Snapshot} snapshot
@param {Object} json
@param {Object} relationship
*/
serializeBelongsTo(snapshot, json, relationship) {
let attr = relationship.key;
if (this.noSerializeOptionSpecified(attr)) {
this._super(snapshot, json, relationship);
return;
}
let includeIds = this.hasSerializeIdsOption(attr);
let includeRecords = this.hasSerializeRecordsOption(attr);
let embeddedSnapshot = snapshot.belongsTo(attr);
if (includeIds) {
let serializedKey = this._getMappedKey(relationship.key, snapshot.type);
if (serializedKey === relationship.key && this.keyForRelationship) {
serializedKey = this.keyForRelationship(relationship.key, relationship.kind, "serialize");
}
if (!embeddedSnapshot) {
json[serializedKey] = null;
} else {
json[serializedKey] = embeddedSnapshot.id;
if (relationship.options.polymorphic) {
this.serializePolymorphicType(snapshot, json, relationship);
}
}
} else if (includeRecords) {
this._serializeEmbeddedBelongsTo(snapshot, json, relationship);
}
},
_serializeEmbeddedBelongsTo(snapshot, json, relationship) {
let embeddedSnapshot = snapshot.belongsTo(relationship.key);
let serializedKey = this._getMappedKey(relationship.key, snapshot.type);
if (serializedKey === relationship.key && this.keyForRelationship) {
serializedKey = this.keyForRelationship(relationship.key, relationship.kind, "serialize");
}
if (!embeddedSnapshot) {
json[serializedKey] = null;
} else {
json[serializedKey] = embeddedSnapshot.serialize({ includeId: true });
this.removeEmbeddedForeignKey(snapshot, embeddedSnapshot, relationship, json[serializedKey]);
if (relationship.options.polymorphic) {
this.serializePolymorphicType(snapshot, json, relationship);
}
}
},
/**
Serializes `hasMany` relationships when it is configured as embedded objects.
This example of a post model has many comments:
```js
Post = DS.Model.extend({
title: DS.attr('string'),
body: DS.attr('string'),
comments: DS.hasMany('comment')
});
Comment = DS.Model.extend({
body: DS.attr('string'),
post: DS.belongsTo('post')
});
```
Use a custom (type) serializer for the post model to configure embedded comments
```app/serializers/post.js
import DS from 'ember-data;
export default DS.RESTSerializer.extend(DS.EmbeddedRecordsMixin, {
attrs: {
comments: { embedded: 'always' }
}
})
```
A payload with an attribute configured for embedded records can serialize
the records together under the root attribute's payload:
```js
{
"post": {
"id": "1"
"title": "Rails is omakase",
"body": "I want this for my ORM, I want that for my template language..."
"comments": [{
"id": "1",
"body": "Rails is unagi"
}, {
"id": "2",
"body": "Omakase O_o"
}]
}
}
```
The attrs options object can use more specific instruction for extracting and
serializing. When serializing, an option to embed `ids`, `ids-and-types` or `records` can be set.
When extracting the only option is `records`.
So `{ embedded: 'always' }` is shorthand for:
`{ serialize: 'records', deserialize: 'records' }`
To embed the `ids` for a related object (using a hasMany relationship):
```app/serializers/post.js
import DS from 'ember-data;
export default DS.RESTSerializer.extend(DS.EmbeddedRecordsMixin, {
attrs: {
comments: { serialize: 'ids', deserialize: 'records' }
}
})
```
```js
{
"post": {
"id": "1"
"title": "Rails is omakase",
"body": "I want this for my ORM, I want that for my template language..."
"comments": ["1", "2"]
}
}
```
To embed the relationship as a collection of objects with `id` and `type` keys, set
`ids-and-types` for the related object.
This is particularly useful for polymorphic relationships where records don't share
the same table and the `id` is not enough information.
By example having a user that has many pets:
```js
User = DS.Model.extend({
name: DS.attr('string'),
pets: DS.hasMany('pet', { polymorphic: true })
});
Pet = DS.Model.extend({
name: DS.attr('string'),
});
Cat = Pet.extend({
// ...
});
Parrot = Pet.extend({
// ...
});
```
```app/serializers/user.js
import DS from 'ember-data;
export default DS.RESTSerializer.extend(DS.EmbeddedRecordsMixin, {
attrs: {
pets: { serialize: 'ids-and-types', deserialize: 'records' }
}
});
```
```js
{
"user": {
"id": "1"
"name": "Bertin Osborne",
"pets": [
{ "id": "1", "type": "Cat" },
{ "id": "1", "type": "Parrot"}
]
}
}
```
@method serializeHasMany
@param {DS.Snapshot} snapshot
@param {Object} json
@param {Object} relationship
*/
serializeHasMany(snapshot, json, relationship) {
let attr = relationship.key;
if (this.noSerializeOptionSpecified(attr)) {
this._super(snapshot, json, relationship);
return;
}
if (this.hasSerializeIdsOption(attr)) {
let serializedKey = this._getMappedKey(relationship.key, snapshot.type);
if (serializedKey === relationship.key && this.keyForRelationship) {
serializedKey = this.keyForRelationship(relationship.key, relationship.kind, "serialize");
}
json[serializedKey] = snapshot.hasMany(attr, { ids: true });
} else if (this.hasSerializeRecordsOption(attr)) {
this._serializeEmbeddedHasMany(snapshot, json, relationship);
} else {
if (this.hasSerializeIdsAndTypesOption(attr)) {
this._serializeHasManyAsIdsAndTypes(snapshot, json, relationship);
}
}
},
/*
Serializes a hasMany relationship as an array of objects containing only `id` and `type`
keys.
This has its use case on polymorphic hasMany relationships where the server is not storing
all records in the same table using STI, and therefore the `id` is not enough information
TODO: Make the default in Ember-data 3.0??
*/
_serializeHasManyAsIdsAndTypes(snapshot, json, relationship) {
let serializedKey = this.keyForAttribute(relationship.key, 'serialize');
let hasMany = snapshot.hasMany(relationship.key);
json[serializedKey] = A(hasMany).map(function (recordSnapshot) {
//
// I'm sure I'm being utterly naive here. Propably id is a configurate property and
// type too, and the modelName has to be normalized somehow.
//
return { id: recordSnapshot.id, type: recordSnapshot.modelName };
});
},
_serializeEmbeddedHasMany(snapshot, json, relationship) {
let serializedKey = this._getMappedKey(relationship.key, snapshot.type);
if (serializedKey === relationship.key && this.keyForRelationship) {
serializedKey = this.keyForRelationship(relationship.key, relationship.kind, "serialize");
}
warn(
`The embedded relationship '${serializedKey}' is undefined for '${snapshot.modelName}' with id '${snapshot.id}'. Please include it in your original payload.`,
typeOf(snapshot.hasMany(relationship.key)) !== 'undefined',
{ id: 'ds.serializer.embedded-relationship-undefined' }
);
json[serializedKey] = this._generateSerializedHasMany(snapshot, relationship);
},
/*
Returns an array of embedded records serialized to JSON
*/
_generateSerializedHasMany(snapshot, relationship) {
let hasMany = snapshot.hasMany(relationship.key);
let manyArray = A(hasMany);
let ret = new Array(manyArray.length);
for (let i = 0; i < manyArray.length; i++) {
let embeddedSnapshot = manyArray[i];
let embeddedJson = embeddedSnapshot.serialize({ includeId: true });
this.removeEmbeddedForeignKey(snapshot, embeddedSnapshot, relationship, embeddedJson);
ret[i] = embeddedJson;
}
return ret;
},
/**
When serializing an embedded record, modify the property (in the json payload)
that refers to the parent record (foreign key for relationship).
Serializing a `belongsTo` relationship removes the property that refers to the
parent record
Serializing a `hasMany` relationship does not remove the property that refers to
the parent record.
@method removeEmbeddedForeignKey
@param {DS.Snapshot} snapshot
@param {DS.Snapshot} embeddedSnapshot
@param {Object} relationship
@param {Object} json
*/
removeEmbeddedForeignKey(snapshot, embeddedSnapshot, relationship, json) {
if (relationship.kind === 'belongsTo') {
let parentRecord = snapshot.type.inverseFor(relationship.key, this.store);
if (parentRecord) {
let name = parentRecord.name;
let embeddedSerializer = this.store.serializerFor(embeddedSnapshot.modelName);
let parentKey = embeddedSerializer.keyForRelationship(name, parentRecord.kind, 'deserialize');
if (parentKey) {
delete json[parentKey];
}
}
} /*else if (relationship.kind === 'hasMany') {
return;
}*/
},
// checks config for attrs option to embedded (always) - serialize and deserialize
hasEmbeddedAlwaysOption(attr) {
let option = this.attrsOption(attr);
return option && option.embedded === 'always';
},
// checks config for attrs option to serialize ids
hasSerializeRecordsOption(attr) {
let alwaysEmbed = this.hasEmbeddedAlwaysOption(attr);
let option = this.attrsOption(attr);
return alwaysEmbed || (option && (option.serialize === 'records'));
},
// checks config for attrs option to serialize records
hasSerializeIdsOption(attr) {
let option = this.attrsOption(attr);
return option && (option.serialize === 'ids' || option.serialize === 'id');
},
// checks config for attrs option to serialize records as objects containing id and types
hasSerializeIdsAndTypesOption(attr) {
let option = this.attrsOption(attr);
return option && (option.serialize === 'ids-and-types' || option.serialize === 'id-and-type');
},
// checks config for attrs option to serialize records
noSerializeOptionSpecified(attr) {
let option = this.attrsOption(attr);
return !(option && (option.serialize || option.embedded));
},
// checks config for attrs option to deserialize records
// a defined option object for a resource is treated the same as
// `deserialize: 'records'`
hasDeserializeRecordsOption(attr) {
let alwaysEmbed = this.hasEmbeddedAlwaysOption(attr);
let option = this.attrsOption(attr);
return alwaysEmbed || (option && option.deserialize === 'records');
},
attrsOption(attr) {
let attrs = this.get('attrs');
return attrs && (attrs[camelize(attr)] || attrs[attr]);
},
/**
@method _extractEmbeddedRecords
@private
*/
_extractEmbeddedRecords(serializer, store, typeClass, partial) {
typeClass.eachRelationship((key, relationship) => {
if (serializer.hasDeserializeRecordsOption(key)) {
if (relationship.kind === "hasMany") {
this._extractEmbeddedHasMany(store, key, partial, relationship);
}
if (relationship.kind === "belongsTo") {
this._extractEmbeddedBelongsTo(store, key, partial, relationship);
}
}
});
return partial;
},
/**
@method _extractEmbeddedHasMany
@private
*/
_extractEmbeddedHasMany(store, key, hash, relationshipMeta) {
let relationshipHash = get(hash, `data.relationships.${key}.data`);
if (!relationshipHash) {
return;
}
let hasMany = new Array(relationshipHash.length);
for (let i = 0; i < relationshipHash.length; i++) {
let item = relationshipHash[i];
let { data, included } = this._normalizeEmbeddedRelationship(store, relationshipMeta, item);
hash.included = hash.included || [];
hash.included.push(data);
if (included) {
hash.included.push(...included);
}
hasMany[i] = { id: data.id, type: data.type };
}
let relationship = { data: hasMany };
set(hash, `data.relationships.${key}`, relationship);
},
/**
@method _extractEmbeddedBelongsTo
@private
*/
_extractEmbeddedBelongsTo(store, key, hash, relationshipMeta) {
let relationshipHash = get(hash, `data.relationships.${key}.data`);
if (!relationshipHash) {
return;
}
let { data, included } = this._normalizeEmbeddedRelationship(store, relationshipMeta, relationshipHash);
hash.included = hash.included || [];
hash.included.push(data);
if (included) {
hash.included.push(...included);
}
let belongsTo = { id: data.id, type: data.type };
let relationship = { data: belongsTo };
set(hash, `data.relationships.${key}`, relationship);
},
/**
@method _normalizeEmbeddedRelationship
@private
*/
_normalizeEmbeddedRelationship(store, relationshipMeta, relationshipHash) {
let modelName = relationshipMeta.type;
if (relationshipMeta.options.polymorphic) {
modelName = relationshipHash.type;
}
let modelClass = store.modelFor(modelName);
let serializer = store.serializerFor(modelName);
return serializer.normalize(modelClass, relationshipHash, null);
},
isEmbeddedRecordsMixin: true
});


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| Functions | ||
| NormalDistParmeters | Create an array of Normal Distribution parameters and return it. | |
| TransitionMatrix | Create an array of Simplex blocks that act as a transition matrix. |
| Public fields | ||
| LB | const | AV compatible, the lower bound |
| UB | const | AV compatible, the upper bound |
| Public methods | ||
| Bounded | Create a parameter bounded above and below. |
| Public fields | ||
| UB | const | AV compatible, the upper bound |
| Public methods | ||
| BoundedAbove | Create a new parameter bounded above. | |
| Menu | virtual |
| Public fields | ||
| LB | const | AV compatible, the lower bound |
| Public methods | ||
| BoundedBelow | A parameter bounded below. |
xj = Free()
| Public methods | ||
| Coefficients | Create a block of free parameters. |
Correlation(L,v0) ≡ Bounded(L,-1.0,1.0,v0)
)
| Public methods | ||
| Correlation | Create a new parameter bounded above by 1 and below by -1. |
x0 = BoundedAbove(UB) or Free() xj = BoundedAbove(xj-1)
| Public methods | ||
| Decreasing | Create a decreasing vector of parameters. |
J
probabilities that sum to strictly less than 1.
x0 = Probability ... xj = Bounded(0.0,1-∑k=0…j-1 xk)
| Public methods | ||
| DecreasingReturns | Create a block of decreasing returns to scale coefficients. |
| Public methods | ||
| Determined | Create a fixed, pre-determined parameter. | |
| SetDoNotVary | Do nothing because DoNotVary does not change for Determined parameter. | |
| ToggleDoNotVary | Do nothing because DoNotVary does not toggle for Determined parameter. |
| Public methods | ||
| Decode | ||
| Encode | Default encoding: no scaling, no constraining. | |
| FixedBlock | create a new vector of parameters that are not changed by algorithms. |
| Public methods | ||
| Encode | virtual | . |
| Free | Create a free, unrestricted parameter. |
x0 = BoundedBelow(LB) or Free() xj = BoundedBelow(xj-1)
| Public methods | ||
| Increasing | Create an increasing vector of parameters. |
| Public fields | ||
| nearflat | starting value is near the flat spot of the transformation. | |
| Public methods | ||
| Limited | . |
Negative(L,v0) ≡ BoundedAbove(L,0.0)
| Public methods | ||
| Negative | Create a parameter bounded above by 0. |
| Public fields | ||
| Anchored | const | list is anchored . |
| B | const | AV compatible, bound for first value. |
| Public methods | ||
| Ordered | Create a block with parameters that maintain an order. |
| Public fields | ||
| curpar | current param in V. | |
| N | length of block. | |
| Psi | array of parameters . | |
| PsiL | array of labels. | |
| Public methods | ||
| AddToBlock | Append parameter to the block. | |
| BlockCode | virtual | Default update for an unstructured block of parameters. |
| Encode | virtual | Default encoding: no scaling, no constraining. |
| Menu | virtual | |
| ParameterBlock | Create a new block of related Parameters. | |
| SetDoNotVary | Set the value of DoNotVary. | |
| ToggleDoNotVary | Toggle DoNotVary for All or some block elements. | |
| Xb | Return \(X\beta = X*CV(beta)\). |
Positive(L,v0) ≡ BoundedBelow(L,0,v0)
| Public methods | ||
| Positive | Create a parameter bounded below by 0. |
xj = Probability()
| Public methods | ||
| Probabilities | Create a block of parameters all constrained to be probabilities. |
Probability(L,v0) &equiv Bounded(L,0.0,1.0)
| Public methods | ||
| Probability | Create a new parameter bounded above by 1 and below by 0. |
J
probabilities that sum to 1.
x0 = Probability xj = Bounded(0.0,1-∑k=0…j-1 xk) ⋮ xJ = Determined(1-∑xj)
| Public fields | ||
| fval | final value, used for cumulating. | |
| Public methods | ||
| Last | static | |
| Simplex | Create a simplex of parameters. |
xj = Positive()
| Public methods | ||
| StDeviations | Create a block of positive parameters. |
pars
vector or array.
| L | Label prefix for the parameters |
| ivals | initial values vector see NormalParams default is <0.0;1.0>, which means parameters start as standard normal if ivals has three elements the last is the correlation coefficient \(\rho\) |
| L | label |
| inmat | a square matrix of initial values for the transitions.
Each column of inmat should be a proper transition (not each row).
|
decl tmat =< 0.90 ~ 0.09 ~0.05; 0.02 ~ 0.80 ~0.3; 0.08 ~ 0.01 ~0.11> decl m = TransitionMatrix("p",tmat); Parameters(m); println("The current Markov transition matrix is ", CV(m));
| L | parameter label |
| LB | double or Parameter, the upper bound |
| v0 | CV compatible default value, v0 |
| L | label for the block (e.g. the equation) |
| ivals | 0: set to length of labels and initialize values to 0 > 0: number of coefficients, initialize to 0 |
| labels | 0: use numbers for labels array of strings, labels for coefficients (e.g. variable names) vector: initial values |
| L | parameter label |
| v0 | CV compatible default value, v0 |
| L | label |
| UB | upper bound for first variable, a double, CV comptible. send .Inf to make the first parameter free |
| ivals | integer, dimension of sequence OR N×1 vector of initial values |
| Anchored | TRUE, UB should be the first element of the block (as a Determined parameter) |
| L | label |
| ivals | N×1 vector, initial values
0< xi < 1 |
| L | string |
| v0 | double, initial value, current value determined by something else CV compatible default value, v0 |
v = start; scale = 1.0; f = v;
| L | label |
| v0 | vector or array of values. |
| L | parameter label |
| v0 | CV compatible default value |
LB < x1 < x2 < … < xN
| L | label |
| LB | lower bound for first variable, a double, Parameter or static function send -.Inf to make the first parameter free |
| ivals | integer, dimension of sequence OR N×1 vector of initial values |
| Anchored | TRUE, LB should be the first element of the block (as a Determined parameter) |
| L | parameter label |
| v0 | CV compatible default value, v0 |
| L | label |
| B | CV-compatible bound or Determined parameter (can be -∞ or +∞) |
| ivals | vector of initial values scalar number of ordered elements (not counting Anchor) |
| sign | ordering |
| Anchored | if TRUE the first element of the block is B |
| psi | Parameter to add. |
| ... | more parameters |
curpar
(and reset 0 when it reaches N).
v = start; scale = 1.0; f = v;
| L | string Label for block |
| ... | a list of parameters to add to the block if only one argument is supplied it should be an array of parameters. |
| setting | TRUE or FALSE |
| elements | DoAll or a vector of parameter indices |
| X | row vector or matrix conforming to the coefficient vector. |
| L | parameter label |
| v0 | CV compatible default value, v0 |
| L | label for the block |
| ivals | 0: set to length of labels and initialize values to 0 > 0: number of postivie parameters, initialize to 1.0 vector: initial values |
| labels | 0: use numbers for labels array of strings, labels for values (e.g. variable names) |
| L | parameter label |
| v0 | CV compatible default value, v0 |
| L | label |
| ivals | integer: dimension of simplex initialized as equal values 1/N
N×1 vector, initial values 0 < x0 < 1 0 < x1 < x0 ⋮ 0 < xi < ∑j=0…i-1 xj xN-1 = 1 - ∑j=0…N-2 xj |
| L | label for the block |
| ivals | 0: set to length of labels and initialize all values at 1 > 0: number of positive parameters, initialize to 1.25 vector: initial values |
| labels | 0: use numbers for labels array of strings, labels for values (e.g. variable names) For example, a vector of standard deviations. |


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/From.20fields.20to.20sets.html ---
Hello,
is there an easy way to get the underlying set of a (discrete) field ?
Thanks
If you have field α
, set.univ α
is the underlying set.
If you have
field α
,set.univ α
is the underlying set.
Great! Thank you Simon.
:+1:
What do you use it for? Typically, I don't see that set used much because the type does most of the work on its own
I think maybe you just want α
?
It's not an "underlying set", it's an "underlying type"
and there is no underlying about it because we define "a field on α" rather than just "a field", so α is the carrier
What do you use it for? Typically, I don't see that set used much because the type does most of the work on its own
You're right, I realized that I don't need it. :+1:
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Notebooks
Story: Arrowheads
Arrowheads
Title
Arrowheads
Collection
Summary
Arrow making, and Arrow poisoning. (L VIII.-26. 8293-8302, 8315-8334). Two kinds of Arrow. (L VIII.-10. 6923 rev. and 6924 rev., note). Arrow heads, etc. (L VIII.-1. 6086 and 6087). Description of the spur, or barblet, sometimes added to the Arrow shaft by Bushmen. (L VIII.-31. 8770–8773, and 8767 rev., note). The adhesive substance used by Bushmen is making Arrows. Preparation thereof. (L VIII.-1. 6088-6091). The marking of Arrows. (L VIII.-26. 8289-8392). Arrow Bags. (L VIII. 30. 8663 rev., note). About the making of arrow-heads: the head is made from a white stone and fixed into the end of a reed; they are used for shooting springbok.
Comments
1) p.6085v: it appears that iron-headed arrow-points are not well made and look like little knives, compared with |han≠kass'o's stone-tipped ones, 2) see also The adhesive substance (|kwae) used in arrowmaking and its preparation for use, 3) This story is found in Book VIII-1
Contributors
|han≠kass'o (Klein Jantje) (VIII)
Date
January 1878
Categories
Artefact and dress, Custom and daily life
Keywords
arrowhead (made of stone) , arrowhead (made of iron) , arrowhead (for shooting springbok) , arrowhead (the stone used to make) , arrowhead (the making of) , arrowhead (and witteklip) , arrow (arrowheads) , arrow (|xam names for the parts of) , arrow (the different parts of) , arrow (poisoned) , arrow (its head) , arrow (the making of) , arrow (looks like a little knife) , arrow (a reed used in the making of) , arrow (for shooting springbok) , springbok (arrowheads for shooting them are not poisoned) , springbok (the making of arrows used for shooting) , hunting (and the making of arrows) , hunting (and the poisoning of arrowheads) , hunting (the arrows used for shooting springbok) , poison (used on arrowheads) , poison (names of) , poison (and the arrows used for shooting springbok) , stones (names of) , stones (used to make arrowheads) , stones (witteklip) , names (of stone used to make arrowheads) , names (for the different parts of arrows) , names (of poisons)
Story Pages
6086-6087


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Chemical mixture at Buffalo Wild Wings kills 1 employee
by The Associated Press, StaffBURLINGTON, Mass. -- Massachusetts authorities say a Buffalo Wild Wings employee has died and at least 10 people have been hospitalized following a chemical mixture at the restaurant.
WHDH-TV reports officials responding Thursday night to reports of a chemical reaction in the kitchen area found a male employee suffering from nausea. Burlington police say they think he inhaled fumes from a cleaning agent.
The employee, whose name wasn't immediately released, was taken to a hospital where he later died. Authorities evacuated the restaurant and urged anyone who was inside at the time to get medical treatment, if needed.
A Buffalo Wild Wings statement says the company is "shocked and saddened" and working with the franchisee and authorities.
The suburban Boston sports bar will remain closed while the investigation continues.


School sorry after student puts Nazi swastika on British war heroes photo
by Matthew EarthFramlingham College have apologised after a student doctored an image of a banner commemorating Britain's war heroes with a swastika before it was shared online.
The banner, depicting the silhouette of a soldier, was altered to include the Nazi symbol by a student.
The image was then shared with another student, who posted it on social media.
Framlingham College said the pair were immediately suspended as a result of the incident.
Louise North, principal of the college, said: "I apologise unreservedly for the offence caused by the behaviour of these pupils, which contradicts in the strongest terms the ethos of this school.
"The Framlingham College community has the most profound respect for the nation's servicemen and women, our veterans and the fallen.
"Every year, the college reflects on the huge sacrifice made by those who have served and those who have fallen, through chapel addresses, classroom discussion, external speakers and charitable undertaking.
"This year was no different, which makes the aberrant behaviour of these pupils all the more deplorable.
"In both cases, their actions were completely out of character and both are full of remorse."


Before Change
kmeans2(data[:, :1], 3, minit="++") // special case (1-D)
// minit="random" can give warnings, filter those
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="At least one cluster is empty. Re-run")
kmeans2(data, 3, minit="random")
kmeans2(data[:, :1], 3, minit="random") // special case (1-D)
@pytest.mark.skipif(sys.platform == "win32",
After Change
kmeans2(data[:, :1], 3, minit="++") // special case (1-D)
// minit="random" can give warnings, filter those
with suppress_warnings() as sup:
sup.filter(message="One of the clusters is empty. Re-run")
kmeans2(data, 3, minit="random")
kmeans2(data[:, :1], 3, minit="random") // special case (1-D)
@pytest.mark.skipif(sys.platform == "win32",


while not done:
pout = pi_func(chainer.Variable(
obs.reshape((1,) + obs.shape)))
// Use the most probale actions for stability of test results
action = pout.most_probable.data[0]
print("state:", obs, "action:", action, "pout:", pout)
model.reset_state()
while not done:
action = agent.act(obs)
print("state:", obs, "action:", action)
obs, reward, done, _ = env.step(action)
total_r += reward
self.assertAlmostEqual(total_r, 1)


WEEK 19 THURSDAY
Year 1 (Year 2)
FIRST READING Joshua 3:7-10.11.13-17
A reading from the Book of Joshua
In those days: The LORD said to Joshua, "This day I will begin to
exalt you in the sight of all
The word of the Lord.
RESPONSORIAL PSALM Psalm 114:1-2.3-4.5-6
R. Alleluia!
When
R. Alleluia!
The sea beheld them and fled; the
R. Alleluia!
Why was it, sea, that you fled; that you turned back,
R. Alleluia!
FIRST READING Ezekiel 12:1-12
A reading from the Book of the Prophet Ezekiel
The word of the LORD came to me: "Son of man,
you dwell in the midst of a rebellious house, who have eyes to see, but see
not, who have ears to hear, but hear not; for they are a rebellious house.
  You shall bring out your baggage by day in their sight, as
baggage for exile; and you shall go forth yourself at evening in their sight,
as men do who must go into exile. Dig through the wall in their sight, and go
out through it. In their sight you shall lift the baggage upon your shoulder,
and carry it out in the dark; you shall cover your face, that you may not see
the land; for I have made you a sign for the house of
The word of the Lord.
RESPONSORIAL PSALM Psalm 78:56-57.58-59.61-62 (R. see 7b)
R. Never forget the deeds of the Lord!
With defiance they tested God Most High; they refused
to obey his decrees. They strayed, faithless like their fathers; they betrayed
him like a faulty bow.
R. Never forget the deeds of the Lord!
 God heard this and was filled with
fury; he utterly rejected
R. Never forget the deeds of the Lord!
He gave his strength into captivity, his splendour
to the hand of the foe. He gave up his people to the sword, and showed his
anger against his heritage.
R. Never forget the deeds of the Lord!
R. Alleluia.  R. Alleluia.
GOSPEL Matthew 18:21-19:1
A reading from the holy Gospel according to Matthew
At that time: Peter came up and said to Jesus, "Lord, how often
shall my brother sin against me, and I forgive him? As many
as seven times?" Jesus said to him, "I do not say to you seven
times, but seventy times seven. "Therefore the
kingdom of heaven may be compared to a king who wished to settle accounts with
his servants. When he began the reckoning, one was brought to him who owed him
ten thousand talents; and as he could not pay, his lord ordered him to be sold,
with his wife and children and all that he had, and payment to be made. So the
servant fell on his knees, imploring him, 'Lord, have patience with me, and I
will pay you everything.' And out of pity for him the lord of that servant
released him and forgave him the debt.  So his fellow
servant fell down and besought him, 'Have patience with me, and I will pay
you.' He refused and went and put him in prison till he should pay the debt.
When his fellow servants saw what had taken place, they were greatly
distressed, and they went and reported to their lord all that had taken place.
Then his lord summoned him and said to him, 'You wicked
servant! I forgave you all that debt because you besought me; and should not
you have had mercy on your fellow servant, as I had mercy on you?' And in anger
his lord delivered him to the jailers, till he should pay all his debt. So also
my heavenly Father will do to every one of you, if you do not forgive your
brother from your heart." Now when Jesus had finished these sayings, he
went away from Galilee and entered the region of Judea beyond the
The Gospel of the Lord.


The following schedule is SUBJECT TO CHANGE. Please contact<EMAIL_ADDRESS>for any questions or comments on the schedule.
Please see the Tutorials and Workshop pages for details on these aspects of the program.
Monday, July 11, 2016
- Workshop: Mobile Multimedia Computing (MMC), 9:00-17:00
- Workshop: Hot Topics in 3D (Hot3D), 9:00-17:00
- Workshop: Multimedia Services and Technologies for E-health (MUST-EH), 9:00-12:00
- Workshop: Multimedia in e-Commerece, 15:45-17:00
- Tutorial: “Mulsemedia”-based Collaborative Mixed/Virtual Reality Environments, 9:00-12:00
- Tutorial: Interactive Search in Video & Lifelog Repositories, 9:00-12:00
- Tutorial: Graph Signal Processing for Image Compression & Restoration, 9:00-12:00
- Tutorial: Quality of Experience in Multimedia Systems and Services, 14:00-17:00
- Tutorial: Situation Recognition from Multimodal Data, 14:00-17:00
- Tutorial: High Efficiency Video Coding – Coding Tools and Specification, 14:00-17:00
- Side Meeting: IEEE Transactions on Multimedia Steering Committee (TMM SC, Chair: Alan Hanjalic), 12:00-14:00
- Morning Coffee Break: 10:15-10:45
- Lunch Break: 12:00-14:00
- Morning Coffee Break: 10:15-10:45
Tuesday, July 12, 2016
8:30-8:40
Opening
8:40-9:40
Plenary Talk: Mark Billinghurst (University of South Australia), Empathic Computing: Design for Understanding
10:00-11:40
Grand Challenge Session: Lightfield Image Compression
Chairs: Touradj Ebrahimi, Peter Schelkens, Fernando Pereira
|
24
|
Pseudo-Sequence-Based Light Field Image Compression
|
Dong Liu, USTC; Lizhi Wang, ; Li Li, ; Zhiwei Xiong, ; Feng Wu, |
|
189
|
HEVC-based Light Field Image Coding with Bi-Predicted Self-Similarity Compensation |
Caroline Conti, ISCTE - IUL, IT; Luis Ducla Soares, ISCTE-IUL, Instituto de Telecomunicações; Paulo Nunes, ISCTE-IUL, Instituto de Telecomunicações |
|
192 |
LIGHT FIELD HEVC-BASED IMAGE CODING USING LOCALLY LINEAR EMBEDDING AND SELF-SIMILARITY COMPENSATED PREDICTION |
Ricardo Monteiro, Instituto Telecomunicacoes; ISCTE, Ins. Univ. de Lisboa; Luis Lucas, Instituto Telecomunicacoes; Caroline Conti, ISCTE - IUL, IT; Paulo Nunes, ISCTE-IUL, Instituto de Telecomunicações; Nuno Rodrigues, Instituto Telecomunicacoes; ESTG, Inst. Pol. de Leiria; Sergio Faria, Instituto Telecomunicacoes; ESTG, Inst. Pol. de Leiria; Carla Pagliari, DEE, Inst. Militar de Engenharias; Eduardo Silva, Universidade Federal do Rio de Janeiro; Luis Ducla Soares, ISCTE-IUL, Instituto de Telecomunicações |
|
193 |
HIGH EFFICIENCY CODING OF LIGHT FIELD IMAGES BASED ON TILING AND PSEUDO-TEMPORAL DATA ARRANGEMENT |
Cristian Perra, University of Cagliari; Pedro Assuncao, Instituto de Telecomunicacoes - IPLeiria |
|
200 |
COMPRESSION OF UNFOCUSED PLENOPTIC IMAGES USING A DISPLACEMENT INTRA PREDICTION |
Yun Li, Mid Sweden University; Roger Olsson, MIUN; Marten Sjostrom, Mid Sweden University |
Oral Session Tue-AM-1: Image/Video Content Analysis
Chair: Tao Mei, Microsoft, USA
|
196
|
Distance Learning by Treating Negative Samples Differently and Exploiting Impostors with Symmetric Triplet Constraint for Person Re-identification
|
Xiaoke Zhu, Wuhan University; Xiao-Yuan Jing, Wuhan University; Fei Wu, Wuhan University; Weishi Zheng, Sun Yat-Sen University; Rui-Min Hu, Wuhan University
|
|
647
|
Approximate Convex Decomposition for 2D Shapes based on Visibility Range
|
Zhiyang Li, Dalian Maritime University; Wenyu Qu, Tianjin University; Heng Qi, Dalian University of Technology; Milos Stojmenovic, Singidunum University
|
|
84
|
Tracking Undulatory Body Motion of Multiple Fish based on Midline Dynamics Modeling
|
Shuo Hong Wang, Fudan University; Xi En Cheng, Fudan University; Yan Qiu Chen, Fudan University
|
|
561
|
Extracting and Describing Liver Capsule Contour in High-frequency Ultrasound Image for Early HBV Cirrhosis Diagnosis
|
Xiang Liu, Fudan University; Jialin Song, ; Jingwen Zhao, Fudan University; Yan Qiu Chen, Fudan University; Zhang Jianquan, ChangZheng Hospital
|
254
+
|
Salient object detection for RGB-D image via saliency evolution
|
Jingfan Guo, Nanjing University; Tongwei Ren, Nanjing University; Jia Bei, Nanjing University
|
|
+ Paper 254 also appears in Poster Session Tue-PO-1: Image/Video Content Analysis I. |
Oral Session Tue-AM-2: Audio/Speech Signal Processing
Chair: Mingxing Xu, Tsinghua University, Beijing, China
|
416
|
MULTICHANNEL REDUCTION BASED ON SOUND FIELD WITHIN TWO EARS
|
Dengshi Li, Wuhan University ; Ruimin Hu, whu; Xiaochen Wang, ; Guo Wu, Wuhan University; zheng zhang, Wuhan University; Weiping Tu,
|
|
230
|
A Perceptually Motivated Approach via Sparse and Low-rank Model for Speech Enhancement
|
Gang MIN, PLA Uni. of Sci. & Tech. ; xiongwei Zhang, ; Jibin Yang, ; Wei Han, ; Xia Zou,
|
|
470
|
Joint Optimization of Audible Noise Suppression and Deep Neural Networks for Single-Channel Speech Enhancemnet
|
Wei Han, ; xiongwei Zhang, ; Gang MIN, PLA Uni. of Sci. & Tech. ; meng Sun, ; Jibin Yang,
|
|
297
|
Phonetic Posteriorgrams for Many-to-One Voice Conversion without Parallel Data Training
|
Lifa Sun, Chinese University of HK; Kun Li, Chinese University of HK; Hao Wang, Chinese University of HK; Shiyin Kang, Chinese University of HK; Helen Meng, Chinese University of HK
|
|
458
|
DBLSTM-BASED MULTI-SCALE FUSION FOR DYNAMIC EMOTION PREDICTION IN MUSIC
|
Xinxing Li, Tsinghua University; Jiashen Tian, Tsinghua University; Mingxing Xu, Tsinghua; Yishuang Ning, Tsinghua University; Lianhong Cai,
|
11:40-13:00
Side Meeting: SPS Multimedia Signal Processing Technical Committee (MMSP TC) Chair: Enrico Magli
Side Meeting: IEEE Multimedia Magazine Editorial Board (MM EB) Chair: Yong Rui
13:00-14:20
Panel: Making the Virtual Real -- The Future of Augmented and Virtual Reality Moderator: Mark Billinghurst (University of South Australia) Panelists: Steve Seitz (University of Washington and Google), Shahram Izadi (Microsoft Research), and Jeremy Selan (Valve Corporation)
14:30-15:50
Oral Session Tues-MD-1: Best Student Paper Award Session
Chair: Chang Wen Chen, State University of New York at Buffalo, USA
|
75
|
Large-Scale Vehicle Re-Identification in Urban Surveillance Videos
|
Xinchen Liu, Beijing Univ. of Posts & Telecomm.; Wu Liu, Beijing University of Posts an; Huadong Ma, ; Huiyuan Fu,
|
|
447
|
EXPLORING AUDITORY NETWORK COMPOSITION DURING FREE LISTENING TO AUDIO EXCERPTS VIA GROUP-WISE SPARSE REPRESENTATION
|
Shijie Zhao, Northwestern Polytechnical Uni; Junwei Han, Northwestern Polytechnical University, China; Xi Jiang, Department of Computer Science,The University of Georgia, US; Xintao Hu, Northwestern Polytechnical University,China; Jinglei Lv, npu;uga; Shu Zhang, uga; Bao Ge, snnu.edu.cn; Lei Guo, ; Tianming Liu,
|
|
455
|
Blind Quality Assessment of Compressed Images via Pseudo Structural Similarity
|
Xiongkuo Min, Shanghai Jiao Tong University; Guangtao Zhai, ; Ke Gu, ; Yuming Fang, Nanyang Technological University
|
|
527 |
EFFECTIVE HEVC INTRA CODING UNIT SIZE DECISION BASED ON ONLINE PROGRESSIVE BAYESIAN CLASSIFICATION |
Jiawei Chen, Zhejiang University; Lu Yu, Zhejiang University, China |
Oral Session Tue-MD-2: Special Session on Free Navigation and Immersive 3D
Chairs: Masayuki Tanimoto (Nagoya Industrial Science Research Institute, Japan; Gauthier Lafruit, Université Libre de Bruxelles, Belgium; Joël Jung, Orange Labs, France
|
698
|
Efficient MRF-based disocclusion inpainting in multiview video
|
Beerend Ceulemans, Vrije Universiteit Brussel; Shao-Ping Lu, Vrije Universiteit Brussel; Gauthier Lafruit, Université Libre de Bruxelles, Belgium; Peter Schelkens, Vrije Universiteit Brussel, Belgium; Adrian Munteanu, Vrije Universiteit Brussel
|
|
669
|
New Results in Free-Viewpoint Television Systems for Horizontal Virtual Navigation
|
Marek Domanski, Poznan University of Technology; Maciej Bartkowiak, Poznan University of Technology; Adrian Dziembowski, Poznan University of Technology; Tomasz Grajek, Poznan University of Technology; Adam Grzelka, Poznan University of Technology; Adam Luczak, Poznan University of Technology; Dawid Mieloch, Poznan University of Technology; jaroslaw Samelak, Poznan University of Technology; Olgierd Stankiewicz, Poznan University of Technology; Jakub Stankowski, Poznan University of Technology; Krzysztof Wegner, Poznan University of Technology
|
|
367
|
EFFICIENT PLENOPTIC IMAGING REPRESENTATION: WHY DO WE NEED IT ?
|
Fernando Pereira, IST-IT; Eduardo Silva, UFRJ
|
492
+
|
Content-adaptive focus configuration for near-eye multi-focal displays
|
Wanmin Wu, Ricoh, USA; Patrick Llull, Duke University; Ivana Tosic, Ricoh Innovations Corp; Noah Bedard, Ricoh Innovations Corp; Kathrin Berkner, Ricoh Innovations Corp; Nikhil Balram, Ricoh Innovations Corp
|
|
+ Paper 492 also appears in Poster Session Tue-PO-3: 3D & Depth Processing. |
Oral Session Tue-MD-3: Object Tracking Chair: Hsu-Yung Cheng, National Central University, Zhongli, Taiwan
|
665
|
3D Tracking targets via kinematic model weighted particle filter
|
Xi En Cheng, Fudan University; Shuo Hong Wang, Fudan University; Yan Qiu Chen, Fudan University
|
|
182
+
|
Structure-Regularized Compressive Tracking
|
Qing Guo, Tianjin University; Wei Feng, Tianjin University; Ce Zhou, Tianjin University; Bin Wu, Tianjin University
|
|
234
|
Online Video Tracking Using Collaborative Convolutional Networks
|
Hao Guan, Fudan University; Xiangyang Xue, Fudan University, China; An Zhiyong,
|
|
525
|
Robust Online Visual Tracking via a Temporal Ensemble Framework
|
Hao Guan, Fudan University; Xiangyang Xue, Fudan University, China
|
|
+ Paper 182 also appears in Poster Session Wed-PO-1: Image/Video Content Analysis II. |
15:20-17:20
Hands-On Expo: Microsoft Cognitive Services
Presenters: Cha Zhang (Microsoft Research), Emad Barsoum (Microsoft Research), Kenneth Tran (Microsoft Research)
Poster Session Tue-PO-1: Image/Video Content Analysis I Chair: Weidong Cai, University of Sydney, Sydney, Australia
|
209
|
Efficient structure-preserving Superpixel Segmentation Based on Minimum Spanning Tree
|
Yu Bai, Uni. of Sci. and Tec. of China; Xuejin Chen, Uni. of Sci. and Tech. of China
|
|
233
|
Unsupervised Visual Domain Adaptation via Dictionary Evolution
|
Songsong Wu, NJUPT; Xiao-Yuan Jing, Wuhan University; Dong Yue, ; Jian Zhang, UTS; Jian Yang, ; Jingyu Yang, NJUST
|
|
241
|
Multi-label Active Learning for Image Classification with Asymmetrical Conditional Dependence
|
Shiquan Zhao, ; Jian Wu, Soochow University; Victor S. Sheng, ; PengPeng Zhao, ; Zhiming Cui,
|
|
258
|
A Bayesian Hierarchical Appearance Model for Robust Object Tracking
|
Raed Almomani, Wayne State University; Ming Dong, Wayne.edu; Dongxiao Zhu, www.wayne.edu
|
|
289
|
Geometry-aware Metric Learning For Similar Face Recognition
|
Nanhai Zhang, Beijing University of Posts an; Jiajie Han, Beijing University of Posts and Telecommunications; Jiani Hu, BUPT; Weihong Deng, Beijing University of Posts and Telecommunications
|
|
305
|
MEMORY-BASED OBJECT DETECTION IN SURVEILLANCE SCENES
|
Xudong Li, University of Electronic Science and Technology of China; Mao Ye, University of Electronic Science and Technology of China; Dan Liu, University of Electronic Science and Technology of China; Feng Zhang, University of Electronic Science and Technology of China; Song Tang, University of Electronic Science and Technology of China
|
|
422
|
Learning to Rank Saliency：a novel salient object detection method in RGBD images
|
Zun Li, Beijing,Jiaotong University; Congyan Lang, Beijing Jiaotong University, China; Songhe Feng,
|
|
326
|
VIDEO SALIENCY PREDICTION WITH OPTIMIZED OPTICAL FLOW AND GRAVITY CENTER BIAS
|
Zhe Wu, University of Chinese Academy ; Li Su, ; Qingming Huang, University of Chinese Academy of Sciences, China; Bo Wu, Ccmu; Jian Li,
|
254
+ |
Salient object detection for RGB-D image via saliency evolution |
Jingfan Guo, Nanjing University; Tongwei Ren, Nanjing University; Jia Bei, Nanjing University |
|
+ Paper 254 also appears in Oral Session Tue-AM-1: Image/Video Content Analysis. |
Poster Session Tue-PO-2: Acoustic/Multimodal Content Analysis Chair: Winston Hsu, National Taiwan University, Taipei, Taiwan
|
623
|
Reducing Manual Labeling in Singing Voice Detection: An Active Learning Approach
|
Wei Li, Fudan University; Xiangyi Feng, Fudan University; Min Xue,
|
|
630
|
Weakly Supervised Scalable Audio Content Analysis
|
Anurag Kumar, Carnegie Mellon University; Bhiksha Raj, Carnegie Mellon University
|
|
699
|
Guitar Solos as Networks
|
Stefano Ferretti, University of Bologna
|
|
36
|
Egocentric Activity Recognition by Leveraging Multiple Mid-level Representations
|
Peng-Ju Hsieh, ; Yen-Liang Lin, National Taiwan University, Taiwan; Winston Hsu, National Taiwan University
|
|
421
|
Discovering Latent Affective Dynamics among Individuals in Online Mental Health-related Communities.
|
BO DUY DAO, Deakin University; Thin Nguyen, Deakin University; Svetha Venkatesh, PRaDA Lab, IT school, Deakin University; Dinh Phung,
|
|
189
|
Learning a pose lexicon for semantic action recognition
|
Lijuan Zhou, University of Wollongong; Wanqing Li, ; Philip Ogunbona,
|
|
444
|
CHINESE SIGN LANGUAGE RECOGNITION WITH ADAPTIVE HMM
|
Jihai Zhang, USTC; Wengang Zhou, University of Science and Technology of China; Junfu Pu, ; Houqiang Li,
|
|
* |
Multimodal Multi-Channel On-Line Speaker Diarization Using Sensor Fusion Through SVM |
Vicente Minotto, ; Claudio Jung, ; Bowon Lee, Inha University |
|
* Paper originally published in IEEE Transactions on Multimedia, presented for first time at ICME 2016. |
Poster Session Tue-PO-3: 3D & Depth Processing Chair: Jian Zhang, University of Technology, Sydney, Australia
|
100
|
Depth Augmented Stereo Panorama for Cinematic Virtual Reality with Head-Motion Parallax
|
Jayant Thatte, Stanford University; Jean-Baptiste Boin, Stanford University; Haricharan Lakshman, Stanford University; Bernd Girod, Stanford University
|
|
331
|
Super-Resolution for 3D Video Using Fully Convolutional Neural Networks
|
Yanchun Xie, XJTLU; Jimin Xiao, XJTLU; Tammam Tillo, Xi'an Jiaotong-Liverpool University; Yunchao Wei, Beijing Jiaotong University; Yao Zhao, Beijing Jiaotong University
|
|
307
|
EXPLICIT MODELING ON DEPTH-COLOR INCONSISTENCY FOR COLOR-GUIDED DEPTH UP-SAMPLING
|
Yifan Zuo, Shanghai University; Qiang Wu, UTS; Jian Zhang, UTS; PING AN, school of communication and information engineering, Shanghai University
|
|
380
|
ITERATIVE COLOR-DEPTH MST COST AGGREGATION FOR STEREO MATCHING
|
Peng Yao, TianjinUniversityofTechnology; Hua Zhang, Tianjin University of Technology; Yanbing Xue, Tianjin University of Technology; Mian Zhou, Tianjin University of Technology; Guangping Xu, Tianjin University of Technology; Zan Gao, Tianjin University of Technology
|
|
580
|
Depth Super-resolution From RGB-D Pairs With Spatial and Transform Regularization
|
Jingyu Yang, Tianjin university; Zhongyu Jiang, Tianjin University in China; Huanjing Yue, ; Chunping Hou,
|
|
244
|
Depth-aware Layered Edge for Object Proposal
|
Jing Liu, Nanjing University; Tongwei Ren, Nanjing University; Bing-kun Bao, Chinese Academy of Sciences; Jia Bei, Nanjing University
|
492
+ |
Content-adaptive focus configuration for near-eye multi-focal displays |
Wanmin Wu, Ricoh, USA; Patrick Llull, Duke University; Ivana Tosic, Ricoh Innovations Corp; Noah Bedard, Ricoh Innovations Corp; Kathrin Berkner, Ricoh Innovations Corp; Nikhil Balram, Ricoh Innovations Corp |
|
* |
Spatio-temporally Consistent Color and Structure Optimization for Multiview Video Color Correction |
Shao-Ping Lu, Vrije Universiteit Brussel (VU; Beerend Ceulemans, Vrije Universiteit Brussel (VUB); Adrian Munteanu, Vrije Universiteit Brussel (VUB); Peter Schelkens, Vrije Universiteit Brussels |
+ Paper 492 also appears in Oral Session Tue-MD-2: Special Session on Free Navigation & Immersive 3D.
* Paper originally published in IEEE Transactions on Multimedia, presented for first time at ICME 2016.
|
Poster Session Tue-PO-4: Quality Assessment Chair: Lu Yu, Zhejiang University, Hangzhou, China
|
512
|
A Reduced-Reference Video Quality Classification Based on Natural Video Statistics and K-means Clustering
|
Haiqing Du, Beijing University of Posts an; Jingtao Xu, Beijing University of Posts and Telecommunications; Yong Liu, Beijing University of Posts and Telecommunications
|
|
167
|
BLeSS: BIO-INSPIRED LOW-LEVEL SPATIOCHROMATIC SIMILARITY ASSISTED IMAGE QUALITY ASSESSMENT
|
Dogancan Temel, Georgia Tech; Ghassan Alregib, Georgia Institute of Technology
|
|
689
|
Visual Attention Analysis on Stereoscopic Images for subjective Discomfort Evaluation
|
Sewoong Ahn, Junghwan Kim, Yonsei Univ.; Haksub Kim, ; Sanghoon Lee,
|
|
355
|
Distortion Recognition for Image Quality Assessment with Convolutional Neural Network
|
Hanli Wang, Tongji University; Lingxuan Zuo, Tongji University; Jie Fu, Tongji University
|
|
560
|
BINOCULAR RIVALRY DETECTION IN NATURAL IMAGE PAIRS
|
yapeng Xue, ; Lu Yu, Zhejiang University, China
|
17:00-18:00
Grand Challenge Session: bitmovin Grand Challenge on Dynamic Adaptive Streaming over HTTP Chair: Christian Timmerer
|
191
|
AN ADAPTATIVE BITRATE ALGORITHM FOR DASH
|
Yunlong Li, Yue Wang, Shanshe Wang and Siwei Ma, Peking University |
|
194
|
Buffer-based Control Theoretic Approach for Dynamically HTTP Streaming
|
Zhimin Xu, Peking University; Chao Zhou, ; Li Liu, ; XINGGONG ZHANG, Peking University; Zongming Guo, Peking University
|
|
190
|
A BIO-INSPIRED HTTP-BASE ADAPTIVE STREAMING PLAYER
|
Yusuf Sani, Lancaster University; Andreas Mauthe, Lancaster University; Christopher Edwards, Lancaster University; Mu Mu, The University of Northampton;
|
Oral Session Tue-PM-1: Special Session on Multimedia Cloud Computing and Big Data Chairs: Zheng-Jun Zha, University of Science and Technology of China, China; Gwendal Simon, Telecom Bretagne, France; Shervin Shirmohammadi, University of Ottawa, Canada; Xiaokang Yang, Shanghai Jiao Tong University, China
|
446
|
A Comparative Evaluation: Different Methods for Simplifying the Deep Compositional Features
|
Shuang Qiu, Beijing Jiaotong University; Shikui Wei, Beijing Jiaotong University; Yao Zhao, Beijing Jiaotong University
|
|
138
|
Person Re-identification via Rich Color-gradient Feature
|
LINGXIANG WU, CASIA NLPR; Jinqiao Wang, National lab of Automation; guibo Zhu, ; Min Xu, Univeristy of Technology Sydney; Hanqing Lu,
|
|
524
|
A Graph-based Geospatial Multimodal Interpolation Framework
|
Mengfan Tang, University of California, Irvine; Pranav Agrawal, University of California, Irvine; Feiping Nie, Northwestern Polytechnical University; Siripen Pongpaichet, ; Ramesh Jain, University of California, Irvine
|
Oral Session Tue-PM-2: Multimodal Interaction Chair: Zicheng Liu, Microsoft, USA
|
276
|
UNDERSTANDING SPATIAL CORRELATION IN EYE-FIXATION MAPS FOR VISUAL ATTENTION IN VIDEOS
|
Tariq Alshawi, Georgia Institute of Technology; zhiling long, Georgia Institue of Technology; Ghassan Alregib, Georgia Institute of Technology
|
603
+ |
Simultaneous Estimation of Gaze Direction and Visual Focus of Attention for Multi-Person and Robot Interaction
|
Benoit Massé, INRIA; Sileye Ba, Inria; Radu Horaud, Inria
|
|
479
|
Hand Gesture Recognition based on Canonical Formed Superpixel Earth Mover's Distance
|
Chong Wang, Ningbo University; Zhong Liu, The University of Hong Kong; JieYu Zhao, ningbouniversity
|
|
+ Paper 603 also appears in Poster Session Wed-PO-1: Image/Video Content Analysis II. |
18:00-20:00
Side Meeting: IEEE Transactions on Multimedia Editorial Board (TMM EB) Chair: Chang Wen Chen
Wednesday, July 13, 2016
8:40-9:40
Plenary Talk: Fei-Fei Li (Stanford University), A Quest for Visual Intelligence in Computers
10:00-11:40
Grand Challenge Session: MSR Image Recognition Challenge
Chairs: Yuxiao Hu and Lei Zhang
|
188
|
Deep Multi-Context Network for Fine-Grained Visual Recognition
|
Xinyu Ou, CASIIE; Zhen Wei, Chinese Academy of Sciences, SKLOIS; Hefei Ling, Huazhong University of Science and Technology; Si Liu, Chinese Academy of Sciences, SKLOIS; Xiaochun Cao, Chinese Academy of Sciences, SKLOIS;
|
|
185
|
Ensemble Deep Neural Networks for Domain-Specific Image Recognition
|
Wenbo Li, ISCAS; Chuan Ke, iscas
Ensemble Deep Neural Networks for Domain-Specific Image Recognition
|
|
186
|
Improve Dog Recognition By Mining More Information From Both Click-through Logs and Pre-trained Models
|
Guotian Xie, Microsoft; Kuiyuan Yang, ; Yalong Bai, ; Min Shang, ; Yong Rui, ; Jianhuang Lai,
|
|
10
|
Learning to Recognition from Bing Clickture Data
|
Li Chenghua, CASIA, UCAS; Song Song, CASIA; Yuhang Wang, CASIA; Hang Song, ; Qi Kang, ; Cheng Jian, CASIA; Hanqing Lu,
|
Oral Session Wed-AM-1: Visual Signal Processing
Chair: Min-Jen Tsai, National Chiao Tung University, Taiwan
|
237
|
Spherical Superpixels
|
Qiang Zhao, Tianjin University; Liang Wan, Tianjin University; Jiawan Zhang, Tianjin University
|
|
569
|
ADAPTIVE THREE-DIMENSIONAL SPARSITY BASED COEFFICIENT ESTIMATION FOR COMPRESSION ARTIFACT REDUCTION
|
Jing Mu, Peking University
|
|
256
+
|
LOCAL- AND HOLISTIC- STRUCTURE PRESERVING IMAGE SUPER RESOLUTION VIA DEEP JOINT COMPONENT LEARNING
|
Yukai Shi, Sun Yat-set University; Liang Lin, Sun Yat-Sen University; Keze Wang,
|
|
310
|
SPARSE TWO-DIMENSIONAL SINGULAR VALUE DECOMPOSITION
|
Junhui Hou, Nanyang Technological University; Jie Chen, ; Lap-Pui Chau, ; Ying He, Nanyang Technological University
|
|
325
+
|
SHAPE-OPTIMIZING HYBRID WARPING FOR IMAGE STITCHING
|
Qingpeng Chai, Tianjin University; Shiguang Liu, Tianjin University
|
+ Paper 256 also appears in Poster Session Thu-PO-1: Image/Video Content Analysis III.
+ Paper 325 also appears in Poster Session Thu-PO-3: Multimedia Systems and Applications.
|
Oral Session Wed-AM-2: Object Detection and Segmentation
Chair: Joern Ostermann, Leibniz Universität Hannover
| 487 | Shape-Guided Segmentation for Fine-Grained Visual Categorization | Ming Sun, ; Jufeng Yang, Nankai University; Bo Sun, ; Kai Wang, |
| 186 + | A NOVEL OBSTACLE DETECTION METHOD BASED ON DISTORTION OF LASER PATTERN | Zichao Guo, Institute of Computing Technology,Chinese Academy of Science; Hong Liu, Institute of Computing Technology, Chinese Academy of Science; Yueliang Qian, Institute of Computing Technology, Chinese Academy of Science; Xiangdong Wang, Institute of Computing Technology, Chinese Academy of Science |
| 219 + | VIDEO OBJECT SEGMENTATION AGGREGATION | Tianfei Zhou, BIT; Yao Lu, ; Huijun Di, ; Jian Zhang, UTS |
| 117 | A Robust Automatic Object Segmentation Method for 3D Printing | Tz-Kuei Huang, National Taiwan University; Ying-Hsuang Wang, National Taiwan University; Ta-Kai Lin, National Taiwan University; Yung-Yu Chuang, |
| 395 | Segmentation of 3D articulated meshes using the shape diameter function and curvature information | Meha Hachani, ENIT tunisia; Azza Ouled Zaid, ISI Tunis; raoua Khwildi, ISI Tunis |
+ Paper 186 also appears in Poster Session Wed-PO-2: Multimedia Understanding.
+ Paper 219 also appears in Poster Session Wed-PO-1: Image/Video Content Analysis II.
|
11:40-13:20
Side Meeting: CAS Multimedia Systems and Applications Technical Committee (MSA TC) Chair: Zicheng Liu
Side Meeting: Computer Society Technical Committee on Multimedia Computing (TCMC)
Chair: Mei-Ling Shyu
13:20-15:00
Industry Forum: Ultra HD – Roadmap of High Quality A/V Content to the Home
Moderator: Patrick Griffis, Dolby
Presenters: Don Eklund, Sony Pictures Home Entertainment; Thierry Fautier, Harmonic; Nandhu Nandhakumar, LG Electronics
Oral Session Wed-MD-1: Special Session on Quality Assessment
Chairs: Yuming Fang, Jiangxi University of Finance and Economics, China; Hantao Liu, Cardiff University, UK; Zhenzhong Chen, Wuhan University, China; Weisi Lin, Nanyang Technological University, Singapore
| 102 | Quality-of-Experience Prediction for Streaming Video | Zhengfang Duanmu, University of Waterloo; Abdul Rehman, University of Waterloo; Kai Zeng, University of Waterloo; Zhou Wang, Dept. of Electrical & Computer Engineering, University of Waterlo |
| 687 | No-reference image quality assessment based on high order derivatives | Qiaohong Li, NTU; Weisi Lin, Nanyang Technological University; Yuming Fang, Nanyang Technological University |
| 564 | FULL-REFERENCE PERCEPTUAL QUALITY ASSESSMENT FOR STEREOSCOPIC IMAGES BASED ON PRIMARY VISUAL PROCESSING MECHANISM | Yu Cao, ; Wenhao Hong, Zhejiang University; Lu Yu, Zhejiang University, China |
| 164 | LEARNING-BASED QUALITY ASSESSMENT OF RETARGETED STEREOSCOPIC IMAGES | Yi Liu, Tsinghua University; Lifeng Sun, Tsinghua University, China; shiqiang yang, Tsinghua University |
Oral Session Wed-MD-2: Multimodal Content Analysis
Chair: Yu-Chang Frank Wang, Academic Sinica, Taipei, Taiwan
|
132
|
A soundtrack generation system to synchronize the climax of a video clip with music
|
Haruki Sato, Waseda University / JST CREST; Tatsunori Hirai, Waseda University; Tomoyasu Nakano, National Institute of Advanced Industrial Science and Technology (AIST); Masataka Goto, National Institute of Advanced Industrial Science and Technology (AIST); Shigeo Morishima, Research Institute for Science and Engineering / JST CREST
|
|
174
|
Recognizing Heterogeneous Cross-Domain Data via Generalized Joint Distribution Adaptation
|
Yuan-Ting Hsieh, National Taiwan University; Shih-Yen Tao, National Taiwan University; Yao-Hung Tsai, CITI, Academia Sinica; Yi-Ren Yeh, National Kaohsiung Normal University; Yu-Chiang Frank Wang, Academia Sinica
|
|
304
|
ROBUST LATENT POISSON DECONVOLUTION FROM MULTIPLE IMPERFECT FEATURES FOR WEB TOPIC DETECTION
|
Fei Tao, UCAS; Junbiao Pang, ; Chunjie Zhang, ; Liang Li, ; Weigang Zhang, ; Qingming Huang, University of Chinese Academy of Science; Guiping Su,
|
|
215
|
INFERRING USERS’ EMOTIONS FOR HUMAN-MOBILE VOICE DIALOGUE APPLICATIONS
|
Boya Wu, Tsinghua University; Jia Jia, Tsinghua; Tao He, Sichuan University; Juan Du, Tsinghua University; Xiaoyuan Yi, Tsinghua University; Yishuang Ning, Tsinghua University
|
14:00-16:00
Side Meeting: ICME Steering Committee (ICME SC) Chair: Haohong Wang
14:40-16:40
Poster Session Wed-PO-1: Image/Video Content Analysis II Chair: Zijung Fang, Shanghai University of Engineering Science, Shanghai, China
|
578
|
OCCLUSION PATTERN-BASED DICTIONARY FOR ROBUST FACE RECOGNITION
|
Cho-Ying Wu, National Taiwan University; Jian-Jiun Ding, National Taiwan University
|
|
599
|
Nonlinear Metric Learning for Visual Tracking
|
Junlin Hu, Nanyang Technological University, Singapore; Jiwen Lu, Tsinghua University; Yap-Peng Tan, NTU, Singapore
|
|
679
|
Collaborative Multi-View Metric Learning for Visual Classification
|
Junlin Hu, Nanyang Technological University, Singapore; Jiwen Lu, Tsinghua University; Junsong Yuan, ; Yap-Peng Tan, NTU, Singapore
|
|
246
|
boosted local classifiers for visual tracking
|
Weijian Ruan, Wuhan University; jun Chen, Wuhan University; Jinqiao Wang, National lab of Automation; Bo Luo, ; wenjun Huang, ; Ruimin Hu, Wuhan University
|
|
247
|
High-order Directional Features and Sparse Representation based Classification for In-air Handwritten Chinese Character Recognition
|
Xiwen Qu, University of Chinese Academy ; Weiqiang Wang, ; Ke Lu, ; Zhangjian Ji,
|
|
637
|
Superpixel Segmentation Algorithm based on Differential Evolution
|
Yue-Jiao Gong, University of Macau; Yicong Zhou, University of Macau; Xinglin Zhang, South China University of Technology
|
|
125
|
Weakly Supervised Image Parsing by Discriminatively Semantic Graph Propagation
|
Xiaocheng Xu, SDU; Jun Ma,
|
|
280
|
IBC127: Video Dataset for Fine-grained Bird Classification
|
Tomoaki Saito, The Univ. of Tokyo; Asako Kanezaki, The Univ. of Tokyo; Tatsuya Harada, The Univ. of Tokyo
|
|
67
|
A NOVEL TRIGNOMETRIC ENERGY FUNCTIONAL FOR IMAGE SEGMENTATION IN THE PRESENCE OF INTENSITY IN-HOMOGENEITY
|
Sajid Hussain, Xi'an Jiaotong University; Qi Chun, Xi'an Jiaotong University; Muhammad Rizwan Asif, Xi'an Jiaotong University; Muhammad Sohrab Khan, Institute of Research and Technology Kashrote, Gilgit-Baltistan, Pakistan ; Zhaoqiang Zhang, 1School of Electronics and Information Engineering, Xi’an Jiaotong University; Muhammad Sadiq Fareed, ; Zhe Zhang, School of Electronics and Information Engineering, Xi’an Jiaotong University
|
182
+ |
Structure-Regularized Compressive Tracking |
Qing Guo, Tianjin University; Wei Feng, Tianjin University; Ce Zhou, Tianjin University; Bin Wu, Tianjin University |
603
+ |
Simultaneous Estimation of Gaze Direction and Visual Focus of Attention for Multi-Person and Robot Interaction |
Benoit Massé, INRIA; Sileye Ba, Inria; Radu Horaud, Inria |
219
+ |
VIDEO OBJECT SEGMENTATION AGGREGATION |
Tianfei Zhou, BIT; Yao Lu, ; Huijun Di, ; Jian Zhang, UTS |
|
*
|
Real-Time Piano Music Transcription Based on Computer Vision
|
Mohammad Akbari, Simon Fraser University; Howard Cheng, University of Lethbridge
|
+ Paper 182 also appears in Oral Session Tue-MD-3: Object Tracking.
+ Paper 603 also appears in Oral Session Tue-PM-2: Multimodal Interaction.
+ Paper 219 also appears in Oral Session Wed-MD-1: Object Detection and Segmentation.
* Paper originally published in IEEE Transactions on Multimedia, presented for first time at ICME 2016.
|
Poster Session Wed-PO-2: Multimedia Understanding Chair: Feng Wu, University of Science and Technology of China
|
78
|
A Semi-Automatic Brain Tumor Segmentation Algorithm
|
Xiaoli Zhang, Jilin University; Xiongfei Li, Jilin University; Hongpeng Li, The Second Hospital of Jilin University; Yuncong Feng, Jilin University
|
|
299
|
BCA: Bi-symmetric Component Analysis for Temporal Symmetry in Human Actions
|
Chenyang Zhang, CUNY City College; Yingli Tian, The City College of New York
|
|
339
|
A Pair Hidden Markov Support Vector Machine For Alignment of Human Actions
|
ZHEN WANG, UTS; Massimo Piccardi, UTS
|
|
379
|
RECOGNIZE HUMAN ACTIVITIES FROM MULTI-PART MISSING VIDEOS
|
Kaiping XU, Tsinghua University; Guolong Wang, Tsinghua University
|
|
198
|
Recognizing Human Actions from Low-resolution Videos by Region-based Mixture Models
|
Ying Zhao, Beijing Institute of Technolog; Huijun Di, ; Jian Zhang, UTS; Yao Lu, ; Feng Lv,
|
|
190
|
Describing Images by Feeding LSTM with Structural Words
|
Shubo Ma, ; Yahong Han, Tianjing University
|
|
494
|
DRIVER CONFUSION STATUS DETECTION USING RECURRENT NEURAL NETWORKS
|
Chiori HORI, MERL; Shinji Watanabe, MERL; Takaaki HORI, MERL; John Hershey, MERL; Yusuke Koji, MELCO; Yoichi Fujii, MELCO; Bret Harsham, MERL; Yuki Furumoto, MELCO
|
|
111
|
SOLAR IRRADIANCE NOW-CASTING WITH RAMP-DOWN EVENT PREDICTION VIA ENHANCED CLOUD DETECTION AND TRACKING
|
Hsu Yung Cheng, National Central University; chih-chang Yu, Institute for Information Industry
|
186
+ |
A NOVEL OBSTACLE DETECTION METHOD BASED ON DISTORTION OF LASER PATTERN |
Zichao Guo, Institute of Computing Technology,Chinese Academy of Science; Hong Liu, Institute of Computing Technology, Chinese Academy of Science; Yueliang Qian, Institute of Computing Technology, Chinese Academy of Science; Xiangdong Wang, Institute of Computing Technology, Chinese Academy of Science |
366
+ |
Human action recognition-based video summarization for RGB-D personal sports video |
Antonio Tejero de Pablos, Nara Insti. of Sci. and Tech.; Yuta Nakashima, NAIST; Tomokazu Sato, ; Naokazu Yokoya, |
|
*
|
Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera
|
Chong Wang, Ningbo University; Zhong Liu, The University of Hong Kong; Shing-Chow Chan, The University of Hong Kong
|
+ Paper 186 also appears in Oral Session Wed-AM-2: Object Detection and Segmentation.
+ Paper 366 also appears in Oral Session Thu-AM-2: Multimedia Systems and Applications.
* Paper originally published in the IEEE Transactions on Multimedia, presented for the first time at ICME 2016. |
Poster Session Wed-PO-3: Mobile & Social Media Chair: Zhenzhong Chen, Wuhan University, China
|
273
|
USING BUSINESS-AWARE LATENT TOPICS FOR IMAGE CAPTIONING IN SOCIAL MEDIA
|
Yan-Ying Chen, FXPAL; Francine Chen, FXPAL; Matthew Cooper, ; Dhiraj Joshi, FXPAL
|
|
572
|
Heterogeneity-Entropy based Unsupervised Feature Learning for Personality Prediction with Cross-Media Data
|
Haishu Xianyu, ; Mingxing Xu, Tsinghua; Zhiyong Wu, ; Lianhong Cai,
|
|
513
|
CLIENT-SIDE CACHE MANAGEMENT FOR SCALABLE AND INTERACTIVE VIDEO STREAMING
|
Kamal Nayfeh, Wayne State University; Nabil Sarhan,
|
|
489
|
Multimedia Transmission over Device-to-Device Wireless Links
|
Chuang Ye, Syracuse University; Mustafa Gursoy, Syracuse University; Senem Velipasalar, Syracuse University
|
|
156
|
Factors Affecting User Preference for Mobile Video Quality
|
Andreea Molnar, University of Portsmouth
|
139
++ |
STUDY ON THE FIELD TEST RESULT OF MOBILE MMT TRIAL SERVICE OVER LTE NETWORK AT OPEN DENSE AREA, SUBWAY AND HIGH SPEED TRAIN |
Hyunmin Jang, SK telecom; Jongmin Lee, SK telecom; Hyeonmin Choi, SK telecom; Sungmin Cho, SK telecom |
|
++ Paper 139 is an Industry Track submission. |
Poster Session Wed-PO-4: Demonstrations Chair: Qiong Liu, FXPAL, USA; Jacob Chakareksi, University of Alabama, USA
| 28 | A 360-DEGREE 4K×2K PANORAMIC VIDEO RECORDING OVER SMART-PHONES | Tsu-Ming Liu, Mediatek Inc.; Chi-Cheng Ju, Mediatek Inc.; Yu-Hao Huang, Mediatek Inc.; Tsui-Shan Chang, Mediatek Inc.; Kai-Min Yang, Mediatek Inc.; Yi-Ting Lin, Mediatek Inc. |
| 55 | CREATIVE COLOR DESIGNS FOR PRODUCT PACKAGING | Ying Li, IBM T. J. Watson Research |
| 56 | Flexible Media Transport Framework for Android | Sándor Molnár, BME; Péter Megyesi, BME; Szilárd Solymos, ; Zsolt Kramer, BME; Zoltán Móczár, BME |
| 70 | Machine learning-Based Behavior Recognition System for a Basketball Player Using Multiple Kinect Cameras | Wei-Yuan Kuo, National Central University; Chien-Hao Kuo, National Central University; Shih-Wei Sun; Pao-Chi Chang, National Central University; Ying-Ting Chen, Taipei National University of the Arts; Wen-Huang Cheng |
| 205 | Automated Recording Platform for High Quality Short Scientific Video Abstract | Samuel Toba, American Chemical Society; Russell Silber, ; Michael David, |
16:40-18:00
Oral Session Wed-PM-1: Visual Coding Chairs João Ascenso, Instituto de Telecomunicações, Portugal
|
112
|
Dictionary Learning for Kernel Sparse Coding
|
Huaping Liu, Tsinghua University
|
|
369
|
DIGITAL HOLOGRAPHY: BENCHMARKING CODING STANDARDS AND REPRESENTATION FORMATS
|
Fernando Pereira, IST-IT; José Pedro Peixeiro, IST-IT; Joao Ascenso, IST-IT; Catarina Brites, IT
|
|
588
+
|
Lossless Depth Map Coding Using Binary Tree Based Decomposition and Context-Based Arithmetic Coding
|
Shampa Shahriyar, Monash University; Manzur Murshed, Federation University Australia; Mortuza Ali, Federation University Australia; Manoranjan Paul, Charles Sturt University, Australia
|
|
23
|
Visual Data Deblocking Using Structural Layer Priors
|
Siyuan Li, TianJin University; Xiaojie Guo, IIE, CAS; Jiawan Zhang, Tianjin University
|
|
+ Paper 588 also appears in Poster Session Thu-PO-4: Multimedia Coding. |
Oral Session Wed-PM-2: Multimedia Search and Retrieval Chair: Yong Rui, Microsoft, China
|
127
|
Robust Image Matching via Feature Guided Gaussian Mixture Model
|
Jiayi Ma, Wuhan University; Junjun Jiang, ; Chengyin Liu, Huazhong University of Science and Technology
|
|
191
|
Cross-modal Hashing Through Ranking Subspace Learning
|
Kai Li, University of Central Florida; Guo-Jun Qi, University of Central Florida, USA; Jun Ye, University of Central Florida; Kien Hua, University of Central Florida
|
|
346
|
Graph-Based Web Video Search Reranking Through Consistency Analysis Using Spectral Clustering
|
Soh Yoshida, Hokkaido University; Takahiro Ogawa, Hokkaido University; Miki Haseyama, Hokkaido University
|
|
41
|
Generalized Residual Vector Quantization for Large Scale Data
|
Shicong Liu, Shanghai Jiaotong University; Junru Shao, ; Hongtao Lu,
|
17:00-18:00
Grand Challenge Summary Session Chairs: Andrew Gallagher and Christian Timmerer
|
N/A
|
Grand Challenge on Light-Field Image Compression
|
Touradj Ebrahimi, Peter Schelkens, Fernando Pereira
|
|
N/A |
bitmovin Grand Challenge on Dynamic Adaptive Streaming over HTTP
|
Christian Timmerer
|
|
104 |
MSR Image Recognition Challenge
|
Yuxiao Hu, Lei Zhang
|
17:00-19:00
Side Meeting: Happy hour mixer , WIE and YP - hosted by IEEE Seattle
Description: Come to join us for happy hour, Hosted by IEEE Seattle. All is welcome.
Chair: Sheree Wen
Room: Vashon
19:00-22:00
Banquet (Grand I & II)
Thursday, July 14, 2016
8:40-9:40
Plenary Talk: Dariu M. Gavrila
(Daimler R&D), The Intelligent Vehicles (R)evolution
10:00-11:40
Oral Session Thu-AM-1: Face Processing and Analysis Chair: Shao-Yi Chien, National Taiwan University, Taipei, Taiwan
|
245
|
One-shot Deep Neural Network for Pose and Illumination Normalization Face Recognition
|
Zhongjun Wu, Beijing Univ. Post. & Telecom.; Weihong Deng, Beijing University of Posts and Telecommunications
|
|
28
|
ROBUST ONLINE FACE TRACKING-BY-DETECTION
|
Francesco Comaschi, TUe; Sander Stuijk, TUE; Twan Basten, TUE; Henk Corporaal, TUE
|
|
343
|
With One Look: 3D Face Shape Estimation from a Single Snapshot
|
Chia-Po Wei, Academia Sinica; Yu-Chiang Frank Wang, Academia Sinica
|
|
21
|
Robust Face Image Alignment Using Structural Priors
|
Xiaojie Guo, IIE, CAS
|
|
552
|
Patch-based face hallucination with multitask deep neural network
|
Wei-Jen Ko, National Taiwan University; Shao-Yi Chien, National Taiwan University, Taiwan
|
Oral Session Thu-AM-2: Multimedia Systems and Applications Chair: Yuta Nakashima, Nara Institute of Science and Technology, Japan
|
142
|
A Pipeline-Based Runtime Technique for Improving Ray-Tracing Algorithm on HSA-Compliant Systems
|
Chih-Chen Kao, National Taiwan University; Yu-Chung Meow, National Taiwan University; Wei-Chung Hsu, National Taiwan University
|
|
366
+
|
Human action recognition-based video summarization for RGB-D personal sports video
|
Antonio Tejero de Pablos, Nara Insti. of Sci. and Tech.; Yuta Nakashima, NAIST; Tomokazu Sato, ; Naokazu Yokoya,
|
|
206
|
Adaptive Affinity Matrix for Unsupervised Metric Learning
|
Yaoyi Li, Shanghai Jiao Tong University; Junxuan Chen, Shanghai Jiaotong University; Yiru Zhao, Shanghai Jiao Tong University; Hongtao Lu,
|
|
179
|
PARAMETERIZED RECONSTRUCTION BASED FOURIER PTYCHOGRAPHY
|
Weixin Jiang, Tsinghua University; Yongbing Zhang, ; Qionghai Dai,
|
|
222
|
Digital Forensics for Printed Character Source Identification
|
Min-Jen Tsai, National Chiao Tung University
|
|
+ Paper 366 also appears in Poster Session Wed-PO-2: Multimedia Understanding. |
10:20-11:40
Three-Minute Thesis (3MT) Competition Chairs: Ivan Bajić and Z. Jane Wang
11:40-13:00
Student-Industry Luncheon
Side Meeting: Comsoc Multimedia Communications Technical Committee (MMTC) Chair: Yonggang Wen
13:00-14:20
Panel: Multimedia Research and Products -- Increasing Impact Moderator: Adriana Dumitras (Microsoft)
Panelists: Touradj Ebrahimi (Ecole Polytechnique Fédérale de Lausanne), Jenq-Neng Hwang (University of Washington), Haohong Wang (TCL), John R. Smith (IBM)
13:00-16:00
Hands-On Expo: Intel RealSense Presenter: Anders Grunnet-Jepsen, Ph.D, CTO & Director of Advanced Technology, Perceptual Computing Intel Corporation
14:20-16:20
Side Meeting: ICME 2017 Organizing Committee (ICME 2017 OC)
Chair: Joern Ostermann
Poster Session Thu-PO-1: Image/Video Content Analysis III Chair: Enrico Magli, Politecnico di Torino, Torino, Italy
|
261
|
Learning deep classifiers with deep features
|
Jie Lei, Zhejiang University; Xinhui Song, ZheJiang University; Li Sun, Zhejiang University; Mingli Song, Zhejiang University
|
|
486
|
Discovering affective regions in deep convolutional neural networks for visual sentiment prediction
|
Ming Sun, ; Jufeng Yang, Nankai University; Kai Wang, ; Hui Shen,
|
|
159
|
LEARNING DEEP REPRESENTATION FROM COARSE TO FINE FOR FACE ALIGNMENT
|
Zhiwen Shao, Shanghai Jiao Tong University; Shouhong Ding, Shanghai Jiao Tong University; Yiru Zhao, Shanghai Jiao Tong University; Qinchuan Zhang, Shanghai Jiao Tong University; Lizhuang Ma, Shanghai Jiaotong University, China
|
|
232
|
Learning kinematic model of targets in videos from fixed cameras
|
Xi En Cheng, Fudan University; Shuo Hong Wang, Fudan University; Yan Qiu Chen, Fudan University
|
|
534
|
KERNELIZED LEARNING in DEEP SCATTERING CONVOLUTION NETWORKS
|
Yuehan Xiong, Shanghai Jiao Tong University, Shanghai, P. R. China, 200240; Can Xu, Shanghai Jiao Tong University; Hongkai Xiong, Shanghai Jiao Tong University, China
|
|
53
|
Recurrent Convolutional Neural Network for Video Classification
|
Zhenqi Xu, BUPT; Weihong Deng, Beijing University of Posts and Telecommunications
|
|
58
|
WEAKLY-SUPERVISED DEEP SELF-LEARNING FOR FACE RECOGNITION
|
Binghui Chen, BUPT; Chi Zhang, BUPT; Weihong Deng, Beijing University of Posts and Telecommunications
|
256
+ |
LOCAL- AND HOLISTIC- STRUCTURE PRESERVING IMAGE SUPER RESOLUTION VIA DEEP JOINT COMPONENT LEARNING |
Yukai Shi, Sun Yat-set University; Liang Lin, Sun Yat-Sen University; Keze Wang, |
+ Paper 256 also appears in Oral Session Wed-AM-1: Visual Signal Processing.
|
Poster Session Thu-PO-2: Multimedia Search and Retrieval Chair: Winston Hsu, National Taiwan University, Taipei, Taiwan
|
110
|
ROBUST FEATURE ENCODING FOR AGE-INVARIANT FACE RECOGNITION
|
Xiaonan Hou, Shanghai Jiaotong University, China; Shouhong Ding, Shanghai Jiao Tong University; Lizhuang Ma, Shanghai Jiaotong University, China
|
|
321
|
Deep Learning Based Supervised Hashing for Efficient Image Retrieval
|
Viet Anh Nguyen, ADSC, Singapore; Minh N. Do,
|
|
538
|
Automatic Suggestion of Presentation Image for Storytelling
|
Yu Liu, University at Buffalo; Tao Mei, Microsoft; Chang Wen Chen, State University of New York at Buffalo
|
|
413
|
Improving the Similarity Estimation Via Score Distribution
|
Lixin Liao, Beijing Jiaotong University; Shikui Wei, Beijing Jiaotong University; Yao Zhao, Beijing Jiaotong University
|
|
105
|
Region Similarity Arrangement for Image Retrieval
|
Jingya Tang, ICT, CAS; Dongming Zhang, ICT, CAS; Yongdong Zhang, Chinese Academy of Sciences, China; Qi Tian , University of Texas at San Antonio, USA
|
|
48
|
Online Self-Organizing Hashing
|
Junxuan Chen, Shanghai Jiaotong University; Yaoyi Li, Shanghai Jiao Tong University; Hongtao Lu,
|
|
452
|
Compact and Robust Video Fingerprinting Using Sparse Represented Features
|
Bo Wu, Ccmu; Sridhar (Sri) Krishnan, Department of Electrical and Computer Engineering, Ryerson University; Nan Zhang, Biomedical Engineering School, Capital Medical School; Li Su,
|
|
*
|
Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset
|
Bor-Chun Chen, University of Maryland; Chu-Song Chen, Academia Sinica; Winston Hsu, National Taiwan University
|
|
* Paper originally published in IEEE Transactions on Multimedia, presented for first time at ICME 2016. |
Poster Session Thu-PO-3: Multimedia Systems and Applications Chair: Shao-Yi Chien, National Taiwan University, Taiwan
|
169
|
ADAPTIVE BACKGROUND FOR REAL-TIME VISUAL TRACKING
|
He Li, ; Daiqin Yang, ; Zhenzhong Chen, Wuhan University, China
|
|
317
|
User-Oriented Stereo Video Refocusing by Computational Cinematographic Model
|
Wenjing Geng, Nanjing University; Dapeng Du, Nanjing University; Tongwei Ren, Nanjing University; Gangshan Wu, Nanjing University
|
|
318
|
Example-based Video Color Transfer
|
Chun-Han Yao, NTU, EE department; Chia-Yang Chang, National Taiwan University ; Shao-Yi Chien, National Taiwan University, Taiwan
|
|
63
|
Automatic Image Dataset Construction with Multiple Textual Metadata
|
Yazhou Yao, Uni of Technology, Sydney; Jian Zhang, UTS; Fumin Shen, University of Electronic Science and Technology of China; xiansheng Hua, Alibaba Group; jingsong Xu, University of Technology Sydney; zhenmin tang, nanjing university of science and technology
|
|
106
|
SPEED-ADAPTIVE STREET VIEW IMAGE GENERATION USING DRIVING VIDEO RECORDER
|
Hua-Tsung Chen, National Chiao Tung Universit; Devi Eddy, National Chiao Tung University; Ray-Lin Chen, National Chiao Tung University; Chien-Li Chou, National Chiao Tung University
|
|
332
|
Bayesian Relevance Feedback Based Chinese Calligraphy Character Synthesis
|
Xueying Du, Zhejiang University; jiangqin Wu, Zhejiang university; Yang Xia,
|
325
+ |
SHAPE-OPTIMIZING HYBRID WARPING FOR IMAGE STITCHING |
Qingpeng Chai, Tianjin University; Shiguang Liu, Tianjin UniversityQingpeng Chai, Tianjin University; Shiguang Liu, Tianjin University |
|
+ Paper 325 also appears in Oral Session Wed-AM-1: Visual Signal Processing. |
Poster Session Thu-PO-4: Multimedia Coding Chair: Anil Fernando, University of Surrey, UK
|
531
|
An Improved Sparse Reconstruction Algorithm for Speech Compressive Sensing Using Structured Priors
|
Xiaobo Jiang, Shanghai Jiao Tong University; Rendong Ying, Shanghai Jiaotong University; Fei Wen, ; Sumxin Jiang, ; Peilin Liu, Shanghai Jiao Tong University
|
|
151
|
JOINT RATE ALLOCATION WITH BOTH LOOK-AHEAD AND FEEDBACK MODEL FOR HIGH EFFICIENCY VIDEO CODING
|
Hongfei Fan, Peking University; Lin Ding, ; Xiaodong Xie, ; Huizhu Jia, ; Wen Gao,
|
|
451
|
SATD-based Joint Decision Algorithm for Parallelized Intra Prediction Encoder in H.265/HEVC
|
YAO JEN CHANG, ITRI; Pei-Hsuan Tsai, ; Chun-Lung Lin, ITRI,Taiwan
|
|
491
|
Decoder Energy-Aware Intra-Coded HEVC Bit Stream Generation
|
Thanuja Mallikarachchi, University of Surrey; Dumidu Talagala, ; Hemantha Kodikara Arachchi, ; Anil Fernando,
|
|
473
|
Quality Assessment of Image Patches Distorted by Image Compression Using Crowdsourcing
|
Sebastian Bosse, Fraunhofer HHI; Mischa Siekmann, ; Jennifer Rasch, ; Wojceich Samek, ; Thomas Wiegand,
|
|
673
|
Multi-view Distributed Coding and Selection of Local Binary Features
|
Joao Ascenso, IST-IT; Fernando Pereira, ; Catarina Brites, IT; Nuno Monteiro, IST-IT
|
|
17
|
Codec Independent Region of Interest Video Coding using a Joint Pre- and Postprocessing Framework
|
Holger Meuel, Leibniz Universitaet Hannover; Marco Munderloh, Leibniz Universitaet Hannover; Florian Kluger, Leibniz Universitaet Hannover; Joern Ostermann, Leibniz Universitaet Hannover
|
|
115
|
Subjective-Quality-Optimized Complexity Control for HEVC Decoding
|
Ren Yang, Beihang University; Mai Xu, Beihang University; Lai Jiang, Beihang University; Zulin Wang, Beihang University
|
588
+ |
Lossless Depth Map Coding Using Binary Tree Based Decomposition and Context-Based Arithmetic Coding |
Shampa Shahriyar, Monash University; Manzur Murshed, Federation University Australia; Mortuza Ali, Federation University Australia; Manoranjan Paul, Charles Sturt University, Australia |
|
+ Paper 588 also appears in Oral Session Wed-PM-1: Visual Coding. |
16:00-17:20
Oral Session Thu-PM-1: Special Session on Deep Learning for Multimedia Computing Chairs: Benoit Huet (Eurecom), Jiebo Luo (University of Rochester), Guo-Jun Qi (University of Central Florida)
|
316
|
Deep Conditional Neural Network for Image Segmentation
|
Qiurui Wang, Tsinghua University; Chun Yuan, Tsinghua university; Yan Liu, Hong Kong Polytechnic University
|
|
314
|
On-premise Signs Detection and Recognition Using Fully Convolutional Networks
|
Yong-Xiang Wang, National Cheng Kung University; Chih-Hsin Hsueh, ; Hung-Yi Lo, ; Min-Chun Hu, National Cheng Kung University, Taiwan
|
|
33
|
DEEP ATTRIBUTE-EMBEDDING GRAPH RANKING FOR CROWD VIDEO RETRIEVAL
|
Yanhao Zhang, Harbin Institute of Technology; Lei Qin, Institute of Computing, Chinese Academy of Sciences ; Sicheng Zhao, Harbin Institute of Technology; Rongrong Ji, Xiamen University; Xiusheng Lu, Harbin Institute of Technology; Hongxun Yao, Harbin Institute of Technology; Qingming Huang, University of Chinese Academy of Science
|
|
577
|
MULTIMEDIA EVENT DETECTION VIA DEEP SPATIAL-TEMPORAL NEURAL NETWORKS
|
Jingyi Hou, Beijing Institute of Technolog; Xinxiao Wu, Beijing Institute of Technology; feiwu Yu, ; Yunde Jia, Beijing Institute of Technology
|
Oral Session Thu-PM-2: Crowd Analysis Chairs: Anthony Vetro, Mitsubishi Electric Research Laboratories, USA
|
243
|
EXAMPLE-BASED VISUAL OBJECT COUNTING WITH A SPARSITY CONSTRAINT
|
Yi Wang, Peking University; Yuexian Zou, Peking University; Jin Chen, Peking University; XiaoLin Huang, Peking University; Cheng Cai, Northwest A&F University
|
|
171
|
ROBUST LOW RANK DYNAMIC MODE DECOMPOSITION FOR COMPRESSED DOMAIN CROWD AND TRAFFIC FLOW ANALYSIS
|
Caglayan Dicle, Northeastern University; Hassan Mansour, MERL; Dong Tian, MERL; Mouhacine Benosman, MERL; Anthony Vetro, Mitsubishi Electric Research Laboratories
|
|
251
|
COST-SENSITIVE SPARSE LINEAR REGRESSION FOR CROWD COUNTING WITH IMBALANCED TRAINING DATA
|
XiaoLin Huang, PekingUniversity; Yuexian Zou, Peking University; Yi Wang, Peking University
|
|
428
|
Reliably detecting humans in crowded and dynamic environments using RGB-D camera
|
Luchao Tian, Fudan University; Guyue Zhang, Fudan University; Mingchen Li, Fudan University; Jun Liu, ; Yan Qiu Chen, Fudan University
|
Oral Session Thu-PM-3: Multimedia Networking and Communication Chair: Sanjeev Mehrotra, Microsoft, USA
|
692
|
Improve HEVC Load-balancing for Network Streaming using Adaptive Tile Boundary
|
Chia-Hsin Chan, National Chiao Tung University; Yu-Xiong Su, National Chiao Tung University; Wen-Jiin Tsai, National Chiao Tung University
|
|
423
|
A General PID-based Rate Adaptation Approach for TCP-based Live Streaming Over Mobile Networks
|
Jiexi Wang, Peking University; Shengbin Meng, Peking University; Jun Sun, Peking University; Zongming Guo, Peking University
|
|
265
|
Power Efficient Multi-view Video Streaming over Multi-antenna Wireless System
|
Zhe Chan, Fudan; Xu Zhang, ; Yuedong XU, Fudan University; Xin Wang, Fudan University
|
|
252
|
Attribute-Based Multi-Dimension Scalable Access Control For Social Media Sharing
|
Changsha Ma, University at Buffalo; Zhisheng Yan, University at Buffalo; Chang Wen Chen, State University of New York at Buffalo
|
Friday, July 15, 2016
- Workshop: Sparsity and Compressive Sensing in MM (MM-SPARSE), 9:00-17:00
- Workshop: Packet Video (PV), 9:00-17:00
- Workshop: Affective Social Multimedia Computing, 9:00-17:00
- Workshop: Privacy Issues in Multimedia (PIM), 9:00-12:00
- Workshop: Multimedia Big Data Processing and Analytics, 9:00-12:00
- Workshop: Multimedia Mobile Cloud for Smart City (MMCloudCity), 9:00-12:00
- Workshop: Multimedia Artworks Analysis (MMArt), 13:30-15:15
- Workshop: Multimedia for Cooking and Eating Activities (CEA), 13:30-16:10
- Morning Coffee Break: 10:15-10:45
- Lunch Break: 12:00-14:00
- Afternoon Coffee Break: 15:15-15:45
|


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+++ title = "Meininger hostel, London" slug = "meininger-hostel-london" date = 2007-10-29T12:00:00+10:00 tags = ["accommodation"] locations = ["england/london"] draft = false summary = """
Meininger London, an English branch of a German chain of hostels, is a funny one. Nice location, in South Kensington; and nice rooms, facilities, and all that. But it's a bit weird, as hostels go. Doesn't seem to be much of a backpacker place: I guess the fact that they have a conference centre, and proper hotel rooms as well, contributes to this being the case. Plus, not much of a hangout area anywhere, which is a bit disappointing. Anyway, it's good if you want to stay somewhere nice for a short time, and if you're not too concerned about meeting fellow travellers or not.
""" [[comments]] title = "pish posh" date = 2007-11-11T14:43:56+10:00 author_name = "your mum" content = """Jeremy, no wonder the hostel was so nice. South Kensington is a very expensive suburb, e.g. it's one of the suburbs where the royal family shop and Harrods is just down the road. Probably equivalent to Double Bay! Hope you didn't buy anything there, you would have been severely ripped off. Luv mum
""" +++Meininger London, an English branch of a German chain of hostels, is a funny one. Nice location, in South Kensington; and nice rooms, facilities, and all that. But it's a bit weird, as hostels go. Doesn't seem to be much of a backpacker place: I guess the fact that they have a conference centre, and proper hotel rooms as well, contributes to this being the case. Plus, not much of a hangout area anywhere, which is a bit disappointing. Anyway, it's good if you want to stay somewhere nice for a short time, and if you're not too concerned about meeting fellow travellers or not.
Rooms: very clean, very quiet, and very secure. No complaints at all!
People: strange crowd for a hostel — lots of families, lots of tour groups, and even a few business people. More of a hotel crowd, really.
Staff: they stay behind the reception desk, they give you the keys (nice, reliable RF electronic keys, by the way), and that's about it.
Location: South Kensington — nice area, with everything you need around the corner, including a tube station.
Food: the breakfast is buffet, but not an amazingly appetising selection. Kind of a school canteen menu.
Internet: total ripoff, £3/hour. Go around the corner, there are places that offer it for £1/hour.
Hot water: very good. The bathrooms in general are very good here.


Black to Play 31
b0-w0/5
pips Black=119 White=104
(CorrectAnswer= 0/0, ErrorScore= 0)
1. 5/2* 2/1 eq:+0.896 72.01% (G:45.17% B:6.59%) 27.99% (G: 9.49% B:0.19%) 2. 20/16 eq:+0.718 (-0.178) 72.61% (G:33.20% B:4.03%) 27.39% (G:11.76% B:0.18%) 3. 6/2* eq:+0.625 (-0.271) 68.25% (G:37.89% B:5.66%) 31.75% (G:18.50% B:0.35%) 4. 20/17 6/5 eq:+0.686 (-0.210) 73.75% (G:31.60% B:2.53%) 26.25% (G: 9.60% B:0.18%) 5. 4/3 4/1 eq:+0.633 (-0.262) 71.32% (G:30.29% B:2.01%) 28.68% (G: 5.74% B:0.05%)


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I have been told that I have an old wise soul, and I take that as a complement. However, I too am a young soul. Filled with idea and hope for the future of this world.  I have been on the most amazing adventures to the most amazing places in the world. I have see the most amazing people, both good and bad. And I have taken not of it.
I have had an amazing life so far, with a few bumps here and there that keeps things interesting. Because I have had such a blessed life, done so much, seen so much, been so much, I plan to give back to the world that has givin to me. My goal with what I am doing here at DBC, it to learn the skills needed to change the world and all who inhabit it. The first part of my plan is provide education, true education not memorization, to people who want it. Only after people become educated can we start to fix this world.
I am a man of many things, goals included. So because you are reading this, just know that I am so serious about everything I say and do. People tend to not believe me at first, but after a while they become believers.
Would you like to change the world with me?
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