2021
Authors: C. Zhou and M. R.D. Rodrigues
Journal/Conference: IEEE Transactions on Geoscience and Remote Sensing
Abstract: Hyperspectral image (HSI) unmixing is an increasingly studied problem in various areas, including remote sensing. It has been tackled using both physical model-based approaches and more recently machine learning-based ones. In this paper, we propose a new HSI unmixing algorithm combining both model- and learning-based techniques, based on algorithm unrolling approaches, delivering improved unmixing performance. Our approach unrolls the Alternating Direction Method of Multipliers (ADMM) solver of a constrained sparse regression problem underlying a linear mixture model. We then propose a neural network structure for abundance estimation that can be trained using supervised learning techniques based on a new composite loss function. We also propose another neural network structure for blind unmixing that can be trained using unsupervised learning techniques. Our proposed networks are also shown to possess a lighter and richer structure containing less learnable parameters and more skip connections compared with other competing architectures. Extensive experiments show that the proposed methods can achieve much faster convergence and better performance even with a very small training dataset size when compared with other unmixing methods such as MNN-AE&BU, UnDIP and EGU-Net.
LinkAuthors: J. Xu, B. Ai, W. Chen, A. Yang, P. Sun, and M. R. D. Rodrigues
Journal/Conference: IEEE Transactions on Circuits and Systems for Video Technology
Abstract: Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.
LinkAuthors: J. Amjad, Z. Lyu, and M. R. D. Rodrigues
Journal/Conference: IEEE Transactions on Signal Processing
Abstract: There are various inverse problems – including reconstruction problems arising in medical imaging - where one is often aware of the forward operator that maps variables of interest to the observations. It is therefore natural to ask whether such knowledge of the forward operator can be exploited in deep learning approaches increasingly used to solve inverse problems. In this paper, we provide one such way via an analysis of the generalisation error of deep learning approaches to inverse problems. In particular, by building on the algorithmic robustness framework, we offer a generalisation error bound that encapsulates key ingredients associated with the learning problem such as the complexity of the data space, the size of the training set, the Jacobian of the deep neural network and the Jacobian of the composition of the forward operator with the neural network. We then propose a ‘plug-and-play’ regulariser that leverages the knowledge of the forward map to improve the generalization of the network. We likewise also use a new method allowing us to tightly upper bound the Jacobians of the relevant operators that is much more computationally efficient than existing ones. We demonstrate the efficacy of our model-aware regularised deep learning algorithms against other state-of-the-art approaches on inverse problems involving various sub-sampling operators such as those used in classical compressed sensing tasks, image super-resolution problems and accelerated Magnetic Resonance Imaging (MRI) setups.
LinkAuthors: G. Aminian, Y. Bu, L. Toni, M. R. D. Rodrigues, and G. Wornell
Journal/Conference: Neural Information Processing Systems (NeurIPS)
Abstract: Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.
LinkAuthors: N. Martinez, M. A. Bertran, A. Papadaki, M. R. D. Rodrigues, and G. Sapiro
Journal/Conference: International Conference on Machine Learning (ICML)
Abstract: Much of the work in the field of group fairness addresses disparities between predefined groups based on protected features such as gender, age, and race, which need to be available at train, and often also at test, time. These approaches are static and retrospective, since algorithms designed to protect groups identified a priori cannot anticipate and protect the needs of different at-risk groups in the future. In this work we analyze the space of solutions for worst-case fairness beyond demographics, and propose Blind Pareto Fairness (BPF), a method that leverages no-regret dynamics to recover a fair minimax classifier that reduces worst-case risk of any potential subgroup of sufficient size, and guarantees that the remaining population receives the best possible level of service. BPF addresses fairness beyond demographics, that is, it does not rely on predefined notions of at-risk groups, neither at train nor at test time. Our experimental results show that the proposed framework improves worst-case risk in multiple standard datasets, while simultaneously providing better levels of service for the remaining population.
LinkAuthors: M. Ferianc, P. Maji, M. Mattina, and M. R. D. Rodrigues
Journal/Conference: Conference on Uncertainty in Artificial Intelligence (UAI)
Abstract: Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation. Being able to quantify uncertainty while making decisions is essential for understanding when the model is over-/under-confident, and hence BNNs are attracting interest in safety-critical applications, such as autonomous driving, healthcare, and robotics. Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their increased memory and compute costs. In this work, we investigate quantisation of BNNs by compressing 32-bit floating-point weights and activations to their integer counterparts, that has already been successful in reducing the compute demand in standard pointwise neural networks. We study three types of quantised BNNs, we evaluate them under a wide range of different settings, and we empirically demonstrate that a uniform quantisation scheme applied to BNNs does not substantially decrease their quality of uncertainty estimation.
Link2020
Authors: M. Zamani, J. Sokolic, D. Jiang, F. Renna, M. R. D. Rodrigues, and A. Demosthenous
Journal/Conference: IEEE Transactions on Biomedical Circuits and Systems
Abstract: This paper presents an adaptable dictionary-based feature extraction approach for spike sorting offering high accuracy and low computational complexity for implantable applications. It extracts and learns identifiable features from evolving subspaces through matched unsupervised subspace filtering. To provide compatibility with the strict constraints in implantable devices such as the chip area and power budget, the dictionary contains arrays of { −1,0 and 1 and the algorithm need only process addition and subtraction operations. Three types of such dictionary were considered. To quantify and compare the performance of the resulting three feature extractors with existing systems, a neural signal simulator based on several different libraries was developed. For noise levels σN between 0.05 and 0.3 and groups of 3 to 6 clusters, all three feature extractors provide robust high performance with average classification errors of less than 8% over five iterations, each consisting of 100 generated data segments. To our knowledge, the proposed adaptive feature extractors are the first able to classify reliably 6 clusters for implantable applications. An ASIC implementation of the best performing dictionary-based feature extractor was synthesized in a 65-nm CMOS process. It occupies an area of 0.09 mm2 and dissipates up to about 10.48 μW from a 1 V supply voltage, when operating with 8-bit resolution at 30 kHz operating frequency.
LinkAuthors: Z. Sabetsarvestani, F. Renna, F. Kiraly and M. R. D. Rodrigues
Journal/Conference: IEEE Transactions on Signal Processing
Abstract: n this paper, we propose an algorithm for source separation with side information where one observes the linear superposition of two source signals plus two additional signals that are correlated with the mixed ones. Our algorithm is based on two ingredients: first, we learn a Gaussian mixture model (GMM) for the joint distribution of a source signal and the corresponding correlated side information signal; second, we separate the signals using standard computationally efficient conditional mean estimators. The paper also puts forth new recovery guarantees for this source separation algorithm. In particular, under the assumption that the signals can be perfectly described by a GMM model, we characterize necessary and sufficient conditions for reliable source separation in the asymptotic regime of low-noise as a function of the geometry of the underlying signals and their interaction. It is shown that if the subspaces spanned by the innovation components of the source signals with respect to the side information signals have zero intersection, provided that we observe a certain number of linear measurements from the mixture, then we can reliably separate the sources; otherwise we cannot. Our proposed framework – which provides a new way to incorporate side information to aid the solution of source separation problems where the decoder has access to linear projections of superimposed sources and side information – is also employed in a real-world art investigation application involving the separation of mixtures of X-ray images. The simulation results showcase the superiority of our algorithm against other state-of-the-art algorithms.
LinkAuthors: Rodrigues, M. R. D. and Eldar, Y. C.
Journal/Conference: Cambridge University Press
Abstract: Learn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine learning, and statistics.
Link2019
Authors: Z. Sabetsarvestani, B. Sober, C. Higgitt, I. Daubechies, M. R. D. Rodrigues
Journal/Conference: Science Advances
Abstract: X-ray images of polyptych wings, or other artworks painted on both sides of their support, contain in one image content from both paintings, making them difficult for experts to “read.” To improve the utility of these x-ray images in studying these artworks, it is desirable to separate the content into two images, each pertaining to only one side. This is a difficult task for which previous approaches have been only partially successful. Deep neural network algorithms have recently achieved remarkable progress in a wide range of image analysis and other challenging tasks. We, therefore, propose a new self-supervised approach to this x-ray separation, leveraging an available convolutional neural network architecture; results obtained for details from the Adam and Eve panels of the Ghent Altarpiece spectacularly improve on previous attempts.
LinkAuthors: P. Song, X. Deng, J. F. C. Mota, N. Deligiannis, P.-L. Dragotti, and M. R. D. Rodrigues
Journal/Conference: IEEE Transactions on Computational Imaging
Abstract: Real-world data processing problems often involve various image modalities associated with a certain scene, including RGB images, infrared images or multi-spectral images. The fact that different image modalities often share certain attributes, such as edges, textures and other structure primitives, represents an opportunity to enhance various image processing tasks. This paper proposes a new approach to construct a high-resolution (HR) version of a low-resolution (LR) image given another HR image modality as guidance, based on joint sparse representations induced by coupled dictionaries. The proposed approach captures complex dependency correlations, including similarities and disparities, between different image modalities in a learned sparse feature domain in lieu of the original image domain. It consists of two phases: coupled dictionary learning phase and coupled super-resolution phase. The learning phase learns a set of dictionaries from the training dataset to couple different image modalities together in the sparse feature domain. In turn, the super-resolution phase leverages such dictionaries to construct a HR version of the LR target image with another related image modality for guidance. In the advanced version of our approach, multi-stage strategy and neighbourhood regression concept are introduced to further improve the model capacity and performance. Extensive guided image super-resolution experiments on real multimodal images demonstrate that the proposed approach admits distinctive advantages with respect to the state-of-the-art approaches, for example, overcoming the texture copying artifacts commonly resulting from inconsistency between the guidance and target images. Of particular relevance, the proposed model demonstrates much better robustness than competing deep models in a range of noisy scenarios.
LinkAuthors: X. Deng, P. Song, M. R. D. Rodrigues, and P.-L. Dragotti
Journal/Conference: IEEE Transactions on Circuits and Systems for Video Technology
Abstract: Depth image super-resolution is a challenging problem, since normally high upscaling factors are required (e.g., 16×), and depth images are often noisy. In order to achieve large upscaling factors and resilience to noise, we propose a Robust Algorithm for Depth imAge super Resolution (RADAR) that combines the power of finite rate of innovation (FRI) theory with multimodal dictionary learning. Given a low-resolution (LR) depth image, we first model its rows and columns as piece-wise polynomials and propose a FRI-based depth upscaling (FDU) algorithm to super-resolve the image. Then, the upscaled moderate quality (MQ) depth image is further enhanced with the guidance of a registered high-resolution (HR) intensity image. This is achieved by learning multimodal mappings from the joint MQ depth and HR intensity pairs to the HR depth, through a recently proposed triple dictionary learning (TDL) algorithm. Moreover, to speed up the super-resolution process, we introduce a new projection-based rapid upscaling (PRU) technique that pre-calculates the projections from the joint MQ depth and HR intensity pairs to the HR depth. Compared with state-of-the-art deep learning based methods, our approach has two distinct advantages: we need a fraction of training data but can achieve the best performance, and we are resilient to mismatches between training and testing datasets. Extensive numerical results show that the proposed method outperforms other state-of-the-art methods on either noise-free or noisy datasets with large upscaling factors up to 16× and can handle unknown blurring kernels well.
LinkAuthors: N. Shlezinger, Y. C. Eldar, and M. R. D. Rodrigues
Journal/Conference: IEEE Transactions on Signal Processing
Abstract: Quantizers take part in nearly every digital signal processing system which operates on physical signals. They are commonly designed to accurately represent the underlying signal, regardless of the specific task to be performed on the quantized data. In systems working with high-dimensional signals, such as massive multiple-input multiple-output (MIMO) systems, it is beneficial to utilize low-resolution quantizers, due to cost, power, and memory constraints. In this work we study quantization of high-dimensional inputs, aiming at improving performance under resolution constraints by accounting for the system task in the quantizers design. We focus on the task of recovering a desired signal statistically related to the high-dimensional input, and analyze two quantization approaches: We first consider vector quantization, which is typically computationally infeasible, and characterize the optimal performance achievable with this approach. Next, we focus on practical systems which utilize hardware-limited scalar uniform analog-to-digital converters (ADCs), and design a taskbased quantizer under this model. The resulting system accounts for the task by linearly combining the observed signal into a lower dimension prior to quantization. We then apply our proposed technique to channel estimation in massive MIMO networks. Our results demonstrate that a system utilizing low-resolution scalar ADCs can approach the optimal channel estimation performance by properly accounting for the task in the system design.
LinkAuthors: N. Shlezinger, Y. C. Eldar, and M. R. D. Rodrigues
Journal/Conference: IEEE Transactions on Signal Processing
Abstract: Quantization plays a critical role in digital signal processing systems. Quantizers are typically designed to obtain an accurate digital representation of the input signal, operating independently of the system task, and are commonly implemented using serial scalar analog-to-digital converters (ADCs). In this work, we study hardware-limited task-based quantization, where a system utilizing a serial scalar ADC is designed to provide a suitable representation in order to allow the recovery of a parameter vector underlying the input signal. We propose hardware-limited task-based quantization systems for a fixed and finite quantization resolution, and characterize their achievable distortion. We then apply the analysis to the practical setups of channel estimation and eigen-spectrum recovery from quantized measurements. Our results illustrate that properly designed hardware-limited systems can approach the optimal performance achievable with vector quantizers, and that by taking the underlying task into account, the quantization error can be made negligible with a relatively small number of bits.
LinkAuthors: P. Song, L. Weizmann, J. M. C. Mota, Y. Eldar, and M. R. D. Rodrigues
Journal/Conference: IEEE Transactions on Medical Imaging
Abstract: Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-weighted, T2-weighted and Fluid-attenuated inversion recovery (FLAIR) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities in either structure level or gray level. In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage the dependency correlation between different contrasts for guided or joint reconstruction from their under-sampled k-space data. Our approach iterates between three stages: coupled dictionary learning, coupled sparse denoising, and enforcing k-space consistency. The first stage learns a set of dictionaries that not only are adaptive to the contrasts, but also capture correlations among multiple contrasts in a sparse transform domain. By capitalizing on the learned dictionaries, the second stage performs coupled sparse coding to remove the aliasing and noise in the corrupted contrasts. The third stage enforces consistency between the denoised contrasts and the measurements in the k-space domain. Numerical experiments, consisting of retrospective under-sampling of various MRI contrasts with a variety of sampling schemes, demonstrate that CDLMRI is capable of capturing structural dependencies between different contrasts. The learned priors indicate notable advantages in multi-contrast MR imaging and promising applications in quantitative MR imaging such as MR fingerprinting.
LinkAuthors: P. Song, G. Mazor, Y. C. Eldar, and M. R. D. Rodrigues
Journal/Conference: Medical Physics
Abstract: PURPOSE:
Magnetic resonance fingerprinting (MRF) methods typically rely on dictionary matching to map the
temporal MRF signals to quantitative tissue parameters. Such approaches suffer from inherent
discretization errors, as well as high computational complexity as the dictionary size grows. To
alleviate these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting (HYDRA) approach,
referred to as HYDRA.
METHODS:
HYDRA involves two stages: a model-based signature restoration phase and a learning-based parameter
restoration phase. Signal restoration is implemented using low-rank based de-aliasing techniques
while
parameter restoration is performed using a deep nonlocal residual convolutional neural network. The
designed network is trained on synthesized MRF data simulated with the Bloch equations and fast
imaging with steady-state precession (FISP) sequences. In test mode, it takes a temporal MRF signal
as
input and produces the corresponding tissue parameters.
RESULTS:
We validated our approach on both synthetic data and anatomical data generated from a healthy
subject.
The results demonstrate that, in contrast to conventional dictionary matching-based MRF techniques,
our approach significantly improves inference speed by eliminating the time-consuming dictionary
matching operation, and alleviates discretization errors by outputting continuous-valued parameters.
We further avoid the need to store a large dictionary, thus reducing memory requirements.
CONCLUSIONS:
Our approach demonstrates advantages in terms of inference speed, accuracy, and storage requirements
over competing MRF methods.
Authors: M. A. Bertran, N. Martinez, A. Papadaki, Q. Qiu, M. R. D. Rodrigues, G. Reeves, and G. Sapiro.
Journal/Conference: International Conference on Machine Learning (ICML)
Abstract: Data collection and sharing are pervasive aspects of modern society. This process can either be voluntary, as in the case of a person taking a facial image to unlock his/her phone, or incidental, such as traffic cameras collecting videos on pedestrians. An undesirable side effect of these processes is that shared data can carry information about attributes that users might consider as sensitive, even when such information is of limited use for the task. It is therefore desirable for both data collectors and users to design procedures that minimize sensitive information leakage. Balancing the competing objectives of providing meaningful individualized service levels and inference while obfuscating sensitive information is still an open problem. In this work, we take an information theoretic approach that is implemented as an unconstrained adversarial game between Deep Neural Networks in a principled, data-driven manner. This approach enables us to learn domain-preserving stochastic transformations that maintain performance on existing algorithms while minimizing sensitive information leakage.
Link2018
Authors: Rodrigues, M., Bolcskei, H., Draper, S., Eldar, Y., & Tan, V
Journal/Conference: IEEE Journal on Selected Topics in Signal Processing
Abstract: The twenty papers that are included in this special section explore applications of information theoretic methods to emerging data science problems. In particular, the papers cover a wide range of topics that can broadly be organized into four themes: (1) data acquisition, (2) data analysis and processing, (3) statistics and machine learning, and (4) privacy and fairness.
LinkAuthors: Chen, M., Renna, F., & Rodrigues, M. R. D
Journal/Conference: IEEE Transactions on Signal Processing
Abstract: This paper studies how to optimally capture side information to aid in the reconstruction of high-dimensional signals from low-dimensional random linear and noisy measurements, by assuming that both the signal of interest and the side information signal are drawn from a joint Gaussian mixture model. In particular, we derive sufficient and (occasionally) necessary conditions on the number of linear measurements for the signal reconstruction minimum mean squared error (MMSE) to approach zero in the low-noise regime; moreover, we also derive closed-form linear side information measurement designs for the reconstruction MMSE to approach zero in the low-noise regime. Our designs suggest that a linear projection kernel that optimally captures side information is such that it measures the attributes of side information that are maximally correlated with the signal of interest. A number of experiments both with synthetic and real data confirm that our theoretical results are well aligned with numerical ones. Finally, we offer a case study associated with a panchromatic sharpening (pan sharpening) application in the presence of compressive hyperspectral data that demonstrates that our proposed linear side information measurement designs can lead to reconstruction peak signal-to-noise ratio (PSNR) gains in excess of 2 dB over other approaches in this practical application.
Link2017
Authors: Deligiannis, N., Mota, J. F. C., Zimos, E., & Rodrigues, M. R. D
Journal/Conference: IEEE Transactions on Communications
Abstract: Large-scale data collection by means of wireless sensor network and Internet-of-Things technology poses various challenges in view of the limitations in transmission, computation, and energy resources of the associated wireless devices. Compressive data gathering based on compressed sensing has been proven a well-suited solution to the problem. Existing designs exploit the spatiotemporal correlations among data collected by a specific sensing modality. However, many applications, such as environmental monitoring, involve collecting heterogeneous data that are intrinsically correlated. In this paper, we propose to leverage the correlation from multiple heterogeneous signals when recovering the data from compressive measurements. To this end, we propose a novel recovery algorithm-built upon belief-propagation principles-that leverages correlated information from multiple heterogeneous signals. To efficiently capture the statistical dependencies among diverse sensor data, the proposed algorithm uses the statistical model of copula functions. Experiments with heterogeneous air-pollution sensor measurements show that the proposed design provides significant performance improvements against the state-of-the-art compressive data gathering and recovery schemes that use classical compressed sensing, compressed sensing with side information, and distributed compressed sensing.
LinkAuthors: Sokolic, J., Giryes, R., Sapiro, G., & Rodrigues, M. R. D
Journal/Conference: IEEE Transactions on Signal Processing
Abstract: The generalization error of deep neural networks via their classification margin is studied in this work. Our approach is based on the Jacobian matrix of a deep neural network and can be applied to networks with arbitrary non-linearities and pooling layers, and to networks with different architectures such as feed forward networks and residual networks. Our analysis leads to the conclusion that a bounded spectral norm of the network's Jacobian matrix in the neighbourhood of the training samples is crucial for a deep neural network of arbitrary depth and width to generalize well. This is a significant improvement over the current bounds in the literature, which imply that the generalization error grows with either the width or the depth of the network. Moreover, it shows that the recently proposed batch normalization and weight normalization re-parametrizations enjoy good generalization properties, and leads to a novel network regularizer based on the network's Jacobian matrix. The analysis is supported with experimental results on the MNIST, CIFAR-10, LaRED and ImageNet datasets.
LinkAuthors: Mota, J. F. C., Deligiannis, N., & Rodrigues, M. R. D
Journal/Conference: IEEE Transactions on Information Theory
Abstract: We address the problem of compressed sensing (CS) with prior information: reconstruct a target CS signal with the aid of a similar signal that is known beforehand, our prior information. We integrate the additional knowledge of the similar signal into CS via l 1 -l 1 and l 1 -l 2 minimization. We then establish bounds on the number of measurements required by these problems to successfully reconstruct the original signal. Our bounds and geometrical interpretations reveal that if the prior information has good enough quality, l 1 -l 1 minimization improves the performance of CS dramatically. In contrast, l 1 -l 2 minimization has a performance very similar to classical CS, and brings no significant benefits. In addition, we use the insight provided by our bounds to design practical schemes to improve prior information. All our findings are illustrated with experimental results.
LinkAuthors: J. Sokolic, R. Gyries, G. Sapiro, and M. R. D. Rodrigue
Journal/Conference: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS)
Abstract: This paper studies the generalization error of invariant classifiers. In particular, we consider the common scenario where the classification task is invariant to certain transformations of the input, and that the classifier is constructed (or learned) to be invariant to these transformations. Our approach relies on factoring the input space into a product of a base space and a set of transformations. We show that whereas the generalization error of a non-invariant classifier is proportional to the complexity of the input space, the generalization error of an invariant classifier is proportional to the complexity of the base space. We also derive a set of sufficient conditions on the geometry of the base space and the set of transformations that ensure that the complexity of the base space is much smaller than the complexity of the input space. Our analysis applies to general classifiers such as convolutional neural networks. We demonstrate the implications of the developed theory for such classifiers with experiments on the MNIST and CIFAR-10 datasets.
Link2016
Authors: Renna, F., Wang, L., Yuan, X., Yang, J., Reeves, G., Calderbank, R., Rodrigues, M. R. D
Journal/Conference: IEEE Transactions on Information Theory
Abstract: This paper offers a characterization of fundamental limits on the classification and reconstruction of high-dimensional signals from low-dimensional features, in the presence of side information. We consider a scenario where a decoder has access both to linear features of the signal of interest and to linear features of the side information signal; while the side information may be in a compressed form, the objective is recovery or classification of the primary signal, not the side information. The signal of interest and the side information are each assumed to have (distinct) latent discrete labels; conditioned on these two labels, the signal of interest and side information are drawn from a multivariate Gaussian distribution that correlates the two. With joint probabilities on the latent labels, the overall signal-(side information) representation is defined by a Gaussian mixture model. By considering bounds to the misclassification probability associated with the recovery of the underlying signal label, and bounds to the reconstruction error associated with the recovery of the signal of interest itself, we then provide sharp sufficient and/or necessary conditions for these quantities to approach zero when the covariance matrices of the Gaussians are nearly low rank. These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are the function of the number of linear features extracted from signal of interest, the number of linear features extracted from the side information signal, and the geometry of these signals and their interplay. Moreover, on assuming that the signal of interest and the side information obey such an approximately low-rank model, we derive the expansions of the reconstruction error as a function of the deviation from an exactly low-rank model; such expansions also allow the identification of operational regimes, where the impact of side information on signal reconstruction is most relevant. Our framework, which offers a principled mechanism to integrate side information in high-dimensional data problems, is also tested in the context of imaging applications. In particular, we report state-of-theart results in compressive hyperspectral imaging applications, where the accompanying side information is a conventional digital photograph.
LinkAuthors: Deligiannis, N., Mota, J. F. C., Cornelis, B., Rodrigues, M. R. D., & Daubechies, I.
Journal/Conference: IEEE Transactions on Image Processing
Abstract: In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front-and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component captures features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data - taken from digital acquisition of the Ghent Altarpiece (1432) - confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation.
LinkAuthors: Reboredo, H., Renna, F., Calderbank, R., & Rodrigues, M. R. D
Journal/Conference: IEEE Transactions on Signal Processing
Abstract: This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient conditions) on the number of measurements required to drive the probability of misclassification to zero in the low-noise regime, both for random measurements and designed ones. Such bounds reveal two important operational regimes that are a function of the characteristics of the source: 1) when the number of classes is less than or equal to the dimension of the space spanned by signals in each class, reliable classification is possible in the low-noise regime by using a one-vs-all measurement design; 2) when the dimension of the spaces spanned by signals in each class is lower than the number of classes, reliable classification is guaranteed in the low-noise regime by using a simple random measurement design. Simulation results both with synthetic and real data show that our analysis is sharp, in the sense that it is able to gauge the number of measurements required to drive the misclassification probability to zero in the low-noise regime.
LinkAuthors: Mota, J. F. C., Deligiannis, N., Sankaranarayanan, A. C., Cevher, V., & Rodrigues, M. R. D.
Journal/Conference: IEEE Transactions on Signal Processing
Abstract: We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements. The signals are assumed sparse, with unknown support, and evolve over time according to a generic nonlinear dynamical model. Our algorithm, based on recent theoretical results for l1 - l1 minimization, is recursive and computes the number of measurements to be taken at each time on-the-fly. As an example, we apply the algorithm to online compressive video foreground extraction, a problem stated as follows: given a set of measurements of a sequence of images with a static background, simultaneously reconstruct each image while separating its foreground from the background. The performance of our method is illustrated on sequences of real images. We observe that it allows a dramatic reduction in the number of measurements or reconstruction error with respect to state-of-the-art compressive background subtraction schemes.
LinkAuthors: Wang, L., Chen, M., Rodrigues, M., Wilcox, D., Calderbank, R., & Carin, L.
Journal/Conference: IEEE Transactions on Pattern Analysis and Machine Intelligence
Abstract: An information-theoretic projection design framework is proposed, of interest for feature design and compressive measurements. Both Gaussian and Poisson measurement models are considered. The gradient of a proposed information-theoretic metric (ITM) is derived, and a gradient-descent algorithm is applied in design; connections are made to the information bottleneck. The fundamental solution structure of such design is revealed in the case of a Gaussian measurement model and arbitrary input statistics. This new theoretical result reveals how ITM parameter settings impact the number of needed projection measurements, with this verified experimentally. The ITM achieves promising results on real data, for both signal recovery and classification.
LinkAuthors: Sokolic, J., Renna, F., Calderbank, R., & Rodrigues, M. R. D
Journal/Conference: IEEE Transactions on Signal Processing
Abstract: This paper considers the classification of linear subspaces with mismatched classifiers. In particular, we assume a model where one observes signals in the presence of isotropic Gaussian noise and the distribution of the signals conditioned on a given class is Gaussian with a zero mean and a low-rank covariance matrix. We also assume that the classifier knows only a mismatched version of the parameters of input distribution in lieu of the true parameters. By constructing an asymptotic low-noise expansion of an upper bound to the error probability of such a mismatched classifier, we provide sufficient conditions for reliable classification in the low-noise regime that are able to sharply predict the absence of a classification error floor. Such conditions are a function of the geometry of the true signal distribution, the geometry of the mismatched signal distributions as well as the interplay between such geometries, namely, the principal angles and the overlap between the true and the mismatched signal subspaces. Numerical results demonstrate that our conditions for reliable classification can sharply predict the behavior of a mismatched classifier both with synthetic data and in a motion segmentation and a hand-written digit classification applications.
LinkAuthors: Sokolic, J., Gyries, R., Sapiro, G., & Rodrigues, M. R. D.
Journal/Conference: International Conference on Learning Representations (ICLR)
Abstract: Understanding the generalization properties of deep learning models is critical for successful applications, especially in the regimes where the number of training samples is limited. We study the generalization properties of deep neural networks via the empirical Rademacher complexity and show that it is easier to control the complexity of convolutional networks compared to general fully connected networks. In particular, we justify the usage of small convolutional kernels in deep networks as they lead to a better generalization error. Moreover, we propose a representation based regularization method that allows to decrease the generalization error by controlling the coherence of the representation. Experiments on the MNIST dataset support these foundations.
Link


We redefine infrastructure!
- syndicate global convergence
- expedite 24/7 markets
- redefine bricks-and-clicks solutions
- embrace one-to-one functionalities
- leverage scalable niches
- orchestrate extensible models
- disintermediate best-of-breed niches
- monetize visionary applications
- leverage value-added web-readiness
- utilize one-to-one initiatives
- drive dynamic paradigms
- seize real-time functionalities


Show specular highlight
Use lighting
Texture:
None
Galvanized
Earth
Material:
Shininess:
Point light:
Location:
X:
Y:
Z:
Specular colour:
R:
G:
B:
Diffuse colour:
R:
G:
B:
Ambient light:
Colour:
R:
G:
B:
Galvanized texture courtesy of
Arroway Textures
.
Earth texture courtesy of
the European Space Agency/Envisat
.


Students
Features
Team
Login
Making it easier to help students.
View student help requests and assign tickets to yourself and others
Get Started


Access the admin application to perform tasks like content and user management.
For users familiar with swagger, this is an interface to explore the API methods. Although it's recommended to use the HAL explorer.
Use the HAL explorer to navigate the entities and their relationships.


Where you work affects your productivity, happiness and success. To all the hustlers, entrepreneurs, techies, and creatives out there: this is your zone.
Community matters. You find inspiration and belonging in those around you. Our spaces create a loving and productive environment. We want you to add to it.


WITNESS LIST
Energy Resources Committee
February 13, 2013 - 2:00 PM or upon final adjourn./recess
The current status of water use in the oil and gas production.
On:
Barron, Andrew (Self; Fracensure and VSW, inc)
Dunkel, Michael (Pioneer)
Ewing, Jay (Devon Energy)
Gesoff, Glenn (TXOGA Water Committee and BP)
Halldorson, Brent (Self; Texas Water Recycling Association)
Holt, David (Colorado river municipal water district)
Jester, Stephen (Conocophillips)
North, Gerald (Self; Texas A&M University)
Pomeroy, Cory (Texas Oil and Gas Association)
Robertson, Michael (Self)
Sinard, Karen (Oxy)
Sumner, Warren (Self)
Registering, but not testifying:
On:
Rila, Kellye (Texas Commission on Environmental Quality)
Streibich, Douglas (Mcr oil tools, llc)
Wilson, Kim (Texas Commission on Environmental Quality)
1


const { CONFIG } = require('./config')
const { PATHS, ERRORS, AUTH_METHODS, ENVIRONMENT } = require('./consts')
const { HttpClient } = require('./http-client')
/**
* @typedef {string} KYCStatus
* @description KYC Status | Description
* :--------- | :----------
* Pending | Waiting on user to provide documents for verification
* Completed | User has provided documents and is waiting to be verified
* Passed | User was successfully verified
* Rejected | User has failed verification
*/
/**
* @typedef {object} BalanceResponse
* @description Contains balances for all supported coins.
*/
/**
* @typedef {object} InterestResponse
* @description Contains interest earned so far for all supported coins and total interest earned in usd.
*/
/**
* @typedef {object} StatisticsResponse
* @description Contains statistical data about users.
*
* @property {string | number} depositCount - Contains number of deposits made by the user
* @property {Object} depositAmount - Contains total amount deposited per coin as well as total amount deposited in usd
* @property {string | number} withdrawalCount - Contains number of withdrawals made by the user
* @property {Object} withdrawalAmount - Contains total amount withdrawn per coin as well as total amount withdrawn in usd
* @property {string | number} interestCount - Contains number of interests earned by the user
* @property {Object} interestAmount - Contains total amount earned per coin as well as total amount earned in usd
*/
/**
* @typedef {Object} Pagination
* @property {number} page - Which page will be returned
* @property {number} perPage - Number of results to be returned per page
*/
/**
* @typedef {Object} FormFields
* @property {string} address - Address where funds need to be sent
* @property {number} amount
*/
/**
* @typedef {Object} Documents
* @property {string} document_front_image
* @property {string} document_back_image
*/
/**
* @typedef {Object} UserData
* @property {string} first_name
* @property {string} last_name
* @property {string} date_of_birth
* @property {string} citizenship
* @property {string} country
* @property {string} state
* @property {string} city
* @property {string} zip
* @property {string} street
* @property {string} building_number
* @property {string} flat_number
* @property {string} itin
* @property {string} national_id
* @property {string} ssn
* @property {string} middle_name
* @property {string} title
* @property {string} gender
* @property {string} phone_number
* @property {string} document_type
*/
/**
* @typedef {object} CelsiusInstance
* @property {function} getKycStatus - Method for sending GET requests
* @property {function} verifyKyc - Method for sending POST requests. Data is transmitted using multipart/form-data
*/
/**
* Celsius js SDK Core
* @module celsius-js-sdk/core
*/
/**
* @description Creates and configures Core.
*
* @function
* @param {object} config - Config has partner key, auth method and user secret.
*
* @returns {Promise<CelsiusInstance>}
*/
const Celsius = function (config) {
let defaultConfig = CONFIG[config.environment]
if (!defaultConfig) {
defaultConfig = CONFIG[ENVIRONMENT.PRODUCTION]
}
config = Object.assign({}, defaultConfig, config)
if (config.authMethod !== AUTH_METHODS.API_KEY && config.authMethod !== AUTH_METHODS.USER_TOKEN) {
throw new Error(ERRORS.INVALID_AUTH_METHOD)
}
if (!config.partnerKey) {
throw new Error(ERRORS.INVALID_PARTNER_KEY)
}
const httpClient = HttpClient(config)
const CelsiusInstance = {
/**
* Contains supported currencies. To refresh, call getSupportedCurrencies method.
* @memberOf module:celsius-js-sdk/core
* @member {String[]} currencies
*
*/
currencies: null,
/**
* Returns KYC status for the given user.
* If partner authenticates users via api-keys, status returned will always be Passed because users are required to
* pass KYC before creating api keys.
*
* @memberOf module:celsius-js-sdk/core
* @function getKycStatus
* @param userSecret {string} - Represents a secret value used to uniquely identify users. Can be user-token or api-key
*
* @returns {KYCStatus | Error}
*
* Error name | Description
* :--------------- | :----------
* Unauthorized | Required user secret and partner key and permission
* Invalid response | Signature failed verification
* Invalid partner| Non existent partner
*/
getKycStatus: function (userSecret) {
return httpClient.get(PATHS.KYC, null, userSecret)
},
/**
* Returns current balance for the given user.
*
* @memberOf module:celsius-js-sdk/core
* @function verifyKyc
* @param userSecret {string} - Represents a secret value used to uniquely identify users. Can be user-token or api-key.
* @param userData {UserData} - Represents user's personal information.
* @param documents {Documents} - Images of user's documents.
*
* @returns {Error}
* Error name | Description
* :--------------- | :----------
* Unauthorized | Required user secret and partner key and permission
* Invalid response | Signature failed verification
* Invalid partner| Non existent partner
* Kyc passed | KYC already passed
* Kyc started | You already sent KYC request
* Kyc data missing | Missing some of the data required for KYC
* Invalid country provided | Country provided wasn't found in our supported countries
* Invalid state provided | State provided wasn't found in our supported US states
* Badly formatted SSN provided | SSN provided was in a wrong format
* Invalid SSN error | SSN provided has failed verification
*/
verifyKyc: function (userData, documents, userSecret) {
return httpClient.post(PATHS.KYC, userData, documents, userSecret)
},
/**
* Returns currently supported currencies and refreshes currencies property of this CelsiusInstance.
*
* @memberOf module:celsius-js-sdk/core
* @function getSupportedCurrencies
*
* @returns {String[]}
*/
getSupportedCurrencies: function () {
return httpClient.get(PATHS.SUPPORTED_CURRENCIES, null, null).then((currencies) => {
this.currencies = currencies.currencies
return currencies
})
},
/**
* Returns balance for the given user.
*
* @memberOf module:celsius-js-sdk/core
* @function getBalanceSummary
* @param userSecret {string} - Represents a secret value used to uniquely identify users. Can be user-token or api-key.
* @returns {BalanceResponse | Error}
* Error name | Description
* :--------------- | :----------
* Unauthorized | Required user secret and partner key and permission
* Invalid response | Signature failed verification
* Invalid partner| Non existent partner
*/
getBalanceSummary: function (userSecret) {
return httpClient.get(PATHS.BALANCE_SUMMARY, null, userSecret)
},
/**
* Returns balance of a single coin for the given user.
*
* @memberOf module:celsius-js-sdk/core
* @function getCoinBalance
* @param userSecret {string} - Represents a secret value used to uniquely identify users. Can be user-token or api-key.
* @param coin {string} - Coin for which to get balance.
*
* @returns {BalanceForCoinResponse | Error}
* Error name | Description
* :--------------- | :----------
* Unauthorized | Required user secret and partner key and permission
* Invalid response | Signature failed verification
* Invalid partner| Non existent partner
* Coin not supported | Coin requested isn't yet supported by us
*/
getCoinBalance: function (coin, userSecret) {
return httpClient.get(PATHS.COIN_BALANCE(coin), null, userSecret)
},
/**
* Returns interest earned so far for the given user.
*
* @memberOf module:celsius-js-sdk/core
* @function getInterestSummary
* @param userSecret {string} - Represents a secret value used to uniquely identify users. Can be user-token or api-key.
* @returns {InterestResponse | Error}
* Error name | Description
* :--------------- | :----------
* Unauthorized | Required user secret and partner key and permission
* Invalid response | Signature failed verification
* Invalid partner| Non existent partner
*/
getInterestSummary: function (userSecret) {
return httpClient.get(PATHS.INTEREST_SUMMARY, null, userSecret)
},
/**
* Returns paginated transactions of a user.
* Also included in the response is a {PaginationHeader}, used to get next chunk of transactions.
*
* @memberOf module:celsius-js-sdk/core
* @function getTransactionSummary
* @param userSecret {string} - Represents a secret value used to uniquely identify users. Can be user-token or api-key.
* @param pagination {PaginationConfig} - Pagination configuration (Which page to get and how many results per page).
*
* @returns {TransactionResponse | Error}
* Error name | Description
* :--------------- | :----------
* Unauthorized | Required user secret and partner key and permission
* Invalid response | Signature failed verification
* Invalid partner| Non existent partner
*/
getTransactionSummary: function (pagination, userSecret) {
return httpClient.get(PATHS.TRANSACTIONS_SUMMARY, pagination, userSecret)
},
/**
* Returns paginated transactions of a user for the specified coin.
* Also included in the response is a {PaginationHeader}, used to get next chunk of transactions.
*
* @memberOf module:celsius-js-sdk/core
* @function getCoinTransactions
* @param userSecret {string} - Represents a secret value used to uniquely identify users. Can be user-token or api-key.
* @param pagination {PaginationConfig} - Pagination configuration (Which page to get and how many results per page).
* @param coin {string} - Coin for which to get transactions.
*
* @returns {TransactionResponse | Error}
* Error name | Description
* :--------------- | :----------
* Unauthorized | Required user secret and partner key and permission
* Invalid response | Signature failed verification
* Invalid partner| Non existent partner
* Coin not supported | Coin requested isn't yet supported by us
*/
getCoinTransactions: function (coin, pagination, userSecret) {
return httpClient.get(PATHS.COIN_TRANSACTIONS(coin), pagination, userSecret)
},
/**
* Returns deposit address of a user's wallet for the specified coin.
*
* @memberOf module:celsius-js-sdk/core
* @function getDeposit
* @param userSecret {string} Represents a secret value used to uniquely identify users. Can be user-token or api-key.
* @param coin {string} Coin for which to get deposit address.
*
* @returns {Error}
* Error name | Description
* :--------------- | :----------
* Unauthorized | Required user secret and partner key and permission
* Invalid response | Signature failed verification
* Invalid partner| Non existent partner
* Coin not supported | Coin requested isn't yet supported by us
* KYC not passed | You have to pass KYC to perform this action
*/
getDeposit: function (coin, userSecret) {
return httpClient.get(PATHS.DEPOSIT(coin), null, userSecret)
},
/**
* Withdraws the specified amount to the provided address.
*
* @memberOf module:celsius-js-sdk/core
* @function withdraw
* @param coin {string} - For witch coin to perform the withdrawal.
* @param formFields {WithdrawalFields} - Contains amount to withdraw and and address that amount is to be withdrawn to.
* @param userSecret {string} Represents a secret value used to uniquely identify users. Can be user-token or api-key.
*
* @returns {Error}
* Error name | Description
* :--------------- | :----------
* Unauthorized | Required user secret and partner key and permission
* Invalid response | Signature failed verification
* Invalid partner| Non existent partner
* Coin not supported | Coin requested isn't yet supported by us
* KYC not passed | You have to pass KYC to perform this action
* Bad address format | Bad address format
* Dusk check | You cannot withdraw less than 1$
* Duplicate withdraw request | You have pending request with the same amount and outgoing address. Please make sure that the first transaction is confirmed before continuing
* Insufficient funds | Insufficient funds.
* Withdrawal limits hit | Withdrawal limits hit
* Daily withdrawal limits hit | Daily withdrawal limits hit
*/
withdraw: function (coin, formFields, userSecret) {
return httpClient.post(PATHS.WITHDRAW(coin), formFields, null, userSecret)
},
/**
* This method returns transaction status for specific transaction.
*
* @memberOf module:celsius-js-sdk/core
* @function getTransactionsStatus
* @param userSecret {string} Represents a secret value used to uniquely identify users. Can be user-token or api-key.
* @param transaction {string} Id of transaction.
*
* @returns {TransactionStatus} Transaction status
* @returns {Error}
*/
getTransactionStatus: function (transaction, userSecret) {
return httpClient.get(PATHS.TRANSACTION_STATUS(transaction), null, userSecret)
},
getUsers: function (pagination, userSecret) {
return httpClient.get(PATHS.USERS, pagination, userSecret)
},
changeMetadata: function (id, data, userSecret) {
return httpClient.post(PATHS.CHANGED_METADATA(id), data, null, userSecret)
},
changeWithdrawalAddress: function (id, data, userSecret) {
return httpClient.post(PATHS.CHANGE_WITHDRAWAL_ADDRESS(id), data, null, userSecret)
},
createUser: function (user, userSecret) {
return httpClient.post(PATHS.CREATE_USER, user, null, userSecret)
},
getInterestRates: function () {
return httpClient.get(PATHS.INTEREST_RATES, null, null, null)
},
/**
* This method returns statistics for the given userSecret.
* Timestamp can be optionally passed to configure start date for getting statistics.
*
* @memberOf module:celsius-js-sdk/core
* @function getStatistics
* @param timestamp {timestamp} Timestamp from which to calculate statistics
* @param userSecret {string} Represents a secret value used to uniquely identify users. Can be user-token or api-key.
*
* @returns { StatisticsResponse }
*/
getStatistics: function (userSecret, timestamp = null) {
return httpClient.get(PATHS.STATISTICS, { timestamp }, userSecret)
}
}
return CelsiusInstance.getSupportedCurrencies().then(() => {
return CelsiusInstance
})
}
module.exports = {
Celsius
}


ts-simple-ast
Source Files
Source files are the root nodes of the AST.
File path and name
Use:
// returns the file path (ex. /home/david/file.ts)
sourceFile.getFilePath();
// returns only the file name (ex. file.ts)
sourceFile.getBaseName();
Check if declaration file
Use:
sourceFile.isDeclarationFile(); // returns: boolean
Save
Save a source file to the file system using one of the following commands:
await sourceFile.save();
sourceFile.saveSync();
Unsaved files
There is a sourceFile.isSaved()
method that will tell you if the file is saved or not, but it might be easier
to call one of the following methods on the main AST object in order to save unsaved source files:
ast.saveUnsavedSourceFiles(); // returns: Promise
ast.saveUnsavedSourceFilesSync(); // could potentially be very slow if there are a lot of files to save
Delete
Delete a source file from the file system using one of the following commands:
await sourceFile.delete(); // or deleteSync()
Copy
Copy a source file to a new file by specifying a new relative or absolute path:
const newSourceFile = sourceFile.copy("newFileName.ts");
// this won't throw if a file exists at the specified path
const otherSourceFile = sourceFile.copy("other.ts", { overwrite: true });
Note that the file, in both these cases, won’t be written to the file system unless you save it.
Move
TODO: Not yet supported.
Refresh from file system
Refresh the source file from the file system:
import {FileSystemRefreshResult} from "ts-simple-ast";
// returns: FileSystemRefreshResult (NoChange, Updated, Deleted)
const result = await sourceFile.refreshFromFileSystem(); // or refreshFromFileSystemSync()
This is useful when you are using a file system watcher and want to refresh a source file from the file system based on changes.
If the file was updated: All the child nodes of the source file will be forgotten and you will have to renavigate the file. If the file was deleted: The source file will be removed and all its nodes forgotten.
Remove
Remove a source file from the AST by calling:
ast.removeSourceFile(sourceFile); // returns: boolean (if was removed)
Note: This does not delete the file from the file system. To do delete it, call .delete()
.
Referenced files
This returns any files that are referenced via /// <reference path="..." />
statements:
const referencedFiles = sourceFile.getReferencedFiles();
Type reference directives
This returns any files that are referenced via /// <reference types="..." />
statements:
const typeReferenceDirectives = sourceFile.getTypeReferenceDirectives();
Get default export symbol
If it exists, the default export symbol can be retrieved:
const defaultExportSymbol = sourceFile.getDefaultExportSymbol(); // returns: Symbol | undefined
Remove default export
Use:
sourceFile.removeDefaultExport();
Note: This is safe to call even when there is no default export.
Import Declarations
See Import Declarations.
Export Declarations
See Export Declarations.
Indenting / Unindenting
Call the .indent
or .unindent
methods.
sourceFile.indent(5); // indent line containing position 5
sourceFile.indent([5, 10]); // indent line or lines within position range [5-10]
sourceFile.indent(10, 3); // indent line containing position 10, 3 times
sourceFile.unindent(10); // unindent line containing position 10
sourceFile.indent(10, -1); // unindent line containing position 10 (specify negative times)
sourceFile.unindent(10, -1); // indent line containing position 10 (specify negative times)
This will indent and unindent based on your manipulation settings.
Formatting Text
Sometimes you might encounter code that looks terrible. For example:
// BadlyFormattedFile.ts
var myVariable : string | number;
function myFunction(param : MyClass){
return "";
}
Automatically format the text of this file by calling format text on it:
sourceFile.formatText();
// or provide optional formatting settings
sourceFile.formatText({
placeOpenBraceOnNewLineForFunctions: true
});
This will run the source file’s text through the TypeScript compiler’s formatting API, which will change the source file to contain the following text:
// BadlyFormattedFile.ts (not anymore!)
var myVariable: string | number;
function myFunction(param: MyClass) {
return "";
}


{% extends 'base.html' %} {% load humanize %} {% load static %} {% block slider %}
{% endblock %} {% block content %}Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum
image and details
image and details
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum


EMI Calculator
Interest Rate
1
20
Loan Amount
1000
500000
Loan Period (in month)
0
12
Find EMI
Total EMI:


At least 3 killed when 2 semis, 3 passenger vehicles collide
DENTON, Texas (AP) - At least three people were killed and three others injured in the collision and explosion of two semitrailers and three passenger vehicles on Interstate 35W in North Texas, north of Fort Worth.
The crash happened just before 1:30 p.m. Friday near the University of North Texas campus in Denton, about 55 miles north of Fort Worth. One of the semis was loaded with tanks of propane, some of which exploded on impact.
Denton Fire-Rescue spokesman Jason Eddington said the three people killed were dead at the scene, while the three others were taken to a hospital for treatment of minor injuries.
Denton police said the northbound lanes of the interstate were to be closed between Farm-to-Market Road 2449 and Interstate 35E until Saturday while cleanup work is underway.
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


This is a paragraph
Hello World!
These parapraphs are styled with CSS.
Every paragraph will be affected by the style.
Me too!
And me!
Hello World!
This paragraph is not affected by this style
Red and center-aligned paragraph.
This paragraph will be red and center-aligned.
This paragraph will be red and center-aligned.
This paragraph will be red, center-aligned, and in a large font-size.
This is a paragraph.
Hello World!
This paragraph is styled with CSS
CSS comments are not shown in the output
This is a paragraph.


Hi! It's great to meet you. I'm currently a Master's student at Georgia Institute of Technology. I have strong interests in nanotechnology, wearable interfaces, and finance. I strive to challenge myself and learn the best from others.
 - Father and Mother
"Opportunity does not knock. It presents itself when you beat down the door." - Kyle Chandler
"Be kind whenever possible. It's always possible." - Dalai Lama
[CS 7646] Machine Learning for Trading
[CS 7450] Information Visualization
[CS 6750] Human Computer Interaction
[CSE 6242] Data and Visual Analytics
[CSE 6220] High Performance Computing
[CS 4911] Senior Design
[CS 4460] Introduction to Information Visualization
[CS 4365] Enterprise Computing
[CX 4230] Computer Simulation
[CX 4140] Computational Modeling Algorithms


[1] THE WORDS of the Preacher, the son of David, king in Jerusalem.
[2] Vanity of vanities, saith the Preacher; vanity of vanities, all is vanity.
[3] What profit hath man of all his labor wherein he laboreth under the sun?
[4] One generation goeth, and another generation cometh; but the earth abideth for ever.
[5] The sun also ariseth, and the sun goeth down, and hasteth to its place where it ariseth.
[6] The wind goeth toward the south, and turneth about unto the north; it turneth about continually in its course, and the wind returneth again to its circuits.
[7] All the rivers run into the sea, yet the sea is not full; unto the place whither the rivers go, thither they go again.
[8] That which hath been is that which shall be; and that which hath been done is that which shall be done: and there is no new thing under the sun.
[10] Is there a thing whereof it may be said, See, this is new? it hath been long ago, in the ages which were before us.
[11] There is no remembrance of the former generations; neither shall there be any remembrance of the latter generations that are to come, among those that shall come after.
[12] I the Preacher was king over Israel in Jerusalem.
[13] And I applied my heart to seek and to search out by wisdom concerning all that is done under heaven: it is a sore travail that God hath given to the sons of men to be exercised therewith.
[14] I have seen all the works that are done under the sun; and, behold, all is vanity and a striving after wind.
[15] That which is crooked cannot be made straight; and that which is wanting cannot be numbered.
[16] I communed with mine own heart, saying, Lo, I have gotten me great wisdom above all that were before me in Jerusalem; yea, my heart hath had great experience of wisdom and knowledge.
[17] And I applied my heart to know wisdom, and to know madness and folly: I perceived that this also was a striving after wind.
[18] For in much wisdom is much grief; and he that increaseth knowledge increaseth sorrow.
[1] I SAID in my heart, Come now, I will prove thee with mirth; therefore enjoy pleasure: and, behold, this also was vanity.
[2] I said of laughter, It is mad; and of mirth, What doeth it?
[3] I searched in my heart how to cheer my flesh with wine, my heart yet guiding me with wisdom, and how to lay hold on folly, till I might see what it was good for the sons of men that they should do under heaven all the days of their life.
[4] I made me great works; I builded me houses; I planted me vineyards;
[5] I made me gardens and parks, and I planted trees in them of all kinds of fruit;
[6] I made me pools of water, to water therefrom the forest where trees were reared;
[7] I bought men-servants and maid-servants, and had servants born in my house; also I had great possessions of herds and flocks, above all that were before me in Jerusalem;
[8] I gathered me also silver and gold, and the treasure of kings and of the provinces; I gat me men-singers and women-singers, and the delights of the sons of men, musical instruments, and that of all sorts.
[9] So I was great, and increased more than all that were before me in Jerusalem: also my wisdom remained with me.
[10] And whatsoever mine eyes desired I kept not from them; I withheld not my heart from any joy; for my heart rejoiced because of all my labor; and this was my portion from all my labor.
[11] Then I looked on all the works that my hands had wrought, and on the labor that I had labored to do; and, behold, all was vanity and a striving after wind, and there was no profit under the sun.
[12] And I turned myself to behold wisdom, and madness, and folly: for what can the mando that cometh after the king? even that which hath been done long ago.
[13] Then I saw that wisdom excelleth folly, as far as light excelleth darkness.
[14] The wise man’s eyes are in his head, and the fool walketh in darkness: and yet I perceived that one event happeneth to them all.
[15] Then said I in my heart, As it happeneth to the fool, so will it happen even to me; and why was I then more wise? Then said I in my heart, that this also is vanity.
[16] For of the wise man, even as of the fool, there is no remembrance for ever; seeing that in the days to come all will have been long forgotten. And how doth the wise man die even as the fool!
[17] So I hated life, because the work that is wrought under the sun was grievous unto me; for all is vanity and a striving after wind.
[18] And I hated all my labor wherein I labored under the sun, seeing that I must leave it unto the man that shall be after me.
[19] And who knoweth whether he will be a wise man or a fool? yet will he have rule over all my labor wherein I have labored, and wherein I have showed myself wise under the sun. This also is vanity.
[20] Therefore I turned about to cause my heart to despair concerning all the labor wherein I had labored under the sun.
[21] For there is a man whose labor is with wisdom, and with knowledge, and with skilfulness; yet to a man that hath not labored therein shall he leave it for his portion. This also is vanity and a great evil.
[22] For what hath a man of all his labor, and of the striving of his heart, wherein he laboreth under the sun?
[23] For all his days are but sorrows, and his travail is grief; yea, even in the night his heart taketh no rest. This also is vanity.
[24] There is nothing better for a man than that he should eat and drink, and make his soul enjoy good in his labor. This also I saw, that it is from the hand of God.
[25] For who can eat, or who can have enjoyment, more than I?
[26] For to the man that pleaseth him God giveth wisdom, and knowledge, and joy; but to the sinner he giveth travail, to gather and to heap up, that he may give to him that pleaseth God. This also is vanity and a striving after wind.
[1] FOR everything there is a season, and a time for every purpose under heaven:
[2] a time to be born, and a time to die; a time to plant, and a time to pluck up that which is planted;
[3] a time to kill, and a time to heal; a time to break down, and a time to build up;
[4] a time to weep, and a time to laugh; a time to mourn, and a time to dance;
[5] a time to cast away stones, and a time to gather stones together; a time to embrace, and a time to refrain from embracing;
[6] a time to seek, and a time to lose; a time to keep, and a time to cast away;
[7] a time to rend, and a time to sew; a time to keep silence, and a time to speak;
[8] a time to love, and a time to hate; a time for war, and a time for peace.
[9] What profit hath he that worketh in that wherein he laboreth?
[10] I have seen the travail which God hath given to the sons of men to be exercised therewith.
[11] He hath made everything beautiful in its time: also he hath set eternity in their heart, yet so that man cannot find out the work that God hath done from the beginning even to the end.
[12] I know that there is nothing better for them, than to rejoice, and to do good so long as they live.
[13] And also that every man should eat and drink, and enjoy good in all his labor, is the gift of God.
[14] I know that, whatsoever God doeth, it shall be for ever: nothing can be put to it, nor anything taken from it; and God hath done it, that men should fear before him
[15] That which is hath been long ago; and that which is to be hath long ago been: and God seeketh again that which is passed away.
[16] And moreover I saw under the sun, in the place of justice, that wickedness was there; and in the place of righteousness, that wickedness was there.
[17] I said in my heart, God will judge the righteous and the wicked; for there is a time there for every purpose and for every work.
[18] I said in my heart, It is because of the sons of men, that God may prove them, and that they may see that they themselves are but as beasts.
[19] For that which befalleth the sons of men befalleth beasts; even one thing befalleth them: as the one dieth, so dieth the other; yea, they have all one breath; and man hath no preeminence above the beasts: for all is vanity.
[20] All go unto one place; all are of the dust, and all turn to dust again.
[21] Who knoweth the spirit of man, whether it goeth upward, and the spirit of the beast, whether it goeth downward to the earth?
[22] Wherefore I saw that there is nothing better, than that a man should rejoice in his works; for that is his portion: for who shall bring him back to see what shall be after him?
[1] THEN I returned and saw all the oppressions that are done under the sun: and, behold, the tears of such as were oppressed, and they had no comforter; and on the side of their oppressors there was power; but they had no comforter.
[2] Wherefore I praised the dead that have been long dead more than the living that are yet alive;
[3] yea, better than them both did I esteem him that hath not yet been, who hath not seen the evil work that is done under the sun.
[4] Then I saw all labor and every skilful work, that for this a man is envied of his neighbor. This also is vanity and a striving after wind.
[5] The fool foldeth his hands together, and eateth his own flesh.
[6] Better is a handful, with quietness, than two handfuls with labor and striving after wind.
[7] Then I returned and saw vanity under the sun.
[8] There is one that is alone, and he hath not a second; yea, he hath neither son nor brother; yet is there no end of all his labor, neither are his eyes satisfied with riches. For whom then, saith he, do I labor, and deprive my soul of good? This also is vanity, yea, it is a sore travail.
[9] Two are better than one, because they have a good reward for their labor.
[10] For if they fall, the one will lift up his fellow; but woe to him that is alone when he falleth, and hath not another to lift him up.
[11] Again, if two lie together, then they have warmth; but how can one be warm alone?
[12] And if a man prevail against him that is alone, two shall withstand him; and a threefold cord is not quickly broken.
[13] Better is a poor and wise youth than an old and foolish king, who knoweth not how to receive admonition any more.
[14] For out of prison he came forth to be king; yea, even in his kingdom he was born poor. I saw all the living that walk under the sun, that they were with the youth, the second, that stood up in his stead.
[15] There was no end of all the people, even of all them over whom he was: yet they that come after shall not rejoice in him. Surely this also is vanity and a striving after wind.
[1] KEEP thy foot when thou goest to the house of God; for to draw nigh to hear is better than to give the sacrifice of fools: for they know not that they do evil. Be not rash with thy mouth, and let not thy heart be hasty to utter anything before God; for God is in heaven, and thou upon earth: therefore let thy words be few.
[2] When thou vowest a vow unto God, defer not to pay it; for he hath no pleasure in fools: pay that which thou vowest.
[4] Better is it that thou shouldest not vow, than that thou shouldest vow and not pay.
[5] For in the multitude of dreams there are vanities, and in many words: but fear thou God.
[7] If thou seest the oppression of the poor, and the violent taking away of justice and righteousness in a province, marvel not at the matter: for one higher than the high regardeth; and there are higher than they.
[8] Moreover the profit of the earth is for all: the king himself is served by the field.
[9] He that loveth silver shall not be satisfied with silver; nor he that loveth abundance, with increase: this also is vanity.
[10] When goods increase, they are increased that eat them; and what advantage is there to the owner thereof, save the beholding of them with his eyes?
[11] The sleep of a laboring man is sweet, whether he eat little or much; but the fulness of the rich will not suffer him to sleep.
[12] There is a grievous evil which I have seen under the sun, namely, riches kept by the owner thereof to his hurt:
[13] and those riches perish by evil adventure; and if he hath begotten a son, there is nothing in his hand.
[14] As he came forth from his mother’s womb, naked shall he go again as he came, and shall take nothing for his labor, which he may carry away in his hand.
[15] And this also is a grievous evil, that in all points as he came, so shall he go: and what profit hath he that he laboreth for the wind?
[16] All his days also he eateth in darkness, and he is sore vexed, and hath sickness and wrath.
[17] Behold, that which I have seen to be good and to be comely is for one to eat and to drink, and to enjoy good in all his labor, wherein he laboreth under the sun, all the days of his life which God hath given him: for this is his portion.
[18] Every man also to whom God hath given riches and wealth, and hath given him power to eat thereof, and to take his portion, and to rejoice in his labor—this is the gift of God.
[19] For he shall not much remember the days of his life; because God answereth him in the joy of his heart.


-
Trustworthy ML & Robust Statistics
As machine learning systems are pervasively deployed in many scientific fields with increasingly sensitive tasks, it has become paramount to develop algorithms that are robust against the numerous sources of uncertainty inherent in those applications including noise in the data, malicious exploitation of vulnerabilities, outliers, variability of the true objective, privacy, and fairness. While current research in machine learning has led to fundamental breakthroughs, there is still a large gap between the theory and the limitations of the existing algorithms used by practitioners in the real world. An important research direction for our group is to lift the current methods out of the sterile lab environment and scale them into the messy real world, by carefully reexamining their limitations, considering more realistic but less perfect conditions, and developing correspondingly robust algorithms.
-
Collaborative Machine Learning & Federated Learning
The human brain is a complex network of functionally interconnected regions whose coordinated effort gives rise to different functions. Understanding what these regions are, how they interact, and how this interaction forms a wide range of behavior has long been an essential question for human neuroscience. Neuroimaging techniques have provided a unique opportunity to tackle this question in a data-driven way. Advances in neuroimaging techniques, such as functional Magnetic Resonance Imaging (fMRI), have allowed us to approximately measure the neural activity in the brain. However, fMRI data are not only massive in size but also spatially and temporally complex. One of the research directions in our group is to develop advanced machine-learning techniques to study brain function and its link to behavior.
-
Functional Connectivity
The human brain is a complex network of functionally interconnected regions whose coordinated effort gives rise to different functions. Understanding what these regions are, how they interact, and how this interaction forms a wide range of behavior has long been an essential question for human neuroscience. Neuroimaging techniques have provided a unique opportunity to tackle this question in a data-driven way. Advances in neuroimaging techniques, such as functional Magnetic Resonance Imaging (fMRI), have allowed us to approximately measure the neural activity in the brain. However, fMRI data are not only massive in size but also spatially and temporally complex. One of the research directions in our group is to develop advanced machine-learning techniques to study brain function and its link to behavior.


Andy Murray misses out for Great Britain in Davis Cup quarter-final
by FSA EditorKyle Edmund will once again play in the singles for Great Britain, meaning Andy Murray will be a spectator for their quarter-final tie.
Andy Murray will not feature for Great Britain in their Davis Cup quarter-final tie against Germany on Friday after Kyle Edmund was once again picked for singles action.
Murray was rested for the Group E victory over Kazakhstan on Thursday that secured Great Britain’s place in the knockout stages of the new-look tournament in Spain.
Captain Leon Smith admitted after the 2-1 win that he was unsure if the Scot – who had laboured to a three-set victory over Tallon Griekspoor of the Netherlands in his team’s opening outing – would return to the line-up.
And Edmund, who played instead against Kazakhstan, recording an impressive victory over Mikhail Kukushkin, has retained his spot in the line-up.
His opponent in the opening match will be Philipp Kohlschreiber, with Dan Evans then going up against Jan-Lennard Struff.
If required, Jamie Murray and Neal Skupski will once again team up for the doubles. Kevin Krawietz and Andreas Mies are scheduled to be their opponents.
The winners of the tie will go up against either Argentina or hosts Spain – who also meet in Friday’s second session in Madrid – in the last four.
In the other half of the draw, Russia knocked out Novak Djokovic’s Serbia courtesy of a hard-fought doubles win, Andrey Rublev and Karen Khachanov saving three match points before eventually prevailing. Next up will be Canada, who qualified for the semi-finals on Thursday.


| dropMenuOptions {shinyWidgets} | R Documentation |
Those options will passed to the underlying JavaScript
library powering dropMenu
: tippy.js. See all available options
here https://atomiks.github.io/tippyjs/all-props/.
dropMenuOptions(duration = c(275, 250), animation = "fade", flip = FALSE, ...)
duration |
Duration of the CSS transition animation in ms. |
animation |
The type of transition animation. |
flip |
Determines if the tippy flips so that it is placed within the viewport as best it can be if there is not enough space. |
... |
Additional arguments. |
a list
of options to be used in dropMenu
.