arXiv Paper Daily: Mon, 16 Jan 2017

Neural and Evolutionary Computing

LIDE: Language Identification from Text Documents

Priyank Mathur , Arkajyoti Misra , Emrah Budur Subjects : Computation and Language (cs.CL) ; Neural and Evolutionary Computing (cs.NE)

The increase in the use of microblogging came along with the rapid growth on

short linguistic data. On the other hand deep learning is considered to be the

new frontier to extract meaningful information out of large amount of raw data

in an automated manner. In this study, we engaged these two emerging fields to

come up with a robust language identifier on demand, namely Language

Identification Engine (LIDE). As a result, we achieved 95.12% accuracy in

Discriminating between Similar Languages (DSL) Shared Task 2015 dataset, which

is comparable to the maximum reported accuracy of 95.54% achieved so far.

Computer Vision and Pattern Recognition

Tumour Ellipsification in Ultrasound Images for Treatment Prediction in Breast Cancer

Recent advances in using quantitative ultrasound (QUS) methods have provided

a promising framework to non-invasively and inexpensively monitor or predict

the effectiveness of therapeutic cancer responses. One of the earliest steps in

using QUS methods is contouring a region of interest (ROI) inside the tumour in

ultrasound B-mode images. While manual segmentation is a very time-consuming

and tedious task for human experts, auto-contouring is also an extremely

difficult task for computers due to the poor quality of ultrasound B-mode

images. However, for the purpose of cancer response prediction, a rough

boundary of the tumour as an ROI is only needed. In this research, a

semi-automated tumour localization approach is proposed for ROI estimation in

ultrasound B-mode images acquired from patients with locally advanced breast

cancer (LABC). The proposed approach comprised several modules, including 1)

feature extraction using keypoint descriptors, 2) augmenting the feature

descriptors with the distance of the keypoints to the user-input pixel as the

centre of the tumour, 3) supervised learning using a support vector machine

(SVM) to classify keypoints as “tumour” or “non-tumour”, and 4) computation of

an ellipse as an outline of the ROI representing the tumour. Experiments with

33 B-mode images from 10 LABC patients yielded promising results with an

accuracy of 76.7% based on the Dice coefficient performance measure. The

results demonstrated that the proposed method can potentially be used as the

first stage in a computer-assisted cancer response prediction system for

semi-automated contouring of breast tumours.

Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehicles

This paper describes the development of a novel algorithm to tackle the

problem of real-time video stabilization for unmanned aerial vehicles (UAVs).

There are two main components in the algorithm: (1) By designing a suitable

model for the global motion of UAV, the proposed algorithm avoids the necessity

of estimating the most general motion model, projective transformation, and

considers simpler motion models, such as rigid transformation and similarity

transformation. (2) To achieve a high processing speed, optical-flow based

tracking is employed in lieu of conventional tracking and matching methods used

by state-of-the-art algorithms. These two new ideas resulted in a real-time

stabilization algorithm, developed over two phases. Stage I considers

processing the whole sequence of frames in the video while achieving an average

processing speed of 50fps on several publicly available benchmark videos. Next,

Stage II undertakes the task of real-time video stabilization using a

multi-threading implementation of the algorithm designed in Stage I.

Active Self-Paced Learning for Cost-Effective and Progressive Face Identification

This paper aims to develop a novel cost-effective framework for face

identification, which progressively maintains a batch of classifiers with the

increasing face images of different individuals. By naturally combining two

recently rising techniques: active learning (AL) and self-paced learning (SPL),

our framework is capable of automatically annotating new instances and

incorporating them into training under weak expert re-certification. We first

initialize the classifier using a few annotated samples for each individual,

and extract image features using the convolutional neural nets. Then, a number

of candidates are selected from the unannotated samples for classifier

updating, in which we apply the current classifiers ranking the samples by the

prediction confidence. In particular, our approach utilizes the high-confidence

and low-confidence samples in the self-paced and the active user-query way,

respectively. The neural nets are later fine-tuned based on the updated

classifiers. Such heuristic implementation is formulated as solving a concise

active SPL optimization problem, which also advances the SPL development by

supplementing a rational dynamic curriculum constraint. The new model finely

accords with the “instructor-student-collaborative” learning mode in human

education. The advantages of this proposed framework are two-folds: i) The

required number of annotated samples is significantly decreased while the

comparable performance is guaranteed. A dramatic reduction of user effort is

also achieved over other state-of-the-art active learning techniques. ii) The

mixture of SPL and AL effectively improves not only the classifier accuracy

compared to existing AL/SPL methods but also the robustness against noisy data.

We evaluate our framework on two challenging datasets, and demonstrate very

promising results. ( this http URL )

Cost-Effective Active Learning for Deep Image Classification

Recent successes in learning-based image classification, however, heavily

rely on the large number of annotated training samples, which may require

considerable human efforts. In this paper, we propose a novel active learning

framework, which is capable of building a competitive classifier with optimal

feature representation via a limited amount of labeled training instances in an

incremental learning manner. Our approach advances the existing active learning

methods in two aspects. First, we incorporate deep convolutional neural

networks into active learning. Through the properly designed framework, the

feature representation and the classifier can be simultaneously updated with

progressively annotated informative samples. Second, we present a

cost-effective sample selection strategy to improve the classification

performance with less manual annotations. Unlike traditional methods focusing

on only the uncertain samples of low prediction confidence, we especially

discover the large amount of high confidence samples from the unlabeled set for

feature learning. Specifically, these high confidence samples are automatically

selected and iteratively assigned pseudo-labels. We thus call our framework

“Cost-Effective Active Learning” (CEAL) standing for the two

advantages.Extensive experiments demonstrate that the proposed CEAL framework

can achieve promising results on two challenging image classification datasets,

i.e., face recognition on CACD database [1] and object categorization on

Caltech-256 [2].

An OpenCL(TM) Deep Learning Accelerator on Arria 10

Convolutional neural nets (CNNs) have become a practical means to perform

vision tasks, particularly in the area of image classification. FPGAs are well

known to be able to perform convolutions efficiently, however, most recent

efforts to run CNNs on FPGAs have shown limited advantages over other devices

such as GPUs. Previous approaches on FPGAs have often been memory bound due to

the limited external memory bandwidth on the FPGA device. We show a novel

architecture written in OpenCL(TM), which we refer to as a Deep Learning

Accelerator (DLA), that maximizes data reuse and minimizes external memory

bandwidth. Furthermore, we show how we can use the Winograd transform to

significantly boost the performance of the FPGA. As a result, when running our

DLA on Intel’s Arria 10 device we can achieve a performance of 1020 img/s, or

23 img/s/W when running the AlexNet CNN benchmark. This comes to 1382 GFLOPs

and is 10x faster with 8.4x more GFLOPS and 5.8x better efficiency than the

state-of-the-art on FPGAs. Additionally, 23 img/s/W is competitive against the

best publicly known implementation of AlexNet on nVidia’s TitanX GPU.

Artificial Intelligence

On the links between argumentation-based reasoning and nonmonotonic reasoning

Zimi Li , Nir Oren , Simon Parsons Subjects : Artificial Intelligence (cs.AI)

In this paper we investigate the links between instantiated argumentation

systems and the axioms for non-monotonic reasoning described in [9] with the

aim of characterising the nature of argument based reasoning. In doing so, we

consider two possible interpretations of the consequence relation, and describe

which axioms are met by ASPIC+ under each of these interpretations. We then

consider the links between these axioms and the rationality postulates. Our

results indicate that argument based reasoning as characterised by ASPIC+ is –

according to the axioms of [9] – non-cumulative and non-monotonic, and

therefore weaker than the weakest non-monotonic reasoning systems they

considered possible. This weakness underpins ASPIC+’s success in modelling

other reasoning systems, and we conclude by considering the relationship

between ASPIC+ and other weak logical systems.

Fuzzy Clustering Data Given in the Ordinal Scale

A fuzzy clustering algorithm for multidimensional data is proposed in this

article. The data is described by vectors whose components are linguistic

variables defined in an ordinal scale. The obtained results confirm the

efficiency of the proposed approach.

A Savage-Like Axiomatization for Nonstandard Expected Utility

Since Leonard Savage’s epoch-making memoir, Subjective Expected Utility

Theory has been the presumptive model for decision-making. Savage provided an

act-based axiomatization of standard expected utility theory. In this article,

we provide a Savage-like axiomatization of nonstandard expected utility theory.

It corresponds to a weakening of Savage’s (6^{th}) axiom.

Deep Probabilistic Programming

Dustin Tran , Matthew D. Hoffman , Rif A. Saurous , Eugene Brevdo , Kevin Murphy , David M. Blei Subjects : Machine Learning (stat.ML) ; Artificial Intelligence (cs.AI); Learning (cs.LG); Programming Languages (cs.PL); Computation (stat.CO)

We propose Edward, a Turing-complete probabilistic programming language.

Edward builds on two compositional representations—random variables and

inference. By treating inference as a first class citizen, on a par with

modeling, we show that probabilistic programming can be as flexible and

computationally efficient as traditional deep learning. For flexibility, Edward

makes it easy to fit the same model using a variety of composable inference

methods, ranging from point estimation, to variational inference, to MCMC. In

addition, Edward can reuse the modeling representation as part of inference,

facilitating the design of rich variational models and generative adversarial

networks. For efficiency, Edward is integrated into TensorFlow, providing

significant speedups over existing probabilistic systems. For example, on a

benchmark logistic regression task, Edward is at least 35x faster than Stan and

PyMC3.

Linear Disentangled Representation Learning for Facial Actions

Limited annotated data available for the recognition of facial expression and

action units embarrasses the training of deep networks, which can learn

disentangled invariant features. However, a linear model with just several

parameters normally is not demanding in terms of training data. In this paper,

we propose an elegant linear model to untangle confounding factors in

challenging realistic multichannel signals such as 2D face videos. The simple

yet powerful model does not rely on huge training data and is natural for

recognizing facial actions without explicitly disentangling the identity. Base

on well-understood intuitive linear models such as Sparse Representation based

Classification (SRC), previous attempts require a prepossessing of explicit

decoupling which is practically inexact. Instead, we exploit the low-rank

property across frames to subtract the underlying neutral faces which are

modeled jointly with sparse representation on the action components with group

sparsity enforced. On the extended Cohn-Kanade dataset (CK+), our one-shot

automatic method on raw face videos performs as competitive as SRC applied on

manually prepared action components and performs even better than SRC in terms

of true positive rate. We apply the model to the even more challenging task of

facial action unit recognition, verified on the MPI Face Video Database

(MPI-VDB) achieving a decent performance. All the programs and data have been

made publicly available.

Information Retrieval

Scalable, Trie-based Approximate Entity Extraction for Real-Time Financial Transaction Screening

Emrah Budur Subjects : Computation and Language (cs.CL) ; Information Retrieval (cs.IR)

Financial institutions have to screen their transactions to ensure that they

are not affiliated with terrorism entities. Developing appropriate solutions to

detect such affiliations precisely while avoiding any kind of interruption to

large amount of legitimate transactions is essential. In this paper, we present

building blocks of a scalable solution that may help financial institutions to

build their own software to extract terrorism entities out of both structured

and unstructured financial messages in real time and with approximate

similarity matching approach.

Computation and Language

LIDE: Language Identification from Text Documents

Priyank Mathur , Arkajyoti Misra , Emrah Budur Subjects : Computation and Language (cs.CL) ; Neural and Evolutionary Computing (cs.NE)

The increase in the use of microblogging came along with the rapid growth on

short linguistic data. On the other hand deep learning is considered to be the

new frontier to extract meaningful information out of large amount of raw data

in an automated manner. In this study, we engaged these two emerging fields to

come up with a robust language identifier on demand, namely Language

Identification Engine (LIDE). As a result, we achieved 95.12% accuracy in

Discriminating between Similar Languages (DSL) Shared Task 2015 dataset, which

is comparable to the maximum reported accuracy of 95.54% achieved so far.

Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network

In this paper, we propose an efficient transfer leaning methods for training

a personalized language model using a recurrent neural network with long

short-term memory architecture. With our proposed fast transfer learning

schemes, a general language model is updated to a personalized language model

with a small amount of user data and a limited computing resource. These

methods are especially useful for a mobile device environment while the data is

prevented from transferring out of the device for privacy purposes. Through

experiments on dialogue data in a drama, it is verified that our transfer

learning methods have successfully generated the personalized language model,

whose output is more similar to the personal language style in both qualitative

and quantitative aspects.

Scalable, Trie-based Approximate Entity Extraction for Real-Time Financial Transaction Screening

Emrah Budur Subjects : Computation and Language (cs.CL) ; Information Retrieval (cs.IR)

Financial institutions have to screen their transactions to ensure that they

are not affiliated with terrorism entities. Developing appropriate solutions to

detect such affiliations precisely while avoiding any kind of interruption to

large amount of legitimate transactions is essential. In this paper, we present

building blocks of a scalable solution that may help financial institutions to

build their own software to extract terrorism entities out of both structured

and unstructured financial messages in real time and with approximate

similarity matching approach.

Kernel Approximation Methods for Speech Recognition

Avner May , Alireza Bagheri Garakani , Zhiyun Lu , Dong Guo , Kuan Liu , Aurélien Bellet , Linxi Fan , Michael Collins , Daniel Hsu , Brian Kingsbury , Michael Picheny , Fei Sha Subjects : Machine Learning (stat.ML) ; Computation and Language (cs.CL); Learning (cs.LG)

We study large-scale kernel methods for acoustic modeling in speech

recognition and compare their performance to deep neural networks (DNNs). We

perform experiments on four speech recognition datasets, including the TIMIT

and Broadcast News benchmark tasks, and compare these two types of models on

frame-level performance metrics (accuracy, cross-entropy), as well as on

recognition metrics (word/character error rate). In order to scale kernel

methods to these large datasets, we use the random Fourier feature method of

Rahimi and Recht (2007). We propose two novel techniques for improving the

performance of kernel acoustic models. First, in order to reduce the number of

random features required by kernel models, we propose a simple but effective

method for feature selection. The method is able to explore a large number of

non-linear features while maintaining a compact model more efficiently than

existing approaches. Second, we present a number of frame-level metrics which

correlate very strongly with recognition performance when computed on the

heldout set; we take advantage of these correlations by monitoring these

metrics during training in order to decide when to stop learning. This

technique can noticeably improve the recognition performance of both DNN and

kernel models, while narrowing the gap between them. Additionally, we show that

the linear bottleneck method of Sainath et al. (2013) improves the performance

of our kernel models significantly, in addition to speeding up training and

making the models more compact. Together, these three methods dramatically

improve the performance of kernel acoustic models, making their performance

comparable to DNNs on the tasks we explored.

Distributed, Parallel, and Cluster Computing

Applying Data Compression Techniques on Systolic Neural Network Accelerator

New directions in computing and algorithms has lead to some new applications

that have tolerance to imprecision. Although, These applications are creating

large volumes of data which exceeds the capability of today’s computing

systems. Therefore, researchers are trying to find new techniques to alleviate

this crisis. Approximate Computing is one promising technique that uses a trade

off between precision and efficiency of computing. Acceleration is another

solution that uses specialized logics in order to do computations in a way that

is more power efficient. Another technique is Data compression which is used in

memory systems in order to save capacity and bandwidth.

Power and Execution Time Measurement Methodology for SDF Applications on FPGA-based MPSoCs

Timing and power consumption play an important role in the design of embedded

systems. Furthermore, both properties are directly related to the safety

requirements of many embedded systems. With regard to availability

requirements, power considerations are of uttermost importance for battery

operated systems. Validation of timing and power requires observability of

these properties. In many cases this is difficult, because the observability is

either not possible or requires big extra effort in the system validation

process. In this paper, we present a measurement-based approach for the joint

timing and power analysis of Synchronous Dataflow (SDF) applications running on

a shared memory multiprocessor systems-on-chip (MPSoC) architecture. As a

proof-of-concept, we implement an MPSoC system with configurable power and

timing measurement interfaces inside a Field Programmable Gate Array (FPGA).

Our experiments demonstrate the viability of our approach being able of

accurately analyzing different mappings of image processing applications (Sobel

filter and JPEG encoder) on an FPGA-based MPSoC implementation.

An OpenCL(TM) Deep Learning Accelerator on Arria 10

Convolutional neural nets (CNNs) have become a practical means to perform

vision tasks, particularly in the area of image classification. FPGAs are well

known to be able to perform convolutions efficiently, however, most recent

efforts to run CNNs on FPGAs have shown limited advantages over other devices

such as GPUs. Previous approaches on FPGAs have often been memory bound due to

the limited external memory bandwidth on the FPGA device. We show a novel

architecture written in OpenCL(TM), which we refer to as a Deep Learning

Accelerator (DLA), that maximizes data reuse and minimizes external memory

bandwidth. Furthermore, we show how we can use the Winograd transform to

significantly boost the performance of the FPGA. As a result, when running our

DLA on Intel’s Arria 10 device we can achieve a performance of 1020 img/s, or

23 img/s/W when running the AlexNet CNN benchmark. This comes to 1382 GFLOPs

and is 10x faster with 8.4x more GFLOPS and 5.8x better efficiency than the

state-of-the-art on FPGAs. Additionally, 23 img/s/W is competitive against the

best publicly known implementation of AlexNet on nVidia’s TitanX GPU.

Computing Scores of Forwarding Schemes in Switched Networks with Probabilistic Faults

Time-triggered switched networks are a deterministic communication

infrastructure used by real-time distributed embedded systems. Due to the

criticality of the applications running over them, developers need to ensure

that end-to-end communication is dependable and predictable. Traditional

approaches assume static networks that are not flexible to changes caused by

reconfigurations or, more importantly, faults, which are dealt with in the

application using redundancy. We adopt the concept of handling faults in the

switches from non-real-time networks while maintaining the required

predictability.

We study a class of forwarding schemes that can handle various types of

failures. We consider probabilistic failures. For a given network with a

forwarding scheme and a constant (ell), we compute the {em score} of the

scheme, namely the probability (induced by faults) that at least (ell)

messages arrive on time. We reduce the scoring problem to a reachability

problem on a Markov chain with a “product-like” structure. Its special

structure allows us to reason about it symbolically, and reduce the scoring

problem to #SAT. Our solution is generic and can be adapted to different

networks and other contexts. Also, we show the computational complexity of the

scoring problem is #P-complete, and we study methods to estimate the score. We

evaluate the effectiveness of our techniques with an implementation.

Beyond NGS data sharing and towards open science

Biosciences have been revolutionized by next generation sequencing (NGS)

technologies in last years, leading to new perspectives in medical, industrial

and environmental applications. And although our motivation comes from

biosciences, the following is true for many areas of science: published results

are usually hard to reproduce either because data is not available or tools are

not readily available, which delays the adoption of new methodologies and

hinders innovation. Our focus is on tool readiness and pipelines availability.

Even though most tools are freely available, pipelines for data analysis are in

general barely described and their configuration is far from trivial, with many

parameters to be tuned.

In this paper we discuss how to effectively build and use pipelines, relying

on state of the art computing technologies to execute them without users need

to configure, install and manage tools, servers and complex workflow management

systems. We perform an in depth comparative analysis of state of the art

frameworks and systems. The NGSPipes framework is proposed showing that we can

have public pipelines ready to process and analyse experimental data, produced

for instance by high-throughput technologies, but without relying on

centralized servers or Web services.

The NGSPipes framework and underlying architecture provides a major step

towards open science and true collaboration in what concerns tools and

pipelines among computational biology researchers and practitioners. We show

that it is possible to execute data analysis pipelines in a decentralized and

platform independent way. Approaches like the one proposed are crucial for

archiving and reusing data analysis pipelines at medium/long-term. NGSPipes

framework is freely available at this http URL

Leader Election with High Probability for Self-Organizing Programmable Matter

Joshua J. Daymude , Robert Gmyr , Andrea W. Richa , Christian Scheideler , Thim Strothmann Subjects : Emerging Technologies (cs.ET) ; Distributed, Parallel, and Cluster Computing (cs.DC)

In this paper we consider programmable matter that consists of

computationally limited devices (which we call particles) that are able to

self-organize in order to achieve a desired collective goal without the need

for central control or external intervention. Particles can establish and

release bonds and can actively move in a self-organized way. We investigate the

feasibility of solving fundamental problems relevant for programmable matter.

As a model for such self-organizing particle systems, we use the geometric

amoebot model that has received some attention in the last years. We present an

efficient local-control algorithm for leader election, that elects a leader

particle in O(n) rounds with high probability, where n is the number of

particles in the system. Our algorithm relies only on local information (e.g.,

particles do not have ids, nor do they know n, or have any sort of global

coordinate system), and requires only a constant-size memory per particle.

Space-time balancing domain decomposition

Santiago Badia , Marc Olm Subjects : Numerical Analysis (cs.NA) ; Distributed, Parallel, and Cluster Computing (cs.DC)

In this work, we propose two-level space-time domain decomposition

preconditioners for parabolic problems discretized using finite elements. They

are motivated as an extension to space-time of balancing domain decomposition

by constraints preconditioners. The key ingredients to be defined are the

sub-assembled space and operator, the coarse degrees of freedom (DOFs) in which

we want to enforce continuity among subdomains at the preconditioner level, and

the transfer operator from the sub-assembled to the original finite element

space. With regard to the sub-assembled operator, a perturbation of the time

derivative is needed to end up with a well-posed preconditioner. The set of

coarse DOFs includes the time average (at the space-time subdomain) of

classical space constraints plus new constraints between consecutive subdomains

in time. Numerical experiments show that the proposed schemes are weakly

scalable in time, i.e., we can efficiently exploit increasing computational

resources to solve more time steps in the same {total elapsed} time. Further,

the scheme is also weakly space-time scalable, since it leads to asymptotically

constant iterations when solving larger problems both in space and time.

Excellent {wall clock} time weak scalability is achieved for space-time

parallel solvers on some thousands of cores.

An Asynchronous Parallel Approach to Sparse Recovery

Asynchronous parallel computing and sparse recovery are two areas that have

received recent interest. Asynchronous algorithms are often studied to solve

optimization problems where the cost function takes the form (sum_{i=1}^M

f_i(x)), with a common assumption that each (f_i) is sparse; that is, each

(f_i) acts only on a small number of components of (xinmathbb{R}^n). Sparse

recovery problems, such as compressed sensing, can be formulated as

optimization problems, however, the cost functions (f_i) are dense with respect

to the components of (x), and instead the signal (x) is assumed to be sparse,

meaning that it has only (s) non-zeros where (sll n). Here we address how one

may use an asynchronous parallel architecture when the cost functions (f_i) are

not sparse in (x), but rather the signal (x) is sparse. We propose an

asynchronous parallel approach to sparse recovery via a stochastic greedy

algorithm, where multiple processors asynchronously update a vector in shared

memory containing information on the estimated signal support. We include

numerical simulations that illustrate the potential benefits of our proposed

asynchronous method.

Learning

Truncation-free Hybrid Inference for DPMM

Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian

non-parametrics. While these models free from choosing the number of components

a-priori, computationally attractive variational inference often reintroduces

the need to do so, via a truncation on the variational distribution. In this

paper we present a truncation-free hybrid inference for DPMM, combining the

advantages of sampling-based MCMC and variational methods. The proposed

hybridization enables more efficient variational updates, while increasing

model complexity only if needed. We evaluate the properties of the hybrid

updates and their empirical performance in single- as well as mixed-membership

models. Our method is easy to implement and performs favorably compared to

existing schemas.

Dictionary Learning from Incomplete Data

This paper extends the recently proposed and theoretically justified

iterative thresholding and (K) residual means algorithm ITKrM to learning

dicionaries from incomplete/masked training data (ITKrMM). It further adapts

the algorithm to the presence of a low rank component in the data and provides

a strategy for recovering this low rank component again from incomplete data.

Several synthetic experiments show the advantages of incorporating information

about the corruption into the algorithm. Finally, image inpainting is

considered as application example, which demonstrates the superior performance

of ITKrMM in terms of speed and/or reconstruction quality compared to its

closest dictionary learning counterpart.

Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints

Restricted Boltzmann machines (RBMs) and their variants are usually trained

by contrastive divergence (CD) learning, but the training procedure is an

unsupervised learning approach, without any guidances of the background

knowledge. To enhance the expression ability of traditional RBMs, in this

paper, we propose pairwise constraints restricted Boltzmann machine with

Gaussian visible units (pcGRBM) model, in which the learning procedure is

guided by pairwise constraints and the process of encoding is conducted under

these guidances. The pairwise constraints are encoded in hidden layer features

of pcGRBM. Then, some pairwise hidden features of pcGRBM flock together and

another part of them are separated by the guidances. In order to deal with

real-valued data, the binary visible units are replaced by linear units with

Gausian noise in the pcGRBM model. In the learning process of pcGRBM, the

pairwise constraints are iterated transitions between visible and hidden units

during CD learning procedure. Then, the proposed model is inferred by

approximative gradient descent method and the corresponding learning algorithm

is designed in this paper. In order to compare the availability of pcGRBM and

traditional RBMs with Gaussian visible units, the features of the pcGRBM and

RBMs hidden layer are used as input ‘data’ for K-means, spectral clustering

(SP) and affinity propagation (AP) algorithms, respectively. A thorough

experimental evaluation is performed with sixteen image datasets of Microsoft

Research Asia Multimedia (MSRA-MM). The experimental results show that the

clustering performance of K-means, SP and AP algorithms based on pcGRBM model

are significantly better than traditional RBMs. In addition, the pcGRBM model

for clustering task shows better performance than some semi-supervised

clustering algorithms.

Symbolic Regression Algorithms with Built-in Linear Regression

Recently, several algorithms for symbolic regression (SR) emerged which

employ a form of multiple linear regression (LR) to produce generalized linear

models. The use of LR allows the algorithms to create models with relatively

small error right from the beginning of the search; such algorithms are thus

claimed to be (sometimes by orders of magnitude) faster than SR algorithms

based on vanilla genetic programming. However, a systematic comparison of these

algorithms on a common set of problems is still missing. In this paper we

conceptually and experimentally compare several representatives of such

algorithms (GPTIPS, FFX, and EFS). They are applied as off-the-shelf,

ready-to-use techniques, mostly using their default settings. The methods are

compared on several synthetic and real-world SR benchmark problems. Their

performance is also related to the performance of three conventional machine

learning algorithms — multiple regression, random forests and support vector

regression.

A dissimilarity-based approach to predictive maintenance with application to HVAC systems

The goal of predictive maintenance is to forecast the occurrence of faults of

an appliance, in order to proactively take the necessary actions to ensure its

availability. In many application scenarios, predictive maintenance is applied

to a set of homogeneous appliances. In this paper, we firstly review taxonomies

and main methodologies currently used for condition-based maintenance;

secondly, we argue that the mutual dissimilarities of the behaviours of all

appliances of this set (the “cohort”) can be exploited to detect upcoming

faults. Specifically, inspired by dissimilarity-based representations, we

propose a novel machine learning approach based on the analysis of concurrent

mutual differences of the measurements coming from the cohort. We evaluate our

method over one year of historical data from a cohort of 17 HVAC (Heating,

Ventilation and Air Conditioning) systems installed in an Italian hospital. We

show that certain kinds of faults can be foreseen with an accuracy, measured in

terms of area under the ROC curve, as high as 0.96.

An Asynchronous Parallel Approach to Sparse Recovery

Asynchronous parallel computing and sparse recovery are two areas that have

received recent interest. Asynchronous algorithms are often studied to solve

optimization problems where the cost function takes the form (sum_{i=1}^M

f_i(x)), with a common assumption that each (f_i) is sparse; that is, each

(f_i) acts only on a small number of components of (xinmathbb{R}^n). Sparse

recovery problems, such as compressed sensing, can be formulated as

optimization problems, however, the cost functions (f_i) are dense with respect

to the components of (x), and instead the signal (x) is assumed to be sparse,

meaning that it has only (s) non-zeros where (sll n). Here we address how one

may use an asynchronous parallel architecture when the cost functions (f_i) are

not sparse in (x), but rather the signal (x) is sparse. We propose an

asynchronous parallel approach to sparse recovery via a stochastic greedy

algorithm, where multiple processors asynchronously update a vector in shared

memory containing information on the estimated signal support. We include

numerical simulations that illustrate the potential benefits of our proposed

asynchronous method.

Deep Probabilistic Programming

Dustin Tran , Matthew D. Hoffman , Rif A. Saurous , Eugene Brevdo , Kevin Murphy , David M. Blei Subjects : Machine Learning (stat.ML) ; Artificial Intelligence (cs.AI); Learning (cs.LG); Programming Languages (cs.PL); Computation (stat.CO)

We propose Edward, a Turing-complete probabilistic programming language.

Edward builds on two compositional representations—random variables and

inference. By treating inference as a first class citizen, on a par with

modeling, we show that probabilistic programming can be as flexible and

computationally efficient as traditional deep learning. For flexibility, Edward

makes it easy to fit the same model using a variety of composable inference

methods, ranging from point estimation, to variational inference, to MCMC. In

addition, Edward can reuse the modeling representation as part of inference,

facilitating the design of rich variational models and generative adversarial

networks. For efficiency, Edward is integrated into TensorFlow, providing

significant speedups over existing probabilistic systems. For example, on a

benchmark logistic regression task, Edward is at least 35x faster than Stan and

PyMC3.

Diffusion-based nonlinear filtering for multimodal data fusion with application to sleep stage assessment

Ori Katz , Ronen Talmon , Yu-Lun Lo , Hau-Tieng Wu Subjects : Machine Learning (stat.ML) ; Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)

The problem of information fusion from multiple data-sets acquired by

multimodal sensors has drawn significant research attention over the years. In

this paper, we focus on a particular problem setting consisting of a physical

phenomenon or a system of interest observed by multiple sensors. We assume that

all sensors measure some aspects of the system of interest with additional

sensor-specific and irrelevant components. Our goal is to recover the variables

relevant to the observed system and to filter out the nuisance effects of the

sensor-specific variables. We propose an approach based on manifold learning,

which is particularly suitable for problems with multiple modalities, since it

aims to capture the intrinsic structure of the data and relies on minimal prior

model knowledge. Specifically, we propose a nonlinear filtering scheme, which

extracts the hidden sources of variability captured by two or more sensors,

that are independent of the sensor-specific components. In addition to

presenting a theoretical analysis, we demonstrate our technique on real

measured data for the purpose of sleep stage assessment based on multiple,

multimodal sensor measurements. We show that without prior knowledge on the

different modalities and on the measured system, our method gives rise to a

data-driven representation that is well correlated with the underlying sleep

process and is robust to noise and sensor-specific effects.

Kernel Approximation Methods for Speech Recognition

Avner May , Alireza Bagheri Garakani , Zhiyun Lu , Dong Guo , Kuan Liu , Aurélien Bellet , Linxi Fan , Michael Collins , Daniel Hsu , Brian Kingsbury , Michael Picheny , Fei Sha Subjects : Machine Learning (stat.ML) ; Computation and Language (cs.CL); Learning (cs.LG)

We study large-scale kernel methods for acoustic modeling in speech

recognition and compare their performance to deep neural networks (DNNs). We

perform experiments on four speech recognition datasets, including the TIMIT

and Broadcast News benchmark tasks, and compare these two types of models on

frame-level performance metrics (accuracy, cross-entropy), as well as on

recognition metrics (word/character error rate). In order to scale kernel

methods to these large datasets, we use the random Fourier feature method of

Rahimi and Recht (2007). We propose two novel techniques for improving the

performance of kernel acoustic models. First, in order to reduce the number of

random features required by kernel models, we propose a simple but effective

method for feature selection. The method is able to explore a large number of

non-linear features while maintaining a compact model more efficiently than

existing approaches. Second, we present a number of frame-level metrics which

correlate very strongly with recognition performance when computed on the

heldout set; we take advantage of these correlations by monitoring these

metrics during training in order to decide when to stop learning. This

technique can noticeably improve the recognition performance of both DNN and

kernel models, while narrowing the gap between them. Additionally, we show that

the linear bottleneck method of Sainath et al. (2013) improves the performance

of our kernel models significantly, in addition to speeding up training and

making the models more compact. Together, these three methods dramatically

improve the performance of kernel acoustic models, making their performance

comparable to DNNs on the tasks we explored.

Perishability of Data: Dynamic Pricing under Varying-Coefficient Models

We consider a firm that sells a large number of products to its customers in

an online fashion. Each product is described by a high dimensional feature

vector, and the market value of a product is assumed to be linear in the values

of its features. Parameters of the valuation model are unknown and can change

over time. The firm sequentially observes a product’s features and can use the

historical sales data (binary sale/no sale feedbacks) to set the price of

current product, with the objective of maximizing the collected revenue. We

measure the performance of a dynamic pricing policy via regret, which is the

expected revenue loss compared to a clairvoyant that knows the sequence of

model parameters in advance.

We propose a pricing policy based on projected stochastic gradient descent

(PSGD) and characterize its regret in terms of time (T), features dimension

(d), and the temporal variability in the model parameters, (delta_t). We

consider two settings. In the first one, feature vectors are chosen

antagonistically by nature and we prove that the regret of PSGD pricing policy

is of order (O(sqrt{T} + sum_{t=1}^T sqrt{t}delta_t)). In the second

setting (referred to as stochastic features model), the feature vectors are

drawn independently from an unknown distribution. We show that in this case,

the regret of PSGD pricing policy is of order (O(d^2 log T + sum_{t=1}^T

tdelta_t)).

Linear Disentangled Representation Learning for Facial Actions

Limited annotated data available for the recognition of facial expression and

action units embarrasses the training of deep networks, which can learn

disentangled invariant features. However, a linear model with just several

parameters normally is not demanding in terms of training data. In this paper,

we propose an elegant linear model to untangle confounding factors in

challenging realistic multichannel signals such as 2D face videos. The simple

yet powerful model does not rely on huge training data and is natural for

recognizing facial actions without explicitly disentangling the identity. Base

on well-understood intuitive linear models such as Sparse Representation based

Classification (SRC), previous attempts require a prepossessing of explicit

decoupling which is practically inexact. Instead, we exploit the low-rank

property across frames to subtract the underlying neutral faces which are

modeled jointly with sparse representation on the action components with group

sparsity enforced. On the extended Cohn-Kanade dataset (CK+), our one-shot

automatic method on raw face videos performs as competitive as SRC applied on

manually prepared action components and performs even better than SRC in terms

of true positive rate. We apply the model to the even more challenging task of

facial action unit recognition, verified on the MPI Face Video Database

(MPI-VDB) achieving a decent performance. All the programs and data have been

made publicly available.

Information Theory

Analysis of Coupled Scalar Systems by Displacement Convexity

Potential functionals have been introduced recently as an important tool for

the analysis of coupled scalar systems (e.g. density evolution equations). In

this contribution, we investigate interesting properties of this potential.

Using the tool of displacement convexity, we show that, under mild assumptions

on the system, the potential functional is displacement convex. Furthermore, we

give the conditions on the system such that the potential is strictly

displacement convex, in which case the minimizer is unique.

The 2-adic complexity of a class of binary sequences with almost optimal autocorrelation

Pseudo-random sequences with good statistical property, such as low

autocorrelation, high linear complexity and large 2-adic complexity, have been

applied in stream cipher. In general, it is difficult to give both the linear

complexity and 2-adic complexity of a periodic binary sequence. Cai and Ding

cite{Cai Ying} gave a class of sequences with almost optimal autocorrelation

by constructing almost difference sets. Wang cite{Wang Qi} proved that one

type of those sequences by Cai and Ding has large linear complexity. Sun et al.

cite{Sun Yuhua} showed that another type of sequences by Cai and Ding has also

large linear complexity. Additionally, Sun et al. also generalized the

construction by Cai and Ding using (d)-form function with difference-balanced

property. In this paper, we first give the detailed autocorrelation

distribution of the sequences was generalized from Cai and Ding cite{Cai Ying}

by Sun et al. cite{Sun Yuhua}. Then, inspired by the method of Hu cite{Hu

Honggang}, we analyse their 2-adic complexity and give a lower bound on the

2-adic complexity of these sequences. Our result show that the 2-adic

complexity of these sequences is at least (N-mathrm{log}_2sqrt{N+1}) and that

it reach (N-1) in many cases, which are large enough to resist the rational

approximation algorithm (RAA) for feedback with carry shift registers (FCSRs).

The Velocity of the Decoding Wave for Spatially Coupled Codes on BMS Channels

We consider the dynamics of belief propagation decoding of spatially coupled

Low-Density Parity-Check codes. It has been conjectured that after a short

transient phase, the profile of “error probabilities” along the spatial

direction of a spatially coupled code develops a uniquely-shaped wave-like

solution that propagates with constant velocity v. Under this assumption, and

for transmission over general Binary Memoryless Symmetric channels, we derive a

formula for v. We also propose approximations that are simpler to compute and

support our findings using numerical data.

The Velocity of the Propagating Wave for General Coupled Scalar Systems

We consider spatially coupled systems governed by a set of scalar density

evolution equations. Such equations track the behavior of message-passing

algorithms used, for example, in coding, sparse sensing, or

constraint-satisfaction problems. Assuming that the “profile” describing the

average state of the algorithm exhibits a solitonic wave-like behavior after

initial transient iterations, we derive a formula for the propagation velocity

of the wave. We illustrate the formula with two applications, namely

Generalized LDPC codes and compressive sensing.

Spectral and Energy Efficiency of Uplink D2D Underlaid Massive MIMO Cellular Networks

One of key 5G scenarios is that device-to-device (D2D) and massive

multiple-input multiple-output (MIMO) will be co-existed. However, interference

in the uplink D2D underlaid massive MIMO cellular networks needs to be

coordinated, due to the vast cellular and D2D transmissions. To this end, this

paper introduces a spatially dynamic power control solution for mitigating the

cellular-to-D2D and D2D-to-cellular interference. In particular, the proposed

D2D power control policy is rather flexible including the special cases of no

D2D links or using maximum transmit power. Under the considered power control,

an analytical approach is developed to evaluate the spectral efficiency (SE)

and energy efficiency (EE) in such networks. Thus, the exact expressions of SE

for a cellular user or D2D transmitter are derived, which quantify the impacts

of key system parameters such as massive MIMO antennas and D2D density.

Moreover, the D2D scale properties are obtained, which provide the sufficient

conditions for achieving the anticipated SE. Numerical results corroborate our

analysis and show that the proposed power control solution can efficiently

mitigate interference between the cellular and D2D tier. The results

demonstrate that there exists the optimal D2D density for maximizing the area

SE of D2D tier. In addition, the achievable EE of a cellular user can be

comparable to that of a D2D user.

Fast Reconstruction of High-qubit Quantum States via Low Rate Measurements

K. Li , J. Zhang , Z. Li , S. Cong Subjects : Information Theory (cs.IT) ; Quantum Physics (quant-ph)

Quantum state tomography is a fundamental technique for quantum technology,

with many applications in quantum control and quantum communication. Due to the

exponential complexity of the resources required for QST, people are looking

for approaches that identify quantum states with less efforts and faster speed.

In this Letter, we provide a tailored efficient method for reconstructing mixed

quantum states up to (12) (or even more) qubits from an incomplete set of

observables subject to noises. Our method is applicable to any pure state

(

ho), and can be extended to many states of interest in quantum information

tasks such as (W) state, GHZ state and cluster states that are matrix product

operators of low dimensions. The method applies the quantum density matrix

constraints to a quantum compressive sensing optimization problem, and exploits

a modified Quantum Alternating Direction Multiplier Method (Quantum-ADMM) to

accelerate the convergence. Our algorithm takes (8,35, 226) seconds to

reconstruct arbitrary superposition state density matrices of (10,11,12) qubits

with acceptable fidelity respectively, using less than (1 /%) measurements of

expectation on a normal desktop, which is the fastest realization to date. We

further discuss applications of this method using experimental data of mixed

states obtained in an ion trap experiment up to (8) qubits.

Joint Tilt Angle Adaptation and Beamforming in Multicell Multiuser Cellular Networks

3D beamforming is a promising approach for interference coordination in

cellular networks which brings significant improvements in comparison with

conventional 2D beamforming techniques. This paper investigates the problem of

joint beamforming design and tilt angle adaptation of the BS antenna array for

maximizing energy efficiency (EE) in downlink of multi-cell multi-user

coordinated cellular networks. An iterative algorithm based on fractional

programming approach is introduced to solve the resulting non-convex

optimization problem. In each iteration, users are clustered based on their

elevation angle. Then, optimization of the tilt angle is carried out through a

reduced complexity greedy search to find the best tilt angle for a given

placement of the users. Numerical results show that the proposed algorithm

achieves higher EE compared to the 2D beamforming techniques.

Secure Lossy Source Coding for Some Classes of Helper and Gray-Wyner Models

In this work, we investigate two source coding models, a emph{Helper}

problem and a emph{Gray-Wyner} problem, under equivocation constraints.

Specifically, in the Helper problem, an encoder communicates with a legitimate

receiver through noise-free rate-limited public and private links; and an

external passive eavesdropper intercepts every information that is sent on the

public link. We study two classes of this model: i) when a pair of arbitrarily

correlated discrete memoryless sources is to be encoded such that one component

has to be recovered lossily at the legitimate receiver while the equivocation

about both components at the eavesdropper must be maintained no smaller than

some prescribed level; and ii) when the legitimate receiver reproduces both

components, one of which, that is recovered losslessly, has to be concealed

from the eavesdropper to some equivocation level. For both classes problems, we

establish single-letter characterizations of optimal

rate-distortion-equivocation tradeoffs in the discrete memoryless case. Next,

we extend our results to the case of two legitimate receivers, i.e., Gray-Wyner

network with equivocation constraints. Here, two legitimate receivers are

connected to the encoder each through a dedicated error-free private link as

well as a common error-free public link; and an external passive eavesdropper

overhears on the public link. We study two classes of this model that are

extensions of the aforementioned instances of Helper problems to the case of

two receivers. For each of the two classes, we establish a single-letter

characterization of the optimal rate-distortion-equivocation region. Throughout

the paper, the analysis sheds light on the role of the private links, and we

illustrate the results by computing them for some binary examples. Also, we

make some meaningful connections, e.g., with problems of secret-sharing and

encryption.

On OR Many-Access Channels

OR multi-access channel is a simple model where the channel output is the

Boolean OR among the Boolean channel inputs. We revisit this model, showing

that employing Bloom filter, a randomized data structure, as channel inputs

achieves its capacity region with joint decoding and the symmetric sum rate of

(ln 2) bits per channel use without joint decoding. We then proceed to the

“many-access” regime where the number of potential users grows without bound,

treating both activity recognition and message transmission problems,

establishing scaling laws which are optimal within a constant factor, based on

Bloom filter channel inputs.

Rate-Distortion Region of a Gray-Wyner Model with Side Information

In this work, we establish a full single-letter characterization of the

rate-distortion region of an instance of the Gray-Wyner model with side

information at the decoders. Specifically, in this model an encoder observes a

pair of memoryless, arbitrarily correlated, sources ((S^n_1,S^n_2)) and

communicates with two receivers over an error-free rate-limited link of

capacity (R_0), as well as error-free rate-limited individual links of

capacities (R_1) to the first receiver and (R_2) to the second receiver. Both

receivers reproduce the source component (S^n_2) losslessly; and Receiver (1)

also reproduces the source component (S^n_1) lossily, to within some prescribed

fidelity level (D_1). Also, Receiver (1) and Receiver (2) are equipped

respectively with memoryless side information sequences (Y^n_1) and (Y^n_2).

Important in this setup, the side information sequences are arbitrarily

correlated among them, and with the source pair ((S^n_1,S^n_2)); and are not

assumed to exhibit any particular ordering. Furthermore, by specializing the

main result to two Heegard-Berger models with successive refinement and

scalable coding, we shed light on the roles of the common and private

descriptions that the encoder should produce and what they should carry

optimally. We develop intuitions by analyzing the developed single-letter

optimal rate-distortion regions of these models, and discuss some insightful

binary examples.

Online Dictionary Learning Aided Target Recognition In Cognitive GPR

Sparse decomposition of ground penetration radar (GPR) signals facilitates

the use of compressed sensing techniques for faster data acquisition and

enhanced feature extraction for target classification. In this paper, we

investigate use of an online dictionary learning (ODL) technique in the context

of GPR to bring down the learning time as well as improve identification of

abandoned anti-personnel landmines. Our experimental results using real data

from an L-band GPR for PMN/PMA2, ERA and T72 mines show that ODL reduces

learning time by 94/% and increases clutter detection by 10/% over the

classical K-SVD algorithm. Moreover, our methods could be helpful in cognitive

operation of the GPR where the system adapts the range sampling based on the

learned dictionary.

Generalized Approximate Message-Passing Decoder for Universal Sparse Superposition Codes

Erdem Biyik , Jean Barbier , Mohamad Dia Subjects : Information Theory (cs.IT)

Sparse superposition (SS) codes were originally proposed as a

capacity-achieving communication scheme over the additive white Gaussian noise

channel (AWGNC) [1]. Very recently, it was discovered that these codes are

universal, in the sense that they achieve capacity over any memoryless channel

under generalized approximate message-passing (GAMP) decoding [2], although

this decoder has never been stated for SS codes. In this contribution we

introduce the GAMP decoder for SS codes, we confirm empirically the

universality of this communication scheme through its study on various channels

and we provide the main analysis tools: state evolution and potential. We also

compare the performance of GAMP with the Bayes-optimal MMSE decoder. We

empirically illustrate that despite the presence of a phase transition

preventing GAMP to reach the optimal performance, spatial coupling allows to

boost the performance that eventually tends to capacity in a proper limit. We

also prove that, in contrast with the AWGNC case, SS codes for binary input

channels have a vanishing error floor in the limit of large codewords.

Moreover, the performance of Hadamard-based encoders is assessed for practical

implementations.

Insider-Attacks on Physical-Layer Group Secret-Key Generation in Wireless Networks

Physical-layer group secret-key (GSK) generation is an effective way of

generating secret keys in wireless networks, wherein the nodes exploit inherent

randomness in the wireless channels to generate group keys, which are

subsequently applied to secure messages while broadcasting, relaying, and other

network-level communications. While existing GSK protocols focus on securing

the common source of randomness from external eavesdroppers, they assume that

the legitimate nodes of the group are trusted. In this paper, we address

insider attacks from the legitimate participants of the wireless network during

the key generation process. Instead of addressing conspicuous attacks such as

switching-off communication, injecting noise, or denying consensus on group

keys, we introduce stealth attacks that can go undetected against

state-of-the-art GSK schemes. We propose two forms of attacks, namely: (i)

different-key attacks, wherein an insider attempts to generate different keys

at different nodes, especially across nodes that are out of range so that they

fail to recover group messages despite possessing the group key, and (ii)

low-rate key attacks, wherein an insider alters the common source of randomness

so as to reduce the key-rate. We also discuss various detection techniques,

which are based on detecting anomalies and inconsistencies on the channel

measurements at the legitimate nodes. Through simulations we show that GSK

generation schemes are vulnerable to insider-threats, especially on topologies

that cannot support additional secure links between neighbouring nodes to

verify the attacks.

Integer-Forcing Linear Receivers: A Design Criterion for Full-Diversity STBCs

In multiple-input multiple-output (MIMO) fading channels, the design

criterion for full-diversity space-time block codes (STBCs) is primarily

determined by the decoding method at the receiver. Although constructions of

STBCs have predominantly matched the maximum-likelihood (ML) decoder, design

criteria and constructions of full-diversity STBCs have also been reported for

low-complexity linear receivers. A new receiver architecture called

Integer-Forcing (IF) linear receiver has been proposed to MIMO channels by Zhan

et al. which showed promising results for the high-rate V-BLAST encoding

scheme. In this work we address the design of full-diversity STBCs for IF

linear receivers. We derive an upper bound on the probability of decoding

error, and show that STBCs that satisfy the non-vanishing singular value (NVS)

property provide full-diversity for the IF receiver. We also present simulation

results to demonstrate that linear designs with NVS property provide full

diversity for IF receiver. As a special case of our analysis on STBCs, we

present an upper bound on the error probability for the V-BLAST architecture

presented by Zhan emph{et al.}, and demonstrate that the IF linear receivers

provide full receive diversity. Our results supplement the existing outage

probability based results for the IF receiver.

Generalized Index Coding Problem and Discrete Polymatroids

The index coding problem has been generalized recently to accommodate

receivers which demand functions of messages and which possess functions of

messages. The connections between index coding and matroid theory have been

well studied in the recent past. Index coding solutions were first connected to

multi linear representation of matroids. For vector linear index codes discrete

polymatroids which can be viewed as a generalization of the matroids was used.

It was shown that a vector linear solution to an index coding problem exists if

and only if there exists a representable discrete polymatroid satisfying

certain conditions. In this work we explore the connections between generalized

index coding and discrete polymatroids. The conditions that needs to be

satisfied by a representable discrete polymatroid for a generalized index

coding problem to have a vector linear solution is established. From a discrete

polymatroid we construct an index coding problem with coded side information

and shows that if the index coding problem has a certain optimal length

solution then the discrete polymatroid satisfies certain properties. From a

matroid we construct a similar generalized index coding problem and shows that

the index coding problem has a binary scalar linear solution of optimal length

if and only if the matroid is binary representable.

Multiple Illumination Phaseless Super-Resolution (MIPS) with Applications To Phaseless DOA Estimation and Diffraction Imaging

Phaseless super-resolution is the problem of recovering an unknown signal

from measurements of the magnitudes of the low frequency Fourier transform of

the signal. This problem arises in applications where measuring the phase, and

making high-frequency measurements, are either too costly or altogether

infeasible. The problem is especially challenging because it combines the

difficult problems of phase retrieval and classical super-resolution