Fourier reconstruction for diffraction tomography of an object rotated into arbitrary orientations

In this paper, we study the mathematical imaging problem of opticaldiffraction tomography (ODT) for the scenario of a microscopic rigid particlerotating in a trap created, for instance, by acoustic or optical forces . We prove a Fourier diffraction theorem and derivenovel backprojection formulae for the reconstruction of the scatteringpotential, which depends on the refractive index distribution inside the object, taking its complicated motion into account .…

NoisyCUR An algorithm for two cost budgeted matrix completion

Matrix completion is a ubiquitous tool in machine learning and data analysis . It is possible to obtain low noise, high costobservations of individual entries or high noise, low cost observations of entire columns . We introduce a regression-based completion algorithm for this setting and experimentally verify the performance of our approach on both synthetic and real data sets .…

HTN Planning Domain for Deployment of Cloud Applications

Hierarchical TaskNetwork (HTN) planning can provide effective means to solve such deployment problems . We present an HTN planning domain that models deployment problems as well as those in realistic Cloud environments . Cloud providers are facing a complex problem in configuring software applications ready for deployment .…

Evolving a Model for Software Process Context An Exploratory Study

In the domain of software engineering, our efforts as researchers to advise industry on which software practices might be applied most effectively are limited by our lack of evidence based information about the relationships between context and practice efficacy . We are in the exploratory stage of evolving amodel for context for situated software practices .…

Modeling Fuzzy Cluster Transitions for Topic Tracing

Twitter can be viewed as a data source for Natural Language Processing (NLP)tasks . The continuously updating data streams on Twitter make it challenging totrace real-time topic evolution . We propose a framework formodeling fuzzy transitions of topic clusters . We extend our previous work oncrisp cluster transitions by incorporating fuzzy logic in order to enrich the underlying structures identified by the framework .…

Autoencoder Based Unequal Error Protection Codes

The proposed design is based on a generalization of an autoencoder loss function that accommodates both message-wise and bit-wise UEP scenarios . In both scenarios, the generalized loss function can be adjusted using an associated weight vector totrade off error probabilities corresponding to different importance classes .…

A Survey on Fundamental Limits of Integrated Sensing and Communication

The integrated sensing and communication (ISAC) has emerged as a key technology in future wireless systems . In this paper, we aim to provide a comprehensive survey for the current research progress on the fundamental limits of ISAC . We propose a systematic classification method for both traditional radio sensing (such as radar sensing) and ISAC so that they can be naturally incorporated into a unified framework .…

A Simple Single RF Chain Multi Antenna Full Duplex Relay Can Outperform An Intelligent Reflecting Surface

In this paper, we propose a single RF-chain multi-antenna full-duplex (FD)relay build with $b$-bit analog phase shifters . We compare the data rate of the proposed FD relaying system with the data rates of the same system but with the FD relay replaced by anideal intelligent reflecting surface (IRS) Our results show that the relayingsystem with 2-bit quantized analog phase .…

Shared Memory n level Hypergraph Partitioning

We present a shared-memory algorithm to compute high-quality solutions to the balanced $k$-way hypergraph partitioning problem . High solution quality is achieved by parallelizing the core technique of the currently best sequential partitionerKaHyPar . This approach is made fast and scalable through intrusive algorithms and data structures that allow precise control of parallelism through atomic operations and fine-grainedlocking .…

libEnsemble A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations

libEnsemble is a library aimed at particular scalability- and capability-stretching uses . It enables running concurrent instances of an application in dynamically allocated ensembles through an extensible Pythonlibrary . We highlight the structure, execution, and capabilities of the library on leading pre-exascale environments as well as advanced capabilities forexascale and beyond .…

A New Pathway to Approximate Energy Expenditure and Recovery of an Athlete

This work proposes to use evolutionary computation as a pathway to allow anew perspective on the modeling of energy expenditure and recovery of an individual athlete during exercise . We revisit a theoretical concept called the”three component hydraulic model” which is designed to simulate metabolicsystems during exercise and which is able to address recently highlighted shortcomings of currently applied performance models .…

Meta Faster R CNN Towards Accurate Few Shot Object Detection with Attentive Feature Alignment

Few-shot object detection (FSOD) aims to detect objects using only few examples . Method incorporates a coarse-to-fine approach into the proposal based object detection framework . We propose to learn a lightweight matching network to measure the similarity between each spatialposition in the query image feature map and spatially-pooled class features, instead of the traditional object/nonobject classifier .…

Embedding Dependencies Within Distributionally Robust Optimization of Modern Power Systems

The increasing share of renewables in the electrical energy generation mix comes along with an increasing uncertainty in power supply . We propose a metric-basedambiguity set with an additional constraint on dependence structure, usingcopula theory . We develop a conic reformulation which is kept generic such that it can be applied to any decision-making problem under uncertainty in powersystems .…

Generating Bug Fixes Using Pretrained Transformers

DeepDebug is a data-driven program repair approach which learns to detect and fixbugs in Java methods mined from real-world GitHub repositories . We framebug-patching as a sequence-to-sequence learning task consisting of two steps . We show that pretraining on source code programs improves thenumber of patches found by 33% as compared to supervised training from scratch .…

Sync Switch Hybrid Parameter Synchronization for Distributed Deep Learning

Stochastic Gradient Descent (SGD) has become the de facto way to train deepneural networks in distributed clusters . A critical factor in determining the training throughput and model accuracy is the choice of the parametersynchronization protocol . While Bulk Synchronous Parallel (BSP) often achieves better converged accuracy, the corresponding training throughput can be negatively impacted by stragglers .…

Citations are not opinions a corpus linguistics approach to understanding how citations are made

A key issue in citation content analysis seeks to understand citations based on the language used during the making of a citation . We find that within citation collocates, there is very low correlationbetween citation type and sentiment . We suggest that citations can be betterunderstood as claims-making devices where the citation type can be explained by understanding how two claims are being compared .…

A Further Study of Quadratic APN Permutations in Dimension Nine

Recently, Beierle and Leander found two new sporadic quadratic APNpermutations in dimension 9 . We show that a function EA-inequivalent to thosesporadic APN permutations can be obtained by just applyingEA transformations and inversion to the original permutations . We conjecture that $C_u$ is not APN if $m$ isgreater than $3$ and $u is not a 7th power .…

Low Row Rank Parity Check Codes

In this paper we present an extended variant of low rank parity check matrix(LRPC) codes that have received significant interests in recent years . It isshown that the extension indeed yields a superfamily of LRPC codes . The decoding method of the proposedcodes is also investigated .…

Lower Bounds on Cross Entropy Loss in the Presence of Test time Adversaries

Understanding the fundamental limits of robust supervised learning hasemerged as a problem of immense interest . In thispaper, we determine optimal lower bounds on the cross-entropy loss in the presence of test-time adversaries, along with the corresponding optimalclassification outputs . We propose and provide a proof of correctnessfor a bespoke algorithm to compute this lower bound efficiently .…

MT Opt Continuous Multi Task Robotic Reinforcement Learning at Scale

A large-scale collective roboticlearning system can acquire a repertoire of behaviors simultaneously, sharing exploration, experience, and representations across tasks . In this framework new tasks can be continuously instantiated from previously learned tasks . We develop a scalable and intuitive framework for specifying new tasks through user-provided examples of desired outcomes .…

ZeRO Infinity Breaking the GPU Memory Wall for Extreme Scale Deep Learning

ZeRO-Infinity can fit models with tens and even hundreds of trillions of parameters for training on currentgeneration GPU clusters . It sustains over 25 petaflops on 512 NVIDIA V100 GPUs, while also demonstrating super linearscalability . An open source implementation is available through DeepSpeed, a deep learning optimization library that makes distributedtraining easy, efficient, and effective .…

Is Your Language Model Ready for Dense Representation Fine tuning

Pre-trained language models (LM) have become go-to text representationencoders . However, dense encoders suffer in low resource situations . One cause liesin the readiness of the LM to expose its knowledge through dense representation, which we term Optimization Readiness . We present Condenser, a general pre-training architecture based on TransformerLMs, to improve dense optimization readiness.…

BERT2Code Can Pretrained Language Models be Leveraged for Code Search

Millions of repetitive code snippets are submitted to code repositories everyday . To search from these large codebases using simple natural language queries would allow programmers to ideate, prototype, and develop easier and faster . We show that our modellearns the inherent relationship between the embedding spaces and furtherprobes into the scope of improvement by empirically analyzing the embeddeddingmethods.…

Quantum Architecture Search via Deep Reinforcement Learning

Recent advances in quantum computing have drawn considerable attention tobuilding realistic application for and using quantum computers . However,designing a suitable quantum circuit architecture requires expert knowledge . We propose aquantum architecture search framework with the power of deep reinforcementlearning (DRL) to address this challenge .…

Hierarchical Human Motion Prediction and Logic Geometric Programming for Minimal Interference Human Robot Tasks

In this paper, we tackle the problem of human-robot coordination in sequences of manipulation tasks . We first devise a hierarchical prediction approach by combining Inverse Reinforcement Learning and short-term motion prediction using a Recurrent Neural Network . In a secondstep, we propose a dynamic version of the TAMP algorithm Logic- GeometricProgramming (LGP) Our version of Dynamic LGP, replans periodically to handle the mismatch between the human motion prediction and the actual human behavior .…

Search oriented Differentiable Product Quantization

Product quantization (PQ) is a popular approach for maximum inner productsearch (MIPS) It is widely used in ad-hoc retrieval . Recent studies proposedifferentiable PQ, where embedding and quantization modules can be trainedjointly . In this work, we proposeSearch-oriented Product Quantization (SoPQ), where a novel training objectiveMCL is formulated .…

An expressiveness hierarchy of Behavior Trees and related architectures

Behavior Trees (BTs) provide a formal framework for comparing the expressivepower of Behavior Trees to other action selection architectures . This leads to an expressiveness hierarchy of control architectures, which includes BTs,Decision Trees (DTs), Teleo-reactive Programs (TRs) and Finite State Machines (FSMs) By distinguishing between BTs with auxiliary variables and thosewithout, we demonstrate the existence of a trade-off in BT design between readability and expressiveness .…

DREAM Lite Simplifying Robot Assisted Therapy for ASD

Robot-Assisted Therapy (RAT) has successfully been used to improve social skills in children with autism spectrum disorders (ASD) The DREAM project explores how to deliver effective RAT interventions to ASD children within asupervisedautonomy framework for controlling the robotic agent . This could reduce the burden on the clinicians delivering such interventions .…