On Optimal Interpolation In Linear Regression

Understanding when and why interpolating methods generalize well has recently been a topic of interest in statistical learning theory . We identify regime where the minimum-norminterpolator provably generalizes arbitrarily worse than the optimalresponse-linear achievable interpolator that we introduce . We extend the notion of optimal response-linearinterpolation to random features regression under a linear data-generatingmodel that has been previously studied in the literature .…

A Decentralized Framework for Serverless Edge Computing in the Internet of Things

Serverless computing is becoming widely adopted among cloud providers . The current technologies are very well suited to data centers, but cannot provide equally good performance in decentralized environments, such as edgecomputing systems . We propose a framework for efficient dispatching of stateless tasks to in-network executors so as to minimize theresponse times while exhibiting short- and long-term fairness, also leveraging information from a virtualized network infrastructure when available .…

CNewSum A Large scale Chinese News Summarization Dataset with Human annotated Adequacy and Deducibility Level

A large-scale Chinese newssummarization dataset CNewSum consists of 304,307 documents and human-written summaries for the news feed . It has long documents with high-abstractive summaries, which can encourage document-level understandingand generation for current summarization models . The test set contains adequacy and deducibilityannotations for the summaries .…

User Level Private Learning via Correlated Sampling

Most works in learning with differential privacy (DP) have focused on thesetting where each user has a single sample . We show that, in this setting, wemay learn with a much fewer number of users . As long as each user receives sufficiently many samples, we can learn anyprivately learnable class via an algorithm using only $O(\log(1/\delta)/\epsilon)$ users .…

Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks

In safety-critical applications like medical diagnosis, certainty associated with a model’s prediction is just as important as its accuracy . Uncertainty inpredictions can be attributed to noise or randomness in data (aleatoric) and incorrect model inferences (epistemic) While model uncertainty can be reduced with more data or bigger models, aleatoric uncertainty is more intricate .…

Intelligent Reflecting Surface for Multi Path Beam Routing with Active Passive Beam Splitting and Combining

Intelligent reflecting surface (IRS) can be densely deployed in wirelessnetworks to significantly enhance the communication channels . In this letter, we consider the downlink transmission from a multi-antenna base station (BS) to a single user, by exploiting the cooperative passive beamforming (CPB)and line-of-sight (LoS) path diversity gains of multi-IRS signal reflection .…

Certifying C program correctness with respect to CompCert with VeriFast

VeriFast is a powerful tool for verification of various correctnessproperties of C programs using symbolic execution . We present a proof-of-concept extension which generates acorrectness certificates for each successful verification run individually . Thiscertificate takes the form of a Coq script containing two proofs which, when checked by Coq, together remove the need for trusting in the correctness of the program itself .…

A Real Time Energy and Cost Efficient Vehicle Route Assignment Neural Recommender System

This paper presents a neural network recommender system algorithm forassigning vehicles to routes based on energy and cost criteria . The new system has been deployed and integrated into the POLARISTransportation System Simulation Tool for use in research conducted by the Department of Energy’s Systems and Modeling for Accelerated Research inTransportation (SMART) Mobility Consortium .…

Learning OFDM Waveforms with PAPR and ACLR Constraints

Most modern systems useorthogonal frequency-division multiplexing (OFDM) for its efficient equalization . This waveform suffers from multiple limitations such as a highadjacent channel leakage ratio (ACLR) and high peak-to-average power ratio (PAPR) In this paper, we propose a learning-based method to design OFDM-basedwaveforms that satisfy selected constraints while maximizing an achievableinformation rate .…

Multiobjective Dijkstra A

We introduce the Multiobjective Dijkstra A* (MDA*) algorithm for the One-to-One MultiObjective Shortest Path Problem . The algorithm requires a monotone node heuristic as part of its input . For any node, the heuristic underestimates the costs of apath from this node to the target node of the search .…

Index Coded NOMA in Vehicular Ad Hoc Networks

The demand for multimedia services is growing day by day in vehicular ad-hocnetworks (VANETs), resulting in high spectral usage and network congestion . Non-orthogonal multiple access (NOMA) is a promising wireless communicationtechnique to solve the problems related to spectral efficiency effectively .…

DAIR Data Augmented Invariant Regularization

Deep learning through empirical risk minimization (ERM) has succeeded at achieving human-level performance at a variety of complex tasks . ERM generalizes poorly to distribution shift, partly explained by overfitting to spurious features such as background in images or named entities in natural language .…

Computer Says No Algorithmic Decision Support and Organisational Responsibility

Algorithmic decision support is increasingly used in various contexts and structures in various areas of society, influencing many people’s lives . Its use raises questions, among others, about accountability,transparency and responsibility . While there is substantial research on the issue of algorithmic systems and responsibility in general, there is little tono prior research on organisational responsibility and its attribution .…

Variational Predictive Routing with Nested Subjective Timescales

Variational Predictive Routing is a neural probabilistic inference system that organizes latent representations of video features in a temporal hierarchy, based on their rates of change . VPR is able to detect event boundaries, disentangle spatiotemporalfeatures across its hierarchy, adapt to the dynamics of the data, and produce accurate time-agnostic rollouts of the future .…

Beamforming Design for Intelligent Reflecting Surface Enhanced Symbiotic Radio Systems

This paper investigates multiuser multi- input single-input single-output downlinksymbiotic radio communication systems . The proposed scheme significantly improves the average sum-rate of the primary system, while guaranteeing the decodingperformance of the secondary system . We exploit the Schur complement thatfacilitates the design of a suboptimal beamforming algorithm based on convex approximation .…

Scheduling of Graph Queries Controlling Intra and Inter query Parallelism for a High System Throughput

The vast amounts of data used in social, business or traffic networks,biology and other natural sciences are often managed in graph-based data sets . This work is concerned with multi-query execution by automaticallycontrolling the degree of parallelization . The underlying concept is three-fold: sampling is used to determine graph statistics, parallelization constraints are derived from algorithm and system properties, and suitable work packages are generated based on the previous two aspects .…

Log concave poset inequalities

We study combinatorial inequalities for various classes of set systems . We use the language formulation of greedoids which allows a linear algebraic setup . The underlying non-commutative nature of matrices associated withgreedoids allows us to proceed beyond polymatroids and prove the equalityconditions .…

A Data Centric Optimization Framework for Machine Learning

EfficientNet is a flexible and user-customizable pipeline for optimizing training of arbitrary deep neuralnetworks . The pipeline begins with standard networks in PyTorch or ONNX and transforms computation through progressive lowering . We define four levels of general-purpose transformations, from local intra-operations to global data movement reduction .…

Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning

We present a novel characterization of the mapping of multiple parallelismforms onto hierarchical accelerator system . We experimentally verify the substantial effect of these mappings onall-reduce performance (up to 448x) We offer a novel syntax-guided programsynthesis framework that is able to decompose reductions over one or moreparallelism axes to sequences of collectives in a hierarchy- and mapping-awareway .…

Monitoring Collective Communication Among GPUs

Communication among devices in multi-GPU systems plays an important role interms of performance and scalability . In order to optimize an application,programmers need to know the type and amount of the communication happening among GPUs . In this work, we extend ComScribe to identify communication among GPUs for collective and P2P communicationprimitives in NVIDIA’s NCCL library .…

Asynchronous parareal time discretization for partial differential equations

Asynchronous iterations are more and more investigated for both scaling and fault-resilience purpose on high performance computing platforms . This paper advocates a novel application direction targeting time-decomposed time-parallel approaches . It turned out that Parareal andasync-Parareal feature very close convergence conditions, asymptoticallyequivalent, including the finite-time termination property .…