## 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 .…

## Analyzing and Improving the Optimization Landscape of Noise Contrastive Estimation

Noise-contrastive estimation (NCE) is a statistically consistent method for learning unnormalized probabilistic models . It has been empirically observed that the choice of the noise distribution is crucial for NCE’s performance . In this work, we prove these challengesarise due to an ill-behaved (more precisely, flat) loss landscape .…

## 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 .…

## Multi stable design of triangulated origami structures on cones of revolution

The Kresling pattern of congruent triangles can be arranged either circularly on a cylinder of revolution or in a helical way . We generalizethese arrangements with respect to cones of revolution, where our approach allows to construct structures which snap between conical realizations whoseapex angles serve as design parameters .…

## 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 .…

## Few Shot Temporal Action Localization with Query Adaptive Transformer

Few-shot TAL (FS-TAL) aims to adapt a model to a new class represented by as few as a single video . However, this setting is not only unnatural actions are typically captured in untrimmed videos, but also ignores background video segments containing vital contextualcues for foreground action segmentation .…

## Multi Object Tracking and Segmentation with a Space Time Memory Network

We propose a method for multi-object tracking and segmentation that does not require fine-tuning or per benchmark hyper-parameter selection . The proposed tracker, MeNToS, addresses particularly the data association problem . We evaluated our tracker on KITTIMOTS and MOTSChallenge and show the benefit of our data association strategy with the HOTA metric .…

## 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 .…

## Personalized Transfer of User Preferences for Cross domain Recommendation

Cold-start problem is still a very challenging problem in recommendersystems . How totransfer user’s preferences from the source domain to the target domain, is the key issue in Cross-domain Recommendation (CDR) which is a promising solution to deal with the cold start problem .…

## Modeling Performance in Open Domain Dialogue with PARADISE

Athena is a dialogue system that has participated in thousands of conversations with real users, while competing as a finalist in the Alexa Prize . We use both user ratings and dialogue length as metrics for quality of dialogue quality .…

## Likelihood Training of Schrödinger Bridge using Forward Backward SDEs Theory

Schrodinger Bridge (SB) is an optimal transport problem that has received attention in deep generative modeling for its mathematicalflexibility compared to the Scored-based Generative Model (SGM) However, it remains unclear whether the optimization principle of SB relates to the moderntraining of deep .…

## A Survey on Methods and Metrics for the Assessment of Explainability under the Proposed AI Act

This study discusses the interplay between metrics used to measure the explainability of AI systems and the proposed EU Artificial Intelligence Act . It proposes that metrics to measure explainability shall be risk-focused, model-agnostic, goal-aware, intelligible & accessible . It does so according to an interdisciplinaryapproach, i.e.…

## FedGEMS Federated Learning of Larger Server Models via Selective Knowledge Fusion

Federated Learning (FL) hasemerged as a viable solution to learn a global model while keeping dataprivate . But model complexity of FL is impeded by the computation resources of edge nodes . In this work, we investigate a novel paradigm to take advantage of a powerful server model to break through model capacity in FL .…

## RoQNN Noise Aware Training for Robust Quantum Neural Networks

Quantum Neural Network (QNN) is a promising application towards quantumadvantage on near-term quantum hardware . The performance of QNN models has a severe degradation on realquantum devices . The accuracy gap between noise-free simulation and noisy results on IBMQ-Yorktown for MNIST-4 classification is over 60% .…

## 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 .…

## Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes

An OCP online solution to depth observation of indoor scenes based on reinforcementlearning is proposed in this paper . The proposed system outperforms seven out often test scenes in obtaining lower depth observation error . The total error inall test scenes is also less than 90% of the baseline ones.…

## Integrating Visuospatial Linguistic and Commonsense Structure into Story Visualization

Little work has been done in text-to-image synthesis, little work has done to explore the usage of linguistic structure of the input text . Suchinformation is even more important for story visualization since its inputshave an explicit narrative structure that needs to be translated into an imagesequence .…

## WENO interpolations and reconstructions using data bounded polynomial approximation

This work characterizes the structure of third and forth order WENO weightsby deducing data bounded condition on third order polynomial approximations . Non-linear weights are defined for third and fourthorder data bounded weighted essentially non-oscillatory (WENO) approximsations . Further with suitable weights, high order data-bounded WENOs are proposed .…

## Single Modal Entropy based Active Learning for Visual Question Answering

Constructing a large-scale labeled dataset in the real world, especially for high-level tasks (eg, Visual Question Answering), can be expensive and time-consuming . We propose a novel method for effectivesample acquisition through the use of ad hoc single-modal branches for each input to leverage its information .…

## 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 .…

## Threshold Tests as Quality Signals Optimal Strategies Equilibria and Price of Anarchy

We study a signaling game between two firms competing to have their product chosen by a principal . The quality of a product can only be estimated via a coarse-grained threshold test . We characterize the unique Bayes-Nash Equilibrium of this game in terms of the principal’s probability of choosing the worse product .…

## DeepBND a Machine Learning approach to enhance Multiscale Solid Mechanics

Effective properties of materials with random heterogeneous structures are typically determined by homogenising the mechanical quantity of interest in awindow of observation . There are relatively standard methods in the literature to completelydetermine the formulation except for two choices: i) the local domain itself and ii) boundary conditions .…

## Finite Volume Least Squares Neural Network FV LSNN Method for Scalar Nonlinear Hyperbolic Conservation Laws

In [4], we introduced the least-squares ReLU neural network (LSNN) method forsolving the linear advection-reaction problem with discontinuous solution . We showed that the number of degrees of freedom for the LSNN method issignificantly less than that of traditional mesh-based methods .…

## 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 .…

## CLOOB Modern Hopfield Networks with InfoLOOB Outperform CLIP

Contrastive learning with the InfoNCE objective is exceptionally successful in various self-supervised learning tasks . The CLIP model yielded impressive results on zero-shot transfer learning . But InfoLOOB upper bound(leave one out bound) works well for high mutual information but suffers from large variance and instabilities .…

## 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 .…

## Ambiguities in Direction of Arrival Estimation with Linear Arrays

Ambiguities arise when there exists a set of distinctdirections-of-arrival, for which the corresponding steering matrix isrank-deficient and associated with nonunique parameter estimation . We derive a method to enumerate such ambiguous setsusing a mixed-integer program and present results on several examples .…

## Performance Analysis for Covert Communications Under Faster than Nyquist Signaling

In this letter, we analyze the performance of covert communications underfaster-than-Nyquist (FTN) signaling in an additive white Gaussian noisechannel . Both Neyman-Pearson criterion- and Kullback-Leibler-based covertness constraints are considered . We prove that both the maximum transmit power and covertrate under FTN signaling are higher than those under Nyquist signaling .…

## On games and simulators as a platform for development of artificial intelligence for command and control

Games and simulators can be a valuable platform to execute complexmulti-agent, multiplayer, imperfect information scenarios with significantparallels to military applications . Multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces .…

## 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 .…

## Human Centered Explainable AI XAI From Algorithms to User Experiences

Explainable AI (XAI) has produced a vast collection of algorithms in recent years . The field is starting to embrace inter-disciplinary perspectives and human-centered approaches . Human-computer interaction (HCI) research and user experience (UX) design in this area are increasingly important .…

## 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 .…

## Development of an Ontology for an Integrated Image Analysis Platform to enable Global Sharing of Microscopy Imaging Data

The Open Microscopy Environment (OME) data model is the de facto standard to describe optical microscopy images and experimental data . We propose 18 upper-level concepts including missing concepts in OME such as electron microscopy, phenotype data, biosample, and imaging conditions .…

## 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 .…

## 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 .…

## Accelerating quantum many body configuration interaction with directives

Many-Fermion Dynamics-nuclear, or MFDn, is a configuration interaction (CI)code for nuclear structure calculations . It is a platform-independent Fortran90 code using a hybrid MPI+X programming model . For CPU platforms the application has a robust and optimized OpenMP implementation for shared memoryparallelism.…

## 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 .…