## FedADC Accelerated Federated Learning with Drift Control

Federated learning (FL) has become de facto framework for collaborativelearning among edge devices . The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner . Largescale implementation of FL brings new challenges, such as the incorporation ofacceleration techniques designed for SGD into the distributed setting, and mitigation of the drift problem due to non-homogeneous distribution of localdatasets .…

## Causality is Graphically Simple

Events in distributed systems include sending or receiving messages, or changing some state in a node . Not all events are related, but some events can influence how other, later events, occur . For instance, a reply to areceived mail message is influenced by that message, and maybe by other priormessages also received .…

## Edge of the Earth Replication Optimizing Content Delivery in Large LEO Satellite Communication Networks

Large low earth orbit (LEO) satellite networks such as SpaceX’s Starlink promise to deliver low-latency, high-bandwidth Internet access with global coverage . They could potentially serve billions of Internet-connecteddevices . Replicating web content within satellites can reduce bandwidth use in the constellation by 93% over an approach without replicationin the network, while storing just 0.01% of all content in individualsatellites .…

## Container Orchestration on HPC Systems

Torque-Operator (a plugin) serves as a bridge between HPC workload managers and container Orchestrators . Containers can encapsulate complex programs with theirdependencies in isolated environments . HPC clusters lack micro-services support and deeply integrated management, as opposed to container orchestrators (e.g.…

## More Industry friendly Federated Learning with High Efficient Design

Researchers propose a high efficient FL methodequipped with the double head design aiming for personalization optimization over non-IID datasets . The method has more stable accuracy and better communication efficient across various datadistributions than other state-of-art methods (SOTAs), makes it more industry-friendly .…

## Online Service Migration in Edge Computing with Incomplete Information A Deep Recurrent Actor Critic Method

Multi-access Edge Computing (MEC) is a key technology in the fifth-generation(5G) network and beyond . Service migration needs to decide where to migrate user services for maintaining high Quality-of-Service(QoS) When users roam between MEC servers with limited coverage and capacity, finding an optimal migration policy is intractable due to the highly dynamic MEC environment and user mobility .…

## Prizes Signal Scientific Revolutions

Scientific revolutions affect funding, investments, and technological advances . We investigated a possible signal predicting a topic’s revolutionarygrowth – its association with a scientific prize . We found that in the year following a prize, a topic experiences an onset of extraordinary growth in impact and talent that continues into the future .…

## Listing Small Minimal Separators of a Graph

A minimal $a,b$-separator of $G$ is an inclusion-wise minimal vertices that separate $a$ and $b$ . We give an algorithm which enumerates such minimal separators, outputting the first $R$ minimalseparators in at most $poly(n) R \cdot \min(4^k, R)$ time . We also discuss barriers for obtaining a polynomial-delay algorithm .…

## Indirect Identification of Horizontal Gene Transfer

Several implicit methods to infer Horizontal Gene Transfer (HGT) focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside . This situation defines the edge set of a graph, thelater-divergence-time (LDT) graph, whose vertices correspond to genes colored by their species .…

## Clustering with Iterated Linear Optimization

We introduce a novel method for clustering using a semidefinite programming(SDP) relaxation of the Max k-Cut problem . The approach is based on a newmethodology for rounding the solution of an SDP using iterated linearoptimization . We show that usingfixed point iteration for rounding for rounding leads to significantly better results when compared to randomized rounding .…

## Nearly tight Trotterization of interacting electrons

We consider simulating quantum systems on digital quantum computers . We use Trotterization for a class of interacting electrons that encompasses various physical systems . We estimate the simulation error by taking the transition amplitude ofnested commutators of Hamiltonian terms within the $eta$-electron manifold .…

## Planar Maximum Coverage Location Problem with Partial Coverage Continuous Spatial Demand and Adjustable Quality of Service

We consider a generalization of the classical planar maximum coveragelocation problem (PMCLP) in which partial coverage is allowed, facilities haveadjustable quality of service (QoS) or service range . Demand zones and service zones of each facility are represented by two-dimensional spatial objects such as rectangles, circles, polygons .…

## Maximum 0 1 Timed Matching on Temporal Graphs

Temporal graphs are graphs where the topology and/or other properties of the graph change with time . They have been used to model applications with temporalinformation in various domains . Problems on static graphs become morechallenging to solve in temporal graphs because of dynamically changing topology .…

## Programmable Quantum Annealers as Noisy Gibbs Samplers

Quantum annealing embodies a promising computationaligm that is intimately related to the complexity of energy landscapes inGibbs distributions . Drawing independent samples from high-dimensional probability distributions represents the major computational bottleneck for modern algorithms, including machine learning frameworks such as deep learning .…

## Physical deep learning based on optimal control of dynamical systems

A central topic in recent artificial intelligence technologies is deeplearning, which can be regarded as a multilayer feedforward neural network . Here, we present a pattern recognitionbased on optimal control of continuous-time dynamical systems . The proposed approach enables to gain insight into mechanisms of deep networkprocessing in the framework of an optimal control problem and opens a novelpathway to realize physical computing hardware .…

## Shape My Face Registering 3D Face Scans by Surface to Surface Translation

Existing surface registration methods focus on fitting in-sample data withlittle to no generalization ability . Shape-My-Face (SMF) is a powerful encoder-decoder architecture based on animproved point cloud encoder . SMF only requires the raw data to be rigidly aligned (with scaling) with apre-defined face template .…

## DECOR GAN 3D Shape Detailization by Conditional Refinement

We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details . The output shape preserves the overall structure (or content) of the input, while its detail generation is conditioned on an input “style code” We demonstrate that ourmethod can refine a coarse shape into a variety of detailed shapes .…

## Temporal Graph Modeling for Skeleton based Action Recognition

Graph Convolutional Networks (GCNs) have obtained remarkable performance for skeleton-based action recognition . The proposed TE-GCN constructs temporal relationgraph to capture complex temporal dynamic . It is state-of-the-art performance by making contribution to temporal modeling for action recognition, says the proposed model achieves the state of theart performance .…

## Visualization and Selection of Dynamic Mode Decomposition Components for Unsteady Flow

Dynamic Mode Decomposition (DMD) is a data-driven and model-freedecomposition technique . It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data . In this paper, wedemonstrate how the components of DMD can be utilized to obtain temporal andspatial information from time-dependent flow fields .…

## Object Centric Neural Scene Rendering

We present a method for composing photorealistic scenes from captured images of objects . Instead of learning a scene radiance field as a NeRF does, we propose to learn object-centric neural scattering functions (OSFs), arepresentation that models per-object light transport implicitly using alighting- and view-dependent neural network .…

## Continuous Positional Payoffs

What payoffs are positional for (deterministic) two-player antagonistic games? In this paper we study this question for payoffsthat are continuous . We establish positionality of contracting payoffs via a Shapley’s fixed pointargument . Our main result states that any continuous positional payoff is acomposition of a non-decreasing continuous function and a contracting payoff .…

## How the emotion s type and intensity affect rumor spreading

This paper sheds light on how DMs’emotional type and intensityaffect rumor spreading . Pessimism has a more significant influence on the stability of the evolutionary game thanoptimism . The government’s emotion types are more sensitive to the game resultsthan netizens, and the emotional intensity is proportional to the evolutionspeed of the game .…

## Incentive Mechanism Design for Distributed Coded Machine Learning

A distributed machine learning platform needs to recruit many heterogeneous nodes to finish computation simultaneously . As a result, the overall performance may be degraded due to straggling workers . By introducingundundancy into computation, coded machine learning can effectively improve the runtime performance by recovering the final computation result through the first $k$ (out of the total $n$) workers who finish computation .…

## Indecision Modeling

AI systems are often used to make or contribute to important decisions in a range of applications, including criminal justice, hiring, and medicine . Since these decisions impact human lives, it is important that the AIsystems act in ways which align with human values .…

## An Assessment of the Usability of Machine Learning Based Tools for the Security Operations Center

Gartner anticipates that by 2024 80% of security operation centers will use machine learning (ML) based solutions to enhance their operations . In light of such widespread adoption, itis vital for the research community to identify and address usability concerns, authors say .…

## Emotion visualization in Virtual Reality An integrative review

Research suggests that it is possible to infer some characteristics of users’ mental states by analyzingelectrophysiological responses in real-time . However, it is not clear how to use the information extracted from electrophysiology signals to create visual representations of the emotional states of Virtual Reality (VR) users .…

## Natural grasp intention recognition based on gaze fixation in human robot interaction

Eye movement is closely related to limb actions, so it can be used to infermovement intentions . Some cases, eye movement is the only way for paralyzed and impaired patients with severe movement disorders to communicate and interact with the environment .…

## AutoDis Automatic Discretization for Embedding Numerical Features in CTR Prediction

Learning sophisticated feature interactions is crucial for Click-Through Rate(CTR) prediction in recommender systems . AutoDis is a framework that discretizesfeatures in numerical fields automatically and is optimized with CTR models in an end-to-end manner . The common methods fornumerical feature embedding are Normalization and Discretization .…

## Batch Constrained Distributional Reinforcement Learning for Session based Recommendation

Most of the existing deep reinforcement learning (RL) approaches for session-based recommendations either rely on costly online interactions with real users, or rely on potentially biased rule-based or data-driven user-behavior models . We propose BCD4Rec:Batch-Constrained Distributional RL for Session-based Recommendations .…

## Scenario aware and Mutual based approach for Multi scenario Recommendation in E Commerce

Recommender systems (RSs) are essential for e-commerce platforms to help meet the enormous needs of users . We observed that the scenarios are heterogeneous because of the hugedifferences among them . Therefore, a unified model is difficult to effectively capture complex correlations (e.g.,…

## Multilingual Evidence Retrieval and Fact Verification to Combat Global Disinformation The Power of Polyglotism

This article investigates multilingual evidence retrieval and factverification as a step to combat global disinformation . A 400 example mixed language English-Romanian dataset is created for cross-lingual transfer learningevaluation . We make code, datasets, and trained models available upon publication .…

## Information retrieval system for silte language using BM25 weighting

The main aim of an information retrieval system is to extract appropriate information from an enormous collection of data based on users need . The system has both indexing and searching part was created . In these modules, different text operations such as tokenization, stemming, stop wordremoval and synonym is included .…

## Session based k NNs with Semantic Suggestions for Next item Prediction

Authors propose aconceptual (cSkNN) model extension for the next-item prediction . They use anNLP technique to parse salient concepts from the product titles to create product generalizations that are used for change detection and a recommendation list post-filtering . Authors say the extension is an improvement over the existing SkNN method on a sparse fashione-commerce dataset.…

## Query expansion with artificially generated texts

A well-known way to improve the performance of document retrieval is to expand the user’s query . In this paper, we explore the use of text generation toautomatically expand the queries . This conceptually simple approach can easily be implemented on any IR system thanks to the availability of GPT code and models .…

## Analyzing and Predicting Purchase Intent in E commerce Anonymous vs Identified Customers

The popularity of e-commerce platforms continues to grow . Being able to predict customer behavior is essential for customizing the userexperience through personalized result presentations, recommendations, and special offers . In this paper, we focus on purchase prediction for both anonymous and identified sessions on ane-commerce platform .…

## Cache aided General Linear Function Retrieval

Coded Caching, proposed by Maddah-Ali and Niesen (MAN), has the potential to reduce network traffic by pre-storing content in the users’ local memories . This paper considers the linear function retrieval version of the original coded cachingsetting, where users are interested in retrieving a number of linearcombinations of the data points stored at the server, as opposed to a single file .…

## On Exploiting Hitting Sets for Model Reconciliation

In human-aware planning, a planning agent may need to provide an explanation to a human user on why its plan is optimal . A popular approach to do this is called model reconciliation, where the agent tries to reconcile the differences in its model and the human’s model .…

## Extracting Smart Contracts Tested and Verified in Coq

We implement extraction of Coq programs to functional languages based on MetaCoq’s certified erasure . We prove the pass correct wrt. a conventionalcall-by-value operational semantics of functional languages . We test several complex contracts such as a DAO-like contract, anescrow contract, and an implementation of a Decentralized Finance (DeFi) contract .…

## A New Connective in Natural Deduction and its Application to Quantum Computing

We defend the ideathat non harmonious connectives model the information erasure, thenon-reversibility, and the non-determinism that occur, among other places, inquantum measurement . We introduce a propositional logic with anon harmonious . connective sup, prove cut elimination for this logic, and show that its proof language forms the core of a quantum programming language .…

## A Novice Friendly Induction Tactic for Lean

In theorem provers based on dependent type theory such as Coq and Lean, induction tactics are omnipresent in proof scripts . Existing induction tactics do not reliably support inductive predicates and relations . This paper describes a new induction tactic, implemented in Lean 3, which addresses these issues .…

## Scalable and Safe Multi Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances

We present a scalable and effective multi-agent safe motion planner that allows agents to move to their desired locations while avoiding collisions with obstacles and other agents . We address this problem by finding a piecewise linearpath for each agent such that the actual trajectories following these paths are guaranteed to satisfy the reach-and-avoid requirement .…

## Lévy walks derived from a Bayesian decision making model in non stationary environments

L\’evy walks are found in the migratory behaviour patterns of variousorganisms . We propose an algorithm that introduces the effects of learning and forgetting into Bayesian inference . We simulate an imitation game in which two decision-making agents estimate eachother’s internal models from their opponent’s observational data .…

## Secret Key Agreement with Physical Unclonable Functions An Optimality Summary

A physical unclonable function (PUF) is a promisingsolution for local security in digital devices . We address security and privacy problems for digital devices and biometrics from an information-theoretic optimality perspective . The optimal trade-offs between the secret-key, privacy-leakage, and storagerates for multiple PUF measurements are given .…

## 3D Trajectory Design for UAV Assisted Oblique Image Acquisition

In this correspondence, we consider a new unmanned aerial vehicle(UAV)-assisted oblique image acquisition system where a UAV is dispatched totake images of multiple ground targets (GTs) The formulatedproblem is shown to be equivalent to a modified 3D traveling salesman problem with neighbourhoods, which is NP-hard in general .…

## An adaptive algorithm for embedding information into compressed JPEG images using the QIM method

A new algorithm proposes a new algorithm for embedding information in JPEG images based on the steganographic QIM method . The main problem of such embedding is the vulnerability to statistical steganalysis . It is proposed to use a variable quantization step, which is adaptively selected for each block of the JPEG cover image .…

## Learning Based Quality Assessment for Image Super Resolution

Image Super-Resolution (SR) techniques improve visual quality by enhancing spatial resolution of images . The resulting SR Image quality databasewith Semi-Automatic Ratings (SISAR) database, so far the largest of SR-IQA database,contains 8,400 images of 100 natural scenes . We train an end-to-end Deep ImageSR Quality (DISQ) model by employing two-stream Deep Neural Networks (DNNs) and a feature fusion network for qualityprediction .…

## A Deep Multi Level Attentive network for Multimodal Sentiment Analysis

The Deep Multi-LevelAttentive network exploits the correlation between image and textmodalities to improve multimodal learning . We generate thebi-attentive visual map along the spatial and channel dimensions to magnify CNNs representation power . Then we model the correlation . between the .…

## Optimizing the Parameters of A Physical Exercise Dose Response Model An Algorithmic Comparison

The purpose of this research was to compare the robustness and performance of a local and global optimization algorithm when given the task of fitting the parameters of a common non-linear dose-response model . The results of ourcomparison over 1000 experimental runs demonstrate the superior performance of the evolutionary computation based algorithm to consistently achieve a stronger model fit and holdout performance in comparison to the local search algorithm .…

## Neural Pruning via Growing Regularization

Regularization has long been utilized to learn sparsity in deep neuralnetwork pruning . In this work, we extend its application to a new scenariowhere the regularization grows large gradually to tackle two central problemsof pruning: pruning schedule and weight importance scoring .…

## Tag based Genetic Regulation for Genetic Programming

Tag-based genetic regulation extends existing tag-based naming schemes to allow programsto “promote” and “repress” code modules . This extension allows evolution to structure a program as a gene regulatory network where program modules areregulated based on instruction executions . We find thattag-based regulation improves problem-solving performance on context-dependent problems .…