Min-plus product of two $n\times n$ matrices is a fundamental problem in algorithm research . It is known to be equivalent to APSP, and in general it hasno truly subcubic algorithms . Our algorithm runs in randomized time $O(n^{2.779) by the fastrectangular matrix multiplication algorithm [Le Gall \& Urrutia 18], better than $2.791$ ($\omega <2.373)$ [Alman \&V.V.Williams 20] …

## Multifractal of mass function

Multifractal plays an important role in many fields, but there is fewattentions about mass function, which can better deal with uncertain information than probability . In this paper, we proposed multifractal of massfunction . One interesting property is that the multifractable dimensionof mass function with maximum entropy is 1.585 no matter the order .…

## Learning First Order Rules with Relational Path Contrast for Inductive Relation Reasoning

Relation reasoning in knowledge graphs aims at predicting missingrelations in incomplete triples . The dominant paradigm is learning theembeddings of relations and entities, which is limited to a transductivesetting . Previous inductive methods are scalable and consume less resource . We propose a novel graph convolutional network(GCN)-based approach for interpretable inductive reasoning with relational pathcontrast, named RPC-IR .…

## State of the Art of Augmented Reality AR Capabilities for Civil Infrastructure Applications

Augmented Reality (AR) is a technology superimposing interactional virtual objects onto a real environment . Civil infrastructure community has not implemented AR technologies to date . This paper compares the capabilities of 16 AR Head-Mounted Devices available in the market since 2017, ranking them in terms of performance for civil infrastructure implementations .…

## Minimal Conditions for Beneficial Local Search

Paper investigates why it is beneficial, when solving a problem, to search in the neighbourhood of a current solution . The paper identifies properties of problems and neighbourhoods that support two novel proofs that neighbourhood search is beneficial over blind search .…

## Multimodal Dialogue Response Generation

Amultimodal dialogue generation model takes the dialogue history as input, then generates a textual sequence or an image as response . Learning sucha model often requires multimodal dialogues containing both texts and images which are difficult to obtain . The method achieves state-of-the-art results in bothautomatic and human evaluation, and can generate informative text and high-resolution image responses.…

## Perturbative construction of mean field equations in extensive rank matrix factorization and denoising

Factorization of matrices where the rank of the two factors diverges linearly with their sizes has many applications in diverse areas such as unsupervisedrepresentation learning, dictionary learning or sparse coding . In the limit where thedimensions of the matrices tend to infinity, but their ratios remain fixed, we expect to be able to derive closed form expressions for the optimal meansquared error on the estimation of two factors .…

## Measuring Total Transverse Reference free Displacements of Railroad Bridges using 2 Degrees of Freedom 2DOF Experimental Validation

Railroad bridge engineers are interested in the displacement of railroadbridges when the train is crossing the bridge for engineering decision making . Measuring displacements under train crossing events is difficult . If simplified reference-free methods would be accurate andvalidated, owners would conduct objective performance assessment of their bridge inventories under trains .…

## Visualization of Real time Displacement Time History superimposed with Dynamic Experiments using Wireless Smart Sensors WSS and Augmented Reality AR

In bridge engineering, inspectors make decisions using objective data from each bridge . They decide about repairs and replacements on the basis of changes in displacements underloads . But access to displacement information in the field and in real-time remains a challenge .…

## Prioritization of COVID 19 related literature via unsupervised keyphrase extraction and document representation learning

The COVID-19 pandemic triggered a wave of novel scientific literature that is impossible to inspect and study in a reasonable time frame manually . Current machine learning methods offer to project such body of literature into the vector space, where similar documents are located close to each other .…

## Towards More Accountable Search Engines Online Evaluation of Representation Bias

People rely on search engines to satisfy their need for information . Search engines deliver results relevant to user requests usually without being or making themselves accountable for the information they deliver . This potentialrisk urges the development of evaluation mechanisms of bias in order to empower the user in judging the results of search engines .…

## Spy game FPT algorithm hardness and graph products

In the $(s,d)$-spy game over a graph $G$, $k$ guards and one spy occupy somevertices of $G$ and, at each turn, the spy may move with speed $s$ and each guard may move along one edge . The spy wins if she reaches a vertice at distance more than the surveilling distance $d$ from every guard .…

## Challenges Porting a C Template Metaprogramming Abstraction Layer to Directive based Offloading

HPC systems employ a growing variety of compute accelerators with differentarchitectures and from different vendors . Large scientific applications need to run efficiently across these systems but need to retain a singlecode-base . Directive-based offloading models set out to provide the required portability, but, to existing codes, they themselves represent yet another API to port to .…

## NeuralArTS Structuring Neural Architecture Search with Type Theory

Neural Architecture Search (NAS) algorithms automate the task of finding optimal deep learning architectures given an initial search space of possible operations . In this paper we present a new framework called Neural ArchitectureType System (NeuralArTS) that categorizes the infinite set of network operations in a structured type system .…

## Learning Continuous Chaotic Attractors with a Reservoir Computer

The exact mechanisms of how dynamical neural systems performabstraction are still not well-understood . We train a 1000-neuron RNN — a reservoircomputer — to abstract a continuous dynamical attractor memory . We propose a theoretical mechanism of this abstractionby combining ideas from differentiable generalized synchronization and feedbackdynamics .…

## An LSTM based Plagiarism Detection via Attention Mechanism and a Population based Approach for Pre Training Parameters with imbalanced Classes

This paper proposes an architecture based on a Long Short-TermMemory (LSTM) and attention mechanism called LSTM-AM-ABC boosted by apopulation-based approach for parameter initialization . The results clearly show that the proposed method can provide competitive performance . Our proposed algorithm can find the initial values for model learning in all LSTm, attentionmechanism, and feed-forward neural network, simultaneously.…

## System Outage Probability and Diversity Analysis of SWIPT Enabled Two Way DF Relaying under Hardware Impairments

This paper investigates the system outage performance of a simultaneouswireless information and power transfer (SWIPT) based two-waydecode-and-forward (DF) relay network . After harvesting energy and decoding messages simultaneously via a power splitting scheme, the energy-limited relaynode forwards the decoded information to both terminals .…

## Making Existing Software Quantum Safe Lessons Learned

In the era of quantum computing, Shor’s algorithm running on quantumcomputers (QCs) can break asymmetric encryption algorithms that classicalcomputers essentially cannot . QCs, with the help of Grover’s algorithm, can speed up the breaking of symmetric encryption . The exact date when QCs will become “dangerous” for practical problems is unknown, the consensus is that this future is near .…

## ACOA Chronological Analysis of the Exhibition of Artistic Works

The ACOA platform supports the organization of multiple sources of information related to creative processes behind complex works . This information is of great interest to conservators and curators, as well as to the general public . This platform houses achronological evolution of the work, through the contextual dissemination of multimedia content .…

## Gemini Practical Reconfigurable Datacenter Networks with Topology and Traffic Engineering

To reduce cost, datacenter network operators are exploring blocking network designs . Gemini is a system designed to achieve these goals on commodityhardware while reconfiguring the network infrequently . The key to Geminiis the joint optimization of topology and routing, using as input a robustestimation of future traffic derived from multiple historical traffic matrices .…

## Verification of MPI programs

In this paper, we outline an approach to verifying parallel programs . A newmathematical model of parallel programs is introduced . The introduced model is illustrated by the verification of the matrix multiplication MPI program .…

## DFW PP Dynamic Feature Weighting based Popularity Prediction for Social Media Content

The over-saturation of content on social media platforms has prompted us to identify the important factors that affect content popularity . The proposed method controls the skewness of the distribution of the features by applying a log-log normalization. The code will be made publicly available athttps://://://github.com/chaitnayabasava/DFW-PP.…

## Design of Link Quality prediction based Software Defined Wireless Sensor Networks

In wireless multi-hop networks, the instability of the wireless links lead to unstable networking . Even in the newly designed Software-Defined WirelessSensor Networks (SDWSN) similar problems exist . To further improve the stability of SDWSN, we introduce a Link Quality (LQ) prediction model into the architecture .…

## What can we learn from universal Turing machines

We construct what we call a pedagogical universalTuring machine . We try to understand which comparisons with biologicalphenomena can be deduced from its encoding and from its working .…

## Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity

Neuro-inspired models and systems have great potential for applications inunconventional computing . Often, the mechanisms of biological neurons aremodeled or mimicked in simulated or physical systems in an attempt to harness some of the computational power of the brain . The aim of this approach was to develop a method of producing models capable of exhibiting complex behavior that may be suitable for use as computationalsubstrates .…

## Self stabilizing Byzantine and Intrusion tolerant Consensus

Consensus applications include services for the Cloud or Blockchain . We propose the first self-stabilizing solution for intrusion-tolerant multivalued consensus for message-passing systems prone to Byzantine failures . This work aims at the design of an even more robust solution than MR, which can deal with up to $t < n/3$ Byzantine processes, where $n$ is the number of processes . In addition totolerating Byzantine and communication failures, self-Stabilizing systems canautomatically recover after the occurrence of \emph{arbitrarytransient-faults}. These faults represent any violation of the assumptions according to which the system was designed to operate (provided that the algorithm code remains intact). We propose, to the best of our knowledge, the first such a solution to such a fault-tolerance solution to this type of problem . Back to Mailing Back to Back to the page you came from :http://www.mailing-backto the page.com/news/post/daily/mailing back to page/back to page to-the-page to the mainline . …

## A Heterogeneous Graph Based Framework for Multimodal Neuroimaging Fusion Learning

Traditional GNN-based models usually assume the brain network is a homogeneous graph with single type of nodes and edges . We present a Heterogeneous Graph neural network for Multimodalneuroimaging fusion learning (HGM) We also propose a self-supervisedpre-training strategy based on heterogeneou brain network to address theoverfitting problem due to the complex model and small sample size .…

## Hydra A System for Large Multi Model Deep Learning

Training deep learning (DL) models that do not fit into the memory of asingle GPU is a vexed process . We present ‘model spilling’, a technique aimed at models such as Transformers and CNNs to move groups of layers, or shards, between DRAM and GPU memory .…

## Fast and Reliable Formal Verification of Smart Contracts with the Move Prover

The Move Prover (MVP) is a formal verifier for smart contracts written in theMove programming language . MVP has an expressive specification language, and is reliable enough that it can be run routinely by developers and inintegration testing in a few minutes .…

## Generalized Out of Distribution Detection A Survey

Out-of-distribution (OOD) detection is critical to ensuring the reliabilityand safety of machine learning systems . Other problems related to OOD detection include anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD) Despite having different definitions and settings, these problems often confuse readers and practitioners, some existing studies misuse terms .…

## Algorithmic Thresholds for Refuting Random Polynomial Systems

We study the basic question of determining the smallest $m$ — the algorithmic threshold — for whichefficient algorithms can find refutations . This setting generalizes problems such as refuting random SAT instances, low-rank matrix sensing and certifyingpseudo-randomness of Goldreich’s candidate generators .…

## On the Pareto Frontier of Regret Minimization and Best Arm Identification in Stochastic Bandits

We study the Pareto frontier of two archetypal objectives in stochasticbandits, namely, regret minimization (RM) and best arm identification (BAI) We show that no algorithm can simultaneously perform optimally for both the RM and BAI objectives . We establish non-trivial lower bounds on the regret achievable byany algorithm with a given BAI failure probability .…

## Voting Theory in the Lean Theorem Prover

In this paper, we report on the development of a framework for formalizing voting theory in the Lean theorem prover . In order to formalize voting theoretic axioms concerning addingor removing candidates and voters, we work in a variable-election setting whoseformalization makes use of dependent types in Lean .…

## Algorithmic Thresholds for Refuting Random Polynomial Systems

We study the basic question of determining the smallest $m$ — the algorithmic threshold — for whichefficient algorithms can find refutations . This setting generalizes problems such as refuting random SAT instances, low-rank matrix sensing and certifyingpseudo-randomness of Goldreich’s candidate generators .…

## Hydra A System for Large Multi Model Deep Learning

Training deep learning (DL) models that do not fit into the memory of asingle GPU is a vexed process . We present ‘model spilling’, a technique aimed at models such as Transformers and CNNs to move groups of layers, or shards, between DRAM and GPU memory .…

## Non existing and ill behaved coequalizers of locally ordered spaces

Categories of locally ordered spaces are especially well-adapted to therealization of most precubical sets, though their colimits are not so easy todetermine . We use the plural here, as the notion of a locally ordered space vary from an author to another, only differing according to seemingly anodynetechnical details .…

## A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer

We propose a variational Bayesian (VB) approach to learning distributions of latent variables in deep neural network (DNN) models for cross-domain knowledgetransfer . To accomplish model transfer, knowledge learnt from a source domainis encoded in prior distributions of . latent variables and optimally combined,in a Bayesian sense, with a small set of adaptation data from a target domain to approximate the corresponding posterior distributions .…

## Sum of Squares Geometry Processing

Geometry processing presents a variety of difficult numerical problems . Sum-of-squares optimization transforms nonlinear polynomial optimization problems into convex problems whose complexity is captured by a single degreeparameter . This allows us to solve a suite of problems on higher-ordersurfaces, such as continuous collision detection and closest point queries oncurved patches, with only minor changes between formulations and geometries .…

## Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness

Ekstrand et al. investigate how recommender performance variesaccording to popularity and demographics . We find that recommendation utility steadily degrades for older users, and islower for women than men . We also find that the utility is higher for usersfrom countries with more representation in the dataset.…

## Directional forces in the evolution of grammar

In modern English grammar, the perfect is formed with have+PP (pastparticiple), but in older English the be+PP form existed as well . This finding strongly suggests that the perfect could have evolved through natural selection rather than random drift . Most intransitive verbs exhibited an increase in the frequency of have +PP, some of which passed the Frequency Increment Test (FIT), indicating a rapid S-shapeincrease .…

## Constructing Many Faces in Arrangements of Lines and Segments

We present new algorithms for computing many faces in arrangements of linesand segments . The problem is to compute the faces of thearrangements of $S$ that contain at least one point of $P$ The results improve the previously bestdeterministic algorithm [Agarwal, 1990] by a factor of $2.22 n$ and the best randomized algorithm .…

## Physical Side Channel Attacks on Embedded Neural Networks A Survey

Deep Neural Networks (DNN) have progressively beenintegrated on all types of platforms, from data centers to embedded systems including low-power processors and, recently, FPGAs . The underlying hardware securityvulnerabilities of embedded NN implementations remain unaddressed . Inparticular, embedded DNN implementations are vulnerable to Side-ChannelAnalysis (SCA) attacks, which are especially important in the IoT and edgecomputing contexts where an attacker can usually gain physical access to thetargeted device .…

## Physics guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty

In this paper, we propose a probabilistic physics-guided framework, termedPhysics-guided Deep Markov Model . The framework is especially targetedto the inference of the characteristics and latent structure of nonlineardynamical systems from measurement data . The proposed framework takes advantage of the expressive power of deep learning, while retaining the driving physics of the dynamical system by imposing physics-driven restrictions on the side of the latent space .…

## Constructing Many Faces in Arrangements of Lines and Segments

We present new algorithms for computing many faces in arrangements of linesand segments . The problem is to compute the faces of thearrangements of $S$ that contain at least one point of $P$ The results improve the previously bestdeterministic algorithm [Agarwal, 1990] by a factor of $2.22 n$ and the best randomized algorithm .…

## Statistics in everyone s backyard an impact study via citation network analysis

In this paper, we take the first step towards understanding the impact statistics has made on other scientific fields in the era of BigData . We use the local clustering technique involving personalized PageRank with conductance for size selection to find the most relevant statistical research area for a given external topic of interest .…

## Deep Reinforcement Learning for Practical Phase Shift Optimization in RIS aided MISO URLLC Systems

Reconfigurable intelligent surfaces (RISs) can assist the wireless systems in providing reliable and low-latency links to realize the requirements inIndustry 4.0.0 . The considered method relies on interacting RIS with industrial scenario by taking actions which are the phase shifts at the RISelements, to maximize the total FBL rate .…

## On the randomness analysis of link quality prediction limitations and benefits

In wireless multi-hop networks, link quality (LQ) is one of the most important metrics and is widely used in higher-layer applications such as routing protocols . Researchers have proposed a lot of link quality prediction models in recent years . However, due to the dynamic and stochastic nature of wirelesstransmission, the performance of the model remains challenging .…

## Verification of MPI programs

In this paper, we outline an approach to verifying parallel programs . A newmathematical model of parallel programs is introduced . The introduced model is illustrated by the verification of the matrix multiplication MPI program .…

## Collaborating with Humans without Human Data

Collaborating with humans requires rapidly adapting to their individual strengths, weaknesses, and preferences . Most standardmulti-agent reinforcement learning techniques produce agents that overfit to their training partners . Alternatively, researchers can collecthuman data, train a human model using behavioral cloning, and then use thatmodel to train “human-aware” agents .…

## Streaming Decision Trees and Forests

Machine learning has successfully leveraged modern data and provided solutions to innumerable real-world problems . Currently, estimators could handle both scenarios with all samples available and situations requiring continuous updates . However, there is still room for improvement on streaming algorithms based onbatch decision trees and random forests, which are the leading methods in batch data tasks .…