In the general AntiFactor problem, a graph $G$ is given with a set$X_v\subseteq \mathbb{N}$ of forbidden degrees . Standard techniques (dynamic programming + fast convolution) can be used to show that if $M$ is the largest forbidden degree, then the problem can be solved in time .…

## Blockchain Enabled Secure Authentication for Unmanned Aircraft Systems

The integration of air and ground smart vehicles is becoming a new paradigm of future transportation . A decent number of smart unmanned vehicles or UAS will be sharing the national airspace for various purposes, such as expressdelivery, surveillance, etc. However, the proliferation of UAS also brings challenges considering the safe integration of them into the current AirTraffic Management (ATM) systems .…

## Mode I and Mode II stress intensity factors and dislocation density behaviour in strain gradient plasticity

In this study, we use the mechanism-based strain gradient plasticity theory to evaluate both crack tip dislocation density behaviour and the coupled effectof the material plastic properties and the intrinsic material length onnon-linear amplitude factors . The constitutive relations are based on Taylor’s dislocation model, which enables gaining insights into the role of the increased dislocationdensity associated with large gradients in plastic strain near cracks .…

## The search of Type I codes

A self-dual binary linear code is called Type I code if it has singly-evencodewords, i.e.~it has codewords with weight divisible by $2.$ The purpose of this paper is to investigate interesting properties of Type I codes . Further, we build up a computer-based code-searching program .…

## Joint Spatial Division and Coaxial Multiplexing for Downlink Multi User OAM Wireless Backhaul

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies (SEs) At last, the proposed methods are extended to the downlink MU-OAM-MIMO wireless backhaul system equipped with uniform concentric circular arrays (UCCAs) for which much higher spectral efficiency (SE) and energy efficiency (EE) can be achieved .…

## Investigating Man in the Middle based False Data Injection in a Smart Grid Laboratory Environment

The security of energy supply is increasingly threatened by cyber-attacks . Traditional cyber-security measures can be used as mitigation and prevention measures, but their effective use requires a deep understanding of the potential threat landscape and complex attack processes in energyinformation systems .…

## A Primer on the Statistical Relation between Wireless Ultra Reliability and Location Estimation

This letter statistically characterizes the impact of location estimationuncertainty in the wireless communication reliability . The reliability – characterized by how likely the outage probabilityis to be above a target threshold – can be sensitive to location errors . We highlight the difficulty of choosing a rate that both meets targetreliability and accounts for the location uncertainty, and that the most directsolutions suffer from being too conservative .…

## Model Based Reinforcement Learning Framework of Online Network Resource Allocation

Online Network Resource Allocation (ONRA) for service provisioning is afundamental problem in communication networks . Reinforcement learning (RL) is promising to approach ONRA via ReinforcementLearning . But, RL solutions suffer from the sample complexity issue; i.e., a large number of interactions with the environment needed to find an efficient policy .…

## Online Motion Planning with Soft Timed Temporal Logic in Dynamic and Unknown Environment

Motion planning of an autonomous system with high-level specifications has wide applications . Research of formal languages involving timedtemporal logic is still under investigation . Many existing resultsrely on a key assumption that user-specified tasks are feasible in the given environment .…

## Category theoretical Semantics of the Description Logic ALC extended version

Category theory can be used to state formulas in First-Order Logic without using set membership . Several notable results in logic such as proof of thecontinuum hypothesis can be elegantly rewritten in category theory . Such a category-theoretical semantics provides a more modularrepresentation of the semantics of $\mathcal{ALC$ and a new way to design algorithms for reasoning .…

## Accurate Baryon Acoustic Oscillations reconstruction via semi discrete optimal transport

Optimal transport theory has recently reemerged as a vastly resourceful field of mathematics with elegant applications across physics and computer science . We report on the efficient implementation for a specific problem in cosmology — the reconstruction of thelinear density field from low redshifts .…

## MeronymNet A Hierarchical Approach for Unified and Controllable Multi Category Object Generation

MeronymNet is a novel hierarchical approach for controllable,part-based generation of multi-category objects using a single unified model . We adopt a guided coarse-to-fine strategy involving semantically conditioned generation of bounding box layouts, pixel-level part layouts and ultimately,the object depictions themselves .…

## On the Statistical Analysis of Complex Tree shaped 3D Objects

A hierarchical organization of subtrees characterizesthese objects — each subtree has the main branch with some side branches attached — and one needs to match these structures across objects formeaningful comparisons . We propose a novel representation that extends theSquare-Root Velocity Function (SRVF), initially developed for Euclidean curves, to tree-shaped 3D objects .…

## 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 .…

## Poisoning Attacks on Fair Machine Learning

A framework seeks to attack fair machine learning models to attack both model accuracy and algorithmicfairness . We develop three online attacks, adversarialsampling, adversarial labeling, and adversarial feature modification . Allthree attacks effectively and efficiently produce poisoning samples . Experiments on two real datasetsdemonstrate the effectiveness and efficiency of our framework .…

## Sahlqvist Correspondence Theory for Second Order Propositional Modal Logic

Modal logic with propositional quantifiers (i.e. second-order propositionalmodal logic) has been considered since the early time of modal logic . We develop the Sahlqvist correspondencetheory for SOPML, which covers and properly extends existing Sahlqist formulas . We define the class of Sahlqqist formulas for SOMPL stepby step in a hierarchical way, each formula of which is shown to have afirst-order correspondent over Kripke frames effectively computable by analgorithm $ALBA^{SOMPL}$.…

## 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 .…

## 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 .…

## 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 .…

## 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 .…

## Developing a novel fair loan predictor through a multi sensitive debiasing pipeline DualFair

Machine learning (ML) models are increasingly used for high-stake applications that can greatly impact people’s lives . Despite their use, these models have the potential to be biased towards certain social groups on the basis of race, gender, or ethnicity .…

## 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.…

## Low Precision Quantization for Efficient Nearest Neighbor Search

K-Nearest Neighbor search over real-valued vector spaces (KNN) is animportant algorithmic task for information retrieval and recommendationsystems . We present a method for using reduced precision to represent vectorsthrough quantized integer values, enabling both a reduction in the memoryoverhead of indexing these vectors and faster distance computations at querytime .…

## 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.…

## 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 .…

## LEO Satellites in 5G and Beyond Networks A Review from a Standardization Perspective

Low Earth Orbit (LEO) Satellite Network (SatNet) with theirmega-constellations are expected to play a key role in providing ubiquitousInternet and communications services in the future . LEO SatNets will providewide-area coverage and support service availability, continuity, andscalability . The satellite communication industry has become increasingly involved with the 3rd Generation Partnership Project (3GPP)standardization activities for 5G .…

## Graph Wedgelets Adaptive Data Compression on Graphs based on Binary Wedge Partitioning Trees and Geometric Wavelets

We introduce graph wedgelets – a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptivelygenerated binary wedge partitionings of a graph . For this, we transferpartitioning and compression techniques known for 2D images to general graphstructures .…

## A Q Learning based Approach for Distributed Beam Scheduling in mmWave Networks

We consider the problem of distributed downlink beam scheduling and powerallocation for millimeter-Wave (mmWave) cellular networks . We propose a distributed scheduling approach to power allocation andadaptation for efficient interference management over the shared spectrum . Experiment results show that the proposed approach adapts well to different interference situations by learning from experience and can achieve higher payoff than the game-based approach .…

## Terminal Embeddings in Sublinear Time

We show how to pre-process $T$ to obtain an almost linear-space data structure that supports computing theterminal embedding image of any $q\in\mathbb{R}^d$ in sublinear time . The downside is that evaluating the embedding on $q$ required solving a semidefinite program with$\Theta(n)$ constraints in $m$ variables .…

## 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 .…

## 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 .…

## 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 .…

## Local Advantage Actor Critic for Robust Multi Agent Deep Reinforcement Learning

Policy gradient methods have become popular in multi-agent reinforcementlearning, but they suffer from high variance due to the presence of stochasticity and exploring agents (i.e., non-stationarity) ROLA allows each agent to learn an individual action-value function as a local critic .…

## n stage Latent Dirichlet Allocation A Novel Approach for LDA

Topic modeling allows determining the semantic structure of a text document . Latent Dirichlet Allocation (LDA) is the mostcommon method among topic modeling methods . In this article, the proposedn-stage LDA method is explained in detail . The positive effect of the method has beendemonstrated by the applied English and Turkish studies .…

## Coordinated Multi Agent Pathfinding for Drones and Trucks over Road Networks

We address the problem of routing a team of drones and trucks over large-scale urban road networks . To conserve their limited flight energy, drones can use trucks as temporary modes of transit en route to their owndestinations . Such coordination can yield significant savings in total vehicledistance traveled, i.e.,…

## Correct Probabilistic Model Checking with Floating Point Arithmetic

Probabilistic model checking computes probabilities and expected values related to designated behaviours of interest in Markov models . To achieve scalability and performance, tools use finite-precision floating-point numbers to represent and calculate probabilities and other values . As aconsequence, their results are affected by rounding errors that may accumulate and interact in hard-to-predict ways .…

## Greedy and Random Broyden s Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations

In this paper, we propose the greedy and random Broyden’s method for solving nonlinear equations . We establish explicit (local) superlinear convergence rates of both methods if the initial point and approximate Jacobian are close enough to a solution and corresponding corresponding Jacobian .…

## HIDE SEEK Privacy Preserving Rebalancing on Payment Channel Networks

Payment channels effectively move the transaction load off-chain therebysuccessfully addressing the inherent scalability problem most cryptocurrencies face . A major drawback of payment channels is the need to ”top up” fundson-chain when a channel is depleted . Rebalancing was proposed to alleviate this issue, where parties with depleting channels move their funds along a cycle toreplenish their channels off the chain .…

## A Framework of Mahalanobis Distance Metric with Supervised Learning for Clustering Multipath Components in MIMO Channel Analysis

As multipath components (MPCs) are experimentally observed to appear inclusters, cluster-based channel models have been focused in the wirelesschannel study . Most of the MPC clustering algorithms for MIMO channels are based on the distance metric that determines the similarity of two MPCs and determines the preferred clustershape .…

## Spectral Efficiency of OTFS Based Orthogonal Multiple Access with Rectangular Pulses

In this paper we consider Orthogonal Time Frequency Space (OTFS) modulationbased multiple-access (MA) methods . We specifically consider orthogonal MA methods(OMA) where the user terminals (UTs) are allocated non-overlapping physical resources in the delay-Doppler (DD) and/or time-frequency (TF) domain . We study the spectral efficiency (SE) performance of these OMA methods with practical rectangular pulses .…

## GP MOOD A positive preserving high order finite volume method for hyperbolic conservation laws

The GP-MOOD method combines two methodologies, thepolynomial-free spatial reconstruction methods of GP (Gaussian Process) and thea posteriori detection algorithms of MOOD (Multidimensional Optimal OrderDetection) We present an a posteriori shock-capturing finite volume method algorithm that solves a compressible hyperbolic conservative system’s athigh-order solution accuracy (e.g.,…

## Minimal Viable IO drivers for TrustZone

TrustZone can isolate IO hardware, but it lacks drivers for modern IO devices . Developers exercise a full driver and record the driver/device interactions; the processed recordings, dubbed driverlets, are played in the TEE at run time to access IO devices.…

## Temporal Knowledge Graph Reasoning Triggered by Memories

Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored . We propose a memory-triggered decision-making (MTDM) network, which incorporates transient memories, long-short-term memories, and deep memories . MTDM utilizes the craftedresidual multi-relational aggregator as the structural encoder to solve themulti-hop coverage problem .…

## 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 .…

## 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 .…

## Contrastive Learning of Visual Semantic Embeddings

Contrastive learning is a powerful technique to learn representations thatare semantically distinctive and geometrically invariant . In a batch, for a given anchor point from one modality, we consider negatives only from another modality . We compare our results with existing visual-semantic embedding methods on cross-modal image-to-text and text retrieval tasks using the MS-COCO and Flickr30K datasets .…

## Scaling Blockchains Can Elected Committees Help

EOS’ DelegatedProof of Stake (DPoS) protocol as a backdrop, we show that identifying theoptimal voting strategy is complex and practically out of reach . We empiricallycharacterize some simpler (suboptimal) voting strategies that token holders resort to in practice and show that these nonetheless converge to optimality,exponentially quickly .…

## On the singular two parameter eigenvalue problem II

Atkinson showed that a nonsingular multiparametereigenvalue problem is equivalent to the associated system of generalizedeigenvalue problems . In 2009, Muhi\v{c} and Plestenjak extended the above relation to a class of singulartwo-parameter eigenvalue . problems with coprime characteristic polynomials and such that all finite eigenvalues are algebraically simple .…

## 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 .…