## An Algorithm for Recommending Groceries Based on an Item Ranking Method

This research proposes a new recommender system algorithm for online groceryshopping . The algorithm figures out the possible dishes a user may cook based on the items added to the basket andrecommends the ingredients accordingly . Instead of using a brute force search, this algorithmlimits the search space to a set of only a few probably food categories .…

## Population Monotonicity in Matching Games

A matching game is a cooperative profit game defined on an edge-weightedgraph . Profit of a coalition is themaximum weight of matchings in the subgraph induced by the coalition . Apopulation monotonic allocation scheme is a collection of rules defining how to share the profit among players in each coalition such that every player isbetter off when the coalition expands .…

## Degrees of Restriction for Two Dimensional Automata

A three-way (resp., two-way) two-dimensional automaton has a read-only inputhead that moves in three directions on a finite array of cells . Restricting the input head movement results in a model that is weaker in terms of recognition power . In this paper, we introduce the notion of “degrees of restriction” for two-deterministic automata models that allow for some bounded number of restricted moves .…

## Generating Extended Resolution Proofs with a BDD Based SAT Solver

In 2006, Biere, Jussila, and Sinz made the key observation that the underlying logic behind algorithms for constructing Reduced, Ordered BinaryDecision Diagrams (BDDs) can be encoded as steps in a proof . A BDD-based SAT solver can generate a checkable proof of unsatisfiability for a set ofclauses .…

## Testing Dynamic Environments Back to Basics

We continue the line of work initiated by Goldreich and Ron (Journal of theACM, 2017) on testing dynamic environments . We propose to pursue a systematic study of the complexity of testing basic dynamic environments and local rules . The meta-algorithm has query complexity poly$(1/\epsilon)$,is non-adaptive and has one-sided error .…

## Multi Target Multi Camera Tracking of Vehicles using Metadata Aided Re ID and Trajectory Based Camera Link Model

In this paper, we propose a novel framework for multi-target multi-cameratracking (MTMCT) of vehicles based on metadata-aided re-identification(MA-ReID) and the trajectory-based camera link model (TCLM) The proposed method isevaluated on the CityFlow dataset, achieving IDF1 76.77%, which outperforms the state-of-the-art MTMCT methods .…

## Modelling and design of FTJs as high reading impedance synaptic devices

We present an in-house modelling framework for Ferroelectric TunnellingJunctions (FTJ) and an insightful study of the design of FTJs as synapticdevices . Results show that a moderately low-k tunnelling dielectric (e.g. SiO2) can increase the read current and the current dynamic range .…

## On the limit of English conversational speech recognition

The study also considers the recently proposed conformer, and more advanced self-attention based language models . Their combination and decoding reaches a new record on Switchboard-300 and 10.0% WER on SWB and CHM parts of Hub5’00 with very simple LSTMmodels. Overall, the conformer showssimilar performance to the L STM; nevertheless, their combination and decode with an improved LM reaches new record .…

## Act the Part Learning Interaction Strategies for Articulated Object Part Discovery

Act the Part (AtP) learns efficient strategies for part discovery, can generalize categories and is capable of conditional reasoning for the task . Although trained in simulation, we show convincing transfer to real world datawith no fine-tuning . AtP is able to isolate structures to make perceptual part recovery possible without semantic labels .…

## What s Decidable about Atomic Polymorphism

In this paper, we investigate System Fat, or atomic System F, a weak predicative fragment of System F whose typable terms coincide with the simply typable ones . We show that the type-checking problem for Fat is decidable and we propose an algorithm which sheds some new light on the source of undecidability in full System F .…

## Explicit constructions of optimal linear codes with Hermitian hulls and their application to quantum codes

We present a new method to construct Hermitian self-orthogonal $[n,k,d]_{q^2} codes with large dimensions$k\frac{n+q-1}{q+1$We provide entanglement-assisted quantum error-correcting codes with new parameters . We prove that any Hermitan self-or-hermitian code gives riseto an$ [n]k, d]_q# code with a $[ n,k]__k-1$ code with $0\le\ell \le k-1$.…

## Accessibility Across Borders

Cultural differences influence user preferences and interaction methods . We believe that it is equally important to apply this inquiry to digital accessibility and how accessibility fits within the design process around the world . We hope that this inquiry will also be applied to how digital accessibility fits into design process .…

## Collision Replay What Does Bumping Into Things Tell You About Scene Geometry

In this paper, we investigate the idea of learning from collisions . We use collision replay to train convolutional neural networks to predict adistribution over collision time from new images . This distribution conveysinformation about the navigational affordances (e.g., corridors vs open spaces)and, as we show, can be converted into the distance function for the scenegeometry.…

## SmoothI Smooth Rank Indicators for Differentiable IR Metrics

Information retrieval (IR) systems traditionally aim to maximize metrics built on rankings, such as precision or NDCG . However, thenon-differentiability of the ranking operation prevents direct optimization of such metrics in state-of-the-art neural IR models . To address this shortcoming, we propose SmoothI, a smooth approximation of rank indicators that serves as abasic building block to devise differentiable approximations of IR metrics .…

## Neural Monocular 3D Human Motion Capture with Physical Awareness

Unlike most neural methods for human motion capture, our approach is aware of physical and environmental constraints . The inputs to our system are 2D joint keypoints, which are canonicalised in anovel way . This enables more accurate global translationestimation without generalisability loss .…

## Abstract clones for abstract syntax

Abstract clonestraditionally describe first-order structures, but by equipping them with algebraic structure, one can further axiomatize second-order, variable-binding operators . We give a construction of free algebras and derive acorresponding induction principle . This provides a syntax-independent representation of simple type theories, such as the simply-typed $\lambda$-calculus, using the framework of abstract clones .…

## Sketches image analysis Web image search engine usingLSH index and DNN InceptionV3

The adoption of an appropriate approximate similarity search method is anessential pre-requisites for developing a fast and efficient CBIR system . In this study we implement a web image search engine on top of a Locality Sensitive Hashing(LSH) Index toallow fast similarity search on deep features .…

## A Gun Detection Dataset and Searching for Embedded Device Solutions

Gun violence is a severe problem in the world, particularly in the United States . Computer vision methods have been studied to detect guns insurveillance video cameras or smart IP cameras . However, due to no public datasets, it is hard to benchmark how well such methods work in real applications .…

## Learning Visually Guided Latent Actions for Assistive Teleoperation

A central problem is that there are many more high-dimensional actions than available low-dimensional inputs . To extract the correct action and maximally assist their human controller, robots must reason over their context . For example, pressing a joystick down when interacting with a coffee cup indicates a different action .…

## Pedestrian Detection in 3D Point Clouds using Deep Neural Networks

Detecting pedestrians is a crucial task in autonomous driving systems . Relying solely on RGB cameras may not be enough to recognize roadenvironments in situations where cameras cannot capture scenes properly . Some approaches aim to compensate for these limitations by combining RGB cameras with TOF sensors, such as LIDARs .…

## Prediction of clinical tremor severity using Rank Consistent Ordinal Regression

Tremor is a key diagnostic feature of Parkinson’s Disease (PD), EssentialTremor (ET), and other central nervous system (CNS) disorders . Clinicians or trained raters assess tremor severity with TETRAS scores by observing patients . In this work, we proposed totrain a deep neural network (DNN) with rank-consistent ordinal regression using276 clinical videos from 36 essential tremor patients .…

## Automated Estimation of Total Lung Volume using Chest Radiographs and Deep Learning

Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases . In this study, we investigate theperformance of several deep-learning approaches for automated measurement of total lung volume from chest radiographs . We demonstrate, for the firsttime, that state-of-the-art deep learning solutions can accurately measuretotal lung volume .…

## Optimal Maximal Leakage Distortion Tradeoff

Most methods for publishing data with privacy guarantees introduce randomness, which reduces the utility of the published data . In this paper, we study the privacy-utility tradeoff by taking maximal leakage as the privacy measure and the expected Hamming distortion as the utility measure .…

## Pseudo Siamese Network for Few shot Intent Generation

Few-shot intent detection is a challenging task due to the scare annotationproblem . In this paper, we propose a Pseudo Siamese Network (PSN) to generatelabeled data for few-shot intents and alleviate this problem . PSN consists of two identical subnetworks with the same structure but different weights: anaction network and an object network .…

## Explaining Behavioural Inequivalence Generically in Quasilinear Time

We provide a generic algorithm for constructing formulae that distinguishbehaviourally inequivalent states in systems of various transition types . The algorithm builds on an existing coalgebraic partition refinement algorithm . It runs in time O((m + n) log n) on systems with n states and m transitions,and the same asymptotic bound applies to the size of the formula itconstructs .…

## Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation

Unsupervised Document Expansion with Generation (UDEG) generates diversesupplementary sentences for the original document without using labels on query-document pairs for training . We validate our framework on two standard IR benchmarkdatasets . The results show that our framework significantly outperformsrelevant expansion baselines for IR.…

## Multiple Output Channel Simulation and Lossy Compression of Probability Distributions

We consider a variant of the channel simulation problem with a single input and multiple outputs . We show that the growth rate of theexpected codeword length is sub-linear in $n$ when a power law bound is satisfied . An application of multiple-outputs channel simulation is thecompression of probability distributions .…

## Pattern Complexity of Aperiodic Substitutive Subshifts

This paper aims to better understand the link between aperiodicity in subshifts and pattern complexity . We prove aquadratic lower bound on their pattern complexity for a class of substitutive sub-shifts . We also prove that the recent bound ofKari and Moutot, showing that any aperiodic subshift has pattern complexity atleast $mn+1$, is optimal for fixed $m$ and $n$.…

## General Knapsack Problems in a Dynamic Setting

The world is dynamic and changes over time, so any optimization problems must address this dynamic nature . In the multistage model we are given aseries of instance of an optimization problem, and a solution is provided foreach instance . The strive for continuous and similar solutions over time are quantified and integrated into the objective function .…

## Ferroelectric based FETs and synaptic devices for highly energy efficient computational technologies

The technological exploitation of ferroelectricity in CMOS electron devices offers new design opportunities, but also significant challenges from anintegration, optimization and modelling perspective . We revisit the working principle and the modelling of some novel . based devices, with an emphasis on energy efficiency and on applications to new computational capabilities .…

## A Rate Splitting Strategy to Enable Joint Radar Sensing and Communication with Partial CSIT

Joint radar and communication (RadCom) systems have attracted increased attention in recent years . Joint RadCom system is designed which marriesthe capabilities of a Multiple-Input Multiple-Output (MIMO) radar withRate-Splitting Multiple Access (RSMA) RSMA providesthe RadCom with more robustness, flexibility and user rate fairness compared to the baseline joint RadCom System based on Space Division Multiple Access(SDMA) System is designed in the presence of partial CSIT to maximize the Average Weighted Sum-Rate (AWSR) under QoS rate constraints and minimize the RadCom Beampattern Squared Error (BSE) against anideal MIMO radar beamp attern .…

## Automatic Collection Creation and Recommendation

We present a collection recommender system that can automatically create andrecommend collections of items at a user level . We employ dimensionality reduction and clusteringtechniques along with intuitive heuristics to create collections with theirratings and titles . We test these ideas in a real-world setting of music recommendation, within apopular music streaming service.…

## Partial Information Decomposition via Deficiency for Multivariate Gaussians

We consider decomposing the information content of three Gaussian random vectors using the partial information decomposition(PID) framework . Barrett previously characterized the Gaussian PID for a scalar”source” or “message” in closed form – we extend this to the case where themessage is a vector .…

## Present and Future of Reconfigurable Intelligent Surface Empowered Communications

Signal processing and communication communities have witnessed the rise of exciting communication technologies in recent years . We discuss the recent developments in the field and put forward promising candidates for future research and development . We also envision an ultimate RIS architecture, which is able to adjust its operation modes dynamically, and introduce the new concept of PHY slicing over RISs towards 6G wireless networks.…

## Impact of Gender Debiased Word Embeddings in Language Modeling

Gender, race and social biases have recently been detected as evident examples of unfairness in applications of Natural Language Processing . A keypath towards fairness is to understand, analyse and interpret our data and algorithms . Recent studies have shown that the human-generated data used intraining is an apparent factor of getting biases .…

## Event Camera Simulator Design for Modeling Attention based Inference Architectures

In recent years, there has been a growing interest in integrating more and more computation at the level of the image sensor . Therising trend has seen an increased research interest in developing novel event-based eventcameras that can facilitate CNN computation directly in the sensor .…

## In search of lost time Axiomatising parallel composition in process algebras

Survey reviews some of the most recent achievements in the saga of theaxiomatisation of parallel composition . We argue that, under some naturalassumptions, the addition of a single auxiliary binary operator to CCS does not yield a finite axiomatising of bisimilarity .…

## Semantic Journeys Quantifying Change in Emoji Meaning from 2012 2018

The less abstract an emoji is, the more likely it is toundergo semantic change . We identify five patterns in emoji semantic development and analyse select emoji in more detail . We make our data publicly available alongwith a web-based interface that anyone can use to explore semantic change inemoji.…

## Generalized Spatially Coupled Parallel Concatenated Convolutional Codes With Partial Repetition

We introduce generalized spatially coupled parallel concatenated codes (GSC-PCCs) as a class of turbo-like turbo-style codes . We show that the proposed codes have some niceproperties such as threshold saturation and that their decoding thresholdsimprove with the repetition factor $q$. We also suggest that the codes asymptotically approach the capacity as $q$ tends toinfinity with any given constituent convolutional code .…

## Weighted Least Squares Twin Support Vector Machine with Fuzzy Rough Set Theory for Imbalanced Data Classification

Support vector machines (SVMs) are powerful supervised learning tools developed to solve classification problems . However, SVMs are likely to performpoorly in the classification of imbalanced data . In this work, we propose an approach that efficiently used fuzzy rough set theory in weighted leastsquares twin support vector machine called FRLSTSVM for classification ofimbalanced data.…

## A Constant factor Approximation for Weighted Bond Cover

Only three cases of minor-closed ${\calF}$ are known to admit constant-factor approximations . We study the problem for the class $\cal F$ of graphs, under theequivalent setting of the \textsc{Weighted $c$-Bond Cover} problem . In the first case, we tame the graph by replacing the protrusion with a special-purpose weighted gadget .…

## Improving Community Detection Performance in Heterogeneous Music Network by Learning Edge type Usefulness Distribution

Music is becoming an essential part of daily life . There is an urgent need todevelop recommendation systems to assist people targeting better songs withfewer efforts . As the interactions between users and songs naturally construct a complex network, community detection approaches can be applied to reveal potential interests on songs .…

## Weakly Supervised Universal Lesion Segmentation with Regional Level Set Loss

Manual annotation is the current clinical practice, being highly time-consuming and inconsistent . AHRNet provides advanced high-resolution deep image features byinvolving a decoder, dual-attention and scale attention mechanisms . RLS can optimize the modelreliably and effectively in a weakly-supervised fashion, forcing thesegmentation close to lesion boundary .…

This paper presents the results of the Russian News Clustering and HeadlineSelection shared task . We propose tasks of Russian newsevent detection, headline selection, and headline generation . The presented datasets for eventdetection and headline selection are the first public Russian datasets for their tasks .…

## Bounds of MIN_NCC and MAX_NCC and filtering scheme for graph domain variables

Beldiceanu et al.presented a generic filtering scheme for global constraints based on graph properties . This scheme strongly relies on the computation of graph properties’bounds and can be used in the context of graph domain variables and constraints . Bounds of MIN_NCC had been defined for the graph-based representation of global constraint for the path_with_loops graphclass .…

## Computationally Efficient Optimization of Plackett Luce Ranking Models for Relevance and Fairness

PL-Rank is a novel algorithm that estimates the gradient of a PLranking model w.r.t. both relevance and fairness metrics . Unlike existing approaches that are based on policy gradients, PL-rank makes use of the structure of PL models and ranking metrics .…

## Thinking Outside the Lab VR Size Depth Perception in the Wild

Size and distance perception in Virtual Reality have been widelystudied in a controlled laboratory setting with a small number of participants . We describe a fully remote perceptual study with a gamified protocol to encourage participant engagement . Varying eye-height from the floor plane showed no significant effect on the judgements .…