Shedding some light on Light Up with Artificial Intelligence

The Light-Up puzzle, also known as the AKARI puzzle, has never been solved using modern artificial intelligence (AI) methods . This project is an effort to apply new AI techniques for solving the Light-up puzzle faster and more computationallyefficient . The algorithms explored for producing optimal solutions include hillclimbing, simulated annealing, feed-forward neural network (FNN), and CNN, and an evolutionary theory algorithm .…

Fast Low Rank Tensor Decomposition by Ridge Leverage Score Sampling

Low-rank tensor decomposition generalizes low-rank matrix approximation and is a powerful technique for discovering low-dimensional structure in high-dimensional data . We study Tucker decompositions and usetools from randomized numerical linear algebra called ridge leverage scores toaccelerate the core tensor update step in the widely-used alternating leastsquares (ALS) algorithm .…

Equidistant Linear Codes in Projective Spaces

Linear codes in projective space $mathbb{P}_q(n)$ were first considered by Braun,Etzion and Vardy . We establish that the normalizedminimum distance of a linear code is maximum if and only if it is equidistant . We prove that the upper bound on the size of such class of linear codes is $2^n$ when $q=2$ as conjectured by Braun et al.…

Super Resolution on the Two Dimensional Unit Sphere

We study the problem of recovering an atomic measure on the unit 2-sphere $\mathbb{S}^2$ given finitely many moments with respect to spherical harmonics . We construct a dual certificate using a kernel given in an explicit form and make a concrete analysis of the interpolation problem .…

Establishing Digital Recognition and Identification of Microscopic Objects for Implementation of Artificial Intelligence AI Guided Microassembly

Many current micro-assembly methods are serial in nature, resulting in unfeasibly low throughput . Alternatively, parallel self-assembly ordirected-assembly techniques can be employed by utilizing forces dominant atthe micro and nano scales such as electro-kinetic, thermal, and capillaryforces . However, these forces are governed by complex equations and often act on microparts simultaneously and competitively, making modeling and simulation difficult .…

Incentive Compatible Mechanism for Influential Agent Selection

In a self-interested setting, agents may strategically hide some connections to make themselves seem to be more important . In this paper, we study the incentive compatible (IC) selectionmechanism to prevent such manipulations . We propose the Geometric Mechanism, which selects an agent with at least 1/2 of the optimal progeny in expectation under the properties of incentive compatibility and fairness .…

LES3 Learning based Exact Set Similarity Search

Set similarity search is a problem of central interest to a wide variety of applications such as data cleaning and web search . Past approaches on setsimilarity search utilize either heavy indexing structures, incurring largesearch costs or indexes that produce large candidate sets .…

Dynamic Cantor Derivative Logic

$d-logics have not previously been studied in the framework of dynamical systems, which are pairs of a topological space $X$ equipped with a continuous function$f\colon X\to X$. We introduce the logics $wK4C$ and $GLC$ and show that they all have the finite Kripke model property and are sound and complete withrespect to the $d$-semantics in this dynamical setting .…

Hodge theoretic reward allocation for generalized cooperative games on graphs

We define cooperative games on general graphs and generalize Lloyd S.Shapley’s celebrated allocation formula for those games in terms of stochasticpath integral driven by the associated Markov chain on each graph . We then show that the value allocation operator, one for each player defined by thestochastic path integral, coincides with the player’s component game which is the solution to the least squares (or Poisson’s) equation .…

Towards Global and Limitless Connectivity The Role of Private NGSO Satellite Constellations for Future Space Terrestrial Networks

Satellite networks are expected to support global connectivity and servicesvia future integrated 6G space-terrestrial networks . In the past few years, many such private constellations have been launched or are in planning, e.g.SpaceX and OneWeb to name a few . We show that the link capacity, delay, and handover rate vary across the constellation, so the optimal handover strategydepends on the constellation design .…

High dimensional expansion implies amplified local testability

In this work we show that high dimensional expansion implies locally testablecode . We define a notion that we callhigh-dimensional-expanding-system (HDE-system) We show that a code that can be modelled over HDE-system islocally testable . This implies that high-dimensional expansion phenomenon solely implies local testability of codes .…

On the Stability Regions of Coded Poisson Receivers with Multiple Classes of Users and Receivers

Motivated by the need to provide differentiated quality-of-service (QoS) ingrant-free uplink transmissions in 5G networks and beyond, we extend theprobabilistic analysis of coded Poisson receivers (CPR) to the setting with multiple classes of users and receivers . For such a CPR system, we prove (undercertain technical conditions) that there is a region, called the stabilityregion in this paper .…

Typing assumptions improve identification in causal discovery

Under assumptions about the data-generative process, the causal graph can often be identified up to anequivalence class . Proposing new realistic assumptions to circumscribe suchequivalence classes is an active field of research . In this work, we propose anew set of assumptions that constrain possible causal relationships based on the nature of the variables .…

Evaluation of contextual embeddings on less resourced languages

The current dominance of deep neural networks in natural language processing is based on contextual embeddings such as ELMo, BERT, and BERT derivatives . Most existing work focuses on English; in contrast, we present here the firstmultilingual empirical comparison of two ELMo and several monolingual and multilingual BERT models using 14 tasks in nine languages .…

Whole Heart Mesh Generation For Image Based Computational Simulations By Learning Free From Deformations

Image-based computer simulation of cardiac function can be used to probe themechanisms of (patho)physiology, and guide diagnosis and personalized treatment of cardiac diseases . This paradigm requires constructing simulation-readymeshes of cardiac structures from medical image data . We propose a novel deep learning approach to reconstruct whole heart meshes from volumetricimage data .…

Did the Cat Drink the Coffee Challenging Transformers with Generalized Event Knowledge

Computational approaches have access to the information about thetypicality of entire events and situations described in language . The evaluation of these models was performed incomparison with SDM, a framework specifically designed to integrate events insentence meaning representations . Our results show that TLMs can reach performances that are comparable to those achieved by SDM .…

Designing a Location Trace Anonymization Contest

Location-based services (LBS) are increasingly used in recent years, and a large amount of location traces are accumulating in a datacenter . The disclosure of these traces raises serious privacy concerns, especially for long traces . To address this issue, we have designed and held a location trace anonymization contest that deals with along trace (400 events per user) and fine-grained locations (1024 regions) Inour contest, each team anonymizes her original traces, and then the other teams perform privacy attacks against the anonymized traces .…

Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications

In multi-dimensional time series analysis, a task is to conductevolutionary subspace clustering, aiming at clustering temporal data according to their evolving low-dimensional subspace structures . We demonstrate that this method can not onlyinterpolate data at any time step, but also achieve higher accuracy than other state-of-the-art evolutionarysubspace-cluing methods .…

The Stationary Prophet Inequality Problem

We study a continuous and infinite time horizon counterpart to the classic prophet inequality problem . Buyers arrive similarly and make take-it-or-leave-it offers for unsold items . The objective is to maximize the (infinite) time average revenue of the seller .…

Abstract Reasoning via Logic guided Generation

Abstract reasoning, i.e., inferring complicated patterns from givenobservations, is a central building block of artificial general intelligence . We propose logic-guided generation (LoGe), a novelgenerative DNN framework that reduces abstract reasoning as an optimization problem in propositional logic . LoGe is composed of three steps: extractpropositional variables from images, reason the answer variables with a logiclayer, and reconstruct the answer image from the variables .…

Randomized Online Algorithms for Adwords

The general adwords problem has remained largely unresolved . We define asubcase called $k-TYPICAL, $k \in \Zplus$ as follows: the total budgetof all the bidders is sufficient to buy $k$ bids for each bidder . We also giverandomized online algorithms for other special cases of adwords .…

MFGNet Dynamic Modality Aware Filter Generation for RGB T Tracking

MFGNet aims to boost the message communication between visible and thermaldata by adaptively adjusting the convolutional kernels for various input images . To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search byexploiting a new direction-aware target-driven attention mechanism .…

CURE Enabling RF Energy Harvesting using Cell Free Massive MIMO UAVs Assisted by RIS

The ever-evolving internet of things (IoT) has led to the growth of numerous wireless sensors, communicating through the internet infrastructure . For extending the lifetime of thesesensors, radio frequency energy harvesting (RFEH) technology has proved to be promising . In this paper, we propose CURE, a novel framework for RFEH that combines the benefits of cell-free massive MIMO (CFmMIMO), unmannedaerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs) toprovide seamless energy harvesting to IoT devices .…

Impacts Towards a comprehensive assessment of the book impact by integrating multiple evaluation sources

The surge in the number of books published makes the manual evaluation methods difficult to efficiently evaluate books . The use of books’ citationsand alternative evaluation metrics can assist manual evaluation and reduce the cost of evaluation . However, relying on a single resource for book assessment may lead to the risk that theevaluation results cannot be obtained due to the lack of the evaluation data, especially for newly published books .…

On the Stability Regions of Coded Poisson Receivers with Multiple Classes of Users and Receivers

Motivated by the need to provide differentiated quality-of-service (QoS) ingrant-free uplink transmissions in 5G networks and beyond, we extend theprobabilistic analysis of coded Poisson receivers (CPR) to the setting with multiple classes of users and receivers . For such a CPR system, we prove (undercertain technical conditions) that there is a region, called the stabilityregion in this paper .…

What Makes Sound Event Localization and Detection Difficult Insights from Error Analysis

Sound event localization and detection (SELD) aims to unify the tasks of sound event detection and direction-of-arrivalestimation . SELD inherits the challenges of both tasks, such as noise, reverberation, interference, polyphony, and non-stationarity of soundsources . Experimental results indicate polyphony as the main challenge in SELD, due to the difficulty in detecting all sound events of interest .…

Codeathon Activity A Design Prototype for Real World Problems

Avirtual codeathon activity, as part of this learning scheme, was conducted for180 undergraduate students to focus on analysis and design of solutions to real-world problems in the Covid-19 pandemic situation . Activity-based learning helps students to learn through participation, AvVirtual Codeathon activity was conducted by students .…

Multi Stream Transformers

Transformer-based encoder-decoder models produce a fused token-wiserepresentation after every encoder layer . We investigate the effects of allowing the encoder to preserve and explore alternative hypotheses, combined at the end of the encoding process . We design and examine a $\textit{Multi-stream Transformer}$ architecture and find that splitting theTransformer encoder into multiple encoder streams and allowing the model to merge multiple representational hypotheses improves performance .…

A local approach to parameter space reduction for regression and classification tasks

New method called localactive subspaces (LAS) combines clustering techniques with a more efficient dimension reduction in the parameter space for the design of accurate response surfaces . The drawback of this approach is the possible scarcity of data in some applications, but in those, where a quantityof data, moderately abundant is available, partitioned or localstudies are beneficial .…