November 2022 arXiv papers — page 152

Xiao Huang

Assessing the statistical significance of parameter estimates is an important step in high-dimensional vector autoregression modeling. Using the least-squares boosting method, we compute the p-value for each selected parameter at every boosting step in a linear model. The p-values are asymptotically valid and also adapt to the iterative nature of the boostin

Yutong Dai, Daniel P. Robinson

We consider the proximal-gradient method for minimizing an objective function that is the sum of a smooth function and a non-smooth convex function. A feature that distinguishes our work from most in the literature is that we assume that the associated proximal operator does not admit a closed-form solution. To address this challenge, we study two adaptive a

Huayi Zhou, Fei Jiang, Hongtao Lu

Domain adaptive object detection (DAOD) aims to alleviate transfer performance degradation caused by the cross-domain discrepancy. However, most existing DAOD methods are dominated by outdated and computationally intensive two-stage Faster R-CNN, which is not the first choice for industrial applications. In this paper, we propose a novel semi-supervised doma

Feifei Shi, Huansheng Ning, Xiaohong Zhang, Rongyang Li

The Metaverse depicts a parallel digitalized world where virtuality and reality are fused. It has economic and social systems like those in the real world and provides intelligent services and applications. In this paper, we introduce the Metaverse from a new technology perspective, including its essence, corresponding technical framework, and potential tech

Shengchao Zhao, Yongchao Liu

The paper studies the distributed stochastic compositional optimization problems over networks, where all the agents' inner-level function is the sum of each agent's private expectation function. Focusing on the aggregative structure of the inner-level function, we employ the hybrid variance reduction method to obtain the information on each agent's private

Sudeep Salgia, Qing Zhao

We consider distributed linear bandits where $M$ agents learn collaboratively to minimize the overall cumulative regret incurred by all agents. Information exchange is facilitated by a central server, and both the uplink and downlink communications are carried over channels with fixed capacity, which limits the amount of information that can be transmitted i

Benoît Assi, Bogdan A. Dobrescu

Within a chiral $SU(15)$ gauge theory in which the Standard Model fermions are bound states of massless preons, we show that proton-decay operators are likely induced at the compositeness scale, $\Lambda_{\rm pre}$. Our estimate of the limit imposed by searches for proton decays is $\Lambda_{\rm pre} > 10^4 \, {\rm TeV}^{1/2} \, C_8^{1/4} \, m_{\cal Q}^{1/2}

Stephen Lack, Adrian Miranda

For a 2-category $\mathcal{K}$, we consider Street's 2-category Mnd($\mathcal{K}$) of monads in $\mathcal{K}$, along with Lack and Street's 2-category EM($\mathcal{K}$) and the identity-on-objects-and-1-cells 2-functor Mnd($\mathcal{K}$) $\to$ EM($\mathcal{K}$) between them. We show that this 2-functor can be obtained as a "free completion" of the 2-functor

Bao Wang, Jun-Jie Wei

The dispersion measure (DM)--redshift relation of fast radio bursts (FRBs) has been proposed as a potential new tool for probing intergalactic medium (IGM) and for studying cosmology. However, the poor knowledge of the baryon fraction in the IGM ($f_{\mathrm{IGM}}$) and its degeneracy with cosmological parameters impose restrictions on the cosmological appli

Aaron Yagnik, Adrian S. -W. Tam

In recent years, Artificial Intelligence (AI) and Computer Vision (CV) have become the pinnacle of technology with new developments seemingly every day. This technology along with more powerful drone technology have made autonomous surveillance more sought after. Here an overview of the Automated Logging Drone (ALD) project is presented along with examples o

D. T. Hoai, P. T. Nhung, M. N. Tan, P. Darriulat

The morpho-kinematics of the circumstellar envelope of oxygen-rich AGB star R Leonis is probed using ALMA (Atacama Large Millimeter/submillimeter Array) observations of the emission of molecular lines, including in particular CO(2-1) and $^{29}$SiO(5-4). Evidence is found for an episode of enhanced mass loss, a few centuries ago, that produced a broad expand

Ryan Humble, Maying Shen, Jorge Albericio Latorre, Eric Darve1

Structured channel pruning has been shown to significantly accelerate inference time for convolution neural networks (CNNs) on modern hardware, with a relatively minor loss of network accuracy. Recent works permanently zero these channels during training, which we observe to significantly hamper final accuracy, particularly as the fraction of the network bei

Yongliang Chen, Simon White, Evgeny A. Ekimov, Carlo Bradac

Cryogenic temperatures are the prerequisite for many advanced scientific applications and technologies. The accurate determination of temperature in this range and at the submicrometer scale is, however, nontrivial. This is due to the fact that temperature reading in cryogenic conditions can be inaccurate due to optically induced heating. Here, we present an

David Peak, Keith Mott, Matthew Hogan

Control of gas exchange between a leafs interior and the surrounding air is accomplished by variations in the turgor pressures in the small epidermal and guard cells that cover the leafs surface. These pressures respond to changes in light intensity and color, temperature, CO2 concentration, and air humidity. The dynamical equations that describe these proce

Andre Manoel, Mirian Hipolito Garcia, Tal Baumel, Shize Su

Federated Learning (FL) is a novel machine learning approach that allows the model trainer to access more data samples, by training the model across multiple decentralized data sources, while data access constraints are in place. Such trained models can achieve significantly higher performance beyond what can be done when trained on a single data source. As

Ben O'Neill

This paper examines the problem of computing a canonical smallest covering region for an arbitrary discrete probability distribution. This optimisation problem is similar to the classical 0-1 knapsack problem, but it involves optimisation over a set that may be countably infinite, raising a computational challenge that makes the problem non-trivial. To solve

Ben O'Neill

We examine the problem of computing the highest density region (HDR) in a computational context where the user has access to a density function and quantile function for the distribution (e.g., in the statistical language R). We examine several common classes of continuous univariate distributions based on the shape of the density function; this includes mon

Casey Clifton, Richard Blythman, Kartika Tulusan

Artificial Intelligence (AI) has the potential to significantly benefit or harm humanity. At present, a few for-profit companies largely control the development and use of this technology, and therefore determine its outcomes. In an effort to diversify and democratize work on AI, various groups are building open AI systems, investigating their risks, and dis

Mengxi Li, Rika Antonova, Dorsa Sadigh, Jeannette Bohg

When humans perform contact-rich manipulation tasks, customized tools are often necessary to simplify the task. For instance, we use various utensils for handling food, such as knives, forks and spoons. Similarly, robots may benefit from specialized tools that enable them to more easily complete a variety of tasks. We present an end-to-end framework to autom

Hieu Nguyen Van, Dat Nguyen, Phuong Minh Nguyen, Minh Le Nguyen

We introduce efficient deep learning-based methods for legal document processing including Legal Document Retrieval and Legal Question Answering tasks in the Automated Legal Question Answering Competition (ALQAC 2022). In this competition, we achieve 1\textsuperscript{st} place in the first task and 3\textsuperscript{rd} place in the second task. Our method

Ming Ji, Holger F. Hofmann

Quantum mechanics describes seemingly paradoxical relations between the outcomes of measurements that cannot be performed jointly. In Hilbert space, the outcomes of such incompatible measurements are represented by non-orthogonal states. In this paper, we investigate how the relation between outcomes represented by non-orthogonal quantum states differs from

Melissa Lee, Gabriel Verret

A finite non-regular primitive permutation group $G$ is extremely primitive if a point stabiliser acts primitively on each of its nontrivial orbits. Such groups have been studied for almost a century, finding various applications. The classification of extremely primitive groups was recently completed by Burness and Lee, who relied on an earlier classificati

Jai-chan Hwang, Hyerim Noh

We present first-order post-Newtonian (1PN) approximations of a general imperfect fluid and of an axion as a coherently oscillating massive scalar field, both in the cosmological context. For the axion, using the Klein transformation and Madelung transformation we derive the Schr\"odinger and Madelung hydrodynamic formulations, respectively, in exact covaria

Christoph Graf, Federico Quaglia, Frank A. Wolak

The negative demand shock due to the COVID-19 lockdown has reduced net demand for electricity -- system demand less amount of energy produced by intermittent renewables, hydroelectric units, and net imports -- that must be served by controllable generation units. Under normal demand conditions, introducing additional renewable generation capacity reduces net

Diego Moura, Vinicius Petrucci, Daniel Mosse

Persistent Memory (PMEM), also known as Non-Volatile Memory (NVM), can deliver higher density and lower cost per bit when compared with DRAM. Its main drawback is that it is typically slower than DRAM. On the other hand, DRAM has scalability problems due to its cost and energy consumption. Soon, PMEM will likely coexist with DRAM in computer systems but the

Guilherme L. Feltes, Hendrik Weber

We study the long time behaviour of a Brownian particle evolving in a dynamic random environment. Recently, [G. Cannizzaro, L. Haunschmid-Sibitz, F. Toninelli, preprint arXiv:2106.06264] proved sharp $\sqrt{log}$-super diffusive bounds for a Brownian particle in the curl of (a regularisation of) the 2-d Gaussian Free Field (GFF) $\underline{\omega}$. We cons

Manon Flageat, Bryan Lim, Luca Grillotti, Maxime Allard

We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control. The suite includes the definition of tasks, environments, behavioral descriptors, and fitness. We specify different benchmarks based on the complexity of both the task and the agent controlled by a deep neural network. The benchmark use

Z. E. Musielak

The irreducible representations of the extended Galilean group are used to derive the symmetric and asymmetric wave equations. It is shown that among these equations only a new asymmetric wave equation is fundamental. By being fundamental the equation gives the most complete description of propagating waves as it accounts for the Doppler effect, forward and

Jacob Stamm, Shuai Li, Bethany Jochim, Stephen H. Yuwono

Strong-field ionization, involving tunnel ionization and electron rescattering, enables femtosecond time-resolved dynamics measurements of chemical reactions involving radical cations. Here, we compare the formation of CH3S+ following the strong-field ionization of the isomers CH3SCN and CH3NCS. The former involves the release of neutral CN, while the latter

Shiv Shankar, Ritwik Sinha, Saayan Mitra, Viswanathan Swaminathan

Consider two brands that want to jointly test alternate web experiences for their customers with an A/B test. Such collaborative tests are today enabled using \textit{third-party cookies}, where each brand has information on the identity of visitors to another website. With the imminent elimination of third-party cookies, such A/B tests will become untenable

Ryan E. G. Bushling

We establish a packing dimension estimate on the exceptional sets of orthogonal projections of sets satisfying an almost dimension conservation law. In particular, the main result applies to homogeneous sets and to certain graph-directed sets. Connections are drawn to results of M. Rams and T. Orponen.

Frank Taipe

In this work, we study another characterization of Yetter-Drinfeld algebras over finite-dimensional Hopf algebras. We show the equivalence between this characterization, called the "only coaction" characterization, and the standard "action-coaction" characterization. This modern approach for Yetter-Drinfeld algebras is one of the key ingredient in a self-dua

Travis Reid, Michael L. Nelson, Michele C. Weigle

We want to make web archiving entertaining so that it can be enjoyed like a spectator sport. To this end, we have been working on a proof of concept that involves gamification of the web archiving process and integrating video games and web archiving. Our vision for this proof of concept involves a web archiving live stream and a gaming live stream. We are c

Alister W. Graham

While dry mergers can produce considerable scatter in the (black hole mass, $M_{\rm bh}$)-(spheroid stellar mass, $M_{\rm *,sph}$) and $M_{\rm bh}$-(spheroid half-light radius, $R_{\rm e,sph}$) diagrams, the virial theorem is used here to explain why the scatter about the $M_{\rm bh}$-(velocity dispersion, $\sigma$) relation remains low in the face of such m

Joel B. Abraham, Daniel Verscharen, Robert T. Wicks, Jefferson A. Agudelo Rueda

We present an observational analysis of the electron thermal energy budget using data from Parker Solar Probe. We use the macroscopic moments, obtained from our fits to the measured electron distribution function, to evaluate the thermal energy budget based on the second moment of the Boltzmann equation. We separate contributions to the overall budget from r

Bappaditya Dey, Enrique Dehaerne, Kasem Khalil, Sandip Halder

In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain. Stochastic defect detection and classification during semiconductor manufacturing has grown to be a challenging task as we

Minjae Jo, Michael Hanks, M. S. Kim

Quantum annealing promises to be an effective heuristic for complex NP-hard problems. However, clear demonstrations of quantum advantage are wanting, primarily constrained by the difficulty of embedding the problem into the quantum hardware. Community detection methods such as the Girvin--Newman algorithm can provide a divide-and-conquer approach to large pr

Katarina Lj Djordjevic, Dragana K Markushev, Marica N Popovic, Mioljub V Nesic

In this paper, the possibility of determining the thermal, elastic and geometric characteristics of a thin TiO2 film deposited on a silicon substrate, thickness 30 mikrons, in the frequency range of 20 to 20 kHz with neural networks was analyzed. For this purpose, the substrate parameters remained the known and constant in the two-layer model and nano layer

Matthew B. Donnelly, Joris G. Keizer, Yousun Chung, Michelle Y. Simmons

A requirement for quantum information processors is the in-situ tunability of the tunnel rates and the exchange interaction energy within the device. The large energy level separation for atom qubits in silicon is well suited for qubit operation but limits device tunability using in-plane gate architectures, requiring vertically separated top-gates to contro

Kevin Cheang, Cameron Rasmussen, Dayeol Lee, David W. Kohlbrenner

We formally verify an open-source hardware implementation of physical memory protection (PMP) in RISC-V, which is a standard feature used for memory isolation in security critical systems such as the Keystone trusted execution environment. PMP provides per-hardware-thread machine-mode control registers that specify the access privileges for physical memory r

Anuj Diwan, Puyuan Peng, Raymond J. Mooney

For the majority of the machine learning community, the expensive nature of collecting high-quality human-annotated data and the inability to efficiently finetune very large state-of-the-art pretrained models on limited compute are major bottlenecks for building models for new tasks. We propose a zero-shot simple approach for one such task, Video Moment Retr

Gabriele Tolomei, Lorenzo Takanen, Fabio Pinelli

In this work, we propose MUSTACHE, a new page cache replacement algorithm whose logic is learned from observed memory access requests rather than fixed like existing policies. We formulate the page request prediction problem as a categorical time series forecasting task. Then, our method queries the learned page request forecaster to obtain the next $k$ pred

Lukas Drexler, Jan Eube, Kelin Luo, Dorian Reineccius

Motivated by an application from geodesy, we introduce a novel clustering problem which is a $k$-center (or k-diameter) problem with a side constraint. For the side constraint, we are given an undirected connectivity graph $G$ on the input points, and a clustering is now only feasible if every cluster induces a connected subgraph in $G$. We call the resultin

Bing Shuai, Alessandro Bergamo, Uta Buechler, Andrew Berneshawi

This paper presents a new large scale multi-person tracking dataset -- \texttt{PersonPath22}, which is over an order of magnitude larger than currently available high quality multi-object tracking datasets such as MOT17, HiEve, and MOT20 datasets. The lack of large scale training and test data for this task has limited the community's ability to understand t

Charles K. Fisher

Restricted Boltzmann Machines (RBMs) are probabilistic generative models that can be trained by maximum likelihood in principle, but are usually trained by an approximate algorithm called Contrastive Divergence (CD) in practice. In general, a CD-k algorithm estimates an average with respect to the model distribution using a sample obtained from a k-step Mark

Artur Avila, David Damanik, Anton Gorodetski

We consider Schr\"odinger operators in $\ell^2(\mathbb{Z})$ whose potentials are given by the sum of an ergodic term and a random term of Anderson type. Under the assumption that the ergodic term is generated by a homeomorphism of a connected compact metric space and a continuous sampling function, we show that the almost sure spectrum arises in an explicitl

Ivis Kerama, Thomas Thorne, Richard G. Everitt

Approximate Bayesian computation (ABC) is a well-established family of Monte Carlo methods for performing approximate Bayesian inference in the case where an ``implicit'' model is used for the data: when the data model can be simulated, but the likelihood cannot easily be pointwise evaluated. A fundamental property of standard ABC approaches is that the numb

Hesham El Faham, Andrea Giammanco, Jan Hajer

Motivated by the expectation that new physics may manifest itself in the form of very heavy new particles, most of the operation time of the LHC is devoted to $pp$ collisions at the highest achievable energies and collision rates. The large collision rates imply tight trigger requirements that include high thresholds on the final-state particles' transverse

Karen L. Collins, Ann N. Trenk, Rebecca Whitman

In this paper, we study split graphs and related classes of graphs from the perspective of their sequence of vertex degrees and an associated lattice under majorization. Following the work of Merris in 2003, we define blocks $[\alpha(\pi)|\beta(\pi)]$, where $\pi$ is the degree sequence of a graph, and $\alpha(\pi)$ and $\beta(\pi)$ are sequences arising fro

Moira MacNeil, Merve Bodur

Two-stage stochastic programs with binary recourse are challenging to solve and efficient solution methods for such problems have been limited. In this work, we generalize an existing binary decision diagram-based (BDD-based) approach of Lozano and Smith (Math. Program., 2018) to solve a special class of two-stage stochastic programs with binary recourse. In

Mathieu Fourment, Christiaan J. Swanepoel, Jared G. Galloway, Xiang Ji

Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via automatic differentiation implemented in general-purpose machine-learning libraries such as TensorFlow and PyTorch. Although these libraries are

Marco Paul E. Apolinario, Adarsh Kumar Kosta, Utkarsh Saxena, Kaushik Roy

Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge devices. However, commercial edge platforms based on standard GPUs are not optimized to deploy SNNs, resulting in high energy and latency. While analog In-Memory Computing (IMC) plat

Guilherme Dean Pelegrina, Leonardo Tomazeli Duarte, Michel Grabisch

Besides accuracy, recent studies on machine learning models have been addressing the question on how the obtained results can be interpreted. Indeed, while complex machine learning models are able to provide very good results in terms of accuracy even in challenging applications, it is difficult to interpret them. Aiming at providing some interpretability fo

Ahmet M. Elbir, Kumar Vijay Mishra, Sergiy A. Vorobyov, Robert W. Heath

Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction. It has been used in several engineering applications such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advances in multi-antenna technologies largely for rada

Muna Al-Harby, Bassam A. Shehadeh

We studied the temperature effect in isospin-singlet pairings in Gamow-Teller excitations. We use theories of a hole-particle in the mean field shell model studied decay transition using the one-particle-one-hole model for the $\beta$-decay of odd-even isotopes and the two-particle-hole models for the $\beta$-decay of even-even and/or odd-odd isotopes. Our r

Stephan C. P. A. van Kalmthout, Laurence I. Midgley, Meik B. Franke

This paper shows the implementation of reinforcement learning (RL) in commercial flowsheet simulator software (Aspen Plus V12) for designing and optimising a distillation sequence. The aim of the SAC agent was to separate a hydrocarbon mixture in its individual components by utilising distillation. While doing so it tries to maximise the profit produced by t

Jiaxiang Li, Shiqian Ma, Tejes Srivastava

We consider a class of Riemannian optimization problems where the objective is the sum of a smooth function and a nonsmooth function, considered in the ambient space. This class of problems finds important applications in machine learning and statistics such as the sparse principal component analysis, sparse spectral clustering, and orthogonal dictionary lea

Shuyang Cao, Lu Wang

In this paper, we study the effects of incorporating timestamps, such as document creation dates, into generation systems. Two types of time-aware prompts are investigated: (1) textual prompts that encode document timestamps in natural language sentences; and (2) linear prompts that convert timestamps into continuous vectors. To explore extrapolation to futu

Thilina Ranbaduge, Dinusha Vatsalan, Ming Ding

Deep learning-based linkage of records across different databases is becoming increasingly useful in data integration and mining applications to discover new insights from multiple sources of data. However, due to privacy and confidentiality concerns, organisations often are not willing or allowed to share their sensitive data with any external parties, thus

Christopher J. McDevitt, Xian-Zhu Tang

Benign termination of mega-ampere (MA) level runaway current has been convincingly demonstrated in recent JET and DIII-D experiments, establishing it as a leading candidate for runaway mitigation on ITER. This comes in the form of a runaway flush by parallel streaming loss along stochastic magnetic field lines formed by global magnetohydrodynamic instabiliti

Aboutaleb Amiri, Charlie Duclut, Frank Jülicher, Marko Popović

We investigate how randomly oriented cell traction forces lead to fluidisation in a vertex model of epithelial tissues. We find that the fluidisation occurs at a critical value of the traction force magnitude $F_c$. We show that this transition exhibits critical behaviour, similar to the yielding transition of sheared amorphous solids. However, we find that

Chase Kimball, Sam Imperato, Vicky Kalogera, Kyle A. Rocha

When a compact object is formed in a binary, any mass lost during core collapse will impart a kick on the binary's center of mass. Asymmetries in this mass loss or neutrino emission would impart an additional natal kick on the remnant black hole or neutron star, whether it was formed in a binary or in isolation. While it is well established that neutron star

Q. M. Wargnier, J. Martinez-Sykora, V. H. Hansteen, B. De Pontieu

Our understanding of magnetic reconnection (MR) under chromospheric conditions remains limited. Recent observations have demonstrated the important role of ion-neutral interactions in the dynamics of the chromosphere. Furthermore, the comparison between spectral profiles and synthetic observations of reconnection events suggest that current MHD approaches ap

David Gonzalez, Antonio Montalbán

We introduce the $\omega$-Vaught's conjecture, a strengthening of the infinitary Vaught's conjecture. We believe that if one were to prove the infinitary Vaught's conjecture in a structural way without using techniques from higher recursion theory, then the proof would probably be a proof of the $\omega$-Vaught's conjecture. We show the existence of an equiv

Vitor Hugo Fernandes

In this paper we study the cyclic inverse monoid $\CI_n$ on a set $\Omega_n$ with $n$ elements, i.e. the inverse submonoid of the symmetric inverse monoid on $\Omega_n$ consisting of all restrictions of the elements of a cyclic subgroup of order $n$ acting cyclically on $\Omega_n$. We show that $\CI_n$ has rank $2$ (for $n\geqslant2$) and $n2^n-n+1$ elements

Luiz Renato Fontes, Pablo Almeida Gomes, Maicon Aparecido Pinheiro

We consider a particle moving in continuous time as a Markov jump process; its discrete chain is given by an ordinary random walk on ${\mathbb Z}^d$ , and its jump rate at $({\mathbf x},t)$ is given by a fixed function $\varphi$ of the state of a birth-and-death (BD) process at $\\mathbf x$ on time $t$; BD processes at different sites are independent and ide

Karol Szałowski, Marko Milivojević, Denis Kochan, Martin Gmitra

Proximity-induced fine features and spin-textures of the electronic bands in graphene-based van der Waals heterostructures can be explored from the point of tailoring a twist angle. Here we study spin-orbit coupling and exchange coupling engineering of graphene states in the proximity of 1T-TaS$_2$ not triggering the twist, but a charge density wave in 1T-Ta

Hoang Giang Pham, Ngan Ha Duong, Tien Mai, Thuy Anh Ta

In this paper, we investigate a class of non-convex sum-of-ratios programs relevant to decision-making in key areas such as product assortment and pricing, and facility location and cost planning. These optimization problems, characterized by both continuous and binary decision variables, are highly non-convex and challenging to solve. To the best of our kno

Martin Pawelczyk, Lea Tiyavorabun, Gjergji Kasneci

The goal of algorithmic recourse is to reverse unfavorable decisions (e.g., from loan denial to approval) under automated decision making by suggesting actionable feature changes (e.g., reduce the number of credit cards). To generate low-cost recourse the majority of methods work under the assumption that the features are independently manipulable (IMF). To

Yue Sun, Zhuoming Huang, Honggang Zhang, Xiaohui Liang

In this paper, we explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space. The UAV hovers at various locations in the space, and its onboard radar senor collects raw radar data via scanning the space with Synthetic Aperture Radar (SAR) operation. The radar data is sent to a dee

Oscar Méndez-Lucio, Christos Nicolaou, Berton Earnshaw

Models that accurately predict properties based on chemical structure are valuable tools in drug discovery. However, for many properties, public and private training sets are typically small, and it is difficult for the models to generalize well outside of the training data. Recently, large language models have addressed this problem by using self-supervised

Tobias Holicki, Carsten W. Scherer

Starting from a linear fractional representation of a linear system affected by constant parametric uncertainties, we demonstrate how to enhance standard robust analysis tests by taking available (noisy) input-output data of the uncertain system into account. Our approach relies on lifting the system and the construction of data-dependent multipliers. It lea

Giuliano Boava, Gilles G. de Castro, Daniel Gonçalves, Daniel W. van Wyk

We introduce two algebras associated with a subshift over an arbitrary alphabet. One is unital and the other not necessarily. We focus on the unital case and describe a conjugacy between Ott-Tomforde-Willis subshifts in terms of a homeomorphism between the Stone duals of suitable Boolean algebras, and in terms of a diagonal-preserving isomorphism of the asso

Pouria Razzaghi, Amin Tabrizian, Wei Guo, Shulu Chen

Compared with model-based control and optimization methods, reinforcement learning (RL) provides a data-driven, learning-based framework to formulate and solve sequential decision-making problems. The RL framework has become promising due to largely improved data availability and computing power in the aviation industry. Many aviation-based applications can

Li Zhang, Yan Zhu, Yifeng Gao, Jessica Lin

Time series motif discovery has been a fundamental task to identify meaningful repeated patterns in time series. Recently, time series chains were introduced as an expansion of time series motifs to identify the continuous evolving patterns in time series data. Informally, a time series chain (TSC) is a temporally ordered set of time series subsequences, in

Zeqi Gu, Wenqi Xian, Noah Snavely, Abe Davis

We propose "factor matting", an alternative formulation of the video matting problem in terms of counterfactual video synthesis that is better suited for re-composition tasks. The goal of factor matting is to separate the contents of video into independent components, each visualizing a counterfactual version of the scene where contents of other components h

Pablo Barceló, Mauricio Duarte, Cristóbal Rojas, Tomasz Steifer

In peer review systems, reviewers are often asked to evaluate various features of submissions, such as technical quality or novelty. A score is given to each of the predefined features and based on these the reviewer has to provide an overall quantitative recommendation. It may be assumed that each reviewer has her own mapping from the set of features to a r

Grzegorz Rypeść, Grzegorz Kurzejamski, Jacek Komorowski

This paper presents a robust end-to-end method for sports cameras extrinsic parameters optimization using a novel evolution strategy. First, we developed a neural network architecture for an edge or area-based segmentation of a sports field. Secondly, we implemented the evolution strategy, which purpose is to refine extrinsic camera parameters given a single

Isaac Benavides-Mata, Saul Calderon-Ramirez

Semi-supervised learning (SSL) leverages both labeled and unlabeled data for training models when the labeled data is limited and the unlabeled data is vast. Frequently, the unlabeled data is more widely available than the labeled data, hence this data is used to improve the level of generalization of a model when the labeled data is scarce. However, in real

Simanta Deb Turja, Mohammad Imrul Jubair, Md. Shafiur Rahman, Md. Hasib Al Zadid

Cartoons are an important part of our entertainment culture. Though drawing a cartoon is not for everyone, creating it using an arrangement of basic geometric primitives that approximates that character is a fairly frequent technique in art. The key motivation behind this technique is that human bodies - as well as cartoon figures - can be split down into va

Stefan Tirkas, Haotian Chen, Gabriele Merlo, Frank Jenko

The derivation of an intermediate-scale gyrokinetic-electron theory in nonuniform tokamak plasmas [Chen H. et al 2021 Nucl. Fusion 61 066017] has shown that a Navier-Stokes type nonlinearity couples electron-temperature-gradient (ETG) modes and zonal flow (ZF) modes with wavelengths much shorter than the ion gyroradius but much longer than the electron gyror

Faisal Hamman, Jiahao Chen, Sanghamitra Dutta

Existing regulations prohibit model developers from accessing protected attributes (gender, race, etc.), often resulting in fairness assessments on populations without knowing their protected groups. In such scenarios, institutions often adopt a separation between the model developers (who train models with no access to the protected attributes) and a compli

Emdadul Haque, Md. Faraz Kabir Khan, Mohammad Imrul Jubair, Jarin Anjum

The cover is the face of a book and is a point of attraction for the readers. Designing book covers is an essential task in the publishing industry. One of the main challenges in creating a book cover is representing the theme of the book's content in a single image. In this research, we explore ways to produce a book cover using artificial intelligence base

Sunkyung Park, Ágnes Kóspál, Péter Ábrahám, Fernando Cruz-Sáenz de Miera

Gaia19fct is one of the Gaia-alerted eruptive young stars that has undergone several brightening events. We conducted monitoring observations using multi-filter optical and near-infrared photometry, as well as near-infrared spectroscopy, to understand the physical properties of Gaia19fct and investigate whether it fits into the historically defined two class

Zijian Jin, Duygu Ataman

Statistical language models conventionally implement representation learning based on the contextual distribution of words or other formal units, whereas any information related to the logographic features of written text are often ignored, assuming they should be retrieved relying on the cooccurence statistics. On the other hand, as language models become l

Nouman Zia, Samu-Pekka Ojanen, Jukka Viheriala, Eero Koivusalo

Tunable lasers emitting at a 2-3 $\mu$m wavelength range and compatible with photonic integration platforms are of great interest for sensing applications. To this end, combining GaSb-based semiconductor gain chips with Si$_3$N$_4$ photonic integrated circuits offers an attractive platform. Herein, we exploit the low-loss features of Si$_3$N$_4$ waveguides a

Bader Alshammari, Aaron Welters

In this paper, we consider the spectral theory of linear differential-algebraic equations (DAEs) for periodic DAEs in canonical form, i.e., \begin{equation*} J \frac{df}{dt}+Hf=\lambda Wf, \end{equation*} where $J$ is a constant skew-Hermitian $n\times n$ matrix that is not invertible, both $H=H(t)$ and $W=W(t)$ are $d$-periodic Hermitian $n\times n$-matrice

Pingchuan Ma, Niko Moritz, Stavros Petridis, Christian Fuegen

In this work, we propose a streaming AV-ASR system based on a hybrid connectionist temporal classification (CTC)/attention neural network architecture. The audio and the visual encoder neural networks are both based on the conformer architecture, which is made streamable using chunk-wise self-attention (CSA) and causal convolution. Streaming recognition with

Isabel Xu, Nicole Lewis, Xiaofeng Wang, James Daniel Brandenburg

The fundamental nature of Dark Matter remains one of the major mysteries of modern physics. Some models postulate the existence of a massive gauge boson, a "dark photon" ($A^\prime$), that may allow Dark Matter particles to interact with Standard Model particles. Ultra-relativistic heavy-ion collisions produce highly Lorentz-contracted electromagnetic fields

Stefano Pini, Christian S. Perone, Aayush Ahuja, Ana Sofia Rufino Ferreira

The goal of autonomous vehicles is to navigate public roads safely and comfortably. To enforce safety, traditional planning approaches rely on handcrafted rules to generate trajectories. Machine learning-based systems, on the other hand, scale with data and are able to learn more complex behaviors. However, they often ignore that agents and self-driving vehi

Austin Atsango, Nathaniel L. Diamant, Ziqing Lu, Tommaso Biancalani

Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS), that implicitly learns a three-dimensional repr

Eoin Ó Colgáin, M. M. Sheikh-Jabbari, Rance Solomon

We construct observational Hubble $H(z)$ and angular diameter distance $D_{A}(z)$ mock data with baseline Planck $\Lambda$CDM input values, before fitting the $\Lambda$CDM model to study evolution of probability density functions (PDFs) of best fit cosmological parameters $(H_0, \Omega_m, \Omega_k)$ across redshift bins. We find that PDF peaks only agree wit

Moawia Alghalith

We discuss and analyze some recent literature that introduced pioneering methods in econophysics. In doing so, we review recent methods of estimating the volatility, volatility of volatility, and probability densities. These methods will have useful applications in econophysics and finance.

Bin Xiao, Yakup Akkaya, Murat Simsek, Burak Kantarci

The global Information and Communications Technology (ICT) supply chain is a complex network consisting of all types of participants. It is often formulated as a Social Network to discuss the supply chain network's relations, properties, and development in supply chain management. Information sharing plays a crucial role in improving the efficiency of the su

Siddharth Nayak, Kenneth Choi, Wenqi Ding, Sydney Dolan

We consider the problem of multi-agent navigation and collision avoidance when observations are limited to the local neighborhood of each agent. We propose InforMARL, a novel architecture for multi-agent reinforcement learning (MARL) which uses local information intelligently to compute paths for all the agents in a decentralized manner. Specifically, InforM

Maya Dotan, Gilad Stern, Aviv Zohar

Consider an asynchronous system where each node begins with some point in $\mathbb{R}^m$. Given some fixed $\epsilon > 0$, we wish to have every nonfaulty node eventually output a point in $\mathbb{R}^m$, where all outputs are within distance $\epsilon$ of each other, and are within the convex hull of the original nonfaulty inputs. This problem, when some of

Jacek Komorowski, Grzegorz Kurzejamski

The paper presents a multi-camera tracking method intended for tracking soccer players in long shot video recordings from multiple calibrated cameras installed around the playing field. The large distance to the camera makes it difficult to visually distinguish individual players, which adversely affects the performance of traditional solutions relying on th

Cameron Strachan

This purpose of this paper is to prove the following result: let phi be a strictly convex, smooth, convex body in the Euclidean plane, if the intersection of n translates of phi has a non-empty interior, and all of the translates contribute to the intersection, then the intersection of these n translates will have exactly n points of singularity along its bo

Venkat Sripad Ganti

This paper gives a proof of the H\"older Inequality by using supersolutions of the Heat Equation. The proof is based on a monotonicity formula for the heat equation presented in Tobias Colding's lectures at MIT.

Vighnesh Nagpal, Sarah Blunt, Brendan P. Bowler, Trent J. Dupuy

Orbital eccentricities directly trace the formation mechanisms and dynamical histories of substellar companions. Here, we study the effect of hyperpriors on the population-level eccentricity distributions inferred for the sample of directly imaged substellar companions (brown dwarfs and cold Jupiters) from hierarchical Bayesian modeling (HBM). We find that t