March 2024 arXiv papers — page 88

King-Yeung Lam, Ray Lee

Inspired by recent studies associating shifting temperature conditions with changes in the efficiency of predator species in converting their prey to offspring, we propose a predator-prey model of reaction-diffusion type to analyze the consequence of such effects on the population dynamics and spread of species. In the model, the predator conversion efficien

Pazit Haim-Kislev, Michael Khanevsky, Asaf Kislev, Daniel Rosen

We prove that the minimal area of a holomorphic triangle whose boundary lies on the union of any three Lagrangian submanifolds is bounded from above by the Lagrangian spectral norm of any pair taken out of the three. We show a relation between this result and the median quasi-state on the 2-sphere. The median quasi-state gives rise to a measure of Poisson no

Juhi Jang, Jiaqi Liu, Matthew Schrecker

We solve the continuation problem for the non-isentropic Euler equations following the collapse of an imploding shock wave. More precisely, we prove that the self-similar G\"uderley imploding shock solutions for a perfect gas with adiabatic exponent $\gamma\in(1,3]$ admit a self-similar extension consisting of two regions of smooth flow separated by an outgo

Carlos Uriarte, Marco A. Fontelos, Manuel Arrayás

We develop and implement numerically a phase field model for the evolution and detachment of a gas bubble resting on a solid substrate and surrounded by a viscous liquid. The bubble has a static contact angle $\theta $ and will be subject to gravitational forces. We compute, as a function of the static contact angle, the cricital Bond number over which bubbl

Yating Lin, Glen Chou, Dmitry Berenson

We propose a method for improving the prediction accuracy of learned robot dynamics models on out-of-distribution (OOD) states. We achieve this by leveraging two key sources of structure often present in robot dynamics: 1) sparsity, i.e., some components of the state may not affect the dynamics, and 2) physical limits on the set of possible motions, in the f

Patanjali Bhamidipati, Advaith Malladi, Manish Shrivastava, Radhika Mamidi

In recent studies, the extensive utilization of large language models has underscored the importance of robust evaluation methodologies for assessing text generation quality and relevance to specific tasks. This has revealed a prevalent issue known as hallucination, an emergent condition in the model where generated text lacks faithfulness to the source and

Jiasheng Shi, Yizhao Zhou, Jing Huang

The Time Since Infection (TSI) models, which use disease surveillance data to model infectious diseases, have become increasingly popular due to their flexibility and capacity to address complex disease control questions. However, a notable limitation of TSI models is their primary reliance on incidence data. Even when hospitalization data are available, exi

William Watson, Lawrence Yong

We explore link prediction as a proxy for automatically surfacing documents from existing literature that might be topically or contextually relevant to a new document. Our model uses transformer-based graph embeddings to encode the meaning of each document, presented as a node within a citation network. We show that the semantic representations that our mod

Bang Nguyen, Mengxia Yu, Yun Huang, Meng Jiang

Reference-based metrics such as BLEU and BERTScore are widely used to evaluate question generation (QG). In this study, on QG benchmarks such as SQuAD and HotpotQA, we find that using human-written references cannot guarantee the effectiveness of the reference-based metrics. Most QG benchmarks have only one reference; we replicate the annotation process and

Stein Vidar Haugan, Terje Fredvik

Until the advent of the SOLARNET recommendations, metadata sharing of simulated data within the Solar Physics community has been mostly on a "private communication" basis, with the description of the data format and content conveyed in an ad hoc manner. This document aims to amend this situation by establishing recommendations for representing such data and

William Watson, Bo Liu

Table extraction has long been a pervasive problem in financial services. This is more challenging in the image domain, where content is locked behind cumbersome pixel format. Luckily, advances in deep learning for image segmentation, OCR, and sequence modeling provides the necessary heavy lifting to achieve impressive results. This paper presents an end-to-

C. P. Burgess, Thomas Colas, R. Holman, Greg Kaplanek

We compute the rate with which unobserved fields decohere other fields to which they couple, both in flat space and in de Sitter space, for spectator scalar fields prepared in their standard adiabatic vacuum. The process is very efficient in de Sitter space once the modes in question pass outside the Hubble scale, displaying the tell-tale phenomenon of secul

Tri Nguyen, Huong Nguyen, Ahmad Ijaz, Saeid Sheikhi

The rapid integration of Generative AI (GenAI) and Large Language Models (LLMs) in sectors such as education and healthcare have marked a significant advancement in technology. However, this growth has also led to a largely unexplored aspect: their security vulnerabilities. As the ecosystem that includes both offline and online models, various tools, browser

J. P. G. Venassi, V. S. Bagnato, G. D. Telles

Bose-Einstein condensates have been the subject of intense research in recent years due to their potential applications in quantum computing and many other areas. However, measuring the shape and size of out-of-equilibrium Bose-Einstein condensates is a challenging task that requires sophisticated image processing techniques. We propose to study perturbed BE

Ibrahim Shaer, Soodeh Nikan, Abdallah Shami

The hyper-parameter optimization (HPO) process is imperative for finding the best-performing Convolutional Neural Networks (CNNs). The automation process of HPO is characterized by its sizable computational footprint and its lack of transparency; both important factors in a resource-constrained Internet of Things (IoT) environment. In this paper, we address

Nishant Jain, Arun S. Suggala, Pradeep Shenoy

Learned reweighting (LRW) approaches to supervised learning use an optimization criterion to assign weights for training instances, in order to maximize performance on a representative validation dataset. We pose and formalize the problem of optimized selection of the validation set used in LRW training, to improve classifier generalization. In particular, w

Liangting Wu, Roberto Tron

Inverse kinematics (IK) is central to robot control and motion planning, yet its nonlinear kinematic mapping makes it inherently nonconvex and particularly challenging under complex constraints. We present IKSPARK (Inverse Kinematics using Semidefinite Programming And RanK minimization), an obstacle-aware IK solver for robots with diverse morphologies, inclu

Vítor H. Fernandes

Following the new description of an oriented full transformation on a finite chain given recently by Higgins and Vernitski in "Orientation-preserving and orientation-reversing mappings: a new description", Semigroup Forum 104 (2022), 509--514, in this short note we present a refinement of this description which is extendable to partial transformations and to

Karl Fiteni, Sven De Rijcke, Victor P. Debattista, Joseph Caruana

It is well established that stellar discs are destabilized by sharp features in their phase space, driving recurrent spiral modes. We explore the extent to which surface-density breaks in disc galaxies - which represent sharp changes in the gradient of the disc density - drive new spiral modes. We employ linear perturbation theory to investigate how disc bre

Elia Buono, Gabriel G. Katul, Davide Poggi

The drag coefficient $C_d$ for a rigid and uniformly distributed rod canopy covering a sloping channel following the instantaneous collapse of a dam was examined using flume experiments. The measurements included space $x$ and time $t$ high resolution images of the water surface $h(x,t)$ for multiple channel bed slopes $S_o$ and water depths behind the dam $

Kelly Isham, Laura Monroe, Kartik Lakhotia, Aleyah Dawkins

A star-product operation may be used to create large graphs from smaller factor graphs. Network topologies based on star-products demonstrate several advantages including low-diameter, high scalability, modularity and others. Many state-of-the-art diameter-2 and -3 topologies~(Slim Fly, Bundlefly, PolarStar etc.) can be represented as star products. In this

Yuguang Meng, Jason W. Allen, Vahid Khalilzad Sharghi, Deqiang Qiu

Purpose: To develop an efficient navigator-based motion and temporal B0 shift correction technique for 3D multi-echo gradient-echo (ME-GRE) MRI for quantitative susceptibility mapping (QSM) and R2* mapping. Theory and Methods: A dual-echo 3D spiral navigator was designed to interleave with the Cartesian ME-GRE acquisitions, allowing the acquisition of both l

Dimitrios Karageorgiou, Giorgos Kordopatis-Zilos, Symeon Papadopoulos

In this work, we introduce OMG-Fuser, a fusion transformer-based network designed to extract information from various forensic signals to enable robust image forgery detection and localization. Our approach can operate with an arbitrary number of forensic signals and leverages object information for their analysis -- unlike previous methods that rely on fusi

Ibrahim Shaer, Abdallah Shami

Cyber-security attacks pose a significant threat to the operation of autonomous systems. Particularly impacted are the Heating, Ventilation, and Air Conditioning (HVAC) systems in smart buildings, which depend on data gathered by sensors and Machine Learning (ML) models using the captured data. As such, attacks that alter the readings of these sensors can se

Miguel Manzano, Marc Mars

In this work, we study null hypersurfaces admitting a privileged vector field $\eta$ which is null and tangent at the hypersurface. We derive an identity that relates the deformation tensor of $\eta$ with tensor fields codifying the intrinsic and extrinsic geometry of the hypersurface. This is done without imposing restrictions either on the topology of the

Ahana Ghosh, Liina Malva, Adish Singla

Computational thinking (CT) and problem-solving skills are increasingly integrated into K-8 school curricula worldwide. Consequently, there is a growing need to develop reliable assessments for measuring students' proficiency in these skills. Recent works have proposed tests for assessing these skills across various CT concepts and practices, in particular,

Qingsong Xu, Yilei Shi, Jonathan Bamber, Chaojun Ouyang

Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to accurately forecast flood crests and issue time-critical hazard warnings. In this work, we build a fast, stable, accurate, resolution-invariant, and geometry-adaptative flood modeli

Yuan Liu, Jack Hau Yung Lo, Janine K. Nunes, Howard A. Stone

There are many fields where it is of interest to measure the elastic moduli of tiny fragile fibers, such as filamentous bacteria, actin filaments, DNA, carbon nanotubes, and functional microfibers. The elastic modulus is typically deduced from a sophisticated tensile test under a microscope, but the throughput is low and limited by the time-consuming and ski

Chinmaya Mishra, Anuj Nandanwar, Sashikala Mishra

With the recent advancements in the field of robotics and the increased focus on having general-purpose robots widely available to the general public, it has become increasingly necessary to pursue research into Human-robot interaction (HRI). While there have been a lot of works discussing frameworks for teaching HRI in educational institutions with a few in

Yifan Ding, Michael Yankoski, Tim Weninger

Information Extraction refers to a collection of tasks within Natural Language Processing (NLP) that identifies sub-sequences within text and their labels. These tasks have been used for many years to link extract relevant information and to link free text to structured data. However, the heterogeneity among information extraction tasks impedes progress in t

Guillermo F. Peñas, Ricardo Puebla, Juan José García-Ripoll

In this article, we consider a realistic waveguide implementation of a quantum network that serves as a testbed to show how to maximize the storage and manipulation of quantum information in QED setups. We analyze two approaches using wavepacket engineering and quantum state transfer protocols. First, we propose and design a family of orthogonal photons in t

Souvik Bhattacharya, Jonathan Boyd, Sven Reichardt, Valentin Allard

Doped semiconductors can exhibit metallic-like properties ranging from superconductivity to tunable localized surface plasmon resonances. Diamond is a wide-bandgap semiconductor that is rendered electronically active by incorporating a hole dopant, boron. While the effects of boron doping on the electronic band structure of diamond are well-studied, any link

Dinorah Barbosa, Rodrigo von Marttens, Javier Gonzalez, Jailson Alcaniz

It is well-known that Einstein's equations constrain only the total energy-momentum tensor of the cosmic substratum, without specifying the characteristics of its individual constituents. Consequently, cosmological models featuring distinct decompositions within the dark sector, while sharing identical values for the sum of dark components' energy-momentum t

M. Griggio, M. Libralato, A. Bellini, L. R. Bedin

Aims. We use near-infrared, ground-based data from the VISTA Variables in the Via Lactea (VVV) survey to indirectly extend the astrometry provided by the Gaia catalog to objects in heavily-extincted regions towards the Galactic bulge and plane that are beyond Gaia's reach. Methods. We make use of the state-of-the-art techniques developed for high-precision a

Santiago Mendez-Moreno

This study introduces the syntropy function ($S_N$) and expectancy function ($E_N$), derived from the novel function $\phi$, to provide a refined perspective on complexity, extending beyond conventional entropy analysis. $S_N$ is designed to detect localized coherent events, whereas $E_N$ encapsulates expected system behaviors, offering a comprehensive frame

Jiaqi Yan, Hideaki Ishii

In this paper, we consider the synchronization of heterogeneous pulse-coupled oscillators (PCOs), where some of the oscillators might be faulty or malicious. The oscillators interact through identical pulses at discrete instants and evolve continuously with different frequencies otherwise. Despite the presence of misbehaviors, benign oscillators aim to reach

Vincent Lahoche, Dine Ousmane Samary

This paper, as a continuation of our previous investigation [arXiv:2403.07577] aims to study the glassy random matrices with quenched Wigner disorder. In this previous work, we have constructed a renormalization group based on the effective deterministic kinetic spectrum emerging from large $N$ limit, and we extended approximate solutions using standard vert

R. Inanc Baykur, A. Nemethi, O. Plamenevskaya

Given a link of a normal surface singularity with its canonical contact structure, we compare the collection of its Stein fillings to its Milnor fillings (that is, Milnor fibers of possible smoothings). We prove that, unlike Stein fillings, Milnor fillings of a given link have bounded topology; for links of sandwiched singularities, we further establish that

Nanda Kishor Panda, Na Li, Simon H. Tindemans

Aggregate peak Electric Vehicle (EV) charging demand is a matter of growing concern for network operators as it severely limits the network's capacity, preventing its reliable operation. Various tariff schemes have been proposed to limit peak demand by incentivizing flexible asset users to shift their demand from peak periods. However, fewer studies quantify

Gerry Chen, Tristan Al-Haddad, Frank Dellaert, Seth Hutchinson

Robot art presents an opportunity to both showcase and advance state-of-the-art robotics through the challenging task of creating art. Creating large-scale artworks in particular engages the public in a way that small-scale works cannot, and the distinct qualities of brush strokes contribute to an organic and human-like quality. Combining the large scale of

Hongjie Chen, Jingqiu Ding, Tommaso d'Orsi, Yiding Hua

We develop the first pure node-differentially-private algorithms for learning stochastic block models and for graphon estimation with polynomial running time for any constant number of blocks. The statistical utility guarantees match those of the previous best information-theoretic (exponential-time) node-private mechanisms for these problems. The algorithm

Ramon Abilio, Guilherme Palermo Coelho, Ana Estela Antunes da Silva

Since 2018, when the Transformer architecture was introduced, Natural Language Processing has gained significant momentum with pre-trained Transformer-based models that can be fine-tuned for various tasks. Most models are pre-trained on large English corpora, making them less applicable to other languages, such as Brazilian Portuguese. In our research, we id

Boqi Chen, Junier Oliva, Marc Niethammer

Medical records often consist of different modalities, such as images, text, and tabular information. Integrating all modalities offers a holistic view of a patient's condition, while analyzing them longitudinally provides a better understanding of disease progression. However, real-world longitudinal medical records present challenges: 1) patients may lack

Antonio Lopez, David Fridovich-Keil

Recent methods using Reinforcement Learning (RL) have proven to be successful for training intelligent agents in unknown environments. However, RL has not been applied widely in real-world robotics scenarios. This is because current state-of-the-art RL methods require large amounts of data to learn a specific task, leading to unreasonable costs when deployin

Y. Y. Shi

In our study, we investigated bright solitons, dark solitons, and quantum droplets in quasi-one-dimensional dipolar Bose gases, and further validated the crossover and coexistence of quantum droplets and solitons using the Lieb-Liniger energy within the framework of local density approximation. Increasing the particle number transforms the bright dipolar sol

E. Kammoun, I. E. Papadakis, M. Dovčiak, C. Panagiotou

Recently, nearby active galactic nuclei (AGN) have been subject to long X-rays/UV/optical monitoring campaigns. These campaigns reveal a strong correlation between the various UV and optical bands, with time lags increasing with wavelength. In a series of papers, we demonstrated that a scenario in which a central X-ray source illuminates the accretion disc e

Melanie Mathys, Marco Willi, Michael Graber, Raphael Meier

The evolution of artificial intelligence (AI) has catalyzed a transformation in digital content generation, with profound implications for cyber influence operations. This report delves into the potential and limitations of generative deep learning models, such as diffusion models, in fabricating convincing synthetic images. We critically assess the accessib

Johannes J. Brust

For minimization problems without 2nd derivative information, methods that estimate Hessian matrices can be very effective. However, conventional techniques generate dense matrices that are prohibitive for large problems. Limited-memory compact representations express the dense arrays in terms of a low rank representation and have become the state-of-the-art

Frédéric Barbaresco, Laurent Rioux, Christophe Labreuche, Michel Nowak

With the support of the national program on measurements, standards, and evaluation of quantum technologies MetriQs-France, a part of the French national quantum strategy, the BACQ project is dedicated to application-oriented benchmarks for quantum computing. The consortium gathering THALES, EVIDEN, an Atos business, CEA, CNRS, TERATEC, and LNE aims at estab

Dengwang Tang, Vijay G. Subramanian

We consider a two-player dynamic information design problem between a principal and a receiver -- a game is played between the two agents on top of a Markovian system controlled by the receiver's actions, where the principal obtains and strategically shares some information about the underlying system with the receiver in order to influence their actions. In

Lucas Böttcher, Luis L. Fonseca, Reinhard C. Laubenbacher

The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology

Jiaxu Xing, Angel Romero, Leonard Bauersfeld, Davide Scaramuzza

Learning visuomotor policies for agile quadrotor flight presents significant difficulties, primarily from inefficient policy exploration caused by high-dimensional visual inputs and the need for precise and low-latency control. To address these challenges, we propose a novel approach that combines the performance of Reinforcement Learning (RL) and the sample

Yunxiao Shi, Manish Kumar Singh, Hong Cai, Fatih Porikli

In this paper, we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. Specifically, we first enhance a baseline convolutional depth completion model by applying attention to 2D features in the bottleneck and skip connections. This effectively improves

Yanli Zhou, Brenden M. Lake, Adina Williams

The ability to learn and compose functions is foundational to efficient learning and reasoning in humans, enabling flexible generalizations such as creating new dishes from known cooking processes. Beyond sequential chaining of functions, existing linguistics literature indicates that humans can grasp more complex compositions with interacting functions, whe

Olga Katkova, Boris Shapiro, Anna Vishnyakova

In this paper, we prove a number of results providing either necessary or sufficient conditions guaranteeing that the number of real roots of real polynomials of a given degree is either less or greater than a given number. We also provide counterexamples to two earlier conjectures refining Descartes rule of signs.

Julio Benitez, Waldemar W. Koczkodaj, Adam Kowalczyk

Orthogonalization is one of few mathematical methods conforming to mathematical standards for approximation. Finding a consistent PC matrix of a given an inconsistent PC matrix is the main goal of a pairwise comparisons method. We introduce an orthogonalization for pairwise comparisons matrix based on a generalized Frobenius inner matrix product. The propose

Dhia Elhaq Rzig, Alaa Houerbi, Rahul Ghanshyam Chavan, Foyzul Hassan

The growing popularity of machine learning (ML) and the integration of ML components with other software artifacts has led to the use of continuous integration and delivery (CI/CD) tools, such as Travis CI, GitHub Actions, etc. that enable faster integration and testing for ML projects. Such CI/CD configurations and services require synchronization during th

Florian Philipp Stilz, Mert Asim Karaoglu, Felix Tristram, Nassir Navab

Reconstruction of endoscopic scenes is an important asset for various medical applications, from post-surgery analysis to educational training. Neural rendering has recently shown promising results in endoscopic reconstruction with deforming tissue. However, the setup has been restricted to a static endoscope, limited deformation, or required an external tra

Ahmad Hassanpour, Fatemeh Jamalbafrani, Bian Yang, Kiran Raja

Face inpainting, the technique of restoring missing or damaged regions in facial images, is pivotal for applications like face recognition in occluded scenarios and image analysis with poor-quality captures. This process not only needs to produce realistic visuals but also preserve individual identity characteristics. The aim of this paper is to inpaint a fa

Nusrat Zahan, Philipp Burckhardt, Mikola Lysenko, Feross Aboukhadijeh

Existing malicious code detection techniques demand the integration of multiple tools to detect different malware patterns, often suffering from high misclassification rates. Therefore, malicious code detection techniques could be enhanced by adopting advanced, more automated approaches to achieve high accuracy and a low misclassification rate. The goal of t

Thomas Garrison, Marijn J. H. Heule, Bernardo Subercaseaux

We present and analyze PackIt!, a turn-based game consisting of packing rectangles on an $n \times n$ grid. PackIt! can be easily played on paper, either as a competitive two-player game or in \emph{solitaire} fashion. On the $t$-th turn, a rectangle of area $t$ or $t+1$ must be placed in the grid. In the two-player format of PackIt! whichever player places

Margaret Hansen, Uland Wong, Terrence Fong

We present the POLAR Traverse Dataset: a dataset of high-fidelity stereo pair images of lunar-like terrain under polar lighting conditions designed to simulate a straight-line traverse. Images from individual traverses with different camera heights and pitches were recorded at 1 m intervals by moving a suspended stereo bar across a test bed filled with regol

Josip Josifovski, Sayantan Auddy, Mohammadhossein Malmir, Justus Piater

Domain Randomization (DR) is commonly used for sim2real transfer of reinforcement learning (RL) policies in robotics. Most DR approaches require a simulator with a fixed set of tunable parameters from the start of the training, from which the parameters are randomized simultaneously to train a robust model for use in the real world. However, the combined ran

Barbara Jäger, Alexander Karlberg, Simon Reinhardt

We present an update of an existing implementation of $WZjj$ production via vector-boson scattering in the framework of the POWHEG BOX program. In particular, previously unavailable semi-leptonic and fully hadronic decay modes of the intermediate vector bosons are provided, and operators of dimension six in an effective-field theory approach to account for p

Maxence Thévenet, Igor A. Andriyash, Luca Fedeli, Ángel Ferran Pousa

Using realistic laser profiles for simulations of laser-plasma interaction is critical to reproduce experimental measurements, but the interface between experiments and simulations can be challenging. Similarly, start-to-end simulations with different codes may require error-prone manipulations to convert between different representations of a laser pulse. I

Kirby S. Heck, Michael F. Howland

Wind turbines operate in the atmospheric boundary layer (ABL), where Coriolis effects are present. As wind turbines with larger rotor diameters are deployed, the wake structures that they create in the ABL also increase in length. Contemporary utility-scale wind turbines operate at rotor diameter-based Rossby numbers, the nondimensional ratio between inertia

Damiano F. G. Fiorillo, Georg Raffelt

A dense neutrino gas exhibiting angular crossings in the electron lepton number is unstable and develops fast flavor conversions. Instead of assuming an unstable configuration from the onset, we imagine that the system is externally driven toward instability. We use the simplest model of two neutrino beams initially of different flavor that either suddenly a

Stefano Zampini, Umberto Zerbinati, George Turkiyyah, David Keyes

In recent years, we have witnessed the emergence of scientific machine learning as a data-driven tool for the analysis, by means of deep-learning techniques, of data produced by computational science and engineering applications. At the core of these methods is the supervised training algorithm to learn the neural network realization, a highly non-convex opt

Tian-Yi Zhou, Namjoon Suh, Guang Cheng, Xiaoming Huo

Motivated by the abundance of functional data such as time series and images, there has been a growing interest in integrating such data into neural networks and learning maps from function spaces to R (i.e., functionals). In this paper, we study the approximation of functionals on reproducing kernel Hilbert spaces (RKHS's) using neural networks. We establis

Chen-An Chou, Tianyi Yu

Pipedreams are combinatorial objects that compute Grothendieck polynomials. We introduce a new combinatorial object that naturally recast the pipedream formula. From this, we obtain the first direct combinatorial formula for the top degree components of Grothendieck polynomials, also known as the Castelnuovo-Mumford polynomials. We also prove the inverse fir

Inti Sodemann Villadiego

An experiment in moir\'e MoTe$_2$ bilayers reported the first observation of a topologically ordered state with zero Hall conductivity and half of the edge conductance of a standard time-reversal invariant quantum spin Hall insulator. This state is believed to emerge at total filling one of a pair of bands with Chern numbers $C=\pm1$ related by time reversal

Ryan Plestid, Mark B. Wise

High energy beams incident on a fixed target may scatter against atomic electrons. To a first approximation, one can treat these electrons as at rest. For precision experiments, however, it is important to be able to estimate the size of, and when necessary calculate, sub-leading corrections. We discuss atomic binding corrections to relativistic lepton-elect

Kirill Rudov

We show how fragile stable matchings are in a decentralized one-to-one matching setting. The classical work of Roth and Vande Vate (1990) suggests simple decentralized dynamics in which randomly-chosen blocking pairs match successively. Such decentralized interactions guarantee convergence to a stable matching. Our first theorem shows that, under mild condit

Tornike Karchkhadze, Hassan Salami Kavaki, Mohammad Rasool Izadi, Bryce Irvin

Foley sound generation, the art of creating audio for multimedia, has recently seen notable advancements through text-conditioned latent diffusion models. These systems use multimodal text-audio representation models, such as Contrastive Language-Audio Pretraining (CLAP), whose objective is to map corresponding audio and text prompts into a joint embedding s

Zohar Barak, Anupam Gupta, Inbal Talgam-Cohen

Algorithms with predictions have attracted much attention in the last years across various domains, including variants of facility location, as a way to surpass traditional worst-case analyses. We study the $k$-facility location mechanism design problem, where the $n$ agents are strategic and might misreport their location. Unlike previous models, where pred

Boming Ning, Kiseop Lee

Statistical arbitrage is a prevalent trading strategy which takes advantage of mean reverse property of spread of paired stocks. Studies on this strategy often rely heavily on model assumption. In this study, we introduce an innovative model-free and reinforcement learning based framework for statistical arbitrage. For the construction of mean reversion spre

Andrew Myers, Weiqun Zhang, Ann Almgren, Thierry Antoun

AMReX is a software framework for the development of block-structured mesh applications with adaptive mesh refinement (AMR). AMReX was initially developed and supported by the AMReX Co-Design Center as part of the U.S. DOE Exascale Computing Project, and is continuing to grow post-ECP. In addition to adding new functionality and performance improvements to t

Christian Wendisch, Christian Muentz, Luis Lopes, Erwin Schwab

The central tracking system of the HADES detector, installed at the SIS-18 synchrotron at GSI/Darmstadt (Germany), employs large-area, low-mass drift chambers, featuring Aluminum potential wires and small cell sizes. The chambers in front of the magnetic field, closest to the interaction point, have developed significant self-sustained currents and discharge

Karol Gietka

Vacuum Rabi splitting relies on symmetrical splitting of the common resonance frequency of atoms and the cavity in which the atoms reside. In this work, we argue that vacuum Rabi splitting is a manifestation of virtual light-matter two-mode squeezing. We establish a connection between squeezing parameters of virtual excitations and frequency shifts of the ph

Shahin Atakishiyev, Mohammad Salameh, Randy Goebel

The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles (AVs), largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integrated sensor devices. However, a lack of explainability in real-time decisions with contemporary learning

Pacheco-Sanjuan Alejandro, Kumar Pradeep, Barraza-Lopez Salvador

The recent discovery of ferroelectric behavior in few-layer materials, accompanied by the observation of antipolar domains in hexagonal boron nitride and transition metal dichalcogenide moir\'e bilayers, is paving the way for revolutionary advancements in the generation and manipulation of intrinsic electric dipoles through stacking. In addition, these cutti

Souvik Mondal, Michael A. Sauer, Matthias Heyden

We aim to automatize the identification of collective variables to simplify and speed up enhanced sampling simulations of conformational dynamics in biomolecules. We focus on anharmonic low-frequency vibrations that exhibit fluctuations on timescales faster than conformational transitions but describe a path of least resistance towards structural change. A k

Mengting Wan, Tara Safavi, Sujay Kumar Jauhar, Yujin Kim

Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application. However, most existing methods for producing label taxonomies and building text-based label classifiers still rely heavily on domain expertise and manual curation, making the p

Ali Karami, Thi Kieu Khanh Ho, Narges Armanfard

Skeleton-based video anomaly detection (SVAD) is a crucial task in computer vision. Accurately identifying abnormal patterns or events enables operators to promptly detect suspicious activities, thereby enhancing safety. Achieving this demands a comprehensive understanding of human motions, both at body and region levels, while also accounting for the wide v

Weikang Zhou, Xiao Wang, Limao Xiong, Han Xia

Jailbreak attacks are crucial for identifying and mitigating the security vulnerabilities of Large Language Models (LLMs). They are designed to bypass safeguards and elicit prohibited outputs. However, due to significant differences among various jailbreak methods, there is no standard implementation framework available for the community, which limits compre

Entong Su, Chengzhe Jia, Yuzhe Qin, Wenxuan Zhou

Using tactile sensors for manipulation remains one of the most challenging problems in robotics. At the heart of these challenges is generalization: How can we train a tactile-based policy that can manipulate unseen and diverse objects? In this paper, we propose to perform Reinforcement Learning with only visual tactile sensing inputs on diverse objects in a

Sabyasachi Chakraborty, Aritra Gupta, Miguel Vanvlasselaer

Compact stellar objects like supernovae and neutron stars are believed to cool by emitting axions predominantly via axion bremsstrahlung ($NN \to NNa$), pion conversion ($\pi^- p^+ \to N a$) and photo-production ($\gamma N \to N a$). In this paper, we study in detail the photo-production channel, from the unavoidable anomaly induced Wess-Zumino-Witten term $

Aristides I. Kechriniotis

Using the properties of the ideal of the coordinate Hermite interpolation on n-dimensional grid [4], we prove that the extension k in k[x1, x2, ..., xn] / (f1(x1), ..., fn(xn)) has a primitive element if and only if at most one of the univariate polynomials f1, ..., fn is inseparable. This result lead to some Corollaries related to the existence of primitive

Sarah Matta, Mathieu Lamard, Philippe Zhang, Alexandre Le Guilcher

Numerous Deep Learning (DL) classification models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and implementation, which encourage healthcare institutions to adopt them, a fundamental questions remain: how can these

Mohammad Jafari, Yimeng Zhang, Yihua Zhang, Sijia Liu

As machine learning tasks continue to evolve, the trend has been to gather larger datasets and train increasingly larger models. While this has led to advancements in accuracy, it has also escalated computational costs to unsustainable levels. Addressing this, our work aims to strike a delicate balance between computational efficiency and model accuracy, a p

Jianfei He, Zheng Zhu

We construct families of rational functions $f \colon \bP^1_k \to \bP^1_k$ of degree $d \geq 2$ over a perfect field $k$ whose associated fixed-point processes fail to be martingales. Conversely, for any normal variety $X \subset \bP^N_{\overline{k}}$ and a finite, generically \'etale morphism $f \colon X \to X$, we establish geometric conditions on the crit

Maxwell Drimmer, Sjoerd Telkamp, Felix L. Fischer, Ines C. Rodrigues

The performance of superconducting microwave circuits is strongly influenced by the material properties of the superconducting film and substrate. While progress has been made in understanding the importance of surface preparation and the effect of surface oxides, the complex effect of superconductor film structure on microwave losses is not yet fully unders

Johan Bylin, Vassilios Kapaklis, Gunnar K. Pálsson

We demonstrate the feasibility of obtaining accurate pair distribution functions of thin amorphous films down to 80 nm, using modern laboratory-based x-ray sources. The pair distribution functions are obtained using a single diffraction scan (one-shot) without the requirement of additional scans of the substrate or of the air. By using a crystalline substrat

Daniel Borrajo, Manuela Veloso

Intelligent robots need to generate and execute plans. In order to deal with the complexity of real environments, planning makes some assumptions about the world. When executing plans, the assumptions are usually not met. Most works have focused on the negative impact of this fact and the use of replanning after execution failures. Instead, we focus on the p

Arijit Das, Tanmoy Nandi, Prasanta Saha, Suman Das

Financial market like the price of stock, share, gold, oil, mutual funds are affected by the news and posts on social media. In this work deep learning based models are proposed to predict the trend of financial market based on NLP analysis of the twitter handles of leaders of different fields. There are many models available to predict financial market base

Divya Pandey, Sugata Kaviraj, Kanak Saha, Saurabh Sharma

Recent surveys have demonstrated the widespread presence of UV emission in early-type galaxies (ETGs), suggesting the existence of star formation in many of these systems. However, potential UV contributions from old and young stars, together with model uncertainties, makes it challenging to confirm the presence of young stars using integrated photometry alo

László Németh

Several articles deal with tilings with various shapes, and also a very frequent type of combinatorics is to examine the walks on graphs or on grids. We combine these two things and give the numbers of the shortest walks crossing the tiled $(1\times n)$ and $(2\times n)$ square grids by covering them with squares and dominoes. We describe these numbers not o

Samyajoy Pal, Christian Heumann

This study tackles the efficient estimation of Kullback-Leibler (KL) Divergence in Dirichlet Mixture Models (DMM), crucial for clustering compositional data. Despite the significance of DMMs, obtaining an analytically tractable solution for KL Divergence has proven elusive. Past approaches relied on computationally demanding Monte Carlo methods, motivating o

Diksha Gupta

In this paper, I obtain an $S$-type fuzzy point when two fuzzy numbers for two independent variables and a corresponding fuzzy number for the dependent variable are given. A comprehensive study on a conceptualization of a fuzzy plane as a collection of fuzzy numbers, or fuzzy points is proposed. A perpendicular fuzzy distance from a fuzzy point to a fuzzy pl

S. Fornasier, A. Wargnier, P. H. Hasselmann, D. Tirsch

This study aims to analyze Phobos' photometric properties using Mars Express mission observations to support the Martian Moons eXploration mission. We analyzed resolved images of Phobos acquired between 2004 and 2022 by the HRSC and the SRC cameras on board the Mars Express spacecraft. We performed photometric analysis using the Hapke model for both integrat