February 2024 arXiv papers — page 139

Vladimir Lazić, Zhixin Xie

A rigid current on a compact complex manifold is a closed positive current whose cohomology class contains only one closed positive current. Rigid currents occur in complex dynamics, algebraic and differential geometry. The goals of the present paper are: (a) to give a systematic treatment of rigid currents, (b) to demonstrate how they appear within the Mini

Lahav Dabah, Tom Tirer

In many classification applications, the prediction of a deep neural network (DNN) based classifier needs to be accompanied by some confidence indication. Two popular approaches for that aim are: 1) Calibration: modifies the classifier's softmax values such that the maximal value better estimates the correctness probability; and 2) Conformal Prediction (CP):

Himanshu Gupta, Aditya Jaiswal

Predicting a fast and accurate model for stock price forecasting is been a challenging task and this is an active area of research where it is yet to be found which is the best way to forecast the stock price. Machine learning, deep learning and statistical analysis techniques are used here to get the accurate result so the investors can see the future trend

Blagoj Mitrevski, Arina Rak, Julian Schnitzler, Chengkun Li

Digital note-taking is gaining popularity, offering a durable, editable, and easily indexable way of storing notes in a vectorized form, known as digital ink. However, a substantial gap remains between this way of note-taking and traditional pen-and-paper note-taking, a practice that is still favored by a vast majority. Our work InkSight, aims to bridge the

Wamiq Reyaz Para, Abdelrahman Eldesokey, Zhenyu Li, Pradyumna Reddy

We introduce an approach for 3D head avatar generation and editing with multi-modal conditioning based on a 3D Generative Adversarial Network (GAN) and a Latent Diffusion Model (LDM). 3D GANs can generate high-quality head avatars given a single or no condition. However, it is challenging to generate samples that adhere to multiple conditions of different mo

Thomas A. Lasko, John M. Still, Thomas Z. Li, Marco Barbero Mota

Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments. With a large enough dataset, it may be possible to use unsupervised machine learning to define clinical disease patterns more precisely. We present an approach to learning these patterns by using probabilistic ind

Jiří Janoš, Joao Pedro Figueira Nunes, Daniel Hollas, Petr Slavíček

This work is part of a prediction challenge that invited theoretical/computational chemists to predict the photochemistry of cyclobutanone in the gas phase, excited at 200 nm by a laser pulse, and the expected signal that will be recorded during a time-resolved megaelectronvolt ultrafast electron diffraction (MeV-UED). We present here our theoretical predict

Eleanor Archer, Matan Shalev

In this paper we introduce a new model of random spanning trees that we call choice spanning trees, constructed from so-called choice random walks. These are random walks for which each step is chosen from a subset of random options, according to some pre-defined rule. The choice spanning trees are constructed by running a choice modified version of Wilson's

Miao Liu, Sheng Meng

A close look of Google's GNoME inorganic materials dataset [Nature 624, 80 (2023)], and 11 things you would like to know.

Shuqi He, Yuqing Chen, Yuxin Xia, Yichun Li

Infographics are visual representations designed for efficient and effective communication of data and knowledge. One crucial aspect of infographic design is the interplay between text and visual elements, particularly in circular visualizations where the textual descriptions can either be embedded within the graphics or placed adjacent to the visual represe

Linjie Li, Zhenyu Wu, Jiaming Liu, Yang Ji

Class-incremental learning is dedicated to the development of deep learning models that are capable of acquiring new knowledge while retaining previously learned information. Most methods focus on balanced data distribution for each task, overlooking real-world long-tailed distributions. Therefore, Long-Tailed Class-Incremental Learning has been introduced,

Di Kevin Gao, Andrew Haverly, Sudip Mittal, Jingdao Chen

Artificial Intelligence (AI) Ethics is a nascent yet critical research field. Recent developments in generative AI and foundational models necessitate a renewed look at the problem of AI Ethics. In this study, we perform a bibliometric analysis of AI Ethics literature for the last 20 years based on keyword search. Our study reveals a three-phase development

Leonard E. C. Romano, Manuel Behrendt, Andreas Burkert

The deposition of energy and momentum by supernova explosions has been subject to numerous studies in the past few decades. However, while there has been some work that focused on the transition from the adiabatic to the radiative stage of a supernova remnant (SNR), the late radiative stage and merging with the interstellar medium (ISM) have received little

Erickson Tjoa, Finnian Gray

In this work we make the connection between the Unruh-DeWitt particle detector model applied to quantum field theory in curved spacetimes and the rigorous construction of the spin-boson model. With some modifications, we show that existing results about the existence of a spin-boson ground state can be adapted to the Unruh-DeWitt model. In the most relevant

Marcellus Amadeus, William Alberto Cruz Castañeda, Wilmer Lobato, Niasche Aquino

Speech technologies rely on capturing a speaker's voice variability while obtaining comprehensive language information. Textual prompts and sentence selection methods have been proposed in the literature to comprise such adequate phonetic data, referred to as a phonetically rich \textit{corpus}. However, they are still insufficient for acoustic modeling, esp

Zixin Huang, Ludovico Lami, Mark M. Wilde

Dephasing is a prominent noise mechanism that afflicts quantum information carriers, and it is one of the main challenges towards realizing useful quantum computation, communication, and sensing. Here we consider discrimination and estimation of bosonic dephasing channels, when using the most general adaptive strategies allowed by quantum mechanics. We reduc

Sergey E. Mikhailov

We consider evolution (non-stationary) space-periodic solutions to the $n$-dimensional non-linear Navier-Stokes equations of anisotropic fluids with the viscosity coefficient tensor variable in space and time and satisfying the relaxed ellipticity condition. Employing the Galerkin algorithm with the basis constituted by the eigenfunctions of the periodic Bes

Pedro A. Castillo, Maribel García Arenas, Nuria Rico, Antonio Miguel Mora

When search methods are being designed it is very important to know which parameters have the greatest influence on the behaviour and performance of the algorithm. To this end, algorithm parameters are commonly calibrated by means of either theoretic analysis or intensive experimentation. When undertaking a detailed statistical analysis of the influence of e

Chukwuma Okolie, Adedayo Adeleke, Julian Smit, Jon Mills

Several methods have been proposed for correcting the elevation bias in digital elevation models (DEMs) for example, linear regression. Nowadays, supervised machine learning enables the modelling of complex relationships between variables, and has been deployed by researchers in a variety of fields. In the existing literature, several studies have adopted ei

Jungjun Choi, Ming Yuan

This paper studies the principal components (PC) estimator for high dimensional approximate factor models with weak factors in that the factor loading ($\boldsymbol{\Lambda}^0$) scales sublinearly in the number $N$ of cross-section units, i.e., $\boldsymbol{\Lambda}^{0\top} \boldsymbol{\Lambda}^0 / N^\alpha$ is positive definite in the limit for some $\alpha

Apashanka Das, Biswajit Pandey

We study the evolution of the red and blue galaxies from $z=3$ to $z=0$ using the EAGLE simulation. The galaxies in the blue cloud and the red sequence are separated at each redshift using a scheme based on Otsu's method. Our analysis shows that the two populations have small differences in the local density and the clustering strength until $z=2$, after whi

Michael E. Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel

Transformers have achieved state-of-the-art performance in language modeling tasks. However, the reasons behind their tremendous success are still unclear. In this paper, towards a better understanding, we train a Transformer model on a simple next token prediction task, where sequences are generated as a first-order autoregressive process $s_{t+1} = W s_t$.

Jazmia Henry

Utilitarian games such as dictator games to measure fairness have been studied in the social sciences for decades. These games have given us insight into not only how humans view fairness but also in what conditions the frequency of fairness, altruism and greed increase or decrease. While these games have traditionally been focused on humans, the rise of AI

Jonathan Thomm, Giacomo Camposampiero, Aleksandar Terzic, Michael Hersche

We analyze the capabilities of Transformer language models in learning compositional discrete tasks. To this end, we evaluate training LLaMA models and prompting GPT-4 and Gemini on four tasks demanding to learn a composition of several discrete sub-tasks. In particular, we measure how well these models can reuse primitives observable in the sub-tasks to lea

Oem Trivedi, Maxim Khlopov, Alexander V. Timoshkin

Holographic principles have proven to be a very interesting approach towards dealing with the issues of the late-time acceleration of the universe, which has resulted in a great amount of work on holographic dark energy models. We consider one such very interesting holographic scenario, namely the Tsallis Holographic dark energy model and consider an ansatz

Fenia Christopoulou, Guchun Zhang, Gerasimos Lampouras

Large pre-trained language models have recently been expanded and applied to programming language tasks with great success, often through further pre-training of a strictly-natural language model--where training sequences typically contain both natural and (linearised) programming language. Such approaches effectively map both modalities of the sequence into

Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan

Learning in general-sum games often yields collectively sub-optimal results. Addressing this, opponent shaping (OS) methods actively guide the learning processes of other agents, empirically leading to improved individual and group performances in many settings. Early OS methods use higher-order derivatives to shape the learning of co-players, making them un

B. Lekshmi, Laurent Gizon, Kiran Jain, Zhi-Chao Liang

We use nearly two decades of helioseismic data obtained from the GONG (2002-2020) and HMI (2010-2020) ring-diagram pipelines to examine the temporal variations of the properties of individual equatorial Rossby modes with azimuthal orders in the range $6 \le m \le 10$. We find that the mode parameters obtained from GONG and HMI are consistent during the data

Kaifeng Bu, Arthur Jaffe, Zixia Wei

The classification of many-body quantum states plays a fundamental role in the study of quantum phases of matter. In this work, we propose an approach to classify quantum states by introducing the concept of magic class. In addition, we introduce an efficient coarse-graining procedure to extract the magic feature of states, which we call the ``convolution gr

Kathleen C. Fraser, Svetlana Kiritchenko

Following on recent advances in large language models (LLMs) and subsequent chat models, a new wave of large vision-language models (LVLMs) has emerged. Such models can incorporate images as input in addition to text, and perform tasks such as visual question answering, image captioning, story generation, etc. Here, we examine potential gender and racial bia

Wenliang Li

We perform an extensive bootstrap study of Hermitian and non-Hermitian theories based on the novel analytic continuation of $\langle\phi^n\rangle$ or $\langle(i\phi)^n\rangle$ in $n$. We first use the quantum harmonic oscillator to illustrate various aspects of the $\phi^n$ trajectory bootstrap method, such as the large $n$ expansion, matching conditions, ex

Vitor Fernandes, Alexei Vernitski

We study permutations on n elements preserving orientation (parity) of every subset of size k. We describe all groups of these permutations. Unexpectedly, these groups (except for some special cases) are either trivial, cyclic or dihedral. In this context, we define and study monoids generalizing monoids of order-preserving mappings and monoids of orientatio

E. M. Tursunov, S. A. Turakulov, K. I. Tursunmakhatov

The differences in potential models used in our work and in the original paper of the authors of the Comment are discussed. The neglecting of simple rules of reaction calculations is shown as a possible origin of the defect in the temperature dependence of the reaction rates in the work of the authors of the Comment.

Soumangsu Chakraborty, Amit Giveon, Akikazu Hashimoto

Aspects of superstring theory on deformed BTZ black holes, formed near $k$~NS5 branes by $p$~fundamental strings, and single-trace $T\bar T$ holography, are presented. The excitation energy of a singly wound long string plus its contribution to the energy of the black hole, $1/p$ fraction of the black-hole energy, is the same as that in $T\bar T$ deformed $C

Aphiwat Yuenyong, Pongwit Srisangyingcharoen

This paper investigates the relationships between closed and mixed string amplitudes at the tree level in string theory. Through the analytic continuation of complex variables, we establish a factorization of closed string amplitudes into those involving ($n-2$) open strings and a single closed string. Expressions for four-, five-, and six-point amplitudes a

Christopher Iliffe Sprague, Arne Elofsson, Hossein Azizpour

In contexts where data samples represent a physically stable state, it is often assumed that the data points represent the local minima of an energy landscape. In control theory, it is well-known that energy can serve as an effective Lyapunov function. Despite this, connections between control theory and generative models in the literature are sparse, even t

Wenhui Chang, Hongming Chen, Xin He, Xiang Chen

Raindrops adhering to the lens of UAVs can obstruct visibility of the background scene and degrade image quality. Despite recent progress in image deraining methods and datasets, there is a lack of focus on raindrop removal from UAV aerial imagery due to the unique challenges posed by varying angles and rapid movement during drone flight. To fill the gap in

Antonio Bueno, Rafael López

We study translators of the mean curvature flow in the product space $\h^2\times\r$. In $\h^2\times\r$ there are three types of translations: vertical translations due to the factor $\r$ and parabolic and hyperbolic translations from $\h^2$. A grim reaper in $\h^2\times\r$ is a translator invariant by a one-parameter group of translations. The variety of tra

Chuyi Zeng, Shiwen Zhang

Verifying a suspicion of Propp and Reiner concerning the cyclic sieving phenomenon (CSP), M. Thiel introduced a Catalan object called noncrossing $(1,2)$-configurations (denoted by $X_n$), which is a class of set partitions of $[n-1]$. More precisely, Thiel proved that, with a natural action of the cyclic group $C_{n-1}$ on $X_n$, the triple $\left(X_n,C_{n-

Li-Xian Wang, Wouter Beugeling, Fabian Schmitt, Lukas Lunczer

The band inversion of topological materials in three spatial dimensions is intimately connected to the parity anomaly of two-dimensional massless Dirac fermions. At finite magnetic fields, the parity anomaly reveals itself as a non-zero spectral asymmetry, i.e., a non-zero difference between the number of conduction and valence band Landau levels, due to the

Javier Cabello Sánchez, Adrián Gordillo-Merino

Our main result states that whenever we have a non-Euclidean norm $\|\cdot\|$ on a two-dimensional vector space $X$, there exists some $x\neq 0$ such that for every $\lambda\neq 1, \lambda>0$, there exist $y, z\in X$ verifying that $\|y\|=\lambda\|x\|$, $z\neq 0$, and $z$ belongs to the bisectors $B(-x,x)$ and $B(-y,y)$. Throughout this paper we also state a

Igor Fernandez de Bustos, Haritz Uriarte, Gorka Urkullu, Vanessa Garcia-Marina

The stability of integrators dealing with high order Differential Algebraic Equations (DAEs) is a major issue. The usual procedures give rise to instabilities that are not predicted by the usual linear analysis, rendering the common checks (developed for ODEs) unusable. The appearance of these difficult-toexplain and unexpected problems leads to methods that

Joseph Steneman, Giuseppe Vinci

Covariance matrix estimation is an important task in the analysis of multivariate data in disparate scientific fields. However, modern scientific data are often incomplete due to factors beyond the control of researchers, and traditional methods may only yield incomplete covariance matrix estimates. For example, it is impossible to obtain a complete sample c

Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires

We introduce off-policy distributional Q($\lambda$), a new addition to the family of off-policy distributional evaluation algorithms. Off-policy distributional Q($\lambda$) does not apply importance sampling for off-policy learning, which introduces intriguing interactions with signed measures. Such unique properties distributional Q($\lambda$) from other ex

Eduardo Peters, Gustavo Funes, Lluís Martínez-León, Enrique Tajahuerce

The subject of calculating the topological charge (TC) or vortex strength of optical vortices has generated divided opinions among scientists. This is due to the fact that proper analytical results are hard to support from the experimental point of view, leading to different results and conclusions. In this work we will present numerical data that shows the

Matt Shearer, Basile Simon, Clément Geiger

We created a software enabling journalists to define a set of criteria they would like to see applied regularly to a constantly-updated dataset, sending them an alert when these criteria are met, thus signaling them that there may be a story to write. The main challenges were to keep the product scalable and powerful, while making sure that it could be used

Tarig Abdelgadir, Ed Segal

We present an explicit GIT construction which produces both the minimal resolution of the type $D_4$ surface singularity, and also the orbifold resolution. Our construction is based on a Tannakian approach which is in principle applicable to arbitrary quotient singularities.

G. Catanzaro, V. Ripepi, M. Salaris, E. Trentin

Classical Cepheids (DCEPs) are important astrophysical objects not only as standard candles for the determination of the cosmic distance ladder but also as a test-bed for the stellar evolution theory, thanks to the connection between their pulsation (periods, amplitudes) and stellar (luminosity, mass, effective temperature, metallicity) parameters. We aim to

G. Piccirilli, G. Fabbian, D. Alonso, K. Storey-Fisher

We make use of the Gaia-Unwise quasar catalogue, Quaia, to constrain the growth history out to high redshifts from the clustering of quasars and their cross-correlation with maps of the Cosmic Microwave Background (CMB) lensing convergence. Considering three tomographic bins, centered at redshifts $\bar{z}_i = [0.69, 1.59, 2.72]$, we reconstruct the evolutio

Arnaud Pierens, Sean N. Raymond

Observations of protoplanetary discs have revealed dust rings which are likely due to the presence of pressure bumps in the disc. Because these structures tend to trap drifting pebbles, it has been proposed that pressure bumps may play an important role in the planet formation process. In this paper, we investigate the orbital evolution of a $0.1$ $M_\oplus$

Xiang I A Yang, Wen Zhang, Mahdi Abkar, William Anderson

Turbulent flow physics regulates the aerodynamic properties of lifting surfaces, the thermodynamic efficiency of vapor power systems, and exchanges of natural and anthropogenic quantities between the atmosphere and ocean, to name just a few applications. The dynamics of turbulent flows are described via numerical integration of the non-linear Navier-Stokes e

R. Rausch, C. Karrasch

The antiferromagnetic sawtooth chain is a prototypical example of a frustrated spin system with vertex-sharing triangles, giving rise to complex quantum states. Depending on the interaction parameters, this system has three phases, of which the gapless non-collinear phase (for strongly coupled basal spins and loosely attached apical spins) has received littl

Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki

Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they can be high-dimensional, contain structured missingness patterns, and measurement time points can be governed by an unknown stochastic process. While various solutions have been su

Batuhan Yardim, Artur Goldman, Niao He

Mean-field reinforcement learning has become a popular theoretical framework for efficiently approximating large-scale multi-agent reinforcement learning (MARL) problems exhibiting symmetry. However, questions remain regarding the applicability of mean-field approximations: in particular, their approximation accuracy of real-world systems and conditions unde

David J. Strachan, Archak Purkayastha, Stephen R. Clark

Since it's rediscovery in the twentieth century, the Mpemba effect, where a far-from-equilibrium state may relax faster than a state closer to equilibrium, has been extensively studied in classical systems and has recently received significant attention in quantum systems. Many theories explaining this counter-intuitive behavior in classical systems rely on

Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang

We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision. UFO employs a dual-agent framework to meticulously observe and analyze the graphical user interface (GUI) and control information of Windows applications. This enables the agent to seamlessly navigate an

Tu Anh Nguyen, Benjamin Muller, Bokai Yu, Marta R. Costa-jussa

We introduce Spirit LM, a foundation multimodal language model that freely mixes text and speech. Our model is based on a 7B pretrained text language model that we extend to the speech modality by continuously training it on text and speech units. Speech and text sequences are concatenated as a single stream of tokens, and trained with a word-level interleav

Gábor P. Nagy, Valentino Smaldore

We prove the switching equivalence of the strongly regular polar graphs $NO^\pm(4m,2)$, $NO^\mp(2m+1,4)$, and $\Gamma(O^\mp(4m,2))$ plus an isolated vertex by giving an analytic description for them and their associated two-graphs.

Vesna Iršič, Bojan Mohar, Alexandra Wesolek

The Cops and Robber game on geodesic spaces is a pursuit-evasion game with discrete steps which captures the behavior of the game played on graphs, as well as that of continuous pursuit-evasion games. One of the outstanding open problems about the game on graphs is to determine which graphs embeddable in a surface of genus $g$ have largest cop number. It is

Nathan Bowler, Max Pitz

We present an elementary construction of an uncountably chromatic graph without uncountable, infinitely connected subgraphs.

Jing Tan, Jian-dong Zhang, Hui-Min Fan, Jianwei Mei

The Einstein-dilaton-Gauss-Bonnet (EdGB) theory is a modified theory of gravity which include a scalar field to couple with the higher order curvature terms. It has already been constrained with various observations include the gravitational wave (GW) with LIGO, Virgo and KAGRA (LVK) Collaboration. In this work, we study the capability for space-borne GW det

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

The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical di

Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello

Offline preference optimization allows fine-tuning large models directly from offline data, and has proved effective in recent alignment practices. We propose generalized preference optimization (GPO), a family of offline losses parameterized by a general class of convex functions. GPO enables a unified view over preference optimization, encompassing existin

M. Yassine Naghmouchi, Wesley da Silva Coelho

This paper presents a new hybrid classical-quantum approach to solve Mixed Integer Linear Programming (MILP) using neutral atom quantum computations. We apply Benders decomposition (BD) to segment MILPs into a master problem (MP) and a subproblem (SP), where the MP is addressed using a neutral-atom device, after being transformed into a Quadratic Unconstrain

Qiuhao Li, Shenghai Yuan

In the context of rapid advancements in industrial automation, vision-based robotic grasping plays an increasingly crucial role. In order to enhance visual recognition accuracy, the utilization of large-scale datasets is imperative for training models to acquire implicit knowledge related to the handling of various objects. Creating datasets from scratch is

Yuxi Wei, Zi Wang, Yifan Lu, Chenxin Xu

Scene simulation in autonomous driving has gained significant attention because of its huge potential for generating customized data. However, existing editable scene simulation approaches face limitations in terms of user interaction efficiency, multi-camera photo-realistic rendering and external digital assets integration. To address these challenges, this

Sevag Abadian, Getulio Souza, Stanislav Winkler, Marian Bogdan Sirbu

On-chip optical isolators, functioning as unidirectional gates for light, play a crucial role in maintaining signal integrity, preventing laser destabilization, and fortifying the overall performance of optical systems. In this paper, we propose a five-layered heterostructure consisting of a magneto-optic material sandwiched between parallel dielectric slab

Nikolay Bazhenov, Ekaterina Fokina, Dino Rossegger, Alexandra Soskova

We adapt the classical notion of learning from text to computable structure theory. Our main result is a model-theoretic characterization of the learnability from text for classes of structures. We show that a family of structures is learnable from text if and only if the structures can be distinguished in terms of their theories restricted to positive infin

Oihana Galparsoro, Rémi Pétuya, Fabio Busnengo, Joseba Iñaki Juaristi

Using molecular dynamics simulations, we predict that the inclusion of nonadiabatic electronic excitations influences the dynamics of preadsorbed hydrogen abstraction from the W(110) surface by hydrogen scattering. The hot-atom recombination, which involves hyperthermal diffusion of the impinging atom on the surface, is significantly affected by the dissipat

ATLAS Collaboration

Measurements of the Higgs boson production times decay rates and differential cross-sections have recently been performed by the ATLAS experiment in several decay channels using up to 139 fb$^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV recorded at the Large Hadron Collider. This paper presents multiple interpretations of these Higgs boson meas

Kento Kawaharazuka, Tatsuya Matsushima, Andrew Gambardella, Jiaxian Guo

Recent developments in foundation models, like Large Language Models (LLMs) and Vision-Language Models (VLMs), trained on extensive data, facilitate flexible application across different tasks and modalities. Their impact spans various fields, including healthcare, education, and robotics. This paper provides an overview of the practical application of found

Jun Wang, Haoxuan Li, Chi Zhang, Dongxu Liang

Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction. However, the observed feedback usually suffer from two issues: selection bias and data sparsity, where biased and insufficient feedback seriously degrade the perform

Jesús A. Moreno López, Sandro Meloni, Jose J. Ramasco

When considering airborne epidemic spreading in social systems, a natural connection arises between mobility and epidemic contacts. As individuals travel, possibilities to encounter new people either at the final destination or during the transportation process appear. Such contacts can lead to new contagion events. In fact, mobility has been a crucial targe

Bhavya Vasudeva, Puneesh Deora, Christos Thrampoulidis

We study the fundamental optimization principles of self-attention, the defining mechanism of transformers, by analyzing the implicit bias of gradient-based optimizers in training a self-attention layer with a linear decoder in binary classification. Building on prior studies in linear logistic regression, recent findings demonstrate that the key-query matri

André S. Proença, Tiago R. Dias, Miguel P. Correia

The real estate market includes complex and inefficient mediation processes. Renting a property envolves multiple entities with different responsibilities and interests. Therefore it is imperative to establish a trustful relationship between parties through intermediaries such as notaries, banks or real estate agencies to avoid eventual disputes. Although an

Muner M. A. Hasan, Ethar A. A. Ahmed, Ahmed F. Ghaleb, Moustafa S. Abou-Dina

An efficient numerical approach based on weighted average finite differences is used to solve the Newtonian plane Couette flow with wall slip, obeying a dynamic slip law that generalizes the Navier slip law with the inclusion of a relaxation term. Slip is exhibited only along the fixed plate, and the motion is triggered by the motion of the other plate. Thre

Chengzhi Zhang, Shubo Wang

Exceptional points (EPs) in non-Hermitian systems can give rise to intriguing effects not available in conventional Hermitian systems due to their unusual properties. Using full-wave simulations, we investigate the scattering, absorption, and transmission of guided light at arbitrary order exceptional points in a non-Hermitian system consisting of coupled sp

D. J. Nader, E. Serrano-Ensástiga

Bose-Einstein condensates in a double-well potential contain the essential ingredients to study many-body systems within a rich classical phase-space that includes an unstable point and a separatrix. Employing a selfconsistent finite difference method, we study some of their quantum properties and their dependency on the strength of the boson-boson interacti

Nora Hoch, Olivia Seidel, Varghese A. Chirayath, Alfredo Enriquez

We report measurements of the transverse diffusion of electrons in P-10 gas (90% Ar, 10% CH4) in a laboratory-scale time projection chamber (TPC) utilizing a novel pixelated signal capture and digitization technique known as Q-Pix. The Q-Pix method incorporates a precision switched integrating transimpedance amplifier whose output is compared to a threshold

Yikai Zhang, Siyu Yuan, Caiyu Hu, Kyle Richardson

Despite remarkable advancements in emulating human-like behavior through Large Language Models (LLMs), current textual simulations do not adequately address the notion of time. To this end, we introduce TimeArena, a novel textual simulated environment that incorporates complex temporal dynamics and constraints that better reflect real-life planning scenarios

Urban Larsson, Richard J. Nowakowski, Carlos P. Santos

There are many combinatorial games in which a move can terminate the game, such as a checkmate in chess. These moves give rise to diverse situations that fall outside the scope of the classical normal play structure. To analyze these games, an algebraic extension is necessary, including infinities as elements. In this work, affine normal play, the algebraic

Pablo Negri, Isabelle Hupont, Emilia Gomez

Face recognition (FR) has reached a high technical maturity. However, its use needs to be carefully assessed from an ethical perspective, especially in sensitive scenarios. This is precisely the focus of this paper: the use of FR for the identification of specific subjects in moderately to densely crowded spaces (e.g. public spaces, sports stadiums, train st

Takumi Maesaka, Shin-ichiro Seki, Taiki Watanabe

We give a new expression of the multiple harmonic sum, which serves as a refinement of the iterated integral expression of the multiple zeta value, and prove it using the so-called connected sum method. Based on this fact, by taking two kinds of limit operations, we obtain new proofs of both the duality for multiple zeta values and the duality for finite mul

Marjan Petreski, Stefan Tanevski, Alejandro D. Jacobo

Using a Taylor rule amended with official reserves movements, we derive country-specific monetary shocks and employ a local projections-estimator for tracking gender-disaggregated labor-market responses in 99 developing economies from 2009 to 2021. Results show that women experience more negative post-shock employment responses than men, contributing to a de

Yi-Ting Pan, Chai-Rong Lee, Shu-Ho Fan, Jheng-Wei Su

The entertainment industry relies on 3D visual content to create immersive experiences, but traditional methods for creating textured 3D models can be time-consuming and subjective. Generative networks such as StyleGAN have advanced image synthesis, but generating 3D objects with high-fidelity textures is still not well explored, and existing methods have li

Patrick Daniels, Alex Youcis

We prove a conjecture of Pappas and Rapoport for all Shimura varieties of abelian type with parahoric level structure when $p>3$ by showing that the Kisin-Pappas-Zhou integral models of Shimura varieties of abelian type are canonical. In particular, this shows that these models of are independent of the choices made during their construction, and that they s

Wei-Ming Chen, Pin-Ju Tsai

Quantum target detection (QTD) utilizes nonclassical resources to enable radar-like detection for identifying reflecting objects in challenging environments, surpassing classical methods. To fully leverage the quantum advantage in QTD, determining the optimal probe states (OPSs) across various detection parameters and gaining a deeper understanding of their

Shilong Mu, Runze Zhao, Zenan Lin, Yan Huang

To foster an immersive and natural human-robot interaction, the implementation of tactile perception and feedback becomes imperative, effectively bridging the conventional sensory gap. In this paper, we propose a dual-modal electronic skin (e-skin) that integrates magnetic tactile sensing and vibration feedback for enhanced human-robot interaction. The dual-

Jiawei Huang, Niao He, Andreas Krause

We study the sample complexity of reinforcement learning (RL) in Mean-Field Games (MFGs) with model-based function approximation that requires strategic exploration to find a Nash Equilibrium policy. We introduce the Partial Model-Based Eluder Dimension (P-MBED), a more effective notion to characterize the model class complexity. Notably, P-MBED measures the

Sophie Xhonneux, David Dobre, Jian Tang, Gauthier Gidel

Despite significant investment into safety training, large language models (LLMs) deployed in the real world still suffer from numerous vulnerabilities. One perspective on LLM safety training is that it algorithmically forbids the model from answering toxic or harmful queries. To assess the effectiveness of safety training, in this work, we study forbidden t

Farshad Rostami Ghadi, Kai-Kit Wong, F. Javier Lopez-Martinez, Wee Kiat New

This paper investigates the performance of physical layer security (PLS) in fluid antenna-aided communication systems under arbitrary correlated fading channels. In particular, it is considered that a single fixed-antenna transmitter aims to send confidential information to a legitimate receiver equipped with a planar fluid antenna system (FAS), while an eav

Giulia De Somma, Marcella Marconi, Santi Cassisi, Roberto Molinaro

Homogeneous multi-wavelength observations of classical Cepheids from the forthcoming Rubin-LSST have the potential to significantly contribute to our understanding of the evolutionary and pulsation properties of these pulsating stars. Updated pulsation models for Classical Cepheid stars have been computed under various assumptions about chemical compositions

Kyu-Hyun Chae

Low-acceleration gravitational anomaly is investigated with a new method of exploiting the normalized velocity profile $\tilde{v}\equiv v_p/v_c$ of wide binary stars as a function of the normalized sky-projected radius $s/r_{\rm{M}}$ where $v_p$ is the sky-projected relative velocity between the pair, $v_c$ is the Newtonian circular velocity at the sky-proje

Mihailo Stojnic

Recent progress in studying \emph{treelike committee machines} (TCM) neural networks (NN) in \cite{Stojnictcmspnncaprdt23,Stojnictcmspnncapliftedrdt23,Stojnictcmspnncapdiffactrdt23} showed that the Random Duality Theory (RDT) and its a \emph{partially lifted}(pl RDT) variant are powerful tools that can be used for very precise networks capacity analysis. Her

Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy, Pierre Nyquist

Information theoretic quantities play a central role in machine learning. The recent surge in the complexity of data and models has increased the demand for accurate estimation of these quantities. However, as the dimension grows the estimation presents significant challenges, with existing methods struggling already in relatively low dimensions. To address

Susana Ramos de Debiaggi, Jose Miguel Campillo-Robles, Alejandro Caro

The phase stability of NiAl clusters of nanometer size was studied by using the embedded atom model and Monte Carlo simulation techniques. For temperatures of 500 and 1000 K and for a range of compositions below 70 at.% Al, the equilibrium structures of the system were determined and compared with the bulk results. We found that the bulk NiAl (B2) and Ni3Al

Valerio Astuti

In order to describe the properties of the observed distribution of wealth in a population, most economic models rely on the existence of an asymptotic equilibrium state. In addition, the process generating the equilibrium distribution is usually assumed to be ergodic, with a finite asymptotic average and bounded inequality. Here we show, using data from Ban

Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos

This paper addresses the multiple two-sample test problem in a graph-structured setting, which is a common scenario in fields such as Spatial Statistics and Neuroscience. Each node $v$ in fixed graph deals with a two-sample testing problem between two node-specific probability density functions (pdfs), $p_v$ and $q_v$. The goal is to identify nodes where the

Mateusz Duda, Luke Brunswick, Luke R. Wilson, Pieter Kok

We demonstrate theoretically that waveguide-coupled cavities with embedded two-level emitters can act as a highly efficient, high-fidelity single-photon switch. The photon switch is an optical router triggered by a classical signal -- the propagation direction of single input photons in the waveguide is controlled by changing the emitter-cavity coupling para

Pranav Kulkarni, Andrew Chan, Nithya Navarathna, Skylar Chan

The proliferation of artificial intelligence (AI) in radiology has shed light on the risk of deep learning (DL) models exacerbating clinical biases towards vulnerable patient populations. While prior literature has focused on quantifying biases exhibited by trained DL models, demographically targeted adversarial bias attacks on DL models and its implication