July 2023 arXiv papers — page 34

Guannan Wei, Oliver Bračevac, Songlin Jia, Yuyan Bao

Reachability types are a recent proposal that has shown promise in scaling to higher-order but monomorphic settings, tracking aliasing and separation on top of a substrate inspired by separation logic. The prior $\lambda^*$ reachability type system qualifies types with sets of reachable variables and guarantees separation if two terms have disjoint qualifier

Shu-Juan Zhang, Jian-Min Yan, F. Tang, Jin Wu

Stoichiometric Cr2Se3 single crystals are particular layer-structured antiferromagnets which possess noncolinear spin configuration, weak ferromagnetic moments, moderate magnetoresistance (MR ~ 14.3%), and bad metallic conductivity below the antiferromagnetic phase transition temperature. Here, we report an interesting >16000% colossal magnetoresistance (CMR

Mominul Islam, Hasib Zunair, Nabeel Mohammed

Crafting effective deep learning models for medical image analysis is a complex task, particularly in cases where the medical image dataset lacks significant inter-class variation. This challenge is further aggravated when employing such datasets to generate synthetic images using generative adversarial networks (GANs), as the output of GANs heavily relies o

Panagiotis Kyriazis, Edmund Lou

Coordination facilitation and efficient decision-making are two essential components of successful leadership. In this paper, we take an informational approach and investigate how followers' information impacts coordination and efficient leadership in a model featuring a leader and a team of followers. We show that efficiency is achieved as the unique ration

A. L. González-Morán, P. Arrabal Haro, C. Muñóz-Tuñón, J. M. Rodríguez-Espinosa

We present an application of unsupervised Machine Learning Clustering to the PAU Survey of galaxy spectral energy distribution (SED) within the COSMOS field. The clustering algorithm is implemented and optimized to get the relevant groups in the data SEDs. We find 12 groups from a total number of 5,234 targets in the survey at $0.01 <$ z $< 0.28$. Among the

Ron Perline, Sergei Tabachnikov

In analogy with the well-known 2-linkage tractor-trailer problem, we define a 2-linkage problem in the plane with novel non-holonomic ``no-slip'' conditions. Using constructs from sub-Riemannian geometry, we look for geodesics corresponding to linkage motion with these constraints (``tricycle kinematics''). The paths of the three vertices turn out to be crit

Krystian Ilkiewicz, Joanna Mikolajewska, Kiril A. Stoyanov

T CrB is a symbiotic recurrent nova that exhibits quiescent and active phases between its classical nova eruptions. The statistical properties of these active phases have been poorly studied thus far. Because of that their nature remained unknown. Here we study statistical properties of the active phases and show that they are consistent with outburst and su

William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen

Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for many of today's probabilistic programming languages (PPLs). The core challenge comes from discrete structure: many of today's PPL inference strategies rely on enumeration, sampling, or differentiation in order to scale, which fail for high-dimensional complex discre

Intesar O. Zalloum, Amin Jafari Sojahrood, Ali A. Paknahad, Michael C. Kolios

The acoustic response of microbubbles (MBs) depends on their resonance frequency, which is dependent on MB size and shell properties. Monodisperse MBs with tunable shell properties are thus desirable for optimizing and controlling MB behavior in acoustics applications. By utilizing a novel microfluidic method that uses lipid concentration to control MB shrin

Jason Fulman

In work on the two alleles Moran model, Ewens showed that the stationary distribution for the number of genes of one type can be approximated by a Beta distribution. In this short note, we provide a sharp error term for this approximation. We show that this example fits perfectly into Doebler's framework for Beta approximation by Stein's method of exchangeab

Gabriel Klasson Landin, Truls Jilborg

Side-channel attacks pose significant challenges to the security of embedded systems, often allowing attackers to circumvent encryption algorithms in minutes compared to the trillions of years required for brute-force attacks. To mitigate these vulnerabilities, various countermeasures have been developed. This study focuses on two specific countermeasures: r

Wenlong Shi, David Keeney, Duyu Chen, Yang Jiao

Disordered hyperuniform materials are an emerging class of exotic amorphous states of matter that endow them with singular physical properties. Here, we generalize the Fourier-space based numerical construction procedure for designing {\it isotropic} disordered hyperuniform two-phase heterogeneous materials (i.e., composites) developed by Chen and Torquato [

Tom Liu, Stefan Zohren

Cryptocurrency trading represents a nascent field of research, with growing adoption in industry. Aided by its decentralised nature, many metrics describing cryptocurrencies are accessible with a simple Google search and update frequently, usually at least on a daily basis. This presents a promising opportunity for data-driven systematic trading research, wh

Yuki Tsukada, Hideaki Iiduka

While stochastic gradient descent (SGD) can use various learning rates, such as constant or diminishing rates, the previous numerical results showed that SGD performs better than other deep learning optimizers using when it uses learning rates given by line search methods. In this paper, we perform a convergence analysis on SGD with a learning rate given by

Andrea Posilicano

We present a much shorter and streamlined proof of an improved version of the results previously given in [A. Posilicano: On the Self-Adjointness of $H+A^{*}+A$, Math. Phys. Anal. Geom. (2020)] concerning the self-adjoint realizations of formal QFT-like Hamiltonians of the kind $H+A^{*}+A$, where $H$ and $A$ play the role of the free field Hamiltonian and of

Umitcan Sahin, Izzet Emre Kucukkaya, Oguzhan Ozcelik, Cagri Toraman

Text-embedded images can serve as a means of spreading hate speech, propaganda, and extremist beliefs. Throughout the Russia-Ukraine war, both opposing factions heavily relied on text-embedded images as a vehicle for spreading propaganda and hate speech. Ensuring the effective detection of hate speech and propaganda is of utmost importance to mitigate the ne

Israel Quiros

We aim at finding static, spherically symmetric, vacuum solutions of a gauge invariant theory of gravity over Weyl integrable geometry spaces. It arises that vacuum wormholes of pure geometric nature are solutions of this theory. This means that there is not necessary to place any exotic matter at the throat of the wormhole in order to support it. A thorough

Amin Jafari Sojahrood, Celina Yang, Claire Counil, Pinunta Nittachayarn

Correct measurement of the shell properties of coated microbubbles (MBs) is essential to understanding and optimizing their response to ultrasound (US) exposure parameters in diagnostic and therapeutic ultrasound. MBs are surrounded by blood; however, the influence of the surrounding medium charges on the MB properties is poorly understood. This study aims t

Zongchen Chen, Daniel Stefankovic, Eric Vigoda

This monograph is an exposition on an exciting new technique known as spectral independence, which has been instrumental in analyzing the convergence rate of Markov Chain Monte Carlo (MCMC) algorithms. For a high-dimensional distribution defined on labelings of the vertices of an $n$-vertex graph, the spectral independence condition, introduced by Anari, Liu

James A. Klimchuk, James E. Leake, Lars K. S. Daldorff, Craig D. Johnston

The thickness of current sheets is extremely important, especially as it relates to the onset of fast magnetic reconnection. Onset determines how much magnetic free energy can build up in a field before it is explosively released. This has implications for many phenomena on the Sun and throughout the universe, including the heating of the solar corona. Signi

Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro

The core challenge of offline reinforcement learning (RL) is dealing with the (potentially catastrophic) extrapolation error induced by the distribution shift between the history dataset and the desired policy. A large portion of prior work tackles this challenge by implicitly/explicitly regularizing the learning policy towards the behavior policy, which is

Marlies Gerber, Philipp Kunde

We consider the problem of classifying Kolmogorov automorphisms (or $K$-automorphisms for brevity) up to isomorphism. Within the collection of measure-preserving transformations, Bernoulli shifts have the ultimate mixing property, and $K$-automorphisms have the next-strongest mixing properties of any widely considered family of transformations. J. Feldman ob

André Henriques, David Penneys, James Tener

In our article [arXiv:1511.05226], we studied the commutant $\mathcal{C}'\subset \operatorname{Bim}(R)$ of a unitary fusion category $\mathcal{C}$, where $R$ is a hyperfinite factor of type $\rm II_1$, $\rm II_\infty$, or $\rm III_1$, and showed that it is a bicommutant category. In other recent work [arXiv:1607.06041, arXiv:2301.11114] we introduced the not

Vincent Lostanlen, Daniel Haider, Han Han, Mathieu Lagrange

Waveform-based deep learning faces a dilemma between nonparametric and parametric approaches. On one hand, convolutional neural networks (convnets) may approximate any linear time-invariant system; yet, in practice, their frequency responses become more irregular as their receptive fields grow. On the other hand, a parametric model such as LEAF is guaranteed

R. Altmann, D. Peterseim, T. Stykel

This paper is devoted to the numerical solution of constrained energy minimization problems arising in computational physics and chemistry such as the Gross-Pitaevskii and Kohn-Sham models. In particular, we introduce the Riemannian Newton methods on the infinite-dimensional Stiefel and Grassmann manifolds. We study the geometry of these two manifolds, its i

C. H. Yuen, C. D. Lin

Probing vibronic coherence in molecules has been a central topic in ultrafast science, as it is an essential prerequisite to monitoring electronic motion. While experiments have demonstrated that attosecond transient absorption spectroscopy (ATAS) can probe the vibronic coherence in a few molecules, its robustness remains largely unexplored. In this Letter,

Marcelo Fiori, Bernardo Marenco, Federico Larroca, Paola Bermolen

The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions. Accordingly, the embedding task of estimating these vectors from an ob

Junze Yin

Urbanization is a phenomenon of concern for planning and public health: projections are difficult because of policy changes and natural events, and indicators are multiple. There are previous studies of development that used fractals, but none for this specific problem, nor extrapolating the future trend. In the first part of this paper, we construct a theor

Xiaowei Gao, James Haworth, Dingyi Zhuang, Huanfa Chen

Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often overlook the importance of model uncertainty. In this paper, we introduce a novel Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) for road-level tra

Jiajun Zhang, Georgina Cosma, Sarah Bugby, Jason Watkins

Artificial intelligence (AI) reasoning and explainable AI (XAI) tasks have gained popularity recently, enabling users to explain the predictions or decision processes of AI models. This paper introduces Forest Monkey (FM), a toolkit designed to reason the outputs of any AI-based defect detection and/or classification model with data explainability. Implement

M. Andrecut

The recently introduced Theory of the Adjacent Possible (TAP) is a model of combinatorial innovation aiming to explain the "hockey-stick" upward trend of human technological evolution, where an explosion in the number of produced items with increasing complexity suddenly occurs. In addition, the TAP model was also used to explain the rapidly emerging biologi

Becky Armstrong, Jonathan H. Brown, Lisa Orloff Clark, Kristin Courtney

In this note, we present criteria that are equivalent to a locally compact Hausdorff groupoid $G$ being effective. One of these conditions is that $G$ satisfies the "C*-algebraic local bisection hypothesis"; that is, that every normaliser in the reduced twisted groupoid C*-algebra is supported on an open bisection. The semigroup of normalisers plays a fundam

Qingming Tang

This thesis focuses on representation learning for sequence data over time or space, aiming to improve downstream sequence prediction tasks by using the learned representations. Supervised learning has been the most dominant approach for training deep neural networks for learning good sequential representations. However, one limiting factor to scale supervis

Dan Busbridge, Jason Ramapuram, Pierre Ablin, Tatiana Likhomanenko

Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size and wall-clock time. This trade-off is typically enabled by a scaling rule, for example, in stochastic gradient descent, one should scale the learning rate linearly with the batch size. Another important machine l

R. J. Cooper, N. Abgrall, G. Aversano, M. S. Bandstra

The detection, identification, and localization of illicit radiological and nuclear material continue to be key components of nuclear non-proliferation and nuclear security efforts around the world. Networks of radiation detectors deployed at strategic locations in urban environments have the potential to provide continuous radiological/nuclear (R/N) surveil

Dake Zhou

Dwarf neutron stars are stable twins of neutron stars but with a maximum mass less than that of neutron stars. Their existence brings into concordance the seemingly conflicting information on the size of neutron stars inferred from gravitational waves from GW170817, from the NICER mission, and from the PREX-II experiment. Their distinctive characteristics le

Damiano Rossi

We prove that Sp\"ath's Character Triple Conjecture holds for every finite group with respect to maximal defect characters at the prime 2. This is done by reducing the maximal defect case of the conjecture to the so-called inductive Alperin-McKay condition whose verification has recently been completed by Ruhstorfer for the prime 2. As a consequence we obtai

Yu Fu, Deyi Xiong, Yue Dong

To mitigate potential risks associated with language models, recent AI detection research proposes incorporating watermarks into machine-generated text through random vocabulary restrictions and utilizing this information for detection. While these watermarks only induce a slight deterioration in perplexity, our empirical investigation reveals a significant

Jishen Zhang, Stéphane Perrard

We investigate the dynamics of pendulum chains immersed in turbulent boundary layers. We combine laboratory experiments and video analysis of the kinetic facade exhibits by the artist Ned Kahn, composed of large-scale clusters of centimeter-sized plates oscillating freely in the wind. At the laboratory scale, we show that a one-dimensional pendulum chain imm

Timothy E. Wang, Alessandro Pinto

We survey the landscape of human operator modeling ranging from the early cognitive models developed in artificial intelligence to more recent formal task models developed for model-checking of human machine interactions. We review human performance modeling and human factors studies in the context of aviation, and models of how the pilot interacts with auto

Bradley E. Schaefer

The evolution of novae and Cataclysmic Variables (CVs) is driven by changes in the binary orbital periods. In a direct and critical test for various evolution models and their physical mechanisms, I measure the sudden changes in the period ($\Delta P$) across 14 nova eruptions and I measure the steady period change during quiescence ($\dot{P}$) for 20 inter-

João M. L. de Freitas, Iberê Kuntz

We describe a mechanism in which the graviton acquires a mass from the functional measure without violating the diffeomorphism symmetry nor including St\"uckelberg fields. Since gauge invariance is not violated, the number of degrees of freedom goes as in general relativity. For the same reason, Boulware-Deser ghosts and the vDVZ disconinuity do not show up.

Xuan Li, John R. Royer, Christopher Ness

Dense suspensions of solid particles in viscous liquid are ubiquitous in both industry and nature, and there is a clear need for efficient numerical routines to simulate their rheology and microstructure. Particles of micron size present a particular challenge: at low shear rates colloidal interactions control their dynamics while at high rates granular-like

Paul Gondolf, Tim Möbus, Cambyse Rouzé

The exponential convergence to invariant subspaces of quantum Markov semigroups plays a crucial role in quantum information theory. One such example is in bosonic error correction schemes, where dissipation is used to drive states back to the code-space - an invariant subspace protected against certain types of errors. In this paper, we investigate perturbat

Neophytos Neophytou, Pankaj Priyadarshi, Zhen Li, Patrizio Graziosi

Over the last two decades a plethora of new thermoelectric materials, their alloys, and their nanostructures were synthesized. The ZT figure of merit, which quantifies the thermoelectric efficiency of these materials increased from values of unity to values consistently beyond two across material families. At the same time, the ability to identify and optimi

Maxime Fontana, Michael Spratling, Miaojing Shi

Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. By using shared resources to simultaneously calculate multiple outputs, this learning paradigm has the potential to have lower memory requirements and inference times compared to the traditional approach of using separate methods for each task.

Fabrizio Frati

We show an $O(n)$-time reduction from the problem of testing whether a multiset of positive integers can be partitioned into two multisets so that the sum of the integers in each multiset is equal to $n/2$ to the problem of testing whether an $n$-vertex biconnected outerplanar DAG admits an upward planar drawing. This constitutes the first barrier to the exi

Ilkay Sikdokur, İnci M. Baytaş, Arda Yurdakul

Deep edge intelligence aims to deploy deep learning models that demand computationally expensive training in the edge network with limited computational power. Moreover, many deep edge intelligence applications require handling distributed data that cannot be transferred to a central server due to privacy concerns. Decentralized learning methods, such as fed

Piero Birello, Michele Re Fiorentin, Boxuan Wang, Vittoria Colizza

Accurate estimates of the reproduction ratio are crucial to project infectious disease epidemic evolution and guide public health response. Here, we prove that estimates of the reproduction ratio based on inference from surveillance data can be inaccurate if the population comprises spatially distinct communities, as the space-mobility interplay may hide the

Gabriella Agazie, Zaven Arzoumanian, Paul T. Baker, Bence Bécsy

We present the results of a Bayesian search for gravitational wave (GW) memory in the NANOGrav 12.5-yr data set. We find no convincing evidence for any gravitational wave memory signals in this data set (Bayes factor = 2.8). As such, we go on to place upper limits on the strain amplitude of GW memory events as a function of sky location and event epoch. Thes

Agnes Bischoff-Kim

To date, pulsational variability has been measured from nearly 70 DBVs and 500 DAVs, with only a fraction of these having been the subjects of asteroseismic analysis. One way to approach white dwarf asteroseismology is forward modeling, where one assumes an interior structure and calculates the model's periods. Many such models are calculated, in the search

Marcus W Beims, Arlans JS Lara

Using the position as an independent variable, and time as the dependent variable, we derive the function ${\cal P}^{(\pm)}$, which generates the space evolution under the potential ${\cal V}(q)$ and Hamiltonian ${\cal H}$. Canonically conjugated variables are the time and minus the Hamiltonian. While the classical dynamics do not change, the corresponding q

Danel Ahman, Matija Pretnar

We explore asynchronous programming with algebraic effects. We complement their conventional synchronous treatment by showing how to naturally also accommodate asynchrony within them, namely, by decoupling the execution of operation calls into signalling that an operation's implementation needs to be executed, and interrupting a running computation with the

Zhiwan Xu, Brian Chan, Gang Wang, Aihong Tang

The search for the chiral magnetic effect (CME) in heavy-ion collisions has been impeded by the significant background arising from the anisotropic particle emission pattern, particularly elliptic flow. To alleviate this background, the event shape selection (ESS) technique categorizes collision events according to their shapes and projects the CME observabl

Deepti Gupta, Shafika Showkat Moni, Ali Saman Tosun

In the present era of advanced technology, the Internet of Things (IoT) plays a crucial role in enabling smart connected environments. This includes various domains such as smart homes, smart healthcare, smart cities, smart vehicles, and many others.With ubiquitous smart connected devices and systems, a large amount of data associated with them is at a prime

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey

We consider estimation of parameters defined as linear functionals of solutions to linear inverse problems. Any such parameter admits a doubly robust representation that depends on the solution to a dual linear inverse problem, where the dual solution can be thought as a generalization of the inverse propensity function. We provide the first source condition

Yi Zhuang, Louis E. Strigari, Lei Jin, Samiran Sinha

We study the prospects for detection of solar and atmospheric neutrino fluxes at future large-scale dark matter detectors through both electron and nuclear recoils. We specifically examine how the detection prospects change for several prospective detector locations (SURF, SNOlab, Gran Sasso, CJPL, and Kamioka), and improve upon the statistical methodologies

Timo Knispel, Jan Berges, Arne Schobert, Erik G. C. P. van Loon

Using scanning tunneling microscopy and spectroscopy, for a monolayer of transition metal dichalcogenide H-NbS$_2$ grown by molecular beam epitaxy on graphene, we provide unambiguous evidence for a charge density wave (CDW) with a 3$\times$3 superstructure, which is not present in bulk NbS$_2$. Local spectroscopy displays a pronounced gap of the order of 20

Jordan K. Matelsky, Felipe Parodi, Tony Liu, Richard D. Lange

Open-ended questions are a favored tool among instructors for assessing student understanding and encouraging critical exploration of course material. Providing feedback for such responses is a time-consuming task that can lead to overwhelmed instructors and decreased feedback quality. Many instructors resort to simpler question formats, like multiple-choice

Valentina De Romeri, Victor Martin Lozano, G. Sanchez Garcia

Leptoquarks are theorized particles of either scalar or vector nature that couple simultaneously to quarks and leptons. Motivated by recent measurements of coherent elastic neutrino-nucleus scattering, we consider the impact of scalar leptoquarks coupling to neutrinos on a few complementary processes, from low energy to colliders. In particular, we set compe

Frank Göhmann, Karol K. Kozlowski, Mikhail D. Minin

Evaluating a lattice path integral in terms of spectral data and matrix elements pertaining to a suitably defined quantum transfer matrix, we derive form-factor series expansions for the dynamical two-point functions of arbitrary local operators in fundamental Yang-Baxter integrable lattice models at finite temperature. The summands in the series are paramet

Jarin Ritu, Ethan Barnes, Riley Martell, Alexandra Van Dine

Underwater acoustic target detection in remote marine sensing operations is challenging due to complex sound wave propagation. Despite the availability of reliable sonar systems, target recognition remains a difficult problem. Various methods address improved target recognition. However, most struggle to disentangle the high-dimensional, non-linear patterns

Ricardo Ribeiro Pereira, Jacopo Bono, João Tiago Ascensão, David Aparício

Machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data. In some domains, such labelled data is unavailable or incomplete. In practice this can lead to low detection rates and high false positive rates, which characterise for example anti-money laundering systems. In fact, it is estimated that 1.7--4 tr

Junhong Yue, Peijun Li

Flexural wave scattering plays a crucial role in optimizing and designing structures for various engineering applications. Mathematically, the flexural wave scattering problem on an infinite thin plate is described by a fourth-order plate-wave equation on an unbounded domain, making it challenging to solve directly using the regular linear finite element met

Soham Mandal, Paul C. Duffell

We present the publicly available moving-mesh hydrodynamics code Sprout. Sprout solves the equations of ideal hydrodynamics on an expanding Cartesian mesh. The expanding mesh can follow fluid outflows for several orders of magnitude with very little numerical diffusion, thereby capturing shocks and fine structures accurately. Following the bulk flow accurate

Daniel Kałuża, Andrzej Janusz, Dominik Ślęzak

Supervised classification algorithms are used to solve a growing number of real-life problems around the globe. Their performance is strictly connected with the quality of labels used in training. Unfortunately, acquiring good-quality annotations for many tasks is infeasible or too expensive to be done in practice. To tackle this challenge, active learning a

Hugo D. Pasinato, Nicólas F. Moguilner Reh

In this study, we explore the application of an artificial recurrent neural network (RNN) called Long Short-Term Memory (LSTM) as an alternative to a turbulent Reynolds-Averaged Navier-Stokes (RANS) model. The LSTM models are utilized to predict the shear Reynolds stress in developed and developing turbulent channel flows. We conduct comparative analyses, co

Federica Fabbri, James Howarth, Theo Maurin

We present a method for identifying $H\rightarrow WW^* \rightarrow \ell \nu j j$ in the presence of large Standard Model backgrounds and illustrate how this decay mode may be applied to the study of Bell-type Inequalities. Our findings reveal the feasibility of complete reconstruction of such Higgs decays and the efficacy of our suggested combination of sele

Anusha Srikanthan, Fengjun Yang, Igor Spasojevic, Dinesh Thakur

We consider joint trajectory generation and tracking control for under-actuated robotic systems. A common solution is to use a layered control architecture, where the top layer uses a simplified model of system dynamics for trajectory generation, and the low layer ensures approximate tracking of this trajectory via feedback control. While such layered contro

Arka Das, Ankur Sinha, Sachin Jayaswal

In this paper, we propose an exact general algorithm for solving non-convex optimization problems, where the non-convexity arises due to the presence of an inverse S-shaped function. The proposed method involves iteratively approximating the inverse S-shaped function through piece-wise linear inner and outer approximations. In particular, the concave part of

Mikhail Nevskii, Alexey Ukhalov

Suppose $\Omega$ is a closed bounded subset of ${\mathbb R}^n,$ $S$ is an $n$-dimensional non-degenerate simplex, $\xi(\Omega;S):=\min \left\{\sigma\geq 1: \, \Omega\subset \sigma S\right\}$. Here $\sigma S$ is the result of homothety of $S$ with respect to the center of gravity with coefficient $\sigma$. Let $d\geq n+1,$ $\varphi_1(x),\ldots,\varphi_d(x)$ b

Ala N. Tak, Jonathan Gratch

This paper investigates the emotional reasoning abilities of the GPT family of large language models via a component perspective. The paper first examines how the model reasons about autobiographical memories. Second, it systematically varies aspects of situations to impact emotion intensity and coping tendencies. Even without the use of prompt engineering,

Xinming Liu, Joseph Y. Halpern

Many studies have shown that humans are "predictably irrational": they do not act in a fully rational way, but their deviations from rational behavior are quite systematic. Our goal is to see the extent to which we can explain and justify these deviations as the outcome of rational but resource-bounded agents doing as well as they can, given their limitation

Sharon Chee Yin Ho, Vahid Majdinasab, Mohayeminul Islam, Diego Elias Costa

Software systems are increasingly relying on deep learning components, due to their remarkable capability of identifying complex data patterns and powering intelligent behaviour. A core enabler of this change in software development is the availability of easy-to-use deep learning libraries. Libraries like PyTorch and TensorFlow empower a large variety of in

Gábor Berend

In this paper, we advocate for using large pre-trained monolingual language models in cross lingual zero-shot word sense disambiguation (WSD) coupled with a contextualized mapping mechanism. We also report rigorous experiments that illustrate the effectiveness of employing sparse contextualized word representations obtained via a dictionary learning procedur

David J. Prömel, David Scheffels

The well-posedness is established for multi-dimensional mean-field stochastic Volterra equations with Lipschitz continuous coefficients and allowing for singular kernels as well as for one-dimensional mean-field stochastic Volterra equations with H\"older continuous diffusion coefficients and sufficiently regular kernels. In these different settings, quantit

M. V. Tkachev, S. V. Pilipenko, E. V. Mikheeva, V. N. Lukash

Modified matter power spectra with approximately Gaussian bump on sub-Mpc scales can be a result of a complex inflation. We consider five spectra with different Gaussian amplitudes $A$ and locations $k_0$ and run N-body simulations in a cube $(5 Mpc/h)^3$ at $z>8$ to reveal the halo mass functions and their evolution with redshift. We have found that the She

Edward M. Molter, Imke de Pater, Chris Moeckel

We report the first near-infrared detection of Uranus's tiny moon Mab, the presumed source of the blue and diffuse $\mu$ ring, using the NIRC2 instrument at Keck Observatory. The detection was permitted by an updated shift-and-stack procedure allowing us to integrate on Mab as it moved across the detector in 23 separate exposures taken over $\sim$2 hours, as

Alfred Lehar, Christine Parlour, Marius Zoican

We investigate how liquidity providers (LPs) choose between high- and low-fee trading venues, in the face of a fixed common gas cost. Analyzing Uniswap data, we find that high-fee pools attract 58% of liquidity supply yet execute only 21% of volume. Large LPs dominate low-fee pools, frequently adjusting out-of-range positions in response to informed order fl

Mohammad Hoseinpour, Milad Hoseinpour, Ali Aghagolzadeh

Machine learning (ML) models can memorize training datasets. As a result, training ML models over private datasets can lead to the violation of individuals' privacy. Differential privacy (DP) is a rigorous privacy notion to preserve the privacy of underlying training datasets. Yet, training ML models in a DP framework usually degrades the accuracy of ML mode

Cheng Han, Qifan Wang, Yiming Cui, Zhiwen Cao

As the size of transformer-based models continues to grow, fine-tuning these large-scale pretrained vision models for new tasks has become increasingly parameter-intensive. Parameter-efficient learning has been developed to reduce the number of tunable parameters during fine-tuning. Although these methods show promising results, there is still a significant

Rupert L. Frank, Ryan W. Matzke

We solve explicitly a certain minimization problem for probability measures involving an interaction energy that is repulsive at short distances and attractive at large distances. We complement earlier works by showing that part of the remaining parameter regime all minimizers are uniform distributions on a surface of a sphere, thus showing concentration on

Chang Liu, Wen-Du Li, Wu-Sheng Dai

This paper presents a nonperturbative method for solving eigenproblems. This method applies to almost all potentials and provides nonperturbative approximations for any energy level. The method converts an eigenproblem into a perturbation problem, obtains perturbation solutions through standard perturbation theory, and then analytically continues the perturb

Zucheng Gao, Zvonimir Vlah, Anthony Challinor

We revisit the flat-sky approximation for evaluating the angular power spectra of projected random fields by retaining information about the correlations along the line of sight. With broad, overlapping radial window functions, these line-of-sight correlations are suppressed and are ignored in the Limber approximation. However, retaining the correlations is

Robin Löwenberg, Gudrid Moortgat-Pick

Heterodyne cavity experiments for gravitational wave (GW) detection experience a rising interest since recent studies showed that they allow to probe the ultra high frequency regime above $10\,\text{kHz}$. In this paper, we present a concise theoretical study of the experiment based on ideas from the former MAGO collaboration which already started experiment

Yu. G. Ignat'ev

Asymptotically exact solutions are obtained for a spherically symmetric field with the Higgs potential generated by a point scalar charge, and a method for numerical integration of the equation for a scalar field with the Higgs potential of a point charge is proposed. Examples of numerical modeling of the equation of the scalar field of a single charge are g

Mohammmadmahdi Maheri, Reza Abdollahzadeh, Bardia Mohammadi, Mina Rafiei

In practical scenarios, the effectiveness of sequential recommendation systems is hindered by the user cold-start problem, which arises due to limited interactions for accurately determining user preferences. Previous studies have attempted to address this issue by combining meta-learning with user and item-side information. However, these approaches face in

A. Mollaebrahimi, D. Amanbayev, S. Ayet San Andrés, S. Beck

In this paper, the major upgrades and technical improvements of the buffer gas handling system for the cryogenic stopping cell of the FRS Ion Catcher at GSI/FAIR (in Darmstadt, Germany) are described. The upgrades include implementation of new gas lines and gas purifiers to achieve a higher buffer gas cleanliness for a more efficient extraction of reactive i

Yantong Liu

The integrity of offshore wind turbines, pivotal for sustainable energy generation, is often compromised by surface material defects. Despite the availability of various detection techniques, limitations persist regarding cost-effectiveness, efficiency, and applicability. Addressing these shortcomings, this study introduces a novel approach leveraging an adv

André L. Oestereich, Marcelo A. Pires, Silvio M. Duarte Queirós, Nuno Crokidakis

We study the Galam's majority-rule model in the presence of an independent behavior that can be driven intrinsically or can be mediated by information regarding the collective opinion of the whole population. We first apply the mean-field approach where we obtained an explicit time-dependent solution for the order parameter of the model. We complement our re

Mark Kozdoba, Binyamin Perets, Shie Mannor

We propose a new approach to non-parametric density estimation that is based on regularizing a Sobolev norm of the density. This method is statistically consistent, and makes the inductive bias of the model clear and interpretable. While there is no closed analytic form for the associated kernel, we show that one can approximate it using sampling. The optimi

Gavin Parpart, Sumedh R. Risbud, Garrett T. Kenyon, Yijing Watkins

Neuromorphic processors have garnered considerable interest in recent years for their potential in energy-efficient and high-speed computing. The Locally Competitive Algorithm (LCA) has been utilized for power efficient sparse coding on neuromorphic processors, including the first Loihi processor. With the Loihi 2 processor enabling custom neuron models and

Yu. G. Ignat'ev

Scale transformations of cosmological models based on a statistical system of degenerate fermions with a scalar Higgs interaction are studied. The similarity properties of cosmological models are revealed under the scale transformation of their fundamental parameters. The laws of transformation of the coordinates of singular points and eigenvalues of the cha

Poula Tadros, Ivan Kolář

We study the Carrollian limit of the (general) quadratic gravity in four dimensions. We find that in order for the Carrollian theory to be a modification of the Carrollian limit of general relativity, the parameters in the action must depend on the speed of light in a specific way. By focusing on the leading and the next-to-leading orders in the Carrollian e

Jia Liu, Liqun Chen, Mehrdad Dianati, Carsten Maple

The use of vehicle-to-everything (V2X) communication is expected to significantly improve road safety and traffic management. We present an efficient protocol, called the AEE protocol, for protecting data authenticity and user privacy in V2X applications. Our protocol provides event-based likability, which enables messages from a subject vehicle to be linked

Zijian Wang, Zhengzhi Wu, Zhong Wang

Decoherence is a major obstacle to the preparation of topological order in noisy intermediate-scale quantum devices. Here, we show that decoherence can also give rise to new types of topological order. Specifically, we construct concrete examples by proliferating fermionic anyons in the toric code via local quantum channels. The resulting mixed states retain

Suraj Jyothi Unni, Paras Sheth, Kaize Ding, Huan Liu

Discovering causal relationships in complex socio-behavioral systems is challenging but essential for informed decision-making. We present Upload, PREprocess, Visualize, and Evaluate (UPREVE), a user-friendly web-based graphical user interface (GUI) designed to simplify the process of causal discovery. UPREVE allows users to run multiple algorithms simultane

Jingjia Shi, Shuaifeng Zhi, Kai Xu

The challenging task of 3D planar reconstruction from images involves several sub-tasks including frame-wise plane detection, segmentation, parameter regression and possibly depth prediction, along with cross-frame plane correspondence and relative camera pose estimation. Previous works adopt a divide and conquer strategy, addressing above sub-tasks with dis

Seyed Mojtaba Marvasti-Zadeh, Nilanjan Ray, Nadir Erbilgin

Semi-supervised object detection (SSOD) aims to improve the performance and generalization of existing object detectors by utilizing limited labeled data and extensive unlabeled data. Despite many advances, recent SSOD methods are still challenged by inadequate model refinement using the classical exponential moving average (EMA) strategy, the consensus of T

Francisco Maion, Raul E. Angulo, Thomas Bakx, Nora Elisa Chisari

The intrinsic alignment of galaxies is an important ingredient for modelling weak-lensing measurements, and a potentially valuable cosmological and astrophysical signal. In this paper, we present HYMALAIA: a new model to predict the intrinsic alignments of biased tracers. HYMALAIA is based on a perturbative expansion of the statistics of the Lagrangian shape