February 2024 arXiv papers — page 57

Luca Rüegg, Gaurav Chaudhary, Robert-Jan Slager

Density-balanced, widely separated quantum Hall bilayers at $\nu_T = 1$ can be described as two copies of composite Fermi liquids (CFLs). The two CFLs have interlayer weak-coupling BCS instabilities mediated by gauge fluctuations, the resulting pairing symmetry of which depends on the CFL hypothesis used. If both layers are described by the conventional Halp

Joseph Briggs, Chris Wells

Barber and Erde asked the following question: if $B$ generates $\mathbb Z^d$ as an additive group, then must the extremal sets for the vertex/edge-isoperimetric inequality on the Cayley graph $\operatorname{Cay}(\mathbb Z^d,B)$ form a nested family? We answer this question negatively for both the vertex- and edge-isoperimetric inequalities, specifically in t

Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach

Data scarcity in low-resource languages can be addressed with word-to-word translations from labeled task data in high-resource languages using bilingual lexicons. However, bilingual lexicons often have limited lexical overlap with task data, which results in poor translation coverage and lexicon utilization. We propose lexicon-conditioned data generation Le

Mauricio Hippert, Jorge Noronha, Paul Romatschke

We point out that there is an upper bound on the speed of sound squared given by $c_s^2 \leq 0.781$ valid for all known systems described by relativistic transient hydrodynamics where calculations of certain ratios of hydrodynamic transport coefficients can be performed from first principles. Assuming this bound is valid for ultradense matter implies that th

Takahiro Morishita, Massimo Stiavelli, Claudio Grillo, Piero Rosati

We present deep JWST NIRSpec observations in the sightline of MACS J1149.5+2223, a massive cluster of galaxies at $z=0.54$. We report the spectroscopic redshift of 28 sources at $3<z<9.1$, including 9 sources with the detection of the [OIII]4363 auroral line. Combining these with 16 [OIII]4363-detected sources from publicly available JWST data, our sample co

Lucas Lehnert, Sainbayar Sukhbaatar, DiJia Su, Qinqing Zheng

While Transformers have enabled tremendous progress in various application settings, such architectures still trail behind traditional symbolic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks. This is accomplished by training an encoder-decoder Transformer model to pre

Jimi Sanchez

Frequency Shift Keying (FSK) modulation is widely utilized in various communication systems for data transmission due to its simplicity and robustness. In this paper, we present a Python-centric approach for demodulating FSK signals and extracting bit strings in Software Defined Radio (SDR) systems. Leveraging the flexibility and power of Python programming

Jacob Bedrossian, Weiren Zhao, Ruizhao Zi

In this paper, we study the Vlasov-Poisson-Fokker-Planck (VPFP) equation with a small collision frequency $0 < \nu \ll 1$, exploring the interplay between the regularity and size of perturbations in the context of the asymptotic stability of the global Maxwellian. Our main result establishes the Landau damping and enhanced dissipation phenomena under the con

Chandrajit Bajaj, Minh Nguyen

Despite extensive research, time series classification and forecasting on noisy data remain highly challenging. The main difficulties lie in finding suitable mathematical concepts to describe time series and effectively separate noise from the true signals. Unlike traditional methods treating time series as static vectors or fixed sequences, we propose a nov

Daniel Nolte, Souparno Ghosh, Ranadip Pal

Deep learning models are being adopted and applied on various critical decision-making tasks, yet they are trained to provide point predictions without providing degrees of confidence. The trustworthiness of deep learning models can be increased if paired with uncertainty estimations. Conformal Prediction has emerged as a promising method to pair machine lea

Pranjal Ralegankar, Mak Pavičević, Matteo Viel

Primordial magnetic fields (PMFs) can enhance matter power spectrum on small scales ($\lesssim$ Mpc) and still agree with bounds from cosmic microwave background (CMB) and Faraday rotation measurements. As modes on scales smaller than Mpc have already become non-linear today, exploring PMFs' impact on small-scale structures requires dedicated cosmological si

Animikh Biswas, Michal Branicki

Bayesian state estimation of a dynamical system utilising a stream of noisy measurements is important in many geophysical and engineering applications. We establish rigorous results on (time-asymptotic) accuracy and stability of these algorithms with general covariance and observation operators. The accuracy and stability results for EnKF and EnSRKF for diss

Scott Taylor

We use thin position of Heegaard splittings to give a new proof of Haken's Lemma that a Heegaard surface of a reducible manifold is reducible and of Scharlemann's ``Strong Haken Theorem'': a Heegaard surface for a 3-manifold may be isotoped to intersect a given collection of essential spheres and discs in a single loop each. We also give a reformulation of C

Hoang Ky Nguyen, Bertrand Chauvineau

We present an exact static spherisymmetric solution for the Brans-Dicke action sourced by a self-gravitating massless Klein-Gordon helicity-0 field. In contrast to the Maxwell electromagnetic field, a Klein-Gordon field possesses an energy-momentum tensor with $\textit{non-vanishing trace}$. Upon a Weyl mapping into the Einstein frame, the transformed Brans-

Arvind N. Parmar, Norbert Schartel, Maria Santos Lleo

We examine the outcomes of the regular announcements of observing opportunities for ESA's X-ray observatory XMM-Newton issued between 2001 and 2021. We investigate how success rates vary with the lead proposer's gender, academic age and the country where the proposer's institute is located. The large number of proposals (10,579) and more than 20 years operat

Ivan Morera, Eugene Demler

We use density matrix renormalization group to investigate the phase diagram of the Fermi Hubbard model on a triangular lattice with densities above half-filling, $1 \leq n < 2$. We discuss the important role of kinetic magnetism and magnetic polarons. For strong interactions and low doublon dopings, attractive interaction between polarons results in phase s

Tianyu Gao, Zirui Wang, Adithya Bhaskar, Danqi Chen

An emerging family of language models (LMs), capable of processing both text and images within a single visual view, has the promise to unlock complex tasks such as chart understanding and UI navigation. We refer to these models as screenshot language models. Despite their appeal, existing screenshot LMs substantially lag behind text-only models on language

James G. Rogers, Hilke E. Schlichting, Edward D. Young

Small, close-in exoplanets are divided into two sub-populations: super-Earths and sub-Neptunes. Most super-Earths are thought to have lost their primordially accreted hydrogen-dominated atmospheres via thermally driven winds. We consider the global chemical equilibrium of super-Earths and the lasting impacts of their fleeting hydrogen atmospheres. We find th

Sayan Banerjee, Mathias S. Scheurer

Non-reciprocal superconductivity, also known as the superconducting diode effect, has been extensively studied in the presence of a magnetic field or some form of ferromagnetic order breaking time-reversal symmetry. We here show that another class of magnetic order known as altermagnetism, which also breaks time-reversal symmetry but does not exhibit a finit

Shi-Fan Chen, Cullan Howlett, Martin White, Patrick McDonald

This paper provides a comprehensive overview of how fitting of Baryon Acoustic Oscillations (BAO) is carried out within the upcoming Dark Energy Spectroscopic Instrument's (DESI) 2024 results using its DR1 dataset, and the associated systematic error budget from theory and modelling of the BAO. We derive new results showing how non-linearities in the cluster

Ana Alexandre, Gia Dvali, Emmanouil Koutsangelas

The mass ranges allowed for Primordial Black Holes (PBHs) to constitute all of Dark Matter (DM) are broadly constrained. However, these constraints rely on the standard semiclassical approximation which assumes that the evaporation process is self-similar. Quantum effects such as memory burden take the evaporation process out of the semiclassical regime late

M. Veresvarska, S. Scaringi, S. Hagen, D. De Martino

The classical nova CP Puppis has been observed to have particularly puzzling and peculiar properties. In particular, this classical nova displays occasional bursts in its long-term ASAS-SN light curve. Here we report on 5 sectors of TESS data displaying 2 of these rapid bursts, lasting ~1 day. Based on the estimated lower energy limits of the bursts we discu

Rikab Gambhir, Benjamin Nachman

Jet energy calibration is an important aspect of many measurements and searches at the LHC. Currently, these calibrations are performed on a per-jet basis, i.e. agnostic to the properties of other jets in the same event. In this work, we propose taking advantage of the correlations induced by momentum conservation between jets in order to improve their jet e

Xiaoyang Huang, Andrew Lucas, Umang Mehta, Marvin Qi

We apply "hydrodynamic" effective field theory techniques to an ersatz Fermi liquid. Our effective theory, which captures the correlation functions of density operators at each angle on the Fermi surface, can only deviate from conventional Fermi liquid behavior if the effective theory is non-local. Neglecting non-local effects, the ersatz Fermi liquid's effe

Daniel Schoenberger, Stefan Hillmich, Matthias Brandl, Robert Wille

Trapped-ion quantum computers exhibit promising potential to provide platforms for high-quality qubits and reliable quantum computation. The Quantum Charge Coupled Device (QCCD) architecture is a leading example that offers a modular solution to enable the realization of scalable quantum computers, paving the way for practical quantum algorithms with large q

Ying Li, Ram Seshadri, Stephen D. Wilson, Anthony K. Cheetham

A few $4d$ and $5d$ transition metal compounds with various electron fillings were recently found to exhibit magnetic susceptibilities $\chi$ and magnetic moments that deviate from the well-established Kotani model. This model has been considered for decades to be the canonical expression to describe the temperature dependence of magnetism in systems with no

Jinmin Yi, A. A. Burkov

One of the fundamental facts of condensed matter physics is that sufficient amount of disorder always turns a Fermi liquid metal into an Anderson insulator: a compressible, but non-conducting phase of matter. Recently, topological semimetals have emerged as another way a metallic phase may be realized. In this paper we point out that, unlike ordinary metals,

Stephan Stieberger, Tomasz R. Taylor, Bin Zhu

Carrollian holography is supposed to describe gravity in four-dimensional asymptotically flat space-time by the three-dimensional Carrollian CFT living at null infinity. We transform superstring scattering amplitudes into the correlation functions of primary fields of Carrollian CFT depending on the three-dimensional coordinates of the celestial sphere and a

Kristian Mæland, Bjørnulf Brekke, Asle Sudbø

Altermagnets exhibit a large electron spin splitting which can be understood as a result of strong coupling between itinerant electrons and localized spins. We consider superconductivity due to electron-magnon scattering, using strong-coupling Eliashberg theory to capture many-body effects that are not covered by a weak-coupling approach. The characteristic

Martin Hoferichter, Peter Stoffer, Maximilian Zillinger

We present a new basis for the hadronic light-by-light (HLbL) tensor that is optimized for the evaluation of narrow-resonance contributions to HLbL scattering in the anomalous magnetic moment of the muon. As main advantage, kinematic singularities are manifestly absent for pseudoscalar, scalar, and axial-vector states, while the remaining singularities for t

Arturo de Giorgi, Marta Fuentes Zamoro, Luca Merlo

We present a model where a GeV axion-like-particle (ALP) is predicted in a large portion of the parameter space due to the presence of explicit Peccei-Quinn symmetry-breaking terms in an exotic leptonic sector. The latter provides a solution to the muon $g-2$ anomaly, within the framework of the Linear Seesaw neutrino mechanism. The spectrum is extended by a

Mudit Garg, Andrea Derdzinski, Shubhanshu Tiwari, Jonathan Gair

We assess the possibility of detecting both eccentricity and gas effects (migration and accretion) in the gravitational wave (GW) signal from LISA massive black hole binaries (MBHBs) at redshift $z=1$. Gas induces a phase correction to the GW signal with an effective amplitude ($C_{\rm g}$) and a semi-major axis dependence (assumed to follow a power-law with

Dumitru Călugăru, Haoyu Hu, Rafael Luque Merino, Nicolas Regnault

We study the interacting transport properties of twisted bilayer graphene (TBG) using the topological heavy-fermion (THF) model. In the THF model, TBG comprises localized, correlated $f$-electrons and itinerant, dispersive $c$-electrons. We focus on the Seebeck coefficient, which quantifies the voltage difference arising from a temperature gradient. We find

Trinity L. Webb, Jennifer A. Rodriguez, Laura A. Lopez, Anna L. Rosen

We present the first Chandra X-ray observations of H72.97-69.39, a highly-embedded, potential super-star cluster (SSC) in its infancy located in the star-forming complex N79 of the Large Magellanic Cloud. We detect particularly hard, diffuse X-ray emission that is coincident with the young stellar objects (YSOs) identified with JWST, and the hot gas fills ca

Gustavo E. Medina, Ricardo R. Muñoz, Jeffrey L. Carlin, A. Katherina Vivas

In order to constrain the evolutionary history of the Milky Way, we hunt for faint RR Lyrae stars (RRLs) using Dark Energy Camera data from the High cadence Transient Survey (HiTS) and the Halo Outskirts With Variable Stars (HOWVAST) survey. We report the detection of $\sim500$ RRLs, including previously identified stars and $\sim90$ RRLs not yet reported. W

Kyohei Mukaida, Masaki Yamada

We investigate the thermalization of high-energy particles injected from the perturbative decay of inflaton during the pre-thermal phase of reheating in detail. In general, thermalization takes a relatively long time in a low-temperature plasma; therefore, the instantaneous thermalization approximation is not justified, even for the reheating of the Standard

Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah

It has recently been shown that adversarial attacks on large language models (LLMs) can "jailbreak" the model into making harmful statements. In this work, we argue that the spectrum of adversarial attacks on LLMs is much larger than merely jailbreaking. We provide a broad overview of possible attack surfaces and attack goals. Based on a series of concrete e

Tobias Boege, Janneke H. Bolt, Milan Studený

We introduce an algebraic concept of the frame for abstract conditional independence (CI) models, together with basic operations with respect to which such a frame should be closed: copying and marginalization. Three standard examples of such frames are (discrete) probabilistic CI structures, semi-graphoids and structural semi-graphoids. We concentrate on th

Di Wu, Wasi Uddin Ahmad, Kai-Wei Chang

This study addresses the application of encoder-only Pre-trained Language Models (PLMs) in keyphrase generation (KPG) amidst the broader availability of domain-tailored encoder-only models compared to encoder-decoder models. We investigate three core inquiries: (1) the efficacy of encoder-only PLMs in KPG, (2) optimal architectural decisions for employing en

Pierre Collet, Servet Martínez

We consider a stationary Markovian evolution with values on a disjointly partitioned set space $I\sqcup {\cal E}$. The evolution is visible (in the sense of knowing the transition probabilities) on the states in $I$ but not for the states in ${\cal E}$. One only knows some partial information on the transition probabilities on ${\cal E}$, the input and outpu

Jun Li, Jihwan Youn, Ryan Wu, Jeroen Overdevest

In automotive radar, time-domain thresholding (TD-TH) and time-frequency domain thresholding (TFD-TH) are crucial techniques underpinning numerous interference mitigation methods. Despite their importance, comprehensive evaluations of these methods in dense traffic scenarios with different types of interference are limited. In this study, we segment automoti

I. Silber, A. Azulay, A. Basha, D. Ketchker

Oxide interfaces feature unique two-dimensional (2D) electronic systems with diverse electronic properties such as tunable spin-orbit interaction and superconductivity. Conductivity emerges in these interfaces when the thickness of an epitaxial polar layer surpasses a critical value, leading to charge transfer to the interface. Here, we show that depositing

Heli Ben-Hamu, Omri Puny, Itai Gat, Brian Karrer

Taming the generation outcome of state of the art Diffusion and Flow-Matching (FM) models without having to re-train a task-specific model unlocks a powerful tool for solving inverse problems, conditional generation, and controlled generation in general. In this work we introduce D-Flow, a simple framework for controlling the generation process by differenti

Vyas Raina, Adian Liusie, Mark Gales

Large Language Models (LLMs) are powerful zero-shot assessors used in real-world situations such as assessing written exams and benchmarking systems. Despite these critical applications, no existing work has analyzed the vulnerability of judge-LLMs to adversarial manipulation. This work presents the first study on the adversarial robustness of assessment LLM

Shashwat Goel, Ameya Prabhu, Philip Torr, Ponnurangam Kumaraguru

Machine Learning models increasingly face data integrity challenges due to the use of large-scale training datasets drawn from the Internet. We study what model developers can do if they detect that some data was manipulated or incorrect. Such manipulated data can cause adverse effects including vulnerability to backdoored samples, systemic biases, and reduc

P. Bouchet, R. Gastaud, A. Coulais, M. J. Barlow

There exist very few mid-infrared (IR) observations of supernovae (SNe) in general. Therefore, SN 1987A, the closest visible SN in 400 years, gives us the opportunity to explore the mid-IR properties of SNe, the dust in their ejecta and surrounding medium, and to witness the birth of a SN remnant (SNR). The James Webb Space Telescope (JWST), with its high sp

Arpit Agarwal, Rad Niazadeh, Prathamesh Patil

In digital health and EdTech, recommendation systems face a significant challenge: users often choose impulsively, in ways that conflict with the platform's long-term payoffs. This misalignment makes it difficult to effectively learn to rank items, as it may hinder exploration of items with greater long-term payoffs. Our paper tackles this issue by utilizing

Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir

We introduce and study a family of online metric problems with long-term constraints. In these problems, an online player makes decisions $\mathbf{x}_t$ in a metric space $(X,d)$ to simultaneously minimize their hitting cost $f_t(\mathbf{x}_t)$ and switching cost as determined by the metric. Over the time horizon $T$, the player must satisfy a long-term dema

Heejong Lee

Let $K/\mathbb{Q}_p$ be a finite extension with residue field $k$. By a work of Emerton--Gee, irreducible components inside the reduced special fiber of the moduli stack of rank $n$ \'etale $(\varphi,\Gamma)$-modules are labeled by Serre weights of $\mathrm{GL}_n(k)$. Let $\sigma$ be a non-Steinberg Serre weight and $\mathcal{C}_\sigma$ be the corresponding

Eduardo Zubizarreta Casalengua, Fabrice P. Laussy, Elena del Valle

We discuss two-photon physics, taking for illustration the particular but topical case of resonance fluorescence. We show that the basic concepts of interferences and correlations provide at the two-photon level an independent and drastically different picture than at the one-photon level, with landscapes of correlations that reveal various processes by span

Arturs Berzins, Andreas Radler, Eric Volkmann, Sebastian Sanokowski

Geometry is a ubiquitous tool in computer graphics, design, and engineering. However, the lack of large shape datasets limits the application of state-of-the-art supervised learning methods and motivates the exploration of alternative learning strategies. To this end, we introduce geometry-informed neural networks (GINNs) -- a framework for training shape-ge

Ankit Bisain, Peter Humphries, Andrei Mandelshtam, Noah Walsh

It is a folklore result in arithmetic quantum chaos that quantum unique ergodicity on the modular surface with an effective rate of convergence follows from subconvex bounds for certain triple product $L$-functions. The physical space manifestation of this result, namely the equidistribution of mass of Hecke-Maass cusp forms, was proven to follow from subcon

Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu

Recent advancements have seen Large Language Models (LLMs) and Large Multimodal Models (LMMs) surpassing general human capabilities in various tasks, approaching the proficiency level of human experts across multiple domains. With traditional benchmarks becoming less challenging for these models, new rigorous challenges are essential to gauge their advanced

Zhiwei He, Binglin Zhou, Hongkun Hao, Aiwei Liu

Text watermarking technology aims to tag and identify content produced by large language models (LLMs) to prevent misuse. In this study, we introduce the concept of cross-lingual consistency in text watermarking, which assesses the ability of text watermarks to maintain their effectiveness after being translated into other languages. Preliminary empirical re

Keenan E. Avers, Yun Suk Eo, Hyeok Yoon, Jarryd A. Horn

FeSi is a curious example of a $d$-electron system that manifests many of the same phenomena associated with $f$-electron Kondo insulators, including conducting surface states with potentially non-trivial topology. Here we investigate the magnetization and magnetotransport of these surface states and how a 2D ferromagnetic state at the surface of FeSi influe

Serena Wang, Michael I. Jordan, Katrina Ligett, R. Preston McAfee

Online platforms and regulators face a continuing problem of designing effective evaluation metrics. While tools for collecting and processing data continue to progress, this has not addressed the problem of "unknown unknowns", or fundamental informational limitations on part of the evaluator. To guide the choice of metrics in the face of this informational

Gustavo Jasso

A theorem of Keller states that the Yoneda algebra of the simple modules over a finite-dimensional algebra is generated in cohomological degrees $0$ and $1$ as a minimal $A_\infty$-algebra. We provide a proof of an extension of Keller's theorem to abelian length categories by reducing the problem to a particular class of Nakayama algebras, where the claim ca

Seungjin Han, Alex Sam

We study multidimensional signaling (cognitive/non-cognitive) as a sender's portfolio choice with a resource constraint. We establish the existence of a unique monotone D1 equilibrium where the cognitive (non-cognitive) signal increases (decreases) in sender type and the sum of the two increases in sender type. The equilibrium is characterized by two thresho

Aline Ioste

Large Language Models with transformer architecture have revolutionized the domain of text generation, setting unprecedented benchmarks. Despite their impressive capabilities, LLMs have been criticized for generating outcomes that deviate from factual accuracy or display logical inconsistencies, phenomena commonly referred to as hallucinations. This term, ho

James P Higham, David Colquhoun

A fundamental mistake in receptor theory has led to an enduring misunderstanding of how to estimate the affinity and efficacy of an agonist. These properties are inextricably linked and cannot be easily separated in any case where the binding of a ligand induces a conformation change in its receptor. Consequently, binding curves and concentration-response re

Doriann Blain, Alejandro Sánchez-López, Paul Mollière

Ground-based high-resolution spectra provide a powerful tool for characterising exoplanet atmospheres. However, they are greatly hampered by the dominating telluric and stellar lines, which need to be removed prior to any analysis. Such removal techniques ("preparing pipelines") deform the spectrum, hence a key point is to account for this process in the for

Shreyash Mishra, Shrey Shah, Rex Pereira

This paper introduces the Multi-Step Action Model (MSAM), a closed-source AI model designed by Empsing to address challenges hindering AI adoption in enterprises. Through a holistic examination, this paper explores MSAM's foundational principles, design architecture, and future trajectory. It evaluates MSAM's performance via rigorous testing methodologies an

Qingyan Bai, Zifan Shi, Yinghao Xu, Hao Ouyang

This work presents 3DPE, a practical method that can efficiently edit a face image following given prompts, like reference images or text descriptions, in a 3D-aware manner. To this end, a lightweight module is distilled from a 3D portrait generator and a text-to-image model, which provide prior knowledge of face geometry and superior editing capability, res

Dominik Schröder, Daniil Dmitriev, Hugo Cui, Bruno Loureiro

For a large class of feature maps we provide a tight asymptotic characterisation of the test error associated with learning the readout layer, in the high-dimensional limit where the input dimension, hidden layer widths, and number of training samples are proportionally large. This characterization is formulated in terms of the population covariance of the f

Yemon Choi

For a finite group $G$, let ${\rm AD}(G)$ denote the Fourier norm of the antidiagonal in $G\times G$. It was shown recently by the author (IMRN, 2023) that ${\rm AD}(G)$ coincides with the amenability constant of the Fourier algebra of $G$, and is equal to the normalized sum of the cubes of character degrees of $G$. Motivated by a gap result for amenability

Joshua Stucky

Let $\phi(n)$denote Euler's phi function. We study the distribution of the numbers $gcd(n,\phi(n))$ and their divisors. Our results generalize previous results of Erd\H{o}s and Pollack.

Gabriel Luz Almeida

Using effective field theory methods, we compute in detail the Lagrangian for the conservative dynamics of compact binary systems, for spinless constituents and in the gravitationally bound case, in massless scalar-tensor (ST) and Einstein-scalar-Gauss-Bonnet gravity (EsGB) to the second post-Newtonian (2PN) order. We employ the Kaluza-Klein parametrization

Jose Rios-Sanchez, German Sborlini

Renormalization is a well-known technique to get rid of ultraviolet (UV) singularities. When relying on Dimensional Regularization (DREG), these become manifest as $\epsilon$-poles, allowing to define counter-terms with useful recursive properties. However, this procedure requires to work at \emph{integral-level} and poses difficulties to achieve a smooth co

Milo Moses, Jacek Horecki, Konrad Deka, Jan Tulowiecki

We present a discussion of the generalized Clifford group over non-cyclic finite abelian groups. These Clifford groups appear naturally in the theory of topological error correction and abelian anyon models. We demonstrate a generalized Gottesman-Knill theorem, stating that every Clifford circuit can be efficiently classically simulated. We additionally prov

Alejandro Salinas, Amit Haim, Julian Nyarko

We employ an audit design to investigate biases in state-of-the-art large language models, including GPT-4. In our study, we prompt the models for advice involving a named individual across a variety of scenarios, such as during car purchase negotiations or election outcome predictions. We find that the advice systematically disadvantages names that are comm

Guiye Li, Guofeng Cao

Addressing the challenges of climate change requires accurate and high-resolution mapping of geospatial data, especially climate and weather variables. However, many existing geospatial datasets, such as the gridded outputs of the state-of-the-art numerical climate models (e.g., general circulation models), are only available at very coarse spatial resolutio

M. Bonnett Del Alamo, R. Helaconde, C. Soncco, J. Bazo

We evaluate the performance of an Omnivision OV5647 CMOS image sensor (5 Mp) for detecting radiation from Sr90 and Cs137 sources. Our experimental setup uses a Raspberry Pi 3 mini-computer for data acquisition, with image processing using Python and OpenCV libraries. We specify the necessary settings to convert a standard camera into a particle detector sens

Jingjin Li, Jiajing Guo, Gilly Leshed

Mindfulness practice has many mental and physical well-being benefits. With the increased popularity of live stream technologies and the impact of COVID-19, many people have turned to live stream tools to participate in online meditation sessions. To better understand the practices, challenges, and opportunities in live-stream meditation, we conducted a thre

Yu Zhao, Yuanbin Qu, Konrad Staniszewski, Szymon Tworkowski

Most language model pre-training frameworks concatenate multiple documents into fixed-length sequences and use causal masking to compute the likelihood of each token given its context; this strategy is widely adopted due to its simplicity and efficiency. However, to this day, the influence of the pre-training sequence composition strategy on the generalisati

Timur Aslyamov, Massimiliano Esposito

We consider Markov jump processes on a graph described by a rate matrix that depends on various control parameters. We derive explicit expressions for the static responses of edge currents and steady-state probabilities. We show that they are constrained by the graph topology (i.e. the incidence matrix) by deriving response relations (i.e. linear constraints

Yongcun Song, Ziqi Wang, Enrique Zuazua

Federated learning (FL) is a promising framework for learning from distributed data while maintaining privacy. The development of efficient FL algorithms encounters various challenges, including heterogeneous data and systems, limited communication capacities, and constrained local computational resources. Recently developed FedADMM methods show great resili

Jun-Kun Wang

We propose an optimization algorithm called Frictionless Hamiltonian Descent, which is a direct counterpart of classical Hamiltonian Monte Carlo in sampling. We analyze Frictionless Hamiltonian Descent for strongly convex quadratic functions and show that the method has a non-trivial accelerated rate as that of Heavy Ball flow. We also propose Frictionless C

Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer, Michalis Vazirgiannis

Graph Neural Networks (GNNs) have emerged as the dominant approach for machine learning on graph-structured data. However, concerns have arisen regarding the vulnerability of GNNs to small adversarial perturbations. Existing defense methods against such perturbations suffer from high time complexity and can negatively impact the model's performance on clean

Ramon Códamo, Plamen Koshlukov

In this paper we study algebras acted on by a finite group $G$ and the corresponding $G$-identities. Let $M_2( \mathbb{C})$ be the $2\times 2$ matrix algebra over the field of complex numbers $ \mathbb{C}$ and let $sl_2( \mathbb{C})$ be the Lie algebra of traceless matrices in $M_2( \mathbb{C})$. Assume that $G$ is a finite group acting as a group of automor

Keren Censor-Hillel, Majd Khoury

The distance of a graph from being triangle-free is a fundamental graph parameter, counting the number of edges that need to be removed from a graph in order for it to become triangle-free. Its corresponding computational problem is the classic minimum triangle edge transversal problem, and its normalized value is the baseline for triangle-freeness testing a

Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan

Machine learning force fields (MLFFs) are an attractive alternative to ab-initio methods for molecular dynamics (MD) simulations. However, they can produce unstable simulations, limiting their ability to model phenomena occurring over longer timescales and compromising the quality of estimated observables. To address these challenges, we present Stability-Aw

Stephan Narison

1. I update my previous comparison of the theoretical value of the muon anomaly with the new measurement and found $\Delta a_\mu\equiv a_\mu^{exp}-a_\mu^{th} = (143\pm 42_{th}\pm 22_{exp})\times 10^{-11}$ which is about 3$\sigma$ discrepancy between the SM predictions and experiment. 2. I improve the estimate of QCD power corrections up to dimension D=12 and

Ramon Códamo, Plamen Koshlukov

Let $D$ be a Noetherian infinite integral domain, denote by $M_2(D)$ and by $sl_2(D)$ the $2\times 2$ matrix algebra and the Lie algebra of the traceless matrices in $M_2(D)$, respectively. In this paper we study the natural grading by the cyclic group $\mathbb{Z}_2$ of order 2 on $M_2(D)$ and on $sl_2(D)$. We describe a finite basis of the graded polynomial

Prasanna Salasiya, Shixu Meng, Bojan B. Guzina

We investigate the scattering of scalar plane waves in two dimensions by a heterogeneous layer that is periodic in the direction parallel to its boundary. On describing the layer as a union of periodic laminae, we develop a solution of the scattering problem by merging the concept of propagator matrices and that of Bloch eigenstates featured by the unit cell

Li-Li Ye, Chen-Di Han, Liang Huang, Ying-Cheng Lai

When a quantum particle moves in a curved space, a geometric potential can arise. In spite of a long history of extensive theoretical studies, to experimentally observe the geometric potential remains to be a challenge. What are the physically observable consequences of such a geometric potential? Solving the Schrodinger equation on a truncated conic surface

Simon Dräger, Maike Sonnewald

Machine Learning has become a pervasive tool in climate science applications. However, current models fail to address nonstationarity induced by anthropogenic alterations in greenhouse emissions and do not routinely quantify the uncertainty of proposed projections. In this paper, we model the Atlantic Meridional Overturning Circulation (AMOC) which is of maj

Christian Gaetz, Oliver Pechenik, Stephan Pfannerer, Jessica Striker

Webs give a diagrammatic calculus for spaces of $U_q(\mathfrak{sl}_r)$-tensor invariants, but intrinsic characterizations of web bases are only known in certain cases. Recently, we introduced hourglass plabic graphs to give the first such $U_q(\mathfrak{sl}_4)$-web bases. Separately, Fraser introduced a web basis for Pl\"{u}cker degree two representations of

Matthew Rosenzweig, Sylvia Serfaty

This note extends the modulated entropy and free energy methods for proving mean-field limits/propagation of chaos to the whole space without any confining potential, in contrast to previous work limited to the torus or requiring confinement in the whole space, for all log/Riesz flows. Our novel idea is a scale transformation, sometimes called self-similar c

Liangbing Luo, Robert W. Neel

We develop an approach to constructing non-Markovian, non-co-adapted couplings for sub-Riemannian Brownian motions in sub-Riemannian manifolds with large symmetry groups by treating the specific cases of the three-dimensional Heisenberg group, higher-dimensional non-isotropic Heisenberg groups, SL(2,R) and its universal cover, and SU(2). Our primary focus is

Alice Cortinovis, Lexing Ying

In this work, we analyze a sublinear-time algorithm for selecting a few rows and columns of a matrix for low-rank approximation purposes. The algorithm is based on an initial uniformly random selection of rows and columns, followed by a refinement of this choice using a strong rank-revealing QR factorization. We prove bounds on the error of the corresponding

Lu Liu, Yuntian Liu, Jiayu Li, Hua Wu

Quantum spin Hall (QSH) effect, where electrons with opposite spin channels are deflected to opposite sides of a two-dimensional system with a quantized conductance, was believed to be characterized by a nontrivial topological index $Z_{2}$. However, spin mixing effects in realistic materials often lead to deviation of the spin Hall conductance from exact qu

Jiahao Zhang, Rui Xue, Wenqi Fan, Xin Xu

In an era of information explosion, recommender systems are vital tools to deliver personalized recommendations for users. The key of recommender systems is to forecast users' future behaviors based on previous user-item interactions. Due to their strong expressive power of capturing high-order connectivities in user-item interaction data, recent years have

K. E. L. de Farias, M. A. Anacleto, F. A. Brito, E. Passos

In this study, we consider the four-dimensional Maxwell electrodynamics extended with CPT-even Myers-Pospelov Lorentz-violating dimension-six operators to investigate the associated two-dimensional properties in the context of quantum vacuum fluctuation effects, namely, the Casimir effect. Upon projecting out the 4D theory down to a 2D theory we obtain analo

Yvain Bruned, Kurusch Ebrahimi-Fard, Yingtong Hou

We propose a novel way to study numerical methods for ordinary differential equations in one dimension via the notion of multi-indice. The main idea is to replace rooted trees in Butcher's B-series by multi-indices. The latter were introduced recently in the context of describing solutions of singular stochastic partial differential equations. The combinator

Eduardo Alves da Silva

This paper aims to study the birational geometry of log Calabi-Yau pairs$(\mathbb{P}^3, D)$ of coregularity 2, where in this case $D$ is an irreducible normal quartic surface with canonical singularities. We completely classify which toric weighted blowups of a point will initiate a volume preserving Sarkisov link starting with this pair. Depending on the ty

Johannes Droschl

Let $\mathrm{F}$ be a local non-archimedean field of residue characteristic $p$ and $\overline{\mathbb{F}}_\ell$ an algebraic closure of a finite field of characteristic $\ell \neq p$. We extend the results of Lapid and M\'inguez concerning $\square$-irreducible representations of inner forms of $\mathrm{GL}_n(\mathrm{F})$ to representations over $\overline{

Eduardo Alves da Silva

This paper aims to study the decomposition group of a nonsingular plane cubic under the light of the log Calabi-Yau geometry. Using this approach we prove that an appropriate algorithm of the Sarkisov Program in dimension 2 applied to an element of this group is automatically volume preserving. From this, we deduce some properties of the (volume preserving)

V. V. Vien

We propose a minimal extended seesaw scheme based on the discrete symmetry $A_4\times Z_4\times Z_2\times Z_8$ which can successfully address neutrino phenomenology and keV sterile neutrino dark matter. The lepton mass hierarchy is naturally achieved. Active neutrino mixing angles can reached the best-fit points with the predictive Dirac CP violation phase.

Edgar Medina, Leyong Loh, Namrata Gurung, Kyung Hun Oh

Human motion prediction is still an open problem extremely important for autonomous driving and safety applications. Due to the complex spatiotemporal relation of motion sequences, this remains a challenging problem not only for movement prediction but also to perform a preliminary interpretation of the joint connections. In this work, we present a Context-b

F. M. Brunbauer, C. Chatterjee, G. Cicala, A. Cicuttin

The proposed new Electron-Ion Collider poses a technical and intellectual challenge for the detector design to accommodate the long-term diverse physics goals envisaged by the program. This requires a 4{\pi} detector system capable of reconstructing the energy and momentum of final state particles with high precision. The Electron-Ion Collider also requires