December 2024 arXiv papers — page 47

Alexander P. Mangerel

Let $\lambda$ be the Liouville function. Assuming the Generalised Riemann Hypothesis for Dirichlet $L$-functions (GRH), we show that for every sufficiently large even integer $N$ there are $a,b \geq 1$ such that $$ a+b = N \text{ and } \lambda(a) = \lambda(b) = -1. $$ This conditionally answers an analogue of the binary Goldbach problem for the Liouville fun

Blaise Gassend

An exponential space elevator is a space elevator with a tether cross-section that varies exponentially with altitude. With such an elevator it is possible to reel in tether material at one end of the elevator while reeling out at the other end, without changing the overall taper profile. I show how to use this property to build up or clone a space elevator

Nelson Colón Vargas

Machine learning models offer powerful predictive capabilities but often lack transparency. Local Interpretable Model-agnostic Explanations (LIME) addresses this by perturbing features and measuring their impact on a model's output. In text-based tasks, LIME typically removes present words (bits set to 1) to identify high-impact tokens. We propose \textbf{Q-

Kam Cheong Au

By introducing a novel integration kernel for Mellin transform, we uncover many previously unknown and intriguing properties of the Witten zeta functions of rank two and three. Detailed results concerning their pole locations, residues, and special values are obtained. We propose a non-trivial conjecture regarding their derivatives at the origin, which seems

Eli Zoghlin, Juliette Plo, Gaihua Ye, Cynthia Nnokwe

Large, high-purity single-crystals of hexagonal BN (h-BN) are essential for exploiting its many desirable and interesting properties. Here, we demonstrate via X-ray tomography, X-ray diffraction and scanning electron microscopy that h-BN crystals can be grown by traveling-solvent floating-zone (TSFZ). The diameters of grown boules range from 3 - 5 mm with le

Qianli Ma, Brianna R. Billingsley, Madalynn Marshall, David A. Dahlbom

We report a comprehensive study on the origin of the enigmatic disordered ground state within the Shastry-Sutherland lattice, BaCe$_2$ZnS$_5$, at low temperatures. The magnetization and heat capacity data show a lack of magnetic ordering down to 73 mK. We deploy a localized spin dimer model which can accurately reproduce the dynamic structure factor of the n

Senne Vervoort, Lukas Nulens, Davi A. D. Chaves, Heleen Dausy

Providing radio frequency (RF) signals to circuits working in cryogenic conditions requires bulky and expensive transmission cabling interfacing specialized RF electronics anchored at room temperature. Superconducting Josephson junction arrays (JJAs) can change this paradigm by placing the RF source and detector inside the chip. In this work, we demonstrate

Francesco Lettich, Emanuele Carlini, Franco Maria Nardini, Raffaele Perego

The rise of Artificial Intelligence and Large Language Models is driving increased GPU usage in data centers for complex training and inference tasks, impacting operational costs, energy demands, and the environmental footprint of large-scale computing infrastructures. This work addresses the online scheduling problem in GPU datacenters, which involves sched

David Krejcirik, Tho Nguyen Duc, Nicolas Raymond

We analyze the spectrum of the Laplace operator, subject to homogeneous complex magnetic fields in the plane. For real magnetic fields, it is well-known that the spectrum consists of isolated eigenvalues of infinite multiplicities (Landau levels). We demonstrate that when the magnetic field has a nonzero imaginary component, the spectrum expands to cover the

Chao Yang, Zhujun Zhang

Recently, two extraordinary results on aperiodic monotiles have been obtained in two different settings. One is a family of aperiodic monotiles in the plane discovered by Smith, Myers, Kaplan and Goodman-Strauss in 2023, where rotation is allowed, breaking the 50-year-old record (aperiodic sets of two tiles found by Roger Penrose in the 1970s) on the minimum

Xinguang Ying, Junji Jia

In this work, we develop a general perturbative procedure to find the off-equatorial plane deflections in the weak deflection limit in general stationary and axisymmetric spacetimes, allowing the existence of the generalized Carter constant. Deflections of both null and timelike rays, with the finite distance effect of the source and detector taken into acco

Stefan Schreieder

For a natural class of cohomology theories with support (including étale or pro-étale cohomology with suitable coefficients), we prove a moving lemma for cohomology classes with support on smooth quasi-projective k-varieties that admit a smooth projective compactification (e.g. if char(k)=0). This has the following consequences for such k-varieties and cohom

Toshiya Ikenobe, Takahiro Yamada, Jun-ichi Yamaura, Tamio Oguchi

Compounds with the anti-PbFCl structure exhibit a variety of electronic instabilities and intriguing physical properties. NaAlSi and NaAlGe are similar topological nodal-line semimetals, but they have distinct properties. NaAlSi is a superconductor at 6.8 K, whereas NaAlGe is an insulator with a pseudogap of approximately 100 K. Using the potassium-indium fl

Israel R. Curbelo

We study the online coloring of $\sigma$-interval graphs, which are interval graphs with interval lengths in $[1,\sigma]$ and 2-count interval graphs, which are interval graphs that require at most two distinct interval lengths. For $\sigma$-interval graphs, the Kierstead-Trotter algorithm has competitive ratio 3 and no online algorithm has competitive ratio

Jakob Gollwitzer, Jeffrey Z. Kaaret, Y. Eren Suyolcu, Guru Khalsa

We investigate the ultrafast structural dynamics of LaAlO3 thin films driven by short mid-infrared laser pulses at 20 THz. Time-resolved X-ray diffraction reveals an immediate lattice expansion and an acoustic breathing mode of the film. First-principles theory and a spring-mass model identify the direct coupling between coherently driven infrared-active pho

Hugo García-Compeán, César I. Ramos

In this work a formalism for proportional generalized double Kerr-Schild ansatz in bigravity is considered, where both metrics are coupled to matter. We study time-dependent and stationary solutions in the framework of the Kerr-Schild classical double copy and obtain the classical Kerr-Schild for the double, single and zeroth copy equations. For the time-dep

Brandon Monsen, Martin Claassen

Conventional theories for Mott insulators involve well-localized electronic orbitals. This picture fails in the presence of topological obstructions in Chern bands which prevent the formation of exponentially localized orbitals and are instead often viewed by analogy to Landau levels. Here, we show that strong interactions in fractionally-filled topological

Jio Oh, Geon Heo, Seungjun Oh, Hyunjin Kim

Large Language Models (LLMs) often struggle with requests related to information retrieval and data manipulation that frequently arise in real-world scenarios under multiple conditions. In this paper, we demonstrate that leveraging tabular structures in LLM interactions, is more effective than utilizing other structures for handling prevalent requests that o

Kevin Luong, Michael Thielscher

Continual Learning models aim to learn a set of tasks under the constraint that the tasks arrive sequentially with no way to access data from previous tasks. The Online Continual Learning framework poses a further challenge where the tasks are unknown and instead the data arrives as a single stream. Building on existing work, we propose a method for identify

Jaehyeok Jin, Gregory A. Voth

Coarse-grained (CG) modeling has gained significant attention in recent years due to its wide applicability in enhancing the spatiotemporal scales of molecular simulations. While CG simulations, often performed with Hamiltonian mechanics, faithfully recapitulate structural correlations at equilibrium, they lead to ambiguously accelerated dynamics. In the fir

Chayan Chatterjee, Karan Jani

Gravitational wave observations by ground based detectors such as LIGO and Virgo have transformed astrophysics, enabling the study of compact binary systems and their mergers. However, transient noise artifacts, or glitches, pose a significant challenge, often obscuring or mimicking signals and complicating their analysis. In this work, we extend the Attenti

Xiao Li, Jaemoon Lee, Anand Rangarajan, Sanjay Ranka

We present a foundation model (FM) for lossy scientific data compression, combining a variational autoencoder (VAE) with a hyper-prior structure and a super-resolution (SR) module. The VAE framework uses hyper-priors to model latent space dependencies, enhancing compression efficiency. The SR module refines low-resolution representations into high-resolution

Fabio Le Piane, Matteo Baldoni, Mauro Gaspari, Francesco Mercuri

Advancements of both computational and experimental tools have recently led to significant progress in the development of new advanced and functional materials, paralleled by a quick growth of the overall amount of data and information on materials. However, an effective unfolding of the potential of advanced and data-intensive methodologies requires systema

Stephen Whitelam, Corneel Casert

We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarde

Wira Azmoon Ahmad, Matthew Sutcliffe

It is known that a quantum circuit may be simulated with classical hardware via stabilizer state (T-)decomposition in $O(2^{\alpha t})$ time, given $t$ non-Clifford gates and a decomposition efficiency $\alpha$. The past years have seen a number of papers presenting new decompositions of lower $\alpha$ to reduce this runtime and enable simulation of ever lar

Zhaoyang Shi, Chinmoy Bhattacharjee, Krishnakumar Balasubramanian, Wolfgang Polonik

We establish Gaussian approximation bounds for covariate and rank-matching-based Average Treatment Effect (ATE) estimators. By analyzing these estimators through the lens of stabilization theory, we employ the Malliavin-Stein method to derive our results. Our bounds precisely quantify the impact of key problem parameters, including the number of matches and

Vanessa Su, Nirmalya Thakur

This study presents a data-driven analysis of COVID-19 discourse on YouTube, examining the sentiment, toxicity, and thematic patterns of video content published between January 2023 and October 2024. The analysis involved applying advanced natural language processing (NLP) techniques: sentiment analysis with VADER, toxicity detection with Detoxify, and topic

Tatiana A. Uaman Svetikova, Igor Ilyakov, Alexey Ponomaryov, Thales V. A. G. de Oliveira

The use of the THz frequency domain in future network generations offers an unparalleled level of capacity, which can enhance innovative applications in wireless communication, analytics, and imaging. Communication technologies rely on frequency mixing, enabling signals to be converted from one frequency to another and transmitted from a sender to a receiver

Jisun Lee, Andrés Gómez, Alper Atamtürk

We study a multi-period convex quadratic optimization problem, where the state evolves dynamically as an affine function of the state, control, and indicator variables in each period. We begin by projecting out the state variables using linear dynamics, resulting in a mixed-integer quadratic optimization problem with a (block-) factorizable cost matrix. We d

Prateeksha Sharma, Dor Oz, Eleftheria Lampadariou, Spyros Doukas

We investigate advanced CMOS-compatible Graphene/Silicon active metasurfaces based on guided-mode resonance filters. The simulated results show a high extinction ratio (>25 dB), narrow linewidth (~1.5 nm @1550 nm), quality factor of Q~1000, and polarization-insensitive operation. By taking advantage of graphene broadband absorption, we present a multispectra

Md Mahmuddun Nabi Murad, Mehmet Aktukmak, Yasin Yilmaz

Time series forecasting is crucial for various applications, such as weather forecasting, power load forecasting, and financial analysis. In recent studies, MLP-mixer models for time series forecasting have been shown as a promising alternative to transformer-based models. However, the performance of these models is still yet to reach its potential. In this

Wooyeon Jo, Hyunsouk Cho

Index tracking is a popular passive investment strategy aimed at optimizing portfolios, but fully replicating an index can lead to high transaction costs. To address this, partial replication have been proposed. However, the cardinality constraint renders the problem non-convex, non-differentiable, and often NP-hard, leading to the use of heuristic or neural

Thomas G. Rizzo

The kinetic mixing (KM) portal, by which the Standard Model (SM) photon mixes with a light dark photon arising from a new $U(1)_D$ gauge group, allows for the possibility of viable scenarios of sub-GeV thermal dark matter (DM) with appropriately suppressed couplings to the SM. This KM can only occur if particles having both SM and dark quantum numbers, here

Amit Shaham, Ariel Epstein

Embedding normal susceptibilities in metasurfaces (MS), in tandem with their tangential counterparts, greatly enriches their spatial dispersion. Particularly, judicious siphoning of the microscopic nonlocality associated with such enhanced meta-atoms facilitates global control over the MS response across the entire angular range, specifically at challenging

Liam Pinchbeck, Csaba Balazs, Eric Thrane

Searches for annihilating dark matter are often designed with a specific dark matter candidate in mind. However, the space of potential dark matter models is vast, which raises the question: how can we search for dark matter without making strong assumptions about unknown physics. We present a model-independent approach for measuring dark matter annihilation

Runjin Chen, Mingxuan Ju, Ngoc Bui, Dimosthenis Antypas

Generative recommendation systems, driven by large language models (LLMs), present an innovative approach to predicting user preferences by modeling items as token sequences and generating recommendations in a generative manner. A critical challenge in this approach is the effective tokenization of items, ensuring that they are represented in a form compatib

Nidhin Harilal, Amit Kiran Rege, Reza Akbarian Bafghi, Maziar Raissi

Self-supervised learning (SSL) has revolutionized learning from large-scale unlabeled datasets, yet the intrinsic relationship between pretraining data and the learned representations remains poorly understood. Traditional supervised learning benefits from gradient-based data attribution tools like influence functions that measure the contribution of an indi

Melissa Lopez, Giada Caneva, Ana Martins, Stefano Schmidt

The direct observation of intermediate-mass black holes (IMBH) populations would not only strengthen the possible evolutionary link between stellar and supermassive black holes, but unveil the details of the pair-instability mechanism and elucidate their influence in galaxy formation. Conclusive observation of IMBHs remained elusive until the detection of gr

Suman Kumar Das, Pujyasmita Nayak

The dynamic environment context necessitates harnessing digital technologies, including artificial intelligence and the Internet of Things, to supply high-resolution, real-time meteorological data to support agricultural decision-making and improve overall farm productivity and sustainability. This study investigates the potential application of various AI-p

Víctor E. Fernández, Natalie M. Paquette

There is a special set of massless four-dimensional gauge theories which admit local and gauge-anomaly-free uplifts to twistor space; we call such theories twistorial. In twistorial theories, generalized towers of soft modes (including states of both helicities) form a 2d chiral algebra even at the quantum level. The 2d OPE limit of this chiral algebra coinc

Piotr M. Hajac, Yang Liu

Cuntz algebras $\mathcal{O}_n$, $n>1$, are celebrated examples of a separable infinite simple C*-algebra with a number of fascinating properties. Their K-theory allows an embedding of $\mathcal O_m$ in $\mathcal O_n$ whenever $n-1$ divides $m-1$. In 2009, Kawamura provided a simple and explicit formula for all such embeddings. His formulas can be easily dedu

William W. Hager

Some classic second-order sufficient optimality conditions in the calculus of variations are shown to be equivalent, while also introducing a new equivalent second-order condition which is extremely easy to apply: simply integrate a linear second-order initial value problem and check that the solution is positive over the problem domain.

Gospel Ozioma Nnadi, Flavio Bertini

The advancements in deep learning, particularly the introduction of transformers, have been pivotal in enhancing various natural language processing (NLP) tasks. These include text-to-text applications such as machine translation, text classification, and text summarization, as well as data-to-text tasks like response generation and image-to-text tasks such

Natalia Tomashenko, Emmanuel Vincent, Marc Tommasi

In this paper, we investigate the impact of speech temporal dynamics in application to automatic speaker verification and speaker voice anonymization tasks. We propose several metrics to perform automatic speaker verification based only on phoneme durations. Experimental results demonstrate that phoneme durations leak some speaker information and can reveal

Ta-Hsin Li

The quantile spectrum was introduced in Li (2012; 2014) as an alternative tool for spectral analysis of time series. It has the capability of providing a richer view of time series data than that offered by the ordinary spectrum especially for nonlinear dynamics such as stochastic volatility. A novel method, called spline autoregression (SAR), is proposed in

Zihan Ding, Chi Jin

This handbook offers a unified perspective on diffusion models, encompassing diffusion probabilistic models, score-based generative models, consistency models, rectified flow, and related methods. By standardizing notations and aligning them with code implementations, it aims to bridge the "paper-to-code" gap and facilitate robust implementations and fair co

Jianming Wen

The classic Young's double-slit experiment exhibits first-order interference, producing alternating bright and dark fringes modulated by the diffraction effect of the slits. In contrast, here we demonstrate that its time-reversed configuration produces an ideal, deterministic second-order 'ghost' interference pattern devoid of diffraction and first-order eff

Adeel Ahmed, Wang Xingfu, Ammar Hawbani, Weijie Yuan

Revolutionary sixth-generation wireless communications technologies and applications, notably digital twin networks (DTN), connected autonomous vehicles (CAVs), space-air-ground integrated networks (SAGINs), zero-touch networks, industry 5.0, and healthcare 5.0, are driving next-generation wireless networks (NGWNs). These technologies generate massive data,

Ansgar Scherp, Gerd Groener, Petr Škoda, Katja Hose

Ever since the vision was formulated, the Semantic Web has inspired many generations of innovations. Semantic technologies have been used to share vast amounts of information on the Web, enhance them with semantics to give them meaning, and enable inference and reasoning on them. Throughout the years, semantic technologies, and in particular knowledge graphs

Vasiliki Koutra, Olga Egorova, Steven G. Gilmour, Luzia A. Trinca

We describe the R package MOODE and demonstrate its use to find multi-objective optimal experimental designs. Multi-Objective Optimal Design of Experiments (MOODE) targets the experimental objectives directly, ensuring that the full set of research questions is answered as economically as possible. In particular, individual criteria aimed at optimizing infer

José M. Mourão, João P. Nunes, Augusto Pereira, Dan Wang

For a symplectic toric manifold we consider half-form quantization in mixed polarizations $\mathcal{P}_\infty$, associated to the action of a subtorus $T^p\subset T^n$. The real directions in these polarizations are generated by components of the $T^p$ moment map. Polarizations of this type can be obtained by starting at a toric Kähler polarization $\mathcal

Charles L. A. Clarke, Laura Dietz

The use of large language models (LLMs) for relevance assessment in information retrieval has gained significant attention, with recent studies suggesting that LLM-based judgments provide comparable evaluations to human judgments. Notably, based on TREC 2024 data, Upadhyay et al make a bold claim that LLM-based relevance assessments, such as those generated

I. Yu. Rybak, L. Sousa

We investigate the emission of vector radiation by superconducting cosmic string loops, deriving general relations to characterize the vector radiation emission efficiency, and study its impact on the evolution of loops. Building on these results, we compute the stochastic gravitational wave background generated by a chiral superconducting cosmic string netw

Saurabh Deochake

In recent years, as more enterprises have moved their infrastructure to the cloud, significant challenges have emerged in achieving holistic cloud spend visibility and cost optimization. FinOps practices provide a way for enterprises to achieve these business goals by optimizing cloud costs and bringing accountability to cloud spend. This paper presents ABAC

Enshu Liu, Xuefei Ning, Yu Wang, Zinan Lin

Autoregressive (AR) models have achieved state-of-the-art performance in text and image generation but suffer from slow generation due to the token-by-token process. We ask an ambitious question: can a pre-trained AR model be adapted to generate outputs in just one or two steps? If successful, this would significantly advance the development and deployment o

Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer

Feature-based explanations, using perturbations or gradients, are a prevalent tool to understand decisions of black box machine learning models. Yet, differences between these methods still remain mostly unknown, which limits their applicability for practitioners. In this work, we introduce a unified framework for local and global feature-based explanations

A. D. Kislovskiy, E. Yu. Lerner, I. A. Senkevich

The well-known problem stated by A. Meir and L. Moser consists in tiling the unit square with rectangles (details), whose side lengths equal $1/n\times 1/(n+1)$, where indices~$n$ range from 1 to infinity. Recently, Terence Tao has proved that it is possible to tile with $1/n^t\times 1/(n+1)^t$ rectangles (squares with the side length of $1/n^t$), $1/2<t<1$,

Kamer Ali Yuksel, Hassan Sawaf

Agentic AI systems use specialized agents to handle tasks within complex workflows, enabling automation and efficiency. However, optimizing these systems often requires labor-intensive, manual adjustments to refine roles, tasks, and interactions. This paper introduces a framework for autonomously optimizing Agentic AI solutions across industries, such as NLP

N. V. Krylov

The paper presents a new short proof of one of Adams's theorems and a $t$-trace-class theorem for parabolic Morrey spaces.

Sourav Chatterjee, Michael Tonks, William Gardner, Marina Sessim

This paper presents a novel diffuse-interface electrochemical model that simultaneously simulates the evolution of the metallic negative electrode and interfacial voids during the stripping and plating processes in solid-state batteries. The utility and validity of this model are demonstrated for the first time on a cell with a sodium (Na) negative electrode

Leidong Xu, Danyal Mohaddes, Yi Wang

Significant advances have been achieved in leveraging foundation models, such as large language models (LLMs), to accelerate complex scientific workflows. In this work we introduce FoamPilot, a proof-of-concept LLM agent designed to enhance the usability of FireFOAM, a specialized solver for fire dynamics and fire suppression simulations built using OpenFOAM

Zayneb Sadek, Abir Hadriche, Rahma Maalej, Nawel Jmail

Over the past decade, high-frequency oscillations (HFOs) have been studied as a promising biomarker for localizing epileptogenic areas in drug-resistant patients requiring pre-surgical intervention, while exploiting intracranial electroencephalographic iEEG. Consequently, it's important to develop accurate methods for predicting epileptic seizures. Seizure p

Jorge Alejandro Amador Herrera, Yi Zhou, Xin Sun, Zhixin Shu

We propose a novel Augmented Mass-Spring (AMS) model for real-time simulation of dense hair at strand level. Our approach considers the traditional edge, bending, and torsional degrees of freedom in mass-spring systems, but incorporates an additional one-way biphasic coupling with a ghost rest-shape configuration. Trough multiple evaluation experiments with

Francesco Calimeri, Giovambattista Ianni, Francesco Pacenza, Simona Perri

DLV2 is an AI tool for Knowledge Representation and Reasoning which supports Answer Set Programming (ASP) - a logic-based declarative formalism, successfully used in both academic and industrial applications. Given a logic program modelling a computational problem, an execution of DLV2 produces the so-called answer sets that correspond one-to-one to the solu

Matteo Ciferri, Matteo Ferrante, Nicola Toschi

Understanding the neural mechanisms behind auditory and linguistic processing is key to advancing cognitive neuroscience. In this study, we use Magnetoencephalography (MEG) data to analyze brain responses to spoken language stimuli. We develop two distinct encoding models: an audio-to-MEG encoder, which uses time-frequency decompositions (TFD) and wav2vec2 l

Yuexing Hao, Tiancheng Yuan, Yuting Yang, Aarushi Gupta

Dairy owners spend significant effort to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce these costs, yet obstacles arise when adapting advanced tools to farming environments. In this work, we adapt AI tools to dairy cow teat localization, teat shape, and teat sk

Pankaj Borah, Pradipta Ghosh, Abhijit Kumar Saha

We explore the gravitational wave probes of a two-component dark matter framework, consisting of an $SU(2)_L$ triplet scalar and a Standard Model singlet fermion. The triplet scalar dark matter typically remains underabundant in the region below $\sim 1.9$ TeV, due to the strong $SU(2)_L$ gauge mediated interactions. We introduce a second dark matter compone

Ágnes Cseh

The presented work focuses on problems from determinant theory, set theory and topology. The term graph is the binding element that connects these problems. Graphs are distinguished by their geometrical simplicity, which helps in showing the equivalence between various seemingly unrelated problems, besides providing solutions to several open questions discus

Victor Kitov, Valentin Abramov, Mikhail Akhtyrchenko

We present a new dataset with the goal of advancing image style transfer - the task of rendering one image in the style of another image. The dataset covers various content and style images of different size and contains 10.000 stylizations manually rated by three annotators in 1-10 scale. Based on obtained ratings, we find which factors are mostly responsib

Hridhaan Banerjee, Carmen Isabel Day, Megan Hunleth, Sarah Hwang

In this paper, we contribute a proof that minimum radius balls over metric spaces with the Heine-Borel property are always LP type. Additionally, we prove that weak metric spaces, those without symmetry, also have this property if we fix the direction in which we take their distances from the centers of the balls. We use this to prove that the minimum radius

Michael W. Hansen, Michael Mandl, Erhard Seiler, Dénes Sexty

Complex Langevin simulations are an attempt to solve the sign (or complex-action) problem encountered in various physical systems of interest. The method is based on a complexification of the underlying degrees of freedom and an evolution in an auxiliary time dimension. The complexification, however, does not come without drawbacks, the most severe of which

David Nabergoj, Erik Štrumbelj

Recent advances in MCMC use normalizing flows to precondition target distributions and enable jumps to distant regions. However, there is currently no systematic comparison of different normalizing flow architectures for MCMC. As such, many works choose simple flow architectures that are readily available and do not consider other models. Guidelines for choo

Marta Dell'Atti, Thomas Kecker

The geometric approach for Painlev\'e and quasi-Painlev\'e differential equations in the complex plane is applied to non-autonomous Hamiltonian systems, quartic in the dependent variables. By computing their defining manifolds (analogue of the Okamoto's space of initial conditions in the quasi-Painlev\'e case), we provide a classification of such systems. We

Jugal Garg, Thorben Tröbst, Vijay V. Vazirani

The fair division of chores, as well as mixed manna (goods and chores), has received substantial recent attention in the fair division literature; however, ours is the first paper to extend this research to matching markets. Indeed, our contention is that matching markets are a natural setting for this purpose, since the manna that fit into the limited numbe

Avishai Weizman, Yehuda Ben-Shimol, Itshak Lapidot

Spoofing-robust automatic speaker verification (SASV) systems are a crucial technology for the protection against spoofed speech. In this study, we focus on logical access attacks and introduce a novel approach to SASV tasks. A novel representation of genuine and spoofed speech is employed, based on the probability mass function (PMF) of waveform amplitudes

Jannis Kockläuner, Dorothea Golze

Recently, the $GW$ approach has emerged as a valuable tool for computing deep core-level binding energies as measured in X-ray photoemission spectroscopy. However, $GW$ fails to accurately predict shake-up satellite features, which arise from charge-neutral excitations accompanying the ionization. In this work, we extend the $GW$ plus cumulant ($GW+C$) appro

Rushendra Sidibomma, Pransh Patwa, Parth Patwa, Aman Chadha

The detection of hate speech has become increasingly important in combating online hostility and its real-world consequences. Despite recent advancements, there is limited research addressing hate speech detection in Devanagari-scripted languages, where resources and tools are scarce. While large language models (LLMs) have shown promise in language-related

Ying Xie

We prove that the generalized Grassmannian flops of both $D_4$ and $G_2^{\dagger}$ type induce derived equivalences, which provide new evidence for the DK conjecture by Bondal-Orlov and Kawamta. The proof is based on Kuznetsov's mutation technique, which takes a sequence of mutations of exceptional objects.

Zhaofeng Lin, Naomi Harte

Audio-Visual Speech Recognition (AVSR) combines auditory and visual speech cues to enhance the accuracy and robustness of speech recognition systems. Recent advancements in AVSR have improved performance in noisy environments compared to audio-only counterparts. However, the true extent of the visual contribution, and whether AVSR systems fully exploit the a

Cameron R. Jones, Benjamin K. Bergen

Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities raise concerns about potential misuse and unintended consequences as these systems become more widely deployed. This review synthesizes recent empirical work examining LLMs' capacity a

Ankan Sur, Roberto Tejada Arevalo, Yubo Su, Adam Burrows

With the recent realization that there likely are stably-stratified regions in the interiors of both Jupiter and Saturn, we construct new non-adiabatic, inhomogeneous evolutionary models with the same microphysics for each that result at the present time in respectable fits for all major bulk observables for both planets. These include the effective temperat

Rei Henigman

There are several different notions of maximal torus actions on smooth manifolds, in various contexts: symplectic, Riemannian, complex. In the symplectic context, for the so-called isotropy-maximal actions, as well as for the weaker notion of almost isotropy-maximal actions, we give classifications up to equivariant symplectomorphism. These classification re

Carsten Lunde Petersen, Saeed Zakeri

We introduce a general framework to study the local dynamics of near-parabolic maps using the meromorphic $1$-form introduced by X.~Buff. As a sample application of this setup, we prove the following tameness result on invariant curves of near-parabolic maps: Let $g(z)=\lambda z+O(z^2)$ have a non-degenerate parabolic fixed point at $0$ with multiplier $\lam

Sagar Basak, Sheela Verma

In this article, we study Steklov eigenvalues and mixed Steklov Neumann eigenvalues on a smooth bounded domain in $\mathbb{R}^{n}$, $n \geq 2$, having a spherical hole. We focus on two main results related to Steklov eigenvalues. First, we obtain explicit expression for the second nonzero Steklov eigenvalue on concentric annular domain. Secondly, we derive a

Sahand Rezaei-Shoshtari, Hanna Yurchyk, Scott Fujimoto, Doina Precup

Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environments. By maximizing their reward without consideration of fairness, AI agents can introduce disparities in their treatment of groups or individuals. In this paper, we establish the connection between bisimulation metrics an

Jin-Yi Cai, Ashwin Maran

We introduce some polynomial and analytic methods in the classification program for the complexity of planar graph homomorphisms. These methods allow us to handle infinitely many lattice conditions and isolate the new P-time tractable matrices represented by tensor products of matchgates. We use these methods to prove a complexity dichotomy for $4 \times 4$

Riccardo Miccini, Clement Laroche, Tobias Piechowiak, Luca Pezzarossa

Speech Enhancement (SE) is essential for improving productivity in remote collaborative environments. Although deep learning models are highly effective at SE, their computational demands make them impractical for embedded systems. Furthermore, acoustic conditions can change significantly in terms of difficulty, whereas neural networks are usually static wit

A. R. Akhiiarov, A. V. Bobu, A. M. Raigorodskii

This work is devoted to lower bounds on independence numbers of distance graphs with vertices in $\{-1,0,1\}^n$. The asymptotic case is studied, yielding new results over a broad range of parameters. Numerical results are presented, highlighting nontrivial relationships between the obtained bounds. Known upper bounds and their potential suboptimality are dis

Husein Natur, Uzi Pereg

We introduce the notion of empirical coordination for quantum correlations. Quantum mechanics enables the calculation of probabilities for experimental outcomes, emphasizing statistical averages rather than detailed descriptions of individual events. This makes empirical coordination a natural and operationally meaningful framework for quantum systems - part

Shrenik Zinage, Vrushabh Zinage, Efstathios Bakolas

This paper presents a novel approach to improve the Model Predictive Path Integral (MPPI) control by using a transformer to initialize the mean control sequence. Traditional MPPI methods often struggle with sample efficiency and computational costs due to suboptimal initial rollouts. We propose TransformerMPPI, which uses a transformer trained on historical

Abhijit Biswas, David I. Ketcheson, Hendrik Ranocha, Jochen Schütz

We study the recently-proposed hyperbolic approximation of the Korteweg-de Vries equation (KdV). We show that this approximation, which we call KdVH, possesses a rich variety of solutions, including solitary wave solutions that approximate KdV solitons, as well as other solitary and periodic solutions that are related to higher-order water wave models, and m

Anna Deza, Andrés Gómez, Alper Atamtürk

This paper introduces mixed-integer optimization methods to solve regression problems that incorporate fairness metrics. We propose an exact formulation for training fair regression models. To tackle this computationally hard problem, we study the polynomially-solvable single-factor and single-observation subproblems as building blocks and derive their close

Tommaso d'Orsi, Chris Jones, Jake Ruotolo, Salil Vadhan

Whether or not the Sparsest Cut problem admits an efficient $O(1)$-approximation algorithm is a fundamental algorithmic question with connections to geometry and the Unique Games Conjecture. Revisiting spectral algorithms for Sparsest Cut, we present a novel, simple algorithm that combines eigenspace enumeration with a new algorithm for the Cut Improvement p

Tomer Jordi Chaffer, Charles von Goins, Bayo Okusanya, Dontrail Cotlage

Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS

Benjamin Ellis, Matthew T. Jackson, Andrei Lupu, Alexander D. Goldie

In reinforcement learning (RL), it is common to apply techniques used broadly in machine learning such as neural network function approximators and momentum-based optimizers. However, such tools were largely developed for supervised learning rather than nonstationary RL, leading practitioners to adopt target networks, clipped policy updates, and other RL-spe

Andrzej Derdzinski

It is known that a Killing field on a compact pseudo-K\"ahler manifold is necessarily (real) holomorphic, as long as the manifold satisfies some relatively mild additional conditions. We provide two further proofs of this fact and discuss the natural open question whether the same conclusion holds for affine -- rather than Killing -- vector fields. The quest

Zhigen Li, Yanmeng Wang, Rizhao Fan, Ye Wang

Paraphrase generation is a longstanding NLP task and achieves great success with the aid of large corpora. However, transferring a paraphrasing model to another domain encounters the problem of domain shifting especially when the data is sparse. At the same time, widely using large pre-trained language models (PLMs) faces the overfitting problem when trainin

Mehdi Letafati, Seyyed Amirhossein Ameli Kalkhoran, Ecenaz Erdemir, Babak Hossein Khalaj

Deep neural network (DNN)-based joint source and channel coding is proposed for privacy-aware end-to-end image transmission against multiple eavesdroppers. Both scenarios of colluding and non-colluding eavesdroppers are considered. Unlike prior works that assume perfectly known and independent identically distributed (i.i.d.) source and channel statistics, t

Dennis Menn, Feng Liang, Hung-Yueh Chiang, Diana Marculescu

Artifact detection algorithms are crucial to correcting the output generated by diffusion models. However, because of the variety of artifact forms, existing methods require substantial annotated data for training. This requirement limits their scalability and efficiency, which restricts their wide application. This paper shows that the similarity of denoise

Subhankar Patra, Bibhas Ranjan Majhi, Santabrata Das

We study the general relativistic transonic accretion flow around the primary black hole, which forms the circumprimary disc (CPD), within a binary black hole (BBH) system. The BBH spacetime is characterized by the mass ratio ($q$) and the separation distance ($z_2$) between the two black holes. We numerically solve the radial momentum and energy equations t