October 2024 arXiv papers — page 31

Sophie Hall, Francesco Micheli, Giuseppe Belgioioso, Ana Radovanović

Data centers are significant contributors to carbon emissions and can strain power systems due to their high electricity consumption. To mitigate this impact and to participate in demand response programs, cloud computing companies strive to balance and optimize operations across their global fleets by making strategic decisions about when and where to place

Djuna Croon, David J. Weir

We summarise the physics of first-order phase transitions in the early universe, and the possible ways in which they might come about. We then focus on gravitational waves, emphasising general qualitative features of stochastic backgrounds produced by early universe phase transitions and the cosmology of their present-day appearance. Finally, we conclude by

Davide Ghilardi, Federico Belotti, Marco Molinari, Tao Ma

SAEs have recently been employed as a promising unsupervised approach for understanding the representations of layers of Large Language Models (LLMs). However, with the growth in model size and complexity, training SAEs is computationally intensive, as typically one SAE is trained for each model layer. To address such limitation, we propose \textit{Group-SAE

Heng Sun, Sai Manoj Jalam, Havish Kodali, Subhash Nerella

Mobile health (mHealth) apps have gained popularity over the past decade for patient health monitoring, yet their potential for timely intervention is underutilized due to limited integration with electronic health records (EHR) systems. Current EHR systems lack real-time monitoring capabilities for symptoms, medication adherence, physical and social functio

Luis F. Abanto-Leon, Robin Neuder, Waqar Ahmed, Alejandro Jimenez Saez

Reconfigurable intelligent surfaces (RISs) have emerged as a key technology for dynamically reshaping wireless propagation, enhancing coverage and mitigating blockages to enable more pervasive network connectivity. However, implementing RISs at high frequencies remains challenging due to the cost and power demands of semiconductor-based components. To addres

Mahdi Goldani, Soraya Asadi Tirvan

The Gulf Cooperation Council countries -- Oman, Bahrain, Kuwait, UAE, Qatar, and Saudi Arabia -- holds strategic significance due to its large oil reserves. However, these nations face considerable challenges in shifting from oil-dependent economies to more diversified, knowledge-based systems. This study examines the progress of Gulf Cooperation Council (GC

Elias Pratschke

The fluid dynamics community has found success in explaining both the onset and coherent structure formation in wall-bounded turbulence through examining transient growth and pseudoresonance. Whether similar effects are important in plasmas well-described by magnetohydrodynamics is an open question. In nuclear fusion experiments, the onset of turbulence ofte

Qihang Liu, Anran Qiao, Jung-Tsung Shen

Quantum state classification and entanglement quantification are of significant importance in the fundamental research of quantum information science and various quantum applications. Traditional methods, such as quantum state tomography, face exponential measurement demands with increasing numbers of qubits, necessitating more efficient approaches. Recent w

Will Sze, Eusebius J. Doedel, Ida Karimfazli, Behrooz Yousefzadeh

The "Fluid Mechanic Sewing Machine" creates periodic patterns through the coiling nature of a viscous fluid falling onto a moving surface. At relatively moderate heights, the reported patterns are translating coiling, alternating loops, W pattern, and meander. A simplified theoretical model based on the geometry and local bending of the contact point can pre

Ella Zeldes, Or Tal, Yossi Adi

This study introduces a refined approach to Text-to-Speech (TTS) generation that significantly enhances sampling stability across languages, with a particular focus on Hebrew. By leveraging discrete semantic units with higher phonetic correlation obtained from a self-supervised model, our method addresses the inherent instability often encountered in TTS sys

Isidora Chara Tourni, Sayontan Ghosh, Brenda Miao, Constantijn van der Poel

This paper explores the problems of Question Answering (QA) and Named Entity Recognition (NER) in five diverse languages. We tested five Large Language Models with various prompting methods, including zero-shot, chain-of-thought reasoning, and translation techniques. Our results show that while some models consistently outperform others, their effectiveness

Gavin Brown, Michael Wemyss

This is an expository article on the noncommutative singularity theory of power series in noncommuting variables, its motivation from deformation theory, and its applications to contractibility of curves and the classification of smooth 3-fold flops.

Kelian Häring, Alexander Zhiboedov

We explore the physical mechanisms responsible for generating the graviton pole in twice-subtracted dispersion relations, both in flat space and in AdS. To characterize these mechanisms, we analyze the energy scale at which the graviton pole is generated in scattering experiments at various impact parameters. At large impact parameters, we identify the eikon

Giuseppe Mignemi, Ioanna Manolopoulou

Rating procedure is crucial in many applied fields (e.g., educational, clinical, emergency). It implies that a rater (e.g., teacher, doctor) rates a subject (e.g., student, doctor) on a rating scale. Given raters variability, several statistical methods have been proposed for assessing and improving the quality of ratings. Model estimation in the presence of

Michiel Nikken, Nicolò Botteghi, Wesley Roozing, Federico Califano

Denoising Diffusion Probabilistic Models (DDPMs) are powerful generative deep learning models that have been very successful at image generation, and, very recently, in path planning and control. In this paper, we investigate how to leverage the generalization and conditional sampling capabilities of DDPMs to generate complex paths for a robotic end effector

Leya Lopez, Derek G. Sahota, J. Steven Dodge

We analyze the optical pump-probe reflection and transmission coefficients when the photoinduced response depends nonlinearly on the incident pump intensity. Under these conditions, we expect the photoconductivity depth profile to change shape as a function of the incident fluence, unlike the case when the photoinduced response is linear in the incident inte

Abel Corrêa Dias, Viviane Pereira Moreira, João Luiz Dihl Comba

Objective: Scientific publications play a crucial role in uncovering insights, testing novel drugs, and shaping healthcare policies. Accessing the quality of publications requires evaluating their Risk of Bias (RoB), a process typically conducted by human reviewers. In this study, we introduce a new dataset for machine reading comprehension and RoB assessmen

Lijie Hu, Songning Lai, Wenshuo Chen, Hongru Xiao

The lack of interpretability in the field of medical image analysis has significant ethical and legal implications. Existing interpretable methods in this domain encounter several challenges, including dependency on specific models, difficulties in understanding and visualization, as well as issues related to efficiency. To address these limitations, we prop

Anusha Pai Asnodkar, Ji Wang, Madelyn Broome, Chenliang Huang

WASP-12 b is an ultra-hot Jupiter (UHJ) of special interest for atmospheric studies since it is on an inspiraling orbit in an extreme environment of intense radiation and circumstellar gas. Previously claimed detections of active mass loss from this planet are controversial across the literature. To address this controversy, we obtain two new transit observa

Jiongxiao Wang, Fangzhou Wu, Wendi Li, Jinsheng Pan

Large language models (LLMs) have been widely deployed as the backbone with additional tools and text information for real-world applications. However, integrating external information into LLM-integrated applications raises significant security concerns. Among these, prompt injection attacks are particularly threatening, where malicious instructions injecte

Hongyang Li, Caesar Wu, Mohammed Chadli, Said Mammar

Distributed learning (DL) uses multiple nodes to accelerate training, enabling efficient optimization of large-scale models. Stochastic Gradient Descent (SGD), a key optimization algorithm, plays a central role in this process. However, communication bottlenecks often limit scalability and efficiency, leading to increasing adoption of compressed SGD techniqu

Rob Sullivan, Nelly Elsayed

The performance of Large language models (LLMs) across a broad range of domains has been impressive but have been critiqued as not being able to reason about their process and conclusions derived. This is to explain the conclusions draw, and also for determining a plan or strategy for their approach. This paper explores the current research in investigating

Yasaman Omid, Marios Aristodemou, Sangarapillai Lambotharan, Mahsa Derakhshani

The integration of low earth orbit (LEO) satellites with terrestrial communication networks holds the promise of seamless global connectivity. The efficiency of this connection, however, depends on the availability of reliable channel state information (CSI). Due to the large space-ground propagation delays, the estimated CSI is outdated. In this paper we co

Jeremy E. B. Guntoro, Benjamin J. Qureshi, Thomas E. Ouldridge

Templated copolymerization, in which information stored in the sequence of a heteropolymer template is copied into another polymer product, is the mechanism behind all known methods of genetic information transfer. A key aspect of templated copolymerization is the eventual detachment of the product from the template. A second key feature of natural biochemic

Yuening Wang, Man Chen, Yaochen Hu, Wei Guo

Many platforms, such as e-commerce websites, offer both search and recommendation services simultaneously to better meet users' diverse needs. Recommendation services suggest items based on user preferences, while search services allow users to search for items before providing recommendations. Since users and items are often shared between the search and re

David C. Groothuizen Dijkema, Vivien Kirk, Claire M. Postlethwaite

Heteroclinic cycles and networks are structures in dynamical systems composed of invariant sets and connecting heteroclinic orbits, and can be robust in systems with invariant subspaces. The usual method for analysing the stability of heteroclinic cycles and networks is to construct return maps to cross-sections near the network. From these return maps, tran

HyoJung Han, Kevin Duh, Marine Carpuat

Recent advances in automatic quality estimation for machine translation have exclusively focused on written language, leaving the speech modality underexplored. In this work, we formulate the task of quality estimation for speech translation (SpeechQE), construct a benchmark, and evaluate a family of systems based on cascaded and end-to-end architectures. In

René Manassé Galekwa, Jean Marie Tshimula, Etienne Gael Tajeuna, Kyamakya Kyandoghere

The sports betting industry has experienced rapid growth, driven largely by technological advancements and the proliferation of online platforms. Machine learning (ML) has played a pivotal role in the transformation of this sector by enabling more accurate predictions, dynamic odds-setting, and enhanced risk management for both bookmakers and bettors. This s

Joakim Hagel, Samuel Brem, Ermin Malic

Twisted transition metal dichalcogenide (TMD) bilayers exhibit periodic moir\'e potentials, which can trap excitons at certain high-symmetry sites. At small twist angles, TMD lattices undergo an atomic reconstruction, altering the moir\'e potential landscape via the formation of large domains, potentially separating the charges in-plane and leading to the fo

Nima Hoda, Timothy Riley

We show that whether loops can be shortcut in a group's Cayley graph depends on the choice of finite generating set. Our example is the direct product of two rank-2 free groups and a consequence is that this group has asymptotic cones with isometrically embedded circles that are null-homotopic.

Ognjen, Rudovic, Pranay Dighe, Yi Su

Follow-up conversations with virtual assistants (VAs) enable a user to seamlessly interact with a VA without the need to repeatedly invoke it using a keyword (after the first query). Therefore, accurate Device-directed Speech Detection (DDSD) from the follow-up queries is critical for enabling naturalistic user experience. To this end, we explore the notion

Vu-Anh Le, Mehmet Dik

Neural operators have emerged as transformative tools for learning mappings between infinite-dimensional function spaces, offering useful applications in solving complex partial differential equations (PDEs). This paper presents a rigorous mathematical framework for analyzing the behaviors of neural operators, with a focus on their stability, convergence, cl

Brendan Hogan, Anmol Kabra, Felipe Siqueira Pacheco, Laura Greenstreet

Trust and interpretability are crucial for the use of Artificial Intelligence (AI) in scientific research, but current models often operate as black boxes offering limited transparency and justifications for their outputs. We introduce AiSciVision, a framework that specializes Large Multimodal Models (LMMs) into interactive research partners and classificati

Iftach Arbel, Yehonathan Refael, Ofir Lindenbaum

Large Language Models (LLMs) have shown promise in highly-specialized domains, however challenges are still present in aspects of accuracy and costs. These limitations restrict the usage of existing models in domain-specific tasks. While fine-tuning pre-trained models have shown promising results, this process can be computationally expensive and require mas

Kemal Altwlkany, Hadžem Hadžić, Amar Kurić, Emanuel Lacic

This paper investigates the industrial setting of real-time classification of early media exchanged during the initialization phase of voice calls. We explore the application of state-of-the-art audio tagging models and highlight some limitations when applied to the classification of early media. While most existing approaches leverage convolutional neural n

Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Annalena Kofler

Inferring atmospheric properties of exoplanets from observed spectra is key to understanding their formation, evolution, and habitability. Since traditional Bayesian approaches to atmospheric retrieval (e.g., nested sampling) are computationally expensive, a growing number of machine learning (ML) methods such as neural posterior estimation (NPE) have been p

Chihaya Jibiki

We construct a continuous map from the space of orders on quandles to the space of quandle actions on one-manifolds, providing an answer to a question posed by Idrissa Ba and Mohamed Elhamdadi. As an application of this map, we characterize isolated orders on quandles in terms of strong rigidity. As another application, we prove that there is no isolated rig

Diego Buccio, Luca Parente, Omar Zanusso

We compute the physical running of a general higher derivative scalar coupled to a nondynamical metric and of higher derivative Weyl invariant gravity with a dynamical metric in four dimensions. In both cases, we find that the physical running differs from the $\mu$-running of dimensional regularization because of infrared divergences which are present in am

Siyi Guo, Myrl G. Marmarelis, Fred Morstatter, Kristina Lerman

Quantifying the effects of textual interventions in social systems, such as reducing anger in social media posts to see its impact on engagement, is challenging. Real-world interventions are often infeasible, necessitating reliance on observational data. Traditional causal inference methods, typically designed for binary or discrete treatments, are inadequat

Siqi Fan, I-Hong Hou

This paper introduces a general framework to analyze and optimize age-of-information (AoI) in CSMA protocols for distributed uplink transmissions. The proposed framework combines two theoretical approaches. First, it employs second-order analysis that characterizes all random processes by their respective means and temporal variances and approximates AoI as

S. I. Alekhin, S. A. Kulagin, R. Petti

We discuss results from our global QCD analyses including nuclear data off deuterium from various measurements, as well as off $\htri$ and $\hetri$ targets from the \mara{} experiment. We simultaneously determine the parton distribution functions of the proton, the higher-twist terms, and the nucleon off-shell correction functions responsible for the modific

Yaopei Zeng, Yuanpu Cao, Bochuan Cao, Yurui Chang

Recent advances in diffusion models have significantly enhanced the quality of image synthesis, yet they have also introduced serious safety concerns, particularly the generation of Not Safe for Work (NSFW) content. Previous research has demonstrated that adversarial prompts can be used to generate NSFW content. However, such adversarial text prompts are oft

Ioannis Iakovoglou

In this paper, we prove that for any flow $\Psi$ obtained by a Dehn-Fried surgery on an expansive (or equivalently topological pseudo-Anosov) flow $\Phi$ in dimension 3, $\Psi$ is expansive if and only if its stable and unstable foliations do not contain $1$-prong singularities.

Matthew Hofkes, Douglas Nychka

Modeling spatial processes that exhibit both smooth and rough features poses a significant challenge. This is especially true in fields where complex physical variables are observed across spatial domains. Traditional spatial techniques, such as Gaussian processes (GPs), are ill-suited to capture sharp transitions and discontinuities in spatial fields. In th

Csaba Biró, André E. Kézdy, Jenő Lehel

The length polyhedron $Q_P$ of an interval order $P$ is the convex hull of integral vectors representing the interval lengths in interval representations of $P$. This polyhedron has been studied by various authors, including Fishburn and Isaak. Notably, $Q_P$ forms a pointed affine cone, a property inherited from being a projection of the representation poly

Gustavo Ivan Angulo Olivares, Burak Kocuk, Diego Moran Ramirez

For equality-constrained linear mixed-integer programs (MIP) defined by rational data, it is known that the subadditive dual is a strong dual and that there exists an optimal solution of a particular form, termed generator subadditive function. Motivated by these results, we explore the connection between Lagrangian duality, subadditive duality and generator

S. E. Chorfi

In this paper, we prove a sharp uniqueness result for the singular Schr\"odinger equation with an inverse square potential. This will be done without assuming geometrical restrictions on the observation region. The proof relies on a recent technique transforming the Schr\"odinger equation into an elliptic equation. We show that this technique is still applic

Hanshi Sun, Li-Wen Chang, Wenlei Bao, Size Zheng

With the widespread deployment of long-context large language models (LLMs), there has been a growing demand for efficient support of high-throughput inference. However, as the key-value (KV) cache expands with the sequence length, the increasing memory footprint and the need to access it for each token generation both result in low throughput when serving l

Jennifer Brennan, Sébastien Lahaie, Adel Javanmard, Nick Doudchenko

In experimental causal inference, we distinguish between two sources of uncertainty: design uncertainty, due to the treatment assignment mechanism, and sampling uncertainty, when the sample is drawn from a super-population. This distinction matters in settings with small fixed samples and heterogeneous treatment effects, as in geographical experiments. The s

Katherine Williams Booth

In this paper, we consider the automorphisms of fine curve graphs restricted to continuously $k$-differentiable curves. We show that for closed surfaces with genus at least 2, they are induced by homeomorphisms of the surface.

Zihan Ren, Sanjay Srinivasan, Dustin Crandall

Pore-scale modeling of rock images based on information in 3D micro-computed tomography data is crucial for studying complex subsurface processes such as CO2 and brine multiphase flow during Geologic Carbon Storage (GCS). While deep learning models can generate 3D rock microstructures that match static rock properties, they have two key limitations: they don

Abdullah Saeed Alghotmi

This paper simulates and analyses the crosstalk interference in off-chip and on-chip communications in multi-stacked chips where Inductive Coupling Links (ICLs) are the communication medium. Crosstalk impacts data signals, leading to errors in the receiver when adjacent transmitters communicate simultaneously. We explored techniques to reduce crosstalk, incl

Nadine H. Soliman, Philip F. Hopkins, Jonathan Squire

We propose a novel dust battery mechanism for generating seed magnetic fields in the early universe, in which charged dust grains are radiatively accelerated, inducing strong electric currents that subsequently generate magnetic fields. Our analysis demonstrates that this process is effective even at very low metallicities (approximately $ \sim 10^{-5} Z_\od

Katherine Williams Booth

In this paper, we study Homeo$^1(S)$, the group of homeomorphisms of a surface that preserve the set of one-dimensional $C^1$ submanifolds of that surface. The group Homeo$^1(S)$ belongs to a family of similarly defined groups Homeo$^k(S)$ that were recently introduced by the author. In a separate paper, we have shown that for most closed surfaces, Homeo$^k(

Viacheslav Surkov, Chris Wendler, Antonio Mari, Mikhail Terekhov

For large language models (LLMs), sparse autoencoders (SAEs) have been shown to decompose intermediate representations that often are not interpretable directly into sparse sums of interpretable features, facilitating better control and subsequent analysis. However, similar analyses and approaches have been lacking for text-to-image models. We investigate th

Théo Simon, Tal Adi, José Luis Bernal, Ely D. Kovetz

Early dark energy, an additional component of dark energy active in the decade of redshift before recombination, has emerged as one of the most effective models at reducing the $H_0$ tension between direct measurement of the Hubble parameter $H_0$ in the late-universe and the $\Lambda$CDM prediction when calibrated on Planck. However, it requires a slight in

Marius Gerbershagen, Dongming He

We determine $1/N$ corrections to a notion of generalized entanglement entropy known as entwinement dual to the length of a winding geodesic in asymptotically AdS$_3$ geometries. We explain how $1/N$ corrections can be computed formally via the FLM formula by relating entwinement to an ordinary entanglement entropy in a fictitious covering space. Moreover, w

Luca Fiorino, Sofia Contarini, Federico Marulli, Ariel G. Sanchez

We investigate the effect of primordial non-Gaussianities on halo number counts using N-body simulations with different values of $f_{\rm NL}^{\rm loc}$. We show how current theoretical models fail to adequately describe the non-Gaussian mass function of halos identified with different overdensity thresholds, $\Delta_{\rm b}$. We explain how these discrepanc

L. Abalo, P. Kretschmar, F. Fürst, C. M. Diez

Strong stellar winds are an important feature in wind-accreting high-mass X-ray binary (HMXB) systems, providing insights into stellar evolution and their impact on surrounding environments. However, the long-term evolution and temporal variability of these winds are not fully understood. This work probes the archetypal wind-accreting HMXB Vela X-1 using MAX

Tobias Cord-Landwehr, Christoph Boeddeker, Reinhold Haeb-Umbach

We propose an approach for simultaneous diarization and separation of meeting data. It consists of a complex Angular Central Gaussian Mixture Model (cACGMM) for speech source separation, and a von-Mises-Fisher Mixture Model (VMFMM) for diarization in a joint statistical framework. Through the integration, both spatial and spectral information are exploited f

Anupama Bhardwaj, Tristen Brisky, Chian Yeong Chuah, Kyle Kawagoe

We study a commutant-closed collection of von Neumann algebras acting on a common Hilbert space indexed by a poset with an order-reversing involution. We give simple geometric axioms for the poset which allow us to construct a braided tensor category of superselection sectors analogous to the construction of Gabbiani and Fr\"ohlich for conformal nets. For co

Wassim Bouaziz, El-Mahdi El-Mhamdi, Nicolas Usunier

Gradient attacks and data poisoning tamper with the training of machine learning algorithms to maliciously alter them and have been proven to be equivalent in convex settings. The extent of harm these attacks can produce in non-convex settings is still to be determined. Gradient attacks can affect far less systems than data poisoning but have been argued to

Shay McBride, Wei Chen, Tanja Cuk, Geoffroy Hautier

Hole transport and localization through small polarons is essential to the performance of TiO$_2$ in photocatalysis applications. The existence of small hole polaron in bulk rutile TiO$_2$ has been however controversial with contradicting evidences from theory and experiments. Here, we use first principles computations and more specifically a Koopmans' compl

Victoria Benjamin, Emily Braca, Israel Carter, Hafsa Kanchwala

This study systematically analyzes the vulnerability of 36 large language models (LLMs) to various prompt injection attacks, a technique that leverages carefully crafted prompts to elicit malicious LLM behavior. Across 144 prompt injection tests, we observed a strong correlation between model parameters and vulnerability, with statistical analyses, such as l

Jake Barrett, Philipp C Verpoort, Kobi Gal

We here present an improved version of the Sortition Foundation's GROUPSELECT software package, which aims to repeatedly allocate participants of a deliberative process to discussion groups in a way that balances demographics in each group and maximises distinct meetings over time. Our result, DREAM, significantly outperforms the prior algorithmic approach L

Mohammad Amir Dastgheib, Jawad A. Salehi, Mohammad Rezai

This paper describes the fundamental principles and mathematical foundations of quantum spread spectrum code division multiple access (QCDMA) communication systems. The evolution of quantum signals through the direct-sequence spread spectrum multiple access communication system is carefully characterized by a novel approach called the decomposition of creati

Sean J. Gunderson, David P. Huenemoerder

We present conditions for which X-ray spectra can be ``unfolded'' to present accurate representation of the true source spectra. The method we use to unfold the data is implemented in the \textit{Interactive Spectral Interpretation Software} \citep{Houck2000} and distinguishes itself as being model-independent. We find that this method of unfolding makes acc

Remi Genet, Hugo Inzirillo

Recent research has challenged the necessity of complex deep learning architectures for time series forecasting, demonstrating that simple linear models can often outperform sophisticated approaches. Building upon this insight, we introduce a novel architecture the Temporal Linear Net (TLN), that extends the capabilities of linear models while maintaining in

Dong Ho Lee, Lasse Peters, David Fridovich-Keil

We study noncooperative games, in which each player's objective is composed of a sequence of ordered- and potentially conflicting-preferences. Problems of this type naturally model a wide variety of scenarios: for example, drivers at a busy intersection must balance the desire to make forward progress with the risk of collision. Mathematically, these problem

Nicholas Strohmeyer, Sriram Vishwanath, David Fridovich-Keil

Stablecoins are a class of cryptocurrencies which aim at providing consistency and predictability, typically by pegging the token's value to that of a real world asset. Designing resilient decentralized stablecoins is a challenge, and prominent stablecoins today either (i) give up on decentralization, or (ii) rely on user-owned cryptocurrencies as collateral

Margaret J. Zhang, Anvay A. Pradhan, Zachary Brei, Xiangyun Bu

Tails serve various functions in both robotics and biology, including expression, grasping, and defense. The vertebrate tails associated with these functions exhibit diverse patterns of vertebral lengths, but the precise mechanisms linking form to function have not yet been established. Vertebrate tails are complex musculoskeletal structures, making both dir

Lukas Allwicher, Marzia Bordone, Gino Isidori, Gioacchino Piazza

The recent observation of the $K^+ \to \pi^+ \nu\bar\nu$ decay by NA62 is an important milestone in precision flavor physics. Together with evidence of $B^+ \to K^+\nu\bar\nu$ reported by Belle-II, they are the only FCNC decays involving third-family leptons where a precision close to the SM expectation has been reached. We study the implications of these re

Adonisz Dimitriu, Tamás Michaletzky, Viktor Remeli

Adversarial attacks threaten the reliability of machine learning models in critical applications like autonomous vehicles and defense systems. As object detectors become more robust with models like YOLOv8, developing effective adversarial methodologies is increasingly challenging. We present Truck Adversarial Camouflage Optimization (TACO), a novel framewor

Taillte May, William E. East, Nils Siemonsen

Oscillating clouds of ultralight bosons can grow around spinning black holes through superradiance, extracting energy and angular momentum, and eventually dissipating through gravitational radiation. Gravitational wave detectors like LIGO, Virgo, KAGRA, and LISA can thus probe the existence of ultralight bosons. In this study, we use fully general-relativist

Michael Przystupa, Kerrick Johnstonbaugh, Zichen Zhang, Laura Petrich

Identifying an appropriate task space that simplifies control solutions is important for solving robotic manipulation problems. One approach to this problem is learning an appropriate low-dimensional action space. Linear and nonlinear action mapping methods have trade-offs between simplicity on the one hand and the ability to express motor commands outside o

Mafu Zhang, Huanghaohe Zou, Saleh Farzamkia, Zibo Chen

This paper reviews commonly used three-phase isolated AC-DC converters and introduces a novel Y-configuration Active Bridge (YAB) converter for single-stage isolated AC-DC power conversion. The proposed YAB addresses the limitations of multi-stage designs by eliminating bulky electrolytic capacitor banks and input boost inductors, thereby enabling a simplifi

Alessio Zaccone

The jamming transition of frictionless athermal particles is a paradigm to understand the mechanics of amorphous materials at the atomic scale. Concepts related to the jamming transition and the mechanical response of jammed packings have cross-fertilized into other areas such as atomistic descriptions of the elasticity and plasticity of glasses. In this per

Zhichao Wang, Bin Bi, Zixu Zhu, Xiangbo Mao

By pretraining on trillions of tokens, an LLM gains the capability of text generation. However, to enhance its utility and reduce potential harm, SFT and alignment are applied sequentially to the pretrained model. Because SFT and alignment have different objectives and underlying processes, performance on certain tasks can decline. To address this, we seamle

Angshuman R. Goswami

In this paper, we present an alternative proof of Fekete's Lemma. We demonstrate that for any subadditive sequence, it is possible to construct a subadditive function that exactly interpolates the sequence. Using this result, along with Hille's theorem on subadditive functions, we naturally arrive at Fekete's Lemma. Additionally, we provide an explicit formu

Kaiqi Yang

We study stable rationality of conic bundles $X$ over $\mathbb{P}^1$ defined over non-closed field $k$ via the cohomology of the Galois group of finite field extension $k'/k$ with action on the geometric Picard lattice of $X$.

Zhong-Bo Kang, Jani Penttala, Congyue Zhang

We have conducted the first simultaneous global fit of the strong coupling constant $\alpha_s$ and the Collins-Soper (CS) kernel using the energy-energy correlators (EEC) of $e^{+} e^{-}$ collisions in the back-to-back limit. This analysis, based on the transverse-momentum-dependent (TMD) factorization of EEC at next-to-next-to-next-to-leading logarithmic ($

Ryan Alvarado, Przemysław Górka, Artur Słabuszewski

In this article, we characterize both Lusin's theorem and the existence of Borel representatives via the regularity properties of the measure in general topological measure spaces. As a corollary, we prove that Borel regularity of the measure is both a necessary and sufficient condition for these results to hold true in metric measure spaces.

Gabriela Araujo-Pardo, Martín Matamala, Juan P. Peña, José Zamora

A set of n non-collinear points in the Euclidean plane defines at least n different lines. Chen and Chv\'tal in 2008 conjectured that the same results is true in metric spaces for an adequate definition of line. More recently, it was conjectured in 2018 by Aboulker et al. that any large enough bridgeless graph on n vertices defines a metric space that has at

Ethan R. Partington, Edward M. Cackett, Rick Edelson, Keith Horne

The Seyfert 1 AGN Fairall 9 was targeted by NICER, Swift, and ground-based observatories for a $\sim$1000-day long reverberation mapping campaign. The following analysis of NICER spectra taken at a two-day cadence provides new insights into the structure and heating mechanisms of the central black hole environment. Observations of Fairall 9 with NICER and Sw

Prabhat Devkota

We prove that the rational cohomology ring of moduli space of multiscale differentials in genus 0 is generated by the boundary divisors. The main idea is the technique of the Chow-K\"unneth generation Property and the observation that the intersection of a collection of boundary divisors in the moduli space is irreducible. We observe that the relations betwe

S. A. van der Giessen, K. Matsumoto, M. Relano, I. De Looze

We study the importance of several processes that influence the evolution of dust and its grain size distribution on spatially resolved scales in nearby galaxies. Here, we compiled several multi-wavelength observations for the nearby galaxies NGC628(M74), NGC5457(M101), NGC598(M33), and NGC300. We applied spatially resolved spectral energy distribution fitti

Ian Chow, Sam Hadden

Planetary systems exhibiting mean-motion resonances (MMRs) offer unique opportunities to study the imprint of disk-induced migration on the orbital architectures of planetary systems. The HD 45364 system, discovered via the radial velocity (RV) method to host two giant planets in a 3:2 MMR, has been the subject of several studies attempting to reconstruct th

Sabila Parveen, Soumya Bonthu, Newton Nath, Ujjal Kumar Dey

It is well-known that within the standard three flavor neutrino oscillation formalism, the Majorana phases appearing in the neutrino mixing matrix cannot have any effect on neutrino oscillation probabilities thereby evading testability at neutrino oscillation experiments. We consider an effective non-Hermitian Hamiltonian describing three flavor neutrino osc

Dmytro S. Inosov, Emil Vlasák

A cryptarithm (or alphametic) is a mathematical puzzle in which numbers are represented with words in such a way that identical letters stand for equal digits and distinct letters for unequal digits. An alphametic puzzle is usually given in the form of an equation that needs to be solved, such as SEND + MORE = MONEY. Alternatively, here we will consider cryp

Ilias Diakonikolas, Sushrut Karmalkar, Shuo Pang, Aaron Potechin

Non-Gaussian Component Analysis (NGCA) is the statistical task of finding a non-Gaussian direction in a high-dimensional dataset. Specifically, given i.i.d.\ samples from a distribution $P^A_{v}$ on $\mathbb{R}^n$ that behaves like a known distribution $A$ in a hidden direction $v$ and like a standard Gaussian in the orthogonal complement, the goal is to app

Lucas Barrault, Stéphane Mathis, Lisa Bugnet

The presence of dips in the gravito-inertial modes period-spacing pattern of gamma-Dor stars is now well established by recent asteroseismic studies. Such Lorentzian-shaped inertial dips arise from the interaction of gravito-inertial modes propagating in the radiative envelope of intermediate-mass main sequence stars with pure inertial modes that propagate i

J. Mögerle, K. Brechtelsbauer, A. T. Gea-Caballero, J. Prior

We present a protocol to implement a spin-1 chain in Rydberg systems using three Rydberg states close to a F\"orster resonance. In addition to dipole-dipole interactions, strong van der Waals interactions naturally appear due to the presence of the F\"orster resonance and give rise to a highly tunable Hamiltonian. The resulting phase diagram is studied using

Adrien Baut, Michael Pereira Martins, Andreas T. Güntner

Metal nitrides possess exceptional catalytic, electronic and physical properties making them widely used in (opto-)electronics and as hard coatings. When used as films in surface-active applications, however, their performance remains limited by poor mass transfer and reduced accessibility of reactive sites. This is associated to compact film architecture yi

Feiyang Cai, Katelin Zacour, Tianyu Zhu, Tzuen-Rong Tzeng

Traditional AI methods often rely on task-specific model designs and training, which constrain both the scalability of model size and generalization across different tasks. Here, we introduce ChemFM, a large foundation model specifically developed for chemicals. By conducting a series of scaling experiments, we identify UniChem as the informative molecular d

Pedro V. P. Cunha, Carlos A. R. Herdeiro, Eugen Radu, Nuno M. Santos

We study stationary clouds of a gauged, complex scalar field on a magnetically (and possibly electrically as well) charged Kerr-Newman black hole (BH). The existence of a magnetic charge $Q_m$ promotes a north-south $\textit{asymmetry}$ of the scalar clouds. This breakdown of the clouds' $\mathbb{Z}_2$-symmetry carries through to the spacetime $\textit{geome

Renwen Yu, Shanhui Fan

We propose the dynamical Casimir effect in a time-modulated near-field system at finite temperatures. The system consists of two bodies made of polaritonic materials, that are brought in close proximity to each other, and the modulation frequency is approximately twice the relevant resonance frequencies of the system. We develop a rigorous fluctuational elec

Chris Camaño, Daniel Huang

We introduce Soft Kernel Interpolation (SoftKI), a method that combines aspects of Structured Kernel Interpolation (SKI) and variational inducing point methods, to achieve scalable Gaussian Process (GP) regression on high-dimensional datasets. SoftKI approximates a kernel via softmax interpolation from a smaller number of interpolation points learned by opti

Yiwei Li, Huaqin Zhao, Hanqi Jiang, Yi Pan

The rapid advances in Large Language Models (LLMs) have the potential to transform manufacturing industry, offering new opportunities to optimize processes, improve efficiency, and drive innovation. This paper provides a comprehensive exploration of the integration of LLMs into the manufacturing domain, focusing on their potential to automate and enhance var

Benjamin Lovitz, Angus Lowe

Tree tensor network states (TTNS) generalize the notion of having low Schmidt-rank to multipartite quantum states, through a parameter known as the bond dimension. This leads to succinct representations of quantum many-body systems with a tree-like entanglement structure. In this work, we study the task of testing whether an unknown pure state is a TTNS on $

Yuhan Sun

For a closed negatively monotone symplectic manifold, we construct quasi-isometric embeddings from the Euclidean spaces to its Hamiltonian diffeomorphism group, assuming it contains an incompressible heavy Lagrangian. We also show the super-heaviness of its skeleton with respect to a Donaldson hypersurface.