October 2024 arXiv papers — page 103

Le Xu, Simone Fasolato, Simona Onori

The increasing adoption of Lithium Iron Phosphate (LFP) batteries in Electric Vehicles is driven by their affordability, abundant material supply, and safety advantages. However, challenges arise in controlling/estimating unmeasurable LFP states such as state of charge (SOC), due to its flat open circuit voltage, hysteresis, and path dependence dynamics duri

Shreya Saha, Ishaan Chadha, Meenakshi Khosla

Over the past decade, predictive modeling of neural responses in the primate visual system has advanced significantly, largely driven by various DNN approaches. These include models optimized directly for visual recognition, cross-modal alignment through contrastive objectives, neural response prediction from scratch, and large language model embeddings.Like

Giangiacomo Mercatali, Andre Freitas, Jie Chen

Interacting systems are prevalent in nature. It is challenging to accurately predict the dynamics of the system if its constituent components are analyzed independently. We develop a graph-based model that unveils the systemic interactions of time series observed at irregular time points, by using a directed acyclic graph to model the conditional dependencie

Stathi Fotiadis, Noah Brenowitz, Tomas Geffner, Yair Cohen

Conditioning diffusion and flow models have proven effective for super-resolving small-scale details in natural images.However, in physical sciences such as weather, super-resolving small-scale details poses significant challenges due to: (i) misalignment between input and output distributions (i.e., solutions to distinct partial differential equations (PDEs

Mohsen Ghassemi, Alan Mishler, Niccolo Dalmasso, Luhao Zhang

Conditional demographic parity (CDP) is a measure of the demographic parity of a predictive model or decision process when conditioning on an additional feature or set of features. Many algorithmic fairness techniques exist to target demographic parity, but CDP is much harder to achieve, particularly when the conditioning variable has many levels and/or when

Sean Trott, Pamela D. Rivière

The success of Large Language Models (LLMs) in other domains has raised the question of whether LLMs can reliably assess and manipulate the readability of text. We approach this question empirically. First, using a published corpus of 4,724 English text excerpts, we find that readability estimates produced ``zero-shot'' from GPT-4 Turbo and GPT-4o mini exhib

Tianle Li, Jia Shu, Qinghong Chen, Murad Mehrab Abrar

This paper presents a threshold-based automated pea weevil detection system, developed as part of the Microsoft FarmVibes project. Based on Internet-of-Things (IoT) and computer vision, the system is designed to monitor and manage pea weevil populations in agricultural settings, with the goal of enhancing crop production and promoting sustainable farming pra

Emma R. Beasor, Nathan Smith, Jacob E. Jencson

Analysis of pre-explosion imaging has confirmed red supergiants (RSGs) as the progenitors to Type II-P supernovae (SNe). However, extracting the RSG's luminosity requires assumptions regarding the star's temperature or spectral type and the corresponding bolometric correction, circumstellar extinction, and possible variability. The robustness of these assump

Mert İnan, Katherine Atwell, Anthony Sicilia, Lorna Quandt

We introduce a goal-oriented conversational AI system enhanced with American Sign Language (ASL) instructions, presenting the first implementation of such a system on a worldwide multimodal conversational AI platform. Accessible through a touch-based interface, our system receives input from users and seamlessly generates ASL instructions by leveraging retri

Brett Saiki, Jackson Brough, Jonas Regehr, Jesús Ponce

New low-precision accelerators, vector instruction sets, and library functions make maximizing accuracy and performance of numerical code increasingly challenging. Two lines of work$\unicode{x2013}$traditional compilers and numerical compilers$\unicode{x2013}$attack this problem from opposite directions. Traditional compiler backends optimize for specific ta

Alex Rybchuk, Luis A. Martínez-Tossas, Stefano Letizia, Nicholas Hamilton

To validate the second-by-second dynamics of turbines in field experiments, it is necessary to accurately reconstruct the winds going into the turbine. Current time-resolved inflow reconstruction techniques estimate wind behavior in unobserved regions using relatively simple spectral-based models of the atmosphere. Here, we develop a technique for time-resol

Olena Hrynenko, Andrea Cavallaro

Privacy personas capture the differences in user segments with respect to one's knowledge, behavioural patterns, level of self-efficacy, and perception of the importance of privacy protection. Modelling these differences is essential for appropriately choosing personalised communication about privacy (e.g. to increase literacy) and for defining suitable choi

Cheng Pan, Kai Junge, Benhui Dai, Qinghua Guan

Human dexterity arises from combining high-level task reasoning with finger-level dexterity control and physical compliance at the muscle and skin layers. In robotics, large Vision-Language-Action (VLA) models demonstrate text-conditioned high-level planning across diverse manipulation tasks, typically using pincher grippers. Smaller imitation-learning polic

Munsif Ali, Leonardo Rossi, Massimo Bertozzi

Few-shot and continual learning face two well-known challenges in GANs: overfitting and catastrophic forgetting. Learning new tasks results in catastrophic forgetting in deep learning models. In the case of a few-shot setting, the model learns from a very limited number of samples (e.g. 10 samples), which can lead to overfitting and mode collapse. So, this p

Matteo Bordin, Andrea Lacava, Michele Polese, Sai Satish

Next-generation wireless systems, already widely deployed, are expected to become even more prevalent in the future, representing challenges in both environmental and economic terms. This paper focuses on improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems through the near-real-time dynamic activation and d

Timothy Mulvany, Daniel Griffiths-King, Jan Novak, Heather Rose

Monitoring of Diffuse Intrinsic Pontine Glioma (DIPG) and Diffuse Midline Glioma (DMG) brain tumors in pediatric patients is key for assessment of treatment response. Response Assessment in Pediatric Neuro-Oncology (RAPNO) guidelines recommend the volumetric measurement of these tumors using MRI. Segmentation challenges, such as the Brain Tumor Segmentation

Liam A. Pocher, Irving Haber, Thomas M. Antonsen, Patrick G. O'Shea

Understanding and predicting complex dynamics in accelerators is necessary for their successful operation. A grand challenge in accelerator physics is to develop predictive virtual accelerators that mitigate design cost and schedule risk. Data-driven techniques greatly appeal to generating virtual accelerators due to their limited dimensionality compared wit

Sanjay Vishwakarma, Vishal Sharathchandra Bajpe, Ryan Mandelbaum, Yuri Kobayashi

Quantum computing is an emerging technology whose positive and negative impacts on society are not yet fully known. As government, individuals, institutions, and corporations fund and develop this technology, they must ensure that they anticipate its impacts, prepare for its consequences, and steer its development in such a way that it enables the most good

Ashok Srinivasan, Satkkeerthi Sriram, Sirish Namilae, Andrew Arash Mahyari

Pedestrian dynamics simulates the fine-scaled trajectories of individuals in a crowd. It has been used to suggest public health interventions to reduce infection risk in important components of air travel, such as during boarding and in airport security lines. Due to inherent variability in human behavior, it is difficult to generalize simulation results to

Jiyoung Park, Günay Doğan

One of the fundamental problems in computer vision is image segmentation, the task of detecting distinct regions or objects in given images. Deep Neural Networks (DNN) have been shown to be very effective in segmenting challenging images, producing convincing segmentations. There is further need for probabilistic DNNs that can reflect the uncertainties from

I. Julián-Macías, F. Soto-Eguibar, I. Ramos Prieto, U. Ruiz

We present a detailed derivation of the Poynting vector for Cauchy-Riemann beams propagating in free space considering a Gaussian modulation with $g \in \mathbb{C}$. The effect generated by this Gaussian modulation is a compression-expansion of the intensity distribution. It is shown that the parameter $g$ can reverse the direction of energy flux and elimina

Praneet Pathak, Simon Blouin, Falk Herwig

Cooling ages of white dwarfs are routinely determined by mapping effective temperatures and masses to ages using evolutionary models. Typically, the reported uncertainties on cooling ages only consider the error propagation of the uncertainties on the spectroscopically or photometrically determined $T_{\rm eff}$ and mass. However, cooling models are themselv

Jonathan Li, Derick Ober, Anton Van der Ven

Hf and Zr nitrides are promising compounds for many technologically important areas, including high temperature structural applications, quantum computing and solar/optical applications. This article reports on a comprehensive first-principles statistical mechanics study of phase stability in the Hf-N and Zr-N binary systems. A high solubility of nitrogen in

Iain Weissburg, Sathvika Anand, Sharon Levy, Haewon Jeong

With the increasing adoption of large language models (LLMs) in education, concerns about inherent biases in these models have gained prominence. We evaluate LLMs for bias in the personalized educational setting, specifically focusing on the models' roles as "teachers." We reveal significant biases in how models generate and select educational content tailor

Gejia Zhang, Robert Mieth

The increasing demand for electricity and the aging infrastructure of power distribution systems have raised significant concerns about future system reliability. Failures in distribution systems, closely linked to system usage and environmental factors, are the primary contributors to electricity service interruptions. The integration of distributed energy

Ömer Faruk Akgül, Rajgopal Kannan, Viktor Prasanna

Graphs play a crucial role in data mining and machine learning, representing real-world objects and interactions. As graph datasets grow, managing large, decentralized subgraphs becomes essential, particularly within federated learning frameworks. These frameworks face significant challenges, including missing neighbor information, which can compromise model

Dmitriy Dmitrishin, Alexander Stokolos

We show that all the zeros of the quadrinomial $p(z)=1+\kappa(z+z^{N-1})+z^N$ lie on the unit circle if and only if the inequalities \[ -1\le\kappa\le 1\; (\mbox{ if $N$ is even}),\;\; -1\le\kappa\le N/(N-2)\; (\mbox{ if $N$ is odd}) \] hold. For the quadrinomial $q(z)=1+\kappa(z-z^{N-1})-z^N$, the corresponding inequalities are \[ -N/(N-2)\le\kappa\le 1\; (

Gabriel Chan, Bart Van Parys, Amine Bennouna

We establish a connection between distributionally robust optimization (DRO) and classical robust statistics. We demonstrate that this connection arises naturally in the context of estimation under data corruption, where the goal is to construct ``minimal'' confidence sets for the unknown data-generating distribution. Specifically, we show that a DRO ambigui

King-Yeung Lam, Gregoire Nadin, Xiao Yu

We consider the spreading dynamics of the Fisher-KPP equation in a shifting environment, by analyzing the limit of the rate function of the solutions. For environments with a weak monotone condition, it was demonstrated in a previous paper that the rate function converges to the unique Ishii solution of the underlying Hamilton-Jacobi equations. In case the e

Khalil Besrour, Abdellah Sebbar

In this paper, we explore the modular differential equation $\displaystyle y'' + F(z)y = 0$ on the upper half-plane $\mathbb{H}$, where $F$ is a weight 4 modular form for $\Gamma_0(2)$. Our approach centers on solving the associated Schwarzian equation $\displaystyle \{h, z\} = 2F(z)$, where $\{h, z\}$ represents the Schwarzian derivative of a meromorphic fu

Hao Li, Chengyi Xing, Saad Khan, Miaoya Zhong

Aquatic mammals, such as pinnipeds, utilize their whiskers to detect and discriminate objects and analyze water movements, inspiring the development of robotic whiskers for sensing contacts, surfaces, and water flows. We present the design and application of underwater whisker sensors based on Fiber Bragg Grating (FBG) technology. These passive whiskers are

Kirill Moiseev

In countries with a growing number of elderly and a shrinking workforce, one of which is Russia, it becomes impossible to maintain a solidary pension system and a need to switch to a more stable funded system appears. This paper analyzes various scenarios of Russia's transition to such a system. This is the first study on the Russian economy in which an Over

Connor Sullivan, Alex Manley, Mohammad Alian, Heechul Yun

Modern commercial-off-the-shelf (COTS) multicore processors have advanced memory hierarchies that enhance memory-level parallelism (MLP), which is crucial for high performance. To support high MLP, shared last-level caches (LLCs) are divided into multiple banks, allowing parallel access. However, uneven distribution of cache requests from the cores, especial

Abdollah Jalilian, Aki Vehtari, Luigi Sedda

We present a novel Bayesian framework to decompose the posterior predictive variance in a fitted Generalized Additive Mixed Model (GAMM) into explained and unexplained components. This decomposition enables a rigorous definition of Bayesian $R^{2}$. We show that the new definition aligns with the intuitive Bayesian $R^{2}$ proposed by Gelman, Goodrich, Gabry

Allison Lau, Younwoo Choi, Vahid Balazadeh, Keertana Chidambaram

Reinforcement Learning from Human Feedback (RLHF) is widely used to align Language Models (LMs) with human preferences. However, existing approaches often neglect individual user preferences, leading to suboptimal personalization. We present the Preference Pretrained Transformer (PPT), a novel approach for adaptive personalization using online user feedback.

Lei Zhang, Yang Yu, Emanuel Gull

We present a minimal pole method for analytically continuing matrix-valued imaginary frequency correlation functions to the real axis, enabling precise access to off-diagonal elements and thus improving the interpretation of self-energies and susceptibilities in quantum simulations. Traditional methods for matrix-valued analytic continuation tend to be eithe

Ryan McCann, Arash Rezaee, Vinod M. Vokkarane

The increasing demand for flexible and efficient optical networks has led to the development of Software-Defined Elastic Optical Networks (SD-EONs). These networks leverage the programmability of Software-Defined Networking (SDN) and the adaptability of Elastic Optical Networks (EONs) to optimize network performance under dynamic traffic conditions. However,

A. Danehkar

The emission lines from ionized nebulae allow us to determine their physical and chemical properties, along with the interstellar extinction. "pyEQUIB" is a pure Python open-source package including several application programming interface (API) functions that can be employed for plasma diagnostics, abundance analysis of collisionally excited lines (CEL) an

Łukasz Merta, Marcin Zieliński

In this note, we examine the arrangements of lines and configurations of points that emerge from Fermat (von Dyck) and Komiya-Kuribayashi quartics. These quartics are characterized by having the maximum number of lines of maximal tangency, that is, lines for which the intersection multiplicity at the tangency point is equal to the degree of the curve. Additi

Mengqi Lu, Jiayue Yang, Robert B. Mann

Wormhole solutions in gravitational theories typically require exotic matter. Here we present a wormhole solution to the field equations of Einsteinian Cubic Gravity -- a phenomenological competitor to general relativity that includes terms cubic in the curvature -- that has no matter, exotic or otherwise. These purely gravitational wormhole geometries are a

Ethan Rathbun, Alina Oprea, Christopher Amato

Recent works have demonstrated the vulnerability of Deep Reinforcement Learning (DRL) algorithms against training-time, backdoor poisoning attacks. The objectives of these attacks are twofold: induce pre-determined, adversarial behavior in the agent upon observing a fixed trigger during deployment while allowing the agent to solve its intended task during tr

Mason L. Klemm, Saif Siddique, Yuan-Chun Chang, Sijie Xu

Two-dimensional (2D) kagome lattice metals are interesting because they display flat electronic bands, Dirac points, Van Hove singularities, and can have interplay between charge density wave (CDW), magnetic order, and superconductivity. In kagome lattice antiferromagnet FeGe, a short-range CDW order was found deep within an antiferromagnetically ordered sta

Dyuman Bhattacharya, Jorma Louko, Robert B. Mann

We consider the transition rate of a static Unruh-DeWitt detector in two $(2+1)$-dimensional black hole spacetimes that are isometric to the static Ba\~nados-Teitelboim-Zanelli black hole outside the horizon but have no asymptotically locally anti-de Sitter exterior behind the horizon. The spacetimes are the $\mathbb{R}\text{P}^{2}$ geon, with spatial topolo

Chenchen Li, Fangxing Li, Sufan Jiang, Jin Zhao

Extreme weather events can cause widespread power outages and huge economic losses. Low-income customers are more vulnerable to power outages because they live in areas with poorly equipped distribution systems. However, existing approaches to improve grid resilience focus on the overall condition of the system and ignore the outage experiences of low-income

Jiping Li, Rishi Sonthalia

Random matrix theory has proven to be a valuable tool in analyzing the generalization of linear models. However, the generalization properties of even two-layer neural networks trained by gradient descent remain poorly understood. To understand the generalization performance of such networks, it is crucial to characterize the spectrum of the feature matrix a

Eva G. Goedhart, Yusuf Gurtas, Pamela E. Harris

In this article, we present a method to construct $e$-power $b$-happy numbers of any height. Using this method, we construct a tree that encodes these happy numbers, their heights, and their ancestry--relation to other happy numbers. For fixed power $e$ and base $b$, we consider happy numbers with at most $k$ digits and we give a formula for the cardinality

Luan Fletcher, Robert van der Klis, Martin Sedláček, Stefan Vasilev

The growing reproducibility crisis in machine learning has brought forward a need for careful examination of research findings. This paper investigates the claims made by Lei et al. (2023) regarding their proposed method, LICO, for enhancing post-hoc interpretability techniques and improving image classification performance. LICO leverages natural language s

D. Cassettari, O. V. Marchukov, B. Carruthers, H. Kendell

In this paper we study transitions of atoms between energy levels of several number-theory-inspired atom potentials, under the effect of time-dependent perturbations. First, we simulate in detail the case of a trap whose one-particle spectrum is given by prime numbers. We investigate one-body Rabi oscillations and the excitation lineshape for two resonantly

Kishan Maharaj, Vitobha Munigala, Srikanth G. Tamilselvam, Prince Kumar

Recent advancements in large language models (LLMs) have significantly enhanced their ability to understand both natural language and code, driving their use in tasks like natural language-to-code (NL2Code) and code summarisation. However, LLMs are prone to hallucination, outputs that stray from intended meanings. Detecting hallucinations in code summarisati

Jiatan Huang, Mingchen Li, Zonghai Yao, Dawei Li

Answering complex real-world questions in the medical domain often requires accurate retrieval from medical Textual Knowledge Graphs (medical TKGs), as the relational path information from TKGs could enhance the inference ability of Large Language Models (LLMs). However, the main bottlenecks lie in the scarcity of existing medical TKGs, the limited expressiv

Aoran Zhang, Wenbin Zhou, Liyan Xie, Shixiang Zhu

Time series data are crucial across diverse domains such as finance and healthcare, where accurate forecasting and decision-making rely on advanced modeling techniques. While generative models have shown great promise in capturing the intricate dynamics inherent in time series, evaluating their performance remains a major challenge. Traditional evaluation me

Aditya Narendra, Maria Dainotti, Milind Sarkar, Aleksander Lenart

Context. Gamma-ray bursts (GRBs), observed at redshifts as high as 9.4, could serve as valuable probes for investigating the distant Universe. However, this necessitates an increase in the number of GRBs with determined redshifts, as currently, only 12% of GRBs have known redshifts due to observational biases. Aims. We aim to address the shortage of GRBs wit

Yasin Hasanpoor, Bahram Tarvirdizadeh, Khalil Alipour, Mohammad Ghamari

Our research introduces a groundbreaking approach to stress classification through Photoplethysmogram (PPG) signals. By combining Continuous Wavelet Transformation (CWT) with the proven VGG16 classifier, our method enhances stress assessment accuracy and reliability. Previous studies highlighted the importance of physiological signal analysis, yet precise st

Zhang Enyan, Zewei Wang, Michael A. Lepori, Ellie Pavlick

Distributional semantics is the linguistic theory that a word's meaning can be derived from its distribution in natural language (i.e., its use). Language models are commonly viewed as an implementation of distributional semantics, as they are optimized to capture the statistical features of natural language. It is often argued that distributional semantics

Wioleta Rzęsa, Maria Stefaniak, Scott Pratt

Modeling proton-deuteron interactions is particularly challenging. Due the deuteron's large size, the interaction can extend over several femtometers. The degree to which it can be modeled as a two-body problem might also be questioned. One way to study these interactions is through femtoscopic correlation measurements of particle pairs, extracting informati

Megan C. Davis, Wilton J. M. Kort-Kamp, Ivana Matanovic, Piotr Zelenay

Direct air capture (DAC) of carbon dioxide is a critical technology for mitigating climate change, but current materials face limitations in efficiency and scalability. We discover novel DAC materials using a combined machine learning (ML) and high-throughput atomistic modeling approach. Our ML model accurately predicts high-quality, density functional theor

Renpu Liu, Ruida Zhou, Cong Shen, Jing Yang

An intriguing property of the Transformer is its ability to perform in-context learning (ICL), where the Transformer can solve different inference tasks without parameter updating based on the contextual information provided by the corresponding input-output demonstration pairs. It has been theoretically proved that ICL is enabled by the capability of Transf

Vera Provatorova, Carlotta Capurro, Evangelos Kanoulas

Social network analysis allows researchers to discover insights from connections between people. While the process of building a social network is relatively straightforward for contemporary social media, deriving connections from historical archives remains a challenging task, with every data collection presenting its unique challenges. Our contribution foc

Shivam Vats, Devesh K. Jha, Maxim Likhachev, Oliver Kroemer

Model-based planners and controllers are commonly used to solve complex manipulation problems as they can efficiently optimize diverse objectives and generalize to long horizon tasks. However, they often fail during deployment due to noisy actuation, partial observability and imperfect models. To enable a robot to recover from such failures, we propose to us

Fan Wu

I study a principal-agent model in which a principal hires an agent to collect information about an unknown continuous state. The agent acquires a signal whose distribution is centered around the state, controlling the signal's precision at a cost. The principal observes neither the precision nor the signal, but rather, using transfers that can depend on the

Bilal Karaman, Ilhan Basturk, Sezai Taskin, Engin Zeydan

As natural disasters become more frequent and severe, ensuring a resilient communications infrastructure is of paramount importance for effective disaster response and recovery. This disaster-resilient infrastructure should also respond to sustainability goals by providing an energy-efficient and economically feasible network that is accessible to everyone.

Neale Ratzlaff, Matthew Lyle Olson, Musashi Hinck, Shao-Yen Tseng

Large Vision Language Models (LVLMs) such as LLaVA have demonstrated impressive capabilities as general-purpose chatbots that can engage in conversations about a provided input image. However, their responses are influenced by societal biases present in their training datasets, leading to undesirable differences in how the model responds when presented with

Barnabas Porfy

In the recent years, research studies in high-energy physics have confirmed the creation of the strongly interacting quark-gluon plasma (sQGP) in ultra-relativistic nucleus-nucleus collisions. NA61/SHINE at CERN SPS investigates hadronic matter properties by varying collision energy ($\sqrt{s_{\rm{NN}}} \approx 5.3, 6.2, 7.7, 8.8, 12$, and 16.8 GeV) and syst

Minhua Lin, Zhiwei Zhang, Enyan Dai, Zongyu Wu

Graph Prompt Learning (GPL) has been introduced as a promising approach that uses prompts to adapt pre-trained GNN models to specific downstream tasks without requiring fine-tuning of the entire model. Despite the advantages of GPL, little attention has been given to its vulnerability to backdoor attacks, where an adversary can manipulate the model's behavio

Ehsan Kazemi, Dechen Gao, Iman Soltani

Autonomous navigation in marine environments can be extremely challenging, especially in the presence of spatially varying flow disturbances and dynamic and static obstacles. In this work, we demonstrate that incorporating local flow field measurements fundamentally alters the nature of the problem, transforming otherwise unsolvable navigation scenarios into

Ryan McCann, Arash Rezaee, Vinod M. Vokkarane

This paper presents an optimization framework for routing in software-defined elastic optical networks using reinforcement learning algorithms. We specifically implement and compare the epsilon-greedy bandit, upper confidence bound (UCB) bandit, and Q-learning algorithms to traditional methods such as K-Shortest Paths with First-Fit core and spectrum assignm

Agnès Beaudry, Chloe Lewis, Clover May, Sabrina Pauli

This article investigates equivariant parametrized cellular cohomology, a cohomology theory introduced by Costenoble-Waner for spaces with an action by a compact Lie group $G$. The theory extends the $RO(G)$-graded cohomology of a $G$-space $B$ to a cohomology graded by $RO(\Pi B)$, the representations of the equivariant fundamental groupoid of $B$. This pap

Will Brian, Christopher Stuart

We prove that every separable Banach space has a barrelled subspace with algebraic dimension $\mathrm{non}(\mathcal M)$, which denotes the smallest cardinality of a non-meager subset of $\mathbb R$. This strengthens a theorem of Sobota. More generally, we prove that every Banach space with density character $\kappa$ contains a barrelled subspace with algebra

Raju Mandal, Sagnik Misra, Partha Paul, Baishali Roy

In this paper, we study the four-point celestial leaf amplitudes of massless scalar and MHV gluon scattering. These leaf amplitudes are non-distributional decompositions of the celestial amplitudes associated with a hyperbolic foliation of the Klein spacetime. Bulk scale invariance imposes constraints on the total conformal weights of the massless scalars or

G. G. L. Nashed, Tiberiu Harko

Physically based changes to general relativity (GR) often predict significant differences in how spacetime behaves near massive neutron stars. One of these modifications is represented by $f(\mathcal{Q}, { \mathcal{T}})$, with $\mathcal{Q}$ being the non-metricity and ${ \mathit{T}}$ representing the energy-momentum tensor trace. This theory is viewed as a n

Andrés Rubiano, Armando Reyes

We investigate the differential smoothness of a certain family of skew Poincar\'e-Birkhoff-Witt extensions.

You Zhou, Jie Wang

Existing tools to detect text generated by a large language model (LLM) have met with certain success, but their performance can drop when dealing with texts in new domains. To tackle this issue, we train a ranking classifier called RoBERTa-Ranker, a modified version of RoBERTa, as a baseline model using a dataset we constructed that includes a wider variety

Pavel Gumenyuk, Maria Kourou, Annika Moucha, Oliver Roth

We characterize two classical types of conformality of a holomorphic self-map of the unit disk at a boundary point - existence of a finite angular derivative in the sense of Carath\'eodory and the weaker property of angle preservation - in terms of the non-tangential asymptotic behaviour of the hyperbolic distortion of the map. These characterizations are gi

Jinze Zhao, Peihao Wang, Junjie Yang, Ruisi Cai

Sparse Mixture-of-Experts (SMoE) architectures have gained prominence for their ability to scale neural networks, particularly transformers, without a proportional increase in computational cost. Despite their success, their role in compositional generalization, i.e., adapting to novel combinations of known components, remains under-explored. This study chal

Dinko Nazor, Andrei Tokovinin

In an effort to determine accurate orbital and physical properties of a large number of bright stars, a method was developed to fit simultaneously stellar parameters (masses, luminosities, effective temperatures), distance, and orbits to the available data on multiple systems, namely the combined and differential photometry, positional measurements, radial v

Subhadip Ghosh, Aydin Zaboli, Junho Hong, Jaerock Kwon

Tele-operated driving (ToD) systems are special types of cyber-physical systems (CPSs) where the operator remotely controls the steering, acceleration, and braking actions of the vehicle. Malicious actors may inject false data in communication channels to manipulate the tele-operators driving commands to cause harm. Hence, protection of this communication is

Catarina G. Belem, Pouya Pezeshkpour, Hayate Iso, Seiji Maekawa

Although many studies have investigated and reduced hallucinations in large language models (LLMs) for single-document tasks, research on hallucination in multi-document summarization (MDS) tasks remains largely unexplored. Specifically, it is unclear how the challenges arising from handling multiple documents (e.g., repetition and diversity of information)

Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat, James Garforth

This whitepaper offers an overview of the ethical considerations surrounding research into or with large language models (LLMs). As LLMs become more integrated into widely used applications, their societal impact increases, bringing important ethical questions to the forefront. With a growing body of work examining the ethical development, deployment, and us

Pranjal Rawat

Traditional methods for computing equilibria in auctions become computationally intractable as auction complexity increases, particularly in multi-item and dynamic auctions. This paper introduces a self-play based reinforcement learning approach that employs advanced algorithms such as Proximal Policy Optimization and Neural Fictitious Self-Play to approxima

Kuldeep Singh, Simerjot Kaur, Charese Smiley

Financial decision-making hinges on the analysis of relevant information embedded in the enormous volume of documents in the financial domain. To address this challenge, we developed FinQAPT, an end-to-end pipeline that streamlines the identification of relevant financial reports based on a query, extracts pertinent context, and leverages Large Language Mode

Shashank Dattathri, Priyamvada Natarajan, Antonio J. Porras-Valverde, Colin J. Burke

We study the co-evolution of black holes (BHs) and their host galaxies in the ASTRID and Illustris-TNG300 cosmological simulations and the Dark Sage Semi-Analytic Model (SAM), focusing on the evolution of the BH mass - stellar mass ($M_{\rm BH}-M_*$) relation. Due to differences in the adopted sub-grid modeling of BH seeding, dynamics, and feedback, the mode

Rachel Ma, Jingyi Qu, Andreea Bobu, Dylan Hadfield-Menell

Embodied AI Agents are quickly becoming important and common tools in society. These embodied agents should be able to learn about and accomplish a wide range of user goals and preferences efficiently and robustly. Large Language Models (LLMs) are often used as they allow for opportunities for rich and open-ended dialog type interaction between the human and

Ihab Bendidi, Shawn Whitfield, Kian Kenyon-Dean, Hanene Ben Yedder

Understanding the relationships among genes, compounds, and their interactions in living organisms remains limited due to technological constraints and the complexity of biological data. Deep learning has shown promise in exploring these relationships using various data types. However, transcriptomics, which provides detailed insights into cellular states, i

Chenyang Zhao, Sam M Lambrick, Nick A von Jeinsen, Yanke Yuan

Scanning helium microscopy (SHeM) is an emerging technique that uses a beam of neutral atoms to image and analyse surfaces. The low energies ($\sim$64 meV) and completely non-destructive nature of the probe particles provide exceptional sensitivity for studying delicate samples and thin devices, including 2D materials. To date, around five such instruments h

Aleksandar Armacki, Shuhua Yu, Pranay Sharma, Gauri Joshi

We study high-probability convergence in online learning, in the presence of heavy-tailed noise. To combat the heavy tails, a general framework of nonlinear SGD methods is considered, subsuming several popular nonlinearities like sign, quantization, component-wise and joint clipping. In our work the nonlinearity is treated in a black-box manner, allowing us

Tonghan Wang, Heng Dong, Yanchen Jiang, David C. Parkes

Multiagent systems grapple with partial observability (PO), and the decentralized POMDP (Dec-POMDP) model highlights the fundamental nature of this challenge. Whereas recent approaches to addressing PO have appealed to deep learning models, providing a rigorous understanding of how these models and their approximation errors affect agents' handling of PO and

Bowen Chen, Zaixi Shang, Jae Won Chung, David Lerner

Demand for streaming services, including satellite, continues to exhibit unprecedented growth. Internet Service Providers find themselves at the crossroads of technological advancements and rising customer expectations. To stay relevant and competitive, these ISPs must ensure their networks deliver optimal video streaming quality, a key determinant of user s

Zhiyu Chen, Jason Choi, Besnik Fetahu, Shervin Malmasi

In e-commerce, high consideration search missions typically require careful and elaborate decision making, and involve a substantial research investment from customers. We consider the task of identifying High Consideration (HC) queries. Identifying such queries enables e-commerce sites to better serve user needs using targeted experiences such as curated QA

Jameson Graber, Elizabeth Matter

We propose a new approach to proving the uniqueness of solutions to a certain class of mean field games of controls. In this class, the equilibrium is determined by an aggregate quantity $Q(t)$, e.g. the market price or production, which then determines optimal trajectories for agents. Our approach consists in analyzing the relationship between $Q(t)$ and co

Anish Mukherjee, Jeremy T. Gaskins, Shoumi Sarkar, Steven Levy

We develop a new longitudinal count data regression model that accounts for zero-inflation and spatio-temporal correlation across responses. This project is motivated by an analysis of Iowa Fluoride Study (IFS) data, a longitudinal cohort study with data on caries (cavity) experience scores measured for each tooth across five time points. To that end, we use

Cogan Shimizu, Shirly Stephe, Adrita Barua, Ling Cai

KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, various place and region identifiers, among other the

Liam Rankin Sheldon, Dylan Sheldon Rankin, Philip Harris

The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning

James O'Quinn

We prove that every ergodic transformation is Shannon orbit equivalent to a weak mixing transformation. The proof is based on the techniques introduced by Fieldsteel and Friedman to show that there is a mixing transformation for a given ergodic transformation $T$ which is, for all $a\geq1$, weak-$a$-equivalent to $T$ and, for all $b\in(0,1)$, strong-$b$-equi

Rongxuan Li, Gary P. T. Choi

Origami, the traditional art of paper folding, has revolutionized science and technology in recent years and has been found useful in various real-world applications. In particular, origami-inspired structures have been utilized for robotics and mechanical information storage, in both of which the rigidity control of origami plays a crucial role. However, mo

Junhong Wu, Yang Zhao, Yangyifan Xu, Bing Liu

Large Language Models (LLMs) have achieved impressive results across numerous NLP tasks but still encounter difficulties in machine translation. Traditional methods to improve translation have typically involved fine-tuning LLMs using parallel corpora. However, vanilla fine-tuning often leads to catastrophic forgetting of the instruction-following capabiliti

Anthony Sicilia, Mert Inan, Malihe Alikhani

Effective human-machine collaboration requires machine learning models to externalize uncertainty, so users can reflect and intervene when necessary. For language models, these representations of uncertainty may be impacted by sycophancy bias: proclivity to agree with users, even if they are wrong. For instance, models may be over-confident in (incorrect) pr

Andrea Sangiovanni, Roberto Valandro

M-theory geometric engineering on non-compact Calabi-Yau fourfolds (CY4) produces 3d theories with 4 supercharges. Carefully establishing a dictionary between the geometry of the CY4 and the QFT in the transverse directions remains, to a large extent, an unresolved challenge, complicated by subtleties arising from M5-brane instanton corrections. Such difficu

Alexander Rawlings, Atte Keitaanranta, Max Mattero, Sonja Soininen

We study stellar core growth in simulations of merging massive ($M_\star>10^{11}\,\mathrm{M}_\odot$) elliptical galaxies by a supermassive black hole (SMBH) displaced by gravitational wave induced recoil velocity. With controlled, dense sampling of the SMBH recoil velocity, we find the core radius originally formed by SMBH binary scouring can grow by a facto

Simon Knapen, Toby Opferkuch, Diego Redigolo, Michele Tammaro

We present strategies for the Mu3e experiment to search for light, weakly coupled particles produced in rare muon decays, focusing on displaced $e^+e^-$ decays within the hollow target. In most scenarios the backgrounds can be fully suppressed with a suitable set of cuts. We furthermore quantify the interplay between displaced and prompt searches at Mu3e and

Gian Michele Graf, Alessandro Tarantola

Bulk-edge correspondence is a wide-ranging principle that applies to topological matter, as well as a precise result established in a large and growing number of cases. According to the principle, the distinctive topological properties of matter, thought of as extending indefinitely in space, are equivalently reflected in the excitations running along its bo