October 2024 arXiv papers — page 88

Srijan Saket, Vivek Chandela, Md. Danish Kalim

Historically, machine learning training pipelines have predominantly relied on batch training models, retraining models every few hours. However, industrial practitioners have proved that real-time training can lead to a more adaptive and personalized user experience. The transition from batch to real-time is full of tradeoffs to get the benefits of accuracy

Yuki Okamoto, Ryotaro Nagase, Minami Okamoto, Yuki Saito

Some datasets with the described content and order of occurrence of sounds have been released for conversion between environmental sound and text. However, there are very few texts that include information on the impressions humans feel, such as "sharp" and "gorgeous," when they hear environmental sounds. In this study, we constructed a dataset with impressi

Kaige Xie, Philippe Laban, Prafulla Kumar Choubey, Caiming Xiong

Evaluating retrieval-augmented generation (RAG) systems remains challenging, particularly for open-ended questions that lack definitive answers and require coverage of multiple sub-topics. In this paper, we introduce a novel evaluation framework based on sub-question coverage, which measures how well a RAG system addresses different facets of a question. We

Zongge Liu, Heejong Bong, Zhao Ren, Matthew A. Smith

We study simultaneous inference for multiple matrix-variate Gaussian graphical models in high-dimensional settings. Such models arise when spatiotemporal data are collected across multiple sample groups or experimental sessions, where each group is characterized by its own graphical structure but shares common sparsity patterns. A central challenge is to con

Tianyu Liang, Meiqiang Zhan, Hulin Wang, Xianglun Wei

In the N$\nu$DEx collaboration, a high-pressure gas TPC is being developed to search for the neutrinoless double beta decay. The use of electronegative $\mathrm{^{82}SeF_{6}}$ gas mandates an ion-TPC. The reconstruction of $z$ coordinate is to be realized exploiting the feature of multiple species of charge carriers. As the initial stage of the development,

Chanseo Lee, Sonu Kumar, Kimon A. Vogt, Sam Meraj

AI-powered medical scribes have emerged as a promising solution to alleviate the documentation burden in healthcare. Ambient AI scribes provide real-time transcription and automated data entry into Electronic Health Records (EHRs), with the potential to improve efficiency, reduce costs, and enhance scalability. Despite early success, the accuracy of AI scrib

Philip Baillargeon, Jina Yoon, Amy Zhang

Human-Computer Interaction (HCI) and Computer Supported Collaborative Work (CSCW) have a longstanding tradition of interrogating the values that underlie systems in order to create novel and accessible experiences. In this work, we use a neurodiversity framing to examine how people with ways of thinking, speaking, and being that differ from normative assumpt

Jinda Jia, Cong Xie, Hanlin Lu, Daoce Wang

Recent years have witnessed a clear trend towards language models with an ever-increasing number of parameters, as well as the growing training overhead and memory usage. Distributed training, particularly through Sharded Data Parallelism (ShardedDP) which partitions optimizer states among workers, has emerged as a crucial technique to mitigate training time

Mahmoud Ranjbar, Mohammad Rahimzadeh

Iran, endowed with abundant hydrocarbon resources, plays a crucial role in the global energy landscape. Gasoline, as a critical fuel, significantly supports the nation's transportation sector. Accurate forecasting of gasoline consumption is essential for strategic resource management and environmental planning. This research introduces a novel approach to pr

Hemida H. Mohammed, Mate Csanad, Y. Mohammed, N. Rashed

In high-energy physics, quantum statistical correlation measurements are very important for getting a good picture of how a particle-emitting source is structured in space and time, as well as its thermodynamic properties and inner dynamics. It is necessary to take into account the various final state effects since they have the potential to alter the observ

Ziqi Xu, Sevvandi Kandanaarachchi, Cheng Soon Ong, Eirini Ntoutsi

Item Response Theory (IRT) has been widely used in educational psychometrics to assess student ability, as well as the difficulty and discrimination of test questions. In this context, discrimination specifically refers to how effectively a question distinguishes between students of different ability levels, and it does not carry any connotation related to f

Ahmed Elbakary, Chaouki Ben Issaid, Tamer ElBatt, Karim Seddik

In this paper, we introduce a method for fine-tuning Large Language Models (LLMs), inspired by Multi-Task learning in a federated manner. Our approach leverages the structure of each client's model and enables a learning scheme that considers other clients' tasks and data distribution. To mitigate the extensive computational and communication overhead often

Srishti Gureja, Lester James V. Miranda, Shayekh Bin Islam, Rishabh Maheshwary

Reward models (RMs) have driven the state-of-the-art performance of LLMs today by enabling the integration of human feedback into the language modeling process. However, RMs are primarily trained and evaluated in English, and their capabilities in multilingual settings remain largely understudied. In this work, we conduct a systematic evaluation of several r

Yuhang Li, Shiqi Chen, Bijie Bai, Aydogan Ozcan

We introduce an all-optical system, termed the "lying mirror", to hide input information by transforming it into misleading, ordinary-looking patterns that effectively camouflage the underlying image data and deceive the observers. This misleading transformation is achieved through passive light-matter interactions of the incident light with an optim

Daniel Cibotaru, Matheus Vieira

We prove, for a class of first order differential operators containing the generalized gradients, Dirac and Penrose twistor operators, a family of Kato inequalities that interpolates between the classical and the refined Kato. For the Hodge-de-Rham operator we get a more detailed result. As a corollary, we get various Kato inequalities from the literature.

Ye Lu, Chaoqian Yuan, Han Guo

This paper presents a convolution tensor decomposition based model reduction method for solving the Allen-Cahn equation. The Allen-Cahn equation is usually used to characterize phase separation or the motion of anti-phase boundaries in materials. Its solution is time-consuming when high-resolution meshes and large time scale integration are involved. To reso

Thinh Phan, Isaac Phillips, Andrew Lockett, Michael T. Kidd

Object tracking, especially animal tracking, is one of the key topics that attract a lot of attention due to its benefits of animal behavior understanding and monitoring. Recent state-of-the-art tracking methods are founded on deep learning architectures for object detection, appearance feature extraction and track association. Despite the good tracking perf

Swarang Joshi, Siddharth Mavani, Joel Alex, Arnav Negi

Misinformation undermines individual knowledge and affects broader societal narratives. Despite growing interest in the research community in multi-modal misinformation detection, existing methods exhibit limitations in capturing semantic cues, key regions, and cross-modal similarities within multi-modal datasets. We propose SceneGraMMi, a Scene Graph-booste

Ange-Clément Akazan, Alexia Jolicoeur-Martineau, Ioannis Mitliagkas

Privacy and regulatory constraints make data generation vital to advancing machine learning without relying on real-world datasets. A leading approach for tabular data generation is the Forest Flow (FF) method, which combines Flow Matching with XGBoost. Despite its good performance, FF is slow and makes errors when treating categorical variables as one-hot c

Radoslav Bucik, Samuel T. Hart, Maher A. Dayeh, Mihir I. Desai

We examined the origin of 3He abundance enhancement in 23 high-energy (25-50 MeV) solar proton events that coincide with 3He-rich periods detected by ACE ULEIS in 1997-2021. In seven events, 3He enhancement was due to 3He leftover from preceding events or independent 3He events occurring during proton events. One event is the most likely impulsive (3He-rich)

Mitsuki Hanada

We construct a basis of the Garsia-Procesi ring using the catabolizability type of standard Young tableaux and the charge statistic. This basis turns out to be equal to the descent basis defined in Carlsson-Chou (2024+). Our new construction connects the combinatorics of the basis with the well-known combinatorial formula for the modified Hall-Littlewood pol

Ryan Magee, Ritwik Sharma, Ananya Agrawal, Rhiannon Udall

Gravitational-wave detection pipelines have helped to identify over one hundred compact binary mergers in the data collected by the Advanced LIGO and Advanced Virgo interferometers, whose sensitivity has provided unprecedented access to the workings of the gravitational universe. The detectors are, however, subject to a wide variety of noise transients (or g

Nishant Balepur, Feng Gu, Abhilasha Ravichander, Shi Feng

Question answering (QA), giving correct answers to questions, is a popular task, but we test reverse question answering (RQA): for an input answer, give a question with that answer. Past work tests QA and RQA separately, but we test them jointly, comparing their difficulty, aiding benchmark design, and checking reasoning consistency. We run 16 LLMs on QA and

Kashob Kumar Roy, Pritom Saha Akash, Kevin Chen-Chuan Chang, Lucian Popa

Open-domain long-form text generation requires generating coherent, comprehensive responses that address complex queries with both breadth and depth. This task is challenging due to the need to accurately capture diverse facets of input queries. Existing iterative retrieval-augmented generation (RAG) approaches often struggle to delve deeply into each facet

Neethu Suma Raveendran, Md Abdul Aziz, Muhammad Mohebujjaman

This paper presents and analyzes two robust, efficient, and optimally accurate fully discrete finite element algorithms for computing the parameterized Navier-Stokes Equations (NSEs) flow ensemble. The timestepping algorithms are linearized, use the backward-Euler method for approximating the temporal derivative, and Ensemble Eddy Viscosity (EEV) regularized

Rohan Saha, Abrar Fahim, Alona Fyshe, Alex Murphy

For specialized domains, there is often not a wealth of data with which to train large machine learning models. In such limited data / compute settings, various methods exist aiming to $\textit{do more with less}$, such as finetuning from a pretrained model, modulating difficulty levels as data are presented to a model (curriculum learning), and considering

Kaveh Pahlevan, Laura Schaefer, Don Porcelli

The Martian isotopic record displays a dichotomy in volatile compositions. Interior volatiles from the mantle record a chondritic heritage (e.g., H, N, Kr, Xe) whereas the atmospheric reservoir of Kr and Xe - which do not currently experience escape - record heritage from a solar-like source. Motivated by disparate inferences on the source of Martian atmosph

Manuel de León, Pablo Soto Martín

In this paper we provide a complete characterisation of coisotropic embeddings of precosymplectic manifolds into cosymplectic manifolds. This result extends a theorem of Gotay about coisotropic embeddings of presymplectic manifolds. We also extend to the cosymplectic case some results of A. Weinstein which generalise the Darboux theorem. While symplectic geo

Xin Li, Songtao Mao

We study uniquely decodable codes and list decodable codes in the high-noise regime, specifically codes that are uniquely decodable from $\frac{1-\varepsilon}{2}$ fraction of errors and list decodable from $1-\varepsilon$ fraction of errors. We present several improved explicit constructions that achieve near-optimal rates, as well as efficient or even linea

Hadas Ben-Eli Yaacov Cnaany, Itay Chowers, Ayelet Goldstein

Background/Objectives: Cataract surgery, a very common and critical procedure for restoring vision, has outcomes that can vary based on patient demographics. This study aimed to elucidate the effects of age and sex on the risk factors, intraoperative complications, and postoperative outcomes of cataract surgery. Subjects/Methods: Conducted as a single-center

Yue Jiang, Christof Lutteroth, Rajiv Jain, Christopher Tensmeyer

Designing adaptive documents that are visually appealing across various devices and for diverse viewers is a challenging task. This is due to the wide variety of devices and different viewer requirements and preferences. Alterations to a document's content, style, or layout often necessitate numerous adjustments, potentially leading to a complete layout rede

Philipp Stammer

The use of energy conservation arguments is ubiquitous in understanding the process of high harmonic generation, yet a complete quantum optical description of exact photon number exchange remained elusive. Here, we solve this gap in description by introducing the energy conserving subspace in high harmonic generation in which many photons of the driving fiel

Elod P. Csirmaz, Laszlo Csirmaz

Enumerating the extremal submodular functions defined on subsets of a fixed base set has only been done for base sets up to five elements. This paper reports the results of attempting to generate all such functions on a six-element base set. Using improved tools from polyhedral geometry, we have computed 360 billion of them, and provide the first reasonable

Jerry Huang, Laura Lewis, Hsin-Yuan Huang, John Preskill

Recent advances have demonstrated that $\mathcal{O}(\log M)$ measurements suffice to predict $M$ properties of arbitrarily large quantum many-body systems. However, these remarkable findings assume that the properties to be predicted are chosen independently of the data. This assumption can be violated in practice, where scientists adaptively select properti

Suhita Ghosh, Melanie Jouaiti, Arnab Das, Yamini Sinha

Speech anonymisation aims to protect speaker identity by changing personal identifiers in speech while retaining linguistic content. Current methods fail to retain prosody and unique speech patterns found in elderly and pathological speech domains, which is essential for remote health monitoring. To address this gap, we propose a voice conversion-based metho

Suhita Ghosh, Tim Thiele, Frederic Lorbeer, Frank Dreyer

The increasing use of cloud-based speech assistants has heightened the need for effective speech anonymization, which aims to obscure a speaker's identity while retaining critical information for subsequent tasks. One approach to achieving this is through voice conversion. While existing methods often emphasize complex architectures and training techniques,

Ralph V. Chamberlin, Stuart M. Lindsay

Nanothermodynamics provides the theoretical foundation for understanding stable distributions of statistically independent subsystems inside larger systems. In this review it is emphasized that adapting ideas from nanothermodynamics to simplistic models improves agreement with the measured properties of many materials. Examples include non-classical critical

Amirreza Fahim Golestaneh, Venanzio Cichella, Caterina Lamuta

The current work is a qualitative study that aims to explore the implementation of Twisted and Coiled Artificial Muscles (TCAMs) for actuating and replicating the bending motion of an octopus-like soft robot arm underwater. Additionally, it investigates the impact of hydrostatic and dynamic forces from steady-state fluid flow on the arm's motion. The artific

Vasile Păiş, Sara Niţă, Alexandru-Iulius Jerpelea, Luca Pană

Memes are becoming increasingly more popular in online media, especially in social networks. They usually combine graphical representations (images, drawings, animations or video) with text to convey powerful messages. In order to extract, process and understand the messages, AI applications need to employ multimodal algorithms. In this paper, we introduce a

Luca Lumetti, Vittorio Pipoli, Kevin Marchesini, Elisa Ficarra

Recently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with their distinctive strengths and limitations. CNNs are constrained by a local receptive field, whereas transformers are hindered by their substantial memory requirements as well

Parsa Esmati, Amirhossein Dadashzadeh, Vahid Goodarzi, Nicolas Larrosa

Current approaches using sequential networks have shown promise in estimating field variables for dynamical systems, but they are often limited by high rollout errors. The unresolved issue of rollout error accumulation results in unreliable estimations as the network predicts further into the future, with each step's error compounding and leading to an incre

Asmar Muqeet, Hassan Sartaj, Aitor Arrieta, Shaukat Ali

Machine learning has been extensively applied for classical software testing activities such as test generation, minimization, and prioritization. Along the same lines, there has been interest in applying quantum machine learning to classical software testing. For example, Quantum Extreme Learning Machines (QELMs) were recently applied for testing classical

Bjoern Bringmann, Sky Cao

We prove the global well-posedness of the dynamical sine-Gordon model up to the third threshold, i.e., for parameters $\beta^2 < 6\pi$. The key novelty in our approach is the introduction of the so-called resonant equation, whose solution is entirely deterministic and completely captures the size of the solution to the dynamical sine-Gordon model. The probab

Arisrei Lim, Abhiram Maddukuri

In recent years, reinforcement learning (RL) has gained popularity and has been applied to a wide range of tasks. One such popular domain where RL has been effective is resource management problems in systems. We look to extend work on RL for resource management problems by considering the novel domain of dynamic memory allocation management. We consider dyn

Sanchit Sinha, Guangzhi Xiong, Aidong Zhang

The rising need for explainable deep neural network architectures has utilized semantic concepts as explainable units. Several approaches utilizing disentangled representation learning estimate the generative factors and utilize them as concepts for explaining DNNs. However, even though the generative factors for a dataset remain fixed, concepts are not fixe

Norbert Tihanyi, Tamas Bisztray, Richard A. Dubniczky, Rebeka Toth

As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often simplistic, allowing models to perform uniformly well and making it difficult to distinguish their capabilities. Additionally, benchmarks typically rely on static question-answer pairs that the models

Jason Jabbour, Vijay Janapa Reddi

The integration of Generative Artificial Intelligence (AI) into autonomous machines represents a major paradigm shift in how these systems operate and unlocks new solutions to problems once deemed intractable. Although generative AI agents provide unparalleled capabilities, they also have unique safety concerns. These challenges require robust safeguards, es

Dimitri Navarro, Jiayin Pan, Xingyu Zhu

Understanding the relationships between geometry and topology is a central theme in Riemannian geometry. We establish two results on the fundamental groups of open (complete and noncompact) $n$-manifolds with nonnegative Ricci curvature and linear volume growth. First, we show that the fundamental group of such a manifold contains a subgroup $\mathbb{Z}^k$ o

T. Henksmeier, P. Mahler, A. Wolff, D. Deutsch

We report on the low-temperature fabrication (300$\deg$C) of ultrathin 2D amorphous carbon layers on III-V semiconductors by plasma-enhanced chemical vapor deposition as a universal template for remote epitaxy. We present growth and detailed characterization of 2D amorphous carbon layers on various host substrates and their subsequent remote epitaxial overgr

Shaun Macdonald, Robin Bretin, Salma ElSayed

Zoomorphic robots have the potential to offer companionship and well-being as accessible, low-maintenance alternatives to pet ownership. Many such robots, however, feature limited emotional expression, restricting their potential for rich affective relationships with everyday domestic users. Additionally, exploring this design space using hardware prototypin

J. -M. Rax, E. J. Kolmes, N. J. Fisch

We describe and analyze a new class of direct energy conversion schemes based on the adiabatic magnetic drift of charged particles in axisymmetric magnetic fields. The efficiency of conversion as well as the geometrical and dynamical limitations of the recoverable power are calculated. The geometries of these axisymmetric field configurations are suited for

Ran Zmigrod, Pranav Shetty, Mathieu Sibue, Zhiqiang Ma

The rise of large language models (LLMs) for visually rich document understanding (VRDU) has kindled a need for prompt-response, document-based datasets. As annotating new datasets from scratch is labor-intensive, the existing literature has generated prompt-response datasets from available resources using simple templates. For the case of key information ex

Heshan Fernando, Han Shen, Parikshit Ram, Yi Zhou

The post-training of LLMs, which typically consists of the supervised fine-tuning (SFT) stage and the preference learning stage (RLHF or DPO), is crucial to effective and safe LLM applications. The widely adopted approach in post-training popular open-source LLMs is to sequentially perform SFT and RLHF/DPO. However, this is suboptimal in terms of SFT and RLH

Sanaz Mohammadi Almas, Ghader Najarbashi

In this paper, we investigate the geometric phase (GP) acquired by two-mode mixed squeezed-coherent states (SCSs) during unitary cyclic evolution, focusing on the influence of squeezing parameters and classical weight. We analyze the GP for three distinct mixed states characterized by different configurations of the SCSs. Our results reveal that increasing t

David Morselli, Giulia Chiari, Federico Frascoli, Marcello E. Delitala

The effectiveness of oncolytic virotherapy is significantly affected by several elements of the tumour microenvironment, which reduce the ability of the virus to infect cancer cells. In this work, we focus on the influence of hypoxia on this therapy and develop a novel continuous mathematical model that considers both the spatial and epigenetic heterogeneity

Rahul Trivedi, Xiehang Yu, Mark Rudner

We consider a general class of spatially local non-Markovian open quantum lattice models, with a bosonic environment that is approximated as Gaussian. Under the assumption of a finite environment memory time, formalized as a finite total variation of the memory kernel, we show that these models satisfy a Lieb-Robinson bound. Our work generalizes Lieb Robinso

Jiaqiang Zhang, Xianjia Yu, Ha Sier, Haizhou Zhang

Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness, offering notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. These distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in

Shuhang Zhang, Bryan Olmos, Basavaraj Naik

Registers in IP blocks of an SoC perform a variety of functions, most of which are essential to the SoC operation. The complexity of register implementation is relatively low when compared with other design blocks. However, the extensive number of registers, combined with the various potential functions they can perform, necessitates considerable effort duri

Abraham Bobadilla Osses, Mauricio Godoy Molina

From the classical theory of Lie algebras, it is well-known that the bilinear form $B(X,Y)={\rm tr}(XY)$ defines a non-degenerate scalar product on the simple Lie algebra ${\mathfrak{sl}}(n,{\mathbb R})$. Diagonalizing the Gram matrix $Gr$ associated with this scalar product we find a basis of ${\mathfrak{sl}}(n,{\mathbb R})$ of eigenvectors of $Gr$ which pr

Matias D. Cattaneo, Carlos Diaz, Rocio Titiunik

Learning about the immediate causal effects of large-scale policy interventions poses a significant challenge for quasi-experimental methods that rely on long-term trends or parametric modeling assumptions. As an alternative, we develop a randomization inference framework for before-and-after studies with multiple units, designed specifically for short-term

Gaétan Leclerc

This is my (reviewed) PhD manuscript. It contains 6 Chapters, which contains mostly already published work, except for Chapter 5 which is new. Chapter 1 introduce basic notions on fractal geometry: the Fourier dimension, the thermodynamical formalism and additive combinatorics. Chapter 2 is a generalized version of arXiv:2211.08088. Chapter 3 is a slightly u

Timofei Gritsaev, Nikita Morozov, Sergey Samsonov, Daniil Tiapkin

Generative Flow Networks (GFlowNets) are a family of generative models that learn to sample objects with probabilities proportional to a given reward function. The key concept behind GFlowNets is the use of two stochastic policies: a forward policy, which incrementally constructs compositional objects, and a backward policy, which sequentially deconstructs t

Peng Wu, Tales Imbiriba, Pau Closas

One of the main challenges of federated learning (FL) is handling non-independent and identically distributed (non-IID) client data, which may occur in practice due to unbalanced datasets and use of different data sources across clients. Knowledge sharing and model personalization are key strategies for addressing this issue. Clustered federated learning is

Fnu Neha, Arvind K. Bansal

Renal tumors, especially renal cell carcinoma (RCC), show significant heterogeneity, posing challenges for diagnosis using radiology images such as MRI, echocardiograms, and CT scans. U-Net based deep learning techniques are emerging as a promising approach for automated medical image segmentation for minimally invasive diagnosis of renal tumors. However, cu

Keri Mallari, Julius Adebayo, Kori Inkpen, Martin T. Wells

Despite strong advisory against it, large generative models (LMs) are already being used for decision making tasks that were previously done by predictive models or humans. We put popular LMs to the test in a high-stakes decision making task: recidivism prediction. Studying three closed-access and open-source LMs, we analyze the LMs not exclusively in terms

David Ryzák, Martin Černý

This paper studies the stochastic setting in cooperative games and suggests a solution concept based on second order stochastic dominance (SSD), which is often applied to robustly model risk averse behaviour of players in different economic and game theoretic models as it enables to model not specified levels of risk aversion among players. The main result o

Christina Hastings Blow, Lijun Qian, Camille Gibson, Pamela Obiomon

AI fairness seeks to improve the transparency and explainability of AI systems by ensuring that their outcomes genuinely reflect the best interests of users. Data augmentation, which involves generating synthetic data from existing datasets, has gained significant attention as a solution to data scarcity. In particular, diffusion models have become a powerfu

Alan Chen, Changliu Liu

A critical goal in robotics and autonomy is to teach robots to adapt to real-world collaborative tasks, particularly in automatic assembly. The ability of a robot to understand the original intent of an incomplete assembly and complete missing features without human instruction is valuable but challenging. This paper introduces 3D combinatorial assembly comp

Sean M. Carroll, Achyuth Parola

We consider emergence from the perspective of dynamics: states of a system evolving with time. We focus on the role of a decomposition of wholes into parts, and attempt to characterize relationships between levels without reference to whether higher-level properties are "novel" or "unexpected." We offer a classification of different varieties of emergence, w

Hangzhi Guo, Pranav Narayanan Venkit, Eunchae Jang, Mukund Srinath

The widespread adoption of large language models (LLMs) and generative AI (GenAI) tools across diverse applications has amplified the importance of addressing societal biases inherent within these technologies. While the NLP community has extensively studied LLM bias, research investigating how non-expert users perceive and interact with biases from these sy

Gal Yona, Or Honovich, Omer Levy, Roee Aharoni

Scaling inference compute in large language models (LLMs) through repeated sampling consistently increases the coverage (fraction of problems solved) as the number of samples increases. We conjecture that this observed improvement is partially due to the answer distribution of standard evaluation benchmarks, which is skewed towards a relatively small set of

Xiao Hu, Ziqi Chen, Bo Peng, Daniel Adu-Ampratwum

Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-based methods to accelerate yield predictions without conducting in vitro experiments. We present log-RRIM, an

Renaud Raquépas, Jeffrey Schenker

We prove a quenched version of the large deviation principle for Birkhoff-like sums along a sequence of random quantum measurements driven by an ergodic process. We apply the result to the study of entropy production in the two-time measurement framework.

Lance Calvin Lim Gamboa, Mark Lee

Work on bias in pretrained language models (PLMs) focuses on bias evaluation and mitigation and fails to tackle the question of bias attribution and explainability. We propose a novel metric, the $\textit{bias attribution score}$, which draws from information theory to measure token-level contributions to biased behavior in PLMs. We then demonstrate the util

Aizan Zafar, Kshitij Mishra, Asif Ekbal

In Medical question-answering (QA) tasks, the need for effective systems is pivotal in delivering accurate responses to intricate medical queries. However, existing approaches often struggle to grasp the intricate logical structures and relationships inherent in medical contexts, thus limiting their capacity to furnish precise and nuanced answers. In this wo

Yonatan Gideoni, Ryan Charles Timms, Oiwi Parker Jones

Non-invasive brainwave decoding is usually done using Magneto/Electroencephalography (MEG/EEG) sensor measurements as inputs. This makes combining datasets and building models with inductive biases difficult as most datasets use different scanners and the sensor arrays have a nonintuitive spatial structure. In contrast, fMRI scans are acquired directly in br

Anton Gorodetski, Victor Kleptsyn

We consider one-parameter families of smooth circle cocycles over an ergodic transformation in the base, and show that their rotation numbers must be log-H\"older regular with respect to the parameter. As an immediate application, we get a dynamical proof of 1D version of the Craig-Simon theorem that establishes that the integrated density of states of an er

Xiaowei Chi, Chun-Kai Fan, Hengyuan Zhang, Xingqun Qi

Video generation models have made significant progress in simulating future states, showcasing their potential as world simulators in embodied scenarios. However, existing models often lack robust understanding, limiting their ability to perform multi-step predictions or handle Out-of-Distribution (OOD) scenarios. To address this challenge, we propose the Re

Shahrad Mohammadzadeh, Juan David Guerra, Marco Bonizzato, Reihaneh Rabbany

As large language models (LLMs) become increasingly prevalent, concerns about their reliability, particularly due to hallucinations - factually inaccurate or irrelevant outputs - have grown. Our research investigates the relationship between the uncertainty in training dynamics and the emergence of hallucinations. Using models from the Pythia suite and sever

Javier Falco, Daniel Isert

We study the reflexivity and strong subdifferentiability within the framework of group invariant mappings. We show that a Banach space is G-reflexive if the norm of its dual is G-strong subdifferentiable. To do this, we extend numerous classical concepts in functional analysis such as weak and weak-star topologies, the polar of a set, duality mapping, to the

Yuan Zhou, Qiuyue Wang, Yuxuan Cai, Huan Yang

Significant advancements have been made in the field of video generation, with the open-source community contributing a wealth of research papers and tools for training high-quality models. However, despite these efforts, the available information and resources remain insufficient for achieving commercial-level performance. In this report, we open the black

Jihao Liu, Lingyao Xie

We show that the anti-canonical bundle of any $\mathbb Q$-factorial surface is numerically effective if and only if it is pseudo-effective. To prove this, we establish a numerical non-vanishing theorem for surfaces polarized with pseudo-effective divisors. The latter answers a question of C. Fontanari.

Francisco M. Fernández

We analyse the exact solutions of a conditionally-solvable Schr\"odinger equation with a rational potential. From the nodes of the exact eigenfunctions we derive a connection between the otherwise isolated exact eigenvalues and the actual eigenvalues of the Hamiltonian operator.

De-Sheng Xiang, Yao-Wen Zhang, Hao-Xiang Liu, Peng Zhou

Interactions of isolated quantum many-body systems typically scramble local information into the entire system and make it unrecoverable. Ergodicity-breaking systems possess the potential to exhibit fundamentally different information scrambling dynamics beyond this paradigm. For many-body localized systems with strong ergodicity breaking, local transport va

Tirthankar Bhattacharyya, Ritul Duhan, Chandan Pradhan

It is shown that van Suijlekom's technique of imposing a set of conditions on operator system spectral triples ensures Gromov-Hausdorff convergence of sequences of sets of unital completely positive maps (equipped with the BW-topology which is metrizable). This implies that even when only a part of the spectrum of the Dirac operator is available together wit

Malvina Nikandrou, Georgios Pantazopoulos, Nikolas Vitsakis, Ioannis Konstas

As Vision and Language models (VLMs) are reaching users across the globe, assessing their cultural understanding has become a critical challenge. In this paper, we introduce CROPE, a visual question answering benchmark designed to probe the knowledge of culture-specific concepts and evaluate the capacity for cultural adaptation through contextual information

Gaia Gavello, Giorgio Tofani, Domenico De Fazio, Stefania Lettieri

Developing novel protocols for hydrogen (H) loading is crucial for furthering the investigation of hydrides as potential high-temperature superconductors at lower pressures compared to recent discoveries. Ionic gating-induced protonation (IGP) has emerged as a promising technique for H loading due to its inherent simplicity, but it can be limited in the maxi

Subhajit Sahu

Community detection, or clustering, identifies groups of nodes in a graph that are more densely connected to each other than to the rest of the network. Given the size and dynamic nature of real-world graphs, efficient community detection is crucial for tracking evolving communities, enhancing our understanding and management of complex systems. The Leiden a

Phillip Harris

Let $X$ be a compact locally symmetric space associated to $SL_n(\mathbb{R})$ and $Y \subset X$ a maximal flat submanifold, not necessarily closed. Using a Euclidean approximation, we give an upper bound in the spectral aspect for Maass forms integrated against a smooth cutoff function on $Y$ .

Yong Zhao, Zhengqiu Zhu, Chen Gao, En Wang

Spatial Crowdsourcing (SC) is gaining traction in both academia and industry, with tasks on SC platforms becoming increasingly complex and requiring collaboration among workers with diverse skills. Recent research works address complex tasks by dividing them into subtasks with dependencies and assigning them to suitable workers. However, the dependencies amo

Pongwit Srisangyingcharoen, Aphiwat Yuenyong

We derive a general expression for on-shell recursion relations of closed string tree-level amplitudes. Starting with the string amplitudes written in the form of the Koba-Nielsen integral, we apply the BCFW shift to deform them. In contrast to open string amplitudes, where poles are explicitly determined by the integration over vertex positions, we utilize

Kosuke Yamato

We study standard processes with no negative jumps under the entrance boundary condition. Similarly to one-dimensional diffusions, we show that the process can be made into a Feller process by attaching the boundary point to the state space. We investigate the spectrum of the infinitesimal generator in detail via the scale function, characterizing it as the

Hongmei Wang, Junlin Hou, Hao Chen

Models based on human-understandable concepts have received extensive attention to improve model interpretability for trustworthy artificial intelligence in the field of medical image analysis. These methods can provide convincing explanations for model decisions but heavily rely on the detailed annotation of pre-defined concepts. Consequently, they may not

Seonjae Lee, Jeong-Eun Lee, Seokho Lee

Hydrogen cyanide (HCN) and hydrogen isocyanide (HNC) are isomers with similar chemical properties. However, HNC can be converted into other molecules by reactions with atomic hydrogen (H) and atomic oxygen (O), resulting in a variation of the HCN/HNC abundance ratio. These reaction rates are sensitive to gas temperature, resulting in different abundance rati

Yiwang Dong, Xiangyu Deng

The accurate segmentation of retinal vessels in fundus images is a great challenge in medical image segmentation tasks due to their highly complex structure from other organs.Currently, deep-learning based methods for retinal cessel segmentation achieved suboptimal outcoms,since vessels with indistinct features are prone to being overlooked in deeper layers

William Smith, Siddharth Singh, Julia Rudy, Yuxiang Guan

Mobile manipulators are finding use in numerous practical applications. The current issues with mobile manipulation are the large state space owing to the mobile base and the challenge of modeling high degree of freedom systems. It is critical to devise fast and accurate algorithms that generate smooth motion plans for such mobile manipulators. Existing tech

Sanguk Lee, Kai-Qi Yang, Tai-Quan Peng, Ruth Heo

Large language models (LLMs) are employed to simulate human-like responses in social surveys, yet it remains unclear if they develop biases like social desirability response (SDR) bias. To investigate this, GPT-4 was assigned personas from four societies, using data from the 2022 Gallup World Poll. These synthetic samples were then prompted with or without a

Akash Harapanahalli, Samuel Coogan

In this work, we provide a global condition for contraction with respect to an invariant Riemannian metric on reductive homogeneous spaces. Using left-invariant frames, vector fields on the manifold are horizontally lifted to the ambient Lie group, where the Levi-Civita connection is globally characterized as a real matrix multiplication. By linearizing in t

Yanggyu Lee, Jihie Kim

In the realm of Large Language Model (LLM) functionalities, providing reliable information is paramount, yet reports suggest that LLM outputs lack consistency. This inconsistency, often at-tributed to randomness in token sampling, under-mines user trust as it leads to varying responses even for identical queries. In this paper, we present a new approach for

Sebastian T. Braun, Jan Stuhler

Despite millions of war widows worldwide, little is known about the economic consequences of being widowed by war. We use life history data from West Germany to show that war widowhood increased women's employment immediately after World War II but led to lower employment rates later in life. War widows, therefore, carried a double burden of employment and c