February 2024 arXiv papers — page 48

Vida Dujmović, Camille La Rose

The crossing number of a graph $G$ is the minimum number of crossings in a drawing of $G$ in the plane. A rectilinear drawing of a graph $G$ represents vertices of $G$ by a set of points in the plane and represents each edge of $G$ by a straight-line segment connecting its two endpoints. The rectilinear crossing number of $G$ is the minimum number of crossin

Ichitaro Yamazaki, Andrew J. Higgins, Erik G. Boman, Daniel B. Szyld

On current computer architectures, GMRES' performance can be limited by its communication cost to generate orthonormal basis vectors of the Krylov subspace. To address this performance bottleneck, its $s$-step variant orthogonalizes a block of $s$ basis vectors at a time, potentially reducing the communication cost by a factor of $s$. Unfortunately, for a la

Yann Bernard

We discuss a large class of conformally invariant curvature energies for immersed hypersurfaces of dimension 4. The class under study includes various examples that have appeared in the recent literature and which arise from different contexts. We show that under natural small-energy hypotheses, critical points satisfy improved energy estimates. Nearly all t

Florian Neukart

The endeavor of interstellar exploration is a convergence of technical innovation and profound ethical inquiry, challenging humanity to extend its reach beyond the confines of our solar system while contemplating the moral implications of such a leap. This paper explores the multifaceted aspects of interstellar travel, exploring advancements in propulsion sy

Syeda Nahida Akter, Sangwu Lee, Yingshan Chang, Yonatan Bisk

Verifying a question's validity before answering is crucial in real-world applications, where users may provide imperfect instructions. In this scenario, an ideal model should address the discrepancies in the query and convey them to the users rather than generating the best possible answer. Addressing this requirement, we introduce a new compositional visua

Diana Taschetto, Ricardo Correa da Silva

This paper combines mathematical, philosophical, and historical analyses in a comprehensive investigation of the dynamical foundations of the formalism of orthodox quantum mechanics. The results obtained include: (i) A deduction of the canonical commutation relations (CCR) from the tenets of Matrix Mechanics; (ii) A discussion of the meaning of Schr\"odinger

Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, Swati Biswas

Multi-gene panel testing allows efficient detection of pathogenic variants in cancer susceptibility genes including moderate-risk genes such as ATM and PALB2. A growing number of studies examine the risk of breast cancer (BC) conferred by pathogenic variants of such genes. A meta-analysis combining the reported risk estimates can provide an overall age-speci

Caleb Rotello, Peter Graf, Matthew Reynolds, Eric B. Jones

Two-stage stochastic programming is a problem formulation for decision-making under uncertainty. In the first stage, the actor makes a best "here and now" decision in the presence of uncertain quantities that will be resolved in the future, represented in the objective function as the expected value function. This function is a multi-dimensional integral of

David J. Grynkiewicz

The $3k-4$ Theorem asserts that, if $A,\,B\subseteq \mathbb Z$ are finite, nonempty subsets with $|A|\geq |B|$ and $|A+B|=|A|+|B|+r< |A|+2|B|-3$, then there are arithmetic progressions $P_A$ and $P_B$ of common difference with $X\subseteq P_X$ with $|P_X|\leq |X|+r+1$ for all $X\in \{A,B\}$. There is much progress extending this result to $\mathbb Z/p\mathbb

Adam Clay, Junyu Lu

Given a $3$-manifold $M$ with multiple incompressible torus boundary components, we develop a general definition of order-detection of tuples of slopes on the boundary components of $M$. In parallel, we arrive at a general definition of representation-detection of tuples of slopes, and show that these two kinds of slope detection are equivalent -- in the sen

Hong-Bin Tan, Ruo-Yu Liu, Markus Böttcher

Blazars, a subclass of active galactic nuclei (AGN), are known to be bright $γ$-ray sources, frequently exhibiting active (flaring) periods. The blazar PKS~2155-304 is a high synchrotron-peaked BL Lac object located at redshift $z=0.116$. On 2006 July 28, an extremely remarkable outburst of VHE $γ$-ray emission from this blazar was reported by the H.E.S.S. e

Di Liu, Sebastian Mair, Kang Yang, Simone Baldi

This work proposes provably-correct self-organizing strategies for platoons of heterogeneous vehicles. We refer to self-organization as the capability of a platoon to autonomously homogenize to a common group behavior. We show that self-organization promotes resilience to acceleration limits and communication failures, i.e., homogenizing to a common group be

A. J. Karran, P. Charland, J-T. Martineau, A. Ortiz de Guinea Lopez de Arana

This study investigates the acceptability of different artificial intelligence (AI) applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI in education, it addresses concerns related to data privacy, AI agency, transparency, explainability and the ethical dep

Shun-Cai Zhao, Jing-Yi Chen

To absorb the photons below the band-gap energy effectively, we proposed a quantum dot (QD) photocell modeled by multi-level system for the quantum yields and photo-to-charge efficiency limits. The theoretical results show the quantum yields are enhanced as compared to the single band-gap solar cell, and the photo-to-charge efficiencies are larger than Shock

Nishanth Kumar, Tom Silver, Willie McClinton, Linfeng Zhao

One promising approach towards effective robot decision making in complex, long-horizon tasks is to sequence together parameterized skills. We consider a setting where a robot is initially equipped with (1) a library of parameterized skills, (2) an AI planner for sequencing together the skills given a goal, and (3) a very general prior distribution for selec

Jing-Yi Chen, Shun-Cai Zhao

The radiative recombination of electron-hole pairs represents a great challenge to the photon-to-charge efficiency in photocell. In this paper, we visit the radiative recombination rate (RRR) in a quantum photocell with or without three dipole-dipole coupled electron donors. The results show that different gaps play the same roles while the ambient temperatu

M. Bonfand, T. Csengeri, S. Bontemps, N. Brouillet

Sites associated with high-mass star and cluster formation exhibit a so-called hot core phase, characterized by high temperatures and column densities of complex organic molecules. We built a comprehensive census of hot core candidates towards the ALMA-IMF protoclusters based on the detection of two CH3OCHO emission lines at 216.1 GHz. We used the source ext

Yassine Nabou, Ion Necoara

In this paper we develop a higher-order method for solving composite (non)convex minimization problems with smooth (non)convex functional constraints. At each iteration our method approximates the smooth part of the objective function and of the constraints by higher-order Taylor approximations, leading to a moving Taylor approximation method (MTA). We prese

Santiago Castro, Amir Ziai, Avneesh Saluja, Zhuoning Yuan

Recent years have witnessed a significant increase in the performance of Vision and Language tasks. Foundational Vision-Language Models (VLMs), such as CLIP, have been leveraged in multiple settings and demonstrated remarkable performance across several tasks. Such models excel at object-centric recognition yet learn text representations that seem invariant

Creston Brooks, Robert Calef, Charlie Cowen-Breen, Anna Sappington

Beam search with masked language models (MLMs) is challenging in part because joint probability distributions over sequences are not readily available, unlike for autoregressive models. However, estimating such distributions has important domain-specific applications such as ancient text restoration and protein engineering. Here we present probabilistically-

Wonjeong Choi, Jungwuk Park, Dong-Jun Han, Younghyun Park

Research interests in the robustness of deep neural networks against domain shifts have been rapidly increasing in recent years. Most existing works, however, focus on improving the accuracy of the model, not the calibration performance which is another important requirement for trustworthy AI systems. Temperature scaling (TS), an accuracy-preserving post-ho

Michael J. Ryan, William Held, Diyi Yang

Before being deployed for user-facing applications, developers align Large Language Models (LLMs) to user preferences through a variety of procedures, such as Reinforcement Learning From Human Feedback (RLHF) and Direct Preference Optimization (DPO). Current evaluations of these procedures focus on benchmarks of instruction following, reasoning, and truthful

Zhuoyan Xu, Zhenmei Shi, Junyi Wei, Fangzhou Mu

Foundation models have emerged as a powerful tool for many AI problems. Despite the tremendous success of foundation models, effective adaptation to new tasks, particularly those with limited labels, remains an open question and lacks theoretical understanding. An emerging solution with recent success in vision and NLP involves finetuning a foundation model

Nitesh Kumar Singh, Ion Necoara

In this paper, we consider constrained optimization problems with convex, smooth objective and constraints. We propose a new stochastic gradient algorithm, called the Stochastic Moving Ball Approximation (SMBA) method, to solve this class of problems, where at each iteration we first take a gradient step for the objective function and then perform a projecti

Florencia Zapata, Sofía Angriman, Amélie Ferran, Pablo J. Cobelli

We show that the unsteadiness of turbulence has a drastic effect on turbulence parameters and in particle cluster formation. To this end we use direct numerical simulations of particle laden flows with a steady forcing that generates an unsteady large-scale flow. Particle clustering correlates with the instantaneous Taylor-based flow Reynolds number, and ant

Shubhendu Kumar, Babu Gaire, Bo Persson, Ali Dhinojwala

Adhesion requires molecular contact, and natural adhesives employ mechanical gradients to achieve complete (conformal) contact to maximize adhesion. Intuitively, one expects that the higher the modulus of the top layer, the lower will be the adhesion strength. However, the relationship between the thickness of the stiff top layer and adhesion is not known. I

Md Sanzeed Anwar, Grant Schoenebeck, Paramveer S. Dhillon

Recommendation algorithms play a pivotal role in shaping our media choices, which makes it crucial to comprehend their long-term impact on user behavior. These algorithms are often linked to two critical outcomes: homogenization, wherein users consume similar content despite disparate underlying preferences, and the filter bubble effect, wherein individuals

Saleh Almohaimeed, Saad Almohaimeed, Mansour Al Ghanim, Liqiang Wang

In Natural Language Processing (NLP), one of the most important tasks is text-to-SQL semantic parsing, which focuses on enabling users to interact with the database in a more natural manner. In recent years, text-to-SQL has made significant progress, but most were English-centric. In this paper, we introduce Ar-Spider 1, the first Arabic cross-domain text-to

Anja Meunier, Michal Robert Žák, Lucas Munz, Sofiya Garkot

We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to accomplish complex tasks by providing high-l

Yanis Labrak, Adrien Bazoge, Beatrice Daille, Mickael Rouvier

Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trained language models. This shift began with Byte-Pair Encoding (BPE) and was later followed by the adoption of SentencePiece and WordPiece. While subword tokenization consistently o

G. Blanchon, H. F. Arellano

This work is part of an ongoing effort to build microscopically-driven nonlocal optical potentials easily tracktable in scattering codes. Based on the separable `$JvH$' structure proposed recently \cite{arellano_22}, where the potential can be cast as the product of a radial and nonlocality form factors, we investigate its angular dependence in momentum spac

Mitchell Pryor, Alex Navarro, Janak Panthi, Kevin Torres

Nuclear facilities must routinely survey their infrastructure for radiation contamination. Generally, this is done by trained professionals, wearing personal protective equipment (PPE) that swipe potentially contaminated surfaces and test the wipes under detectors. This approach leaves personnel vulnerable to radiation exposure and is not comprehensive. Robo

Tobias Schmidt

In arXiv:2212.14023 a decomposition of Gaussian measures on finite-dimensional spaces was introduced, which turned out to be a central technical tool to improve currently known bounds on a long standing conjecture in statistical mechanics called the Polaron problem. This note slightly generalizes this decomposition and provides numerical values for all occur

Yuzan Xiong, Andrew Christy, Yun Dong, Andrew Comstock

Developing hybrid materials and structures for electromagnetic wave engineering has been a promising route towards novel functionalities and tunabilities in many modern applications and perspectives in new quantum technologies. Despite its established success in engineering optical light and terahertz waves, the implementation of meta-resonators operating at

Cathie So, KD Conway, Xiaohang Yu, Suning Yao

The convergence of Artificial Intelligence (AI) and blockchain technology is reshaping the digital world, offering decentralized, secure, and efficient AI services on blockchain platforms. Despite the promise, the high computational demands of AI on blockchain raise significant privacy and efficiency concerns. The Optimistic Privacy-Preserving AI (opp/ai) fr

Nabil Kahouadji

The use of machine learning algorithms in healthcare can amplify social injustices and health inequities. While the exacerbation of biases can occur and compound during the problem selection, data collection, and outcome definition, this research pertains to some generalizability impediments that occur during the development and the post-deployment of machin

Junwei Pan, Wei Xue, Ximei Wang, Haibin Yu

We present Tencent's ads recommendation system and examine the challenges and practices of learning appropriate recommendation representations. Our study begins by showcasing our approaches to preserving prior knowledge when encoding features of diverse types into embedding representations. We specifically address sequence features, numeric features, and pre

Minge Xie, Peng Wang

Rapid advancements in data science require us to have fundamentally new frameworks to tackle prevalent but highly non-trivial "irregular" inference problems, to which the large sample central limit theorem does not apply. Typical examples are those involving discrete or non-numerical parameters and those involving non-numerical data, etc. In this article, we

Jianqi Hu, Matias Eriksson, Sylvain Gigan, Robert Fickler

The Talbot effect describes periodic revivals of field patterns and is ubiquitous across wave systems. In optics, it is mostly known for its manifestations in space and time, but is also observed in the wavevector and frequency spectra owing to the Fourier duality. Recently, the Talbot self-imaging has been shown separately in the azimuthal angle and orbital

Etash Jhanji

Sentiment analysis is a continuously explored area of text processing that deals with the computational analysis of opinions, sentiments, and subjectivity of text. However, this idea is not limited to text and speech, in fact, it could be applied to other modalities. In reality, humans do not express themselves in text as deeply as they do in music. The abil

Nandita Shankar Naik, Christopher Potts, Elisa Kreiss

Current visual question answering (VQA) models tend to be trained and evaluated on image-question pairs in isolation. However, the questions people ask are dependent on their informational needs and prior knowledge about the image content. To evaluate how situating images within naturalistic contexts shapes visual questions, we introduce CommVQA, a VQA datas

Allison M. McCarthy, Philip S. Muirhead, Patrick Tamburo, Johanna M. Vos

Multi-wavelength photometry of brown dwarfs and planetary-mass objects provides insight into their atmospheres and cloud layers. We present near-simultaneous $J-$ and $K_s-$band multi-wavelength observations of the highly variable T2.5 planetary-mass object, SIMP J013656.5+093347. We reanalyze observations acquired over a single night in 2015 using a recentl

Yuanjian Liu, Huihao Luo, Zhijun Han, Yao Hu

Storing and archiving data produced by next-generation sequencing (NGS) is a huge burden for research institutions. Reference-based compression algorithms are effective in dealing with these data. Our work focuses on compressing FASTQ format files with an improved reference-based compression algorithm to achieve a higher compression ratio than other state-of

Zhuofeng Wu, He Bai, Aonan Zhang, Jiatao Gu

Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first. In this paper we devise a similar strategy that breaks down reasoning tasks into a problem decomposition phase and a problem solving phase and show that the strategy is able to outperform a sin

Sebastien Galtier

Wave turbulence and eddy turbulence are the two regimes that we may encounter in nature. The attention of fluid mechanics being mainly focused on incompressible hydrodynamics, it is usually the second regime that is treated in books, whereas waves are often present in geophysics and astrophysics. In these lecture notes, I present the theory of wave turbulenc

M. Rashed Khan, Taliman Afroz, Caleb Shaw

Here, we describe a simple and unique architecture of a microfluidic mixer that can mix two streams (Water and Fluorescein Isothiocyanate (FITC) buffer) with 30% efficiency. The overall mixing in this design is dominated by 1D diffusion, and to enhance mixing, we used three different types of geometric obstacles inside the channel. Comsol multiphysics simula

Javad Mashreghi, Marek Ptak, William T. Ross

For a given unitary operator $U$ on a separable complex Hilbert space $\h$, we describe the set $\mathscr{C}_{c}(U)$ of all conjugations $C$ (antilinear, isometric, and involutive maps) on $\h$ for which $C U C = U$. As this set might be empty, we also show that $\mathscr{C}_{c}(U) \not = \varnothing$ if and only if $U$ is unitarily equivalent to $U^{*}$.

Owen Eckart, Alexandros Psomas, Paritosh Verma

Allocating items in a fair and economically efficient manner is a central problem in fair division. We study this problem for agents with additive preferences, when items are all goods or all chores, divisible or indivisible. The celebrated notion of Nash welfare is known to produce fair and efficient allocations for both divisible and indivisible goods; the

Javad Mashreghi, Marek Ptak, William T. Ross

If $U$ is a unitary operator on a separable complex Hilbert space $\mathcal{H}$, an application of the spectral theorem says there is a conjugation $C$ on $\mathcal{H}$ (an antilinear, involutive, isometry on $\mathcal{H}$) for which $ C U C = U^{*}.$ In this paper, we fix a unitary operator $U$ and describe all of the conjugations $C$ which satisfy this pro

Wilson Souza, José Carlos Marinello, Taufik Abrão

This chapter delves into the critical aspects of optimizing energy efficiency (EE) in active reconfigurable intelligent surface (RIS)-assisted massive MIMO (M-MIMO) wireless communication systems. We develop a comprehensive and unified theoretical framework to analyze the boundaries of EE within M-MIMO systems integrated with active RIS while adhering to pra

Thomas Hitchcox, James Richard Forbes

Laser line scanners are increasingly being used in the subsea industry for high-resolution mapping and infrastructure inspection. However, calibrating the 3D pose of the scanner relative to the vehicle is a perennial source of confusion and frustration for industrial surveyors. This work describes three novel algorithms for laser-to-vehicle extrinsic calibra

Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun

The versatility of large language models (LLMs) led to the creation of diverse benchmarks that thoroughly test a variety of language models' abilities. These benchmarks consist of tens of thousands of examples making evaluation of LLMs very expensive. In this paper, we investigate strategies to reduce the number of evaluations needed to assess the performanc

Nicola Mariella, Albert Akhriev, Francesco Tacchino, Christa Zoufal

Optimal Transport (OT) has fueled machine learning (ML) across many domains. When paired data measurements $(\boldsymbol{\mu}, \boldsymbol{\nu})$ are coupled to covariates, a challenging conditional distribution learning setting arises. Existing approaches for learning a $\textit{global}$ transport map parameterized through a potentially unseen context utili

M. J. Rain, M. S. Pera, G. Perren, O. Benvenuto

Building on the recent release of a new \emph{Gaia}-based blue straggler star catalog in Galactic open star clusters (OCs), we explored the properties of these stars in a cluster sample spanning a wide range in fundamental parameters. We employed \emph{Gaia} EDR3 to assess the membership of any individual blue or yellow straggler to their parent cluster. We

Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese, Giulio Ermanno Pibiri

Verifiable learning advocates for training machine learning models amenable to efficient security verification. Prior research demonstrated that specific classes of decision tree ensembles -- called large-spread ensembles -- allow for robustness verification in polynomial time against any norm-based attacker. This study expands prior work on verifiable learn

Adam Block, Alexander Rakhlin, Abhishek Shetty

In order to circumvent statistical and computational hardness results in sequential decision-making, recent work has considered smoothed online learning, where the distribution of data at each time is assumed to have bounded likeliehood ratio with respect to a base measure when conditioned on the history. While previous works have demonstrated the benefits o

Maxine E. Calle, Julian J. Gould

We provide a framework for abstract reconstruction problems using the $K$-theory of categories with covering families, which we then apply to reformulate the edge reconstruction conjecture in graph theory. Along the way, we state some fundamental theorems for the $K$-theory of categories with covering families.

Zhaoyang Shi, Krishnakumar Balasubramanian, Wolfgang Polonik

We develop nonparametric regression methods for the case when the true regression function is not necessarily smooth. More specifically, our approach is using the fractional Laplacian and is designed to handle the case when the true regression function lies in an $L_2$-fractional Sobolev space with order $s\in (0,1)$. This function class is a Hilbert space l

Emily M. Boudreaux, Aylin Garcia Soto, Brian C. Chaboyer

The Gaia M dwarf gap, also known as the Jao Gap, is a novel feature discovered in the Gaia DR2 G vs. BP-RP color magnitude diagram. This gap represents a 17 percent decrease in stellar density in a thin magnitude band around the convective transition mass ($\sim 0.35 M_{\odot}$) on the main sequence. Previous work has demonstrated a paucity of Hydrogen Alpha

Panyi Dong, Zhiyu Quan, Brandon Edwards, Shih-han Wang

The report demonstrates the benefits (in terms of improved claims loss modeling) of harnessing the value of Federated Learning (FL) to learn a single model across multiple insurance industry datasets without requiring the datasets themselves to be shared from one company to another. The application of FL addresses two of the most pressing concerns: limited d

Mahsa Salehi, Kalin Stefanov, Ehsan Shareghi

In this paper we study the variations in human brain activity when listening to real and fake audio. Our preliminary results suggest that the representations learned by a state-of-the-art deepfake audio detection algorithm, do not exhibit clear distinct patterns between real and fake audio. In contrast, human brain activity, as measured by EEG, displays dist

Niranjan Balachandran, Shagnik Das, Brahadeesh Sankarnarayanan

Using the sunflower method, we show that if $\theta \in (0,1) \cap \mathbb{Q}$ and $\mathcal{F}$ is a $O(n^{1/3})$-bounded $\theta$-intersecting family over $[n]$, then $\lvert \mathcal{F} \rvert = O(n)$, and that if $\mathcal{F}$ is $o(n^{1/3})$-bounded, then $\lvert \mathcal{F} \rvert \leq (\frac{3}{2} + o(1))n$. This partially solves a conjecture of Balac

Arshmeet Kaur, Morteza Sarmadi

Rapid advancements in genome sequencing have led to the collection of vast amounts of genomics data. Researchers may be interested in using machine learning models on such data to predict the pathogenicity or clinical significance of a genetic mutation. However, many genetic datasets contain imbalanced target variables that pose challenges to machine learnin

Victoria Lin, Eli Ben-Michael, Louis-Philippe Morency

As large language models (LLMs) see greater use in academic and commercial settings, there is increasing interest in methods that allow language models to generate texts aligned with human preferences. In this paper, we present an initial exploration of language model optimization for human preferences from direct outcome datasets, where each sample consists

Orit Shaer, Angelora Cooper, Osnat Mokryn, Andrew L. Kun

The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process - the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative grou

Hongbin Liu, Michael K. Reiter, Neil Zhenqiang Gong

Foundation model has become the backbone of the AI ecosystem. In particular, a foundation model can be used as a general-purpose feature extractor to build various downstream classifiers. However, foundation models are vulnerable to backdoor attacks and a backdoored foundation model is a single-point-of-failure of the AI ecosystem, e.g., multiple downstream

Dmitry Kangin, Plamen Angelov

The vision transformer-based foundation models, such as ViT or Dino-V2, are aimed at solving problems with little or no finetuning of features. Using a setting of prototypical networks, we analyse to what extent such foundation models can solve unsupervised domain adaptation without finetuning over the source or target domain. Through quantitative analysis,

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The LHCb collaboration measures production of the exotic hadron $\chi_{c1}$(3872) in proton-nucleus collisions for the first time. Comparison with the charmonium state $\psi$(2$S$) suggests that the exotic $\chi_{c1}$(3872) experiences different dynamics in the nuclear medium than conventional hadrons, and comparison with data from proton-proton collisions i

Majid Farhadloo, Arun Sharma, Jayant Gupta, Alexey Leontovich

Given multi-category point sets from different place-types, our goal is to develop a spatially-lucid classifier that can distinguish between two classes based on the arrangements of their points. This problem is important for many applications, such as oncology, for analyzing immune-tumor relationships and designing new immunotherapies. It is challenging due

Lele Cao, Valentin Buchner, Zineb Senane, Fangkai Yang

Multimodal Large Language Models (MLLMs) are typically assessed using expensive annotated multimodal benchmarks, which often lag behind the rapidly evolving demands of MLLM evaluation. This paper outlines and validates GenCeption, a novel, annotation-free evaluation method that requires only unimodal data to measure inter-modality semantic coherence and inve

Kai North, Tharindu Ranasinghe, Matthew Shardlow, Marcos Zampieri

Lexical Simplification (LS) automatically replaces difficult to read words for easier alternatives while preserving a sentence's original meaning. LS is a precursor to Text Simplification with the aim of improving text accessibility to various target demographics, including children, second language learners, individuals with reading disabilities or low lite

A. P. Meilakhs

We introduce a concept of non-coherent evolution of macroscopic quantum systems. We show that for weakly interacting systems such evolution is a Markovian stochastic process. The transition rates between system states, which characterize the process, are determined by Fermi's golden rule. Such evolution is time-irreversible and leads to the equidistribution

Dao Thanh Hai

A new architectural paradigm, named, optical-computing-enabled network, is proposed as a potential evolution of the currently used optical-bypass framework. The main idea is to leverage the optical computing capabilities performed on transitional lightpaths at intermediate nodes and such proposal reverses the conventional wisdom in optical-bypass network, th

Jiongxiao Wang, Jiazhao Li, Yiquan Li, Xiangyu Qi

Despite the general capabilities of Large Language Models (LLM), these models still request fine-tuning or adaptation with customized data when meeting specific business demands. However, this process inevitably introduces new threats, particularly against the Fine-tuning based Jailbreak Attack (FJAttack) under the setting of Language-Model-as-a-Service (LMa

Billel Guelmame, Stéphane Junca, Didier Clamond

This paper studies the smoothing effect for entropy solutions of conservation laws with general nonlinear convex fluxes on $\mathbb{R}$. Beside convexity, no additional regularity is assumed on the flux. Thus, we generalize the well-known $\mathrm{BV}$ smoothing effect for $\mathrm{C}^2$ uniformly convex fluxes discovered independently by P. D. Lax and O. Ol

Haotian Lin, Matthew Reimherr

Many existing two-phase kernel-based hypothesis transfer learning algorithms employ the same kernel regularization across phases and rely on the known smoothness of functions to obtain optimality. Therefore, they fail to adapt to the varying and unknown smoothness between the target/source and their offset in practice. In this paper, we address these problem

Oswin Aichholzer, Florian Lehner, Christian Lindorfer

Which polyominoes can be folded into a cube, using only creases along edges of the square lattice underlying the polyomino, with fold angles of $\pm 90^\circ$ and $\pm 180^\circ$, and allowing faces of the cube to be covered multiple times? Prior results studied tree-shaped polyominoes and polyominoes with holes and gave partial classifications for these cas

G. Singh

While forming an ($A$+2) nucleus from a nucleus $A$ via a two-neutron transfer reaction, the constructive interference of the many possible reaction channels favors significant pairing enhancement through the continuum of the intermediate ($A$+1) nucleus [Phys. Lett. B \textbf{834} 137413 (2022)]. I analyse this situation in more generality, from the point o

Hanqi Yan, Qinglin Zhu, Xinyu Wang, Lin Gui

While Large language models (LLMs) have the capability to iteratively reflect on their own outputs, recent studies have observed their struggles with knowledge-rich problems without access to external resources. In addition to the inefficiency of LLMs in self-assessment, we also observe that LLMs struggle to revisit their predictions despite receiving explic

S. Bähr, H. Bae, J. Becker, M. Bertemes

We describe the principles and performance of the first-level ("L1") hardware track trigger of Belle II, based on neural networks. The networks use as input the results from the standard Belle II trigger, which provides "2D" track candidates in the plane transverse to the electron-positron beams. The networks then provide estimates for the origin of the 2D t

Dong Wang, Giovanni Beltrame

Traditional Reinforcement Learning (RL) policies are typically implemented with fixed control rates, often disregarding the impact of control rate selection. This can lead to inefficiencies as the optimal control rate varies with task requirements. We propose the Multi-Objective Soft Elastic Actor-Critic (MOSEAC), an off-policy actor-critic algorithm that us

Anna Zapaishchykova, Benjamin H. Kann, Divyanshu Tak, Zezhong Ye

Synthetic longitudinal brain MRI simulates brain aging and would enable more efficient research on neurodevelopmental and neurodegenerative conditions. Synthetically generated, age-adjusted brain images could serve as valuable alternatives to costly longitudinal imaging acquisitions, serve as internal controls for studies looking at the effects of environmen

Duc N. M. Hoang, Vu Tuan Truong, Hung Duy Le, Long Bao Le

This paper studies the wireless scheduling design to coordinate the transmissions of (local) model parameters of federated learning (FL) for a swarm of unmanned aerial vehicles (UAVs). The overall goal of the proposed design is to realize the FL training and aggregation processes with a central aggregator exploiting the sensory data collected by the UAVs but

Pier-Emmanuel Tremblay, Antoine Bédard, Mairi W. O'Brien, James Munday

This review highlights the role of the Gaia space mission in transforming white dwarf research. These stellar remnants constitute 5-7% of the local stellar population in volume, yet before Gaia the lack of trigonometric parallaxes hindered their identification. The mission's Data Release 2 in 2018 provided the first unbiased colour-absolute magnitude diagram

Jessy Xinyi Han, Andrew Miller, S. Craig Watkins, Christopher Winship

We are interested in developing a data-driven method to evaluate race-induced biases in law enforcement systems. While the recent works have addressed this question in the context of police-civilian interactions using police stop data, they have two key limitations. First, bias can only be properly quantified if true criminality is accounted for in addition

Jakub Kolář, Radim Špetlík, Jiří Matas

We introduce a novel method for measuring properties of periodic phenomena with an event camera, a device asynchronously reporting brightness changes at independently operating pixels. The approach assumes that for fast periodic phenomena, in any spatial window where it occurs, a very similar set of events is generated at the time difference corresponding to

Abhishek Jha, Matthew B. Blaschko, Yuki M. Asano, Tinne Tuytelaars

Last couple of years have witnessed a tremendous progress in self-supervised learning (SSL), the success of which can be attributed to the introduction of useful inductive biases in the learning process to learn meaningful visual representations while avoiding collapse. These inductive biases and constraints manifest themselves in the form of different optim

Yannis Voet, Espen Sande, Annalisa Buffa

Mass lumping techniques are commonly employed in explicit time integration schemes for problems in structural dynamics and both avoid solving costly linear systems with the consistent mass matrix and increase the critical time step. In isogeometric analysis, the critical time step is constrained by so-called "outlier" frequencies, representing the inaccurate

Jaachinma Okafor, Lateefah Isegen, Ark Ifeanyi

This study introduces and evaluates the Quantile Regressor Tree (QRT), a novel methodology merging the robust characteristics of quantile regression with the versatility of decision trees. The quantile regressor tree introduces non-linearity to the quantile regression due to the splitting by features in the decision tree, enhancing flexibility while maintain

Isabel L. Colman, Ruth Angus, Trevor David, Jason Curtis

For ongoing studies of the role of rotation in stellar evolution, we require large catalogs of rotation periods for testing and refining gyrochronology. While there is a wealth of data from the Kepler and K2 missions, TESS presents both an opportunity and a challenge: despite its all-sky coverage, rotation periods remain hard to detect. We analyzed individua

Sean Bailey, David Brown, Michael Snyder, Nicole Turner

A dot-product representation of a graph is a mapping of its vertices to vectors of length $k$ so that vertices are adjacent if and only if the inner product (a.k.a. dot product) of their corresponding vertices exceeds some threshold. Minimizing dimension of the vector space into which the vectors must be mapped is a typical focus. We investigate this and str

Justin Chan

Cooperative games can be distinguished as non-cooperative games in which players can freely sign binding agreements to form coalitions. These coalitions inherit a joint strategy set and seek to maximize collective payoffs. When the payoffs to each coalition under some non-cooperative solution concept coincide with their value in the cooperative game, the coo

Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett

We study the \emph{in-context learning} (ICL) ability of a \emph{Linear Transformer Block} (LTB) that combines a linear attention component and a linear multi-layer perceptron (MLP) component. For ICL of linear regression with a Gaussian prior and a \emph{non-zero mean}, we show that LTB can achieve nearly Bayes optimal ICL risk. In contrast, using only line

Arpit Agarwal, Sanjeev Khanna, Huan Li, Prathamesh Patil

We present a parallel algorithm for the $(1-\epsilon)$-approximate maximum flow problem in capacitated, undirected graphs with $n$ vertices and $m$ edges, achieving $O(\epsilon^{-3}\text{polylog} n)$ depth and $O(m \epsilon^{-3} \text{polylog} n)$ work in the PRAM model. Although near-linear time sequential algorithms for this problem have been known for alm

Mats Gustafsson, Lukas Jelinek, Miloslav Capek, Johan Lundgren

The problem of substructure characteristic modes is developed using a scattering matrix-based formulation, generalizing subregion characteristic mode decomposition to arbitrary computational tools. It is shown that the modes of the scattering formulation are identical to the modes of the classical formulation based on the background Green's function for loss

Carl Edwards, Qingyun Wang, Lawrence Zhao, Heng Ji

Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released which are 1) small and scraped from existing databases, 2) large but noisy and constructed by performing enti

Ahmad Mohammad Saber, Amr Youssef, Davor Svetinovic, Hatem Zeineldin

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal's amplitude and frequency. Building on the prior PQE cl

Yuepei Li, Kang Zhou, Qiao Qiao, Qing Wang

Distantly-Supervised Named Entity Recognition (DS-NER) uses knowledge bases or dictionaries for annotations, reducing manual efforts but rely on large human labeled validation set. In this paper, we introduce a real-life DS-NER dataset, QTL, where the training data is annotated using domain dictionaries and the test data is annotated by domain experts. This

William Theisen, Michael Yankoski, Kristina Hook, Ernesto Verdeja

Governments use propaganda, including through visual content -- or Politically Salient Image Patterns (PSIP) -- on social media, to influence and manipulate public opinion. In the present work, we collected Telegram post-history of from 989 Russian milbloggers to better understand the social and political narratives that circulated online in the months surro

Xinyuan Zheng, Mahmoud Jalali Mehrabad, Jonathan Vannucci, Kevin Li

Non-Hermitian models describe the physics of ubiquitous open systems with gain and loss. One intriguing aspect of non-Hermitian models is their inherent topology that can produce intriguing boundary phenomena like resilient higher-order topological insulators (HOTIs) and non-Hermitian skin effects (NHSE). Recently, time-multiplexed lattices in synthetic dime