December 2024 arXiv papers — page 206

S. A. Pavlenko, E. N. Govorun

We analyze the aggregation of locally amphiphilic copolymers with blocky architecture and uniformly distributed amphiphilic moieties in terms of a mean-field theory. Locally amphiphilic structure is characteristic of many thermoresponsive polymers, both linear and grafted, which endows them with local surface activity. Self-assembly of such copolymers exhibi

Paul C. Kainen

A book embedding of the complete graph $K_n$ needs $\lceil \frac{n}{2} \rceil$ pages and the page-subgraphs can be chosen to be spanning paths (for $n$ even) and one spanning star for $n$ odd. We show that all page-subgraphs can be chosen to be {\rm star forests} by including one extra {\rm cross-cap} page or two new ordinary pages.

Jiayi Wang, Ruiwei Xiao, Ying-Jui Tseng

Artificial intelligence (AI) is transforming society, making it crucial to prepare the next generation through AI literacy in K-12 education. However, scalable and reliable AI literacy materials and assessment resources are lacking. To address this gap, our study presents a novel approach to generating multiple-choice questions (MCQs) for AI literacy assessm

Tomasz Zawadzki

On the domain of a Riemannian submersion, we consider variations (i.e., smooth one-parameter families) of Riemannian metrics, for which the submersion is Riemannian and which all keep the metric induced on its fibers fixed. We obtain a formula for the variation of the second fundamental form of the fibers with respect to such changes of metric. We find a cho

Xinyi Chen, Nikhil Padmanabhan, Daniel J. Eisenstein

We propose to constrain the primordial (local-type) non-Gaussianity signal by first reconstructing the initial density field to remove the late time non-Gaussianities introduced by gravitational evolution. Our reconstruction algorithm combines perturbation theory on large scales with a convolutional neural network on small scales. We reconstruct the squared

Henry Papadatos, Rachel Freedman

Large language models (LLMs) are often sycophantic, prioritizing agreement with their users over accurate or objective statements. This problematic behavior becomes more pronounced during reinforcement learning from human feedback (RLHF), an LLM fine-tuning stage intended to align model outputs with human values. Instead of increasing accuracy and reliabilit

Ali Marashian, Enora Rice, Luke Gessler, Alexis Palmer

Many of the world's languages have insufficient data to train high-performing general neural machine translation (NMT) models, let alone domain-specific models, and often the only available parallel data are small amounts of religious texts. Hence, domain adaptation (DA) is a crucial issue faced by contemporary NMT and has, so far, been underexplored for low

V. M. Gorkavenko, A. O. Zaporozhchenko, M. S. Tsarenkova

We considered the vacuum polarization of a quantized charged scalar matter field in the background of a topological defect modeled by a finite-thickness tube with magnetic flux inside. The tube is impenetrable for quantum matter, and a generalized boundary condition of the Robin type is imposed at its surface. We have shown that in the flat space-time, the t

Benjamin Bergner, Christoph Lippert, Aravindh Mahendran

The adoption of Vision Transformers (ViTs) in resource-constrained applications necessitates improvements in inference throughput. To this end several token pruning and merging approaches have been proposed that improve efficiency by successively reducing the number of tokens. However, it remains an open problem to design a token reduction method that is fas

John A. Kypriotakis, Siddharth Maharana, Ramya M. Anche, Chaitanya V. Rajarshi

The Wide Area Linear Optical Polarimeter North (WALOP-North) is an optical polarimeter designed for the needs of the PASIPHAE survey. It will be installed on the 1.3m telescope at the Skinakas Observatory in Crete, Greece. After commissioning, it will measure the polarization of millions of stars at high Galactic latitude, aiming to measure hundreds of stars

Christopher W. Brown

How to handle division in systems that compute with logical formulas involving what would otherwise be polynomial constraints over the real numbers is a surprisingly difficult question. This paper argues that existing approaches from both the computer algebra and computational logic communities are unsatisfactory for systems that consider the satisfiability

Mijntje Meijer, Hadi Mohammadi, Ayoub Bagheri

Large language models (LLMs) have become increasingly pivotal in various domains due the recent advancements in their performance capabilities. However, concerns persist regarding biases in LLMs, including gender, racial, and cultural biases derived from their training data. These biases raise critical questions about the ethical deployment and societal impa

Ashley Ferreira, Mahip Singh, Andrea Capra, Ina Carli

The ALPHA-g experiment at CERN aims to perform the first-ever direct measurement of the effect of gravity on antimatter, determining its weight to within 1% precision. This measurement requires an accurate prediction of the vertical position of annihilations within the detector. In this work, we present a novel approach to annihilation position reconstructio

H. N. Yang, C. Maitra, G. Vasilopoulos, F. Haberl

A new giant outburst of the Be X-ray binary RX J0520.5-6932 was detected and subsequently observed with several space-borne and ground-based instruments. This study presents a comprehensive analysis of the optical and X-ray data, focusing on the spectral and timing characteristics of selected X-ray observations. A joint fit of spectra from simultaneous obser

Summra Saleem, Muhammad Nabeel Asim, Ludger Van Elst, Andreas Dengel

Traditional language models have been extensively evaluated for software engineering domain, however the potential of ChatGPT and Gemini have not been fully explored. To fulfill this gap, the paper in hand presents a comprehensive case study to investigate the potential of both language models for development of diverse types of requirement engineering appli

Philipp Kleinpaß, Thomas Walther, Erik Fitzke

We present time- and frequency-resolved simulations of quantum key distribution~(QKD) systems employing highly entangled biphoton quantum states. Our simulations are based on expansions of the covariance matrix and photon detection probabilities of biphoton states in terms of increasing orders of the joint spectral amplitude that were introduced in the first

Philipp Kleinpaß, Thomas Walther, Erik Fitzke

We discuss an expansion of the detection probabilities of biphoton states in terms of increasing orders of the joint spectral amplitude. The expansion enables efficient time- or frequency-resolved numerical simulations involving quantum states exhibiting a high degree of spectral entanglement. Contrary to usual approaches based on one- or two-pair approximat

Evi Papadopoulou, Hadi Mohammadi, Ayoub Bagheri

Prior research has demonstrated that language models can, to a limited extent, represent moral norms in a variety of cultural contexts. This research aims to replicate these findings and further explore their validity, concentrating on issues like 'homosexuality' and 'divorce'. This study evaluates the effectiveness of these models using information from two

Keren Ganon, Morris Alper, Rachel Mikulinsky, Hadar Averbuch-Elor

Buildings are a central feature of human culture and are increasingly being analyzed with computational methods. However, recent works on computational building understanding have largely focused on natural imagery of buildings, neglecting the fundamental element defining a building's structure -- its floorplan. Conversely, existing works on floorplan unders

Peter Balazs, Michael Multerer

Recently, samplets have been introduced as localized discrete signed measures which are tailored to an underlying data set. Samplets exhibit vanishing moments, i.e., their measure integrals vanish for all polynomials up to a certain degree, which allows for feature detection and data compression. In the present article, we extend the different construction s

Xie Yu, Jingyuan Wang, Yifan Yang, Qian Huang

Typical dynamic ST data includes trajectory data (representing individual-level mobility) and traffic state data (representing population-level mobility). Traditional studies often treat trajectory and traffic state data as distinct, independent modalities, each tailored to specific tasks within a single modality. However, real-world applications, such as na

Burak Bekci, Nassir Navab, Federico Tombari, Mahdi Saleh

Shape completion, a crucial task in 3D computer vision, involves predicting and filling the missing regions of scanned or partially observed objects. Current methods expect known pose or canonical coordinates and do not perform well under varying rotations, limiting their real-world applicability. We introduce ESCAPE (Equivariant Shape Completion via Anchor

Karim Elgammal, Marc Maußner

This work demonstrates a systematic implementation of hybrid quantum-classical computational methods for investigating corrosion inhibition mechanisms on aluminum surfaces. We present an integrated workflow combining density functional theory (DFT) with quantum algorithms through an active space embedding scheme, specifically applied to studying 1,2,4-Triazo

Toni Peter, Joseph S. W. Lewis, Ralf S. Klessen, Simon C. O. Glover

We introduce the radiative transfer postprocessing code Subsweep. The code is based on the method of transport sweeps, in which the exact solution to the scattering-less radiative transfer equation is computed in a single pass through the entire computational grid. The radiative transfer module is coupled to radiation chemistry, and chemical compositions as

Nicholas Lenzen, Amogh Raut, Andrew Melnik

Recently, the STEVE-1 approach has been introduced as a method for training generative agents to follow instructions in the form of latent CLIP embeddings. In this work, we present a methodology to extend the control modalities by learning a mapping from new input modalities to the latent goal space of the agent. We apply our approach to the challenging Mine

Edward Bayes, Israel Abebe Azime, Jesujoba O. Alabi, Jonas Kgomo

Evaluations of Large Language Models (LLMs) on knowledge-intensive tasks and factual accuracy often focus on high-resource languages primarily because datasets for low-resource languages (LRLs) are scarce. In this paper, we present Uhura -- a new benchmark that focuses on two tasks in six typologically-diverse African languages, created via human translation

Ryo Kamoi, Yusen Zhang, Sarkar Snigdha Sarathi Das, Ranran Haoran Zhang

Large Vision Language Models (LVLMs) have achieved remarkable performance in various vision-language tasks. However, it is still unclear how accurately LVLMs can perceive visual information in images. In particular, the capability of LVLMs to perceive geometric information, such as shape, angle, and size, remains insufficiently analyzed, although the percept

Matteo Manzoni, Sergio Mascetti, Dragan Ahmetovic, Ryan Crabb

For individuals who are blind or have low vision, tactile maps provide essential spatial information but are limited in the amount of data they can convey. Digitally augmented tactile maps enhance these capabilities with audio feedback, thereby combining the tactile feedback provided by the map with an audio description of the touched elements. In this conte

N. A. Cruz, J. D. Toloza-Delgado, O. O. Melo

This paper presents the generalized spatial autoregression (GSAR) model, a significant advance in spatial econometrics for non-normal response variables belonging to the exponential family. The GSAR model extends the logistic SAR, probit SAR, and Poisson SAR approaches by offering greater flexibility in modeling spatial dependencies while ensuring computatio

Narmeen Oozeer, Sinem Erisken, Alice Rigg

Efforts to interpret reinforcement learning (RL) models often rely on high-level techniques such as attribution or probing, which provide only correlational insights and coarse causal control. This work proposes replacing nonlinearities in convolutional neural networks (ConvNets) with bilinear variants, to produce a class of models for which these limitation

Alireza Torabian, Ruth Urner

Calibration is a frequently invoked concept when useful label probability estimates are required on top of classification accuracy. A calibrated model is a function whose values correctly reflect underlying label probabilities. Calibration in itself however does not imply classification accuracy, nor human interpretable estimates, nor is it straightforward t

Dylan Hamitouche, Youcef Barkat, Deven Parekh, Eva Hammer

Background: Effective use of mobile health technologies requires high participant adherence and retention. However, remote digital health studies often face high attrition and low adherence, potentially introducing bias and limiting generalizability. Objective: This study aims to identify longitudinal indicators of participant retention and adherence to deve

Ali Asadi, Krishnendu Chatterjee, Raimundo Saona, Ali Shafiee

A standard model that arises in several applications in sequential decision making is partially observable Markov decision processes (POMDPs) where a decision-making agent interacts with an uncertain environment. A basic objective in such POMDPs is the reachability objective, where given a target set of states, the goal is to eventually arrive at one of them

Shoki Kishida, Norihiko. L. Okamoto, Ryoji Katsube, Akira Nagaoka

Practical applications of thermoelectric (TE) materials are constrained by less developments of high-performance n-type materials compared to their p-type counterparts. Chalcopyrite CdSnAs2 is a promising n-type semiconductor for thermoelectrics from its narrow bandgap around 0.2 eV and exceptionally high electron mobility. In this study, we investigated the

Hayk L. Gevorgyan

High-precision, robust quantum gates are essential components in quantum computation and information processing. In this study, we present an alternative perspective, exploring the potential applicability of quantum gates that exhibit heightened sensitivity to errors. We investigate such sensitive quantum gates, which, beyond their established use in in vivo

Marit Nuijten, Gijs Nelemans

Context. Wolf-Rayet (WR) and O-star binaries can be the progenitors of X-ray binaries and double black hole binaries. Their formation is not yet fully understood, however. For 21 observed WR+O systems, we aim to infer whether the mass transfer started on the main sequence (Case A) or later (Case B). We also calculated (limits on) the mass-transfer efficiency

Tien-Tien Yeh, Hennadii Yerzhakov, Logan Bishop-Van Horn, Srinivas Raghu

Challenge to control the quantum states of matter via light have been at the forefront of modern research on driven quantum matter. We explore the imprinting effects of structured light on superconductors, demonstrating how the quantum numbers of light-specifically spin angular momentum, orbital angular momentum, and radial order-can be transferred to the su

T. Y. S. S. Santosh, Hassan Sarwat, Matthias Grabmair

In this paper, we introduce QABISAR, a novel framework for statutory article retrieval, to overcome the semantic mismatch problem when modeling each query-article pair in isolation, making it hard to learn representation that can effectively capture multi-faceted information. QABISAR leverages bipartite interactions between queries and articles to capture di

Stefan Pietrusky

The use of artificial intelligence (AI) offers various possibilities to expand and support educational research. Specifically, the implementation of AI can be used to develop new frameworks to establish new research tools that accelerate and meaningfully expand the efficiency of data evaluation and interpretation (Buckingham Shum et al., 2023). This article

Qing Liu, Roberto Abraham, Peter G. Martin, William P. Bowman

Diffuse Galactic cirrus, or Diffuse Galactic Light (DGL), can be a prominent component in the background of deep wide-field imaging surveys. The DGL provides unique insights into the physical and radiative properties of dust grains in our Milky Way, and it also serves as a contaminant on deep images, obscuring the detection of background sources such as low

Kumar Ashutosh, Georgios Pavlakos, Kristen Grauman

Anticipating how a person will interact with objects in an environment is essential for activity understanding, but existing methods are limited to the 2D space of video frames-capturing physically ungrounded predictions of "what" and ignoring the "where" and "how". We introduce FIction for 4D future interaction prediction from videos. Given an input video o

Yashi Tiwari, Ujjwal Upadhyay, Rajeev Kumar Jain

In the current era of precision cosmology, the persistence of cosmological tensions, most notably the Hubble tension and the $S_8$ tension, challenges the standard $\Lambda$CDM model. To reconcile these tensions via late-time modifications to expansion history, various features such as phantom crossing in the dark energy equation of state, a negative energy

A. R. Vieira

The diagrammatic computation of anomalies is usually associated with the breaking of the momentum routing invariance. This is because the momentum routing is usually chosen to fulfill the desired Ward identity. In the case of the chiral anomaly, the momentum routing is chosen in order to fulfill the gauge Ward identity and break the chiral Ward identity. Alt

Faidon Andriopoulos

In the article of Hesselholt [Hes05], a set of conjectures is laid out. Given a smooth scheme $X$ over the ring of integers $\mathcal{O}_K$ of a $p$-adic field $K$, these conjectures concern the expected relation between log topological restriction homology $\mathrm{TR}^r (X,M_X)$ and the absolute log de Rham--Witt complex $W_r\Omega_{(X,M_X)}$. In this note

Yuxuan Ou, Jingyi Zhao, Austin Tripp, Morteza Rasoulianboroujeni

Lipid nanoparticles (LNPs) are vital in modern biomedicine, enabling the effective delivery of mRNA for vaccines and therapies by protecting it from rapid degradation. Among the components of LNPs, ionizable lipids play a key role in RNA protection and facilitate its delivery into the cytoplasm. However, designing ionizable lipids is complex. Deep generative

Weiming Ren, Huan Yang, Jie Min, Cong Wei

Current large multimodal models (LMMs) face significant challenges in processing and comprehending long-duration or high-resolution videos, which is mainly due to the lack of high-quality datasets. To address this issue from a data-centric perspective, we propose VISTA, a simple yet effective Video Spatiotemporal Augmentation framework that synthesizes long-

Lei Yang, Michael J. Daniels, Fan Li

Causal inference in the presence of intermediate variables is a challenging problem in many applications. Principal stratification (PS) provides a framework to estimate principal causal effects (PCE) in such settings. However, existing PS methods primarily focus on settings with binary intermediate variables. We propose a novel approach to estimate PCE with

Huayu Bai, Andriy Shevchenko, Radoslaw Kolkowski

Bound states in the continuum (BICs) in planar photonic structures have attracted broad scientific interest owing to their exceptional capability to confine light. Topological robustness of certain BICs allows them to be moved in the momentum space by tuning the geometric parameters of the structure. In this work, we study such a BIC in a one-dimensional per

Tomer Ravid

I review the formalism of patch bosonization of Fermi surfaces, with a focus on the problem of a two-dimensional metal at a quantum critical point. I argue that this formalism is fundamentally inapplicable to the problem, except in synthetic limits. One such limit is the small-$N$ limit, which was already discussed in early studies of the problem; a similar

Subhayan Sahu, Guifre Vidal

We investigate the entanglement structure of a generic $M$-particle Bethe wavefunction (not necessarily an eigenstate of an integrable model) on a 1d lattice by dividing the lattice into $L$ parts and decomposing the wavefunction into a sum of products of $L$ local wavefunctions. Using the fact that a Bethe wavefunction accepts a \textit{fractal} multipartit

Roy Fairstein, Dan Vilenchik, Kobi Gal

Participatory Budgeting (PB) offers a democratic process for communities to allocate public funds across various projects through voting. In practice, PB organizers face challenges in selecting aggregation rules either because they are not familiar with the literature and the exact details of every existing rule or because no existing rule echoes their expec

Marko Nonhoff, Johannes Köhler, Matthias A. Müller

This paper proposes a modular approach that combines the online convex optimization framework and reference governors to solve a constrained control problem featuring time-varying and a priori unknown cost functions. Compared to existing results, the proposed framework is uniquely applicable to nonlinear dynamical systems subject to state and input constrain

Stavya Puri, Tanoy Kanti Konar, Leela Ganesh Chandra Lakkaraju, Aditi Sen De

Achieving quantum advantage in energy storage and power extraction is a primary objective in the design of quantum-based batteries. We explore how long-range (LR) interactions in conjunction with Floquet driving can improve the performance of quantum batteries, particularly when the battery is initialized in a fully polarized state. In particular, we analyti

Kirill Safonov

This paper contributes to the literature on parametric demand estimation by using deep learning to model consumer preferences. Traditional econometric methods often struggle with limited within-product price variation, a challenge addressed by the proposed neural network approach. The proposed method estimates the functional form of the demand and demonstrat

Guillaume Bal

This paper reviews recent results on the classification of partial differential operators modeling bulk and interface topological insulators in Euclidean spaces. Our main objective is the mathematical analysis of the unusual, robust-to-perturbations, asymmetric transport that necessarily appears at interfaces separating topological insulators in different ph

Wei Bu, Sean Seet

In this paper, we present a simple chiral 2d theory living on a momentum space celestial sphere whose behaviour exactly produces various IR dynamics of recent resurged interests for 4d (selfdual) Einstein gravity in asymptotically flat spacetimes. We demonstrate how to use simple 2d CFT computations to reproduce 4d BMS algebra and $w_{1+\infty}$ algebra, ded

Quentin Dubroff, Jeff Kahn, Jinyoung Park

We show that a restricted version of a conjecture of M. Talagrand on the relation between "expectation thresholds" and "fractional expectation thresholds" follows easily from a strong version of a second conjecture of Talagrand, on "selector processes." The selector process conjecture was proved by Park and Pham, and the quantitative strengthening used here

Nonthakorn Olaranont, Chaozhen Wei, John Lowengrub, Min Wu

It is widely recognized that reciprocal interactions between cells and their microenvironment, via mechanical forces and biochemical signaling pathways, regulate cell behaviors during normal development, homeostasis and disease progression such as cancer. However, it is still not well understood how complex patterns of tissue growth emerge. Here, we propose

Xulong Yuan, Jian-dong Zhang, Jianwei Mei

In the typical data analysis and waveform modeling of the gravitational waves (GWs) signals for binary black holes (BBHs), it is assumed to be isolate sources in the vacuum within the theory of general relativity (GR). However, various kinds of matter may exist around the source or on the path to the detector, and there also exist many different kinds of mod

Faidon Andriopoulos

Given an animated ring $S$, we define a filtration on its absolute prismatic cohomology $\mathcal{N}_r^{\geq i} \mathbb{\Delta}_S$, which we call the $r$-Nygaard filtration and study some of its main properties using a mixture of algebraic and homotopy theoretic techniques. This filtration is obtained by suitably gluing $r$-copies of the usual Nygaard filtra

Thomas H. Li, Francisco Barreras

Human mobility datasets have seen increasing adoption in the past decade, enabling diverse applications that leverage the high precision of measured trajectories relative to other human mobility datasets. However, there are concerns about whether the high sparsity in some commercial datasets can introduce errors due to lack of robustness in processing algori

Andrei Grekov

This paper is intended to serve as a review of a series of papers with Nikita Nekrasov, where we achieved several important results concerning the relation between the moduli space of instantons and classical integrable systems. We derive I. Krichever's Lax matrix for the elliptic Calogero-Moser system from the equivariant cohomology of the moduli space of i

Donya Sadat Rezaeishad, Mohammad Rezai, Foroogh Sadat Tabataba, Jawad A. Salehi

The diversity of quantum communication protocols and their rapid growth entail the development of the Quantum Internet, the interconnection of various quantum communication nodes and systems. One of the challenges posed by this development is all-quantum mode adaptation, which is essential for adapting different quantum nodes to the common network, enhancing

Suresh Kumar Amalapuram, Shreya Kumar, Bheemarjuna Reddy Tamma, Sumohana Channappayya

Fully supervised continual learning methods have shown improved attack traffic detection in a closed-world learning setting. However, obtaining fully annotated data is an arduous task in the security domain. Further, our research finds that after training a classifier on two days of network traffic, the performance decay of attack class detection over time (

Gaspard Ohlmann

We establish an explicit formula for the Half-Wave maps equation for rational functions with simple poles. The Lax pair provides a description of the evolution of the poles. By considering a half-spin formulation, we use linear algebra to derive a time evolution equation followed by the half-spins, in the moving frame provided by the Lax pair. We then rewrit

Srikumar Warrier, Gaurav Tomar

Linear stability of a locally parallel annular swirling jet laden with particles in a swirl flow combustor is considered. At low Stokes numbers, the eigenspectra of the particle-laden jet with uniform particle concentration shows three unstable modes namely centre, sinuous and varicose modes. As the Stokes number is increased to unity, the growth rates of th

A. P. Saiko, G. A. Rusetsky, S. A. Markevich, R. Fedaruk

Closed-form expressions for the average amplitude of the optical field in optomechanical systems are obtained, in which, in addition to the linear interaction, quadratic and cubic interactions of the vibrational mode of the mechanical resonator with the mode of the optical resonator are considered. In the framework of the non-secular perturbation theory, usi

Philipp Schröer, Francesca Randone, Raúl Pardo, Andrzej Wąsowski

It is of utmost importance to ensure that modern data intensive systems do not leak sensitive information. In this paper, the authors, who met thanks to Joost-Pieter Katoen, discuss symbolic methods to compute information-theoretic measures of leakage: entropy, conditional entropy, Kullback-Leibler divergence, and mutual information. We build on two semantic

Einar Broch Johnsen, Eduard Kamburjan, Raúl Pardo, Erik Voogd

Whereas the semantics of probabilistic languages has been extensively studied, specification languages for their properties have received less attention -- with the notable exception of recent and on-going efforts by Joost-Pieter Katoen and collaborators. In this paper, we revisit probabilistic dynamic logic (pDL), a specification logic for programs in the p

Youjia Zhang, Anpei Chen, Yumin Wan, Zikai Song

In this paper, we introduce Ref-GS, a novel approach for directional light factorization in 2D Gaussian splatting, which enables photorealistic view-dependent appearance rendering and precise geometry recovery. Ref-GS builds upon the deferred rendering of Gaussian splatting and applies directional encoding to the deferred-rendered surface, effectively reduci

Hans Rabus, Leo Thomas

This work reviews the concepts of an event used in micro- and nanodosimetry and analyzes how single event distributions could theoretically be derived from probability distributions related to interactions of the primary particle which produce secondary electrons. It is shown that the corresponding mathematical expressions of conditional ionization cluster s

Grace Colverd, Jumpei Takami, Laura Schade, Karol Bot

Tree height estimation serves as an important proxy for biomass estimation in ecological and forestry applications. While traditional methods such as photogrammetry and Light Detection and Ranging (LiDAR) offer accurate height measurements, their application on a global scale is often cost-prohibitive and logistically challenging. In contrast, remote sensing

Tetsushi Ito, Ren Tatematsu, Takahiro Tsushima

The van der Geer-van der Vlugt curves are Artin-Schreier coverings of the affine line defined by linearized polynomials over finite fields. We give several criteria for them to be maximal or minimal, i.e. attaining the upper or lower bound in the Hasse-Weil inequalities. We also study the $L$-polynomials of certain generalizations of van der Geer-van der Vlu

Evgeny Shindin, Roi Ben Gigi, Odellia Boni

Fluid models provide a tractable approach to approximate multiclass processing networks. This tractability is a due to the fact that optimal control for such models is a solution of a Separated Continuous Linear Programming (SCLP) problem. Recently developed revised SCLP-simplex algorithm allows to exactly solve very large instances of SCLPs in a reasonable

Shivank Sharma, Ral Bielawski, Oliver Gibson, Shuzhi Zhang

This work presents a comprehensive framework for the efficient implementation of finite-volume-based reacting flow solvers, specifically tailored for high speed propulsion applications. Using the exascale computing project (ECP) based AMReX framework, a compressible flow solver for handling high-speed reacting flows is developed. This work is complementary t

Sina Kazemdehbashi

Unmanned aerial vehicles (UAVs) are increasingly utilized in search and rescue (SAR) operations to enhance efficiency by enabling rescue teams to cover large search areas in a shorter time. Reducing coverage time directly increases the likelihood of finding the target quickly, thereby improving the chances of a successful SAR operation. In this context, UAVs

Huaijin Wang, Zhibo Liu, Yanbo Dai, Shuai Wang

Software composition analysis (SCA) denotes the process of identifying open-source software components in an input software application. SCA has been extensively developed and adopted by academia and industry. However, we notice that the modern SCA techniques in industry scenarios still need to be improved due to privacy concerns. Overall, SCA requires the u

Wesley H. Holliday

We propose a model of unawareness that remains close to the paradigm of Aumann's model for knowledge [R. J. Aumann, International Journal of Game Theory 28 (1999) 263-300]: just as Aumann uses a correspondence on a state space to define an agent's knowledge operator on events, we use a correspondence on a state space to define an agent's awareness operator o

Weizhe Ren, Yichen Qin, Yang Li

Traditional genetic programming (GP) often struggles in stock alpha factor discovery due to its vast search space, overwhelming computational burden, and sporadic effective alphas. We find that GP performs better when focusing on promising regions rather than random searching. This paper proposes a new GP framework with carefully chosen initialization and st

Emma Cardwell, Aida Maraj, Alvaro Ribot

Given a rooted tree $T$ on $n$ non-root leaves with colored and zeroed nodes, we construct a linear space $L_T$ of $n\times n$ symmetric matrices with constraints determined by the combinatorics of the tree. When $L_T$ represents the covariance matrices of a Gaussian model, it provides natural generalizations of Brownian motion tree (BMT) models in phylogene

Yuanwei Liu, Chongjun Ouyang, Zhaolin Wang, Jiaqi Xu

In this paper, a novel continuous-aperture array (CAPA)-based wireless communication architecture is proposed, which relies on an electrically large aperture with a continuous current distribution. First, an existing prototype of CAPA is reviewed, followed by the potential benefits and key motivations for employing CAPAs in wireless communications. Then, thr

Thu Ha Trieu

We express the Mahler measure of an exact polynomial in arbitrarily many variables in terms of Deligne-Beilinson cohomology. We then focus on the relationship between the Mahler measure of four-variable exact polynomials and the special value of the $L$-function of $K3$ surfaces at $s = 4$. This result extends the three-variable case studied in \cite{Tri23}.

Nataša Raičević

The non-perturbative processes - the internal transverse motion of partons inside hadrons, which gives rise to their intrinsic transverse momentum (intrinsic-kT) - and multiple soft gluon emissions that need to be resummed, are dominant contributions to the low transverse momentum of the Drell-Yan (DY) pair cross section. Therefore, this part of the DY spect

Micheline Fakhoury

We describe the surjective isometries of the unit sphere of real Schreier spaces of all orders and their $p$-convexifications, for $1 < p < \infty$. This description allows us to provide for those spaces a positive answer to a special case of Tingley's problem, which asks whether every surjective isometry of the unit sphere of a real Banach space can be exte

Kun Qian, Tianyu Sun, Wenhong Wang

Industrial anomaly detection (IAD) plays a crucial role in the maintenance and quality control of manufacturing processes. In this paper, we propose a novel approach, Vision-Language Anomaly Detection via Contrastive Cross-Modal Training (CLAD), which leverages large vision-language models (LVLMs) to improve both anomaly detection and localization in industr

Saeed Noori Gashti

In this paper, we delve into the thermodynamic topology of AdS Reissner-Nordstr$\ddot{o}$m (R-N) black holes by employing nonextensive entropy frameworks, specifically R$\acute{e}$nyi (with nonextensive parameter $\lambda$) and Sharma-Mittal entropy (with nonextensive parameter $\alpha, \beta$). Our investigation spans two frameworks: bulk boundary and restr

Malik Abdul Manan, Feng Jinchao, Shahzad Ahmed, Abdul Raheem

In medical imaging, efficient segmentation of colon polyps plays a pivotal role in minimally invasive solutions for colorectal cancer. This study introduces a novel approach employing two parallel encoder branches within a network for polyp segmentation. One branch of the encoder incorporates the dual convolution blocks that have the capability to maintain f

Mingyu Yang, Junyou Li, Zhongbin Fang, Sheng Chen

In recent years, Artificial Intelligence Generated Content (AIGC) has advanced from text-to-image generation to text-to-video and multimodal video synthesis. However, generating playable games presents significant challenges due to the stringent requirements for real-time interaction, high visual quality, and accurate simulation of game mechanics. Existing a

N. J. Chater, R. S. MacKay

An axiomatic approach to macroeconomics based on the mathematical structure of thermodynamics is presented. It deduces relations between aggregate properties of an economy, concerning quantities and flows of goods and money, prices and the value of money, without any recourse to microeconomic foundations about the preferences and actions of individual econom

Mirajul Islam, Michael J. Daniels, Zeynab Aghabazaz, Juned Siddique

Cardiovascular disease (CVD) cohorts collect data longitudinally to study the association between CVD risk factors and event times. An important area of scientific research is to better understand what features of CVD risk factor trajectories are associated with the disease. We develop methods for feature selection in joint models where feature selection is

Antoine Bendimerad-Hohl, Ghislain Haine, Laurent Lefèvre, Denis Matignon

In this paper, we extend the port-Hamiltonian framework by introducing the concept of Stokes-Lagrange structure, which enables the implicit definition of a Hamiltonian over an $N$-dimensional domain and incorporates energy ports into the system. This new framework parallels the existing Dirac and Stokes-Dirac structures. We propose the Stokes-Lagrange struct

Emily Liu

Neural collapse is a phenomenon observed during the terminal phase of neural network training, characterized by the convergence of network activations, class means, and linear classifier weights to a simplex equiangular tight frame (ETF), a configuration of vectors that maximizes mutual distance within a subspace. This phenomenon has been linked to improved

Qianren Mao, Weifeng Jiang, Junnan Liu, Chenghua Lin

The semi-supervised learning (SSL) strategy in lightweight models requires reducing annotated samples and facilitating cost-effective inference. However, the constraint on model parameters, imposed by the scarcity of training labels, limits the SSL performance. In this paper, we introduce PS-NET, a novel framework tailored for semi-supervised text mining wit

Rongkun Zheng, Lu Qi, Xi Chen, Yi Wang

Recent DETR-based methods have advanced the development of Video Instance Segmentation (VIS) through transformers' efficiency and capability in modeling spatial and temporal information. Despite harvesting remarkable progress, existing works follow asynchronous designs, which model video sequences via either video-level queries only or adopting query-sensiti

Naixing Xu, Qian Li, Xu Wang, Bingchen Liu

Knowledge graph (KG) embedding methods map entities and relations into continuous vector spaces, improving performance in tasks like link prediction and question answering. With rising privacy concerns, machine unlearning (MU) has emerged as a critical AI technology, enabling models to eliminate the influence of specific data. Existing MU approaches often re

Sheyda Salehirad, Amy E. Reines, Mallory Molina

We present a sample of 398 galaxies with ionized gas outflow signatures in their spectra from the Galaxy and Mass Assembly (GAMA) Survey Data Release 4, including 45 low-mass galaxies with stellar masses $M_*<10^{10}$ $M_\odot$. We assemble our sample by systematically searching for the presence of a second velocity component in the [O III]$\lambda\lambda 49

Xiao Zhang, Peiyang Chen, Mei Li, Yuzhi Shi

Brownian systems are characterized by spatiotemporal disorder, which arises from the erratic motion of particles driven by thermal fluctuations. When light interacts with such systems, it typically produces unpolarized and uncorrelated fields. Here, we report the observation of a large-scale spin-locking effect of light within a Brownian medium. In an observ

Haoze Sun, Wenbo Li, Jiayue Liu, Kaiwen Zhou

Generalization has long been a central challenge in real-world image restoration. While recent diffusion-based restoration methods, which leverage generative priors from text-to-image models, have made progress in recovering more realistic details, they still encounter "generative capability deactivation" when applied to out-of-distribution real-world data.

Hongxuan Lu, Shenjian Wang, Biao Li

During the entire training process of the ASR model, the intensity of data augmentation and the approach of calculating training loss are applied in a regulated manner based on preset parameters. For example, SpecAugment employs a predefined strength of augmentation to mask parts of the time-frequency domain spectrum. Similarly, in CTC-based multi-layer mode

Wenxuan Huang, Zijie Zhai, Yunhang Shen, Shaosheng Cao

Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision understanding, reasoning, and interaction. However, the inference computation and memory increase progressively with the generation of output tokens during decoding, directly affecting the efficacy of MLLMs. Existing methods attempt to reduce the vision context redundancy to