March 2025 arXiv papers — page 100

Hongyu Zhang, Yufan Deng, Shenghai Yuan, Yian Zhao

In the domain of text-to-video (T2V) generation, reliably synthesizing compositional content involving multiple subjects with intricate relations is still underexplored. The main challenges are twofold: 1) Subject presence, where not all subjects can be presented in the video; 2) Inter-subject relations, where the interaction and spatial relationship between

Seungwon Lim, Sungwoong Kim, Jihwan Yu, Sungjae Lee

Escape rooms present a unique cognitive challenge that demands exploration-driven planning: with the sole instruction to 'escape the room', players must actively search their environment, collecting information, and finding solutions through repeated trial and error. Motivated by this, we introduce VisEscape, a benchmark of 20 virtual escape rooms specifical

David Carchedi

Derived geometry provides powerful tools to handle non-transverse intersections and singular moduli problems arising in geometry and theoretical physics. While derived algebraic geometry has been extensively developed, classical field theories -- formulated as variational problems involving sections of smooth fiber bundles over manifolds -- naturally require

Adam E. A. Fouda, Bhavnesh Jangid, Eetu Pelimanni, Stephen H. Southworth

Efficiently and accurately computing molecular Auger electron spectra for larger systems is limited by the increasing complexity of the scaling in the number of doubly ionized final states with respect to the system size. In this work, we benchmark the application of multiconfiguration pair-density functional theory with a restricted active space (RAS) refer

Akshay A. Murthy, Mustafa Bal, Michael J. Bedzyk, Hilal Cansizoglu

The Superconducting Materials and Systems (SQMS) Center, a DOE National Quantum Information Science Research Center, has conducted a comprehensive and coordinated study using superconducting transmon qubit chips with known performance metrics to identify the underlying materials-level sources of device-to-device performance variation. Following qubit coheren

Justus Teller, Christian Schäfer, Kristof Moors, Benjamin Bennemann

We study the frustration pattern of a square lattice with in-situ fabricated Nb-Pt-Nb four-terminal Josephson junctions. The four-terminal geometry gives rise to a checker board pattern of alternating fluxes f, f' piercing the plaquettes, which stabilizes the Berezinskii-Kosterlitz-Thouless transition even at irrational flux quanta per plaquette, due to an u

Richard Pinčák, Alexander Pigazzini, Michal Pudlák, Erik Bartoš

This work introduces the G$_2$-Ricci flow on seven-dimensional manifolds with non-zero torsion and explores its physical implications. By extending the Ricci flow to manifolds with G$_2$ structures, we study the evolution of solitonic solutions and their role in spontaneous symmetry breaking in gauge theories. In particular, this model proposes that the mass

George FitzGerald, Will Yeadon

We introduce QSTToolkit, a Python library for performing quantum state tomography (QST) on optical quantum state measurement data. The toolkit integrates traditional Maximum Likelihood Estimation (MLE) with deep learning-based techniques to reconstruct quantum states. It includes comprehensive noise models to simulate both intrinsic state noise and measureme

Vlad Hondru, Eduard Hogea, Darian Onchis, Radu Tudor Ionescu

The ever growing realism and quality of generated videos makes it increasingly harder for humans to spot deepfake content, who need to rely more and more on automatic deepfake detectors. However, deepfake detectors are also prone to errors, and their decisions are not explainable, leaving humans vulnerable to deepfake-based fraud and misinformation. To this

Marc Levine, Anna M. Viergever

Using virtual localization in Witt sheaf cohomology, we show that the generating series of quadratic Donaldson-Thomas invariants of $(\mathbb{P}^1)^3$, valued in the Witt ring of $\mathbb{R}$, $W(\mathbb{R})\cong \mathbb{Z}$, is equal to $M(q^2)^{-8}$, where $M(q)$ is the MacMahon function. This confirms a modified version of a conjecture of Viergever. We al

Gustavo F. S. Alves, Matheus Hostert, Maxim Pospelov

Current data on ultra-high-energy (UHE) cosmic rays suggest they are predominantly made of heavy nuclei. This indicates that the flux of neutrinos produced from proton collisions on the cosmic microwave background is small and hard to observe. Motivated by the recent extremely-high-energy muon event reported by KM3NeT, we explore the possibility of enhancing

Cristian F. Nino, Omkar Sudhir Patil, Sage C. Edwards, Warren E. Dixon

A distributed implementation of a Robust Integral of the Sign of the Error (RISE) controller is developed for multi-agent target tracking problems with exponential convergence guarantees. Previous RISE-based approaches for multi-agent systems required 2-hop communication, limiting practical applicability. New insights from a Lyapunov-based design-analysis ap

Loïc Foissy, Claudia Malvenuto, Frédéric Patras

A fundamental result by L. Solomon in algebraic combinatorics and representation theory states that Mackey formulas for products of characters of a symmetric group, or equivalently the computation of tensor products of representations thereof, can be lifted to the corresponding Solomon's descent algebra, a subalgebra of the group algebra with a very rich str

Timo Richarz, Jakob Scholbach

The manuscript at hand systematically studies K\"unneth formulas at a categorical level. We give criteria for an abstract six functor formalism to satisfy the categorical K\"unneth formula, and use this to formulate conjectures for categories of \'etale motives. As supporting evidence for these conjectures, we prove categorical K\"unneth formulas for adic sh

Robert Cardona, Andreu Vega

The goal of this work is to show that, mathematically, screw dislocation loops with distinct topological properties can be introduced locally on a given smectic liquid crystal configuration. Concretely, given a configuration, we show that a new screw dislocation loop can be introduced along any knot or link transverse to the regular layers through a purely l

Pierre Yves Gaudreau Lamarre

Let $\Lambda=\{\Lambda_0,\Lambda_1,\Lambda_2,\ldots\}$ be the point process that describes the edge scaling limit of either (i) "regular" beta-ensembles with inverse temperature $\beta>0$, or (ii) the top eigenvalues of Wishart or Gaussian invariant random matrices perturbed by $r_0\geq1$ critical spikes. In other words, $\Lambda$ is the eigenvalue point pro

Fedor Pakovich

Let $A$ and $B$ be non-constant rational functions over $\mathbb{C}$, and let $K \subset \mathbb{P}^1(\mathbb{C})$ be an infinite set. Using height functions, we prove that the inclusion $ A^{-1}(K) \subseteq B^{-1}(K) $ implies the inequality $ {\rm deg} B \geq {\rm deg} A $ in the following two cases: the set $K$ is contained in $\mathbb{P}^1(k)$, where $

Gionnieve Lim, Juho Kim, Simon T. Perrault

Social platforms have expanded opportunities for deliberation with the comments being used to inform one's opinion. However, using such information to form opinions is challenged by unsubstantiated or false content. To enhance the quality of opinion formation and potentially confer resistance to misinformation, we developed Iffy-Or-Not (ION), a browser exten

Siwei Zhang, Yun Xiong, Yateng Tang, Jiarong Xu

Temporal graph neural networks (TGNNs) have shown remarkable performance in temporal graph modeling. However, real-world temporal graphs often possess rich textual information, giving rise to temporal text-attributed graphs (TTAGs). Such combination of dynamic text semantics and evolving graph structures introduces heightened complexity. Existing TGNNs embed

G. Karapetyan

This paper investigates high-energy hadron-hadron scattering utilizing AdS/QCD correspondence, gravitational form factors, and the Brower-Polchinski-Strassler-Tan pomeron exchange kernel. We use the configurational complexity to estimate the slope of the total cross section for hadron-hadron interactions at the high-energy regime. Our approach agrees well wi

Merijn Floren, Jean-Philippe Noël, Jan Swevers

Estimating the parameters of nonlinear block-oriented state-space models from input-output data typically involves solving a highly non-convex optimization problem, which is prone to poor local minima and slow convergence. This paper presents a computationally efficient initialization method for nonlinear linear fractional representation (NL-LFR) models usin

Parisa Ghanad Torshizi, Laura B. Hensel, Ari Shapiro, Stacy C. Marsella

Co-speech gestures convey a wide variety of meanings and play an important role in face-to-face human interactions. These gestures significantly influence the addressee's engagement, recall, comprehension, and attitudes toward the speaker. Similarly, they impact interactions between humans and embodied virtual agents. The process of selecting and animating m

Loïc Chaumont, Clément Lamoureux

A branching process $Z$ is said to be non conservative if it hits $\infty$ in a finite time with positive probability. It is well known that this happens if and only if the branching mechanism $\varphi$ of $Z$ satisfies $\int_{0+}d\lambda/|\varphi(\lambda)|<\infty$. We construct on the same probability space a family of conservative continuous state branchin

Ipsita Mandal

We investigate the influence of a time-periodic drive on three-dimensional Weyl and multi-Weyl semimetals in planar-Hall/planar-thermal-Hall set-ups. The drive is modelled here by circularly-polarized electromagnetic fields, whose effects are incorporated by a combination of the Floquet theorem and the van Vleck perturbation theory, applicable in the high-fr

Mert Bulent Sariyildiz, Philippe Weinzaepfel, Thomas Lucas, Pau de Jorge

Recent multi-teacher distillation methods have unified the encoders of multiple foundation models into a single encoder, achieving competitive performance on core vision tasks like classification, segmentation, and depth estimation. This led us to ask: Could similar success be achieved when the pool of teachers also includes vision models specialized in dive

Daniel An, Ruth Lawrence, Dennis Sullivan

This paper constructs a graded-commutative, associative, differential Transverse Intersection Algebra TIA {on the torus (in any dimension) with its cubical decomposition by using a probabilistic wiggling interpretation. This structure agrees with the combinatorial graded intersection algebra (graded by codimension) defined by transversality on pairs of `cubo

Theodoros G. Tsironis, Aris L. Moustakas

We use the Kac-Rice formula and results from random matrix theory to obtain the average number of critical points of a family of high-dimensional empirical loss functions, where the data are correlated $d$-dimensional Gaussian vectors, whose number has a fixed ratio with their dimension. The correlations are introduced to model the existence of structure in

Lisha Li, Jingwen Hou, Weide Liu, Yuming Fang

Facial Aesthetics Enhancement (FAE) aims to improve facial attractiveness by adjusting the structure and appearance of a facial image while preserving its identity as much as possible. Most existing methods adopted deep feature-based or score-based guidance for generation models to conduct FAE. Although these methods achieved promising results, they potentia

Sean Jaffe

We study the evolution of majority dynamics on Erd\H{o}s-R\'enyi $G(n,p)$ random graphs. In this process, each vertex of a graph is assigned one of two initial states. Subsequently, on every day, each vertex simultaneously updates its state to the most common state in its neighbourhood. If the difference in the numbers of vertices in each state on day $0$ is

Jong E. Han

Numerical renormalization group (NRG) is formulated for nonequilibrium steady-state by converting finite-lattice many-body eigenstates into scattering states. Extension of the full-density-matrix NRG for a biased Anderson impurity model, simplified by formulating with the original orbital basis as the Hamiltonian, enables detailed studies of the sub-Kondo sp

Ravi Varma Pakalapati, Gary E. Davis

Acts of political violence in the continental United States have increased dramatically in the last decade. For this rise in political violence, we are interested in where and when such incidents occur: how are the locations and times of incidents of political violence distributed across the continental United States, and what can we learn from a detailed ex

Zoe Nieraeth, Michael Penrod

In this paper we prove boundedness of Calder\'on-Zygmund operators and the Christ-Goldberg maximal operator in the matrix-weighted variable Lebesgue spaces recently introduced by Cruz-Uribe and the second author. Our main tool to prove these bounds is through bounding a Goldberg auxiliary maximal operator. As an application, we obtain a quantitative extrapol

Timothe Alezraa, Giacomo Cacciapaglia, Aldo Deandrea

Asymptotic grand unification models can be constructed in five dimensions compactified on an orbifold. We demonstrate that the parameter space of such models admit solutions that naturally achieve the baryon asymmetry via leptogenesis, these solutions indicating that the scale of the extra dimensions is much higher than the TeV scale.

Archie Licudi, Anshul Thakur, Soheila Molaei, Danielle Belgrave

Asynchronous federated learning (FL) with heterogeneous clients faces two key issues: curvature-induced loss barriers encountered by standard linear parameter interpolation techniques (e.g. FedAvg) and interference from stale updates misaligned with the server's current optimisation state. To alleviate these issues, we introduce a geometric framework that ca

Jing Wang, Ruirui Liu, Yu Lei, Michael J. Baine

Purpose: Accurate 3D MRI-ultrasound (US) deformable registration is critical for real-time guidance in high-dose-rate (HDR) prostate brachytherapy. We present a weakly supervised spatial implicit neural representation (SINR) method to address modality differences and pelvic anatomy challenges. Methods: The framework uses sparse surface supervision from MRI/U

Rebecca Scholefield, Samuel Martin, Otto Barten

The malicious use or malfunction of advanced general-purpose AI (GPAI) poses risks that, according to leading experts, could lead to the 'marginalisation or extinction of humanity.' To address these risks, there are an increasing number of proposals for international agreements on AI safety. In this paper, we review recent (2023-) proposals, identifying area

Boris Alexeev, Wenyan Luo, Dustin G. Mixon, Yan X Zhang

Things can go spectacularly wrong when clustering timeseries data that has been preprocessed with a sliding window. We highlight three surprising failures that emerge depending on how the window size compares with the timeseries length. In addition to computational examples, we present theoretical explanations for each of these failure modes.

Qiantong Wang

This paper explores hallucination phenomena in large language models (LLMs) through the lens of language philosophy and psychoanalysis. By incorporating Lacan's concepts of the "chain of signifiers" and "suture points," we propose the Anchor-RAG framework as a novel approach to mitigate hallucinations. In contrast to the predominant reliance on trial-and-err

Shadi Hamdan, Deniz Yuret

Large language models (LLMs) undergo a three-phase training process: unsupervised pre-training, supervised fine-tuning (SFT), and learning from human feedback (RLHF/DPO). Notably, it is during the final phase that these models are exposed to negative examples -- incorrect, rejected, or suboptimal responses to queries. This paper delves into the role of negat

Maxwell D. Radin, Alexander Kunitsa

Oxygen resonant inelastic X-ray scattering has become a prominent tool for unveiling the electronic structure of solids, especially redox mechanisms in Li-rich cathode materials. A number of studies have observed a strong absorption feature at 531 eV associated with the anomalous capacity of Li-rich cathodes. The most prominent emission feature arising from

Valentýn Číhala, Martin Pecka, Tomáš Svoboda, Karel Zimmermann

We investigated the performance of existing semi- and fully autonomous methods for controlling flipper-based skid-steer robots. Our study involves reimplementation of these methods for fair comparison and it introduces a novel semi-autonomous control policy that provides a compelling trade-off among current state-of-the-art approaches. We also propose new me

Yekatierina Churakova, Mathias Ekstedt, Larissa Schmid

The Vulnerability Exploitability eXchange (VEX) format has been introduced to complement Software Bill of Materials (SBOM) with security advisories of known vulnerabilities. VEX gives an accurate understanding of vulnerabilities found in the dependencies of third-party software, which is critical for secure software development and risk analysis. In this pap

Mahmoud Alawashra, Jan Benáček, Martin Pohl, Mikhail Medvedev

Super-strongly magnetized plasmas play a crucial role in extreme environments of magnetar and laboratory laser experiments, demanding comprehensive understanding of how quantum electrodynamic (QED) effects influence plasma behaviour. Earlier analytical and semi-analytical calculations have shown that QED effects can significantly modify the plasma polarizati

Jianing Sun, Jigang Duan, Guangyin Li, Xu Jiang

Compared to single-source imaging systems, dual-source imaging systems equipped with two cross-distributed scanning beams significantly enhance temporal resolution and capture more comprehensive object scanning information. Nevertheless, the interaction between the two scanning beams introduces more complex scatter signals into the acquired projection data.

Juan Beccuti, Thunj Chantramonklasri, Matthias Hafner, Nicolas Oderbolz

This paper examines how various categories of Ethereum stakers respond to changes in the consensus issuance schedule, and the potential impact of such changes on the composition of the staking market. To this end, we have develop and calibrate a game-theoretic model of the Ethereum staking market, incorporating strategic interactions between various staking

Bowen Xue, Giuseppe Claudio Guarnera, Shuang Zhao, Zahra Montazeri

Despite recent advances in text-to-image generation, controlling geometric layout and PBR material properties in synthesized scenes remains challenging. We present a pipeline that first produces a G-buffer (albedo, normals, depth, roughness, shading, and metallic) from a text prompt and then renders a final image through a PBR-inspired branch network. This i

Shuoguang Liu, Peter B. Littlewood, Ryo Hanai

Unveiling universal non-equilibrium scaling laws has been a central theme in modern statistical physics, with recent attention increasingly directed toward non-equilibrium phases that exhibit rich dynamical phenomena. A striking example arises in non-reciprocal systems, where asymmetric interactions between components lead to inherently dynamic phases and un

Harry Walden, Alexander Stegmaier, Jörn Dunkel, Alexander Mietke

Non-reciprocal interactions in elastic media give rise to rich non-equilibrium behaviors, but controllable experimental realizations of such odd elastic phenomena remain scarce. Building on recent breakthroughs in electrical analogs of non-Hermitian solid-state systems, we design and analyze scalable odd electrical circuits (OECs) as exact analogs of an odd

Rikuto Tsuchida, Hibiki Yokoyama, Takehito Utsuro

The purpose of this paper is to examine whether large language models (LLMs) can understand what is good and evil with respect to judging good/evil reputation of celebrities. Specifically, we first apply a large language model (namely, ChatGPT) to the task of collecting sentences that mention the target celebrity from articles about celebrities on Web pages.

Chien-Ming Chi

Evidence suggests that oblique splits can significantly enhance the performance of decision trees. This paper explores the optimization of high-dimensional oblique splits for decision tree construction, establishing the Sufficient Impurity Decrease (SID) convergence that takes into account $s_0$-sparse oblique splits. We demonstrate that the SID function cla

Sara R. Cabo, Yasuhiro Nishimura, Sergio Luis Suárez Gómez, Laura Bonavera

Due to the progressive increase in size of the latest Cherenkov-type detectors, it is becoming increasingly important to design a suitable compensation system based on coils of the Earth's magnetic field to ensure the correct operation of the photomultipliers (PMTs). Until now, most studies have assessed the correct functioning of such a system by the propor

Pouria Sarhadi

The traditional control theory and its application to basic and complex systems have reached an advanced level of maturity. This includes aerial, marine, and ground vehicles, as well as robotics, chemical, transportation, and electrical systems widely used in our daily lives. The emerging era of data-driven methods, Large Language Models (LLMs), and AI-based

Chenyu Liu, Luca Rossi

The accurate diagnosis of Alzheimer's disease (AD) and prognosis of mild cognitive impairment (MCI) conversion are crucial for early intervention. However, existing multimodal methods face several challenges, from the heterogeneity of input data, to underexplored modality interactions, missing data due to patient dropouts, and limited data caused by the time

Zechen Bai, Hai Ci, Mike Zheng Shou

Synthetic videos nowadays is widely used to complement data scarcity and diversity of real-world videos. Current synthetic datasets primarily replicate real-world scenarios, leaving impossible, counterfactual and anti-reality video concepts underexplored. This work aims to answer two questions: 1) Can today's video generation models effectively follow prompt

Negin Baghbanzadeh, Adibvafa Fallahpour, Yasaman Parhizkar, Franklin Ogidi

Despite the growing scale of medical Vision-Language datasets, the impact of dataset quality on model performance remains under-explored. We introduce Open-PMC, a high-quality medical dataset from PubMed Central, containing 2.2 million image-text pairs, enriched with image modality annotations, subfigures, and summarized in-text references. Notably, the in-t

Maximilian Beck, Korbinian Pöppel, Phillip Lippe, Sepp Hochreiter

Linear RNNs with gating recently demonstrated competitive performance compared to Transformers in language modeling. Although their linear compute scaling in sequence length offers theoretical runtime advantages over Transformers, realizing these benefits in practice requires optimized custom kernels, as Transformers rely on the highly efficient Flash Attent

Sai Coumar, Zachary Kingston

Generating structured ASCII art using computational techniques demands a careful interplay between aesthetic representation and computational precision, requiring models that can effectively translate visual information into symbolic text characters. Although Convolutional Neural Networks (CNNs) have shown promise in this domain, the comparative performance

Tabitha K. Peter, Patrick J. Breheny

In this paper, we develop an implementation of cross-validation for penalized linear mixed models. While these models have been proposed for correlated high-dimensional data, the current literature implicitly assumes that tuning parameter selection procedures developed for independent data will also work well in this context. We argue that such naive assumpt

Zihao Guo, Shuqing Shi, Richard Willis, Tristan Tomilin

Sequential social dilemmas pose a significant challenge in the field of multi-agent reinforcement learning (MARL), requiring environments that accurately reflect the tension between individual and collective interests. Previous benchmarks and environments, such as Melting Pot, provide an evaluation protocol that measures generalization to new social partners

Cristian F. Nino, Omkar Sudhir Patil, Jordan C. Insinger, Marla R. Eisman

A generalized ResNet architecture for adaptive control of nonlinear systems with black box uncertainties is developed. The approach overcomes limitations in existing methods by incorporating pre-activation shortcut connections and a zeroth layer block that accommodates different input-output dimensions. The developed Lyapunov-based adaptation law establishes

Dimitris Moustos, Obinna Abah

We investigate an Otto thermodynamic cycle with a qubit Unruh-DeWitt detector as the working medium, coupled to a massless, conformally coupled scalar quantum field in the Hartle-Hawking vacuum in a (2+1)-dimensional BTZ black hole spacetime. We employ the thermal properties of the field to model heat and cold reservoirs between which the thermal machine ope

Giuseppe Bruni, Sepehr Maleki, Senthil K Krishnababu

The field of scientific machine learning and its applications to numerical analyses such as CFD has recently experienced a surge in interest. While its viability has been demonstrated in different domains, it has not yet reached a level of robustness and scalability to make it practical for industrial applications in the turbomachinery field. The highly comp

Sapna Mishra, Andrew J. Fox, J. V. Smoker, Scott Lucchini

The Magellanic Stream (MS) is a large tail of neutral and ionized gas originating from tidal and hydrodynamical interactions between the Magellanic Clouds as they orbit the Milky Way (MW). It carries a significant gas reservoir that could impact the future evolution of the MW. Despite its importance, no direct observational constraints on the Stream's distan

Oisín Creaner, Anna Preis, Cormac Ryan, Nika Gorchakova

We present the progress of work to streamline and simplify the process of exoplanet observation by citizen scientists. International collaborations such as ExoClock and Exoplanet Watch enable citizen scientists to use small telescopes to carry out transit observations. These studies provide essential supports for space missions such as JWST and ARIEL. Contri

Dimitra Kyriakopoulou

We study uniqueness of an elliptic Riemannian polyhedron using the elliptic version for Boundary Control method, which we presented in [1]. We also present interface detection criteria for hyperbolic Riemannian manifolds through introduction of the waveguide notion, the four-wave mixing notion, etc.

Senwei Liang, Linghua Zhu, Xiaosong Li, Chao Yang

Variational quantum algorithms (VQAs) offer a promising approach to solving computationally demanding problems by combining parameterized quantum circuits with classical optimization. Estimating probabilistic outcomes on quantum hardware requires repeated measurements (shots). However, in practice, the limited shot budget introduces significant noise in the

Akinari Hoshi, Aiichi Yamasaki

Let $k$ be a global field, $K/k$ be a finite separable field extension and $L/k$ be the Galois closure of $K/k$ with Galois groups $G={\rm Gal}(L/k)$ and $H={\rm Gal}(L/K)\lneq G$. In 1931, Hasse proved that if $G$ is cyclic, then the Hasse norm principle holds for $K/k$. We show that if $G$ is metacyclic with trivial Schur multiplier $M(G)=0$, then $H$ is c

Martina Giachello, Stefano Iubini, Roberto Livi, Giacomo Gradenigo

We perform a detailed numerical study of the very peculiar thermodynamic properties of the localized high-energy phase of the Discrete Non-Linear Schr\"odinger Equation (DNLSE). A numerical sampling of the microcanonical ensemble done by means of Hamiltonian dynamics reveals a new and subtle relation between the presence of the localized phase and a property

A. Flores--Pérez, M. A. González--Olvera, V. F. Breña--Medina

This study analyses the dynamical consequences of heterogeneous temporal delays within a quorum sensing-inspired (QS-inspired) system, specifically addressing the differential response kinetics of two sub-populations to signalling molecules. A nonlinear delay differential equation (DDE) model, predicated upon an activator-inhibitor framework, is formulated t

Nicolas Menand, Erik Waingarten

Given a set of vectors $X = \{ x_1,\dots, x_n \} \subset \mathbb{R}^d$, the Euclidean max-cut problem asks to partition the vectors into two parts so as to maximize the sum of Euclidean distances which cross the partition. We design new algorithms for Euclidean max-cut in models for massive datasets: $\bullet$ We give a fully-scalable constant-round MPC algo

Changfeng Zhou, Taige Wang

In this article, we use results of Number Theory to prove the conjecture on eigenvalue problem of a 2D elliptic PDE proposed by P. Korman in his recent paper \cite{ref}: for any even integer $2k$, one can find an eigenvalue $N$ that can be represented as $N=a^{2}+b^{2}$, with integers $a\neq b$ and multiplicity $2k$, while for any odd integer $2k + 1$, one c

Javier Sivianes, Peio Garcia-Goiricelaya, Daniel Hernangómez-Pérez, Julen Ibañez-Azpiroz

We systematically explore a pathway for generating nonlinear charge and spin photocurrents using spin-orbit-split surface states. This mechanism enables net charge and spin flow along the surface plane even in centrosymmetric bulk environments like the Rashba prototype Au(111), where we establish the key principles by combining model predictions with density

Chao Wang, Giulio Franzese, Alessandro Finamore, Pietro Michiardi

Rectified Flow (RF) models trained with a Flow matching framework have achieved state-of-the-art performance on Text-to-Image (T2I) conditional generation. Yet, multiple benchmarks show that synthetic images can still suffer from poor alignment with the prompt, i.e., images show wrong attribute binding, subject positioning, numeracy, etc. While the literatur

Alfredo Oneto, Blazhe Gjorgiev, Giovanni Sansavini

Many data clustering applications must handle objects that cannot be represented as vectors. In this context, the bag-of-vectors representation describes complex objects through discrete distributions, for which the Wasserstein distance provides a well-conditioned dissimilarity measure. Kernel methods extend this by embedding distance information into featur

Taslim Murad, Sarwan Ali, Murray Patterson

The analysis of sequences (e.g., protein, DNA, and SMILES string) is essential for disease diagnosis, biomaterial engineering, genetic engineering, and drug discovery domains. Conventional analytical methods focus on transforming sequences into numerical representations for applying machine learning/deep learning-based sequence characterization. However, the

Alexander Partin, Priyanka Vasanthakumari, Oleksandr Narykov, Andreas Wilke

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent dataset

Runqi Meng, Sifan Song, Pengfei Jin, Yujin Oh

Accurate tumor segmentation is crucial for cancer diagnosis and treatment. While foundation models have advanced general-purpose segmentation, existing methods still struggle with: (1) limited incorporation of medical priors, (2) imbalance between generic and tumor-specific features, and (3) high computational costs for clinical adaptation. To address these

Omkar Kokane, Gopal Raut, Salim Ullah, Mukul Lokhande

A CORDIC-based configuration for the design of Activation Functions (AF) was previously suggested to accelerate ASIC hardware design for resource-constrained systems by providing functional reconfigurability. Since its introduction, this new approach for neural network acceleration has gained widespread popularity, influencing numerous designs for activation

Erik G. Larsson, Nicolo Michelusi

The decentralized gradient descent (DGD) algorithm, and its sibling, diffusion, are workhorses in decentralized machine learning, distributed inference and estimation, and multi-agent coordination. We propose a novel, principled framework for the analysis of DGD and diffusion for strongly convex, smooth objectives, and arbitrary undirected topologies, using

Xiyu Fan, Minghao Lu, Bowen Xu, Peng Lu

Obstacle avoidance for unmanned aerial vehicles like quadrotors is a popular research topic. Most existing research focuses only on static environments, and obstacle avoidance in environments with multiple dynamic obstacles remains challenging. This paper proposes a novel deep-reinforcement learning-based approach for the quadrotors to navigate through highl

Remi Hendriks, Mattijs Jonker, Roland van Rijswijk-Deij, Raffaele Sommese

Load Balancing (LB) is a routing strategy that increases performance by distributing traffic over multiple outgoing paths. In this work, we introduce a novel methodology to detect the influence of LB on anycast routing, which can be used by operators to detect networks that experience anycast site flipping, where traffic from a single client reaches multiple

Shoubin Yu, Difan Liu, Ziqiao Ma, Yicong Hong

Recent video diffusion models have enhanced video editing, but it remains challenging to handle instructional editing and diverse tasks (e.g., adding, removing, changing) within a unified framework. In this paper, we introduce VEGGIE, a Video Editor with Grounded Generation from Instructions, a simple end-to-end framework that unifies video concept editing,

Henrik Rüping, Marc Stephan

For the nontrivial action by the cyclic group $C_3$ of order $3$ on the graded polynomial ring $\mathbb{F}_2[a,b]$, we classify the $C_3$-invariant parameter ideals that are closed under Steenrod operations. The classification has applications to free actions by the Klein four-group $\mathbb{Z}/2\times\mathbb{Z}/2$ on products of two spheres (and more genera

Roberto Balestri

This study evaluates the biases in Gemini 2.0 Flash Experimental, a state-of-the-art large language model (LLM) developed by Google, focusing on content moderation and gender disparities. By comparing its performance to ChatGPT-4o, examined in a previous work of the author, the analysis highlights some differences in ethical moderation practices. Gemini 2.0

Lewis McCallum, Kenneth Wood, Robert Benjamin, Dhanesh Krishnarao

We combine parallax distances to nearby O stars with parsec-scale resolution three-dimensional dust maps of the local region of the Milky Way (within 1.25 kpc of the Sun) to simulate the transfer of Lyman continuum photons through the interstellar medium. Assuming a fixed gas-to-dust ratio, we determine the density of ionized gas, electron temperature, and H

Zishun Liu, Sam Power, Yongxin Chen

We present a new method for proving the norm concentration inequality of sub-Gaussian variables. Our proof is based on an averaged version of the moment generating function, termed the averaged moment generating function. Our method applies to both vector cases to bound the vector norm and matrix cases to bound the operator norm. Compared with the widely ado

Zeqian Ju, Dongchao Yang, Jianwei Yu, Kai Shen

Recent advances in text-to-speech synthesis have achieved notable success in generating high-quality short utterances for individual speakers. However, these systems still face challenges when extending their capabilities to long, multi-speaker, and spontaneous dialogues, typical of real-world scenarios such as podcasts. These limitations arise from two prim

Shivam Dubey, Mrinal Kanti Roychowdhury, Saurabh Verma

For a given $r \in (0, +\infty)$, the quantization dimension of order $r$, if it exists, denoted by $D_r(\mu)$, represents the rate at which the $n$th quantization error of order $r$ approaches to zero as the number of elements $n$ in an optimal set of $n$-means for $\mu$ tends to infinity. If $D_r(\mu)$ does not exist, we define $\underline{D}_r(\mu)$ and $

Yali Bi, Enyu Che, Yinan Chen, Yuanpeng He

Medical image segmentation aims to identify anatomical structures at the voxel-level. Segmentation accuracy relies on distinguishing voxel differences. Compared to advancements achieved in studies of the inter-class variance, the intra-class variance receives less attention. Moreover, traditional linear classifiers, limited by a single learnable weight per c

Kinga Anna Wozniak, Stephen Mulligan, Jan Kieseler, Markus Klute

We introduce a novel approach for end-to-end black-box optimization of high energy physics (HEP) detectors using local deep learning (DL) surrogates. These surrogates approximate a scalar objective function that encapsulates the complex interplay of particle-matter interactions and physics analysis goals. In addition to a standard reconstruction-based metric

Andrew Roxburgh, Floriana Grasso, Terry R. Payne

Predicting the words that a child is going to learn next can be useful for boosting language acquisition, and such predictions have been shown to be possible with both neural network techniques (looking at changes in the vocabulary state over time) and graph model (looking at data pertaining to the relationships between words). However, these models do not f

Yisen Xu, Feng Lin, Jinqiu Yang, Tse-Hsun

Maintaining and scaling software systems relies heavily on effective code refactoring, yet this process remains labor-intensive, requiring developers to carefully analyze existing codebases and prevent the introduction of new defects. Although recent advancements have leveraged Large Language Models (LLMs) to automate refactoring tasks, current solutions are

Chao Zhai, Zhenyuan Xue

Nowadays, the subsistent anisotropic non-Kolmogorov (ANK) turbulence models are all established on the supposition that the long axis of turbulence cell ought to be level with the ground. Nevertheless, Beason et al. and Wang et al. have illustrated through their recent experimental results that there is an anisotropic tilt angle in the turbulence cell, i.e.,

Murong Yue, Ziyu Yao

Batch prompting, which combines a batch of multiple queries sharing the same context in one inference, has emerged as a promising solution to reduce inference costs. However, our study reveals a significant security vulnerability in batch prompting: malicious users can inject attack instructions into a batch, leading to unwanted interference across all queri

Daniel Herbst, Stefanie Jegelka

Graph limit models, like graphons for limits of dense graphs, have recently been used to study size transferability of graph neural networks (GNNs). While most literature focuses on message passing GNNs (MPNNs), in this work we attend to the more powerful higher-order GNNs. First, we extend the $k$-WL test for graphons (B\"oker, 2023) to the graphon-signal s

Chenxiao Yang, Nathan Srebro, David McAllester, Zhiyuan Li

While state-of-the-art LLMs have demonstrated great promise of using long Chains-of-Thought (CoT) to boost reasoning, scaling it up to more challenging problems at test-time is fundamentally limited by suboptimal memory usage -- intermediate computations accumulate indefinitely in context even when no longer needed for future thoughts. We introduce PENCIL, w

Michael Anastos, Joshua Erde, Mihyun Kang, Vincent Pfenninger

There has been much interest in the distribution of the circumference, the length of the longest cycle, of a random graph $G(n,p)$ in the sparse regime, when $p = \Theta\left(\frac{1}{n}\right)$. Recently, the first author and Frieze established a scaling limit for the circumference in this regime, along the way establishing an alternative 'structural' appro

G. Campi, A. Alimenti, G. Logvenov, G. A. Smith

Artificial high-Tc superlattices (AHTS) composed of quantum building blocks with tunable superconducting critical temperature have been synthesized by engineering their nanoscale geometry using the Bianconi-Perali-Valletta (BPV) two gaps superconductivity theory. These quantum heterostructures consist of quantum wells made of superconducting, modulation-dope

Alejandro Sepulveda-Peñaloza, Isabelle S. Beaudry

Respondent-driven sampling (RDS) is a sampling scheme used in socially connected human populations lacking a sampling frame. One of the first steps to make design-based inferences from RDS data is to estimate the sampling probabilities. A classical approach for such estimation assumes that a first-order Markov chain over a fully connected and undirected netw

Hojjat Azimi Asrari, Megan A. K. Peters

Brains construct not only "first-order" representations of the environment but also "higher-order" representations about those representations -- including higher-order uncertainty estimates that guide learning and adaptive behavior. Higher-order expectations about representational uncertainty -- i.e., learned through experience -- may play a key role in gui