November 2025 arXiv papers — page 166

Nsibiet E. Udo, Praise Adeyemo

This paper investigates a novel connection between reductions of companion matrices associated with a symmetric family of certain binomial ideals in the coordinate ring of affine n-space and permutation matrices. Specifically, for fixed monomial orders, we observe that the reduced companion matrices yield permutation matrices satisfying group-theoretic relat

Rui-Chong Hu, Bing Zhang

The formation channels of magnetars remain an open question. Although core collapse supernovae of isolated massive stars are important, binary interactions -- such as tidal interaction, common envelope evolution, and stellar mergers -- may also play a significant role in making magnetars. Understanding the relative contributions of these channels is crucial

Mario Bukal

This paper investigates the asymptotic behavior of strong solutions to a family of nonlinear fourth-order evolution equations on the real line, with particular focus on the thin-film equation $\partial_tu = -(uu_{xxx})_x$. The method builds on the framework introduced by Carrillo and Toscani (Nonlinearity 27 (2014), 3159) for second-order nonlinear diffusion

Mostafijur Rahman Akhond, Saikat Chakraborty, Gias Uddin

A loop invariant is a property of a loop that remains true before and after each execution of the loop. The identification of loop invariants is a critical step to support automated program safety assessment. Recent advancements in Large Language Models (LLMs) have demonstrated potential in diverse software engineering (SE) and formal verification tasks. How

Keerati Keeratikarn, Christoph Ortner, Jarvist Moore Frost

Calculating intermolecular charge transfer integrals in organic semiconductors requires substantial computer resource for each individual calculation. We might alternatively construct a machine learning model for transfer integrals, which model the full six-degrees of freedom for the relative position of dimer pairs, trained on representative calculations fo

Adrian F. Chlebowski, Lukasz A. Sterczewski, Jaroslaw Sotor

Semiconductor lasers merge coherent light emission with photodetection and, owing to third-order nonlinearities in their active region, function as sensitive room temperature two-photon absorption (TPA) detectors. Here, we leverage these capabilities offered by a commercially available InGaAsP semiconductor laser diode with an integrated InGaAs monitor photo

Tobias Rueckert, Raphaela Maerkl, David Rauber, Leonard Klausmann

Robotic- and computer-assisted minimally invasive surgery (RAMIS) is increasingly relying on computer vision methods for reliable instrument recognition and surgical workflow understanding. Developing such systems often requires large, well-annotated datasets, but existing resources often address isolated tasks, neglect temporal dependencies, or lack multi-c

Lydia Bieri

We show that the stochastic background of gravitational wave memory of growing type leads to a fractional Brownian motion increasing at the order of $t^{H}$ for large $t$ where $\frac{1}{2} < H <1$. This beats the scaling law of Brownian motion. In this article we investigate sources of gravitational waves in the early universe as well as in astrophysical se

Hassan Nagib, Lorenzo Lazzarini, Gabriele Bellani, Alessandro Talamelli

The CICLoPE facility at the University of Bologna in Forli, Italy, is a unique facility that provides fully developed pipe flow up to Reynolds numbers of about $Re_\tau$ of 50,000 with exceptional spatial resolution and stable operating conditions. Measurements obtained over the last two years, on the centerline of the pipe in the fully developed test sectio

Nagaraj Vernekar, Lorenzo Spina, Sara Lucatello, Carmelo Arcidiacono

Aims. This paper introduces LRPayne, a novel algorithm designed for the efficient determination of stellar parameters and chemical abundances from low-resolution optical spectra, with a primary focus on data from large-scale galactic surveys such as WEAVE. Methods. LRPayne employs a model-driven approach, utilising a fully connected artificial neural network

Ruiqing Cao, Abhishek Bhatia

As generative artificial intelligence (GenAI) automates coding tasks and expands access to technical resources, this paper examines how GenAI-enabled coding automation, colloquially known as "vibecoding," affects digital entrepreneurial entry and venture performance. We exploit ex-ante variation in ventures' exposure to vibecoding based on the product charac

Shihao Zhang, Zudi Lu, Chao Zheng

In this paper, we propose Random Forests by Random Weights (RF-RW), a theoretically grounded and practically effective alternative RF modelling for nonlinear time series data, where existing RF-based approaches struggle to adequately capture temporal dependence. RF-RW reconciles the strengths of classic RF with the temporal dependence inherent in time series

Konstantinos Maronikolakis

This paper complements the work done on simultaneous approximation results in classical Banach spaces, by focusing on approximation by finite Blaschke products. We prove the existence of a finite Blaschke product that approximates a prescribed holomorphic function bounded by 1 locally uniformly on the unit disc, and simultaneously approximates a prescribed u

Chaegeun Song, Zhong Zheng, Bing Li, Lingzhou Xue

Categorical predictors are omnipresent in everyday regression practice: in fact, most regression data involve some categorical predictors, and this tendency is increasing in modern applications with more complex structures and larger data sizes. However, including too many categories in a regression model would seriously hamper accuracy, as the information i

Ngartelbaye Guerngar, Erkan Nane

In this article, we study the space-time SPDE $$ \partial_t^\beta u=-(-\Delta)^{\alpha/2} u+I_t^{1-\beta}[b(u)+\sigma(u)\dot{W}],$$ where $u=u(t,x)$ is defined for $(t,x)\in\mathbb{R}_+\times \mathbb{R},$ $\beta\in(0,1), \alpha\in(0,2)$ and $\dot{W}$ denotes a space-time white noise. It has long been conjectured that this equation has a unique solution with

Sirus Shahini, Robert Ricci

Network steganography and covert communication channels have been studied extensively in the past. However, prior works offer minimal practical use for their proposed techniques and are limited to specific use cases and network protocols. In this paper, we show that covert channels in networking have a much greater potential for practical secret communicatio

Bojan Nikolic, Marko Djukanovic

This paper addresses two open questions posed in [27] regarding the balanced domination number in graphs. We show that three new classes of graphs, those of convex polytopes A_n, D_n, and Rn&#39;&#39;, are d-balanced. Further, we provide a characterization of d-balancedness for rooted trees with two levels of descendants and prove that each full binary tree

Ghazal Farhani, Taufiq Rahman, Kieran Humphries

Accurate EV power estimation underpins range prediction and energy management, yet practitioners need both point accuracy and trustworthy uncertainty. We propose an anchored-ensemble Long Short-Term Memory (LSTM) with a Student-t likelihood that jointly captures epistemic (model) and aleatoric (data) uncertainty. Anchoring imposes a Gaussian weight prior (MA

Jonathan S. Gómez, Hugo Messias, Neil M. Nagar, Gustavo Orellana-González

Hyperluminous infrared galaxies (HyLIRGs; SFRs up to about 1000 Msun yr-1), though rare, provide key constraints on galaxy evolution. H-ATLAS J084933.4+021443, a z = 2.41 binary HyLIRG (galaxies W and T) with two additional luminous companions (C and M), offers an ideal laboratory for studying star formation during "cosmic noon". We use ALMA to obtain resolv

Ikram Hamoudy, Julian Westerweck, Ofek Birnholtz

We perform a long-duration Bayesian analysis of gravitational-wave data to constrain the near-horizon geometry of black holes formed in binary mergers. Deviations from the Kerr geometry are parameterized by replacing the horizon's absorbing boundary with a reflective surface at a fractional distance epsilon. This modification produces long-lived monochromati

Sushant Mehta

Machine learning systems exhibit diverse failure modes: unfairness toward protected groups, brittleness to spurious correlations, poor performance on minority sub-populations, which are typically studied in isolation by distinct research communities. We propose a unifying theoretical framework that characterizes when different bias mechanisms produce quantit

Tinatin Baratashvili, Haopeng Wang, Daria Sorokina, Andrea Lani

Coronal modelling is crucial for a better understanding of solar and helio-physics. Due to the strong brightness of the Sun and the lack of white light observations of the solar atmosphere and low corona (1-1.5R$_\odot$), total solar eclipses have become a standard approach for validating the coronal models. In this study, we validate the COCONUT coronal mod

J. D. Koenig, G. Barbieri, F. Fani Sani, C. A. Potts

The ability to efficiently simulate a variety of interacting quantum systems on a single device is an overarching goal for digital and analog quantum simulators. In circuit quantum electrodynamical systems, strongly nonlinear superconducting oscillators are typically realized using transmon qubits, featuring a wide range of tunable couplings that are mainly

Klaus Stephan, Maximilian Eibl, Albrecht Kurze

What information can we get using inflatables as sensors? While using inflatables as actuators for various interactions has been widely adopted in the HCI community, using the sensing capabilities of inflatables is much less common. Almost all inflatable setups include air pressure sensors as part of the automation when pressurizing or deflating, but the ful

Oluwadara Kalejaiye, Luel Hagos Beyene, David Ifeoluwa Adelani, Mmekut-Mfon Gabriel Edet

Nigeria is the most populous country in Africa with a population of more than 200 million people. More than 500 languages are spoken in Nigeria and it is one of the most linguistically diverse countries in the world. Despite this, natural language processing (NLP) research has mostly focused on the following four languages: Hausa, Igbo, Nigerian-Pidgin, and

Xiaonan Luo, Yue Huang, Ping He, Xiangliang Zhang

High-quality Question-Answer (QA) datasets are foundational for reliable Large Language Model (LLM) evaluation, yet even expert-crafted datasets exhibit persistent gaps in domain coverage, misaligned difficulty distributions, and factual inconsistencies. The recent surge in generative model-powered datasets has compounded these quality challenges. In this wo

Hongnan Ma, Yiwei Shi, Guanxiong Sun, Mengyue Yang

In decision-making processes, stakeholders often rely on counterfactual explanations, which provide suggestions about what should be changed in the queried instance to alter the outcome of an AI system. However, generating these explanations for multivariate time series presents challenges due to their complex, multi-dimensional nature. Traditional Nearest U

Qinghua Ma, Seyyedali Hosseinalipour, Ming Shi, Jan Drgona

This paper aims to proactively diagnose and manage the voltage collapse risks, i.e., the risk of bus voltages violating the safe operational bounds, which can be caused by extreme events and contingencies. We jointly answer two resilience-related research questions: (Q1) Survivability: Upon having an extreme event/contingency, will the system remain feasible

Navin Khoshnan, Claudia K Petritsch, Bryce-Allen Bagley

The identification of high-dimensional nonlinear dynamical systems via the Volterra series has significant potential, but has been severely hindered by the curse of dimensionality. Tensor Network (TN) methods such as the Modified Alternating Linear Scheme (MVMALS) have been a breakthrough for the field, offering a tractable approach by exploiting the low-ran

Siiri Kivimäki

A wide Aronszajn tree is a tree of size $\aleph_1$ with no uncountable branches. Assuming the consistency of the existence of a weakly compact cardinal, we show the consistency of the existence of a wide Aronszajn tree that is \textit{universal} in the sense that it contains an isomorphic copy of every wide Aronszajn tree.

Philip Trippenbach, Isabella Scala, Jai Bhambra, Rowan Emslie

Effective governance of artificial intelligence (AI) requires public engagement, yet communication strategies centered on existential risk have not produced sustained mobilization. In this paper, we examine the psychological and opinion barriers that limit engagement with extinction narratives, such as mortality avoidance, exponential growth bias, and the ab

Haihui Gao, Alessandro Bosso, Lei Wang, David Saussié

In this paper, we address the problem of data-driven stabilization of continuous-time multi-input multi-output (MIMO) linear time-invariant systems using the input-output data collected from an experiment. Building on recent results for data-driven output-feedback control based on non-minimal realizations, we propose an approach that can be applied to a broa

Selma Grebovic, Abdulah Aksamovic, Bozidar Filipovic-Grcic, Samim Konjicija

The increasing integration of solar power plants into transmission grids has raised concerns about their vulnerability to disturbances, particularly lightning strokes. Solar energy, while offering significant environmental and economic benefits, faces challenges when connected to transmission lines that are prone to lightning discharges. This paper investiga

Jan Ondras, Marek Šuppa

Mathematical reasoning requires abstracting symbolic rules from visual patterns -- inferring the infinite from the finite. We investigate whether multimodal AI systems possess this capability through FractalBench, a benchmark evaluating fractal program synthesis from images. Fractals provide ideal test cases: Iterated Function Systems with only a few contrac

Tinatin Baratashvili, Emma Davies, Eva Weiler, Brigitte Schmieder

Coronal mass ejections (CMEs) are the main drivers of disturbances in the solar heliosphere because they propagate and interact with the magnetic field of the solar wind. It is crucial to investigate the evolution of CMEs and their deformation for understanding the interaction between the solar wind and CMEs. We quantify the effect of the dynamic solar wind

Franjo Vukovic, Bozidar Filipovic-Grcic, Nina Stipetic, Bojan Franc

At a wind farm in Croatia, lightning activity is monitored across the entire site using a lightning location system, and on a single wind turbine equipped with a Rogowski-coil-based current measurement system and a high-speed camera, all independently GPS-synchronized. In addition to recording lightning flash currents on the monitored turbine, the system is

Nina Stipetic, Bozidar Filipovic-Grcic, Igor Ziger, Silvio Jancin

Unearthed neutral is commonly used in networks which require continuous power supply. This is common in MV circuits of industrial and power plants. Unearthed networks can remain in operation during an earth-fault, but fast determination of the faulty line is key for prevention of further fault escalation. Signal injection is one of the fault location methods

Sanaz Saki Norouzi, Mohammad Masjedi, Pascal Hitzler

Artificial Neural Networks, the building blocks of AI, were inspired by the human brain's network of neurons. Over the years, these networks have evolved to replicate the complex capabilities of the brain, allowing them to handle tasks such as image and language processing. In the realm of Large Language Models, there has been a keen interest in making the l

Salam Afiouni, Jakub Cerny, Chun Kai Ling, Christian Kroer

We study a class of two-player zero-sum Colonel Blotto games in which, after allocating soldiers across battlefields, players engage in (possibly distinct) normal-form games on each battlefield. Per-battlefield payoffs are parameterized by the soldier allocations. This generalizes the classical Blotto setting, where outcomes depend only on relative soldier a

Su Gao, Feng Li, André Nies, Gianluca Paolini

We prove that the epimorphism relation is a complete analytic quasi-order on the space of countable groups. In the process, we obtain the result of independent interest that the epimorphism relation on pointed reflexive graphs is complete.

Cormac O'Neill, Jasmine Terrones, H. Harry Asada

Controlling robots that dynamically engage in contact with their environment is a pressing challenge. Whether a legged robot making-and-breaking contact with a floor, or a manipulator grasping objects, contact is everywhere. Unfortunately, the switching of dynamics at contact boundaries makes control difficult. Predictive controllers face non-convex optimiza

Vamsi Addanki, Julien Dallot, Leon Kellerhals, Maciej Pacut

The problem of online buffer sharing is expressed as follows. A switch with $n$ output ports receives a stream of incoming packets. When an incoming packet is accepted by the switch, it is stored in a shared buffer of capacity $B$ common to all packets and awaits its transmission through its corresponding output port determined by its destination. Each outpu

Alexander Baumgartner

We consider a family of operators connected with the geodesic flow on the modular surface. We show certain spectral information is retained after expanding their domain to the space of $\alpha$-H\"older continuous functions on the unit interval. For example, the point spectra associated with the Maass cusp forms and non-trivial zeroes of the Riemann zeta fun

Haonan Shi, Guoli Wang, Tu Ouyang, An Wang

Small language models (SLMs) are increasingly deployed on edge devices, making their safety alignment crucial yet challenging. Current shallow alignment methods that rely on direct refusal of malicious queries fail to provide robust protection, particularly against adversarial jailbreaks. While deliberative safety reasoning alignment offers deeper alignment

Dharmateja Priyadarshi Uddandarao, Ravi Kiran Vadlamani

This study presents a novel framework for counterfactual user behavior forecasting that combines structural causal models with transformer-based generative artificial intelligence. To model fictitious situations, the method creates causal graphs that map the connections between user interactions, adoption metrics, and product features. The framework generate

Jean Lévine, Jaume Franch

It is widely recognized that no tractable necessary and sufficient conditions exist for determining whether a system is, in general, differentially flat. However, specific cases do provide such conditions. For instance, driftless systems with two inputs have known necessary and sufficient conditions. For driftless systems with three or more inputs, the avail

Marx M. M. Freitas, Giovanni Interdonato, Stefano Buzzi

In the downlink of CF-mMIMO systems, spectral efficiency gains critically rely on joint coherent transmission, as all APs must align their transmitted signals in phase at the UE. Achieving such phase alignment is challenging, as it requires tight synchronization among geographically distributed APs. In this paper, we address this issue by introducing a DSTBC

Alexander S. Bratus, Sergei V. Drozhzhin, Artem S. Novozhilov

A novel mathematical framework is proposed to describe the ecological and evolutionary consequences of consumer--resource interactions. Both the consumer and resource are assumed to consist of several (sub)species, which interact between themselves in addition to incorporating the deleterious effects of the consumer on the resource. Separating the ecological

Grace E. Calkins, Jay W. McMahon, Jackson Kulik

An optimal rank-1 approximation of state transition tensors was developed as an efficient alternative to state transition tensors for nonlinear uncertainty quantification. While previous directional state transition tensors used the dominant right singular subspace of the state transition matrix to construct a reduced-dimension representation of the state tr

Harrison Horn, Allona Vazan, Stella Chariton, Vitali Prakapenka

Close-in transiting sub-Neptunes are abundant in our galaxy \cite{fulton2017california}. Planetary interior models based on their observed radius-mass relationship suggest that sub-Neptunes contain a discernible amount of either hydrogen (dry planets) or water (wet planets) blanketing a core composed of rocks and metal \cite{bean2021nature}. Water-rich sub-N

S. Azadi, A. Principi, T. D. Kühne, M. S. Bahramy

We determine the resonating-valence-bond (RVB) state in graphene using real-space quantum Monte Carlo with correlated variational wave functions. Variational and diffusion quantum Monte Carlo (DMC) calculations with Jastrow-Slater-determinant and Jastrow-antisymmetrized-geminal-power ansatze are employed to evaluate the RVB pairing energy. Using a rectangula

Jannik Matuschke

We study the following fundamental network optimization problem known as Maximum Robust Flow (MRF): A planner determines a flow on $s$-$t$-paths in a given capacitated network. Then, an adversary removes $k$ arcs from the network, interrupting all flow on paths containing a removed arc. The planner's goal is to maximize the value of the surviving flow, antic

Mahsa Derakhshan, Mohammad Roghani, Mohammad Saneian, Tao Yu

In this paper, we study Ranking, a well-known randomized greedy matching algorithm, for general graphs. The algorithm was originally introduced by Karp, Vazirani, and Vazirani [STOC 1990] for the online bipartite matching problem with one-sided vertex arrivals, where it achieves a tight approximation ratio of 1 - 1/e. It was later extended to general graphs

A. M. Mora, A. I. Esparcia-Alcázar, M. S. Cruz

Volume containing the Late-Breaking Abstracts submitted to the Evo* 2025 Conference, held in Trieste (Italy) from April 23rd to 25th. These extended abstracts showcase ongoing research and preliminary findings exploring the application of various Bioinspired Methods (primarily Evolutionary Computation) to a range of problems, many of which address real-world

Malaspina R., Pierini L., Shekhovtsova O., Pacetti S

We propose a model for the QCD running coupling constant based on the Analytical Inverse QCD Coupling Constant concept with an additional regularization in the low momentum region. Analyticity in the $q^2$-complex plane, where $q$ is the 4-momentum transfer, is imposed by methods of the Analytic Perturbation Theory. The model incorporates a peculiar low-mome

Vasileios Aravantinos-Sotiropoulos

We give an elementary construction of the exact completion of a weakly lex category for categories enriched in the cartesian closed category $\mathsf{Pos}$ of partially ordered sets. Paralleling the ordinary case, we characterize categories which arise as such completions in terms of projective objects. We then apply the results to categories of Eilenberg-Mo

Antu Saha, Mehedi Sun, Oscar Chaparro

During issue resolution, software developers rely on issue reports to discuss solutions for defects, feature requests, and other changes. These discussions contain proposed solutions--from design changes to code implementations--as well as their evaluations. Locating solution-related content is essential for investigating reopened issues, addressing regressi

Haotian Xia, Haonan Ge, Junbo Zou, Hyun Woo Choi

Deeply understanding sports requires an intricate blend of fine-grained visual perception and rule-based reasoning - a challenge that pushes the limits of current multimodal models. To succeed, models must master three critical capabilities: perceiving nuanced visual details, applying abstract sport rule knowledge, and grounding that knowledge in specific vi

Jonathan Ansari, Sebastian Fuchs

We introduce a new dependence order, termed the conditional convex order, whose minimal and maximal elements characterize independence and perfect dependence. Moreover, it characterizes conditional independence, satisfies information monotonicity, and exhibits several invariance properties. Consequently, it is an ordering for the strength of functional depen

Quang Phuoc Nguyen, David Anugraha, Felix Gaschi, Jun Bin Cheng

Realignment is a promising strategy to improve cross-lingual transfer in multilingual language models. However, empirical results are mixed and often unreliable, particularly for typologically distant or low-resource languages (LRLs) compared to English. Moreover, word realignment tools often rely on high-quality parallel data, which can be scarce or noisy f

Keke Long, Jiacheng Guo, Tianyun Zhang, Hongkai Yu

Vision Language Models (VLMs) are increasingly used in autonomous driving to help understand traffic scenes, but they sometimes produce hallucinations, which are false details not grounded in the visual input. Detecting and mitigating hallucinations is challenging when ground-truth references are unavailable and model internals are inaccessible. This paper p

Peter Blohm, Patrick Indri, Thomas Gärtner, Sagar Malhotra

We propose and investigate probabilistic guarantees for the adversarial robustness of classification algorithms. While traditional formal verification approaches for robustness are intractable and sampling-based approaches do not provide formal guarantees, our approach is able to efficiently certify a probabilistic relaxation of robustness. The key idea is t

Tiansheng Wen, Yifei Wang, Aosong Feng, Long Ma

Mixture-of-Experts (MoE) architectures scale large language models (LLMs) by activating only a subset of experts per token, but the standard TopK routing assigns the same fixed number of experts to all tokens, ignoring their varying complexity. Prior adaptive routing methods introduce additional modules and hyperparameters, often requiring costly retraining

Sivaram Krishnan, Jinho Choi, Jihong Park

A wide variety of real-world data, such as sea measurements, e.g., temperatures collected by distributed sensors and multiple unmanned aerial vehicles (UAV) trajectories, can be naturally represented as graphs, often exhibiting non-Euclidean structures. These graph representations may evolve over time, forming time-varying graphs. Effectively modeling and an

Atharva Thakur, Shruti Dhumal

Sepsis is a life-threatening condition that requires rapid detection and treatment to prevent progression to severe sepsis, septic shock, or multi-organ failure. Despite advances in medical technology, it remains a major challenge for clinicians. While recent machine learning models have shown promise in predicting sepsis onset, their black-box nature limits

Maurice de Gosson

In earlier work, we introduced quantum blobs as minimum-uncertainty symplectic ellipsoids in phase space. These objects may be viewed as geometric monads in the Leibnizian sense, representing the elementary units of phase-space structure consistent with the uncertainty principle. We establish a one-to-one correspondence between such monads and generalized co

Yule Chen, Yufan Ren, Sabine Süsstrunk

Complex visual narratives, such as comics, present a significant challenge to Vision-Language Models (VLMs). Despite excelling on natural images, VLMs often struggle with stylized line art, onomatopoeia, and densely packed multi-panel layouts. To address this gap, we introduce AI4VA-FG, the first fine-grained and comprehensive benchmark for VLM-based comic u

Abhay Jindal, Igor Klep, Scott McCullough

We establish operator-valued versions of the earlier foundational factorization results for noncommutative polynomials due to Helton (Ann.~Math., 2002) and one of the authors (Linear Alg.~Appl., 2001). Specifically, we show that every positive operator-valued noncommutative polynomial $p$ admits a single-square factorization $p=r^{*}r$. An analogous statemen

Andrei Arhire, Matei Chiriac, Radu Timofte

Twin-width is a recently formulated graph and matrix invariant that intuitively quantifies how far a graph is from having the structural simplicity of a co-graph. Since its introduction in 2020, twin-width has received increasing attention and has driven research leading to notable advances in algorithmic fields, including graph theory and combinatorics. The

Duaa Abdullah, Jasem Hamoud

In this paper, we investigate the combinatorial and density properties of infinite words generated by Fibonacci-type morphisms, focusing on their subword structure, palindrome density, and extremal statistical behaviors. Using the morphism $0 \to 01$, $1 \to 0$, we define a derived ternary word $\mathbb{Y}$ and establish new results relating its density comp

Cinzia Bisi, Paolo Cascini, Luca Tasin

We study the intersection form $F_X$ on the second cohomology group $H^2(X, \mathbb{Z})$ of a compact K\"ahler manifold $X$ of dimension $n$. Although the structure of $F_X$ is relatively well understood in dimensions two and three, much less is known for $n \geq 4$. We investigate the fundamental properties of $F_X$ in higher dimensions and discuss several

Termeh Taheri, Yinghao Ma, Emmanouil Benetos

Large language models (LLMs) have advanced in text and vision, but their reasoning on audio remains limited. Most existing methods rely on dense audio embeddings, which are difficult to interpret and often fail on structured reasoning tasks. Caption-based approaches, introduced in recent benchmarks such as MMAU, improve performance by translating audio into

Om Prakash Bhardwaj, Trung Chau, Omkar Javadekar

Numerical semigroups with multiplicity $e$, width $e-1$, and embedding dimension $e-2$ are of the form $$S(e,m,n) = \langle \{e, e+1, \ldots, 2e-1\} \setminus \{e+m, e+n\} \rangle,$$ for some $1 \leq m < n \leq e-2$. Inspired by the work of Sally, Herzog and Stamate studied the special case $S(e,2,3)$, which they called the ``Sally numerical semigroups''. Re

Benjamin Gregg, Daniela Calzetti, Angela Adamo, Alex Pedrini

We use JWST/NIRCam and MIRI imaging acquired by the Feedback in Emerging extrAgalactic Star clusTers (FEAST) program along with archival HST imaging to map ionized gas (Pa$\alpha$, Br$\alpha$, and H$\alpha$) and Polycyclic Aromatic Hydrocarbon (PAH) emission (3.3 and 7.7 $\mu$m) across a sample of four nearby galaxies (NGC 5194, 5236, 628, and 4449). These m

Alice Requier, Andrea Plati, Emmanuelle Rio, Anniina Salonen

External driving leads to the emergence of unique phenomena and properties in soft matter systems. We show that driving quasi-2D foams by mechanical vibration results in significant bubble coalescence, which is enhanced by the continuous phase yield stress. The competition between coarsening and coalescence can be modulated through vibration amplitude and fo

Qianxi He, Qingyu Ren, Shanzhe Lei, Xuhong Wang

Recent advancements in large language models (LLMs) have shifted the post-training paradigm from traditional instruction tuning and human preference alignment toward reinforcement learning (RL) focused on reasoning capabilities. However, numerous technical reports indicate that purely rule-based reward RL frequently results in poor-quality reasoning chains o

Muhammad Shahnawaz, Adeel Safder

Supply chain disruptions and volatile demand pose significant challenges to the UK automotive industry, which relies heavily on Just-In-Time (JIT) manufacturing. While qualitative studies highlight the potential of integrating Artificial Intelligence (AI) with traditional optimization, a formal, quantitative demonstration of this synergy is lacking. This pap

Murat Uzundag, Mukremin Kilic, Francisco C. De Gerónimo, Alejandro H. Córsico

2MASS J05513444+4135297 (herafter J0551+4135) is the only pulsating DAQ white dwarf known with a carbon and hydrogen atmosphere. Its unusual atmospheric composition and kinematics indicate a white dwarf merger origin. We present time-series photometry of J0551+4135 obtained using the Apache Point Observatory 3.5m, Gemini North 8m, and Gran Telescopio Canaria

Nikolay Yudin, Ekaterina Grishina, Andrey Veprikov, Alexandr Beznosikov

Recently, optimizers that explicitly treat weights as matrices, rather than flattened vectors, have demonstrated their effectiveness. This perspective naturally leads to structured approximations of the Fisher matrix as preconditioners, where the matrix view induces a Kronecker-factorized form that enables memory-efficient representation. However, constructi

Mulan Wu, Mengyu Xu, Dongyun Kim

Small sample sizes in clinical studies arises from factors such as reduced costs, limited subject availability, and the rarity of studied conditions. This creates challenges for accurately calculating confidence intervals (CIs) using the normal distribution approximation. In this paper, we employ a quadratic-form based statistic, from which we derive more ac

Kyuho Lee, Euntae Kim, Jinwoo Choi, Buru Chang

Video large language models (Video LLMs) have recently achieved strong performance on tasks such as captioning, summarization, and question answering. Many models and training methods explicitly encourage continuity across events to enhance narrative coherence. While this improves fluency, it also introduces an inductive bias that prioritizes storyline consi

Matias D. Cattaneo, Rocio Titiunik, Ruiqi Rae Yu

The boundary discontinuity (BD) design is a non-experimental method for identifying causal effects that exploits a thresholding rule based on a bivariate score and a boundary curve. This widely used method generalizes the univariate regression discontinuity design but introduces unique challenges arising from its multidimensional nature. We synthesize over 8

Janosch Fuchs, Rin Saito, Tatsuhiro Suga, Takahiro Suzuki

In the \textsc{Coloring Reconfiguration} problem, we are given two proper $k$-colorings of a graph and asked to decide whether one can be transformed into the other by repeatedly applying a specified recoloring rule, while maintaining a proper coloring throughout. For this problem, two recoloring rules have been widely studied: \emph{single-vertex recoloring

Adele Olof-Ors, Martin Smit

In this paper, we argue that anthropomorphized technology, designed to simulate emotional realism, are not neutral tools but cognitive infrastructures that manipulate user trust and behaviour. This reinforces the logic of surveillance capitalism, an under-regulated economic system that profits from behavioural manipulation and monitoring. Drawing on Nicholas

Jingtao Tang, Hang Ma

We study GCS-TSP, a new variant of the Traveling Salesman Problem (TSP) defined over a Graph of Convex Sets (GCS) -- a powerful representation for trajectory planning that decomposes the configuration space into convex regions connected by a sparse graph. In this setting, edge costs are not fixed but depend on the specific trajectory selected through each co

Mingde "Harry" Zhao

Existing Reinforcement Learning (RL) systems encounter significant challenges when applied to real-world scenarios, primarily due to poor generalization across environments that differ from their training conditions. This thesis explores the direction of enhancing agents' zero-shot systematic generalization abilities by granting RL agents reasoning behaviors

Johnathon Taylor

We construct the universal realized limit sketch associated to a given limit sketch. The construction uses factorization systems to organize the classical argument of [2], yielding a streamlined and conceptually unified formulation of the technical steps. This provides a structured framework for understanding realizations of limit sketches in terms of factor

Dan Zhang

This project proposes an attention-aware LLM that integrates EEG and eye tracking to monitor and measure user attention dynamically. To realize this, the project will integrate real-time EEG and eye-tracking data into an LLM-based interactive system and classify the user's attention state on the fly. The system can identify five attention states: High Attent

Soumya Pratap Tripathy, Sayan Saha, Saurabh Kumar Gupta, Pallavee Das

Hydrothermal synthesis offers an economical and scalable way to produce nanodiamonds under relatively mild, low-pressure and low-temperature conditions. However,its sustainability and the detailed mechanisms behind diamond formation in such environments are still not fully understood. In this work, we designed ten hydrothermal synthesis protocols using diffe

Sai Karan Mukthapuram, Sandeep Kumar Kataria

The black hole shadow, a direct probe of the event horizon's gravitational influence, has been observationally confirmed by the Event Horizon Telescope (EHT). While theoretical models of shadows in vacuum are mature, real astrophysical black holes like M87* and Sgr A* are enveloped in plasma, which can alter photon trajectories through dispersion. Current un

Lingfan Bao, Tianhu Peng, Chengxu Zhou

This chapter addresses the critical challenge of simulation-to-reality (sim-to-real) transfer for deep reinforcement learning (DRL) in bipedal locomotion. After contextualizing the problem within various control architectures, we dissect the ``curse of simulation'' by analyzing the primary sources of sim-to-real gap: robot dynamics, contact modeling, state e

Zhangyong Liang, Huanhuan Gao, Ji Zhang

Data valuation quantifies data importance, but existing methods cannot ensure validity in a single training process. The neural dynamic data valuation (NDDV) method [3] addresses this limitation. Based on NDDV, we are the first to explore error estimation and convergence analysis in data valuation. Under Lipschitz and smoothness assumptions, we derive quadra

Lun Zhang, Chenxi Wang, Nan Lu, Zhen Zhang

The classical phase-field modeling approaches for multiphase problems represent each phase using a regularized characteristic function, which necessarily introduces a simplex constraint for the phase-field variables. Additionally, the consistency requirement for phase-field modeling brings difficulties to the construction of nonlinear potentials in the energ

Shay Moran, Elizaveta Nesterova

Consider the task of locating an unknown target point using approximate distance queries: in each round, a reconstructor selects a query point and receives a noisy version of its distance to the target. This problem arises naturally in various contexts ranging from localization in GPS and sensor networks to privacy-aware data access, and spans a wide variety

Fangnuo Wu, Mingkai Dong, Wenjun Cai, Jingsheng Yan

The \emph{Partial Cache-Coherence (PCC)} model maintains hardware cache coherence only within subsets of cores, enabling large-scale memory sharing with emerging memory interconnect technologies like Compute Express Link (CXL). However, PCC's relaxation of global cache coherence compromises the correctness of existing single-machine software. This paper focu

Huanbo Lyu, Miqing Li, Shiqiao Zhou, Daniel Herring

In offline data-driven multi-objective optimization (MOO), optimization is performed using surrogate models trained only on an offline dataset. These surrogate models contain inherent errors and uncertainty. This epistemic uncertainty can lead to incorrect dominance judgments, thereby misleading the search process. Existing methods mitigate this issue by inc

Chenpei Huang, Lingfeng Yao, Kyu In Lee, Lan Emily Zhang

Acoustic Environment Matching (AEM) is the task of transferring clean audio into a target acoustic environment, enabling engaging applications such as audio dubbing and auditory immersive virtual reality (VR). Recovering similar room impulse response (RIR) directly from reverberant speech offers more accessible and flexible AEM solution. However, this capabi

Dev Patel, Gabrielle Gervacio, Diekola Raimi, Kevin Zhu

Large Language Models require substantial computational resources for inference, posing deployment challenges. While dynamic pruning offers superior efficiency over static methods through adaptive circuit selection, it exacerbates alignment degradation by retaining only input-dependent safety-critical circuit preservation across diverse inputs. As a result,

Shaoxiang Wang, Shihong Zhang, Christen Millerdurai, Rüdiger Westermann

Despite recent advances in single-object front-facing inpainting using NeRF and 3D Gaussian Splatting (3DGS), inpainting in complex 360{\deg} scenes remains largely underexplored. This is primarily due to three key challenges: (i) identifying target objects in the 3D field of 360{\deg} environments, (ii) dealing with severe occlusions in multi-object scenes,

Huili Huang, Chengeng Liu, Danrong Zhang, Shail Patel

Rapid post-earthquake damage assessment is crucial for rescue and resource planning. Still, existing remote sensing methods depend on costly aerial images, expert labeling, and produce only binary damage maps for early-stage evaluation. Although ground-level images from social networks provide a valuable source to fill this gap, a large pixel-level annotated