April 2026 arXiv papers — page 135

Rupert Li, Elchanan Mossel

t-SNE has gained popularity as a dimension reduction technique, especially for visualizing data. It is well-known that all dimension reduction techniques may lose important features of the data. We provide a mathematical framework for understanding this loss for t-SNE by establishing a number of results in different scenarios showing how important features o

Wei Jiang, Wei Wang

Existing video coding for machines is often trained for a specific downstream task and model. As a result, the compressed representation becomes tightly coupled to the end task, making it difficult to scale across multiple tasks or adapt to model updates. We propose PAT-VCM, a plug-and-play auxiliary-token framework for video coding for machines. PAT-VCM kee

Mahyar Ghazanfari, Peng Wei

Autonomous drone delivery systems are rapidly advancing, but ensuring safe and reliable package drop-offs remains highly challenging in cluttered urban and suburban environments where accurately identifying suitable package drop zones is critical. Existing approaches typically rely on either geometry-based analysis or semantic segmentation alone, but these m

Harun Ur Rashid, Mingxin Li, Aleksandra Pachalieva, Georg Stadler

Accurate characterization of subsurface heterogeneity is challenging but essential for applications such as reservoir pressure management, geothermal energy extraction and CO$_2$, H$_2$, and wastewater injection operations. This challenge becomes especially acute in extreme pressure events, which are rarely observed but can strongly affect operational risk.

Rui Dong, Qingyue Wu, Danny Ding, Zheng Guo

Abstract semantics has proven to be instrumental for accelerating search-based program synthesis, by enabling the sound pruning of a set of incorrect programs (without enumerating them). One may expect faster synthesis with increasingly finer-grained abstract semantics. Unfortunately, to the best of our knowledge, this is not the case, yet. The reason is bec

Wei He, Wensheng Gan, Philip S. Yu

Fraud detection on graph data can be viewed as a demanding task that requires distinguishing between different types of nodes. Because graph neural networks (GNNs) are naturally suited for processing information encoded in graph form through their message-passing operations, methods based on GNN models have increasingly attracted attention in the fraud detec

Victor Kebande

Modern stream ciphers rely on strong diffusion and pseudorandom keystream generation (PKG) to resist cryptanalysis. While conventional evaluation methods such as statistical randomness tests and differential analysis provide important security assurances, they may fail to detect localized structural patterns embedded within cipher outputs. In this paper, a N

John E. Ortega, Rodolfo Zevallos, Fabricio Carraro

We present a unified pipeline for synthesizing high-quality Quechua and Spanish speech for the Peruvian Constitution using three state-of-the-art text-to-speech (TTS) architectures: XTTS v2, F5-TTS, and DiFlow-TTS. Our models are trained on independent Spanish and Quechua speech datasets with heterogeneous sizes and recording conditions, and leverage bilingu

Gabriel Afriat, Xiang Meng, Shibal Ibrahim, Hussein Hazimeh

Weight pruning is a common technique for compressing large neural networks. We focus on the challenging post-training one-shot setting, where a pre-trained model is compressed without any retraining. Existing one-shot pruning methods typically optimize a single objective, such as a layer-wise reconstruction loss or a second-order Taylor approximation of the

Mehak Dhaliwal, Shashwat Chaurasia, Yao Qin, Dezhi Hong

Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing to performance disparities across languages. We present a systematic, controlled study of the interplay between training language coverage, model scale, and task domain, based on 220 supervised fine-tuning runs o

John B. DeBrota, Christian List

Consciousness and quantum mechanics are among the most puzzling phenomena studied in the sciences. Some scholars suggest they are related, though others think this claim commits a "minimization of mystery" fallacy. The aim of this programmatic paper is to draw attention to a less widely discussed parallel between consciousness and quantum mechanics: both cha

Rishik Kondadadi, John E. Ortega

Clinical text classification requires choosing between specialized fine-tuned models (BERT variants) and general-purpose large language models (LLMs), yet neither dominates across all instances. We introduce Learning to Defer for clinical text (L2D-Clinical), a framework that learns when a BERT classifier should defer to an LLM based on uncertainty signals a

Anoop Mutneja, Kenneth S. Schweizer

The re-solidification of amorphous solids after mechanically driven yielding from a nonequilibrium state is a fundamental soft matter science problem of broad relevance in materials science, with implications for material strength, processing, and printing-based additive manufacturing. We present a microscopic statistical mechanical theory that predicts in a

Mohamed-Bachir Belaid

Earth Observation (EO) satellite scheduling (deciding which imaging tasks to perform and when) is a well-studied combinatorial optimization problem. Existing methods typically assume that the operational constraint model is fully specified in advance. In practice, however, constraints governing separation between observations, power budgets, and thermal limi

Moritz Zaiss, Amr Aly, Jonathan Endres, Tobias Dornstetter

Purpose: Novel MR sequence developments still today allow generation of new diagnostic tools or novel imaging biomarkers. Programming MRI pulse sequences, however, is time-consuming and requires deep expertise in sequence design, restrictions by hardware constraints and MRI physics; even small modifications often require substantial debugging and validation.

Xiaoyu K. Zhang, Mehdi Senoussi, Tom Verguts

Humans and artificial agents must often learn and switch between multiple tasks in dynamic environments. Success in such settings requires cognitive flexibility: the ability to retain prior knowledge (cognitive stability) while also transferring it to novel tasks (cognitive generalization). Cognitive flexibility research has largely focused on the role of mo

K. H. Pham, D. Mumma, C. M. Deibel, L. T. Baby

Observation of the 511-keV positron-annihilation line would be a powerful probe of classical novae, with the primary source of positrons likely from the $\beta^+$ decay of \textsuperscript{18}F. We have determined the properties of important resonances in $^{19}$Ne which govern the \textsuperscript{18}F($p,\alpha$)\textsuperscript{15}O reaction rate and the

Mohammad Saleh, Azadeh Tabatabaei

Reliable fall detection in elderly care requires monitoring systems that are not only accurate but also capable of producing stable, interpretable explanations of motion dynamics, a requirement that existing post hoc explainability methods rarely satisfy when applied to sequential biosignals. This study introduces a lightweight framework for skeleton-based f

Yann V. Bellec

Aerial object detection in UAV imagery presents unique challenges due to the high prevalence of tiny objects, adverse environmental conditions, and strict computational constraints. Standard YOLO-based detectors fail to address these jointly: their minimum detection stride of 8 pixels renders sub-32px objects nearly undetectable, their CIoU loss produces zer

Muhammad Ovais Ahmad

Generative Artificial Intelligence (GenAI) tools (e.g., ChatGPT, Calude) have rapidly become integral to software development. These tools are especially attractive to students, as they can reduce cognitive load. However, their adoption also introduces a socio-cognitive risk: the accumulation of Comprehension Debt (CD). CD refers to the growing gap between w

Joseph Moore

The IEC-61850 GOOSE protocol underpins time-critical communication in modern digital substations but lacks native security mechanisms, leaving it vulnerable to replay, masquerade, and data injection attacks. Intrusion detection in this setting is challenging due to strict latency constraints (sub-4ms) and limited availability of labeled attack data. This pap

Dikshant Kukreja, Kshitij Sah, Gautam Gupta, Avinash Anand

Larger language models become simultaneously better and worse at handling contextual information -- better at ignoring false claims, worse at ignoring irrelevant tokens. We formalize this apparent paradox through the first scaling laws for contextual entrainment, the tendency of models to favor tokens that appeared in context regardless of relevance. Analyzi

Lixing Zhang, Liyan Xie, Ruizhi Zhang

Sequential change-point detection seeks to rapidly identify distributional changes in streaming data while controlling false alarms. Existing multi-stream detection methods typically rely on non-private access to raw observations or intermediate statistics, limiting their usage in privacy-sensitive settings. We study sequential change-point detection for mul

Vladimir Baikalov, Iskander Bagautdinov, Sergey Muravyov

Generative retrieval with Semantic IDs (SIDs) assigns each item a discrete identifier and treats retrieval as a sequence generation problem rather than a nearest-neighbor search. While content-only SIDs are stable, they do not take into account user-item interaction patterns, so recent systems construct interaction-informed SIDs. However, as interaction patt

Chenyang Qiu, Mihitha Maithripala, Zongli Lin

This paper studies a stochastic algorithm for linearly constrained nonconvex optimization, where the objective function is smooth but only unbiased stochastic gradients with bounded variance are available. We propose a momentum-based augmented Lagrangian method that employs a Polyak-type gradient estimator and requires only one stochastic gradient evaluation

Anton Saenko, Pranshav Gajjar, Abiodun Ganiyu, Vijay K. Shah

Large Language Models (LLMs) are increasingly applied to complex telecommunications tasks, including 3GPP specification analysis and O-RAN network troubleshooting. However, a critical limitation remains: LLM-generated confidence scores are often biased and unreliable, frequently exhibiting systematic overconfidence. This lack of trustworthy self-assessment m

Mohammad Sahraei, Yasaman Hosseini, Mojtaba Mohammadi Najafabadi

Vector-like top partners with electric charge $+2/3$ are predicted in many extensions of the Standard Model and are actively searched for at the LHC through their electroweak decays $T\to Wb$, $Zt$, and $Ht$. More general scenarios, however, allow dipole interactions that induce radiative decays $T\to t\gamma$ and $T\to tg$. We reinterpret precision measurem

Shayan Nadeem, Walter Wilcox, Frank X. Lee

We present a lattice QCD calculation of the electric polarizability of the charged kaon using a four-point function approach, which is the Euclidean analog of low-energy Compton scattering. In the case of the charged kaon, the polarizability is separated into an elastic term, determined from the charge radius extracted via the kaon electromagnetic form facto

Bahey Tharwat, Giorgos Kordopatis-Zilos, Pavel Suma, Ian Reid

Multimodal Large Language Models (MLLMs) have demonstrated strong cross-modal reasoning capabilities, yet their potential for vision-only tasks remains underexplored. We investigate MLLMs as training-free similarity estimators for instance-level image-to-image retrieval. Our approach prompts the model with paired images and converts next-token probabilities

Kanak Saha, Roland Bacon, Anne Verhamme, Soumil Maulick

The end of the Cosmic Dark Age marked the onset of reionization, driven by extreme-UV photons from the first galaxies. Direct detection of such photons has remained challenging due to strong intergalactic attenuation. Here, we report the first direct detection of ionizing photons at rest-frame wavelengths $350\r{A}$, $392\r{A}$, and $485\r{A}$, using deep UV

Seung Hun Lee, Yuting Qian, Xi Dai, Bohm-Jung Yang

Compensated magnets with vanishing net magnetization can exhibit both pronounced spin splitting and unconventional band degeneracies. In altermagnets, such degeneracies are enforced by crystal and magnetic symmetries. In compensated ferrimagnets, however, they may arise even in the absence of the corresponding symmetry protection, raising a fundamental quest

Pan Zhao, Peter B. Gilbert, Oliver Dukes, Bo Zhang

Refined vaccine regimens containing variant-matched inserts are often authorized based on historical phase 3 efficacy trials together with immunobridging studies. Phase 3 trials are essential for establishing immune biomarkers that reliably predict disease risk or vaccine efficacy against clinical endpoints. Once such immune correlates are identified, update

Lucia Ameis, Niklas Hagemann, Kathrin Möllenhoff

Evaluating the influence of continuous covariates, like exposure time or dose, on a response variable is a pivotal objective in the assessment of a compound's effect, particularly when determining toxicity in pre-clinical research or pharmacokinetics in clinical trials. The determination of an alert, such as the ED50 value, at which a pre-specified threshold

Yilang Zhang, Abraham Jaeger Mountain, Bingcong Li, Georgios B. Giannakis

Meta-learning offers a principled framework leveraging \emph{task-invariant} priors from related tasks, with which \emph{task-specific} models can be fine-tuned on downstream tasks, even with limited data records. Gradient-based meta-learning (GBML) relies on gradient descent (GD) to adapt the prior to a new task. Albeit effective, these methods incur high c

Saket Maganti

In medical image segmentation, uncertainty estimates are often reported but rarely used to guide decisions. We study the missing step: how uncertainty maps are converted into actionable policies such as accepting, flagging, or deferring predictions. We formulate segmentation as a two-stage pipeline, estimation followed by decision, and show that optimizing u

Sriram Akella, Norihiro Iizuka

We study replica symmetry breaking (RSB) for multi-entropy in the random-tensor-network (RTN) domain-wall spin model. Our main result is that, within this framework, multi-entropy has a structural obstruction to RSB for any R\'enyi index $n$ and any multipartite number $\mathtt{q}$. This obstruction arises because the boundary permutations relevant to multi-

Karmanpartap Singh Sidhu, Junyi Fan, Maryam Pishgar

We utilize FinBERT, a domain-specific transformer model, to parse 6.5 million sentences from 16,428 S&P 500 quarterly earnings call transcripts (2015-2025) and demonstrate that post-earnings stock returns are not equally affected by all speakers in a conference call. Our section-weighted sentiment, with empirically derived speaker weights (Analyst 49%, CFO 3

Vladyslav Prytula

When a learning algorithm reshapes the data distribution it trains on, the long-run behavior depends on the joint evolution of the policy, the value estimate, and the data distribution. We study finite-state actor-critic mean dynamics on the enlarged phase space $(\theta,w,\mu)$, where $\theta$ is the actor parameter, $w$ is an auxiliary critic state, and $\

Vishal Pramanik, Maisha Maliha, Nathaniel D. Bastian, Sumit Kumar Jha

Attribution methods seek to explain language model predictions by quantifying the contribution of input tokens to generated outputs. However, most existing techniques are designed for encoder-based architectures and rely on linear approximations that fail to capture the causal and semantic complexities of autoregressive generation in decoder-only models. To

Jose Soto-Garcia, Natalia Chepiga

Arrays of Rydberg atoms provide a powerful platform for exploring constrained quantum dynamics and nonergodic many-body phenomena. While most work has focused on single-species systems, multispecies architectures offer additional interaction channels and enable new forms of dynamical constraints. We study the nonequilibrium dynamics of one-dimensional dual-s

Sanjar Khudoyberdiev, Arman Bekov

Neural models for TCR-pMHC binding prediction are susceptible to shortcut learning: they exploit spurious correlations in training data -- such as peptide length bias or V-gene co-occurrence -- rather than the physical binding interface. This renders predictions brittle under family-held-out and distance-aware evaluation, where such shortcuts do not transfer

Negin Musavi, Melkior Ornik

We study sequential decision-making in time-varying Markov decision processes (TVMDPs) under limited update rates, where the decision-maker observes the system and updates its model only intermittently. Such settings arise in applications with sensing, communication, or computational constraints that preclude continuous adaptation. Our goal is to understand

Arman Bekov, Timur Bekzhanov, Bekzat Sadykov

Predicting T-cell receptor (TCR)--peptide-MHC (pMHC) binding is central to vaccine design and T-cell therapy, yet deployed models frequently encounter epitopes unseen during training, causing silent overconfidence and unreliable prioritization. We address this by framing TCR--pMHC prediction as a \emph{selective prediction} problem: a calibrated model should

Vageesh Kumar Saxena

This research investigated how online criminal activities can be better understood and connected using data-driven machine learning methods. Many illegal activities, such as human trafficking and illicit trade, have moved to online platforms where offenders hide behind anonymous accounts and frequently change identities. This makes it difficult for authoriti

Ankit Lade, Sai Krishna J., Indar Kumar

Adaptive Conformal Inference (ACI) provides distribution-free prediction intervals with asymptotic coverage guarantees for time series under distribution shift. However, ACI only adapts the quantile threshold -- it cannot shift the interval center. When a base forecaster develops persistent bias after a regime change, ACI compensates by widening intervals sy

Kevin Wilkinghoff, Neelu Madan, Juan Miguel Valverde, Kamal Nasrollahi

Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are trained exclusively on normal data to learn a single reference model of normality. This implicitly assumes that normal behavior can be captured by a single, unconditional reference distribution. In practice,

Sofia Berloff, Pavel Koptev, Konstantin Malkov

Analog optical computers promise large efficiency gains for machine learning inference, yet no demonstration has moved beyond small-scale image benchmarks. We benchmark the analog optical computer (AOC) digital twin on mortgage approval classification from 5.84 million U.S. HMDA records and separate three sources of accuracy loss. On the original 19 features

Olivia S. Moro, Nigel B. Wilding, Vincent Ballenegger

We introduce a simulation technique to compute the free energy difference between two hydrate structures of different stoichiometry connected to a reservoir of gas molecules at a prescribed pressure. The method permits the determination of coexistence parameters for the system when the two hydrate structures have the same number of water molecules $N_w$. The

Parham Kebria, Soheil Sabri, Laura J Brattain

Remote medical response systems are increasingly being deployed to support emergency care in disaster-affected and infrastructure-limited environments. Enabled by GeoVision capabilities, this paper presents a Digital Twin architecture for hybrid autonomous-teleoperated medical response systems. The proposed framework integrates perception and adaptive naviga

Jakub Kowalski, Magdalena Piotrowska

Learner satisfaction prediction from MOOC reviews and behavioral logs is valuable for course quality improvement and platform operations. In practice, models trained on one platform degrade significantly when deployed on another due to domain shift in review style, learner population, behavioral logging schemas, and platform-specific rating norms. We study \

Dorin Bucur, Andrea Gentile, Antoine Henrot

Refining the sharp upper bounds $μ_{k,d}^* $ obtained by Kröger (1999) for the $k$-th Neumann eigenvalue of a convex domain $Ω\subset \mathbb{R}^d$, we prove the following inequalities: for any $k\in \mathbb{N}$ there exists a constant $C(k,d) >0$ such that $$D_Ω^2 μ_k(Ω) \leq μ_{k,d}^* - C(k,d) a_2(Ω)^2/D_Ω^2$$ where $D_Ω$ is the diameter of $Ω$ and $a_2(Ω)

Joonkyung Kim, Yanze Zhang, Wenhao Luo, Yiwei Lyu

Safe navigation for multi-robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision-making. Existing decentralized methods often assume robot homogeneity, making shared safety requirements non-uniformly interpreted across heterogeneous agents with structurally different dynamics, which could lead to avoidance ob

Benjamin Kiefer, Jan Lukas Augustin, Jon Muhovič, Mingi Jeong

The 4th Workshop on Maritime Computer Vision (MaCVi) is organized as part of CVPR 2026. This edition features five benchmark challenges with emphasis on both predictive accuracy and embedded real-time feasibility. This report summarizes the MaCVi 2026 challenge setup, evaluation protocols, datasets, and benchmark tracks, and presents quantitative results, qu

Dongyang Guo, Yasmeen Abdrabou, Enkelejda Kasneci

Gaze event detection is fundamental to vision science, human-computer interaction, and applied analytics. However, current workflows often require specialized programming knowledge and careful handling of heterogeneous raw data formats. Classical detectors such as I-VT and I-DT are effective but highly sensitive to preprocessing and parameterization, limitin

Giorgio Franceschelli, Mirco Musolesi

Large language models (LLMs), particularly when integrated into agentic systems, have demonstrated human- and even superhuman-level performance across multiple domains. Whether these systems can truly be considered creative, however, remains a matter of debate, as conclusions heavily depend on the definitions, evaluation methods, and specific use cases emplo

Anna Kowalczyk, Jakub Kowalski

Learner satisfaction is a critical quality signal in massive open online courses (MOOCs), directly influencing retention, engagement, and platform reputation. Most existing methods infer satisfaction \emph{post hoc} from end-of-course reviews and star ratings, which are too late for effective intervention. In this paper, we study \textbf{early-warning satisf

Michael Hartz, Marcel Scherer

We give a direct proof of the Cotlar-Stein lemma, which does not rely on the power trick.

Yifeng Huang

We introduce a quadratic form $Q$ on the space of functions on the gap poset $G$ of the numerical semigroup $\langle a,b\rangle$. We prove combinatorially that when evaluated on the indicator function of an upward closed subset $D$, this quadratic form precisely recovers the Gorsky--Mazin $\mathtt{dinv}$ statistic of $D$, viewed as a Young subdiagram of $G$.

Shivam Chand Kaushik

Semiconductor failure analysis (FA) requires engineers to examine inspection images, correlate equipment telemetry, consult historical defect records, and write structured reports, a process that can consume several hours of expert time per case. We present SemiFA, an agentic multi-modal framework that autonomously generates structured FA reports from semico

Mohammad Nasir Uddin

The Spatial-Temporal Graph Attention Network (ST-GAT) framework was created to serve as an explainable GNN-based solution for detecting bank distress early warning signs and for conducting macro-prudential surveillance of the interbank system in the United States. The ST-GAT framework models 8,103 FDIC insured institutions across 58 quarterly snapshots (2010

Melonie de Almeida, George Brydon, Divya M. Persaud, John H. Williamson

Digital elevation modeling of planetary surfaces is essential for studying past and ongoing geological processes. Wide-angle imagery acquired during spacecraft descent promises to offer a low-cost option for high-resolution terrain reconstruction. However, accurate 3D reconstruction from such imagery is challenging due to strong radial distortion and limited

Zs. Regaly, V. Frohlich, Cs. Kalup, Cs. Kiss

We investigated the millennial-scale evolution of narrow innermost rings composed of pebble-sized to sub-millimeter particles around the four known ring-bearing small bodies Chiron, Chariklo, Quaoar, and Haumea. Using a GPU-accelerated 8th-order Hermite integrator, we modeled the combined effects of solar radiation pressure (RP), shadowing of the rings by th

Mohammad Nasir Uddin, Md Munna Aziz

Financial crime costs U.S. institutions over $32 billion each year. Although AI tools for fraud detection have become more advanced, their use in real-world systems still faces a major obstacle: many of these models operate as black boxes that cannot provide the transparent, auditable explanations required by regulations such as OCC Bulletin 2011-12 and Fede

Aurosweta Mahapatra, Ismail Rasim Ulgen, Kong Aik Lee, Nicholas Andrews

Speech deepfake detection (SDD) systems perform well on standard benchmarks datasets but often fail to generalize to expressive and emotional spoofing attacks. Many methods rely on spoof-heavy training data, learning dataset-specific artifacts rather than transferable cues of natural speech. In contrast, humans internalize variability in real speech and dete

Rahul Jain, Pierre Trepagnier, Rick Gentile, Joey Botero

AI-enhanced interference rejection in radio frequency (RF) transmissions has recently attracted interest because deep learning approaches trained on both the signal of interest (SOI) and the signal mixture (SOI plus interference) can outperform traditional approaches which only consider the SOI. The goal is to detect, demodulate, and decode signals over a ra

John Vinson, Terrence Jach, Rainer Unterumsberger, Michael A. Woodcox

While highly successful, density functional theory is known to have limitations owing to its neglect of many-body electron-electron interactions. This neglect leads to errors in the single-particle energies, leading to underestimated band gaps and band widths as well as errors in band alignment at interfaces. Many-body perturbation theory, in the form of the

Yuyuan Zhou, Shixu Meng

This work proposes a hybrid method (ULR) which integrates a rotation-equivariance-aware neural network and a low-rank structure to solve the two dimensional inverse medium scattering problem. The neural network is to model the data corrector which maps the full data to the Born data, and the low-rank structure is to design an inverse Born solver that finds a

Zonghai Yao, Benlu Wang, Yifan Zhang, Junda Wang

Large language models perform well on many medical QA benchmarks, but real clinical reasoning often requires integrating evidence across multiple images rather than interpreting a single view. We introduce MedThinkVQA, an expert-annotated benchmark for thinking with multiple images, where models must interpret each image, combine cross-view evidence, and ans

Chuangtao Chen, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo

Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: reusing a cached document in a new context requires recomputing KV states to account for shifts in attention distribution. Existing solutions such as CacheBlend, EPIC, and SAM-KV mitigate this issue by selectiv

Michelangelo Pantaleoni González, João Alves, Cameren Swiggum, Isak Niederbrunner

We reassess the long-standing idea of Gould's Belt using Gaia DR3 for a sample of young massive stars and nearby young clusters. The structure surrounding the Sun, often interpreted as an inclined, expanding, and rotating ring, emerges in our analysis as a transient alignment of a few cluster families rather than an individual, coherent dynamical feature. By

John Fernald, Amit Gandhi, Dimitrije Ruzic, James Traina

We review the "production approach" to estimating markups, the ratio of price to marginal cost. The approach is uniquely scalable: it requires no model of consumer demand or market structure and applies broadly across firms, industries, and time. Our organizing insight is that the production-based markup is a residual. Like the Solow residual, it is clean in

Rocco D'Agostino, Vittorio De Falco

In this paper, we study the gravitational lensing around the static and spherically symmetric DD black holes, which we recently derived as perturbations of the Schwarzschild geometry within the revised Deser-Woodard theory of nonlocal gravity. We first present general analytical expressions for the deflection angle in both weak- and strong-deflection limits,

F. V. Pereyra Aponte, E. A. Jagla

We propose a model that extends the standard depinning paradigm by incorporating an aging mechanism into the local pinning force. This favors oscillations between a stuck state of large pinning, and a slipping state of smaller pinning. We show that for mean field interactions between sites this mechanism can lead to the appearance of ``king avalanches" and g

Bo Ning, Yan Yang

Let \( G \) be a graph of order \( n \) with maximum degree $\Delta$, and let $P(G,x)$ denote its chromatic polynomial. We investigate several properties of $P(G,x)$ related to its derivatives and higher-order derivatives. First, we study the monotonicity of $P(G,x)/x^n$. Dong proved that $(x-1)^nP(G,x)\geq x^nP(G,x-1)$ for all real $x\geq n$. In particular,

Miqueas E. Gamero, Guillermo Fernández Moroni, Fernando Chierchie, Agustin J. Lapi

Low-noise detectors are a key technology for the next generation of astronomical instruments aimed at spectroscopy of faint objects and the search for exoplanets. In this context, the multiple-amplifier sensing charge-coupled device (MAS-CCD) emerges as a promising technology for future scientific instruments. A critical parameter affecting the performance o

Ali Kaazempur-Mofrad, Xiaowu Dai, Xuming He

The academic job market for new statisticians is highly congested at the interview stage, where departments must rank and select candidates from large applicant pools without credible signals of candidate interest. As a result, interviews and offers are often misallocated, leading to unfilled positions and poor mutual fit. We frame interview allocation as a

Colburn Riffel, Reece Robertson, Peter Hendrickson

A fully fault-tolerant implementation of the quantum error-detecting Iceberg $[[2m, 2m-2, 2]]$ code applied to a Toffoli circuit achieved beyond-break-even error detection on a leading trapped-ion quantum computer, where the effect of encoding a circuit with a quantum error-detection code enables increased fidelity compared to an unencoded circuit. This code

Danru Xu, Sébastien Lachapelle, Sara Magliacane

Causal representation learning (CRL) aims to identify the underlying latent variables from high-dimensional observations, even when variables are dependent with each other. We study this problem for latent variables that follow a potentially degenerate Gaussian mixture distribution and that are only observed through the transformation via a piecewise affine

Nahid Khoshk Angabini, Mohsen Tajgardan, Mahesh Madhavan, Zahra Asghari Varzaneh

Reliable evaluation of blastocyst quality is critical for the success of in vitro fertilization (IVF) treatments. Current embryo grading practices primarily rely on visual assessment of morphological features, which introduces subjectivity, inter-embryologist variability, and challenges in standardizing quality assurance. In this study, we propose a multitas

Dalya Baron, David J. Setton, Yilun Ma, J. X. Prochaska

The Sparks survey provides rest-frame near-infrared spectroscopy for 93 local massive galaxies spanning the rapid transition from starburst to post-starburst, including Balmer-strong galaxies as well as systems with active galactic nuclei (AGN). Interpreting these extreme systems requires reliable physical properties, yet these can vary substantially when de

Tania Rifat Jahan, Donald J. Docimo

This work explores methods to identify energy system designs for infeasible control co-design optimization problems. Control co-design, or CCD, has been recognized as a powerful tool to maximize energy system capabilities through simultaneous determination of plant and controller parameters. However, due to the inherent nonlinearities, complexity, and confli

Fabrizio Colombo, Francesco Mantovani, Peter Schlosser

The present work is devoted to the study of fractional powers of vector operators, with particular emphasis on the gradient operator with non-constant coefficients. Within the setting of Clifford algebra $\mathbb{R}_n$, this operator turns out to have bisectorial properties. By applying the spectral theory on the $S$-spectrum, we address a fundamental mathem

Daniel Dominik, Jeffrey A. Mudrock

DP-coloring (also called correspondence coloring) is a generalization of list coloring introduced by Dvo\v{r}\'{a}k and Postle in 2015. The DP-chromatic number of a graph $G$, $\chi_{_{DP}}(G)$, is the analogue of the chromatic number of $G$ in the DP context and is bounded above by the degeneracy of $G$ plus one. Over the last two years a plethora of author

Dalya Baron, David J. Setton, Yilun Ma, J. X. Prochaska

Rapid transitions from starburst to quiescence constitute a key evolutionary pathway in galaxy formation. Post-starburst galaxies trace this brief phase, exhibiting optical spectra dominated by intermediate-age stellar populations with strong Balmer absorption features. Although rare locally, such systems are commonly revealed by JWST observations among mass

Laura Portero, Andrés Arrarás, Francisco J. Gaspar, Florin A. Radu

We study the slightly compressible Darcy-Forchheimer equations modeling gas flow in porous media, particularly in applications related to combustion processes. The equations are discretized in time using the backward Euler method and in space via a mixed finite element scheme. As a result, a nonlinear algebraic system is obtained at each time step. We propos

Massimo Guiggiani

Classical vehicle dynamics contains several widely adopted misconceptions that, while intuitively appealing, may lead to inconsistencies when examined under a rigorous mechanical framework. This paper revisits a number of such misconceptions, clarifying their domains of validity and highlighting potential sources of misunderstanding. The aim is not to dismis

Yu-Qian, Ouyang, Alex I. Malz, Ming Lian

The development of the observing strategy for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) requires a broad optimization across science cases inside and outside of time-domain astronomy. We introduce a novel metric for transient science with LSST based on information-theoretic cross-entropy. We demonstrate its utility for distinguishi

Chashi Mahiul Islam, Alan Villarreal, Mao Nishino, Shaeke Salman

As Large Language Models (LLMs) are increasingly integrated into agentic workflows, their unpredictability stemming from numerical instability has emerged as a critical reliability issue. While recent studies have demonstrated the significant downstream effects of these instabilities, the root causes and underlying mechanisms remain poorly understood. In thi

Archana Radhakrishnan, Debsubhra Chakraborty, Nilmani Mathur

We present a lattice QCD study of heavy baryons containing charm and bottom quarks, with particular emphasis on the relativistic treatment of all valence quarks. We use $N_f=2+1+1$ HISQ ensembles at the physical point to compute ground-state energies of spin-$3/2^+$ baryons, including singly-, doubly-, and triply-heavy charmed and bottom baryons. This work r

Ruiqi Ni, Yuchen Liu, Ahmed H. Qureshi

The motion planning problem requires finding a collision-free path between start and goal configurations in high-dimensional, cluttered spaces. Recent learning-based methods offer promising solutions, with self-supervised physics-informed approaches such as Neural Time Fields (NTFields) solving the Eikonal equation to learn value functions without expert dem

Ibrahim Bilau, Nicole Li, Terrence Malayvong, Eunhwa Yang

Mild Cognitive Impairment (MCI) affects 15-20% of adults aged 65 and older, often making kitchen navigation and independent living difficult, particularly in lower-income communities with limited access to professional design help. This study created an AI system that converts standard kitchen photos into MCI-friendly designs using the Home Design Guidelines

Oliver Bentham, Vivek Srikumar

Large language models are emerging as scientific assistants, but evaluating their ability to reason from empirical data remains challenging. Benchmarks derived from published studies and human annotations inherit publication bias, known-knowledge bias, label noise, and substantial storage requirements. We present InfiniteScienceGym, a procedurally generated

Matija Ćuk, Harrison F. Agrusa, Marina Brozović, Matthew M. Hedman

The two innermost moons of Neptune, Naiad and Thalassa, are currently in a 73:69 mean-motion resonance. This resonance relies on the large inclination of Naiad, and we estimate that Naiad requires multiple Gyr to reach its $4.7^{\circ}$ inclination through this resonance. However, we find through direct numerical simulations that the current Naiad-Thalassa r

Yiyang Lin, Erlin Qiao, Jifeng Liu, Meng Guo

Quasi-periodic oscillation (QPO) has been detected in several accreting supermassive black hole (SMBH) systems, including active galactic nuclei (AGNs) and tidal disruption events (TDEs). However, despite that several models have been proposed, the physical origin of QPO is still unclear. In this paper, we performed radiation hydrodynamic simulations of accr

Shih-Kai Chiu, Ronan J. Conlon, Frédéric Rochon

We construct examples of complete Calabi-Yau metrics on smoothings of 3-dimensional Calabi-Yau cones that are not products of lower-dimensional Calabi-Yau cones and that have orbifold singularities away from the vertex.

Arian Moghni, Namrata Roy, Timothy M. Heckman, Kevin Bundy

Red geysers are a population of massive (log[M/M$_\odot$]~10.5), quiescent galaxies that exhibit large-scale but weak, bi-symmetric ionized gas outflows, interpreted as signatures of ongoing, low-level active galactic nucleus (AGN) feedback. We investigate the kinematics and prevalence of cool (T~100-1000K), neutral gas traced by Na I D absorption, and its c

Marcel Novaes

We discuss the semiclassical approximation to transport problems in quantum chaotic systems. The figures of merit are moments of the transmission matrix and of the time delay matrix. After reviewing a few results obtained by treating these matrices are random matrices, we show how expressions for their elements in terms of sums over trajectories lead to diag

Donggeon David Oh, Duy P. Nguyen, Haimin Hu, Jaime Fernández Fisac

Robust control barrier functions (CBFs) provide a principled mechanism for smooth safety enforcement under worst-case disturbances. However, existing approaches typically rely on explicit, closed-form structure in the dynamics (e.g., control-affine) and uncertainty models. This has led to limited scalability and generality, with most robust CBFs certifying o

Javier Fabre, Carlos Castillo, Carlos Rodriguez-Pardo, Jorge Lopez-Moreno

Many materials show anisotropic light scattering patterns due to the shape and local alignment of their underlying micro structures: surfaces with small elements such as fibers, or the ridges of a brushed metal, are very sparse and require a high spatial resolution to be properly represented as a volume. The acquisition of voxel data from such objects is a t

Fabio Pasticci

This study examines the effectiveness of a structured instructional approach to decomposition and recomposition of large numbers in six primary school classes (three Year 4 and three Year 5, N = 120) using a quasi - experimental design with a control group. The 12 - week intervention is grounded in the Concrete Pictorial Abstract (CPA) progression. The exper