arXiv papers from October 2025

Isaac Meza

We extend the approximate residual balancing (ARB) framework to nonlinear models, answering an open problem posed by Athey et al. (2018). Our approach addresses the challenge of estimating average treatment effects in high-dimensional settings where the outcome follows a generalized linear model. We derive a new bias decomposition for nonlinear models that r

Da-Wei Luo, Edward Yu, Ting Yu

We considered the transfer of continuous-variable entangled states in coupled oscillator chains embedded in a generic environment. We demonstrate high-fidelity transfer via optimal control in two configurations - a linear chain and an X-shaped chain. More specifically, we use the Krotov optimization algorithm to design control fields that achieve the desired

Brian Kintu

In this follow-up paper, we again inspect a surprising relationship between the set of $n$-periodic points of a polynomial map $\varphi_{d, c}$ defined by $\varphi_{d, c}(z) = z^d + c$ for all $c, z \in \mathbb{Z}_{p}$ or $\in \mathbb{F}_{p}[t]$ and the coefficient $c$, where $d>2$ is an integer and $n\in \mathbb{Z}_{\geq 2}$ is any fixed (period). As before

Dowon Kim, MinJae Lee, Janghyeon Kim, HyuckSung Kwon

The expansion of context windows in large language models (LLMs) to multi-million tokens introduces severe memory and compute bottlenecks, particularly in managing the growing Key-Value (KV) cache. While Compute Express Link (CXL) enables non-eviction frameworks that offload the full KV-cache to scalable external memory, these frameworks still suffer from co

M. G. Masteghin, Z. P. Aslam, A. P. Brown, M. J. Whiting

Focused ion beams (FIBs) are widely used in nanofabrication for applications such as circuit repair, ultra-thin lamella preparation, strain engineering, and quantum device prototyping. Although the lateral spread of the ion beam is often overlooked, it becomes critical in precision tasks such as impurity placement in host substrates, where accurate knowledge

Leonel Corado, Sérgio Godinho, Carlos Alberto Silva, Juan Guerra-Hernández

Accurate geolocation is essential for the reliable use of GEDI LiDAR data in footprint-scale applications such as aboveground biomass modeling, data fusion, and ecosystem monitoring. However, residual geolocation errors arising from both systematic biases and random ISS-induced jitter can significantly affect the accuracy of derived vegetation and terrain me

Dana Kim, Yichen Xu, Tiffany Lin

Large Language Models (LLMs) offer a flexible means to generate synthetic tabular data, yet existing approaches often fail to preserve key causal parameters such as the average treatment effect (ATE). In this technical exploration, we first demonstrate that state-of-the-art synthetic data generators, both GAN- and LLM-based, can achieve high predictive fidel

Vincent Hickl, Gabriel Gmünder, René M. Rossi, Antonia Neels

Bacterial colonies are a well-known example of living active matter, exhibiting collective behaviors such as nematic alignment and collective motion that play an important role in the spread of microbial infections. While the underlying mechanics of these behaviors have been described in model systems, many open questions remain about how microbial self-orga

Khakim Akhunov, Eren Yildiz, Kasim Sinan Yildirim

Low-power multicore platforms are suitable for running data-intensive tasks in parallel, but they are highly inefficient for computing on intermittent power. In this work, we present PEARL (PowEr And eneRgy-aware MuLticore Intermittent Computing), a novel systems support that can make existing multicore microcontroller (MCU) platforms suitable for efficient

Lee Xiong, Maksim Tkachenko, Johanes Effendi, Ting Cai

We propose Factorization Memory, an efficient recurrent neural network (RNN) architecture that achieves performance comparable to Transformer models on short-context language modeling tasks while also demonstrating superior generalization in long-context scenarios. Our model builds upon Mamba-2, enabling Factorization Memory to exploit parallel computations

Rohit Kishan Ray

Quantum nonlocality is often judged by violations of Bell-type inequalities for a given state. The computation of such violations is a global task, requiring evaluation of global correlations and subsequent testing against a Bell functional. We ask instead: when is a given state local (classical)? We formalize this question via local perception operators (LP

Calvin W. Johnson, Mark A. Caprio

Ab initio calculations of atomic nuclei have had many successes in recent years. Nonetheless, important challenges that resist even brute-force calculation remain. As archetypal examples of these challenges, we consider $^{11}$Li and $^{29}$F, well known halo nuclides situated on islands of inversion. The deformed intruder levels, which are primarily two-par

J. -H. Eschenburg, K. K. Santos, R. Tribuzy

In this article we investigate some properties of equivariant embeddings of a symmetric K\"ahlerian manifold. Motivated by a theorem of Cartan and Wallach on equivariant embeddings of symmetric spaces we characterize these embeddings in the special case of $\mathbb{CP}^n$. Further, we verify that if a equivariant embedding has parallel plurimean curvature th

Zawad Chowdhury, Francois Clement, Max Horwitz

We investigate a family of $4$-regular graphs constructed to test for the presence of combinatorial structure in a sequence of distinct real numbers. We show that the graphs constructed from the Kronecker sequence can be embedded into the torus, while the graphs constructed from the binary van der Corput sequence can be embedded into the Chamanara surface, i

Juan Rivera Cázares, Xavier Valencia Díaz, Christian Lambarri Martínez

We analyzed the body structure of the Blackstripe Cichlid Vieja fenestrata (G\"unther, 1860), a species with highly phenotypic variability, by the Systemics Morphometrics Methodology, previously proposed by one of the authors. From this perspective and considering the properties of its bauplan, we describe the expected morphometrics variability of this speci

Chaopeng Tan, Hao Liu, Dingshan Sun, Marco Rinaldi

Max-pressure (MP) control stands out among real-time network traffic signal control methods due to its simplicity, decentralized nature, and theoretical stability. However, existing MP control methods have limited consideration of public transportation and do not address the network stability problem of transit-prioritized MP in partially connected vehicle (

Peter Yegon, W. Brent Lindquist, Svetlozar T. Rachev

We present two models for incorporating the total effect of market microstructure noise into dynamic pricing of assets and European options. The first model is developed under a Black-Scholes-Merton, continuous-time framework. The second model is a discrete, binomial tree model developed as an extension of the static Grossman-Stiglitz model. Both models are

Amrit Gautam

The study of exclusive photoproduction of multi-hadron final states in ultra-peripheral collisions (UPCs) provides a unique avenue to explore quantum chromodynamics (QCD) and the nature of resonances emerging from gluonic interactions. The ALICE Collaboration has recently performed measurements of exclusive four-pion photoproduction using Run 2 data, favorin

Cesar Mello, Fernando Medina da Cunha

Malignant membranes cluster nutrient transporters within glycan-rich domains, sustaining metabolism through redundant intake routes. A theoretical framework links interfacial chemistry to transport suppression and energetic or redox collapse. The model unites a screened Poisson-Nernst-Planck electrodiffusion problem, an interfacial potential of mean force, a

Yegor Goncharov

We describe a self-contained procedure for constructing the traceless projection of mixed tensor products (built out of a finite-dimensional complex vector space and its dual). The construction relies on the Schur-Weyl duality for the general linear group and regards rational representations thereof. By identifying the traceless subspace as a particular rati

Andreia Chapouto, Justin Forlano, Thierry Laurens

We consider a family of intermediate nonlinear Schr\"{o}dinger equations (INLS) on the real line, which includes the continuum Calogero-Moser models (CCM). We prove that INLS is locally well-posed in $H^{s}(\mathbb{R})$ for any $s>\frac 14$, which improves upon the previous best result of $s>\frac 12$ by de Moura-Pilod (2008). This result is also new in the

Ciaran Bench, Oskar Pfeffer, Vivek Desai, Mohammad Moulaeifard

In principle, deep learning models trained on medical time-series, including wearable photoplethysmography (PPG) sensor data, can provide a means to continuously monitor physiological parameters outside of clinical settings. However, there is considerable risk of poor performance when deployed in practical measurement scenarios leading to negative patient ou

Fuhad Ahmed Opu, Moddassir Khan Nayeem, Hamid Najafzad, Omar Abbaas

This study addresses the multi-item multi-period order allocation problem under all-unit quantity discounts (AUQD) and blending ratios. A manufacturer makes a single product that requires mixing/assembling multiple ingredients/components with pre-determined blending ratios. We consider a single supplier offering quantity-based discounts which introduces non-

Alexei A. Bokov, Haiyan Guo, Zuo-Guang Ye

Information about the crystal structures in the range of morphotropic phase boundary of ferroelectric perovskite solid solutions is important for understanding their intricate properties which result in wide opportunities for practical applications. However, for the (1-x)Pb(Mg1/3Nb2/3)O3-xPbTiO3 solid solution system this information is contradictory. Differ

Dániel Garamvölgyi, Bill Jackson, Tibor Jordán, Soma Villányi

We consider three matroids defined by Kalai in 1985: the symmetric completion matroid $\mathcal{S}_d$ on the edge set of a looped complete graph; the hyperconnectivity matroid $\mathcal{H}_d$ on the edge set of a complete graph; and the birigidity matroid $\mathcal{B}_d$ on the edge set of a complete bipartite graph. These matroids arise in the study of low

Linhan Fang, Xingpeng Li

The escalating adoption of electric vehicles (EVs) and the growing demand for charging solutions are driving a surge in EV charger installations in distribution networks. However, this rising EV load strains the distribution grid, causing severe voltage drops, particularly at feeder extremities. This study proposes a proactive voltage management (PVM) framew

Peng Wang, Zhengmao Li, Luis Badesa

In low-carbon grids, system flexibility can be enhanced through mechanisms such as Demand Response (DR), enabling the efficient utilization of renewable energy. However, as Synchronous Generators (SGs) are being replaced by renewable energy sources characterized by Inverter-Based Resources (IBR), system stability is severely affected. Due to the limited over

Kosmas Alexandridis, Giorgos Dimitrakopoulos

Transformers have significantly advanced AI and machine learning through their powerful attention mechanism. However, computing attention on long sequences can become a computational bottleneck. FlashAttention mitigates this by fusing the softmax and matrix operations into a tiled computation pattern that decouples performance from sequence length. Though de

Lucas Almeida, Maycon Peixoto

An Edge-Cloud Continuum integrates edge and cloud resources to provide a flexible and scalable infrastructure. This paradigm can minimize latency by processing data closer to the source at the edge while leveraging the vast computational power of the cloud for more intensive tasks. In this context, module application placement requires strategic allocation p

Hengjia Li, Jianjin Xu, Keli Cheng, Lei Wang

Recent advances in personalized generative models have demonstrated impressive capabilities in producing identity-consistent images of the same individual across diverse scenes. However, most existing methods lack explicit viewpoint control and fail to ensure multi-view consistency of generated identities. To address this limitation, we present MagicView, a

Michal Habera, Andreas Zilian

Trigonometric formulas for eigenvalues of $3 \times 3$ matrices that build on Cardano's and Vi\`ete's work on algebraic solutions of the cubic are numerically unstable for matrices with repeated eigenvalues. This work presents numerically stable, closed-form evaluation of eigenvalues of real, diagonalizable $3 \times 3$ matrices via four invariants: the trac

Christos Mavridis, Fernando S. Barbosa, Hamed Farhadi, Karl H. Johansson

Network digital twin (NDT) models are virtual models that replicate the behavior of physical communication networks and are considered a key technology component to enable novel features and capabilities in future 6G networks. In this work, we focus on NDTs that model the communication quality properties of a multi-cell, dynamically changing wireless network

Ashwin Gerard Colaco, Sharad Mehrotra, Michael J De Lucia, Kevin Hamlen

NOMAD (Navigating Optimal Model Application for Datastreams) is an intelligent framework for data enrichment during ingestion that optimizes realtime multiclass classification by dynamically constructing model chains, i.e ,sequences of machine learning models with varying cost-quality tradeoffs, selected via a utilitybased criterion. Inspired by predicate or

You-Jin Kim, Radha Kumaran, Ehsan Sayyad, Anne Milner

By situating computer-generated content in the physical world, mobile augmented reality (AR) can support many tasks that involve effective search and inspection of physical environments. Currently, there is limited information regarding the viability of using AR in realistic wide-area outdoor environments and how AR experiences affect human behavior in these

Mao Fabrice Djete

This paper introduces and analyzes a new class of mean-field control (\textsc{MFC}) problems in which agents interact through a \emph{fixed but controllable} network structure. In contrast with the classical \textsc{MFC} framework -- where agents are exchangeable and interact only through symmetric empirical distributions -- we consider systems with heteroge

Dimitar Grantcharov, Khoa Nguyen

We study properties of two families, $E_{+}(g)$ and $E_-(g)$, of non-weight modules over the orthosymplectic Lie superalgebra $\mathfrak{osp}(1|2)$ that are parameterized by a nonconstant polynomial $g(x) \in \mathbb C [x]$. These families appear naturally from the two oscillator homomorphisms and the exponential modules over the first Weyl algebra $\mathcal

José A. Rueda, Sergio Ramírez, Miguel A. Sánchez, Cecilio U. Aguilar

The subsolar point, the closest location on Earth's surface to the Sun, marks the Sun-Earth line of gravity that governs Earth's coupled orbital-rotational cycle. We examined the dynamic interactions among the Sun meridian declination (SMD), the Equation of Time (EoT), Earth's rotational speed (ER$_\omega$) -- equatorial and with respect to the Sun -- and th

Gregory Mattson

The sPHENIX experiment is a next-generation collider detector at the Relativistic Heavy Ion Collider (RHIC) designed for rare jet and heavy-flavor probes of Au + Au, $p$ + Au, and polarized $p+p$ collisions. The experiment includes a large acceptance, granular electromagnetic calorimeter and very high-rate data acquisition plus trigger system. In RHIC Run-24

Pierluigi Contucci, Godwin Osabutey, Filippo Zimmaro

We introduce the Economic Productivity of Energy (EPE), GDP generated per unit of energy consumed, as a quantitative lens to assess the sustainability of the Artificial Intelligence (AI) revolution. Historical evidence shows that the first industrial revolution, pre-scientific in the sense that technological adoption preceded scientific understanding, initia

Stefany P. Carvalho, Guilherme S. L. Fabris, Ana Carolina F. de Brito, Raphael B. de Oliveira

We investigate the air-induced degradation of few-layer hafnium diselenide (HfSe$_2$) through combined experimental and theoretical approaches. AFM and SEM reveal the formation of selenium-rich spherical features upon ambient exposure, while EDS confirms Se segregation. \textit{Ab initio} molecular dynamics simulations show that Se atoms migrate to flake edg

Yuki Takahashi

If $T$ has dependent dividing, then the burden agrees with the dp-rank witnessed by NIP formulas. We use this observation to prove that if $T$ has dependent dividing, then the burden is sub-additive. We also state a connection between the burden and the dual VC density.

Tristan Aurégan, Noé Daniel, Megan Mazzatenta, Luc Deike

Bubbles bursting at the surface of the ocean produce drops that heavily influence ocean-atmosphere interactions. One of the mechanisms through which drops are formed is called jet drop production, where the collapse of the bubble cavity leads to the formation of a fast upwards jet that breaks to form drops. While isolated bubble bursting has been extensively

Abhinav Joshi, Areeb Ahmad, Ashutosh Modi

Large Language Models (LLMs) have demonstrated inherent calibration capabilities, where predicted probabilities align well with correctness, despite prior findings that deep neural networks are often overconfident. Recent studies have linked this behavior to specific components in the final layer, such as entropy neurons and the unembedding matrix null space

Meituan LongCat Team, Bairui Wang, Bayan, Bin Xiao

We introduce LongCat-Flash-Omni, a state-of-the-art open-source omni-modal model with 560 billion parameters, excelling at real-time audio-visual interaction. By adopting a curriculum-inspired progressive training strategy that transitions from simpler to increasingly complex modality sequence modeling tasks, LongCat-Flash-Omni attains comprehensive multimod

I. Čurlik, F. Akbar, S. Gabani, M. Giovannini

Within the family of cubic YbCu$_4$X compounds ($X$ = Ni, Au and Zn), we have investigated the YbCu$_{5-x}$Zn$_x$ ($1\geq x \geq 0.7$) alloys by means of structural, magnetic, thermal and transport measurements. In the $\tau 1-$ YbCu$_{5-x}$Zn$_x$ (cubic AuBe$_5$ type, $0.7 \leq x \leq 1.5$) structural phase, Yb ion is in its Yb$^{3+}$ magnetic configuration

Mohammad Hadi Akbarzadeh, Mahmood Ahmadi, Mohammad Saeed Jahangiry, Jae Young Hur

The exponential growth of Internet of Things (IoT) devices, smart vehicles, and latency-sensitive applications has created an urgent demand for efficient distributed computing paradigms. Multi-Fog Computing (MFC), as an extension of fog and edge computing, deploys multiple fog nodes near end users to reduce latency, enhance scalability, and ensure Quality of

Jan Wiegerinck

We give an example of a Laplace transform $\int_\gamma e^{\zeta z} d\mu(\zeta)$ that does not have regular growth. This answers a question in Hayman's List

Katharine Rucker, Ian Baxter, Pedram Hassanzadeh, Tiffany A. Shaw

AI models have emerged as potential complements to physics-based models, but their skill in capturing observed regional climate trends with important societal impacts has not been explored. Here, we benchmark satellite-era regional thermodynamic trends, including extremes, in an AI emulator (ACE2) and a hybrid model (NeuralGCM). We also compare the AI models

Michiel Straat, Thorben Markmann, Sebastian Peitz, Barbara Hammer

Chaotic convective flows arise in many real-world systems, such as microfluidic devices and chemical reactors. Stabilizing these flows is highly desirable but remains challenging, particularly in chaotic regimes where conventional control methods often fail. Reinforcement Learning (RL) has shown promise for control in laminar flow settings, but its ability t

Saadat Izadi, Shakib Komasi, Ali Salimi, Alireza Rezaei

The rapid growth of the Internet of Things (IoT) offers new opportunities but also expands the attack surface of distributed, resource-limited devices. Intrusion detection in such environments is difficult due to data heterogeneity from diverse sensing modalities and the non-IID distribution of samples across clients. Federated Learning (FL) provides a priva

Abhinav Joshi, Vaibhav Sharma, Sanjeet Singh, Ashutosh Modi

Sign language translation remains a challenging task due to the scarcity of large-scale, sentence-aligned datasets. Prior arts have focused on various feature extraction and architectural changes to support neural machine translation for sign languages. We propose POSESTITCH-SLT, a novel pre-training scheme that is inspired by linguistic-templates-based sent

Long Li, Jiajia Li, Dong Chen, Lina Pu

Accurate classification plays a pivotal role in smart agriculture, enabling applications such as crop monitoring, fruit recognition, and pest detection. However, conventional centralized training often requires large-scale data collection, which raises privacy concerns, while standard federated learning struggles with non-independent and identically distribu

Prithvi Anickode, Fabio Milner

Acidosis in tumors arises from reprogrammed metabolism and compromised vasculature, creating a harsh, acidic microenvironment that drives the evolutionary selection of acid-resistant cell phenotypes. A mathematical model is proposed to integrate phenotypic evolution, microenvironmental acidification, and tumor density dynamics. Three key mechanisms are incor

Shounak Paul, Dhananjay Ghumare, Pawan Goyal, Saptarshi Ghosh

Identifying/retrieving relevant statutes and prior cases/precedents for a given legal situation are common tasks exercised by law practitioners. Researchers to date have addressed the two tasks independently, thus developing completely different datasets and models for each task; however, both retrieval tasks are inherently related, e.g., similar cases tend

Christian Prothmann, Vijay Gadepally, Jeremy Kepner, Koley Borchard

The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent years, AI Accelerator

Arman Anwar, Zefang Liu

Traditional cybersecurity tabletop exercises (TTXs) provide valuable training but are often scripted, resource-intensive, and difficult to scale. We introduce AgentBnB, a browser-based re-imagining of the Backdoors & Breaches game that integrates large language model teammates with a Bloom-aligned, retrieval-augmented copilot (C2D2). The system expands a cur

A. Somov, V. V. Berdnikov, H. Voskanyan, A. Asaturyan

A new electromagnetic calorimeter composed of 1596 lead tungstate (PbWO$_4$) scintillating crystals has been constructed for the GlueX detector in Hall D at Jefferson Lab. The calorimeter is equipped with a light monitoring system that uses light-emitting diodes. The light monitoring system was fabricated, installed, and integrated into the GlueX trigger sys

Xiaocong Yang

Modern dense information retrieval (IR) models usually rely on costly large-scale pretraining. In this paper, we introduce LLM2IR, an efficient unsupervised contrastive learning framework to convert any decoder-only large language model (LLM) to an information retrieval model. Despite its simplicity, the effectiveness is proven among different LLMs on multip

Jinyuan Chen

COOL (Chen'21) is an error-free, information-theoretically secure Byzantine agreement (BA) protocol proven to achieve BA consensus in the synchronous setting for an $\ell$-bit message, with a total communication complexity of $O(\max\{n\ell, nt \log q\})$ bits, four communication rounds in the worst case, and a single invocation of a binary BA, under the opt

Neha Balamurugan, Sarah Wu, Adam Chun, Gabe Gaw

Humans excel at visual social inference, the ability to infer hidden elements of a scene from subtle behavioral cues such as other people's gaze, pose, and orientation. This ability drives everyday social reasoning in humans and is critical for developing more human-like AI agents. We introduce Spot The Ball, a challenging benchmark for evaluating visual soc

Linzhe Jiang, Jiayuan Huang, Sophia Bano, Matthew J. Clarkson

Accurate 3D point cloud registration underpins reliable image-guided colonoscopy, directly affecting lesion localization, margin assessment, and navigation safety. However, biological tissue exhibits repetitive textures and locally homogeneous geometry that cause feature degeneracy, while substantial domain shifts between pre-operative anatomy and intra-oper

Andria J. Farrens, Luis Garcia-Fernandez, Raymond Diaz Rojas, Jillian Obeso Estrada

Precision rehabilitation aims to tailor movement training to improve outcomes. We tested whether proprioceptively-tailored robotic training improves hand function and neural processing in stroke survivors. Using a robotic finger exoskeleton, we tested two proprioceptively-tailored approaches: Propriopixel Training, which uses robot-facilitated, gamified move

Salvador Rey Gomez, Tomek Jaroslawski

Experimental mean flows are commonly used to study wall-bounded turbulence. However, these measurements are often unable to resolve the near-wall region and thus introduce ambiguity in the velocity closest to the wall. This poses a source of uncertainty in equation-based approaches that rely on these mean flow measurements such as resolvent analysis. Resolve

Zachary Chase, Shinji Ito, Idan Mehalel

We determine the minimax optimal expected regret in the classic non-stochastic multi-armed bandit with expert advice problem, by proving a lower bound that matches the upper bound of Kale (2014). The two bounds determine the minimax optimal expected regret to be $\Theta\left( \sqrt{T K \log (N/K) } \right)$, where $K$ is the number of arms, $N$ is the number

Zongyang Du, Shreeram Suresh Chandra, Ismail Rasim Ulgen, Aurosweta Mahapatra

Everyday speech conveys far more than words, it reflects who we are, how we feel, and the circumstances surrounding our interactions. Yet, most existing speech datasets are acted, limited in scale, and fail to capture the expressive richness of real-life communication. With the rise of large neural networks, several large-scale speech corpora have emerged an

Fangxun Liu, S M Rayeed, Samuel Stevens, Alyson East

In entomology and ecology research, biologists often need to collect a large number of insects, among which beetles are the most common species. A common practice for biologists to organize beetles is to place them on trays and take a picture of each tray. Given the images of thousands of such trays, it is important to have an automated pipeline to process t

Michael Koss, Nafisa Aftab, Steven W. Allen, Roberta Amato

The AXIS Community Science Book represents the collective effort of 592 scientists worldwide to define the transformative science enabled by the Advanced X-ray Imaging Satellite (AXIS), a next-generation X-ray mission selected by NASA's Astrophysics Probe Program for Phase A study. AXIS will advance the legacy of high-angular-resolution X-ray astronomy with

Aaron Sun, Subhransu Maji, Grant Van Horn

In the single-positive multi-label (SPML) setting, each image in a dataset is labeled with the presence of a single class, while the true presence of other classes remains unknown. The challenge is to narrow the performance gap between this partially-labeled setting and fully-supervised learning, which often requires a significant annotation budget. Prior SP

Felix Bartel, Pascal Schröter

We present a Fourier-based approach for high-dimensional function approximation. To this end, we analyze the truncated ANOVA (analysis of variance) decomposition and learn the anisotropic smoothness properties of the target function from scattered data. This smoothness information is then incorporated into our approximation algorithm to improve the accuracy.

Courtney B. Watson, Elizabeth L. Blanton, Scott W. Randall, Tracy E. Clarke

We present results from very deep (485 ks) Chandra X-ray observations of the relaxed, cool core cluster Abell 2029 (z = 0.0767). A2029 hosts one of the longest, most continuous sloshing spirals ever observed, which we find extends nearly 600 kpc from the cluster core. In addition to providing detailed views of the sloshing spiral, imaging and spectroscopic a

Sushil Khairnar

The growth in IoT devices means an ongoing risk of data vulnerability. The transition from centralized ecosystems to decentralized ecosystems is of paramount importance due to security, privacy, and data use concerns. Since the majority of IoT devices will be used by consumers in peer-to-peer applications, a centralized approach raises many issues of trust r

Alexandra V. Antoniouk, Anatoly N. Kochubei

We introduce and study an analog of the Kelvin transformation connected with the Vladimirov-Taibleson operator acting on real- or complex-valued functions on a space $K^n$ over a non-Archimedean local field $K$.

Shurui Gui, Deep Anil Patel, Xiner Li, Martin Renqiang Min

Recent advances in video diffusion models have enabled the generation of high-quality videos. However, these videos still suffer from unrealistic deformations, semantic violations, and physical inconsistencies that are largely rooted in the absence of 3D physical priors. To address these challenges, we propose an object-aware 4D human motion generation frame

Saini Jatin Rao, Akhil Aravind, Saptarshi Basu

Shock tubes have been a crucial device, facilitating studies across a wide range of practical applications. An open-ended shock tube employing the wire-explosion technique with a rectangular cross section is used in the present study to generate blast waves over a Mach number range of 1.2-1.8, enabling detailed investigation of unsteady compressible flow at

Zhiwen Li, Cheuk Hin Ho, Lok Ming Lui

Traditional methods for high-dimensional diffeomorphic mapping often struggle with the curse of dimensionality. We propose a mesh-free learning framework designed for $n$-dimensional mapping problems, seamlessly combining variational principles with quasi-conformal theory. Our approach ensures accurate, bijective mappings by regulating conformality distortio

Wadduwage Shanika Perera, ABM Islam, Van Vung Pham, Min Kyung An

Melanoma is one of the most aggressive and deadliest skin cancers, leading to mortality if not detected and treated in the early stages. Artificial intelligence techniques have recently been developed to help dermatologists in the early detection of melanoma, and systems based on deep learning (DL) have been able to detect these lesions with high accuracy. H

Iain Smears

This article provides a brief introduction to the a posteriori error analysis of parabolic partial differential equations, with an emphasis on challenges distinct from those of steady-state problems. Using the heat equation as a model problem, we examine the crucial influence of the choice of error norm, as well as the choice of notion of reconstruction of t

Yan Bin Ng, Xianfeng Gu

The optimal transport (OT) problem aims to find the most efficient mapping between two probability distributions under a given cost function, and has diverse applications in many fields such as machine learning, computer vision and computer graphics. However, existing methods for computing optimal transport maps are primarily developed for Euclidean spaces a

Deep Bodra, Sushil Khairnar

Cloud resource allocation has emerged as a major challenge in modern computing environments, with organizations struggling to manage complex, dynamic workloads while optimizing performance and cost efficiency. Traditional heuristic approaches prove inadequate for handling the multi-objective optimization demands of existing cloud infrastructures. This paper

Gavin Crowder, Lora Ramunno, Stephen Hughes

The preparation of photonic qubits in the excited state is an integral part of the performance of an on-demand single photon source (SPS). Conventional resonant excitation, an excellent approach to maximize the coherence and indistinguishability of the SPS, often requires polarization filtering to remove the pump signal and isolate the qubit emission, but th

Arne Burdack, Maximilian Stargardt, Christoph Winkler, Konrad Klein

In a world increasingly powered by renewables and aiming for greenhouse gas-neutral industrial production, the future competitiveness of todays top manufacturing countries is questioned. This study applies detailed energy system modeling to quantify the Renewable Pull, an incentive for industry relocation exerted by countries with favorable renewable conditi

Maria Clara Cavalcante-Siviero, K. Menéndez-Delmestre, P. P. B. Beaklini, T. S. Gonçalves

The structure, extent, and mass of the Milky Way's (MW) dark matter (DM) halo are observationally challenging to determine due to our position within the Galaxy. To overcome this limitation, we study a combined sample of 127 MW analogs from the IllustrisTNG-50 cosmological simulation with observations of 11 nearby galaxies. Using both spatial and spectral hi

J A Sellwood

This paper presents an equilibrium model of a Milky Way-like spiral galaxy that supports open, mostly 2- and 3-arm spiral patterns but does not form a bar. It is suggested as a more realistic alternative model to that employed by the Agora collaboration; their model has a much lower disk mass and therefore forms only multi-arm spiral patterns. This improved

Oisin O Sullivan

The security of enterprise-grade networking hardware and software is critical to ensuring the integrity, availability, and confidentiality of data in modern cloud and data center environments. Network interface controllers (NICs) play a pivotal role in high-performance computing and virtualization, but their privileged access to system resources makes them a

Anita L. Cochran, Adam J. McKay, Youssef Moulane

We report high spectral resolving power optical observations of comet C/2017\,K2 (PanSTARRS) as it approached the Sun. This comet was discovered when it was 16\,{\sc au} from the Sun. At discovery, the comet had a large and relatively bright coma. However, the spectrum at discovery showed only signatures of dust. We used the coud{\'e} spectrograph on the McD

Alexander Leithes

At linear order we study perturbations to a G\"odel background spacetime which includes expansion in addition to rotation. We investigate the transformation behaviour of these perturbations under gauge transformations and construct gauge invariant quantities. Using the perturbed energy conservation equation we find that there are conserved quantities in Expa

Oisin O Sullivan, Colin Flanagan, Eoin O Connell

Read-Copy-Update (RCU) is widely used in the Linux kernel to manage concurrent access to shared data structures.However, improper synchronization when removing RCU protected hash table entries can lead to stale pointers, inconsistent lookups, and critical use after free (UAF) vulnerabilities. This paper investigates a driver-level synchronization issue arisi

Yu. I. Pylypchuk, P. O. Nakaznyi, O. V. Barabash, A. O. Zaporozhchenko

A linear magnetic topological defect (cosmic string) is modeled as a magnetic flux-carrying tube that is impenetrable to external spinor matter. The matter field is quantized in the background of this tube, with the most general set of boundary conditions ensuring both the tube's impenetrability and the self-adjointness of the Dirac Hamiltonian operator. We

Evgeniya Egorova, Kathryn Kreckel, Oleg Egorov, Alexei Moiseev

Accretion of metal-poor gas is expected to be an important channel of gas replenishment in galaxy evolution studies. However, observational evidence of this process is still relatively scarce. The unusual polar disk galaxy VGS 12 was found in the Void Galaxy Survey. It appears to be isolated and resides in the cosmological wall between two large voids. The s

Pamud Akalanka Bethmage, Ryker Fish, Brennan Sprinkle, Michelle M. Driscoll

Driven suspensions, where energy is input at a particle scale, are a framework for understanding general principles of out-of-equilibrium organization. A large number of simple interacting units can give rise to non-trivial structure and hierarchy. Rotationally driven colloidal particles are a particularly nice model system for exploring this pattern formati

Rayehe Karimi Mahabadi, Jianfeng Lu, Hossein Salahshoor

Parametrized measures (or Young measures) enable to reformulate non-convex variational problems as convex problems at the cost of enlarging the search space from space of functions to space of measures. To benefit from such machinery, we need powerful tools for approximating measures. We develop a deep neural network approximation of Young measures in this p

Karol Bołbotowski, Guy Bouchitté

Based on a new Kantorovich-Rubinstein duality principle for the Hessian that was recently established by the two authors, we extend the Rio inequality to any dimension $d \ge 1$ with an optimal constant. Similarly, we propose an optimal upper bound for the ratio of Zolotarev distance $Z_2(\mu,\nu)$ to Wasserstein distance $W_2(\mu,\nu)$ when $\mu,\nu \in \ma

Fuming Yang, Yicong Li, Hanspeter Pfister, Jeff W. Lichtman

Petascale electron microscopy (EM) datasets push storage, transfer, and downstream analysis toward their current limits. We present a vector-quantized variational autoencoder-based (VQ-VAE) compression framework for EM that spans 16x to 1024x and enables pay-as-you-decode usage: top-only decoding for extreme compression, with an optional Transformer prior th

Sheer Karny, Anthony Baez, Pat Pataranutaporn

Millions of users now design personalized LLM-based chatbots that shape their daily interactions, yet they can only roughly anticipate how their design choices will manifest as behaviors in deployment. This opacity is consequential: seemingly innocuous prompts can trigger excessive sycophancy, toxicity, or other undesirable traits, degrading utility and rais

Zalán Gyenis

In this paper we study the interaction between logic and probability. In particular, we show that the convex hull of evaluations of a broad class of logics is always effectively axiomatizable. We define a Birkhoff-style calculus for probability axioms for which compactness, and finite completeness is proved. We give example for a logic for which probabilitie

Mihai Iancu, Veronica-Oana Nechita

We give sharp bounds for the hyperbolic curvature of the level curve $|z|=|f(z)|$, when $f:\mathbb{D}\to\mathbb{D}$ is holomorphic on the unit disc $\mathbb{D}$ and $f(0)\neq0$, as well as for other related level curves. As a consequence, we point out a rigidity theorem: if the hyperbolic curvature of the above level curve vanishes at some point, then the le

Mohamed Barakat, Diane Guignard

We develop and analyze a nonlinear reduced basis (RB) method for parametrized elliptic partial differential equations based on a binary-tree partition of the parameter domain into tensor-product structured subdomains. Each subdomain is associated with a local RB space of prescribed dimension, constructed via a greedy algorithm. A splitting strategy along the

Pinjun Zheng, Md. Jahangir Hossain, Anas Chaaban

Channel estimation is fundamental to wireless communications, yet it becomes increasingly challenging in massive multiple-input multiple-output (MIMO) systems where base stations employ hundreds of antennas. Traditional least-squares methods require prohibitive pilot overhead that scales with antenna count, while sparse estimation methods depend on precise c

Tomonori Shirakawa, Javier Robledo-Moreno, Toshinari Itoko, Vinay Tripathi

Quantum computers must operate in concert with classical computers to deliver on the promise of quantum advantage for practical problems. To achieve that, it is important to understand how quantum and classical computing can interact together, and how one can characterize the scalability and efficiency of hybrid quantum-classical workflows. So far, early exp

Hussein Nassar, Andrew Weber

Many compliant shell mechanisms are periodically corrugated or creased. Being thin, their preferred deformation modes are inextensional, i.e., isometric. Here, we report on a recent characterization of the isometric deformations of periodic surfaces. In a way reminiscent of Gauss theorem, the result builds a constraint that relates the ways in which the peri