October 2025 arXiv papers — page 124

Thomas Klatzer, David Cardona-Vasquez, Luca Santosuosso, Sonja Wogrin

Time series aggregation (TSA) aims to construct temporally aggregated optimization models that accurately represent the output space of their full-scale counterparts while using a significantly reduced temporal dimensionality. This paper presents a theoretical approach that achieves exact temporal aggregation of full-scale power system models -- even in the

François Durand

Previous studies have shown that Instant-Runoff Voting (IRV) is highly resistant to coalitional manipulation (CM), though the theoretical reasons for this remain unclear. To address this gap, we analyze the susceptibility to CM of three major voting rules-Plurality, Two-Round System, and IRV-within the Perturbed Culture model. Our findings reveal that each r

Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel

Multi-class wine classification presents fundamental trade-offs between model accuracy, feature dimensionality, and interpretability - critical factors for production deployment in analytical chemistry. This paper presents a comprehensive empirical study of One-vs-Rest logistic regression on the UCI Wine dataset (178 samples, 3 cultivars, 13 chemical feature

ALICE Collaboration

In this letter, the first measurement of the femtoscopic correlation of protons and $\Sigma^+$ hyperons is presented and used to study the p$-\Sigma^+$ interaction. The measurement is performed with the ALICE detector in high-multiplicity triggered pp collisions at $\sqrt{s} = 13$ TeV. The $\Sigma^+$ hyperons are reconstructed using a missing-mass approach i

Mohammad Shafiei, Farhad Fazileh, Milorad V. Milošević

Floquet engineering with high-frequency light offers dynamic control over topological phases in quantum materials. While in 3D Dirac systems circularly polarized light is known to induce topological phase transitions via gap opening, linearly polarized light (LPL) has generally been considered ineffective. Here we show that in quasi-2D Dirac materials the se

M. Aguilar-Janita, V. Martin-Mayor, J. Moreno-Gordo, J. J. Ruiz-Lorenzo

We study the critical behavior of the Ising spin glass in five spatial dimensions through large-scale Monte Carlo simulations and finite-size scaling analysis. Numerical evidence for a phase transition is found both with and without an externally applied magnetic field. The critical exponents are computed in both cases. We compute with a 10% accuracy the low

Guillaume Rongier, Luk Peeters

The distribution of resources in the subsurface is deeply linked to the variations of its physical properties. Generative modeling has long been used to predict those physical properties while quantifying the associated uncertainty. But current approaches struggle to properly reproduce geological structures, and fluvial deposits in particular, because of the

Mayuri Kate, Suresh Neethirajan

The convergence of IoT sensing, edge computing, and machine learning is transforming precision livestock farming. Yet bioacoustic data streams remain underused because of computational complexity and ecological validity challenges. We present one of the most comprehensive bovine vocalization datasets to date, with 569 curated clips covering 48 behavioral cla

Ke Wang, Li-Na Sheng, Tao Li, Geng Wang

The High Intensity heavy ion Accelerator Facility (HIAF) successfully accelerated the 18O6+ beam on October 27, 2025. This paper presents a further simulation study on the high-precision optics, namely sliced optics, of the Booster Ring (BRing) at HIAF based on measured magnetic fields, focusing on three aspects: (1) closed-orbit distortion (COD) and variati

Francesco De Paolis, Faryal Naseem, Noraiz Tahir

The discovery of a temperature asymmetry in the cosmic microwave background (CMB) data towards various galaxies has opened a window for a deeper comprehension of galactic halos. A crucial step forward is that of estimating the fraction of missing baryons in the halos, but it relies on understanding the real cause of the observed CMB temperature asymmetry sin

Alexander Gottstein, Lorenzo Mercolli, Eva Kasanda, Isidre Mateu

We present a practical method to measure the energy of proton beams at a medical cyclotron using the stacked foil technique in combination with a Bayesian inference method. By measuring the $^{48}$V activity induced in a stack of irradiated titanium foils, the proton energy can be inferred without relying on direct current or charge measurements, making the

Nishant Doshi

This paper presents an approach for accelerated learning of optimal plans for a given task represented using Linear Temporal Logic (LTL) in multi-agent systems. Given a set of options (temporally abstract actions) available to each agent, we convert the task specification into the corresponding Buchi Automaton and proceed with a model-free approach which col

Satyaranjan Pradhan, Abhishek Senapati, Madan Mohan Soren

This article discusses the convergence properties of the Max Product and Max Min variants of Durrmeyer type exponential sampling series. We first establish pointwise and uniform convergence of both operators in the space of log uniformly continuous and bounded functions. The rates of convergence are then analyzed in terms of the logarithmic modulus of contin

Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang

The hallmark of Deep Research agents lies in compositional reasoning, the capacity to aggregate distributed, heterogeneous information into coherent logical insights. However, current agentic systems are often retrieval-heavy but reasoning-light, where success is predominantly determined by simple entity-seeking rather than the multi-step aggregation of scat

Davinder Siwal, P. K. Mohanty, D. Bose, A. Jain

A systematic study is conducted to understand the coincident resolving time (CRT) for a pair of Lutetium-yttrium oxyorthosilicate (LYSO) and the plastic scintillation detector bars under the Geant4 framework. Crystals are coupled to a silicon photomultiplier single pad wafer with an appropriate optical coupling for signal readout. Pad reads the light photons

Ruijie Miao, Yilun Yao, Zihan Wang, Zhiming Wang

The Mixture-of-Experts (MoE) technique has proven to be a promising solution to efficiently scale the model size, which has been widely applied in recent LLM advancements. However, the substantial memory overhead of MoE models has made their compression an important research direction. In this work, we provide a theoretical analysis of expert merging, a rece

Nicola Borri, Yukun Liu, Aleh Tsyvinski, Xi Wu

We organize existing empirical regularities of cryptocurrencies into seven stylized facts and analyze cryptocurrencies through the lens of empirical asset pricing. We find important similarities with traditional markets--risk-adjusted performance so far is broadly comparable, and the cross-section of returns can be summarized by a small set of factors. Howev

Bjorn Poonen, Michael Stoll

Let $R$ be a discrete valuation ring, with valuation $v \colon R \twoheadrightarrow \mathbb{Z}_{\ge 0} \cup \{\infty\}$ and residue field $k$. Let $H$ be a hypersurface $\operatorname{Proj}(R[x_0,\ldots,x_n]/\langle f \rangle)$. Let $H_k$ be the special fiber, and let $(H_k)_{\mathrm{sing}}$ be its singular subscheme. Let $\Delta(f)$ be the discriminant of $

Alain Comtet, Pierre Le Doussal, Naftali R. Smith

We study the Tracy-Widom (TW) distribution $f_\beta(a)$ in the limit of large Dyson index $\beta \to +\infty$. This distribution describes the fluctuations of the rescaled largest eigenvalue $a_1$ of the Gaussian (alias Hermite) ensemble (G$\beta$E) of (infinitely) large random matrices. We show that, at large $\beta$, its probability density function takes

Kaushik Bal, Shilpa Gupta

We investigate a class of elliptic and parabolic partial differential equations driven by p(u) laplacian. This dependence necessitates the use of variable exponent Sobolev spaces specifically tailored to the anisotropic framework. For the elliptic case, we establish the existence of a weak solution by employing the theory of pseudomonotone operators in conju

Paolo Foschi

The geometrical structure of PLS shrinkages is here considered. Firstly, an explicit formula for the shrinkage vector is provided. In that expression, shrinkage factors are expressed a averages of a set of basic shrinkages that depend only on the data matrix. On the other hand, the weights of that average are multilinear functions of the observed responses.

Oscar Céspedes Vicente, Karim Achouri, Christophe Caloz

Artificial magnetic conductors (AMCs) mimic the idealized boundary condition of a perfect magnetic conductor (PMC), which reflects electromagnetic waves with a preserved electric field and inverted magnetic field. Despite their usefulness, existing AMC implementations often rely on complex or impractical designs, and lack a clear electromagnetic theory expla

Ho Yin Au, Jie Chen, Junkun Jiang, Jingyu Xiang

Recent research on motion generation has shown significant progress in generating semantically aligned motion with singular semantics. However, when employing these models to create composite sequences containing multiple semantically generated motion clips, they often struggle to preserve the continuity of motion dynamics at the transition boundaries betwee

Song-lin Zhao, Han Wang, Da-jun Zhang

In this paper we derive bilinear forms and present their solutions in Casoratians for several fourth-order lattice Gel'fand-Dikii (lattice GD-4) equations. These equations were recently formulated from the direct linearization approach and exhibit the multidimensionally consistent property in multi-component form. Based on the obtained soliton solutions, we

Jin Zhang

The complete one loop corrections to the trilinear self coupling of the Higgs boson is firstly evaluated in the Standard Model (SM) by the effective potential approach. Then we extend the SM by including a singlet vector-like top partner and assume that the top partner only mixes with the top quark, the effects of the mass of the top partner and the mixing p

Andrea Di Giusto, Alberto Ravagnani

We investigate the asymptotic number of equivalence classes of linear codes with prescribed length and dimension. While the total number of inequivalent codes of a given length has been studied previously, the case where the dimension varies as a function of the length has not yet been considered. We derive explicit asymptotic formulas for the number of equi

Antonio Quim Cervantes, Ajinkya Desai, Tirtha Banerjee

This study investigates the influence of forest canopy heterogeneity on buoyant plume dynamics resulting from surface thermal anomalies representing wildland fires, utilizing Large Eddy Simulation (LES). The Parallelized Large-Eddy Simulation Model (PALM) was employed to simulate six canopy configurations: no canopy, homogeneous canopy, external plume-edge c

F. M. Brunbauer, C. Chatterjee, G. Cicala, D. D'Ago

This study investigates the suitability of Hydrogenated NanoDiamond (HND) materials as an alternative for CsI in MPGD-based photon detectors. The research focuses on characterizing HND photocathodes coupled with THGEM + Micromegas-based detectors. The HND grains were prepared via hydrogenation and stored in water for more than two years. They were then coate

Enzo Zanchini, Francesco Zanchini, Claudia Naldi

An accurate design of a ground-coupled heat pump system requires the knowledge of the outlet fluid temperature from the borehole heat exchangers (BHEs), both in the short and long term. This paper fucuses on the short and medium term. In this time range, either 3D finite-element simulations or Thermal Resistance Capacity Models (TRCMs) can be applied. The fo

Qingyu Ren, Qianyu He, Powei Chang, Jie Zeng

Language models often struggle to follow multi-constraint instructions that are crucial for real-world applications. Existing reinforcement learning (RL) approaches suffer from dependency on external supervision and sparse reward signals from multi-constraint tasks. We propose a label-free self-supervised RL framework that eliminates dependency on external s

Tapio Pahikkala, Riikka Numminen, Parisa Movahedi, Napsu Karmitsa

Consider two sets of entities and their members' mutual affinity values, say drug-target affinities (DTA). Drugs and targets are said to interact in their effects on DTAs if drug's effect on it depends on the target. Presence of interaction implies that assigning a drug to a target and another drug to another target does not provide the same aggregate DTA as

Hai Ha Pham, Ngoc-Sang Pham

We investigate the effects of wariness (defined as individuals' concern for their minimum utility over time) on poverty traps and equilibrium multiplicity in an overlapping generations (OLG) model. We explore conditions under which (i) wariness amplifies or mitigates the likelihood of poverty traps in the economy and (ii) it gives rise to multiple intertempo

Oleg Lunin

We analyze equations describing gravitational waves in the Myers-Perry and Gibbons-Lu-Page-Pope geometries with arbitrary rotation parameters. Assuming that at least one rotation parameter vanishes, we demonstrate full separability of equations for several polarizations of gravitational waves and analyze the resulting ODEs. We also construct some examples of

Hrishikesh Chakrabarty, Daniele Malafarina

We propose a unified model of dark energy and inflation through the Markov-Mukhanov modification of the Einstein-Hilbert action, where the matter sector is coupled to gravity via a scalar coupling function depending only on the energy density of the matter content. We assume that the coupling function encodes the UV corrections to the standard model of cosmo

Tadao Hoshino

This paper develops a sensitivity analysis framework that transfers the average total treatment effect (ATTE) from source data with a fully observed network to target data whose network is completely unknown. The ATTE represents the average social impact of a policy that assigns the treatment to every individual in the dataset. We postulate a covariate-shift

Baris Baysal, Omid Arfaie, Ramazan Unal

This study presents a powered transtibial prosthesis with complete push-off assistance, RoboANKLE. The design aims to fulfill specific requirements, such as a sufficient range of motion (RoM) while providing the necessary torque for achieving natural ankle motion in daily activities. Addressing the challenges faced in designing active transtibial prostheses,

Runlin Zhou, Letian Li, Zemin Zheng

We study personalized federated learning for multivariate responses where client models are heterogeneous yet share variable-level structure. Existing entry-wise penalties ignore cross-response dependence, while matrix-wise fusion over-couples clients. We propose a Sparse Row-wise Fusion (SROF) regularizer that clusters row vectors across clients and induces

Claudia Grundke, Gabriele Röger

Axioms are a feature of the Planning Domain Definition Language PDDL that can be considered as a generalization of database query languages such as Datalog. The PDDL standard restricts negative occurrences of predicates in axiom bodies to predicates that are directly set by actions and not derived by axioms. In the literature, authors often deviate from this

Enis Oğuz

The developments in Generative AI technologies have paved the way for numerous innovations in different fields. Recently, Generative AI has been proposed as a competitor to AES systems in evaluating student essays automatically. Considering the potential limitations of AI in processing idioms, this study assessed the scoring performances of Generative AI mod

Xiaoyu Ma, Hao Chen

Multimodal learning integrates information from different modalities to enhance model performance, yet it often suffers from modality imbalance, where dominant modalities overshadow weaker ones during joint optimization. This paper reveals that such an imbalance not only occurs during representation learning but also manifests significantly at the decision l

Michael Röckner, Yiming Su, Yanjun Sun, Deng Zhang

We consider the stochastic nonlinear Schr\"odinger equation driven by linear multiplicative noise in the mass-supercritical case. Given arbitrary $K$ solitary waves with distinct speeds, we construct stochastic multi-solitons pathwisely in the sense of controlled rough path, which behave asymptotically as the sum of the $K$ prescribed solitons as time tends

Tatsuru Kikuchi

I develop a comprehensive theoretical framework for dynamic spatial treatment effect boundaries using continuous functional definitions grounded in Navier-Stokes partial differential equations. Rather than discrete treatment effect estimators, the framework characterizes treatment intensity as a continuous function $\tau(\mathbf{x}, t)$ over space-time, enab

Germain Bourcier, Stephane Balac, Safia Mohand-Ousaid, Julien Lumeau

Fiber Fabry--Perot (FFP) resonators of a few centimeters are optimized as a function of the reflectivity of the mirrors and the dimensions of the intra-cavity waveguide. Loaded quality factor in excess of 10^9, with an optimum of 4___x___10^9, together with an intrinsic quality factor larger than 10^10 and intrinsic finesse in the range of 10^5 have been mea

Jiawei Li, Qingyuan Zhang, Didier Sornette

Forecast models in statistical seismology are commonly evaluated with log-likelihood scores of the full distribution P(n) of earthquake numbers, yet heavy tails and out-of-range observations can bias model ranking. We develop a tail-aware evaluation framework that estimates cell-wise P(n) using adaptive Gaussian kernel density estimation and tests three stra

Xikai Zhang, Bo Wang, Likang Xiao, Yongzhi Li

Although large language models (LLMs) have made significant strides across various tasks, they still face significant challenges in complex reasoning and planning. For example, even with carefully designed prompts and prior information explicitly provided, GPT-4o achieves only a 7% Final Pass Rate on the TravelPlanner dataset in the sole-planning mode. Simil

Hsiao-Yi Chen, Ryotaro Arita, Yusuke Nomura

We present a first-principles method based on density functional theory and many-body perturbation theory for computing spin excitations in magnetic systems with noncollinear spin textures. Traditionally, the study of magnetic excitations has relied on spin models that assume magnetic moments to be localized. Beyond this restriction, recent $ab~initio$ metho

Harold J. W. Zandvliet, Pantelis Bampoulis, Cristiane Morais Smith, Lumen Eek

A twisted honeycomb bilayer exhibits a moir\'e superstructure that is composed of a hexagonal arrangement of AB and BA stacked domains separated by domain boundaries. In the case of twisted bilayer graphene, the application of an electric field normal to the bilayer leads to the opening of inverted band gaps in the AB and BA stacked domains. The inverted ban

Chao Tu, Kun Huang, Jie Zhang, Qianjin Feng

The burgeoning discipline of computational pathology shows promise in harnessing whole slide images (WSIs) to quantify morphological heterogeneity and develop objective prognostic modes for human cancers. However, progress is impeded by the computational bottleneck of gigapixel-size inputs and the scarcity of dense manual annotations. Current methods often o

Sean Cowan, Ron Noomen

This work aims to automate the design of Multiple Gravity-Assist (MGA) transfers between planets using low-thrust propulsion. In particular, during the preliminary design phase of space missions, the combinatorial complexity of MGA sequencing is very large, and current optimization approaches require extensive experience and can take many days to simulate. T

Prateek Gupta, Qiankun Zhong, Hiromu Yakura, Thomas Eisenmann

A growing body of multi-agent studies with LLMs explores how norms and cooperation emerge in mixed-motive scenarios, where pursuing individual gain can undermine the collective good. While prior work has explored these dynamics in both richly contextualized simulations and simplified game-theoretic environments, most LLM systems featuring common-pool resourc

Yingpeng Ning, Yuanyuan Sun, Ling Luo, Yanhua Wang

Biomedical question answering (QA) requires accurate interpretation of complex medical knowledge. Large language models (LLMs) have shown promising capabilities in this domain, with retrieval-augmented generation (RAG) systems enhancing performance by incorporating external medical literature. However, RAG-based approaches in biomedical QA suffer from halluc

Juan Pablo Aguilera, Anton Fernández, Joost J. Joosten

Using relativized ordinal analysis, we give a proof-theoretic characterization of the provably total set-recursive-from-$\omega$ functions of KPl and related theories.

Shiyao Ding, Takayuki Ito

Large language models (LLMs) trained for general \textit{next-token prediction} often fail to generate responses that reflect how specific individuals communicate. Progress on personalized alignment is further limited by the difficulty of collecting real-world personal communication data due to privacy constraints. We propose Your Next Token Prediction (YNTP

Kaoru Sano

We study rational iterated preimages of the origin under unicritical maps $f_{d,c}(x)=x^d+c$. Earlier works of Faber--Hutz--Stoll and Hutz--Hyde--Krause established finiteness and conditional bounds in the quadratic case. Building on this, we prove that for $d=2$ and $c \in \mathbb Q\setminus\{0,-1\}$ there are no rational fourth preimages of the origin, and

Philippe Baranek, James P. Connolly, Antoine Gissler, Philip Schulz

This paper presents a multiscale approach to evaluate perovskite solar cell performance which determines material properties at the atomistic scale with first-principles calculations, and applies them in macro-scale device models. This work focuses on the MAPbI3 (MA = CH3NH3) perovskite and how its phase transitions impact on its optical, electronic, and str

Jorge Antonio González Layja

The main objective of this paper is to evaluate six new Ap\'ery-like series of weight $5$ in closed form. These series involve harmonic numbers and exhibit the characteristic reciprocal central binomial coefficient structure. Generating functions for the inverse sine and identities related to harmonic numbers are used to link each series to a variety of inte

Jun Li, Qun Zhao

Suicide remains a critical global public health issue. While previous studies have provided valuable insights into detecting suicidal expressions in individual social media posts, limited attention has been paid to the analysis of longitudinal, sequential comment trees for predicting a user's evolving suicidal risk. Users, however, often reveal their intenti

Fatao Wang, Guodong Wang

We provide a short proof of the $L^2$-orbital stability of a class of explicit steady Euler flows in a disk by establishing a quantitative estimate. The main idea is to exploit the conserved quantities of the Euler equation, including the kinetic energy, the enstrophy, and the moment of fluid impulse. Our result seems to suggest that more radial symmetry lea

Ching-Lin Hsiung, Tian-Sheuan Chang

Current transformer accelerators primarily focus on optimizing self-attention due to its quadratic complexity. However, this focus is less relevant for vision transformers with short token lengths, where the Feed-Forward Network (FFN) tends to be the dominant computational bottleneck. This paper presents a low power Vision Transformer accelerator, optimized

Hongtao Lyu, Boyue Liu, Mingyu Wu, Haibo Chen

Large language model (LLM) inference systems face a fundamental tension between minimizing Time-to-First-Token (TTFT) latency for new requests and maintaining a high, steady token generation rate (low Time-Per-Output-Token, or TPOT) for ongoing requests. Existing stall-free batching schedulers proposed by Sarathi, while effective at preventing decode stalls,

Jaehoon Ahn, Moon-Ryul Jung

Recent beat and downbeat tracking models (e.g., RNNs, TCNs, Transformers) output frame-level activations. We propose reframing this task as object detection, where beats and downbeats are modeled as temporal "objects." Adapting the FCOS detector from computer vision to 1D audio, we replace its original backbone with WaveBeat's temporal feature extractor and

Yi-Ying Wang, Yin-Jie Li, Yi-Zhong Fan

Hubble constant tension, together with the recent indications of dynamical dark energy proposed from the Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillation (BAO) measurements, poses significant challenges to the standard cosmological model. We investigate the possible redshift evolution of dark energy and the Hubble constant through a da

Brandon Hill, Kma Solaiman

Motherboard defect detection is critical for ensuring reliability in high-volume electronics manufacturing. While prior research in PCB inspection has largely targeted bare-board or trace-level defects, assembly-level inspection of full motherboards inspection remains underexplored. In this work, we present BoardVision, a reproducible framework for detecting

Zhe Wu, Hongjin Lu, Junliang Xing, Changhao Zhang

Building agents that autonomously operate mobile devices has attracted increasing attention. While Vision-Language Models (VLMs) show promise, most existing approaches rely on direct state-to-action mappings, which lack structured reasoning and planning, and thus generalize poorly to novel tasks or unseen UI layouts. We introduce Hi-Agent, a trainable hierar

Yijie Hu, Zihao Zhou, Kaizhu Huang, Xiaowei Huang

Math reasoning has been one crucial ability of large language models (LLMs), where significant advancements have been achieved in recent years. However, most efforts focus on LLMs by curating high-quality annotation data and intricate training (or inference) paradigms, while the math reasoning performance of multi-modal LLMs (MLLMs) remains lagging behind. S

Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi Nagabhushana

Spiking neural networks have garnered increasing attention due to their energy efficiency, multiplication-free computation, and sparse event-based processing. In parallel, state space models have emerged as scalable alternatives to transformers for long-range sequence modelling by avoiding quadratic dependence on sequence length. We propose SHaRe-SSM (Spikin

Ding Cao, Guangyou Fang, Xuequan Chen

Microorganisms are ubiquitous in nature, and microbial activities are closely intertwined with the entire life cycle system and human life. Developing novel technologies for the detection, characterization and manipulation of microorganisms promotes their applications in clinical, environmental and industrial areas. Over the last two decades, terahertz (THz)

Danish Ali, Ajmal Mian, Naveed Akhtar, Ghulam Mubashar Hassan

Accurate brain tumor segmentation is significant for clinical diagnosis and treatment but remains challenging due to tumor heterogeneity. Mamba-based State Space Models have demonstrated promising performance. However, despite their computational efficiency over other neural architectures, they incur considerable overhead for this task due to their sequentia

Yohei Yamada, Zenas C. Chao

The brain anticipates future events using internal models that specify not only what will occur, but also when it will occur and with what probability. We refer to this joint specification of identity, timing, and likelihood as a complete prediction object. Existing computational models typically capture identity and timing separately, omit probability as an

Andrew Zhao, Reshmi Ghosh, Vitor Carvalho, Emily Lawton

Large language model (LLM) systems increasingly power everyday AI applications such as chatbots, computer-use assistants, and autonomous robots, where performance often depends on manually well-crafted prompts. LLM-based prompt optimizers reduce that effort by iteratively refining prompts from scored feedback, yet the security of this optimization stage rema

Miaomiao Zhang, Jouni Kainulainen, He Zhao, Yang Su

Dust plays a critical role in the study of the interstellar medium (ISM). Extinction maps derived from optical surveys often fail to capture regions with high column density due to the limited photometric depth in optical wavelengths. To address these limitations, we developed the XPNICER method based on near-infrared (NIR) photometric survey data. This meth

Ming-Han Lin, Tian-Sheuan Chang

Computing-in-Memory (CIM) macros have gained popularity for deep learning acceleration due to their highly parallel computation and low power consumption. However, limited macro size and ADC precision introduce throughput and accuracy bottlenecks. This paper proposes a two-stage CIM-aware model adaptation process. The first stage compresses the model and rea

Ruifeng Yuan, Lei Wu

While flow optimization has been extensively studied in the continuum regime, its extension to rarefied gas flows remains less explored. Here, based on the Boltzmann model equation, an adjoint topology optimization method is employed to design two-dimensional single inlet multi outlet manifolds, aiming to maximize the total mass flow rate while maintaining o

Mykolas Sveistrys, Richard Kunert

Retrieval-Augmented Generation (RAG) has been used in question answering (QA) systems to improve performance when relevant information is in one (single-hop) or multiple (multi-hop) passages. However, many real life scenarios (e.g. dealing with financial, legal, medical reports) require checking all documents for relevant information without a clear stopping

Dongnam Byun, Jungwon Park, Jungmin Ko, Changin Choi

Recent progress in text-to-image (T2I) generative models has led to significant improvements in generating high-quality images aligned with text prompts. However, these models still struggle with prompts involving multiple objects, often resulting in object neglect or object mixing. Through extensive studies, we identify four problematic scenarios, Similar S

Xiaofeng Cai, Zhen Hao, Liu Liu, Jiayu Wan

In this work, we present an asymptotic-preserving semi-Lagrangian discontinuous Galerkin scheme for the Boltzmann equation that effectively handles multi-scale transport phenomena. The main challenge lies in designing appropriate moments update for penalization within the semi-Lagrangian framework. Inspired by [M. Ding, J. M. Qiu, and R. Shu, Multiscale Mode

Han Qiu, Peng Gao, Lewei Lu, Xiaoqin Zhang

Multimodal large language models~(MLLMs) have demonstrated promising spatial understanding capabilities, such as referencing and grounding object descriptions. Despite their successes, MLLMs still fall short in fine-grained spatial perception abilities, such as generating detailed region descriptions or accurately localizing objects. Additionally, they often

Van Chien Le, Karel Van Bockstal

This paper develops the necessary ingredients for the variational approach of initial boundary-value problems of parabolic partial differential equations on a fixed spatial domain containing evolving subdomains. In particular, we introduce function spaces for the variational solution that extend standard Sobolev-Bochner spaces to account for a coefficient as

Md Shakhawath Hossain, Jiatong Xu, Thi Ngoc Anh Mai, Nhat Minh Nguyen

Diamonds hosting color centers possess intrinsically high thermal conductivity; therefore, laser-induced heating has often received little attention. However, when placed on substrates with low thermal conductivity, localized heating of diamonds under laser excitation can become significant, and the presence of an interfacial polymer layer between substrate

Jianchun Chu, Jie Deng, Zihang Hao, Jian Li

In this paper, we obtain the optimal rigidity of dimension estimate for holomorphic functions with polynomial growth on K\"ahler manifolds with non-negative holomorphic bisectional curvature. There is a specific gap between the largest and the second largest dimension. We also determine the optimal dimension that ensures the maximal volume growth which impli

Romario Mondal, Sk. Soyeb Ali, Saikat Nandi, S. Chattopadhyay

A triple perovskite oxide Ba$_{3}$MnTa$_{2}$O$_{9}$ has been synthesized and its magnetic properties have been investigated through dc and ac magnetization, specific heat, electron spin resonance (ESR) measurements, and density functional theory (DFT) calculations. Mn$^{2+}$ ($S$ = 5/2) ions are the only magnetic species present in the material. These Mn$^{2

Tianhua Xia, Sai Qian Zhang

Running Large Language Models (LLMs) on edge devices is crucial for reducing latency, improving real-time processing, and enhancing privacy. By performing inference directly on the device, data does not need to be sent to the cloud, ensuring faster responses and reducing reliance on network connectivity. However, implementing LLMs on edge devices presents ch

Joseph E. Trujillo-Falcon, Monica L. Bozeman, Liam E. Llewellyn, Samuel T. Halvorson

To advance a Weather-Ready Nation, the National Weather Service (NWS) is developing a systematic translation program to better serve the 68.8 million people in the U.S. who do not speak English at home. This article outlines the foundation of an automated translation tool for NWS products, powered by artificial intelligence. The NWS has partnered with LILT,

Mei Dong, Lin Liu, Dingke Tang, Geoffrey Liu

Non-binary instrumental variables, especially continuous ones, are common in practice. A binary recoding induces a Wald ratio but may discard useful variation and reduce efficiency. Although fully nonparametric approaches can in principle use the entire instrument, they often require high-dimensional nuisance estimation which can be unstable with rich covari

Xiaocun Zong, Hao Shi, Fan Yang, Yong Liu

In this letter, we present the design and implementation of a 2-bit digaital metasurface operating in the Ku-band, engineered to exhibit advanced polarization conversion characteristics and support dual-polarization control for both X- and Y-polarizations. To address the challenge of array size scalability hindered by extensive DC control routing in 2-bit me

Shan Jiang, He Yan, Chenxia Xie, Lei Wang

In this study, the thermocapillary actuation behavior of an odd viscous droplet on a uniformly heated surface is numerically investigated using a phase-field-based lattice Boltzmann method. The numerical results reveal that unlike a conventional viscous droplet that remains stationary on a uniformly heated surface, the presence of odd viscosity converts tang

Isaac Ohavi

The aim of this work is to establish the well-posedness of fully nonlinear partial differential equations (PDE) posed on a star-shaped network, having nonlinear Kirchhoff's boundary condition at the vertex, and possibly degenerate. We obtain a comparison theorem, for discontinuous viscosity solutions, following the recent ideas obtained by Ohavi for second o

Lachlan Passenger, Sharan Banagiri, Eric Thrane, Paul D. Lasky

The binary black hole merger GW231123 is both the most massive gravitational-wave event observed and has the highest component spins measured to date. The dimensionless spins of the more massive (primary) and less massive (secondary) black holes are measured to be $\chi_1 = 0.90^{+0.10}_{-0.19}$ and $\chi_2 = 0.80^{+0.20}_{-0.51}$ ($90\%$ credible intervals)

Xiaocun Zong, Fan Yang, Shenheng Xu, Maokun Li

This letter proposes a novel anti-interference communication method leveraging computational antennas, utilizing time averaging and 1-bit reconfigurable intelligent surfaces (RIS) to achieve robust signal modulation with minimal hardware complexity. We develop a communication model for computational antennas and propose an efficient signal processing algorit

Pawel Pawlowski

\textbf{T-BAT} logic is a formal system designed to express the notion of informal provability. This type of provability is closely related to mathematical practice and is quite often contrasted with formal provability, understood as a formal derivation in an appropriate formal system. \textbf{T-BAT} is a non-deterministic four-valued logic. The logical valu

Po-Hsun Wu, Yu-Chieh Lo, Yen-Chieh Huang

A robust and infrared laser-excited photocathode with high quantum efficiency, high brightness, and low cost, operating under a moderate vacuum, has long been sought by the accelerator and microscopy communities. This study investigates various types of graphite photocathodes, including bulk, sheet, and flake graphite, in the regime of thermionically assiste

Zichen Wen, Yiyu Wang, Chenfei Liao, Boxue Yang

In an era where AI is evolving from a passive tool into an active and adaptive companion, we introduce AI for Service (AI4Service), a new paradigm that enables proactive and real-time assistance in daily life. Existing AI services remain largely reactive, responding only to explicit user commands. We argue that a truly intelligent and helpful assistant shoul

Yuanhao Cui, Jiali Nie, Fan Liu, Weijie Yuan

The capacity-maximization design philosophy has driven the growth of wireless networks for decades. However, with the slowdown in recent data traffic demand, the mobile industry can no longer rely solely on communication services to sustain development. In response, Integrated Sensing and Communications (ISAC) has emerged as a transformative solution, embedd

Xiaobei Zhao, Xingqi Lyu, Xin Chen, Xiang Li

Agricultural robots are emerging as powerful assistants across a wide range of agricultural tasks, nevertheless, they are still heavily relying on manual operations or fixed railways for movement. The A2A benchmark and the AgriVLN method pioneeringly extended Vision-and-Language Navigation (VLN) to the agricultural domain, successfully navigating agricultura

Liwen Yan, Linyuan Che, Jing Li

Existing phase-resolved wave reconstruction methods are mostly based on potential flow theory, which limits their ability to capture strongly nonlinear phenomena such as wave breaking dynamics. In such cases, the importance of multiphase incompressible Navier--Stokes solvers becomes particularly evident. These solvers form the foundation of the widely used n

Zhiqiang Kou, Junyang Chen, Xin-Qiang Cai, Ming-Kun Xie

Large language models (LLMs) have achieved impressive results across a range of natural language processing tasks, but their potential to generate harmful content has raised serious safety concerns. Current toxicity detectors primarily rely on single-label benchmarks, which cannot adequately capture the inherently ambiguous and multi-dimensional nature of re

Ian Edmonds, Peter Killen

We introduce a new method for predicting sunspot number (SSN) that, based on successful back projections, can predict features of the SSN several solar cycles in advance. The method applies Fourier analysis to the annual SILSO SSN record, from 1700.5 to 2023.5, to identify in the spectrum, four strong components in the decadal, 10 to 11 year period, range an

Siddharth Tourani, Jayaram Reddy, Sarvesh Thakur, K Madhava Krishna

With the rise in consumer depth cameras, a wealth of unlabeled RGB-D data has become available. This prompts the question of how to utilize this data for geometric reasoning of scenes. While many RGB-D registration meth- ods rely on geometric and feature-based similarity, we take a different approach. We use cycle-consistent keypoints as salient points to en

Ziad Elshaer, Essam A. Rashed

High-performing medical Large Language Models (LLMs) typically require extensive fine-tuning with substantial computational resources, limiting accessibility for resource-constrained healthcare institutions. This study introduces a confidence-driven multi-model framework that leverages model diversity to enhance medical question answering without fine-tuning

Rijul Tandon, Peter Vamplew, Cameron Foale

In most value-based reinforcement learning (RL) algorithms, the agent estimates only the expected reward for each action and selects the action with the highest reward. In contrast, Distributional Reinforcement Learning (DRL) estimates the entire probability distribution of possible rewards, providing richer information about uncertainty and variability. C51