Computational Geometry
CuACD: A Fully GPU-Resident Approximate Convex Decomposition
Ruoxi Shi, Xinyue Wei, Fanbo Xiang et al. (+2 more)
Approximate convex decomposition (ACD) converts triangle meshes into small sets of convex parts and is a standard preprocessing step for physics simulation, collision detection, and large-scale
robot learning. The majority of modern ACD methods produce high-quality decompositions through an expensive search over candidate cutting planes, with per-mesh runtimes of tens of seconds that force game pipelines into overnight bakes and keep articulated-object
datasets on CPU clusters for days. Prior work has accelerated isolated stages, most recently VisACD's GPU-based visibility metric, yet the dominant costs – search, mesh cutting, and convex hull construction – have remained on the CPU because their natural decomposition into many small homogeneous phases trails off in a fading last wave at every kernel boundary, and the variable-sized output of each phase forces a host round trip simply to allocate the next launch's input. We address these obstacles by adopting the warp, rather than the thread or thread block, as the unit of
algorithm design, an idea introduced in the graph-processing community for a different pathology and which we adapt here to fuse the many heterogeneous phases of a computational-geometry pipeline into single warp-resident kernels, paired with a device-side heap allocator that lets the buffers between fused phases be sized and allocated on the device. Building on this template, we present CuACD (CUDA ACD), the first fully GPU-resident ACD system, together with a suite of reusable GPU components, released as open-source standalone CUDA modules that drop into any search-based ACD pipeline. On the V-HACD
benchmark, PartNet-Mobility, and an Objaverse subset, CuACD achieves more than an order of magnitude of speedup over CoACD at matched or better quality.
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All authors Ruoxi Shi, Xinyue Wei, Fanbo Xiang, Zexiang Xu, Hao Su
Plasma Physics
AI-Accelerated Gyrokinetic Predictions of Turbulent Transport for Stellarator Design Optimization and Experimental Planning
R. Michael Churchill, Matt Landreman, Jong Youl Choi et al. (+10 more)
Previous work built AI-based surrogates for a nonlinear gyrokinetic simulation code with the goal of using them for fast, direct calculations of turbulent ion heat flux in stellarator design optimizations and scenario planning for experiments. These AI surrogates were trained on data from >200k nonlinear, adiabatic electron gyrokinetic simulations with the gyrokinetic flux-tube code GX, using a wide range of stellarator
magnetic configurations ($\sim$23k), positions in the
plasma, and gradient scale lengths. In this paper, we demonstrate the use of the AI-based
turbulence surrogate in the optimization of stellarator
magnetic equilibrium and to speed up stellarator transport solvers. Due to its speed ($\sim$ms), the AI-based surrogate enables previously unattainable optimization objectives, such as full radial profiles of ion turbulent heat flux, or directly optimizing to maximize the turbulent critical gradient at multiple locations across the
plasma. These direct calculations provide a potentially more accurate optimization target and reduce reliance on ad-hoc heuristics that may not accurately capture the variation of turbulent transport with
magnetic configuration. By including the AI-based surrogate for turbulent heat flux in a transport solver, we can quickly postprocess and confirm the improved ion temperature resulting from the optimized
equilibrium. Finally, we demonstrate the use of AI
agents with strong
reasoning AI models to automate the outer loop, exploring many objective and hyperparameter configurations with this AI-based
turbulence surrogate to discover improved
turbulence optimized
magnetic equilibria.
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All authors R. Michael Churchill, Matt Landreman, Jong Youl Choi, Byoungchan Jang, Rory Conlin, Noah Mandell, Anima Anandkumar, Valentin Duruisseaux, Jeffrey Larson, Dario Panici, Yigit Gunsur Elmacioglu, Jai Sachdev, Tony Qian
Numerical Analysis
Error analysis of penalized $H({\rm div})$-conforming finite element methods for surface Stokes problems
Alan Demlow and Orsan Kilicer
In recent years a number of finite element methods have been developed for the surface Stokes equations. Constructing standard conforming surface finite element methods for the velocity-pressure formulation of this problem would require simultaneously enforcing $H^1$ conformity and tangentiality of the velocity field, which is not possible on the generalized polyhedral approximating surfaces on which surface FEM are typically posed. Thus methods for this problem weakly enforce either tangentiality or $H^1$ conformity of the velocity field. In this paper we provide an error analysis for an $H({\rm div})$-conforming method based on classical Brezzi-Douglas-Marini elements. In this method continuity of the velocity field is weakly enforced using an interior penalty-type formulation. Advantages of this formulation include exact enforcement of the incompressibility constraint and its construction based on a standard mixed finite element pair. Error analysis of this method in previous work was only valid for lowest-order elements and did not account for “
geometric errors”, i.e., variational crimes due to approximation of the continuous surface on which the PDE is posed by an approximating surface on which the finite element method is posed. Here we provide a full error analysis in a natural discontinuous Galerkin energy norm, with
geometric errors accounted for. Optimal estimates for the $L_2$ velocity error are also presented. In the case of lowest-order (polyhedral) surface approximations, our proof of optimal $L_2$ estimates requires a stricter than usual condition on the placement of mesh nodes relative to the surface. Numerical experiments are used to illustrate our results.
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Systems and Control
Multivariable Newton-Based Extremum Seeking with Bounded Update Rates
Farzaneh Karimi, Mohsen Mojiri, Mohammadali Ghadiri-Modarres et al. (+1 more)
We propose a multivariable Newton-based extremum seeking scheme, incorporating known bounds on update rates. The design extends the recent bounded extremum seeking scheme to the Newton-based approach, which makes the convergence rate independent of the unknown Hessian by estimating and inverting the Hessian
matrix. As a vital part of the Newton-based
algorithm, we design an appropriate demodulation
matrix to generate an estimate of the Hessian in an average sense. The local exponential stability of the bounded extremum seeking
algorithm is proven for general multivariable static maps using averaging analysis. In comparison with previous practical stability results, it guarantees convergence to a neighborhood of the extremum point with an exact convergence rate exhibiting exponential decay. Subsequently, the main stability result is established for the Newton-based bounded extremum seeking approach under a weak Hessian coupling assumption. Simulation results demonstrate the advantage of the proposed approach over bounded extremum seeking by assigning equal, desired convergence rates to all parameters using the Newton approach.
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All authors Farzaneh Karimi, Mohsen Mojiri, Mohammadali Ghadiri-Modarres, Azadeh Mohammadpour
Computation and Language
PTC-Bias: Phoneme-Level Temporal Competition for Bias Retrieval and Post-Decoding Correction in Speech LLMs
Zhiqi Ai, Han Cheng, Shiyi Mu et al. (+2 more)
Contextual biasing improves rare-word recognition in
speech large
language models (SpeechLLMs), but efficiently exploiting large bias lists remains challenging. We propose PTC-Bias, a two-stage framework based on phoneme-level temporal competition. At the prefill stage, PTC
Retrieval performs frame-synchronous phoneme decoding and temporal competition among candidate pronunciations, producing a compact bias-word shortlist and corresponding
speech intervals. After SpeechLLM decoding, PTC Correction conducts a second local competition between the retrieved candidates and mismatched transcript spans within these intervals. Selective correction reduces near-homophone and word-
segmentation errors while preserving correct transcriptions. Both stages share the same phoneme posteriors and require no additional SpeechLLM forward pass. Experiments on LibriSpeech show consistent gains across two SpeechLLMs and bias lists of up to 2000 words. With
Prompt-SLAM-ASR-7B and 2000 bias words, PTC-Bias reduces B-WER by 23.4%/23.9% relative to CTC-Filter on test-clean/test-other, while keeping U-WER nearly unchanged.
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All authors Zhiqi Ai, Han Cheng, Shiyi Mu, Yongjin Zhou, Shugong Xu
Quantum Physics
Associative Memory for Quantum Entangled States
Qiyuan Hu, Kartiek Agarwal and Ivar Martin
We generalize the canonical Hopfield model of associative
memory, which stores classical states of individual
spins, to store entangled
quantum states. The construction is based on truncated projector-sum
Hamiltonian, and we explicitly consider a special case that stores dimer singlet coverings of a
spin-1/2 system. We analyze the stability of the stored
memories and find that the capacity – the number of stable patterns – scales faster with the system size than one would expect from a naive analogy with the standard Hopfield model, suggesting a possible
quantum advantage.
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Cryptography and Security
Unmasking Shortcut Learning in IoT Intrusion Detection: A Forensic, Multi-Paradigm Evaluation of Feature Dependence and Data Leakage
Uday Shankar Roy and Mahbuba Jahan Minu
Machine learning-based Network Intrusion Detection Systems often report near-perfect performance on IoT
benchmarks. However, whether these models learn generalizable attack behavior or exploit spurious
dataset shortcuts- such as static testbed IP/MAC addresses and chronological recording artifacts-remains an important question. We evaluate the CyberFlowIoT-GICAP
benchmark, containing 3,617,388 flow records across 126 PCAP sessions with 849,395 benign flows. Four learning paradigms are evaluated across four feature configurations using PCAP-disjoint splits; LightGBM is additionally evaluated using conventional random-flow splitting. When only statistical flow behavior is used (Fbehav), LightGBM (92.58% +/- 8.18%), Random Forest (92.59% +/- 8.18%), and Deep MLP (92.55% +/- 8.18%) achieve nearly identical Macro-F1, indicating that performance is constrained by feature representation rather than model complexity. With raw timestamps (Ftstamp), tree-based models reach 99.28% Macro-F1, while the linear model remains at 90.62%, showing that nonlinear models can exploit
dataset-specific temporal structure. Attack detectability is highly asymmetric: high-rate and active attacks maintain >99.8% recall from flow behavior alone in nonlinear models, whereas the DNS Beaconing drops from 27.78% to 0.00% recall when contextual features are removed. Conventional random-flow splitting increases attack recall by up to 14.00%, highlighting the effect of placing flows from the same sessions in both training and test sets. We conclude with a 4-point protocol checklist for realistic IoT NIDS evaluation.
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Performance
The Canonical Parallel Form as a Substrate for Parallelizing Compilers and Agentic Optimizers
Yakup Koray Budanaz, Pratyai Mazumder, Alexandru Calotoiu et al. (+1 more)
Imperative code fixes an execution order the computation does not require, and a parallelizing compiler must prove which parts of that order it can remove. We introduce the Canonical Parallel Form (CPF), a device-neutral program state from which every ordering constraint our analyses prove unnecessary has been removed. CPF is reached by output-preserving normalization, by lifting semantic operations such as tensor contractions, and by deriving parallelism in three levels ordered by decidability: syntactic subscript tests, exact affine dependence tests over integer sets, and SMT queries over non-linear integer arithmetic, which also admit parallelism guarded behind a runtime check. Heuristics can then specialize the canonical form for each architecture. Across 248 loop-level
reasoning kernels on an AMD MI300A, CPF reaches 4.4x over Numba on its 24 Zen 4 cores and 25.4x on its CDNA 3 GPU. Against the other auto-parallelizing optimizers, CPF is 2.9x faster on the CPU and 8.7x on the GPU than DaCe's own auto-parallelizer, 1.9x faster than Pluto, and 1.5x faster than PPCG on the affine subset of the kernels. Because the pipeline is deterministic, CPF also serves as an
agent's starting source, cutting the
token cost per kernel by up to a factor of 2.72x while leaving the achieved speed-up unchanged, since the coding
agents reason less about parallelism.
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All authors Yakup Koray Budanaz, Pratyai Mazumder, Alexandru Calotoiu, Torsten Hoefler
Human-Computer Interaction
How Spatial Biologists Direct and Verify AI-Assisted Analyses
Ella Hugie, Alexandra Irger, Chiara Schiller et al. (+3 more)
Spatial biologists use visualization to assess computational analyses of tissue data. We examine how they direct and verify analyses when an AI
agent performs this work. We synthesized workflows from fourteen contextual inquiries and conducted a formative pilot followed by an observational study with ten spatial biologists using Claude Science on their own data. Participants valued help with plotting, locating
cells of interest, and tasks they found laborious or could not otherwise perform. Assessing the
agent's work involved obtaining suitable evidence, sometimes through additional work in external tools, and interpreting it using knowledge of the tissue and its markers. Scientists also sought information about ongoing computation to decide how analysis should proceed. We contribute a workflow synthesis, an empirical account of scientists directing and verifying agentic analyses, and four design directions addressing execution control, familiar interactive views, source and execution information, and accessible verification across computing setups and experience.
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All authors Ella Hugie, Alexandra Irger, Chiara Schiller, Lukas Hatscher, Denis Schapiro, Hanspeter Pfister
Machine Learning
Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities
Pravija Raj P V, Ashish Gupta, Andrea Augello et al. (+1 more)
While
Federated Learning (FL) has been widely adopted for protecting user
privacy in machine learning, it remains vulnerable to various robustness challenges, including performance-impairment risks, information-stealing threats, and aggregation vulnerabilities. This work offers a holistic synthesis of FL robustness along three tightly coupled angles: (i) a threat-centric view of robustness that categorizes the multifaceted attack surfaces, (ii) a structured taxonomy of robust aggregation strategies distinguishing outcome-centric approaches from security-centric strategies, and (iii) a layered taxonomy of defensive strategies. We rigorously examine current evaluation practices for FL robustness and identify major applications and open research challenges to guide future research.
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All authors Pravija Raj P V, Ashish Gupta, Andrea Augello, Sajal K. Das
Human-Computer Interaction
Assembling, Breaking, and Refusing the Mask: Agency in AI-Mediated Self-Presentation in Livestreaming
Yang Hong, Nusrat Jahan Mim and Sharifa Sultana
Our mixed-method study examines how Chinese women livestreamers use masking to construct idealized mediated personas while navigating gendered, commercial, organizational, and platform pressures alongside personal agendas. We built on the concept of masking, analyzed 627 recruitment posts, and conducted livestream observations and interviews with 26 Chinese women streamers. We found that streamers assembled masks across bodies, AI-mediated technologies, spaces, performances, and social relations to become recognizable while protecting personal boundaries. These masks were continually negotiated, and participants sometimes broke, resisted, or refused them when demands became misaligned or unsustainable. We conceptualize masking as a sociotechnical assemblage in which agency lies in preserving, disrupting, and reconfiguring relations rather than controlling a single interface. We further theorize breaking as a consequential part of masking that exposes hidden labor and unequal costs of visibility. We offer theoretical and design directions for more negotiable, contestable, and agency-supporting AI-mediated self- presentation.
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General Relativity and Quantum Cosmology
Exploring mutated hilltop inflation in $f(\phi, T)$ gravity with latest CMB and DESI observations
Biswajit Deb and Atri Deshamukhya
The high-precision observations from the $Planck$ and BICEP/$Keck$ have imposed stringent bounds on the tensor-to-scalar ratio, ruling out many standard inflationary models. The recent bound from Atacama and SPT along with DESI provides a different viable landscape, making it difficult for the inflationary models to sustain. Amid these increasingly stringent and conflicting observational constraints, modified
gravity theories offer a compelling avenue to restore the viability of inflationary models. With this motivation, we study mutated hilltop inflation within $f(\phi, T)$
gravity, where a scalar field is non-minimally coupled to the trace of the energy-momentum tensor. The scalar spectral index $n_s$, the tensor spectral index $n_t$, and the tensor-to-scalar ratio $r$ are calculated, and their trajectories are presented in the $n_s-r$ plane. The results are then confronted with the latest combined CMB bounds from $Planck$, BICEP/$Keck$, ACT DR6, SPT, and DESI DR2. We report that MHI can predict $r \sim 10^{-3}$ and is compatible with the latest CMB & DESI bound SPA+BK+DESI at $ 1\sigma $ CL.
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Solar and Stellar Astrophysics
A comprehensive multi-wavelength study of two X-ray emitting Be stars
Hema Anilkumar, Blesson Mathew, Savithri H. Ezhikode et al. (+4 more)
We present a multi-wavelength study of two X-ray-emitting Be
stars, CXOU J052218.4+332820 (
Star 1) and CXOU J052304.2+332846 (
Star 2), identified from the LAMOST DR5 Hot emission-line
stars catalog. Our spectroscopic and photometric analyses indicate that
Star 1 is a main-sequence B5e
star, whereas
Star 2 is a pre-main-sequence B7e
star. The LAMOST spectrum of
Star 1 exhibits shell-type Feii, Paschen, and Oi emission from circumstellar regions extending to ~ 3 - 5Rstar. TESS period analysis reveals a rapid rotational period of ~ 0.355 d and an inclination angle of ~ 68 deg, confirming the Classical Be nature of
Star 1. In contrast,
Star 2 shows strong
accretion signatures, including inverse P-Cygni Feii and redshifted Hei absorption features. Its ZTF light curves display irregular dimming events of up to ~ 3 mag and quasi-periodic variability on timescales of ~ 50 - 110 d, consistent with circumstellar obscuration in an accreting Herbig Be
star. To further investigate the high-energy properties of the two systems, we analyzed their Chandra X-ray spectra. Chandra spectroscopy yields moderate
plasma temperatures (kT ~ 1.7-1.8 keV) and X-ray luminosities of logL_X ~ 30.4-30.6 erg/s, comparable to those observed in magnetically active coronae. Furthermore, the positional agreement between the Chandra and
Gaia coordinates indicates that the X-ray emission is spatially associated with the optical systems rather than a nearby resolved companion. These results suggest that the observed X-ray emission originates from magnetically influenced circumstellar activity, consistent with magnetically confined wind-shock (MCWS) or magnetically torqued disk scenarios.
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All authors Hema Anilkumar, Blesson Mathew, Savithri H. Ezhikode, Sreeja S. Kartha, Suman Bhattacharyya, Sneha Nedhath, Ajith N
Machine Learning (Statistics)
Exact Bayes Regret and Asymptotic Optimality in High-Dimensional Gaussian Bandits
Prakhar Singhvi, Yi Zou and Abhishek Bhattacharjee
We study
Bayesian linear bandits with an isotropic Gaussian parameter, independent Gaussian candidate arms, and Gaussian reward noise when the horizon is proportional to the dimension. The normalized posterior uncertainty has an explicit limit that is uniform over all causal policies. Gaussian posterior identities then determine the limiting parameter overlaps without an assumed closure of the adaptive recursion. These results yield exact regret curves for Thompson sampling, posterior-mean greedy selection, and a family of policies that scale the posterior sampling covariance. The normalized realized cumulative regret converges in L1, uniformly on compact proportional-time intervals. A policy-uniform lower bound identifies the limiting optimal Bayes regret and proves that posterior-mean greedy selection attains it. Thompson sampling incurs a strictly larger leading regret; its instantaneous regret ratio relative to greedy selection lies between one and two and approaches two at long proportional horizons. Closed-form cumulative curves also identify a different comparison in the vanishing-noise limit. Finally, the instantaneous regret converges to a nondegenerate Gaussian decision-loss distribution, rather than to its mean. The analysis separates the amount of information acquired by a bandit policy from the quality of the decisions made using that information.
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Hardware Architecture
HeteroReason: Heterogeneous FPGA-GPU Acceleration for Disaggregated Speculative Reasoning
Zehuan Zhang, Quan Deng, Zibo Ren et al. (+8 more)
Large
Reasoning Models (LRMs) have achieved state-of-the-art performance in
reasoning tasks by utilizing Chain-of-Thought (CoT)
reasoning. To achieve fast execution speed, speculative
reasoning techniques adopt a lightweight draft model for candidate
token generation followed by process reward models (PRMs) for verification and a strong target model for refinements. This paper identifies that the existing speculative
reasoning paradigm follows a strictly forward-only
reasoning trajectory, which lacks robustness and can lead to severe error propagation if early
reasoning steps are suboptimal. Furthermore, executing these disparate inference schemes, including sequential drafting and parallel verification on homogeneous GPU platforms, can lead to severe resource underutilization. To address this, we propose HeteroReason, an
algorithm-hardware co-designed heterogeneous FPGA-GPU inference paradigm specifically tailored for LRM speculative
reasoning. At the algorithmic level, we introduce a backtracking-enhanced workflow that enables the system to recover from low-quality states and explore alternative
reasoning trajectories, significantly improving
reasoning robustness. At the system level, the draft model is offloaded to the FPGA while deploying the PRM and target models on GPUs. A specialized workflow is optimized to achieve prefill-decode disaggregation, which exploits shadow synchronization to overlap GPU-side refinements with FPGA-side
token updates to effectively hide synchronization latency. To mitigate inherent sequential constraints, we propose a step-ahead speculation and refinement scheduling scheme, transitioning the system from a sequential execution scheme to a parallel pipeline. Experimental evaluations show an average 4.2% accuracy improvement, with 1.01x-1.42x latency speedups and 1.25x-1.57x improvements in energy efficiency compared to homogeneous GPU baselines.
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All authors Zehuan Zhang, Quan Deng, Zibo Ren, Hao Mark Chen, Guoyu Li, Xuchun Hu, Jose G. F. Coutinho, Ce Guo, Wayne Luk, Zhiqiang Que, Hongxiang Fan
Robotics
Temporal Learning for End-Effector Position Estimation under Aerodynamic Disturbances in Aerial Continuum Manipulation
Niloufar Amiri, Houman Masnavi and Farrokh Janabi-Sharifi
This paper investigates temporal neural networks for \mbox{end-effector} position \mbox{estimation} of an aerial continuum manipulator (ACM) operating under aerodynamic effects induced by the unmanned aerial vehicle (
UAV). An experimental
dataset is collected under stationary (\mbox{rotor-off}) and \mbox{free-hovering} conditions across continuum
robot (CR) configurations and
UAV altitudes, providing \mbox{end-effector} position measurements with and without aerodynamic residuals. To establish a nominal framework, \mbox{strain-parameterized} kinematic models with progressively richer strain bases are evaluated to balance model complexity and prediction accuracy. The selected nominal model then serves as the baseline for 3D position residual estimation using a \mbox{closed-form} \mbox{continuous-time} (CfC) neural network, with a multilayer perceptron (MLP) and a gated recurrent unit (GRU) used for comparison. On unseen test experiments, the CfC achieves an RMSE of \(22.00\pm1.70~\mathrm{mm}\) over five random seeds, compared with \(36.38\pm3.58~\mathrm{mm}\) for the MLP and \(27.72\pm2.92~\mathrm{mm}\) for the GRU, corresponding to reductions of \(39.52\%\) and \(20.62\%\), respectively. These results demonstrate the effectiveness of \mbox{continuous-time} learning for \mbox{end-effector} position estimation under aerodynamic disturbances relative to static and \mbox{discrete-time} learning methods.
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Human-Computer Interaction
Understanding Creative Design Practices among Data Artists
Tianwei Ma, Anna Offenwanger and Naimul Hoque
Creative and artistic data visualizations communicate stories and invite engagement, yet how designers develop their expressive forms remains poorly understood. We investigate the design process of data artists through three complementary studies: an analysis of 40 public project accounts, artifact-anchored interviews with seven experienced data artists, and a design task-based study with eight data artists. We find that stories and visual forms develop together through data exploration, reference adaptation, sketching, and prototyping. Inspiration comes from data, existing work, everyday imagery, and personal experience. Sketches help develop mappings, while prototypes with real data can reshape representations and intended stories. Designers consider multiple possibilities but typically develop one direction at a time, partly because producing alternatives is costly. Their choices balance meaning, visual appeal, readability, and feasibility. These findings inform tools connecting stories, references, and real data, including AI assistance that supports testing and revising alternatives while preserving designers' creative judgment.
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Analysis of PDEs
Generic Structural Stability for Riemann Solutions to $n \times n$ Systems of Hyperbolic Conservation Laws
Hong Kiat Tan and Andrea L. Bertozzi
This paper proves generic structural stability for Riemann solutions to $n \times n$ systems of
hyperbolic conservation laws in one spatial dimension. Under assumptions of strict hyperbolicity, genuine nonlinearity, and a regular
manifold hypothesis on the Rankine-Hugoniot map, we show that for almost every pair of left and right states, any $n$-wave Riemann solution consisting of Lax-admissible shocks and rarefactions is structurally stable under perturbations of the left state, the right state, and the flux function in the $C^2$
topology. The central new idea is sequential transversality, which chains the $n$ waves through intermediate states and transports their tangent contributions to a common reference point via pushforward maps, reducing the structural stability condition to the invertibility of an $n \times n$ transversality
matrix. We apply the results to the $p$-system, polydisperse particle-laden thin films, and machine-learned flux approximations.
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Audio and Speech Processing
Spooftral: Can Voxtral Audio-Language Model Detect Speech Spoofing?
Avishai Weizman, Yehuda Ben-Shimol and Itshak Lapidot
Self-supervised learning (SSL) countermeasures (CMs) have shown strong performance in recent years. However, they often show degraded performance while facing unseen spoofing attacks and mismatched conditions. This study examines the Voxtral audio-
language model (ALM) framework for spoofing detection, as a step toward combining CM capabilities within the ALM framework. We analyze how Voxtral captures spoofing cues through audio-text processing and propose an instruction-guided approach that uses label-sequence likelihoods to evaluate bonafide and spoofed
speech. Experiments on the ASVspoof databases show that without task-specific adaptation, the LLM layers emphasize semantic representations, reducing the separability of spoof-discriminative acoustic cues compared to the Whisper-based audio encoder. Consequently, spoofing-related information becomes less separable after
language-model processing. We also applied lightweight adaptation using weight-decomposed low-rank adaptation (DoRA) to the Voxtral model and propose the Spooftral model, achieving an equal error rate (EER) of 4.25% on the ASVspoof5 evaluation set.
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High Energy Astrophysical Phenomena
Tomography analysis of the intermediate-luminosity Type Iax SN 2024pxl
Barnabás Barna, Mridweeka Singh, Lindsey A. Kwok et al. (+90 more)
We present an abundance
tomography analysis of SN 2024pxl, an intermediate luminosity Type Iax
supernova ($M_{r}=-16.82 \pm 0.19$ mag), with the most-detailed follow-up in the Type Iax subclass to-date. As one of the few intermediate luminosity Type Iax objects, SN 2024pxl may link the two extremes of the peculiar thermonuclear
supernova subclass. To test this hypothesis, we analyze its spectral
evolution through the first 100 days after the explosion and aim to probe the structure of its ejecta. We conduct an abundance
tomography analysis using synthetic spectra produced with the one dimensional radiative transfer code TARDIS. The fit of the spectral time series provides a radial scan of physical properties and probes the stratification of chemical elements throughout most of the SN ejecta. The observed spectral
evolution is well fit with the final model, similar to the general predictions of the pure deflagration scenarios, but significant modifications are required in the density function of the inner ejecta and in the chemical profiles of the outermost regions. The constrained physical characteristics, such as the photospheric velocities and the time of maximum light, are also consistent with other SNe~Iax. Despite their small number, intermediate luminosity Type Iax SNe are not outliers in the subclass but demonstrate the continuous nature of SNe Iax through their luminosity range. Following this observation, we argue that all SNe~Iax share the same progenitor and explosion origin.
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All authors Barnabás Barna, Mridweeka Singh, Lindsey A. Kwok, Saurabh W. Jha, R. Dastidar, Conor Larison, Alexei V. Filippenko, J. P. Anderson, Jennifer E. Andrews, Moira Andrews, G. C. Anupama, Prasiddha Arunachalam, Katie Auchettl, Dominik Bánhidi, K. Azalee Bostroem, Thomas G. Brink, Régis Cartier, Ping Chen, T.-W. Chen, Collin T. Christy, David A. Coulter, Sofia Covarrubias, Kyle W. Davis, Connor B. Dickinson, Yize Dong, Joseph Farah, Andreas Flörs, Ryan J. Foley, Noah Franz, Christoffer Fremling, Lluís Galbany, Anjasha Gangopadhyay, Aarna Garg, Elinor L. Gates, Or Graur, Mariusz Gromadzki, Daichi Hiramatsu, Emily Hoang, D. Andrew Howell, Brian Hsu, Joel Johansson, Arti Joshi, Lordrick A. Kahinga, Ravjit Kaur, Sahana Kumar, Piramon Kumnurdmanee, Hanindyo Kuncarayakti, Chang Liu, Keiichi Maeda, Kate Maguire, Bailey Martin, Curtis McCully, Darshana Mehta, Luca M. Menotti, Anne J. Metevier, A. A. Miller, Kuntal Misra, Erika Mochnács, T. E. Müller-Bravo, Megan Newsome, Estefania Padilla Gonzalez, Kishore C. Patra, Jeniveve Pearson, Anthony L. Piro, Abigail Polin, Aravind P. Ravi, Armin Rest, Nicolas Meza Retamal, O. M. Robinson, César Rojas-Bravo, Devendra K. Sahu, David J. Sand, Brian P. Schmidt, Steve Schulze, Michaela Schwab, Manisha Shrestha, Matthew R. Siebert, Sunil Simha, Nathan Smith, Jesper Sollerman, Shubham Srivastav, Bhagya M. Subrayan, Tamás Szalai, Kirsty Taggart, Rishabh Singh Teja, Jacco H. Terwel, Samaporn Tinyanont, Stefano Valenti, József Vinkó, Aya L. Westerling, J. Craig Wheeler, Yi Yang, WeiKang Zheng
Chemical Physics
Pauli exclusion as a necessary condition for the existence of the H$_2$ + H$^{\bullet}$ atom transfer barrier
Shehan H. Fernando and Alistair J. Sterling
The transition state barrier is a foundational concept in chemical kinetics, yet its physical origin is seldom investigated. In this study we analyze the barrier to hydrogen atom transfer in collinear H$_2$+H$^{\bullet}$ on its Born$-$Oppenheimer surface. We extend upon the energy component analysis of chemical bonds to develop an electronic kinetic energy-based interpretation of the transition state barrier. This approach reveals that Pauli exclusion is necessary for the existence of the energy barrier to H$_2$+H$^{\bullet}$ atom transfer, which is consistent with a complementary analysis using absolutely-localized molecular orbitals.
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High Energy Astrophysical Phenomena
Stability of hybrid stars via catastrophe theory
Eduardo S. Fraga and Sergio E. Jorás
We discuss the possible
topologies of the mass-radius relation for hybrid
stars in light of catastrophe theory. We show that different branches of compact
stars emerge or disappear depending on the bifurcation structure of the underlying catastrophe potential function, the mathematical analog of an effective potential. For that we assume a single first-order phase transition between
nuclear and
quark matter. After a warm up with incompressible fluids, we present a qualitative discussion explaining the (dis)appearance of (un)stable branches for hybrid
stars as the control parameters change.
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Robotics
OA-MPPI: Occlusion-Aware Model Predictive Path Integral Control for UAV Flight
Vittorio Palladino, Teaya Yang, Ruiqi Zhang et al. (+1 more)
Autonomous
UAV flight through cluttered and partially unknown environments requires
reasoning not only about observed obstacles but also about occluded regions that the sensor cannot observe. We present OA-MPPI, an obstacle- and occlusion-aware extension of Model Predictive Path Integral (MPPI) control for quadrotor flight that accounts for potential moving
agents emerging from these regions into the vehicle's path. At every planning step, we extract a 3D occlusion boundary from the online occupancy map and use it to model the regions that hidden
agents could reach over the prediction horizon. We penalize trajectories that enter these expanding regions within MPPI rollouts generated using nonlinear quadrotor dynamics and accounting for individual rotor thrust limits. We validate the proposed approach in simulation and hardware flight experiments, with the complete pipeline running onboard the vehicle in real time. Results show increased clearance from occlusion boundaries compared to baseline MPPI in both settings, as well as avoidance of an
agent emerging from occlusion in simulation.
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All authors Vittorio Palladino, Teaya Yang, Ruiqi Zhang, Mark W. Mueller
Data Structures and Algorithms
Tight Approximation Results for Matroid Optimization with a Linear Constraint
Ilan Doron-Arad, Hadas Shachnai and Gilad Shmerler
We study the following class of matroid optimization problems with a linear constraint (P-MOL). Given a matroid M=(E,I), two weight functions $v,w:E\to R_{\ge 0}$, and a threshold $L\in R_{\ge 0}$, find $opt v(S)$ where S is either an independent set or a base of M satisfying a budget-type constraint: $w(S)\le L$ or $w(S)\ge L$, and $opt\in\{min,max\}$. P-MOL provides a unified representation for a broad family of NP-hard optimization problems, including budgeted matroid independent set, constrained minimum-basis, and knapsack-cover variants with a matroid constraint. Also, it naturally extends to multiple matroid constraints. In particular, we consider the matroid intersection cover (MIC) problem, where feasibility is defined by the common independent sets of two matroids and one seeks minimum $v(S)$ subject to $w(S)\ge L$. Our main result is a unified EPTAS for all nontrivial P-MOL variants, obtained by generalizing a technique of Hassin and Levin (SIAM J. Comput., 2004) for solving the constrained minimum spanning tree problem. Specifically, for any fixed $\epsilon>0$, we present an
algorithm running in time $|E|^{O(1)} (1/{\epsilon^2})^{O(1/{\epsilon})}$ that outputs a feasible solution S whose value is at most $(1+\epsilon)OPT$ for minimization variants and at least $(1-\epsilon)OPT$ for maximization variants. This resolves the complexity status of all members of P-MOL, as none of these problems admits an FPTAS (Doron-Arad, Kulik and Shachnai, ICALP'24). Finally, we separate the P-MOL family from its extension to matroid intersection. We show that an EPTAS is unlikely to exist for the matroid intersection variant of P-MOL under a covering constraint, whereas an EPTAS is known to exist under a budget constraint. This highlights a qualitative difference between these two types of linear constraints that does not arise in the single-matroid setting.
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Classical Analysis and ODEs
On the Alzer-Berg problem: an optimal Bernstein boundary and a uniqueness conjecture
Valmir Krasniqi
We study the two-parameter exponential family associated with the complete-monotonicity problem of Alzer and Berg. Strict necessary parameter bounds place every possible Bernstein function in the domain of a regularized Laplace representation. A quantitative positivity-transfer inequality then yields a linear sufficient condition with the largest possible universal coefficient, expressed in terms of the exact one-parameter critical exponent. To describe the whole admissible region, we derive convolution identities for parameter derivatives and prove that increasing either normalized parameter destroys positivity at every zero of a nonnegative density. Combined with uniform tail estimates, this excludes gaps in the admissible parameter intervals and gives a continuous, strictly monotone optimal boundary. Global nonnegativity of the density and contact with zero characterize that boundary; its inverse determines the complete admissible interval for the second parameter. The characterization is implicit and requires neither uniqueness nor nondegeneracy of the contact points. Published numerical approximations of the one-parameter exponent are distinguished from the exact results and from the finite rational certificate used in the proofs. We also derive a variational formula and a rigorous framework for validated numerical enclosure of the boundary, and formulate a boundary-contact
conjecture asserting uniqueness and quadratic contact at every interior boundary point.
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Combinatorics
Nonregular graphs of odd maximum degree with maximum spectral radius
Liangdong Fan, Liying Kang and Yaojun Chen
Let $\rho(n,d)$ denote the maximum adjacency spectral radius among all connected nonregular graphs of order $n$ and maximum degree $d$. A graph attaining this maximum is called an extremal graph. Liu [J. Combin. Theory Ser. B, 2024] determined the extremal graphs for $d=3,4$ and formulated two
conjectures for general $d$. For each fixed odd integer $d\ge3$, the
conjectures assert that: (1) $\displaystyle\lim_{n\to\infty}n^2\bigl(d-\rho(n,d)\bigr) =(d-1)\pi^2/4$. (2) For all sufficiently large $n$, the degree sequence of every extremal graph is $(d,\ldots,d,d-1)$ for odd $n$ and $(d,\ldots,d,1)$ for even $n$. We prove the first
conjecture for every fixed odd $d\ge3$ and, more precisely, obtain the asymptotic expansion \[ \rho(n,d) =d-\frac{(d-1)\pi^2}{4n^2} +\frac{(d-1)^2\pi^2}{4n^3} +O_d(n^{-4}) \qquad(n\to\infty). \] We further prove the second
conjecture for every fixed odd $d\ge3$.
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Superconductivity
A Renormalized Ginzburg-Landau Framework for Dimensional Crossover and Fluctuation Specific Heat in High-$T_c$ Superconductors in a Magnetic Field
Roger Magloire Keumo Tsiaze, Jeremeie Edmond Danga and Cornelius Fai Lukong
This paper presents a theoretical analysis of phase transitions and critical phenomena in high-temperature
superconductors using a renormalized Ginzburg-Landau framework. Rather than assuming a conventional linear temperature dependence, we treat the quadratic coefficient as a self-consistent, Hartree-renormalized quantity determined by fluctuation-loop corrections. This renormalization regularizes mean-field divergences and yields a finite, dimensionality-dependent specific-heat anomaly near the transition. When an external
magnetic field is applied, minimal coupling quantizes order-parameter fluctuations into discrete Landau levels, reducing the effective dimensionality via an effective spectral dimension. In this framework, the intrinsic fluctuation-coupling strength self-consistently determines the temperature width of the critical Ginzburg region, while the cyclotron energy of the quantized fluctuations uniquely establishes both the specific-heat peak position and the upper-critical-field crossover boundary. Comparison with $\text{YBa}_2\text{Cu}_3\text{O}_{7-\delta}$ experimental data demonstrates that this approach captures the suppression of the sharp mean-field discontinuity and accurately reproduces the
vortex-
lattice topological structure under finite
magnetic fields.
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High Energy Physics (Phenomenology)
Plasma instability splits axion-photon conversion lines
Elizabeth A. Tolman
The
axion coupling to electromagnetism may leave signatures of uncertain nature in astrophysical spectra; one is thought to be a single line centered at the
axion frequency $\omega = \sqrt{\left(m_a c^2/\hbar\right)^2 + c^2 k_a^2}$, with $m_a c^2/\hbar$ the
axion Compton frequency and $\hbar k_a$ its momentum. If a parametric
plasma instability is excited, this signature is transformed into a set of lines, which may be proportionally spaced and linearly polarized. A Crab
pulsar signal at a frequency near recent cosmological predictions for the
axion Compton frequency has some qualitative similarity to this signature, though this similarity is not evidence that the signal is caused by
axions.
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Computation and Language
An Explainable DistilBERT-BiLSTM-Attention Framework for Binary and Multi-Class Hate Speech Detection
Rameesha Zia and Muhammad Shahid Iqbal Malik
Hate
speech on social media poses serious risks to social harmony, mental well-being, and public safety, making its timely and accurate detection essential for content moderation systems. Most existing studies focus on binary classification, evaluated their frameworks on a single
dataset, and provide limited insight into how decisions are made, which limits their real-world applicability. In addition, limited work is done on the explainability of their predictive inference. To address these challenges, this study proposes a multilevel and explainable hate
speech detection framework. The proposed model integrates DistilBERT (Distilled Bidirectional Encoder Representations from
Transformers)
embeddings with a Bi-LSTM (Bidirectional Long Short-Term
Memory) model, and an
attention mechanism to capture both contextual meaning and sequential dependencies in text. To enhance trust and transparency, LIME (Local Interpretable Model-agnostic Explanations) is employed to explain model predictions by highlighting influential textual features. The framework is evaluated on two
benchmark datasets using both binary and multi-class classification to examine robustness and generalization. In addition, an ablation study is presented to highlight the significance of various components of proposed framework. For binary classification, the proposed model achieves F1-scores of 96.78% on the Davidson
dataset and 99.53% on the SMHS
dataset. In the multi-class setting, it attains F1-scores of 97.00% and 94.99% on the Davidson and SMHS
datasets, respectively, outperforming existing baseline approaches. The results demonstrate that multilevel evaluation improves the reliability that the proposed framework effectively balances performance and efficiency. This makes the framework suitable for practical hate
speech moderation systems that require accurate, generalizable, and explainable decisions.
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Combinatorics
There are no nontrivial chordal square-complementary graphs
Pierre Aboulker, Martin Milanič, Peter Muršič et al. (+1 more)
We study square-complementary graphs $G$ (satisfying $G^2 \cong \overline{G}$). We show that in such graphs, no two vertices have comparable closed neighborhoods. This implies that nontrivial square-complementary graphs have no simplicial vertices and are not chordal, thus solving two open problems posed in Discrete Mathematics 327 (2014) 62-75. We also show that no nontrivial square-complementary graph is distance-hereditary.
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All authors Pierre Aboulker, Martin Milanič, Peter Muršič, Miguel A. Pizaña