Published: 2025-09-08 05:38:10
Authors: Yuanteng Chen, Peisong Wang, Yuantian Shao, Jian Cheng
Categories: cs.LG, cs.AI
Abstract:
Sparse Mixture-of-Experts (MoE) has become a key architecture for scaling
large language models (LLMs) efficiently. Recent fine-grained MoE designs
introduce hundreds of experts per layer, with multiple experts activated per
token, enabling stronger specialization. However, during pre-training, routers
are optimized mainly for stability and robustness: they converge prematurely
and enforce balanced usage, limiting the full potential of model performance
and efficiency. In this work, we uncover two overlooked issues: (i) a few
highly influential experts are underutilized due to premature and balanced
routing decisions; and (ii) enforcing a fixed number of active experts per
token introduces substantial redundancy. Instead of retraining models or
redesigning MoE architectures, we introduce Ban&Pick, a post-training,
plug-and-play strategy for smarter MoE routing. Pick discovers and reinforces
key experts-a small group with outsized impact on performance-leading to
notable accuracy gains across domains. Ban complements this by dynamically
pruning redundant experts based on layer and token sensitivity, delivering
faster inference with minimal accuracy loss. Experiments on fine-grained
MoE-LLMs (DeepSeek, Qwen3) across math, code, and general reasoning benchmarks
demonstrate that Ban&Pick delivers free performance gains and inference
acceleration without retraining or architectural changes. For instance, on
Qwen3-30B-A3B, it improves accuracy from 80.67 to 84.66 on AIME2024 and from
65.66 to 68.18 on GPQA-Diamond, while accelerating inference by 1.25x under the
vLLM.
Published: 2025-09-08 05:34:53
Authors: Jie Ma, Rongxing Xu
Categories: math.CO
Abstract:
Bridges are a classical concept in structural graph theory and play a
fundamental role in the study of cycles. A conjecture of Voss from 1991 asserts
that if disjoint bridges $B_1, B_2, \ldots, B_k$ of a longest cycle $L$ in a
$2$-connected graph overlap in a tree-like manner (i.e., induce a tree in the
{\it overlap graph} of $L$), then the total {\it length} of these bridges is at
most half the length of $L$. Voss established this for $k \leq 3$ and used it
as a key tool in his 1991 monograph on cycles and bridges. In this paper, we
confirm the conjecture in full via a reduction to a cycle covering problem.
Published: 2025-09-08 05:33:50
Authors: Pedro Luiz Ramos, Enrique Achire Quispe, Ricardo Puziol de Oliveira, Jorge A. Achcar
Categories: stat.ME
Abstract:
In this work, we develop an objective Bayesian framework for the Dhillon
probability distribution. We explicitly derive three objective priors: the
Jeffreys prior, the overall reference prior, and the maximal data information
prior. We show that both Jeffreys and reference priors yields a proper
posterior distribution, whereas the maximal data information prior leads to an
improper posterior. Moreover, we establish sufficient conditions for the
existence of its respective posterior moments. Bayesian inference is carried
out via Markov chain Monte Carlo, using the Metropolis-Hastings algorithm. A
comprehensive simulation study compares the Bayesian estimators to maximum
likelihood estimators in terms of bias, mean squared error, and coverage
probability. Finally, a real-data application illustrates the practical utility
of the proposed Bayesian approach.
Published: 2025-09-08 05:33:02
Authors: Fedor Tairli
Categories: astro-ph.IM
Abstract:
Rejection of cloud-contaminated data is a complex and important process at
the Pierre Auger Observatory, one which combines information from several
sources, including IR cameras, lidars, and satellite imaging. With the
deteriorating quality of the IR cameras and challenges in using other sources,
we propose a new method. We use continuous detector monitoring measurements to
build a large database of night sky background fluxes for each pixel across 27
telescopes. Using this database, we generate the expected background flux and
define cloud rejection thresholds. Through a straightforward analysis we
construct boolean cloud-contamination masks. We demonstrate some results of the
analysis, including comparisons with cloud detected using infra-red
observations.
Published: 2025-09-08 05:32:28
Authors: Filip Bjelonic, Fabian Tischhauser, Marco Hutter
Categories: cs.RO
Abstract:
Legged robots must achieve both robust locomotion and energy efficiency to be
practical in real-world environments. Yet controllers trained in simulation
often fail to transfer reliably, and most existing approaches neglect
actuator-specific energy losses or depend on complex, hand-tuned reward
formulations. We propose a framework that integrates sim-to-real reinforcement
learning with a physics-grounded energy model for permanent magnet synchronous
motors. The framework requires a minimal parameter set to capture the
simulation-to-reality gap and employs a compact four-term reward with a
first-principle-based energetic loss formulation that balances electrical and
mechanical dissipation. We evaluate and validate the approach through a
bottom-up dynamic parameter identification study, spanning actuators,
full-robot in-air trajectories and on-ground locomotion. The framework is
tested on three primary platforms and deployed on ten additional robots,
demonstrating reliable policy transfer without randomization of dynamic
parameters. Our method improves energetic efficiency over state-of-the-art
methods, achieving a 32 percent reduction in the full Cost of Transport of
ANYmal (value 1.27). All code, models, and datasets will be released.
Published: 2025-09-08 05:12:03
Authors: Issue Yishu Wang, Kakam Chong, Xiaofeng Wang, Xu Yan, DeXin Kong, Chen Ju, Ming Chen, Shuai Xiao, Shuguang Han, jufeng chen
Categories: cs.AI
Abstract:
In online second-hand marketplaces, multi-turn bargaining is a crucial part
of seller-buyer interactions. Large Language Models (LLMs) can act as seller
agents, negotiating with buyers on behalf of sellers under given business
constraints. A critical ability for such agents is to track and accurately
interpret cumulative buyer intents across long negotiations, which directly
impacts bargaining effectiveness. We introduce a multi-turn evaluation
framework for measuring the bargaining ability of seller agents in e-commerce
dialogues. The framework tests whether an agent can extract and track buyer
intents. Our contributions are: (1) a large-scale e-commerce bargaining
benchmark spanning 622 categories, 9,892 products, and 3,014 tasks; (2) a
turn-level evaluation framework grounded in Theory of Mind (ToM) with annotated
buyer intents, moving beyond outcome-only metrics; and (3) an automated
pipeline that extracts reliable intent from massive dialogue data.
Published: 2025-09-08 05:10:18
Authors: Shivam Mahajan, Long-Zhou Huang, Cunyuan Jiang, Yun-Jiang Wang, Massimo Pica Ciamarra, Jie Zhang, Matteo Baggioli
Categories: cond-mat.soft
Abstract:
The boson peak is a characteristic anomaly of amorphous solids broadly
defined as a low-energy excess in the density of states and heat capacity
compared to the textbook predictions of Debye theory. The origin of this
anomaly has long been the subject of ongoing debate and remains a topic of
active controversy. While remaining agnostic about the microscopic origin of
the phenomenon, we propose that the boson peak (BP) may universally originate
from a dispersionless, optic-like excitation, which we refer to as the 'flat
mode'. We revisit both experimental and simulation data from the literature
through this lens and conduct further simulations in 2D and 3D amorphous
systems. These analyses collectively provide supporting evidence for this
interpretation. Notably, if this is indeed the case, a striking analogy emerges
with similar anomalies observed in crystalline materials, where the nonphononic
flat mode is effectively replaced by anomalously low-energy optical phonons.
Published: 2025-09-08 05:01:05
Authors: Ingrid Irmer
Categories: math.GT, math.GR
Abstract:
In [10] it was shown that there is a mapping class group-equivariant
deformation retraction of the Teichm\"uller space of a closed surface onto a CW
complex with dimension equal to the virtual cohomological dimension of the
mapping class group. This paper studies the image of this deformation
retraction and shows that when the analogy with the well-rounded deformation
retraction of $SL(n,\mathbb{Z})$ is defined correctly via a notion of duality,
this deformation retraction is analogous to the well-rounded deformation
retractions of [2], [24] and [26]. In the process, an elementary necessary
condition is derived for a cycle in the geometric realisation of Harvey's curve
complex to represent a nontrivial homology class.
Published: 2025-09-08 05:00:58
Authors: Shuai Yuan, Zhibo Zhang, Yuxi Li, Guangdong Bai, Wang Kailong
Categories: cs.CR, cs.LG
Abstract:
The widespread distribution of Large Language Models (LLMs) through public
platforms like Hugging Face introduces significant security challenges. While
these platforms perform basic security scans, they often fail to detect subtle
manipulations within the embedding layer. This work identifies a novel class of
deployment phase attacks that exploit this vulnerability by injecting
imperceptible perturbations directly into the embedding layer outputs without
modifying model weights or input text. These perturbations, though
statistically benign, systematically bypass safety alignment mechanisms and
induce harmful behaviors during inference. We propose Search based Embedding
Poisoning(SEP), a practical, model agnostic framework that introduces carefully
optimized perturbations into embeddings associated with high risk tokens. SEP
leverages a predictable linear transition in model responses, from refusal to
harmful output to semantic deviation to identify a narrow perturbation window
that evades alignment safeguards. Evaluated across six aligned LLMs, SEP
achieves an average attack success rate of 96.43% while preserving benign task
performance and evading conventional detection mechanisms. Our findings reveal
a critical oversight in deployment security and emphasize the urgent need for
embedding level integrity checks in future LLM defense strategies.
Published: 2025-09-08 04:59:00
Authors: Jianpeng Zhao, Chenyu Yuan, Weiming Luo, Haoling Xie, Guangwei Zhang, Steven Jige Quan, Zixuan Yuan, Pengyang Wang, Denghui Zhang
Categories: cs.AI
Abstract:
Questionnaire-based surveys are foundational to social science research and
public policymaking, yet traditional survey methods remain costly,
time-consuming, and often limited in scale. This paper explores a new paradigm:
simulating virtual survey respondents using Large Language Models (LLMs). We
introduce two novel simulation settings, namely Partial Attribute Simulation
(PAS) and Full Attribute Simulation (FAS), to systematically evaluate the
ability of LLMs to generate accurate and demographically coherent responses. In
PAS, the model predicts missing attributes based on partial respondent
profiles, whereas FAS involves generating complete synthetic datasets under
both zero-context and context-enhanced conditions. We curate a comprehensive
benchmark suite, LLM-S^3 (Large Language Model-based Sociodemographic Survey
Simulation), that spans 11 real-world public datasets across four sociological
domains. Our evaluation of multiple mainstream LLMs (GPT-3.5/4 Turbo, LLaMA
3.0/3.1-8B) reveals consistent trends in prediction performance, highlights
failure modes, and demonstrates how context and prompt design impact simulation
fidelity. This work establishes a rigorous foundation for LLM-driven survey
simulations, offering scalable and cost-effective tools for sociological
research and policy evaluation. Our code and dataset are available at:
https://github.com/dart-lab-research/LLM-S-Cube-Benchmark
Published: 2025-09-08 04:53:46
Authors: Jeongmin Yu, Susang Kim, Kisu Lee, Taekyoung Kwon, Won-Yong Shin, Ha Young Kim
Categories: cs.CV, cs.AI, cs.CR
Abstract:
Recent face anti-spoofing (FAS) methods have shown remarkable cross-domain
performance by employing vision-language models like CLIP. However, existing
CLIP-based FAS models do not fully exploit CLIP's patch embedding tokens,
failing to detect critical spoofing clues. Moreover, these models rely on a
single text prompt per class (e.g., 'live' or 'fake'), which limits
generalization. To address these issues, we propose MVP-FAS, a novel framework
incorporating two key modules: Multi-View Slot attention (MVS) and Multi-Text
Patch Alignment (MTPA). Both modules utilize multiple paraphrased texts to
generate generalized features and reduce dependence on domain-specific text.
MVS extracts local detailed spatial features and global context from patch
embeddings by leveraging diverse texts with multiple perspectives. MTPA aligns
patches with multiple text representations to improve semantic robustness.
Extensive experiments demonstrate that MVP-FAS achieves superior generalization
performance, outperforming previous state-of-the-art methods on cross-domain
datasets. Code: https://github.com/Elune001/MVP-FAS.
Published: 2025-09-08 04:52:00
Authors: Tz-Ying Wu, Sharath Nittur Sridhar, Subarna Tripathi
Categories: cs.CV
Abstract:
We propose to improve the time-sensitive video understanding (TSV) capability
of video large language models (Video-LLMs) with grounded objects (GO). We
hypothesize that TSV tasks can benefit from GO within frames, which is
supported by our preliminary experiments on LITA, a state-of-the-art Video-LLM
for reasoning temporal localization. While augmenting prompts with textual
description of these object annotations improves the performance of LITA, it
also introduces extra token length and susceptibility to the noise in object
level information. To address this, we propose GO-Tokenizer, a lightweight
add-on module for Video-LLMs leveraging off-the-shelf object detectors to
encode compact object information on the fly. Experimental results demonstrate
that pretraining with GO-Tokenizer outperforms the vanilla Video-LLM and its
counterpart utilizing textual description of objects in the prompt. The gain
generalizes across different models, datasets and video understanding tasks
such as reasoning temporal localization and dense captioning.
Published: 2025-09-08 04:43:28
Authors: Konstantinos Georgiou
Categories: cs.DM
Abstract:
This work resolves the optimal average-case cost of the Disk-Inspection
problem, a variant of Bellman's 1955 lost-in-a-forest problem. In
Disk-Inspection, a mobile agent starts at the center of a unit disk and follows
a trajectory that inspects perimeter points whenever the disk does not obstruct
visibility. The worst-case cost was solved optimally in 1957 by Isbell, but the
average-case version remained open, with heuristic upper bounds proposed by
Gluss in 1961 and improved only recently.
Our approach applies Fermat's Principle of Least Time to a recently proposed
discretization framework, showing that optimal solutions are captured by a
one-parameter family of recurrences independent of the discretization size. In
the continuum limit these recurrences give rise to a single-parameter optimal
control problem, whose trajectories coincide with limiting solutions of the
original Disk-Inspection problem. A crucial step is proving that the optimal
initial condition generates a trajectory that avoids the unit disk, thereby
validating the optics formulation and reducing the many-variable optimization
to a rigorous one-parameter problem. In particular, this disproves Gluss's
conjecture that optimal trajectories must touch the disk.
Our analysis determines the exact optimal average-case inspection cost, equal
to $3.549259\ldots$ and certified to at least six digits of accuracy.
Published: 2025-09-08 04:39:07
Authors: Penelope Brown, Julie Stephany Berrio Perez, Mao Shan, Stewart Worrall
Categories: cs.CV, cs.RO
Abstract:
Vulnerable road users (VRUs) such as pedestrians, cyclists, and motorcyclists
represent more than half of global traffic deaths, yet their detection remains
challenging in poor lighting, adverse weather, and unbalanced data sets. This
paper presents a multimodal detection framework that integrates RGB and thermal
infrared imaging with a fine-tuned YOLOv8 model. Training leveraged KITTI,
BDD100K, and Teledyne FLIR datasets, with class re-weighting and light
augmentations to improve minority-class performance and robustness, experiments
show that 640-pixel resolution and partial backbone freezing optimise accuracy
and efficiency, while class-weighted losses enhance recall for rare VRUs.
Results highlight that thermal models achieve the highest precision, and
RGB-to-thermal augmentation boosts recall, demonstrating the potential of
multimodal detection to improve VRU safety at intersections.
Published: 2025-09-08 04:31:12
Authors: Roussel Rahman, Aashwin Ananda Mishra
Categories: cs.LG, cs.AI
Abstract:
Large Language Models (LLMs) have demonstrated remarkable emergent
capabilities, yet the robustness of their numerical reasoning remains an open
question. While standard benchmarks evaluate LLM reasoning on complex problem
sets using aggregated metrics, they often obscure foundational weaknesses. In
this work, we probe LLM mathematical numeracy by evaluating performance on
problems of escalating complexity, from constituent operations to combinatorial
puzzles. We test several state-of-the-art LLM-based agents on a 100-problem
challenge comprising four categories: (1) basic arithmetic, (2) advanced
operations, (3) primality checking, and (4) the Game of 24 number puzzle. Our
results show that while the agents achieved high accuracy on the first three
categories, which require deterministic algorithmic execution, they
consistently failed at the number puzzle, underlining its demand for a
heuristic search over a large combinatorial space to be a significant
bottleneck. These findings reveal that the agents' proficiency is largely
confined to recalling and executing known algorithms, rather than performing
generative problem-solving. This suggests their apparent numerical reasoning is
more akin to sophisticated pattern-matching than flexible, analytical thought,
limiting their potential for tasks that require novel or creative numerical
insights.
Published: 2025-09-08 04:24:31
Authors: Md Sultanul Islam Ovi, Mainul Hossain, Md Badsha Biswas
Categories: cs.CV
Abstract:
Currency recognition systems often overlook usability and authenticity
assessment, especially in low-resource environments where visually impaired
users and offline validation are common. While existing methods focus on
denomination classification, they typically ignore physical degradation and
forgery, limiting their applicability in real-world conditions. This paper
presents a unified framework for currency evaluation that integrates three
modules: denomination classification using lightweight CNN models, damage
quantification through a novel Unified Currency Damage Index (UCDI), and
counterfeit detection using feature-based template matching. The dataset
consists of over 82,000 annotated images spanning clean, damaged, and
counterfeit notes. Our Custom_CNN model achieves high classification
performance with low parameter count. The UCDI metric provides a continuous
usability score based on binary mask loss, chromatic distortion, and structural
feature loss. The counterfeit detection module demonstrates reliable
identification of forged notes across varied imaging conditions. The framework
supports real-time, on-device inference and addresses key deployment challenges
in constrained environments. Results show that accurate, interpretable, and
compact solutions can support inclusive currency evaluation in practical
settings.
Published: 2025-09-08 04:23:48
Authors: Guanlan Hu, Adit Anand, Pooja M. Desai, Iñigo Urteaga, Lena Mamykina
Categories: cs.LG
Abstract:
This study examined the use of machine learning and domain specific
enrichment on patient generated health data, in the form of free text meal
logs, to classify meals on alignment with different nutritional goals. We used
a dataset of over 3000 meal records collected by 114 individuals from a
diverse, low income community in a major US city using a mobile app. Registered
dietitians provided expert judgement for meal to goal alignment, used as gold
standard for evaluation. Using text embeddings, including TFIDF and BERT, and
domain specific enrichment information, including ontologies, ingredient
parsers, and macronutrient contents as inputs, we evaluated the performance of
logistic regression and multilayer perceptron classifiers using accuracy,
precision, recall, and F1 score against the gold standard and self assessment.
Even without enrichment, ML outperformed self assessments of individuals who
logged meals, and the best performing combination of ML classifier with
enrichment achieved even higher accuracies. In general, ML classifiers with
enrichment of Parsed Ingredients, Food Entities, and Macronutrients information
performed well across multiple nutritional goals, but there was variability in
the impact of enrichment and classification algorithm on accuracy of
classification for different nutritional goals. In conclusion, ML can utilize
unstructured free text meal logs and reliably classify whether meals align with
specific nutritional goals, exceeding self assessments, especially when
incorporating nutrition domain knowledge. Our findings highlight the potential
of ML analysis of patient generated health data to support patient centered
nutrition guidance in precision healthcare.
Published: 2025-09-08 04:21:27
Authors: Ruiming Du, Guangxun Zhai, Tian Qiu, Yu Jiang
Categories: cs.CV, q-bio.QM
Abstract:
The precise characterization of plant morphology provides valuable insights
into plant environment interactions and genetic evolution. A key technology for
extracting this information is 3D segmentation, which delineates individual
plant organs from complex point clouds. Despite significant progress in general
3D computer vision domains, the adoption of 3D segmentation for plant
phenotyping remains limited by three major challenges: i) the scarcity of
large-scale annotated datasets, ii) technical difficulties in adapting advanced
deep neural networks to plant point clouds, and iii) the lack of standardized
benchmarks and evaluation protocols tailored to plant science. This review
systematically addresses these barriers by: i) providing an overview of
existing 3D plant datasets in the context of general 3D segmentation domains,
ii) systematically summarizing deep learning-based methods for point cloud
semantic and instance segmentation, iii) introducing Plant Segmentation Studio
(PSS), an open-source framework for reproducible benchmarking, and iv)
conducting extensive quantitative experiments to evaluate representative
networks and sim-to-real learning strategies. Our findings highlight the
efficacy of sparse convolutional backbones and transformer-based instance
segmentation, while also emphasizing the complementary role of modeling-based
and augmentation-based synthetic data generation for sim-to-real learning in
reducing annotation demands. In general, this study bridges the gap between
algorithmic advances and practical deployment, providing immediate tools for
researchers and a roadmap for developing data-efficient and generalizable deep
learning solutions in 3D plant phenotyping. Data and code are available at
https://github.com/perrydoremi/PlantSegStudio.
Published: 2025-09-08 04:21:04
Authors: Changhyun Ahn, Man Hea Kim
Categories: hep-th
Abstract:
From the classical $SO({\cal N}=8)$ extended superconformal algebra between
the lowest ${\cal N}=8$ multiplet in two dimensions obtained by Ademollo et al.
(1976), we generalize it for the arbitrary ${\cal N}=8$ multiplet with manifest
$SU(8)$ symmetry containing the bosonic $w_{1+\infty}$ algebra. By modifying
this ${\cal N}=8$ supersymmetric $w_{1+\infty}$ algebra, we show that the
celestial soft current algebra between the graviton, the gravitinos, the
graviphotons, the graviphotinos, and the scalars in two dimensions appears in
the ${\cal N}=8$ supergravity theory with $SO(8)$ (or $SU(8)$) global symmetry
in four dimensions initiated by de Wit and Freedman (at Stony Brook in 1977).
The twenty five couplings in this celestial algebra can be written in terms of
eight arbitrary couplings via the Jacobi identity.
Published: 2025-09-08 04:21:01
Authors: Yitong Li, Qian Ai, Lalith Krishna Samanth Bonagiri, Yingjie Zhang
Categories: cond-mat.mtrl-sci, physics.chem-ph
Abstract:
Platinum-water interfaces underpin many electrochemical energy conversion
processes. However, despite decades of research, the real-space liquid
structure of these interfaces remains elusive. Using three-dimensional atomic
force microscopy (3D-AFM), we mapped Pt-water interface in real space with
angstrom-level resolution. Topographic imaging revealed atomically flat (type
I) and stripe-like (type II) surface nanodomains. Force-distance profiles above
type I domains exhibited oscillatory decay patterns with periodicity of ~0.33
nm, consistent with water. In contrast, type II domains showed stronger
oscillations with larger periodicity of ~0.45 nm and extended decay lengths,
indicative of a different liquid structure with stronger correlation and
ordering. Wide-angle X-ray scattering (WAXS) measurements of pure water and a
series of liquid n-alkanes revealed peaks at ~0.31 nm and ~0.46 nm, in
agreement with 3D-AFM observations of type I and type II structures,
respectively. Our findings uncover the coexistence of two types of liquid
structures at Pt-water interfaces modulated by surface heterogeneity, enabling
new understandings and design principles for energy conversion applications.
Published: 2025-09-08 04:17:02
Authors: Ruisi Zhang, Yifei Zhao, Neusha Javidnia, Mengxin Zheng, Farinaz Koushanfar
Categories: cs.CR, cs.AI
Abstract:
As on-device LLMs(e.g., Apple on-device Intelligence) are widely adopted to
reduce network dependency, improve privacy, and enhance responsiveness,
verifying the legitimacy of models running on local devices becomes critical.
Existing attestation techniques are not suitable for billion-parameter Large
Language Models (LLMs), struggling to remain both time- and memory-efficient
while addressing emerging threats in the LLM era. In this paper, we present
AttestLLM, the first-of-its-kind attestation framework to protect the
hardware-level intellectual property (IP) of device vendors by ensuring that
only authorized LLMs can execute on target platforms. AttestLLM leverages an
algorithm/software/hardware co-design approach to embed robust watermarking
signatures onto the activation distributions of LLM building blocks. It also
optimizes the attestation protocol within the Trusted Execution Environment
(TEE), providing efficient verification without compromising inference
throughput. Extensive proof-of-concept evaluations on LLMs from Llama, Qwen,
and Phi families for on-device use cases demonstrate AttestLLM's attestation
reliability, fidelity, and efficiency. Furthermore, AttestLLM enforces model
legitimacy and exhibits resilience against model replacement and forgery
attacks.
Published: 2025-09-08 04:13:57
Authors: Steven M. Baksa, Lin-Ding Yuan, Stephen D. Wilson, James M. Rondinelli
Categories: cond-mat.mtrl-sci
Abstract:
Wurtzite-type nitrides have recently emerged as promising candidates for
ferroelectric applications, yet their magnetic counterparts remain largely
unexplored. Here, we establish MnSiN$_2$ and MnGeN$_2$ as aristotypes of a new
multiferroic wurtzite family that simultaneously exhibits ferroelectricity and
antiferromagnetism. These Mn(II)-based nitrides crystallize in polar structures
and display robust G-type antiferromagnetism at room temperature.
First-principles calculations reveal that nonmagnetic analogs incorporating Zn
and Mg possess high polarization reversal barriers (0.735 and 0.683 eV per
formula unit) and wide band gaps (4.0 and 4.8 eV), making them ideal
ferroelectric candidates. In contrast, MnSiN$_2$ and MnGeN$_2$ exhibit strong
antiferromagnetic exchange interactions (5--9 meV per Mn site) and moderate
band gaps (1.6 and 1.0 eV), with reversal barriers of 0.963 and 0.460 eV per
formula unit, respectively. Despite their limited magnetoelectric coupling, we
show this family of Type-1 multiferroics exhibits altermagnetic spin splitting
which reverses sign upon polarization switching. By strategically substituting
alkaline-earth metals, we engineer multiple materials with coexisting
switchable polarization, spin texture, and magnetic order. These findings open
new avenues for the design of nitride-based altermagnetic multiferroics,
offering a platform for integrated antiferromagnetic spintronic devices.
Published: 2025-09-08 04:11:28
Authors: Zhuohang Shen, Mohammed Yaseen, Denini Silva, Kevin Guan, Junho Lee, Marcelo d'Amorim, Owolabi Legunsen
Categories: cs.SE
Abstract:
Runtime verification (RV) now scales for testing thousands of open-source
Java projects, helping find hundreds of bugs. The popular Python ecosystem
could use such benefits. But, today's Python RV systems are limited to a domain
or specification logic, or slow. We propose PyMOP, a generic, extensible, and
efficient RV system for Python. PyMOP supports five logics, implements five
existing monitoring algorithms, ships with 73 API specs of Python and
widely-used libraries, supports three instrumentation strategies, and users can
easily add more of these. On 290,133 unit tests in 1,463 GitHub projects, we
find mainly that (i) the default monitoring algorithm for Java is often not the
fastest for Python; (ii) PyMOP is up to 1,168.3x faster than two recent dynamic
analysis systems; and (iii) 44 of 121 bugs that PyMOP helped find so far were
fixed by developers. PyMOP's generality and efficiency position it well as an
excellent platform for the next advances on RV for Python.
Published: 2025-09-08 04:09:52
Authors: Rama Srinivas Varanasi, Motomichi Koyama, Shuya Chiba, Saya Ajito, Eiji Akiyama
Categories: cond-mat.mtrl-sci
Abstract:
This study clarifies the hydrogen embrittlement (HE) behavior in a 1.5 GPa
ferrite-martensite dual-phase (DP) steel. Hydrogen pre-charging (3.8 mass ppm
diffusible hydrogen), followed by slow strain tensile testing (10-4 s-1),
resulted in a brittle fracture at 900 MPa within the elastic regime.
Fractographic studies indicated that surface crack initiation consists of
intergranular and quasi-cleavage morphology; site-specific transmission
electron microscopy (TEM) investigations revealed sub-surface secondary crack
blunting by ferrite. A mixed-mode morphology consisting of ductile and brittle
features was observed adjacent to crack initiation. It differs from the
previous investigation of uncharged DP steel, wherein a predominant brittle
fracture was observed. Following significant crack growth, the pre-charged
specimen exhibited predominant brittle fracture; site-specific TEM and
transmission Kikuchi diffraction studies revealed {100} ferrite cleavage
cracking. Electron backscatter diffraction studies were performed on the
cross-sectional cracks. We explain the HE via hydrogen-induced fast fracture
mechanism. During loading, hydrogen diffuses to the prior austenite grain
boundary, resulting in hydrogen-induced decohesion. Subsequent hydrogen
diffusion to the crack tip promotes brittle fracture at high crack velocity
(>Vcrit). The high crack velocity effectively inhibits crack blunting via
dislocation emission, ensuring sustained brittle crack growth even after
hydrogen depletion at the crack tip, resulting in {100} ferrite cleavage
cracking. Based on TEM observations, we explain the formation of river pattern
features on the {100} cleavage surface.
Published: 2025-09-08 04:08:50
Authors: Jiajun Bao, Nicolas Boullé, Toni J. B. Liu, Raphaël Sarfati, Christopher J. Earls
Categories: cs.LG
Abstract:
Large language models (LLMs) have demonstrated emergent in-context learning
(ICL) capabilities across a range of tasks, including zero-shot time-series
forecasting. We show that text-trained foundation models can accurately
extrapolate spatiotemporal dynamics from discretized partial differential
equation (PDE) solutions without fine-tuning or natural language prompting.
Predictive accuracy improves with longer temporal contexts but degrades at
finer spatial discretizations. In multi-step rollouts, where the model
recursively predicts future spatial states over multiple time steps, errors
grow algebraically with the time horizon, reminiscent of global error
accumulation in classical finite-difference solvers. We interpret these trends
as in-context neural scaling laws, where prediction quality varies predictably
with both context length and output length. To better understand how LLMs are
able to internally process PDE solutions so as to accurately roll them out, we
analyze token-level output distributions and uncover a consistent ICL
progression: beginning with syntactic pattern imitation, transitioning through
an exploratory high-entropy phase, and culminating in confident, numerically
grounded predictions.
Published: 2025-09-08 04:07:14
Authors: Mengcheng Lan, Chaofeng Chen, Jiaxing Xu, Zongrui Li, Yiping Ke, Xudong Jiang, Yingchen Yu, Yunqing Zhao, Song Bai
Categories: cs.CV
Abstract:
Multimodal Large Language Models (MLLMs) have shown exceptional capabilities
in vision-language tasks. However, effectively integrating image segmentation
into these models remains a significant challenge. In this work, we propose a
novel text-as-mask paradigm that casts image segmentation as a text generation
problem, eliminating the need for additional decoders and significantly
simplifying the segmentation process. Our key innovation is semantic
descriptors, a new textual representation of segmentation masks where each
image patch is mapped to its corresponding text label. We first introduce
image-wise semantic descriptors, a patch-aligned textual representation of
segmentation masks that integrates naturally into the language modeling
pipeline. To enhance efficiency, we introduce the Row-wise Run-Length Encoding
(R-RLE), which compresses redundant text sequences, reducing the length of
semantic descriptors by 74% and accelerating inference by $3\times$, without
compromising performance. Building upon this, our initial framework Text4Seg
achieves strong segmentation performance across a wide range of vision tasks.
To further improve granularity and compactness, we propose box-wise semantic
descriptors, which localizes regions of interest using bounding boxes and
represents region masks via structured mask tokens called semantic bricks. This
leads to our refined model, Text4Seg++, which formulates segmentation as a
next-brick prediction task, combining precision, scalability, and generative
efficiency. Comprehensive experiments on natural and remote sensing datasets
show that Text4Seg++ consistently outperforms state-of-the-art models across
diverse benchmarks without any task-specific fine-tuning, while remaining
compatible with existing MLLM backbones. Our work highlights the effectiveness,
scalability, and generalizability of text-driven image segmentation within the
MLLM framework.
Published: 2025-09-08 04:05:38
Authors: Ying Zheng, Jiacheng Bao, Zhiqiang Yu
Categories: math.QA, 18M20
Abstract:
For any cyclic group $\mathbb{Z}_n$, we first determine the Casimir number
and determinant of the Haagerup-Izumi fusion ring
$\mathcal{HI}_{\mathbb{Z}_n}$, it turns out that they do not share the same set
of prime factors. Then we show that all finite-dimensional irreducible
representations of $\mathcal{HI}_{\mathbb{Z}_n}$ are defined over certain
cyclotomic fields. As a direct result, we obtain the formal codegrees of
$\mathcal{HI}_{\mathbb{Z}_n}$, which satisfy the pseudo-unitary inequality.
Published: 2025-09-08 03:59:41
Authors: Avinash Kumar Jha, Shiladitya Sengupta
Categories: cond-mat.soft, cond-mat.dis-nn, cond-mat.stat-mech
Abstract:
Glasses are mechanically rigid, still undergo structural relaxation which
changes their properties and affects potential technological applications.
Understanding the underlying physical processes is a problem of broad
theoretical and practical interest. We investigate intermittent structural
relaxation events or ``avalanches'' occurring inside glassy regime. Contrary to
the more well-known avalanches due to shear, here they are induced by thermal
fluctuations in undeformed glass. By analyzing changes in structural,
mechanical, dynamical, topological and vibrational properties of the system, we
provide a multi-faceted characterization of avalanches. Overall we find that
the system softens due to avalanches. Further, we develop a formalism to
extract local measures of non-Affine displacement and tensorial strain for
thermal amorphous solids in absence of any external deformation. Our analysis
highlights a key difference between two types of driving: while the shear
deformation response is dominated by volume preserving deviatoric strain,
changes in local density must be considered to model response of undeformed
glass under thermal noise. The observations suggest the idea of Generalized
Strain Transformation Zones (GSTZ), where coupled shear and volume-changing
deformations govern thermally-mediated plasticity. Our work paves the way for a
unified description of elasto-plastic response of (athermal) mechanically
deformed and thermally driven undeformed glasses.
Published: 2025-09-08 03:55:17
Authors: Devon Campbell
Categories: quant-ph, cs.CR
Abstract:
Residual cross-talk in superconducting qubit devices creates a security
vulnerability for emerging quantum cloud services. We demonstrate a
Clifford-only Quantum Rowhammer attack-using just X and CNOT gates-that injects
faults on IBM's 127-qubit Eagle processors without requiring pulse-level
access. Experiments show that targeted hammering induces localized errors
confined to the attack cycle and primarily manifests as phase noise, as
confirmed by near 50% flip rates under Hadamard-basis probing. A full lattice
sweep maps QR's spatial and temporal behavior, revealing reproducible
corruption limited to qubits within two coupling hops and rapid recovery in
subsequent benign cycles. Finally, we leverage these properties to outline a
prime-and-probe covert channel, demonstrating that the clear separability
between hammered and benign rounds enables highly reliable signaling without
error correction. These findings underscore the need for hardware-level
isolation and scheduler-aware defenses as multi-tenant quantum computing
becomes standard.
Published: 2025-09-08 03:47:28
Authors: Tanay Kumar, Raktim Bhattacharya
Categories: math.OC
Abstract:
Navigation in the cislunar domain presents significant challenges due to
chaotic and unmodeled dynamics, as well as state-dependent sensor errors. This
paper develops a robust estimation framework based on Linear Fractional
Transformation (LFT) models, and state estimation in $\mathcal{H}_\infty$ and
$\mu$ synthesis framework to address these challenges. The cislunar dynamics
are embedded into an LFT form that captures nonlinearities in the gravitational
model and state-dependent sensor errors as structured uncertainty. A nonlinear
estimator is then synthesized in the $\mathcal{H}_\infty$ sense to ensure
robust performance guarantees in the presence of the stated uncertainties.
Simulation results demonstrate the effectiveness of the estimator for
navigation in a surveillance constellation.
Published: 2025-09-08 03:43:22
Authors: Devon Campbell
Categories: quant-ph, cs.IT, math.IT
Abstract:
Quantum processors are often affected by biased noise and noisy readout,
which reduce reliability and reproducibility. This work combines two
complementary strategies to address these challenges. The first is bias
tailoring, which aligns stabilizers with the dominant error type. The second is
single-shot (SS) decoding, which uses metachecks to identify measurement faults
from just one noisy round. We implement these ideas in a four-dimensional
lifted hypergraph product (4D-LHP) code constructed from quasi-cyclic
protograph seeds. Simulation results show that bias tailoring lowers the
word-error rate (WER) by 20-60 percent across realistic Z:X bias ratios (from
1:1 up to 1000:1), with the largest improvements at moderate bias. When
measurement noise is present, a single SS round recovers more than one third of
the performance lost to readout errors. Moreover, metachecks identify over 99.8
percent of faulty syndromes, providing near-complete fault visibility even with
limited correction power. Together, these findings demonstrate that 4D-LHP
codes maintain strong resilience under realistic noise, making them promising
candidates for integration into orchestrated QPU-CPU workflows.
Published: 2025-09-08 03:40:58
Authors: Csanád Horváth, Natasha Hurley-Walker, Samuel J. McSweeney, Timothy J. Galvin, John Morgan
Categories: astro-ph.IM, astro-ph.HE
Abstract:
We present an automated search method for radio transients on the minute
timescale focused on the emerging long period transients (LPTs) in image-plane
radio data. The method is tuned for use with the Murchison Widefield Array
(MWA) and tested on archival observations from the GaLactic and Extragalactic
All-Sky MWA Extended Survey (GLEAM-X) in the 70--300 MHz range. The images are
formed from model-subtracted visibilities, before applying three filters to the
time series of each pixel in an image, with each filter designed to be
sensitive to a different transient behaviour. Due to the nature of radio
interferometry and the refraction of the fluctuating ionosphere, the vast
majority of candidates at this stage are artefacts which we identify and remove
using a set of flagging measures. Of the 336 final candidates, 7 were genuine
transients; 1 new LPT, 1 new pulsar, and 5 known pulsars. The performance of
the method is analysed by injecting modelled transient pulses into a subset of
the observations and applying the method to the result.
Published: 2025-09-08 03:36:47
Authors: Mehmet Can Yavuz, Berrin Yanikoglu
Categories: cs.LG, cs.CV
Abstract:
A central challenge in representation learning is constructing latent
embeddings that are both expressive and efficient. In practice, deep networks
often produce redundant latent spaces where multiple coordinates encode
overlapping information, reducing effective capacity and hindering
generalization. Standard metrics such as accuracy or reconstruction loss
provide only indirect evidence of such redundancy and cannot isolate it as a
failure mode. We introduce a redundancy index, denoted rho(C), that directly
quantifies inter-dimensional dependencies by analyzing coupling matrices
derived from latent representations and comparing their off-diagonal statistics
against a normal distribution via energy distance. The result is a compact,
interpretable, and statistically grounded measure of representational quality.
We validate rho(C) across discriminative and generative settings on MNIST
variants, Fashion-MNIST, CIFAR-10, and CIFAR-100, spanning multiple
architectures and hyperparameter optimization strategies. Empirically, low
rho(C) reliably predicts high classification accuracy or low reconstruction
error, while elevated redundancy is associated with performance collapse.
Estimator reliability grows with latent dimension, yielding natural lower
bounds for reliable analysis. We further show that Tree-structured Parzen
Estimators (TPE) preferentially explore low-rho regions, suggesting that rho(C)
can guide neural architecture search and serve as a redundancy-aware
regularization target. By exposing redundancy as a universal bottleneck across
models and tasks, rho(C) offers both a theoretical lens and a practical tool
for evaluating and improving the efficiency of learned representations.
Published: 2025-09-08 03:36:44
Authors: Mio Hashimoto, Takako Konoike, Tomoki Kobayashi, Shintaro Hoshino, Takuya Kawada, Tomoyuki Yokouchi, Shinya Uji, Atsutaka Maeda, Yuki Shiomi
Categories: cond-mat.supr-con
Abstract:
We report the nonreciprocal charge transport along the longitudinal and
transverse directions in the vortex flow regime of FeSe superconducting films.
Clear nonreciprocal signals under an inplane magnetic field reveals symmetry
breaking at the film surfaces since the crystal structure of FeSe is
centrosymmetric. Although the symmetry in such polar superconductors allows the
nonreciprocal transverse response under a magnetic field parallel to the
electric current, its observation is physically counterintuitive because vortex
motion is not expected in this configuration. We propose that thermally excited
(anti)vortices due to the two-dimensional nature of FeSe give rise to the
nonreciprocal transverse signals when the mirror symmetry is broken by the
inplane magnetic field.
Published: 2025-09-08 03:34:56
Authors: Guangyu Lei, Tianhao Liang, Yuqi Ping, Xinglin Chen, Longyu Zhou, Junwei Wu, Xiyuan Zhang, Huahao Ding, Xingjian Zhang, Weijie Yuan, Tingting Zhang, Qinyu Zhang
Categories: eess.SY, cs.LG, cs.SY, 68T07, 68T45, 93C85, 94A12, I.2.10; I.2.6; I.2.9; C.2.1
Abstract:
The rapid development of the low-altitude economy emphasizes the critical
need for effective perception and intent recognition of non-cooperative
unmanned aerial vehicles (UAVs). The advanced generative reasoning capabilities
of multimodal large language models (MLLMs) present a promising approach in
such tasks. In this paper, we focus on the combination of UAV intent
recognition and the MLLMs. Specifically, we first present an MLLM-enabled UAV
intent recognition architecture, where the multimodal perception system is
utilized to obtain real-time payload and motion information of UAVs, generating
structured input information, and MLLM outputs intent recognition results by
incorporating environmental information, prior knowledge, and tactical
preferences. Subsequently, we review the related work and demonstrate their
progress within the proposed architecture. Then, a use case for low-altitude
confrontation is conducted to demonstrate the feasibility of our architecture
and offer valuable insights for practical system design. Finally, the future
challenges are discussed, followed by corresponding strategic recommendations
for further applications.
Published: 2025-09-08 03:26:18
Authors: Hang Fan, Yu Shi, Zongliang Fu, Shuo Chen, Wei Wei, Wei Xu, Jian Li
Categories: cs.LG
Abstract:
High-quality wind power forecasting is crucial for the operation of modern
power grids. However, prevailing data-driven paradigms either train a
site-specific model which cannot generalize to other locations or rely on
fine-tuning of general-purpose time series foundation models which are
difficult to incorporate domain-specific data in the energy sector. This paper
introduces WindFM, a lightweight and generative Foundation Model designed
specifically for probabilistic wind power forecasting. WindFM employs a
discretize-and-generate framework. A specialized time-series tokenizer first
converts continuous multivariate observations into discrete, hierarchical
tokens. Subsequently, a decoder-only Transformer learns a universal
representation of wind generation dynamics by autoregressively pre-training on
these token sequences. Using the comprehensive WIND Toolkit dataset comprising
approximately 150 billion time steps from more than 126,000 sites, WindFM
develops a foundational understanding of the complex interplay between
atmospheric conditions and power output. Extensive experiments demonstrate that
our compact 8.1M parameter model achieves state-of-the-art zero-shot
performance on both deterministic and probabilistic tasks, outperforming
specialized models and larger foundation models without any fine-tuning. In
particular, WindFM exhibits strong adaptiveness under out-of-distribution data
from a different continent, demonstrating the robustness and transferability of
its learned representations. Our pre-trained model is publicly available at
https://github.com/shiyu-coder/WindFM.
Published: 2025-09-08 03:23:39
Authors: Guang-Yuan Song, Zhen-Zhao Tao, Bo-Lun Huang, Yan Cui, Bo Yu, Tong-Jie Zhang
Categories: astro-ph.IM, astro-ph.EP
Abstract:
The Five-hundred-meter Aperture Spherical Telescope (FAST) is the world's
largest single-dish radio telescope, and the search for extraterrestrial
intelligence (SETI) is one of its five key science objectives. We conducted a
targeted narrowband search toward the TRAPPIST-1 system using FAST. The
observations consisted of five independent L-band pointings, each with a
20-minute integration, for a total on-source time of 1.67h. The frequency
coverage spanned 1.05--1.45GHz with a spectral resolution of ~7.5Hz. We
searched for narrowband drifting signals with Doppler drift rates within
+_4Hz/s and a signal-to-noise ratio threshold of S/N>10 in two orthogonal
linear polarizations separately.Based on the system parameters adopted in this
work, we estimate a minimum detectable equivalent isotropic radiated power of
2.04x10^10W, placing one of the most stringent constraints to date on
persistent or high-duty-cycle narrowband transmitters in this system. No
credible technosignature candidates were identified within the searched
parameter space. Nevertheless,TRAPPIST-1 remains a compelling target for future
SETI efforts. We plan to extend our search to other signal types, such as
periodic or transient transmitters, and to carry out broader surveys of nearby
exoplanetary systems with FAST.
Published: 2025-09-08 03:18:56
Authors: James Tian
Categories: math.FA, math.OA, Primary: 47A20, secondary: 46L53, 47A60, 47A63, 47B80
Abstract:
We develop a dilation theory for tuples of random operators on a Hilbert
space. Their joint distribution defines a moment kernel by expectation of
operator products. We prove that this kernel admits a dilation to a Cuntz
family of isometries precisely when the associated shifted kernel is dominated
in the positive-definite order. As consequences, we establish a mean-square
version of von Neumann's inequality and construct a free functional calculus
for random operators, extending from polynomials to the free disk algebra and
the free Hardy algebra. These results extend classical dilation theory into a
probabilistic setting and provide new tools for analyzing noncommutative random
systems.
Published: 2025-09-08 03:16:05
Authors: Seung Hyun Moon
Categories: stat.ML, cs.LG, math.ST, stat.TH
Abstract:
This paper studies high-dimensional additive regression under the transfer
learning framework, where one observes samples from a target population
together with auxiliary samples from different but potentially related
regression models. We first introduce a target-only estimation procedure based
on the smooth backfitting estimator with local linear smoothing. In contrast to
previous work, we establish general error bounds under sub-Weibull($\alpha$)
noise, thereby accommodating heavy-tailed error distributions. In the
sub-exponential case ($\alpha=1$), we show that the estimator attains the
minimax lower bound under regularity conditions, which requires a substantial
departure from existing proof strategies. We then develop a novel two-stage
estimation method within a transfer learning framework, and provide theoretical
guarantees at both the population and empirical levels. Error bounds are
derived for each stage under general tail conditions, and we further
demonstrate that the minimax optimal rate is achieved when the auxiliary and
target distributions are sufficiently close. All theoretical results are
supported by simulation studies and real data analysis.
Published: 2025-09-08 03:13:47
Authors: Lei Shu, Dong Zhao
Categories: cs.AI
Abstract:
Conventional approaches to building energy retrofit decision making suffer
from limited generalizability and low interpretability, hindering adoption in
diverse residential contexts. With the growth of Smart and Connected
Communities, generative AI, especially large language models (LLMs), may help
by processing contextual information and producing practitioner readable
recommendations. We evaluate seven LLMs (ChatGPT, DeepSeek, Gemini, Grok,
Llama, and Claude) on residential retrofit decisions under two objectives:
maximizing CO2 reduction (technical) and minimizing payback period
(sociotechnical). Performance is assessed on four dimensions: accuracy,
consistency, sensitivity, and reasoning, using a dataset of 400 homes across 49
US states. LLMs generate effective recommendations in many cases, reaching up
to 54.5 percent top 1 match and 92.8 percent within top 5 without fine tuning.
Performance is stronger for the technical objective, while sociotechnical
decisions are limited by economic trade offs and local context. Agreement
across models is low, and higher performing models tend to diverge from others.
LLMs are sensitive to location and building geometry but less sensitive to
technology and occupant behavior. Most models show step by step, engineering
style reasoning, but it is often simplified and lacks deeper contextual
awareness. Overall, LLMs are promising assistants for energy retrofit decision
making, but improvements in accuracy, consistency, and context handling are
needed for reliable practice.
Published: 2025-09-08 03:12:57
Authors: Zhang Jing, Pu Nan, Xie Yu Xiang, Guo Yanming, Lu Qianqi, Zou Shiwei, Yan Jie, Chen Yan
Categories: cs.CV
Abstract:
Generalized Category Discovery (GCD) is an emerging and challenging
open-world problem that has garnered increasing attention in recent years. Most
existing GCD methods focus on discovering categories in static images. However,
relying solely on static visual content is often insufficient to reliably
discover novel categories. To bridge this gap, we extend the GCD problem to the
video domain and introduce a new setting, termed Video-GCD. Thus, effectively
integrating multi-perspective information across time is crucial for accurate
Video-GCD. To tackle this challenge, we propose a novel Memory-guided
Consistency-aware Contrastive Learning (MCCL) framework, which explicitly
captures temporal-spatial cues and incorporates them into contrastive learning
through a consistency-guided voting mechanism. MCCL consists of two core
components: Consistency-Aware Contrastive Learning(CACL) and Memory-Guided
Representation Enhancement (MGRE). CACL exploits multiperspective temporal
features to estimate consistency scores between unlabeled instances, which are
then used to weight the contrastive loss accordingly. MGRE introduces a
dual-level memory buffer that maintains both feature-level and logit-level
representations, providing global context to enhance intra-class compactness
and inter-class separability. This in turn refines the consistency estimation
in CACL, forming a mutually reinforcing feedback loop between representation
learning and consistency modeling. To facilitate a comprehensive evaluation, we
construct a new and challenging Video-GCD benchmark, which includes action
recognition and bird classification video datasets. Extensive experiments
demonstrate that our method significantly outperforms competitive GCD
approaches adapted from image-based settings, highlighting the importance of
temporal information for discovering novel categories in videos. The code will
be publicly available.
Published: 2025-09-08 03:12:36
Authors: Shengyong Li, Yanjin Yue, Ying Hu, Rui-Yang Gong, Qianchuan Zhao, Zhihui Peng, Pengtao Song, Zeliang Xiang, Jing Zhang
Categories: quant-ph
Abstract:
Quantum states encoded in electromagnetic fields, also known as bosonic
states, have been widely applied in quantum sensing, quantum communication, and
quantum error correction. Accurate characterization is therefore essential yet
difficult when states cannot be reconstructed with sparse Pauli measurements.
Tomography must work with dense measurement bases, high-dimensional Hilbert
spaces, and often sample-based data. However, existing convex
optimization-based techniques are not efficient enough and scale poorly when
extended to large and multi-mode systems. In this work, we explore convex
optimization as an effective framework to address problems in bosonic state
tomography, introducing three techniques to enhance efficiency and scalability:
efficient displacement operator computation, Hilbert space truncation, and
stochastic convex optimization, which mitigate common limitations of existing
approaches. Then we propose a sample-based, convex maximum-likelihood
estimation (MLE) method specifically designed for flying mode tomography.
Numerical simulations of flying four-mode and nine-mode problems demonstrate
the accuracy and practicality of our methods. This method provides practical
tools for reliable bosonic mode quantum state reconstruction in
high-dimensional and multi-mode systems.
Published: 2025-09-08 03:11:51
Authors: Hai-Bo Li, Hong-Fei Shen
Categories: hep-ex
Abstract:
This proceeding presents recent advances in the study of hyperon physics
using data from the BESIII experiment. The BESIII detector has been in full
operation at the BEPCII collider since 2008, providing excellent resolution,
particle identification (PID), and large coverage for both neutral and charged
particles. Leveraging the outstanding capability of the detector, the BESIII
experiment has collected 10 billion $J/\psi$ and 2.7 billion $\psi(3686)$
events. In recent years, BESIII has conducted a series of analyses focusing on
hyperon physics, utilizing the pair production of quantum-entangled
hyperon-antihyperon pairs from these charmonium decays. The transverse
polarizations of the $\Lambda$, $\Sigma^{+,0}$, and $\Xi^{-,0}$ hyperons have
been observed in $J/\psi$ and $\psi(3686)$ decays, providing excellent
opportunities to search for the CP violation in hyperon decays. Additionally,
BESIII investigates weak radiative hyperon decays, semi-leptonic hyperon
decays, and hyperon-nucleon interactions.
Published: 2025-09-08 03:09:50
Authors: Yingying Fan, Jingyuan Liu, Jinchi Lv, Ao Sun
Categories: stat.ML, cs.LG, stat.ME
Abstract:
We propose a new inference framework, named MOSAIC, for change-point
detection in dynamic networks with the simultaneous low-rank and sparse-change
structure. We establish the minimax rate of detection boundary, which relies on
the sparsity of changes. We then develop an eigen-decomposition-based test with
screened signals that approaches the minimax rate in theory, with only a minor
logarithmic loss. For practical implementation of MOSAIC, we adjust the
theoretical test by a novel residual-based technique, resulting in a pivotal
statistic that converges to a standard normal distribution via the martingale
central limit theorem under the null hypothesis and achieves full power under
the alternative hypothesis. We also analyze the minimax rate of testing
boundary for dynamic networks without the low-rank structure, which almost
aligns with the results in high-dimensional mean-vector change-point inference.
We showcase the effectiveness of MOSAIC and verify our theoretical results with
several simulation examples and a real data application.
Published: 2025-09-08 03:05:48
Authors: Geva Yashfe
Categories: math.CO, cs.IT, math.IT, 05B35, 68P30, 20F10
Abstract:
We prove that there is no algorithm to decide whether a given integer vector
is in the closure of the entropic cone $\overline{\Gamma_{n}^{*}}$.
Equivalently, there is no decision procedure to determine whether a given
integer-valued function
$h:\mathcal{P}(\{1,\ldots,n\})\rightarrow\mathbb{Z}_{\ge 0}$ is a pointwise
limit of joint entropy functions. In other words, given such an $h$, it is
undecidable whether for all $\varepsilon > 0$ there exists a finite probability
space $(\Omega,P)$ with random variables $X_{1},\ldots,X_{n}$ such that their
joint entropy $H$ satisfies
$\max_{I\subseteq\{1,\ldots,n\}}\left|H\left(X_{I}\right)-h\left(I\right)\right|<\varepsilon$.
This settles the last open case in a sequence of related undecidability results
proved by L. K\"{u}hne and the author, with applications in algorithmic
information theory. The main new tool is a Desargues'-type theorem for almost
entropic polymatroids.
Published: 2025-09-08 03:02:53
Authors: Dharun Anandayuvaraj, Zain Hammadeh, Andreas Lund, Alexandra Holloway, James C. Davis
Categories: cs.SE
Abstract:
Software failures can have significant consequences, making learning from
failures a critical aspect of software engineering. While software
organizations are recommended to conduct postmortems, the effectiveness and
adoption of these practices vary widely. Understanding how engineers gather,
document, share, and apply lessons from failures is essential for improving
reliability and preventing recurrence. High-reliability organizations (HROs)
often develop software systems where failures carry catastrophic risks,
requiring continuous learning to ensure reliability. These organizations
provide a valuable setting to examine practices and challenges for learning
from software failures. Such insight could help develop processes and tools to
improve reliability and prevent recurrence. However, we lack in-depth industry
perspectives on the practices and challenges of learning from failures.
To address this gap, we conducted a case study through 10 in-depth interviews
with research software engineers at a national space research center. We
examine how they learn from failures: how they gather, document, share, and
apply lessons. To assess transferability, we include data from 5 additional
interviews at other HROs. Our findings provide insight into how engineers learn
from failures in practice. To summarize: (1) failure learning is informal, ad
hoc, and inconsistently integrated into SDLC; (2) recurring failures persist
due to absence of structured processes; and (3) key challenges, including time
constraints, knowledge loss from turnover and fragmented documentation, and
weak process enforcement, undermine systematic learning. Our findings deepen
understanding of how software engineers learn from failures and offer guidance
for improving failure management practices.
Published: 2025-09-08 03:02:03
Authors: Oem Trivedi, Abraham Loeb
Categories: astro-ph.EP, astro-ph.GA, astro-ph.IM, astro-ph.SR
Abstract:
We investigate dynamical constraints on the population of large interstellar
objects (ISOs) by combining encounter rate analysis, Eddington inversion, and
Liouville mapping. Encounter rate scaling demonstrates that detections of
kilometer-scale ISOs require flux enhancements beyond natural Maxwellian
expectations. Using Eddington inversion, we show how steep density profiles
imply phase-space biases consistent with strong gravitational focusing and we
then develop a Liouville mapping formalism that propagates the interstellar
velocity distribution inward under conservation of energy and angular momentum,
revealing that low-angular momentum anisotropies can reproduce the observed
size dependent detection rates. These results provide a self consistent
dynamical framework for interpreting the observed population of ISOs and for
assessing whether the required anisotropies arise from natural or artificial
origins. The main results are framed in the context of the parameters for
3I/ATLAS, but the implications are general and go on to sharpen the distinction
between natural dynamical mechanisms and potential artificial origins for ISOs.
Published: 2025-09-08 02:53:13
Authors: V. Berejnov, B. Y. Rubinstein
Categories: physics.optics
Abstract:
A new method for phase recovery from a single two-beam interferogram is
presented. Conventional approaches, relying on trigonometric inversion followed
by phase unfolding and unwrapping, are hindered by discontinuities, typically
addressed through intricate algorithms. Our method bypasses the unfolding and
unwrapping, instead formulating a first-order differential equation directly
relating the phase to the interferogram. Integration of this equation enables
continuous retrieval of phase along any straight path. Representing a new class
of analytical tools for single-interferogram phase retrieval, this approach is
derived from first principles and accommodates both Newton-type and Fizeau-type
interferograms. Its performance is demonstrated on multiple idealized synthetic
interferograms of increasing complexity, validating against the known seed
phase.
Published: 2025-09-08 02:52:45
Authors: Zihan Yan, Rui Xi, Mengshu Hou
Categories: cs.DB
Abstract:
Database knob tuning is essential for optimizing the performance of modern
database management systems, which often expose hundreds of knobs with
continuous or categorical values. However, the large number of knobs and the
vast configuration space make it difficult to identify optimal settings
efficiently. Although learning-based tuning has shown promise, existing
approaches either ignore domain knowledge by relying solely on benchmark
feedback or struggle to explore the high-dimensional knob space, resulting in
high tuning costs and suboptimal performance. To address these challenges, we
propose MCTuner, an adaptive knob tuning framework that minimizes exploration
in ineffective regions of the configuration space. MCTuner employs a
Mixture-of-Experts (MoE) mechanism with specialized LLMs to identify
performance-critical knobs. In further, MCTuner introduces the first spatial
decomposition algorithm that recursively partitions the space into hierarchical
subspaces, on which Bayesian Optimization is performed to efficiently search
for near-optimal configurations. Evaluated on different benchmarks (OLAP, OLTP,
and HTAP), MCTuner achieves up to 19.2% performance gains and 1.4x faster
configuration discovery per iteration compared to state-of-the-art methods.
Published: 2025-09-08 02:50:11
Authors: Li Lin, Xiaojun Wan
Categories: cs.LG
Abstract:
A natural and intuitive idea in model quantization is to approximate each
component's quantized output to match its original. Layer-wise post-training
quantization (PTQ), though based on this idea, adopts a strictly local view and
can achieve, at best, only activation-aware approximations of weights. As a
result, it often leads to insufficient approximations and practical deviations
from this guiding intuition. Recent work has achieved a more accurate
approximation of linear-layer outputs within the framework of layer-wise PTQ,
but such refinements remain inadequate for achieving alignment with the full
model output. Based on a deeper understanding of the structural characteristics
of mainstream LLMs, we propose $LoaQ$, an output-approximation method for
layer-wise PTQ that explicitly targets output-level consistency. It better
aligns with this intuition and can feature a simple closed-form solution,
making it orthogonal to existing techniques and readily integrable into
existing quantization pipelines. Experiments on the LLaMA and Qwen model
families demonstrate that LoaQ performs effectively in both weight-only and
weight-activation joint quantization. By integrating seamlessly with existing
quantization strategies, it further enhances overall quantization quality and
shows strong potential to advance the frontier of post-training quantization.