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2512.22380
|
Creating multicomponent Schrödinger cat states in a coupled qubit-oscillator system
|
We present a method for preparing various exotic modifications of Schrödinger cat states by coupling a semiclassical oscillator to a system of qubits. Varying the number of qubits and parameters of the quantum quench performed in the coupled system, we bring the oscillator into a~highly non-classical state composed of an arbitrary number of partly coherent wavepackets in tunable proportions and motion relations. The method can be implemented with the aid of current experimental techniques and may find applications in quantum information and sensing protocols.
| 2025-12-26
| 2025-12-30
|
[
"quant-ph"
] |
Pavel Stránský, Pavel Cejnar
|
2512.22349
|
Human-like visual computing advances explainability and few-shot learning in deep neural networks for complex physiological data
|
Machine vision models, particularly deep neural networks, are increasingly applied to physiological signal interpretation, including electrocardiography (ECG), yet they typically require large training datasets and offer limited insight into the causal features underlying their predictions. This lack of data efficiency and interpretability constrains their clinical reliability and alignment with human reasoning. Here, we show that a perception-informed pseudo-colouring technique, previously demonstrated to enhance human ECG interpretation, can improve both explainability and few-shot learning in deep neural networks analysing complex physiological data.
We focus on acquired, drug-induced long QT syndrome (LQTS) as a challenging case study characterised by heterogeneous signal morphology, variable heart rate, and scarce positive cases associated with life-threatening arrhythmias such as torsades de pointes. This setting provides a stringent test of model generalisation under extreme data scarcity. By encoding clinically salient temporal features, such as QT-interval duration, into structured colour representations, models learn discriminative and interpretable features from as few as one or five training examples. Using prototypical networks and a ResNet-18 architecture, we evaluate one-shot and few-shot learning on ECG images derived from single cardiac cycles and full 10-second rhythms. Explainability analyses show that pseudo-colouring guides attention toward clinically meaningful ECG features while suppressing irrelevant signal components. Aggregating multiple cardiac cycles further improves performance, mirroring human perceptual averaging across heartbeats. Together, these findings demonstrate that human-like perceptual encoding can bridge data efficiency, explainability, and causal reasoning in medical machine intelligence.
| 2025-12-26
| 2025-12-30
|
[
"cs.CV",
"cs.AI",
"cs.HC",
"cs.LG"
] |
Alaa Alahmadi, Mohamed Hasan
|
2501.08609
|
Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet)
|
Motor imitation impairments are commonly reported in individuals with autism spectrum conditions (ASCs), suggesting that motor imitation could be used as a phenotype for addressing autism heterogeneity. Traditional methods for assessing motor imitation are subjective, labor-intensive, and require extensive human training. Modern Computerized Assessment of Motor Imitation (CAMI) methods, such as CAMI-3D for motion capture data and CAMI-2D for video data, are less subjective. However, they rely on labor-intensive data normalization and cleaning techniques, and human annotations for algorithm training. To address these challenges, we propose CAMI-2DNet, a scalable and interpretable deep learning-based approach to motor imitation assessment in video data, which eliminates the need for data normalization, cleaning and annotation. CAMI-2DNet uses an encoder-decoder architecture to map a video to a motion encoding that is disentangled from nuisance factors such as body shape and camera views. To learn a disentangled representation, we employ synthetic data generated by motion retargeting of virtual characters through the reshuffling of motion, body shape, and camera views, as well as real participant data. To automatically assess how well an individual imitates an actor, we compute a similarity score between their motion encodings, and use it to discriminate individuals with ASCs from neurotypical (NT) individuals. Our comparative analysis demonstrates that CAMI-2DNet has a strong correlation with human scores while outperforming CAMI-2D in discriminating ASC vs NT children. Moreover, CAMI-2DNet performs comparably to CAMI-3D while offering greater practicality by operating directly on video data and without the need for ad-hoc data normalization and human annotations.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
Kaleab A. Kinfu, Carolina Pacheco, Alice D. Sperry, Deana Crocetti, Bahar Tunçgenç, Stewart H. Mostofsky, René Vidal
|
2512.22116
|
General Construction of Quantum Error-Correcting Codes from Multiple Classical Codes
|
The hypergraph product (HGP) construction of quantum error-correcting codes (QECC) offers a general and explicit method for building a QECC from two classical codes, thereby paving the way for the discovery of good quantum low-density parity-check codes. In this letter, we propose a general and explicit construction recipe for QECCs from a total of D classical codes for arbitrary D. Following this recipe guarantees the obtainment of a QECC within the stabilizer formalism and nearly exhausts all possible constructions. As examples, we demonstrate that our construction recovers the HGP construction when D = 2 and leads to four distinct types of constructions for D = 3, including a previously studied case as one of them. When the input classical codes are repetition codes, our D = 3 constructions unify various three-dimensional lattice models into a single framework, encompassing the three-dimensional toric code model, a fracton model, and two other intriguing models not previously investigated. Among these, two types of constructions exhibit a trade-off between code distance and code dimension for a fixed number of qubits by adjusting the lengths of the different classical codes, and the optimal choice can simultaneously achieve relatively large values for both code distance and code dimension. Our general construction protocol provides another perspective for enriching the structure of QECCs and enables the exploration of richer possibilities for good codes.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph",
"cond-mat.quant-gas",
"cond-mat.str-el"
] |
Yue Wu, Meng-Yuan Li, Chengshu Li, Hui Zhai
|
2512.22357
|
Dispersionless version of multi-component Pfaff-Toda hierarchy
|
We consider the dispersionless limit of the recently introduced multi-component Pfaff-Toda hierarchy. Its dispersionless version is a set of nonlinear differential equations for the dispersionless limit of logarithm of the tau-function (the F-function). They are obtained as limiting cases of bilinear equations of the Hirota-Miwa type. The analysis of the Pfaff-Toda hierarchy is substantially simplified by using the observation that the full (not only dispersionless) N-component Pfaff-Toda hierarchy is actually equivalent to the 2N-component DKP hierarchy. In the dispersionless limit, there is an elliptic curve built in the structure of the hierarchy, with the elliptic modular parameter being a dynamical variable. This curve can be uniformized by elliptic functions, and in the elliptic parametrization the hierarchy acquires a compact and especially nice form.
| 2025-12-26
| 2025-12-30
|
[
"nlin.SI",
"math-ph",
"math.MP"
] |
A. Savchenko, A. Zabrodin
|
2512.21827
|
Securing Cross-Domain Internet of Drones: An RFF-PUF Allied Authenticated Key Exchange Protocol With Over-the-Air Enrollment
|
The Internet of Drones (IoD) is an emerging and crucial paradigm enabling advanced applications that require seamless, secure communication across heterogeneous and untrusted domains. In such environments, access control and the transmission of sensitive data pose significant security challenges for IoD systems, necessitating the design of lightweight mutual authentication and key exchange protocols. Existing solutions are often hampered by high computation overhead, reliance on third parties, the requirement for secret storage in resource-constrained drones, and the need for a strictly controlled enrollment environment. These limitations make them impractical for dynamic cross-domain deployment. To address these limitations, we propose a lightweight mutual authentication mechanism that integrates Radio Frequency Fingerprint (RFF) and Physical Unclonable Function (PUF) technologies for secure drone-to-drone (D2D) and drone-to-ground station server (D2G) communication. RFF-based device identification is used to achieve over-the-air (OTA) enrollment, while the PUF serves as the root of trust for establishing mutual authentication among communication parties. Additionally, the on-the-fly key generation capability of the PUF is co-designed with One-Time-Pad (OTP) encryption to realize ephemeral keying and eliminate the need for storing secrets within drones. Both informal security analysis and ProVerif-based formal security verification comprehensively demonstrate the resilience of our protocol against common security attacks. The proposed protocol also outperforms existing IoD authentication schemes in terms of security features, as well as computation, communication, and storage overhead.
| 2025-12-26
| 2025-12-29
|
[
"cs.CR"
] |
Xuanyu Chen, Yue Zheng, Junqing Zhang, Guanxiong Shen, Chip-Hong Chang
|
2503.02176
|
Client-Aided Secure Two-Party Computation of Dynamic Controllers
|
In this paper, we propose a secure two-party computation protocol for dynamic controllers using a secret sharing scheme. The proposed protocol realizes outsourcing of controller computation to two servers, while controller parameters, states, inputs, and outputs are kept secret against the servers. Unlike previous encrypted controls in a single-server setting, the proposed method can operate a dynamic controller for an infinite time horizon without controller state decryption or input re-encryption. We show that the control performance achievable by the proposed protocol can be made arbitrarily close to that attained by the unencrypted controller. Furthermore, system-theoretic and cryptographic modifications of the protocol are presented to improve the communication complexity. The feasibility of the protocol is demonstrated through numerical examples of PID and observer-based controls.
| 2025-12-26
| 2025-12-29
|
[
"eess.SY",
"cs.CR",
"cs.SY"
] |
Kaoru Teranishi, Takashi Tanaka
|
2408.03068
|
Valence Quark Distributions in Pions: Insights from Tsallis Entropy
|
We investigate the valence quark distributions of pions at a low initial scale ($Q^2_0$) by employing Tsallis entropy, a non-extensive measure that effectively captures long-range correlations among internal constituents. Utilizing the maximum entropy approach, we adopt two distinct functional forms and fit experimental data through the elegant GLR-MQ-ZRS evolution equation to derive the model parameters. Our findings indicate that the resulting valence quark distributions provide an optimal fit to experimental data, with the values of the $q$ parameter deviating from unity. This deviation indicates the significant role that correlations among valence quarks play in shaping our understanding of pion internal structure. Additionally, our computations of the first three moments of pion quark distributions at $ Q^2 = 4 \, \mathrm{GeV}^2$ display consistency with other theoretical models, thereby reinforcing the importance of incorporating valence quark correlations within this analytical framework.
| 2025-12-26
| 2025-12-30
|
[
"hep-ph"
] |
Jingxuan Chen, Xiaopeng Wang, Yanbing Cai, Xurong Chen, Qian Wang
|
2512.22385
|
LLM-Guided Exemplar Selection for Few-Shot Wearable-Sensor Human Activity Recognition
|
In this paper, we propose an LLM-Guided Exemplar Selection framework to address a key limitation in state-of-the-art Human Activity Recognition (HAR) methods: their reliance on large labeled datasets and purely geometric exemplar selection, which often fail to distinguish similar weara-ble sensor activities such as walking, walking upstairs, and walking downstairs. Our method incorporates semantic reasoning via an LLM-generated knowledge prior that captures feature importance, inter-class confusability, and exemplar budget multipliers, and uses it to guide exemplar scoring and selection. These priors are combined with margin-based validation cues, PageRank centrality, hubness penalization, and facility-location optimization to obtain a compact and informative set of exemplars. Evaluated on the UCI-HAR dataset under strict few-shot conditions, the framework achieves a macro F1-score of 88.78%, outperforming classical approaches such as random sampling, herding, and $k$-center. The results show that LLM-derived semantic priors, when integrated with structural and geometric cues, provide a stronger foundation for selecting representative sensor exemplars in few-shot wearable-sensor HAR.
| 2025-12-26
| 2025-12-30
|
[
"cs.CL",
"cs.AI",
"cs.CV"
] |
Elsen Ronando, Sozo Inoue
|
2512.22121
|
Information Critical Phases under Decoherence
|
Quantum critical phases are extended regions of phase space characterized by a diverging correlation length. By analogy, we define an information critical phase as an extended region of a mixed state phase diagram where the Markov length, the characteristic length scale governing the decay of the conditional mutual information (CMI), diverges.
We demonstrate that such a phase arises in decohered $\mathbb{Z}_{N}$ Toric codes by assessing both the CMI and the coherent information, the latter quantifying the robustness of the encoded logical qudits. For $N>4$, we find that the system hosts an information critical phase intervening between the decodable and non-decodable phases where the coherent information saturates to a fractional value in the thermodynamic limit, indicating that a finite fraction of logical information is still preserved. We show that the density matrix in this phase can be decomposed into a convex sum of Coulombic pure states, where gapped anyons reorganize into gapless photons. We further consider the ungauged $\mathbb{Z}_{N}$ Toric code and interpret its mixed state phase diagram in the language of strong-to-weak spontaneous symmetry breaking. We argue that in the dual model, the information critical phase arises because the spontaneously broken off-diagonal $\mathbb{Z}_{N}$ symmetry gets enhanced to a U(1) symmetry, resulting in a novel superfluid phase whose gapless modes involve coherent excitations of both the system and the environment. Finally, we propose an optimal decoding protocol for the corrupted $\mathbb{Z}_{N}$ Toric code and evaluate its effectiveness in recovering the fractional logical information preserved in the information critical phase. Our findings identify a gapless analog for mixed-state phases that still acts as a fractional topological quantum memory, thereby extending the conventional paradigm of quantum memory phases.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph",
"cond-mat.dis-nn",
"cond-mat.stat-mech"
] |
Akash Vijay, Jong Yeon Lee
|
2511.11342
|
Lorentz Transformation in Quantum Mechanics
|
The compatibility of special relativity and Quantum Mechanics has been questioned by several authors. The origin of this tension can be traced back mainly to the introduction of the measurement processes and the corresponding wave function reduction, which play a crucial role in Quantum Mechanics. We approach this problem with the help of a recent proposal for a model of Quantum Mechanics, where the measurement is explicitly described as a specific stochastic process. This implements ordinary Quantum Mechanics, where measurement and reduction are treated as phenomenological events of unknown origin without any physical justification. To state clearly the question in general, we first discuss and establish the effect of a Lorentz transformation on a generic wave function in space-time. Alongside the analysis we formulate the relativistic version of the model. We then consider few thought experiments in order to analyze to what extent Quantum Mechanics follows relativistic invariance and find the specific critical points where non invariance possibly occurs. The analysis can shade light to the interpretation of the existing experimental observations.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph"
] |
Marcello Baldo
|
2512.22401
|
$U_q(\mathfrak{gl}(m|n))$ bounds on the minimal genus of virtual links
|
For links $L \subset Σ\times [0,1]$, where $Σ$ is a closed orientable surface, we define a $U_q(\mathfrak{gl}(1|1))$ Reshetikhin-Turaev invariant with coefficients in $\mathbb{Z}[H_1(Σ)]$. This invariant turns out to be equivalent to an infinite cyclic version of the Carter-Silver-Williams (CSW) polynomial. The importance of the CSW polynomial is that half its symplectic rank gives strong lower bounds on the virtual genus. Recall that the virtual genus of a virtual link $J$ is the smallest genus of all closed orientable surfaces $Σ$ on which $J$ can be represented by a link diagram on $Σ$. Here we generalize the CSW lower bound to all quantum supergroups $U_q(\mathfrak{gl}(m|n))$ with $m,n>0$. For $(m,n)=(1,1)$, the $U_q(\mathfrak{gl}(m|n))$ bound is the same as the CSW bound. However, changing the value of the pair $(m,n)$ can give lower bounds better than those available from other known methods. We compare the $U_q(\mathfrak{gl}(m|n))$ lower bounds to those coming from the CSW polynomial, the surface bracket, the arrow polynomial, hyperbolicity, and the Gordon-Litherland determinant test. As a first application, we show that the Seifert genus of homologically trivial knots in thickened surfaces is not additive under the connected sum operation of virtual knots. As a second application, we prove that the Jaeger-Kauffman-Saleur invariant of a virtual knot is always realizable as the Alexander polynomial of an infinite cyclic cover of a knot complement in some $Σ\times [0,1]$, but is not always so on a surface of minimal genus. This is accomplished with a generalization of the $Zh$-construction, called the homotopy $Zh$-construction.
| 2025-12-26
| 2025-12-30
|
[
"math.GT"
] |
Micah Chrisman, Killian Davis, Anup Poudel
|
2507.14270
|
APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
|
We propose the APTx Neuron, a novel, unified neural computation unit that integrates non-linear activation and linear transformation into a single trainable expression. The APTx Neuron is derived from the APTx activation function, thereby eliminating the need for separate activation layers and making the architecture both optimization-efficient and elegant. The proposed neuron follows the functional form $y = \sum_{i=1}^{n} ((α_i + \tanh(β_i x_i)) \cdot γ_i x_i) + δ$, where all parameters $α_i$, $β_i$, $γ_i$, and $δ$ are trainable. We validate our APTx Neuron-based architecture on the MNIST dataset, achieving up to $96.69\%$ test accuracy within 11 epochs using approximately 332K trainable parameters. The results highlight the superior expressiveness and training efficiency of the APTx Neuron compared to traditional neurons, pointing toward a new paradigm in unified neuron design and the architectures built upon it. Source code is available at https://github.com/mr-ravin/aptx_neuron.
| 2025-12-26
| 2025-12-29
|
[
"cs.NE",
"cs.AI",
"cs.CV",
"cs.LG"
] |
Ravin Kumar
|
2512.21864
|
The trinacria graphs $T_{(b+2)b2}$ are $e$-positive
|
In this paper, we identify a new family of $e$-positive graphs, called the trinacria graphs $T_{(b+2)b2}$, thereby providing a partial answer to Stanley's question on which graphs are $e$-positive. The trinacria graph $T_{abc}$ is the graph on $a+b+c+3$ vertices obtained by attaching paths $P_a$, $P_b$ and~$P_c$ to the vertices of a triangle, respectively. Our proof relies on several ad hoc combinatorial ideas, and employs divide-and-conquer techniques, charging arguments, and progressive repair methods.
| 2025-12-26
| 2025-12-29
|
[
"math.CO"
] |
Simon Y. M. Gong, David G. L. Wang, K. Zhang
|
2512.22381
|
PHANTOM: Physics-Aware Adversarial Attacks against Federated Learning-Coordinated EV Charging Management System
|
The rapid deployment of electric vehicle charging stations (EVCS) within distribution networks necessitates intelligent and adaptive control to maintain the grid's resilience and reliability. In this work, we propose PHANTOM, a physics-aware adversarial network that is trained and optimized through a multi-agent reinforcement learning model. PHANTOM integrates a physics-informed neural network (PINN) enabled by federated learning (FL) that functions as a digital twin of EVCS-integrated systems, ensuring physically consistent modeling of operational dynamics and constraints. Building on this digital twin, we construct a multi-agent RL environment that utilizes deep Q-networks (DQN) and soft actor-critic (SAC) methods to derive adversarial false data injection (FDI) strategies capable of bypassing conventional detection mechanisms. To examine the broader grid-level consequences, a transmission and distribution (T and D) dual simulation platform is developed, allowing us to capture cascading interactions between EVCS disturbances at the distribution level and the operations of the bulk transmission system. Results demonstrate how learned attack policies disrupt load balancing and induce voltage instabilities that propagate across T and D boundaries. These findings highlight the critical need for physics-aware cybersecurity to ensure the resilience of large-scale vehicle-grid integration.
| 2025-12-26
| 2025-12-30
|
[
"cs.ET",
"cs.LG"
] |
Mohammad Zakaria Haider, Amit Kumar Podder, Prabin Mali, Aranya Chakrabortty, Sumit Paudyal, Mohammad Ashiqur Rahman
|
2410.23066
|
Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank
|
State-of-the-art Extreme Multi-Label Text Classification models rely on multi-label attention to focus on key tokens in input text, but learning good attention weights is challenging. We introduce PLANT - Pretrained and Leveraged Attention - a plug-and-play strategy for initializing attention. PLANT works by planting label-specific attention using a pretrained Learning-to-Rank model guided by mutual information gain. This architecture-agnostic approach integrates seamlessly with large language model backbones such as Mistral-7B, LLaMA3-8B, DeepSeek-V3, and Phi-3. PLANT outperforms state-of-the-art methods across tasks including ICD coding, legal topic classification, and content recommendation. Gains are especially pronounced in few-shot settings, with substantial improvements on rare labels. Ablation studies confirm that attention initialization is a key driver of these gains. For code and trained models, see https://github.com/debjyotiSRoy/xcube/tree/plant
| 2025-12-26
| 2025-12-29
|
[
"cs.CL",
"cs.LG"
] |
Debjyoti Saha Roy, Byron C. Wallace, Javed A. Aslam
|
2511.07944
|
A Method for On-Orbit Calibration of the VLAST-P Electromagnetic Calorimeter
|
The Very Large Area Gamma-ray Space Telescope Pathfinder (VLAST-P), as the technology validation satellite for the VLAST mission, is designed to observe high-energy solar bursts on orbit. The CsI electromagnetic calorimeter (ECAL) is one of the key sub-detectors of VLAST-P. To investigate the on-orbit energy calibration method of the ECAL, a Geant4-based simulation of VLAST-P was carried out. The results show an energy resolution better than 10% in the 0.1 to 5 GeV range and a linearity deviation below 2%. A dedicated minimum-ionization-particle (MIP) calibration method was developed to ensure accurate energy reconstruction and to monitor detector stability throughout the in-orbit calibration period.
| 2025-12-26
| 2025-12-29
|
[
"hep-ex"
] |
Jiaxuan Wang, Zhen Wang, Borong Peng, Renjun Wang, Yunlong Zhang, Zhongtao Shen, Yifeng Wei, Dengyi Chen, Xiang Li, Yiming Hu, Jianhua Guo
|
2308.15451
|
Metawisdom of the Crowd: Experimental Evidence of Crowd Accuracy Through Diverse Choices of Decision Aids
|
The provision of information can improve individual judgments but also fail to make group decisions more accurate; if individuals choose to attend to the same information in the same manner, the predictive diversity that enables crowd wisdom may be lost. Decision support systems, from search engines to business intelligence platforms, present individuals with decision aids -- relevant information, interpretative frames, or heuristics -- to enhance the quality and speed of decision-making but potentially influence judgments through the selective presentation of information and interpretative frames. We describe decision-making as often containing two decisions: the choice of decision aids followed by the primary decision, and define \textit{metawisdom of the crowd} as any pattern by which individuals' choice of aids leads to higher crowd accuracy than equal assignment to the same aids, a comparison that accounts for the information content of the aids. The theoretical model accounting for aid bias and variance shows that an optimal distribution of aid usage can produce metawisdom based on the characteristics of aids within a collection. Three studies -- two estimation tasks (N=900, 728) and the nowcasting of inflation (N=1,956; across three collections) -- support this claim. Metawisdom emerges from the use of diverse aids, not through widespread use of the aids that induce the most accurate estimates. Thus, the microfoundations of crowd wisdom appear in the first choice, suggesting crowd wisdom can be robust in information choice problems. Given the implications for collective decision-making, the insights warrant future research investigations into the nature and use of decision aids.
| 2025-12-26
| 2025-12-29
|
[
"econ.GN",
"q-fin.EC"
] |
Jon Atwell, Marlon Twyman
|
2512.21973
|
When Indemnity Insurance Fails: Parametric Coverage under Binding Budget and Risk Constraints
|
In high-risk environments, traditional indemnity insurance is often unaffordable or ineffective, despite its well-known optimality under expected utility. This paper compares excess-of-loss indemnity insurance with parametric insurance within a common mean-variance framework, allowing for fixed costs, heterogeneous premium loadings, and binding budget constraints. We show that, once these realistic frictions are introduced, parametric insurance can yield higher welfare for risk-averse individuals, even under the same utility objective. The welfare advantage arises precisely when indemnity insurance becomes impractical, and disappears once both contracts are unconstrained. Our results help reconcile classical insurance theory with the growing use of parametric risk transfer in high-risk settings.
| 2025-12-26
| 2025-12-29
|
[
"econ.GN",
"math.OC",
"q-fin.EC",
"q-fin.RM"
] |
Benjamin Avanzi, Debbie Kusch Falden, Mogens Steffensen
|
2512.22058
|
Spin dynamics in the van der Waals ferromagnet CrTe2 engineered by Nb doping
|
Understanding and controlling spin dynamics in two-dimensional (2D) van der Waals (vdW) ferromagnets is essential for their application in magnonics and hybrid quantum platforms. Here, we investigate the spin dynamics of the vdW ferromagnet 1T-CrTe_{2} and demonstrate their systematic tunability via niobium (Nb) substitution in Cr_{1-x}Nb_{x}Te_{2}(x=0-0.2). Ferromagnetic resonance (FMR) spectroscopy reveals that Nb doping enables wide-band tuning of the resonance frequency from 40 GHz down to the few-GHz regime, accompanied by a moderate increase in the Gilbert damping constant from ~0.066 to ~0.14, while preserving robust room-temperature ferromagnetism. Complementary magnetometry shows a concurrent reduction of the Curie temperature and saturation magnetization with increasing Nb content. Density functional theory calculations attribute the observed spin-dynamic trends to Nb-induced modifications of magnetic anisotropy and magnetic exchange interactions. Furthermore, CrTe_{2} flakes (~100nm thick) exhibit lower resonance frequencies than bulk crystals, consistent with thickness-dependent magnetic anisotropy. These results establish Nb-doped CrTe_{2} as a tunable vdW ferromagnet with controllable spin dynamics, extending its functionality from spintronics to broadband magnonics and quantum magnonics.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.mtrl-sci",
"cond-mat.mes-hall"
] |
Dhan Raj Lawati, Prem Bahadur Karki, Jitender Kumar, Karishma Prasad, Mohamed A. Elekhtiar, Kai Huang, Bibek Tiwari, Suvechhya Lamichhane, Rupak Timalsina, Zane Hubble, John Watt, Sy-Hwang Liou, Evgeny Y. Tsymbal, Jian Wang, Kapildeb Ambal, Abdelghani Laraoui
|
2512.22005
|
Asymptotics for the spectrum of the Laplacian in thin bars with varying cross sections
|
We consider spectral problems for Laplace operator in 3D rod structures with a small cross section of diameter $O(\varepsilon)$, $\varepsilon$ being a positive parameter. The boundary conditions are Dirichlet (Neumann, respectively) on the bases of this structure and Neumann on the lateral boundary. As $\varepsilon\to 0$, we show the convergence of the spectrum with conservation of the multiplicity towards that of a 1D spectral model with Dirichlet (Neumann, respectively) boundary conditions. This 1D model may arise in diffusion or vibrations models of nonhomogeneous media with different physical characteristics and it takes into account the geometry of the 3D domain. We deal with the low frequencies and the approach to eigenfunctions in the suitable Sobolev spaces is also outlined.
| 2025-12-26
| 2025-12-29
|
[
"math.AP"
] |
Pablo Benavent-Ocejo, Delfina Gómez, Maria-Eugenia Pérez-MartÃnez
|
2511.02813
|
A Construction of Infinite Families of Self-Orthogonal Quasi-Cyclic Codes Using Constituent Codes
|
Quasi-cyclic codes have been recently employed in the constructions of quantum error-correcting codes. In this paper, we propose a construction of infinite families of quasi-cyclic codes which are self-orthogonal with respect to the Euclidean and Hermitian inner products. In particular, their dimension and a lower bound for their minimum distance are computed using their constituent codes defined over field extensions of $\mathbb{F}_q$. We also show that the lower bound for the minimum distance satisfies the square-root-like lower bound and also show how self-dual quasi-cyclic codes can arise from our construction. Using the CSS construction, we show the existence of quantum error-correcting codes with good parameters.
| 2025-12-26
| 2025-12-30
|
[
"cs.IT",
"math.IT"
] |
Gustavo Terra Bastos, Angelynn Ãlvarez, Cameron Williams
|
2312.02519
|
Creative Agents: Empowering Agents with Imagination for Creative Tasks
|
We study building embodied agents for open-ended creative tasks. While existing methods build instruction-following agents that can perform diverse open-ended tasks, none of them demonstrates creativity -- the ability to give novel and diverse solutions implicit in the language instructions. This limitation comes from their inability to convert abstract language instructions into concrete goals and perform long-horizon planning for such complicated goals. Given the observation that humans perform creative tasks with imagination, we propose a class of solutions, where the controller is enhanced with an imaginator generating detailed imaginations of task outcomes conditioned on language instructions. We introduce several approaches to implementing the components of creative agents. We implement the imaginator with either a large language model for textual imagination or a diffusion model for visual imagination. The controller can either be a behavior-cloning policy or a pre-trained foundation model generating executable codes in the environment. We benchmark creative tasks with the challenging open-world game Minecraft, where the agents create diverse buildings given free-form language instructions. We propose novel evaluation metrics for open-ended creative tasks utilizing GPT-4V, which holds many advantages over existing metrics. We perform a detailed experimental analysis of creative agents, showing that creative agents are the first AI agents accomplishing diverse building creation in the survival mode of Minecraft. Our benchmark and models are open-source for future research on creative agents (https://github.com/PKU-RL/Creative-Agents).
| 2025-12-26
| 2025-12-29
|
[
"cs.AI",
"cs.LG"
] |
Penglin Cai, Chi Zhang, Yuhui Fu, Haoqi Yuan, Zongqing Lu
|
2512.22400
|
Canonical description of Pontryagin and Euler classes with a Barbero-Immirzi parameter
|
A detailed canonical analysis for Pontryagin and Euler classes with a Barbero-Immirzi [BI] parameter is developed. We rewrite the topological invariants by introducing a set of Holst-like variables, and then study the set of all constraints. We report the complete canonical structure and the symmetries of the theory; we count the physical degrees of freedom and identify reducibility conditions among the constraints. In addition, in our results, if we consider the $BI$ parameter takes the value of $γ= \pm i $, then the self-dual representation of these invariants is reproduced. Finally, we couple the invariants to the Holst action and explore the canonical analysis.
| 2025-12-26
| 2025-12-30
|
[
"gr-qc"
] |
Alberto Escalante, Edmundo Suárez-Polo, Luis A. Huerta-del Campo
|
2512.22331
|
The Multi-View Paradigm Shift in MRI Radiomics: Predicting MGMT Methylation in Glioblastoma
|
Non-invasive inference of molecular tumor characteristics from medical imaging is a central goal of radiogenomics, particularly in glioblastoma (GBM), where O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation carries important prognostic and therapeutic significance. Although radiomics-based machine learning methods have shown promise for this task, conventional unimodal and early-fusion approaches are often limited by high feature redundancy and an incomplete modeling of modality-specific information. In this work, we introduce a multi-view latent representation learning framework based on variational autoencoders (VAE) to integrate complementary radiomic features derived from post-contrast T1-weighted (T1Gd) and Fluid-Attenuated Inversion Recovery (FLAIR) magnetic resonance imaging (MRI). By encoding each modality through an independent probabilistic encoder and performing fusion in a compact latent space, the proposed approach preserves modality-specific structure while enabling effective multimodal integration. The resulting latent embeddings are subsequently used for MGMT promoter methylation classification.
| 2025-12-26
| 2025-12-30
|
[
"cs.CV",
"cs.AI"
] |
Mariya Miteva, Maria Nisheva-Pavlova
|
2506.14898
|
Self-Interacting Dark Matter with Mass Segregation: A Unified Explanation of Dwarf Cores and Small-Scale Lenses
|
In two-component self-interacting dark matter (SIDM) models with inter-species interactions, mass segregation arises naturally from collisional relaxation, enhancing central densities and gravothermal evolution. We demonstrate that models with velocity-dependent interactions, both within and between species, can connect several small-scale observations while remaining consistent with cluster-scale constraints. This combination enables core formation in dwarf halos, where the presence of baryons increases the inner densities and enhances the predicted strong lensing signatures. Using cosmological and controlled simulations alongside an accurate parametric model, we show that this framework explains the structure of dark perturbers observed in strong lensing systems, and significantly increases the efficiency of small-scale lenses by a factor of $\sim 5$, consistent with the galaxy-galaxy strong lensing excess reported in clusters. Importantly, mass segregation can enhance the Einstein radii of SIDM halos relative to their CDM counterparts, overcoming a key challenge in one-component SIDM scenarios. Our results establish mass segregation in two-component SIDM as a self-consistent and testable model capable of simultaneously addressing multiple small-scale challenges in structure formation.
| 2025-12-26
| 2025-12-29
|
[
"astro-ph.CO",
"astro-ph.GA"
] |
Daneng Yang, Yi-Zhong Fan, Siyuan Hou, Yue-Lin Sming Tsai
|
2512.22348
|
Reddit Deplatforming and Toxicity Dynamics on Generalist Voat Communities
|
Deplatforming, the permanent banning of entire communities, is a primary tool for content moderation on mainstream platforms. While prior research examines effects on banned communities or source platform health, the impact on alternative platforms that absorb displaced users remains understudied. We analyze four major Reddit ban waves (2015--2020) and their effects on generalist communities on Voat, asking how post-ban arrivals reshape community structure and through what mechanisms transformation occurs. Combining network analysis, toxicity detection, and dynamic reputation modeling, we identify two distinct regimes of migration impact: (1) Hostile Takeover (2015--2018), where post-ban arrival cohorts formed parallel social structures that bypassed existing community cores through sheer volume, and (2) Toxic Equilibrium (2018--2020), where the flattening of existing user hierarchy enabled newcomers to integrate into the now-dominant toxic community. Crucially, community transformation occurred through peripheral dynamics rather than hub capture: fewer than 5% of newcomers achieved central positions in most months, yet toxicity doubled. Migration structure also shaped outcomes: loosely organized communities dispersed into generalist spaces, while ideologically cohesive groups concentrated in dedicated enclaves. These findings suggest that receiving platforms face a narrow intervention window during the hostile takeover phase, after which toxic norms become self-sustaining.
| 2025-12-26
| 2025-12-30
|
[
"cs.SI",
"cs.CY",
"physics.soc-ph"
] |
Aleksandar TomaÅ¡eviÄ, Ana VraniÄ, Aleksandra AloriÄ, Marija MitroviÄ Dankulov
|
2512.22347
|
Reinforcement Learning for Optimal Stopping in POMDPs with Application to Quickest Change Detection
|
The field of quickest change detection (QCD) focuses on the design and analysis of online algorithms that estimate the time at which a significant event occurs. In this paper, design and analysis are cast in a Bayesian framework, where QCD is formulated as an optimal stopping problem with partial observations. An approximately optimal detection algorithm is sought using techniques from reinforcement learning. The contributions of the paper are summarized as follows: (i) A Q-learning algorithm is proposed for the general partially observed optimal stopping problem. It is shown to converge under linear function approximation, given suitable assumptions on the basis functions. An example is provided to demonstrate that these assumptions are necessary to ensure algorithmic stability. (ii) Prior theory motivates a particular choice of features in applying Q-learning to QCD. It is shown that, in several scenarios and under ideal conditions, the resulting class of policies contains one that is approximately optimal. (iii) Numerical experiments show that Q-learning consistently produces policies that perform close to the best achievable within the chosen function class.
| 2025-12-26
| 2025-12-30
|
[
"math.OC"
] |
Austin Cooper, Sean Meyn
|
2512.22026
|
Proton therapy range uncertainty reduction using vendor-agnostic tissue characterization on a virtual photon-counting CT head scan
|
In this work, we proposed virtual imaging simulators as an alternative approach to experimental validation of beam range uncertainty in complex patient geometry using a computational model of a human head and a photon-counting CT scanner. We validate the accuracy of stopping power ratio (SPR) calculations using a conventional stoichiometric calibration approach and a prototype software, TissueXplorer. A validated CT simulator (DukeSim) was used to generate photon-counting CT projections of a computational head model, which were reconstructed with an open-source toolbox (ASTRA). The dose of 2 Gy was delivered through protons in a single fraction to target two different cases of nasal and brain tumors with a single lateral beam angle. Ground truth treatment plan was made directly on the computational head model using clinical treatment planning software (RayStation). This plan was then recalculated on the corresponding CT images for which SPR values were estimated using both the conventional method and the prototype software TissueXplorer. The mean percentage difference in estimating the stopping power ratio with TissueXplorer in all head tissues inside the scanned volume was 0.28%. Stopping power ratios obtained with this method showed smaller dose distribution differences from the ground truth plan than the conventional stoichiometric calibration method on the computational head model. Virtual imaging offers an alternative approach to validation of the SPR prediction from CT imaging, as well as its effect on the dose distribution and thus downstream clinical outcomes. According to this simulation study, software solutions that utilize spectral information, such as TissueXplorer, hold promise for more accurate prediction of the stopping power ratio than the conventional stoichiometric approach.
| 2025-12-26
| 2025-12-29
|
[
"physics.med-ph"
] |
S. VrbaÅ¡ki, G. StaniÄ, S. Mollineli, M. Bhattarai, E. Abadi, M. Ciocca, E. Samei
|
2512.21847
|
Quantum Breakdown Condensate as a Disorder-Free Quantum Glass
|
We study the phase diagram of a one-dimensional spin quantum breakdown model, which has an exponential $U(1)$ symmetry with charge unit decaying as $2^{-j}$ with site position $j$. By exact diagonalization (ED), we show that the model with spin $S\ge2$ exhibits an exponential $U(1)$ spontaneous symmetry breaking (SSB) phase dubbed a quantum breakdown condensate. It exhibits a bulk gap violating the Goldstone theorem, and an edge mode only on the left edge if in open boundary condition. In a length $L$ lattice, the condensate has $\mathcal{O}(2^L)$ number of SSB ground states originating from the $\mathcal{O}(2^L)$ number of exponential $U(1)$ charge sectors, leading to a finite entropy density $\ln 2$. This enforces a first order SSB phase transition into this phase, as observed in ED and verified in the large $S$ limit on an exactly solvable Rokhsar-Kivelson line. The condensate has an SSB order parameter being the local in-plane spin, which points in angles related by the chaotic Bernoulli (dyadic) map and thus is effectively random. Moreover, we show the condensate exhibits non-decaying local autocorrelations, and does not have an off-diagonal long-range order. The quantum breakdown condensate thus behaves as a disorder-free quantum glass and is beyond the existing classifications of phases of matter.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.str-el",
"cond-mat.quant-gas",
"cond-mat.stat-mech"
] |
Yu-Min Hu, Zhaoyu Han, Biao Lian
|
2512.22031
|
From In Silico to In Vitro: Evaluating Molecule Generative Models for Hit Generation
|
Hit identification is a critical yet resource-intensive step in the drug discovery pipeline, traditionally relying on high-throughput screening of large compound libraries. Despite advancements in virtual screening, these methods remain time-consuming and costly. Recent progress in deep learning has enabled the development of generative models capable of learning complex molecular representations and generating novel compounds de novo. However, using ML to replace the entire drug-discovery pipeline is highly challenging. In this work, we rather investigate whether generative models can replace one step of the pipeline: hit-like molecule generation. To the best of our knowledge, this is the first study to explicitly frame hit-like molecule generation as a standalone task and empirically test whether generative models can directly support this stage of the drug discovery pipeline. Specifically, we investigate if such models can be trained to generate hit-like molecules, enabling direct incorporation into, or even substitution of, traditional hit identification workflows. We propose an evaluation framework tailored to this task, integrating physicochemical, structural, and bioactivity-related criteria within a multi-stage filtering pipeline that defines the hit-like chemical space. Two autoregressive and one diffusion-based generative models were benchmarked across various datasets and training settings, with outputs assessed using standard metrics and target-specific docking scores. Our results show that these models can generate valid, diverse, and biologically relevant compounds across multiple targets, with a few selected GSK-3$β$ hits synthesized and confirmed active in vitro. We also identify key limitations in current evaluation metrics and available training data.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG",
"cs.AI"
] |
Nagham Osman, Vittorio Lembo, Giovanni Bottegoni, Laura Toni
|
2512.22321
|
Interface Modeling of Perovskite Polymer Heterostructures for Enhanced Charge Transfer Efficiency in Hybrid Photovoltaic Materials
|
Perovskite solar cells (PSCs) based on methylammonium lead iodide (MAPbI3) exhibit remarkable photovoltaic performance, where interface engineering with hole transport layers (HTLs) is crucial for optimizing charge transfer and device efficiency. In this work, we present a density functional theory (DFT) study of the MAPbI3/poly(3-hexylthiophene) (P3HT) hybrid interface, focusing on the role of perovskite surface termination in determining interfacial stability and electronic structure. We model MAI- and PbI-terminated MAPbI3 surfaces interfaced with P3HT and compare their interfacial electronic properties. Electronic structure calculations reveal distinct differences in orbital hybridization and band alignment: the MAI/m-P3HT interface exhibits weak coupling, whereas the PbI/m-P3HT interface shows stronger hybridization and enhanced charge transfer. Band alignment confirms type-II, hole-selective character in both cases, with more pronounced valence band maximum adjustment for PbI. Charge difference maps, Bader analysis, and local density of states consistently indicate higher charge transfer and stronger electronic coupling for PbI termination. Electrostatic potential offsets and transport parameters further highlight termination-dependent differences, with lighter effective masses at PbI/m-P3HT and higher hole velocity at MAI/m-P3HT. These findings provide theoretical insight into interfacial charge transport mechanisms and offer guidelines for optimizing perovskite-organic hybrid solar cells.
| 2025-12-26
| 2025-12-30
|
[
"cond-mat.mtrl-sci",
"cond-mat.mes-hall"
] |
Somayyeh Alidoust, V. Ongun Ãzçelik
|
2512.22053
|
Local identifiability of a parameter function in a system of differential equations
|
In this paper, we consider the problem of local parameter identifiability of a parameter function in a system of ordinary differential equations. Previously, in this problem, the case where the dimensions of a parameter and a solution of a system coincide was considered, and a specific class of systems was identified, for which sufficient conditions for local parametric identifiability were obtained. We extend these results and consider a wider class of systems of differential equations, as well as the case where the dimension of a parameter is less than or equal to the dimension of a solution of a system. In both cases, sufficient conditions are derived for the local identifiability of a parameter function based on observations of a solution at a finite number of points.
| 2025-12-26
| 2025-12-29
|
[
"math.DS"
] |
V. S. Shalgin
|
2512.22090
|
Abstraction of Trusted Execution Environments as the Missing Layer for Broad Confidential Computing Adoption: A Systematization of Knowledge
|
Trusted Execution Environments (TEEs) protect sensitive code and data from the operating system, hypervisor, or other untrusted software. Different solutions exist, each proposing different features. Abstraction layers aim to unify the ecosystem, allowing application developers and system administrators to leverage confidential computing as broadly and efficiently as possible. We start with an overview of representative available TEE technologies. We describe and summarize each TEE ecosystem, classifying them in different categories depending on their main design choices. Then, we propose a systematization of knowledge focusing on different abstraction layers around each design choice. We describe the underlying technologies of each design, as well as the inner workings and features of each abstraction layer. Our study reveals opportunities for improving existing abstraction layer solutions. It also highlights WebAssembly, a promising approach that supports the largest set of features. We close with a discussion on future directions for research, such as how future abstraction layers may evolve and integrate with the confidential computing ecosystem.
| 2025-12-26
| 2025-12-29
|
[
"cs.CR"
] |
Quentin Michaud, Sara Ramezanian, Dhouha Ayed, Olivier Levillain, Joaquin Garcia-Alfaro
|
2601.00837
|
Pediatric Pneumonia Detection from Chest X-Rays:A Comparative Study of Transfer Learning and Custom CNNs
|
Pneumonia is a leading cause of mortality in children under five, with over 700,000 deaths annually. Accurate diagnosis from chest X-rays is limited by radiologist availability and variability.
Objective: This study compares custom CNNs trained from scratch with transfer learning (ResNet50, DenseNet121, EfficientNet-B0) for pediatric pneumonia detection, evaluating frozen-backbone and fine-tuning regimes.
Methods: A dataset of 5,216 pediatric chest X-rays was split 80/10/10 for training, validation, and testing. Seven models were trained and assessed using accuracy, F1-score, and AUC. Grad-CAM visualizations provided explainability.
Results: Fine-tuned ResNet50 achieved the best performance: 99.43\% accuracy, 99.61\% F1-score, and 99.93\% AUC, with only 3 misclassifications. Fine-tuning outperformed frozen-backbone models by 5.5 percentage points on average. Grad-CAM confirmed clinically relevant lung regions guided predictions.
Conclusions: Transfer learning with fine-tuning substantially outperforms CNNs trained from scratch for pediatric pneumonia detection, showing near-perfect accuracy. This system has strong potential as a screening tool in resource-limited settings. Future work should validate these findings on multi-center and adult datasets.
Keywords: Pneumonia detection, deep learning, transfer learning, CNN, chest X-ray, pediatric diagnosis, ResNet, DenseNet, EfficientNet, Grad-CAM.
| 2025-12-26
| 2026-01-06
|
[
"cs.CV",
"cs.AI"
] |
Agniv Roy Choudhury
|
2512.21913
|
GQ-VAE: A gated quantized VAE for learning variable length tokens
|
While most frontier models still use deterministic frequency-based tokenization algorithms such as byte-pair encoding (BPE), there has been significant recent work to design learned neural tokenizers. However, these schemes generally add to underlying language model complexity and force large changes to architecture, making them hard to implement at large scales. To overcome these challenges, we propose the gated quantized variational autoencoder (GQ-VAE), a novel architecture that can be independently pre-trained to serve as a drop-in replacement for existing tokenizers. The key innovation of the architecture is to learn to encode variable-length discrete tokens. GQ-VAE improves compression and language modeling performance over a standard VQ-VAE tokenizer, and approaches the compression rate and language modeling performance of BPE. Interestingly, if we use BPE with a smaller vocabulary, such that the compression is equivalent between GQ-VAE and BPE, we find that GQ-VAE improves downstream language model learning. We conclude with a discussion of several exciting avenues for future work. Code can be found at https://github.com/Theo-Datta-115/gq-vae.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG"
] |
Theo Datta, Kayla Huang, Sham Kakade, David Brandfonbrener
|
2512.21942
|
Multi-reference Trial State for Lattice Quantum Monte Carlo Simulations
|
Nuclear lattice effective field theory (NLEFT) is an efficient \textit{ab initio} tool for solving nuclear many-body systems using the imaginary-time projection technique, where the preparation of trial states is essential for substantially reducing the computational cost required to achieve the desired numerical precision. It has been challenging in forming optimal multi-reference trial states using multiple Slater determinants within auxiliary-field based quantum Monte Carlo frameworks like NLEFT. In this work, we develop a novel sampling method for efficiently incorporating such multi-reference trial states into NLEFT calculations. We applied the optimized trial state to $^7$Li and $^8$Li, finding overall improvements in calculated energies, electromagnetic properties, and transitions compared to results obtained without these optimizations. Our approach provides a reliable foundation for accurately simulating nuclear ground and low-lying excited states within the NLEFT framework.
| 2025-12-26
| 2025-12-29
|
[
"nucl-th"
] |
Teng Wang, Xu Feng, Bing-Nan Lu
|
2208.12937
|
Pseudodifferential arithmetic and a rejection of the Riemann hypothesis
|
The Weyl symbolic calculus of operators leads to the construction, if one takes for symbol a certain distribution decomposing over the zeros of the Riemann zeta function, of an operator with the following property: the Riemann hypothesis is equivalent to the validity of a collection of estimates involving this operator. Pseudodifferential arithmetic, a novel chapter of pseudodifferential operator theory, makes it possible to make the operator under study fully explicit. This leads to a disproof of the conjecture: the set of real parts of non-trivial zeros of zeta is a dense subset of [0,1]..
| 2025-12-26
| 2025-12-29
|
[
"math.NT"
] |
André Unterberger
|
2505.19393
|
Spectral selections, commutativity preservation and Coxeter-Lipschitz maps
|
Let $(W,S)$ be a Coxeter system whose graph is connected, with no infinite edges. A self-map $Ï$ of $W$ such that $Ï_{Ïθ}\in \{Ï_θ,\ ÏÏ_θ\}$ for all $θ\in W$ and all reflections $Ï$ (analogous to being 1-Lipschitz with respect to the Bruhat order on $W$) is either constant or a right translation. A somewhat stronger version holds for $S_n$, where it suffices that $Ï$ range over smaller, $θ$-dependent sets of reflections.
These combinatorial results have a number of consequences concerning continuous spectrum- and commutativity-preserving maps $\mathrm{SU}(n)\to M_n$ defined on special unitary groups: every such map is a conjugation composed with (a) the identity; (b) transposition, or (c) a continuous diagonal spectrum selection. This parallels and recovers Petek's analogous statement for self-maps of the space $H_n\le M_n$ of self-adjoint matrices, strengthening it slightly by expanding the codomain to $M_n$.
| 2025-12-26
| 2025-12-29
|
[
"math.SP",
"math.CO",
"math.GN",
"math.GR",
"math.MG"
] |
Alexandru Chirvasitu
|
2512.22402
|
Efficient Multi-Model Orchestration for Self-Hosted Large Language Models
|
Self-hosting large language models (LLMs) is increasingly appealing for organizations seeking privacy, cost control, and customization. Yet deploying and maintaining in-house models poses challenges in GPU utilization, workload routing, and reliability. We introduce Pick and Spin, a practical framework that makes self-hosted LLM orchestration scalable and economical. Built on Kubernetes, it integrates a unified Helm-based deployment system, adaptive scale-to-zero automation, and a hybrid routing module that balances cost, latency, and accuracy using both keyword heuristics and a lightweight DistilBERT classifier. We evaluate four models, Llama-3 (90B), Gemma-3 (27B), Qwen-3 (235B), and DeepSeek-R1 (685B) across eight public benchmark datasets, with five inference strategies, and two routing variants encompassing 31,019 prompts and 163,720 inference runs. Pick and Spin achieves up to 21.6% higher success rates, 30% lower latency, and 33% lower GPU cost per query compared with static deployments of the same models.
| 2025-12-26
| 2025-12-30
|
[
"cs.DC",
"cs.AI"
] |
Bhanu Prakash Vangala, Tanu Malik
|
2410.15353
|
Detection of very high-energy gamma-ray emission from the radio galaxy M87 with LHAASO
|
The nearby radio galaxy M87 is a very-high-energy (VHE) gamma-ray emitter established by observations with ground-based gamma-ray detectors. Here we report the long-term monitoring of M87 from 2021 to 2024 with Large High Altitude Air Shower Observatory (LHAASO). M87 has been detected by LHAASO with a statistical significance $\sim 9Ï$. The observed energy spectrum extends to 20 TeV, with a possible hardening at $\sim 20$ TeV and then a clear softening at higher energies. Assuming that the intrinsic spectrum is described by a single power law up to 20 TeV, a tight upper bound on the extragalactic background light (EBL) intensity is obtained. A strong VHE flare lasting eight days, with the rise time of $Ï_{r}^{\rm rise} = 1.05\pm0.49$~days and decay time of $Ï_{d}^{\rm decay} = 2.17\pm0.58$~days, was found in early 2022. A possible GeV flare is seen also in the Fermi-LAT data during the VHE flare period. The variability time as short as one day seen in the LHAASO data suggests a compact emission region with a size of $\sim 3\times 10^{15}δ\, {\rm cm}$ ($δ$ being the Doppler factor of the emitting region), corresponding to a few Schwarzschild radii of the central supermassive black hole in M87. The continuous monitoring of the source reveals a duty cycle of $\sim 1\%$ for VHE flares with a flux above $ 10^{-11}{\rm~erg~cm^{-2}~s^{-1}}$.
| 2025-12-26
| 2025-12-29
|
[
"astro-ph.HE"
] |
Zhen Cao, F. Aharonian, Axikegu, Y. X. Bai, Y. W. Bao, D. Bastieri, X. J. Bi, Y. J. Bi, W. Bian, A. V. Bukevich, Q. Cao, W. Y. Cao, Zhe Cao, J. Chang, J. F. Chang, A. M. Chen, E. S. Chen, H. X. Chen, Liang Chen, Lin Chen, Long Chen, M. J. Chen, M. L. Chen, Q. H. Chen, S. Chen, S. H. Chen, S. Z. Chen, T. L. Chen, Y. Chen, N. Cheng, Y. D. Cheng, M. C. Chu, M. Y. Cui, S. W. Cui, X. H. Cui, Y. D. Cui, B. Z. Dai, H. L. Dai, Z. G. Dai, Danzengluobu, X. Q. Dong, K. K. Duan, J. H. Fan, Y. Z. Fan, J. Fang, J. H. Fang, K. Fang, C. F. Feng, H. Feng, L. Feng, S. H. Feng, X. T. Feng, Y. Feng, Y. L. Feng, S. Gabici, B. Gao, C. D. Gao, Q. Gao, W. Gao, W. K. Gao, M. M. Ge, T. T. Ge, L. S. Geng, G. Giacinti, G. H. Gong, Q. B. Gou, M. H. Gu, F. L. Guo, J. Guo, X. L. Guo, Y. Q. Guo, Y. Y. Guo, Y. A. Han, O. A. Hannuksela, M. Hasan, H. H. He, H. N. He, J. Y. He, Y. He, Y. K. Hor, B. W. Hou, C. Hou, X. Hou, H. B. Hu, Q. Hu, S. C. Hu, C. Huang, D. H. Huang, T. Q. Huang, W. J. Huang, X. T. Huang, X. Y. Huang, Y. Huang, Y. Y. Huang, X. L. Ji, H. Y. Jia, K. Jia, H. B. Jiang, K. Jiang, X. W. Jiang, Z. J. Jiang, M. Jin, M. M. Kang, I. Karpikov, D. Khangulyan, D. Kuleshov, K. Kurinov, B. B. Li, C. M. Li, Cheng Li, Cong Li, D. Li, F. Li, H. B. Li, H. C. Li, Jian Li, Jie Li, K. Li, S. D. Li, W. L. Li, W. L. Li, X. R. Li, Xin Li, Y. Z. Li, Zhe Li, Zhuo Li, E. W. Liang, Y. F. Liang, S. J. Lin, B. Liu, C. Liu, D. Liu, D. B. Liu, H. Liu, H. D. Liu, J. Liu, J. L. Liu, M. Y. Liu, R. Y. Liu, S. M. Liu, W. Liu, Y. Liu, Y. N. Liu, Q. Luo, Y. Luo, H. K. Lv, B. Q. Ma, L. L. Ma, X. H. Ma, J. R. Mao, Z. Min, W. Mitthumsiri, H. J. Mu, Y. C. Nan, A. Neronov, K. C. Y. Ng, L. J. Ou, P. Pattarakijwanich, Z. Y. Pei, J. C. Qi, M. Y. Qi, B. Q. Qiao, J. J. Qin, A. Raza, D. Ruffolo, A. Sáiz, M. Saeed, D. Semikoz, L. Shao, O. Shchegolev, X. D. Sheng, F. W. Shu, H. C. Song, Yu. V. Stenkin, V. Stepanov, Y. Su, D. X. Sun, Q. N. Sun, X. N. Sun, Z. B. Sun, J. Takata, P. H. T. Tam, Q. W. Tang, R. Tang, Z. B. Tang, W. W. Tian, L. H. Wan, C. Wang, C. B. Wang, G. W. Wang, H. G. Wang, H. H. Wang, J. C. Wang, Kai Wang, Kai Wang, L. P. Wang, L. Y. Wang, P. H. Wang, R. Wang, W. Wang, X. G. Wang, X. Y. Wang, Y. Wang, Y. D. Wang, Y. J. Wang, Z. H. Wang, Z. X. Wang, Zhen Wang, Zheng Wang, D. M. Wei, J. J. Wei, Y. J. Wei, T. Wen, C. Y. Wu, H. R. Wu, Q. W. Wu, S. Wu, X. F. Wu, Y. S. Wu, S. Q. Xi, J. Xia, G. M. Xiang, D. X. Xiao, G. Xiao, Y. L. Xin, Y. Xing, D. R. Xiong, Z. Xiong, D. L. Xu, R. F. Xu, R. X. Xu, W. L. Xu, L. Xue, D. H. Yan, J. Z. Yan, T. Yan, C. W. Yang, C. Y. Yang, F. Yang, F. F. Yang, L. L. Yang, M. J. Yang, R. Z. Yang, W. X. Yang, Y. H. Yao, Z. G. Yao, L. Q. Yin, N. Yin, X. H. You, Z. Y. You, Y. H. Yu, Q. Yuan, H. Yue, H. D. Zeng, T. X. Zeng, W. Zeng, M. Zha, B. B. Zhang, F. Zhang, H. Zhang, H. M. Zhang, H. Y. Zhang, J. L. Zhang, Li Zhang, P. F. Zhang, P. P. Zhang, R. Zhang, S. B. Zhang, S. R. Zhang, S. S. Zhang, X. Zhang, X. P. Zhang, Y. F. Zhang, Yi Zhang, Yong Zhang, B. Zhao, J. Zhao, L. Zhao, L. Z. Zhao, S. P. Zhao, X. H. Zhao, F. Zheng, W. J. Zhong, B. Zhou, H. Zhou, J. N. Zhou, M. Zhou, P. Zhou, R. Zhou, X. X. Zhou, X. X. Zhou, B. Y. Zhu, C. G. Zhu, F. R. Zhu, H. Zhu, K. J. Zhu, Y. C. Zou, X. Zuo
|
2512.17384
|
Influence of Pt/Ru ratios on the oxidation mechanism of MCrAlYTa coatings modified with Pt-Ru overlays
|
This study investigates the influence of varying Pt/Ru ratios on the oxidation mechanism of NiCoCrAlYTa coatings with electrodeposited, vacuum-annealed Ptsingle bondRu overlays. Weight change measurements, scanning electron microscopy/energy dispersive X-ray spectrometry (SEM/EDS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) were used for high-temperature oxidation analyses, showing superior resistance with higher Pt contents. This was attributed to the creation of a denser, thinner, and more homogeneous layer of alumina (alpha-Al2O3) in the thermally-grown oxide (TGO) layer. On the contrary, an increase in Ru contents led to the development of other oxides and microcracks along with alumina in the TGO layer, undermining oxidation protection. The accommodation of Ti and Ta, in the minimally-deteriorative form of carbide, along with Y into the TGO layer with increasing Pt contents further enhanced oxidation resistance. In addition to the explored significant impact of the Pt/Ru ratio on oxide scale characteristics and oxidation resistance, the lower cost of Ru compared to Pt suggests the potential for designing cost-effective systems through optimized Pt/Ru ratios and microstructural engineering.
| 2025-12-26
| 2025-12-30
|
[
"cond-mat.mtrl-sci",
"physics.app-ph",
"physics.atom-ph",
"physics.chem-ph"
] |
Majid Hosseinzadeh, Erfan Salahinejad
|
2512.21906
|
Wave propagation for 1-dimensional reaction-diffusion equation with nonzero random drift
|
We consider the wave propagation for a reaction-diffusion equation on the real line, with a random drift and Fisher-Kolmogorov-Petrovskii-Piscounov (FKPP) type nonlinear reaction. We show that when the average drift is positive, the asymptotic wave fronts propagating to the positive and negative directions are both pushed in the negative direction, leading to the possibility that both wave fronts propagate toward negative infinity. Our proof is based on the Large Deviations Principle for diffusion processes in random environments, as well as an analysis of the Feynman-Kac formula. Such probabilistic arguments also reveal the underlying physical mechanism of the wave fronts formation: the drift acts as an external field that shifts the (quenched) free-energy reference level without altering the intrinsic fluctuation structure of the system.
| 2025-12-26
| 2025-12-29
|
[
"math.AP",
"math-ph",
"math.MP",
"math.PR"
] |
Dihang Guan, Hui He, Wenqing Hu, Jiaojiao Yang
|
2507.08681
|
Searching for the neutral triple gauge couplings in the process $μ^+μ^-\to γν\barν$ at muon colliders
|
We investigate the sensitivity of future high-energy muon colliders to neutral triple gauge couplings (nTGCs) through the process $μ^{+}μ^{-}\toγν\barν$ within the Standard Model Effective Field Theory (SMEFT) framework. Extending beyond previous studies, we consider a set of 14 dimension-8 operators, including both Higgs-related and pure gauge structures. By computing the cross sections and performing Monte Carlo simulations at multiple center-of-mass energies (3-30 TeV), we demonstrate that the annihilation process dominates over vector boson fusion (VBF) at TeV scales. We also explore the impact of beam polarization and show that the $(-+)$ polarization enhances sensitivity to several operators. After the study of the event selection strategies, we show that muon colliders can impose stronger expected constraints on nTGCs operators than current LHC bounds, with two of the pure gauge operators yielding the most stringent expected constraints. We also evaluate the contribution of CP-violating pure gauge operators to the electron electric dipole moment (EDM), finding that the expected constraints from muon colliders are stronger than those from EDM measurements.
| 2025-12-26
| 2025-12-29
|
[
"hep-ph"
] |
Wei Xie, Ji-Chong Yang
|
2512.21946
|
An alternative characterisation of graphs quasi-isometric to graphs of bounded treewidth
|
Quasi-isometry is a measure of how similar two graphs are at `large-scale'. Nguyen, Scott, and Seymour [arXiv:2501.09839] and Hickingbotham [arXiv:2501.10840] independently gave a characterisation of graphs quasi-isometric to graphs of treewidth $k$. In this paper, we give a new characterisation of such graphs. Specifically, we show that such graphs $G$ are characterised by the existence of a partition whose quotient has treewidth at most $k$ and such that each part has bounded weak diameter in $G$. The primary contribution of our characterisation is a structural description of graphs that admit such a quasi-isometry. This differs from the characterisation mentioned above, which primarily shows the existence of such a quasi-isometry. The characterisations are complementary, and neither immediately implies the other.
| 2025-12-26
| 2025-12-29
|
[
"math.CO"
] |
Marc Distel
|
2505.00920
|
Giant exciton binding energy in bulk CrCl3
|
Van der Waals (vdW) materials, with their unique combination of electronic, optical, and magnetic properties, are emerging as promising platforms for exploring excitonic phenomena. Thus far, the choice of materials with exceptional excitonic response has been limited to two-dimensional (2D) configurations of vdW materials. At the same time, large interlayer distance and the possibility to create a variety of heterostructures offers an opportunity to control the dielectric screening in van der Waals heterostructures and van der Waals 3D materials, thus engineering the excitonic properties. Here, we reveal that bulk vdW crystal CrCl3 answers this quest with a record exciton binding energy of 1.64 eV owing to a delicate interplay of quasi-2D electronic confinement and local magnetic correlations. We also suggest that the non-local magnetic correlations play an important role in the temperature dependence of photoluminescence intensity. Furthermore, we observe colossal binding energies in vdW crystals NbOCl2 (0.66 eV) and MoCl3 (0.35 eV) and formulate a universal exciton binding energy dependence on bandgap for 2D and 3D vdW materials. Hence, our findings establish a fundamental link between the layered structure of vdW materials and their excitonic properties.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.mtrl-sci",
"physics.optics"
] |
Georgy Ermolaev, Tagir Mazitov, Anton Minnekhanov, Arslan Mazitov, Gleb Tselikov, Aleksandr Slavich, Mikhail Tatmyshevskiy, Mikhail Kashchenko, Nikolay Pak, Andrey Vyshnevyy, Valentin Solovei, Ivan Kazantsev, Gleb Tikhonowski, Alexander Melentev, Elena Zhukova, Dmitriy Grudinin, Junhua Luo, Ivan Kruglov, Aleksey Arsenin, Sangen Zhao, Kostya S. Novoselov, Andrey Katanin, Valentyn S. Volkov
|
2512.22391
|
Derived Gamma Geometry II: Stable $\infty$-Categories of Gamma-Modules, Derived Monoidal Structures, and Obstructions to Binary Shadows
|
Let \(\T\) be a commutative ternary \(\Gm\)-semiring in the sense of the triadic, \(\Gm\)-parametrized multiplication \(\{a,b,c\}_γ\). Building on the affine \(\Gm\)-spectrum \(\SpecG(\T)\), the structure sheaf, and the equivalence between \(\Gm\)-modules and quasi-coherent \(\Gm\)-sheaves on affine \(\Gm\)-schemes, we construct and organize the derived formalism at the level of stable \(\infty\)-categories.
Our first contribution is a technically explicit construction of a stable \(\infty\)-category \(\Dinfty(\T,\Gm)\) enhancing the unbounded derived category of \(\Gm\)-modules, obtained by dg-nerve and \(\infty\)-localization of chain complexes. We further explain the derived monoidal structure induced by the ternary \(\Gm\)-tensor product and the corresponding internal \(\RHom\), under standard exactness/projectivity hypotheses.
Our second contribution is an obstruction theory to \emph{binary reduction}: we formalize the nonexistence of any conservative ``binary module shadow'' compatible with the cubic localization calculus intrinsic to ternary \(\Gm\)-semirings. In particular, any attempt to represent the triadic \(\Gm\)-action by binary scalars forces \(\Gm\)-mode data to be absorbed into the scalars, hence ceases to be a genuine reduction.
Finally, we give a detailed affine derived equivalence between derived quasi-coherent \(\Gm\)-sheaves on \(X=\SpecG(\T)\) and \(\Dinfty(\T,\Gm)\), and we include worked examples illustrating the cubic localization relation and its derived consequences.
| 2025-12-26
| 2025-12-30
|
[
"math.RA"
] |
Chandrasekhar Gokavarapu
|
2512.22315
|
VideoZoomer: Reinforcement-Learned Temporal Focusing for Long Video Reasoning
|
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language tasks yet remain limited in long video understanding due to the limited context window. Consequently, prevailing approaches tend to rely on uniform frame sampling or static pre-selection, which might overlook critical evidence and unable to correct its initial selection error during its reasoning process. To overcome these limitations, we propose VideoZoomer, a novel agentic framework that enables MLLMs to dynamically control their visual focus during reasoning. Starting from a coarse low-frame-rate overview, VideoZoomer invokes a temporal zoom tool to obtain high-frame-rate clips at autonomously chosen moments, thereby progressively gathering fine-grained evidence in a multi-turn interactive manner. Accordingly, we adopt a two-stage training strategy: a cold-start supervised fine-tuning phase on a curated dataset of distilled exemplar and reflection trajectories, followed by reinforcement learning to further refine the agentic policy. Extensive experiments demonstrate that our 7B model delivers diverse and complex reasoning patterns, yielding strong performance across a broad set of long video understanding and reasoning benchmarks. These emergent capabilities allow it to consistently surpass existing open-source models and even rival proprietary systems on challenging tasks, while achieving superior efficiency under reduced frame budgets.
| 2025-12-26
| 2025-12-30
|
[
"cs.CV",
"cs.AI"
] |
Yang Ding, Yizhen Zhang, Xin Lai, Ruihang Chu, Yujiu Yang
|
2512.19337
|
Real-time propagators resummed with nontrivial boundary wavefunctions in a constant electric field
|
We present the derivation of an alternative representation of the real-time in-in formalism under a spatially homogeneous and time independent electric field. Because the system exhibits instability associated with pair production of particles and antiparticles, the perturbation theory should be reorganized depending on the choice of the reference vacuum. We recast the boundary wavefunctions into the quadratic self-energy-like terms in the functional integration formalism. The resulting generating functional in the modified in-in formalism leads to the propagators that resum infinite diagrams necessary to capture the vacuum-instability effects. The proper-time representations of the propagators reproduce the known expressions from the canonical operator formalism, but our derivation based on the generating functional along the closed-time path clarifies the origin of the additional proper-time contour and provides a better physical understanding. Finally, as a concrete example of the application, we compute the in-in expectation value of the vector current in a constant electric field, and find that the simple one-loop calculation captures the pair production effect.
| 2025-12-26
| 2025-12-29
|
[
"hep-ph",
"hep-th"
] |
Kenji Fukushima, Shuhei Minato
|
2512.22049
|
Quantum Secret Sharing Rates
|
This paper studies the capacity limits for quantum secret sharing (QSS). The goal of a QSS scheme is to distribute a quantum secret among multiple participants, such that only authorized parties can recover it through collaboration, while no information can be obtained without such collaboration. Following the approach of Zou et al. (2015) on classical secret sharing, we introduce an information-theoretic model for the rate analysis of QSS and its relation to compound quantum channels. We establish a regularized characterization for the QSS capacity, and determine the capacity for QSS with dephasing noise.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph",
"cs.IT",
"math.IT"
] |
Gabrielle Lalou, Husein Natur, Uzi Pereg
|
2512.22407
|
Learning to Program != "One-Size-Fits-All": Exploring Variations of Parsons Problems as Scaffolding
|
Lowering the barriers to computer programming requires understanding how to scaffold learning. Parsons problems, which require learners to drag-and-drop blocks of code into the correct order and indentation, are proving to be beneficial for scaffolding learning how to write code from scratch. But little is known about the ability of other problem types to do so. This study explores learners' perceptions of a new programming environment called Codespec, which was developed to make computer programming more accessible and equitable by offering multiple means of engagement. Retrospective think-aloud interviews were conducted with nine programmers who were given the choice between Faded Parsons and Pseudocode Parsons problems as optional scaffolding toward solving write-code problems. The results showed that offering Faded and Pseudocode Parsons problems as optional scaffolds supported comprehension monitoring, strategy formation, and refinement of prior knowledge. Learners selectively used Faded Parsons problems for syntax/structure and Pseudocode Parsons problems for high-level reasoning. The costs noted included the time it takes to drag-and-drop the blocks and the confusion experienced when a solution diverges from a learners' mental model. Faded Parsons problems were also perceived as a desirable challenge. This study contributes to the field of computing education and human-computer interaction by extending the functionality of problem spaces that support Parsons problems and by providing empirical evidence of the effectiveness of using other problem types as scaffolding techniques.
| 2025-12-26
| 2025-12-30
|
[
"cs.HC"
] |
Carl Christopher Haynes-Magyar
|
2506.23956
|
Topological two-body interaction obstructing trivial ground states: an indicator of fractional Chern insulators
|
The search for candidate materials for fractional Chern insulators (FCIs) has mainly focused on the topological and geometrical structures of single-particle Chern bands. However, there are inherent limitations in approaches that neglect interaction effects, highlighting the need for complementary methods. In this work, we discuss how the Chern number defined for the effective interaction projected onto a Chern band is related to the stabilization of FCIs. Specifically, by formulating both the effective interaction and the two-particle problem using a common matrix, we establish a connection between the two-particle band structure and the effective interaction. This formulation allows us to characterize the effective interaction through the topology of the two-particle band. To investigate the relationship between topological effective interactions and FCIs, we perform numerical calculations primarily based on exact diagonalization. We find a notable correlation between the fact that the dominant two-particle bands carry a unit Chern number and the realization of a robust FCI at the filling fraction $ν= 1/3$. This result is consistent with the presumed correspondence between pseudopotentials in the fractional quantum Hall effect and the two-particle band structure. From another perspective, our findings suggest that the topology inherent in the interaction itself can obstruct trivial ground states. We also discuss this in the context of scattering channels. Extending such topological two-body interactions could pave the way for realizing exotic states beyond FCIs.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.str-el",
"cond-mat.mes-hall",
"cond-mat.mtrl-sci",
"cond-mat.supr-con",
"quant-ph"
] |
Nobuyuki Okuma, Tomonari Mizoguchi
|
2512.21993
|
Estimating the Role of Bag Constant and Modified Theory on Anisotropic Stellar Models
|
In this article, we are devoted to discuss different compact stars admitting anisotropic interiors in a particular modified theory of gravity. For this purpose, a spherically symmetric metric is adopted to formulate the field equations corresponding to two different $f(\mathcal{R},\mathcal{T},\mathcal{Q})$ models, where $\mathcal{Q}=\mathcal{R}_{αγ}\mathcal{T}^{αγ}$. Since the field equations contain extra degrees of freedom, we choose Finch-Skea metric and MIT bag model equation of state to make them solvable. We also use matching conditions to calculate a constant triplet in the chosen ansatz. The resulting solutions are then graphically analyzed for particular values of the bag constant and model parameter in the interior of 4U 1820-30 compact star. The viability and stability of the modified models are also checked through certain tests. Further, we calculate the values of model parameter through the vanishing radial pressure constraint that correspond to the observed data (radii and masses) of eight different star candidates. Finally, we conclude that our models I and II are in well-agreement with the conditions needed for physically relevant interiors to exist.
| 2025-12-26
| 2025-12-29
|
[
"gr-qc"
] |
Tayyab Naseer, M. Sharif
|
2512.16756
|
Structure of the mean-field yrast spectrum of a two-component Bose gas in a ring: role of interaction asymmetry
|
The mean-field yrast spectrum of an SU(2)-symmetric two-component Bose gas confined to a ring geometry is known to exhibit an intricate nonanalytic structure that is absent in single-component systems. In particular, due to the interplay between the species concentration and the atomic interactions, a sequence of plane-wave states can emerge as yrast states at fractional values of the angular momentum per particle. This behavior stands in sharp contrast to the single-component case, where plane-wave states occur only at integer angular momenta. In this paper, we investigate how the structure of the yrast spectrum in a two-component Bose gas is modified by interaction asymmetry. By numerically solving the coupled Gross-Pitaevskii equations for propagating soliton states, we compute the mean-field yrast spectrum and, in particular, determine the critical curves associated with the emergence of various plane-wave yrast states. We find that both the behavior of these critical curves and the mechanisms by which plane-wave yrast states arise depend sensitively on the relative strengths of the inter- and intra-component interactions. When the inter-component interaction is weaker, the plane-wave yrast states replace soliton states through a continuous evolution, as in the SU(2)-symmetric case, although the conditions for their existence become more restrictive. In contrast, when the inter-component interaction is stronger, plane-wave yrast states emerge by overtaking soliton states via branch crossings, and their stability is significantly enhanced. Our results have important implications for the existence and stability of persistent currents in asymmetric, two-component Bose gases.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.quant-gas",
"physics.atom-ph"
] |
Hui Tang, Guan-Hua Huang, Eugene Zaremba, Shizhong Zhang, Zhigang Wu
|
2512.22360
|
Generalized K-theoretic invariants and wall-crossing via non-abelian localization
|
Given an abelian category and a stability condition satisfying appropriate conditions, we define generalized $K$-theoretic invariants and prove that they satisfy wall-crossing formulas. For this, we introduce a new associative algebra structure on the $K$-homology of the stack of objects of an abelian category, which we call the $K$-Hall algebra. We first define $δ$-invariants directly coming from the stack of semistable objects and use the $K$-Hall algebra to take a formal logarithm and construct $\varepsilon$-invariants. We prove that these satisfy appropriate wall-crossing formulas using the non-abelian localization theorem. Based on work of Joyce in the cohomological setting, Liu had previously defined similar invariants assuming the existence of a framing functor; we show that when their definition of invariants makes sense it agrees with ours. Our results extend Joyce--Liu wall-crossing to non-standard hearts of $D^b(X)$, for which framing functors are not known to exist.
| 2025-12-26
| 2025-12-30
|
[
"math.AG",
"math.KT"
] |
Ivan Karpov, Miguel Moreira
|
2512.22009
|
iSHIFT: Lightweight Slow-Fast GUI Agent with Adaptive Perception
|
Multimodal Large Language Models (MLLMs) show strong potential for interpreting and interacting with complex, pixel-rich Graphical User Interface (GUI) environments. However, building agents that are both efficient for high-level tasks and precise for fine-grained interactions remains challenging. GUI agents must perform routine actions efficiently while also handling tasks that demand exact visual grounding, yet existing approaches struggle when accuracy depends on identifying specific interface elements. These MLLMs also remain large and cannot adapt their reasoning depth to the task at hand. In this work, we introduce iSHIFT: Implicit Slow-fast Hybrid Inference with Flexible Tokens, a lightweight agent that integrates latent thinking (implicit chain-of-thought) with a perception control module. iSHIFT enables an MLLM to switch between a slow mode, which leverages detailed visual grounding for high precision and a fast mode that uses global cues for efficiency. Special perception tokens guide attention to relevant screen regions, allowing the model to decide both how to reason and where to focus. Despite its compact 2.5B size, iSHIFT matches state-of-the-art performance on multiple benchmark datasets.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
Sarthak Mehrotra, Sairam V C Rebbapragada, Mani Hemanth Reddy Bonthu, Vineeth N Balasubramanian
|
2512.22383
|
Symbolic Specification and Reasoning for Quantum Data and Operations
|
In quantum information and computation research, symbolic methods have been widely used for human specification and reasoning about quantum states and operations. At the same time, they are essential for ensuring the scalability and efficiency of automated reasoning and verification tools for quantum algorithms and programs. However, a formal theory for symbolic specification and reasoning about quantum data and operations is still lacking, which significantly limits the practical applicability of automated verification techniques in quantum computing.
In this paper, we present a general logical framework, called Symbolic Operator Logic $\mathbf{SOL}$, which enables symbolic specification and reasoning about quantum data and operations. Within this framework, a classical first-order logical language is embedded into a language of formal operators used to specify quantum data and operations, including their recursive definitions. This embedding allows reasoning about their properties modulo a chosen theory of the underlying classical data (e.g., Boolean algebra or group theory), thereby leveraging existing automated verification tools developed for classical computing. It should be emphasised that this embedding of classical first-order logic into $\mathbf{SOL}$ is precisely what makes the symbolic method possible.
We envision that this framework can provide a conceptual foundation for the formal verification and automated theorem proving of quantum computation and information in proof assistants such as Lean, Coq, and related systems.
| 2025-12-26
| 2025-12-30
|
[
"cs.PL",
"cs.LO",
"quant-ph"
] |
Mingsheng Ying
|
2512.21992
|
Measure of entanglement and the monogamy relation: a topical review
|
Characterizing entanglement, including quantifying and distribution of entanglement, which lies at heart of the quantum resource theory, have been investigated extensively ever since Bennett \etal proposed three seminal measures of entanglement in 1996. Up to now, there are numerous measures of entanglement that have been proposed from different point of view and plenty of monogamy relations have been explored which make the distribution of entanglement became more and more clear. While this is relatively easy in the case of pure states, it is much more intricate for the case of mixed quantum states especially with higher dimension and more particles in the system. We present here an overview of the theory along this line. We outline most of the results in this field historically and focus on the finite-dimensional systems. In particular we emphasize the point of view that (i) which yardsticks haven been applied in quantifying entanglement and its distribution, (ii) what are the substantive characteristics and interrelations of these measures and their monogamy relations mathematically by comparing, and (iii) which concepts should be improved or revised and how they were developed accordingly.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph"
] |
Yu Guo, Zhixiang Jin
|
2512.21954
|
Latency-Optimal Cache-aided Multicast Streaming via Forward-Backward Reinforcement Learning
|
We consider a cellular network equipped with cache-enabled base-stations (BSs) leveraging an orthogonal multipoint multicast (OMPMC) streaming scheme. The network operates in a time-slotted fashion to serve content-requesting users by streaming cached files. The users being unsatisfied by the multicat streaming face a delivery outage, implying that they will remain interested in their preference at the next time-slot, which leads to a forward dynamics on the user preference. To design a latency-optimal streaming policy, the dynamics of latency is properly modeled and included in the learning procedure. We show that this dynamics surprisingly represents a backward dynamics. The combination of problem's forward and backward dynamics then develops a forward-backward Markov decision process (FB-MDP) that fully captures the network evolution across time. This FB-MDP necessitates usage of a forward-backward multi-objective reinforcement learning (FB-MORL) algorithm to optimize the expected latency as well as other performance metrics of interest including the overall outage probability and total resource consumption. Simulation results show the merit of proposed FB-MORL algorithm in finding a promising dynamic cache policy.
| 2025-12-26
| 2025-12-29
|
[
"cs.IT",
"math.IT"
] |
Mohsen Amidzadeh
|
2601.02394
|
Hydrodynamic Whispering: Enabling Near-Field Silent Communication via Artificial Lateral Line Arrays
|
To address the imperative for covert underwater swarm coordination, this paper introduces "Hydrodynamic Whispering," a near-field silent communication paradigm utilizing Artificial Lateral Line (ALL) arrays. Grounded in potential flow theory, we model the transmitter as an oscillating dipole source. The resulting pressure field exhibits steep nearfield attenuation (scaling with 1/r^2, naturally delimiting a secure "communication bubble" with intrinsic Low Probability of Interception (LPI) properties. We propose a transceiver architecture featuring a Binary Phase Shift Keying (BPSK) modulation scheme adapted for mechanical actuator inertia, coupled with a bio-inspired 24-sensor conformal array. To mitigate low Signal-to-Noise Ratio (SNR) in turbulent environments,a Spatio-Temporal Joint Processing framework incorporating Spatial Matched-Field Beamforming is developed. Simulation results demonstrate that the system achieves an array gain of approximately 13.8 dB and maintains a near-zero Bit Error Rate (BER) within the effective range. This study validates the feasibility of utilizing localized hydrodynamic pressure fluctuations for reliable and secure short-range underwater networking.
| 2025-12-26
| 2026-01-07
|
[
"eess.SP",
"physics.flu-dyn"
] |
Yuan-Jie Chen
|
2512.21987
|
Optimal Placement of Data Centers to Support Power Distribution Networks Using Intelligent Algorithms with Economic Indicators
|
Data centers are among the fastest growing electricity consumers and can impose severe voltage drops and feeder losses when connected to weak distribution networks. This paper formulates a techno economic siting problem in which each candidate data center site is mapped to a bus of the distribution network and is assumed to deploy on site renewable generation and power electronic interfaces, resulting in a controllable net active power injection equivalent to distributed generation. A mixed integer nonlinear optimization model is developed to jointly select the connection bus and size the DG capacity while respecting network operating limits. The objective combines three normalized terms including active power losses, a voltage deviation index capturing profile quality, and investment cost derived from location dependent land price and unit DG cost. To address the discrete continuous search space, an intelligent genetic algorithm is embedded in a multi scenario decision framework with adaptive weight tuning. Three stakeholder scenarios prioritize losses, voltage quality, or techno economic balance, and additional balanced scenarios are generated automatically until the optimal bus decision converges. A case study on the IEEE 33 bus radial system demonstrates the effectiveness of the approach. The converged design selects bus 14 with 1.10 MW DG, reducing total losses from 202.67 kW to 129.37 kW while improving the minimum bus voltage to 0.933 per unit at a moderate investment cost of 1.33 MUSD. The proposed framework provides an interpretable pathway to integrate economic indicators into distribution aware data center siting.
| 2025-12-26
| 2025-12-29
|
[
"eess.SY",
"cs.SY"
] |
Amin Hajihasani, Mahmoud Modaresi
|
2512.22388
|
BLISS: Bandit Layer Importance Sampling Strategy for Efficient Training of Graph Neural Networks
|
Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their application to large graphs is hindered by computational costs. The need to process every neighbor for each node creates memory and computational bottlenecks. To address this, we introduce BLISS, a Bandit Layer Importance Sampling Strategy. It uses multi-armed bandits to dynamically select the most informative nodes at each layer, balancing exploration and exploitation to ensure comprehensive graph coverage. Unlike existing static sampling methods, BLISS adapts to evolving node importance, leading to more informed node selection and improved performance. It demonstrates versatility by integrating with both Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), adapting its selection policy to their specific aggregation mechanisms. Experiments show that BLISS maintains or exceeds the accuracy of full-batch training.
| 2025-12-26
| 2025-12-30
|
[
"cs.LG",
"cs.AI",
"cs.SI",
"math.OC",
"stat.ML"
] |
Omar Alsaqa, Linh Thi Hoang, Muhammed Fatih Balin
|
2505.07982
|
Perfect state transfer on graphs with clusters
|
Using graphs with clusters, we provide a unified approach for constructing graphs with pair state transfer-relative to the adjacency, Laplacian, and signless Laplacian matrix-between the same pair of states at the same time, despite being non-regular. We show that for each $k\geq 5$, there are infinitely many connected graphs with maximum valency $k$ admitting this property. This framework also aids in establishing sufficient conditions for pair state transfer in edge-perturbed graphs, including complete graphs and complete bipartite graphs. Furthermore, we utilize graph products to generate new infinite families of graphs with the above property.
| 2025-12-26
| 2025-12-29
|
[
"math.CO",
"quant-ph"
] |
Hermie Monterde, Hiranmoy Pal
|
2502.09990
|
X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from Multi-Turn Jailbreaks without Compromising Usability
|
Despite the rapid development of safety alignment techniques for LLMs, defending against multi-turn jailbreaks is still a challenging task. In this paper, we conduct a comprehensive comparison, revealing that some existing defense methods can improve the robustness of LLMs against multi-turn jailbreaks but compromise usability, i.e., reducing general capabilities or causing the over-refusal problem. From the perspective of mechanism interpretability of LLMs, we discover that these methods fail to establish a boundary that exactly distinguishes safe and harmful feature representations. Therefore, boundary-safe representations close to harmful representations are inevitably disrupted, leading to a decline in usability. To address this issue, we propose X-Boundary to push harmful representations away from boundary-safe representations and obtain an exact distinction boundary. In this way, harmful representations can be precisely erased without disrupting safe ones. Experimental results show that X-Boundary achieves state-of-the-art defense performance against multi-turn jailbreaks, while reducing the over-refusal rate by about 20% and maintaining nearly complete general capability. Furthermore, we theoretically prove and empirically verify that X-Boundary can accelerate the convergence process during training. Please see our code at: https://github.com/AI45Lab/X-Boundary.
| 2025-12-26
| 2025-12-29
|
[
"cs.CR",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] |
Xiaoya Lu, Dongrui Liu, Yi Yu, Luxin Xu, Jing Shao
|
2501.18581
|
Bias-variance decompositions: the exclusive privilege of Bregman divergences
|
Bias-variance decompositions are widely used to understand the generalization performance of machine learning models. While the squared error loss permits a straightforward decomposition, other loss functions - such as zero-one loss or $L_1$ loss - either fail to sum bias and variance to the expected loss or rely on definitions that lack the essential properties of meaningful bias and variance. Recent research has shown that clean decompositions can be achieved for the broader class of Bregman divergences, with the cross-entropy loss as a special case. However, the necessary and sufficient conditions for these decompositions remain an open question.
In this paper, we address this question by studying continuous, nonnegative loss functions that satisfy the identity of indiscernibles (zero loss if and only if the two arguments are identical), under mild regularity conditions. We prove that so-called $g$-Bregman or rho-tau divergences are the only such loss functions that have a clean bias-variance decomposition. A $g$-Bregman divergence can be transformed into a standard Bregman divergence through an invertible change of variables. This makes the squared Mahalanobis distance, up to such a variable transformation, the only symmetric loss function with a clean bias-variance decomposition. Consequently, common metrics such as $0$-$1$ and $L_1$ losses cannot admit a clean bias-variance decomposition, explaining why previous attempts have failed. We also examine the impact of relaxing the restrictions on the loss functions and how this affects our results.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG"
] |
Tom Heskes
|
2512.22021
|
Site-Order Optimization in the Density Matrix Renormalization Group via Multi-Site Rearrangement
|
In the approaches based on matrix-product states (MPSs), such as the density-matrix renormalization group (DMRG) method, the ordering of the sites crucially affects the computational accuracy. We investigate the performance of an algorithm that searches for the optimal site order by iterative local site rearrangement. We improve the algorithm by expanding the range of site rearrangement and apply it to a one-dimensional quantum Heisenberg model with random site permutation. The results indicate that increasing the range of the site rearrangement significantly improves the computational accuracy of the DMRG method. In particular, increasing the rearrangement range from two to three sites reduces the average relative error in the ground-state energy by 65% to 94% in the cases we tested. We also discuss the computational cost of the algorithm and its application as a preprocessing for MPS-based calculations.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.stat-mech",
"quant-ph"
] |
Ryo Watanabe, Toshiya Hikihara, Hiroshi Ueda
|
2505.24011
|
Interaction between shallow NV$^-$ and spin active azafullerenes on hydrogenated and fluorinated (001) diamond surfaces
|
The interaction between surface-lying nitrogen-substituted fullerenes (radical azafullerene, C$_{59}$N$^\bullet$) with sub-surface negative nitrogen-vacancy complexes (NV$^-$) in diamond is investigated using first principles calculations. We consider (2$\times$1) reconstructed (001) oriented diamond surfaces with both H- and F-surface termination. The charge stability of NV$^-$, when in close proximity to both the nearby surface and the spin active azafullerene is discussed, in the context of diamond band bending arising from surface-induced changes in electron affinity (EA). In the case of the hydrogenated surface, the system spin is quenched, yielding a negatively charged azafullerene (C$_{59}$N$^-$) and neutrally charged NV$^0$ as the most stable electronic configuration. In contrast, fluorinating the surface favours the negatively charged NV$^-$, and conserves the C$_{59}$N$^\bullet$, neutrality and stabilizes uncompensated free spins. This opposing behaviour is attributed to surface charge doping emerging from different band bending effects associated with the surface EA. This study is consistent with experimentally observed photoluminescence quenching, and shows that surface passivation by fluorination could efficiently tackle the problem of charge transfer between adsorbed molecules and shallow NV centers.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.mtrl-sci"
] |
Bastien Anézo, Denis ArÄon, Chris Ewels
|
2512.22396
|
HalluMat: Detecting Hallucinations in LLM-Generated Materials Science Content Through Multi-Stage Verification
|
Artificial Intelligence (AI), particularly Large Language Models (LLMs), is transforming scientific discovery, enabling rapid knowledge generation and hypothesis formulation. However, a critical challenge is hallucination, where LLMs generate factually incorrect or misleading information, compromising research integrity. To address this, we introduce HalluMatData, a benchmark dataset for evaluating hallucination detection methods, factual consistency, and response robustness in AI-generated materials science content. Alongside this, we propose HalluMatDetector, a multi-stage hallucination detection framework that integrates intrinsic verification, multi-source retrieval, contradiction graph analysis, and metric-based assessment to detect and mitigate LLM hallucinations. Our findings reveal that hallucination levels vary significantly across materials science subdomains, with high-entropy queries exhibiting greater factual inconsistencies. By utilizing HalluMatDetector verification pipeline, we reduce hallucination rates by 30% compared to standard LLM outputs. Furthermore, we introduce the Paraphrased Hallucination Consistency Score (PHCS) to quantify inconsistencies in LLM responses across semantically equivalent queries, offering deeper insights into model reliability.
| 2025-12-26
| 2025-12-30
|
[
"cs.AI",
"cond-mat.mtrl-sci",
"cs.IR"
] |
Bhanu Prakash Vangala, Sajid Mahmud, Pawan Neupane, Joel Selvaraj, Jianlin Cheng
|
2403.03297
|
"It's the only thing I can trust": Envisioning Large Language Model Use by Autistic Workers for Communication Assistance
|
Autistic adults often experience stigma and discrimination at work, leading them to seek social communication support from coworkers, friends, and family despite emotional risks. Large language models (LLMs) are increasingly considered an alternative. In this work, we investigate the phenomenon of LLM use by autistic adults at work and explore opportunities and risks of LLMs as a source of social communication advice. We asked 11 autistic participants to present questions about their own workplace-related social difficulties to (1) a GPT-4-based chatbot and (2) a disguised human confederate. Our evaluation shows that participants strongly preferred LLM over confederate interactions. However, a coach specializing in supporting autistic job-seekers raised concerns that the LLM was dispensing questionable advice. We highlight how this divergence in participant and practitioner attitudes reflects existing schisms in HCI on the relative privileging of end-user wants versus normative good and propose design considerations for LLMs to center autistic experiences.
| 2025-12-26
| 2025-12-29
|
[
"cs.HC"
] |
JiWoong Jang, Sanika Moharana, Patrick Carrington, Andrew Begel
|
2512.22042
|
Esakia order-compactifications and locally Esakia spaces
|
We introduce Esakia order-compactifications and study how they fit in the general theory of Priestley order-compactifications. We provide an analog of Dwinger's theorem by characterizing Esakia order-compactifications by means of special rings of upsets. These considerations naturally lead to the notion of a locally Esakia space, for which we prove that taking the largest Esakia order-compacification is functorial, thus obtaining an analog of Banaschewski's theorem.
| 2025-12-26
| 2025-12-29
|
[
"math.LO",
"math.GN"
] |
Rodrigo Nicolau Almeida, Guram Bezhanishvili, Nick Bezhanishvili
|
2512.22364
|
Cost-Aware Text-to-SQL: An Empirical Study of Cloud Compute Costs for LLM-Generated Queries
|
Text-to-SQL systems powered by Large Language Models (LLMs) achieve high accuracy on standard benchmarks, yet existing efficiency metrics such as the Valid Efficiency Score (VES) measure execution time rather than the consumption-based costs of cloud data warehouses. This paper presents the first systematic evaluation of cloud compute costs for LLM-generated SQL queries. We evaluate six state-of-the-art LLMs across 180 query executions on Google BigQuery using the StackOverflow dataset (230GB), measuring bytes processed, slot utilization, and estimated cost. Our analysis yields three key findings: (1) reasoning models process 44.5% fewer bytes than standard models while maintaining equivalent correctness (96.7%-100%); (2) execution time correlates weakly with query cost (r=0.16), indicating that speed optimization does not imply cost optimization; and (3) models exhibit up to 3.4x cost variance, with standard models producing outliers exceeding 36GB per query. We identify prevalent inefficiency patterns including missing partition filters and unnecessary full-table scans, and provide deployment guidelines for cost-sensitive enterprise environments.
| 2025-12-26
| 2025-12-30
|
[
"cs.DB",
"cs.AI",
"cs.DC"
] |
Saurabh Deochake, Debajyoti Mukhopadhyay
|
2505.13010
|
To Bias or Not to Bias: Detecting bias in News with bias-detector
|
Media bias detection is a critical task in ensuring fair and balanced information dissemination, yet it remains challenging due to the subjectivity of bias and the scarcity of high-quality annotated data. In this work, we perform sentence-level bias classification by fine-tuning a RoBERTa-based model on the expert-annotated BABE dataset. Using McNemar's test and the 5x2 cross-validation paired t-test, we show statistically significant improvements in performance when comparing our model to a domain-adaptively pre-trained DA-RoBERTa baseline. Furthermore, attention-based analysis shows that our model avoids common pitfalls like oversensitivity to politically charged terms and instead attends more meaningfully to contextually relevant tokens. For a comprehensive examination of media bias, we present a pipeline that combines our model with an already-existing bias-type classifier. Our method exhibits good generalization and interpretability, despite being constrained by sentence-level analysis and dataset size because of a lack of larger and more advanced bias corpora. We talk about context-aware modeling, bias neutralization, and advanced bias type classification as potential future directions. Our findings contribute to building more robust, explainable, and socially responsible NLP systems for media bias detection.
| 2025-12-26
| 2025-12-30
|
[
"cs.CL",
"cs.AI",
"cs.HC"
] |
Himel Ghosh, Ahmed Mosharafa, Georg Groh
|
2502.03534
|
Many-Body Non-Hermitian Skin Effect with Exact Steady States in the Dissipative Quantum Link Model
|
We introduce a dissipative lattice gauge model that exhibits the many-body version of the non-Hermitian skin effect. The dissipative couplings between dynamical gauge fields on the lattice links and the surrounding environment generate chiral motions of particles residing on lattice sites. Despite the complexity arising from many-body interactions, the local gauge symmetry enables the exact construction of a steady state that displays the many-body non-Hermitian skin effect. Furthermore, our approach can be generalized to realize a new type of many-body non-Hermitian skin effect, dubbed the hierarchical skin effect, where different subsystem degrees of freedom exhibit boundary accumulation of multiple moments at different orders. Our findings can be readily observed by engineering dissipation in state-of-the-art lattice gauge simulators.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph",
"cond-mat.mes-hall",
"cond-mat.quant-gas",
"cond-mat.str-el",
"physics.optics"
] |
Yu-Min Hu, Zijian Wang, Biao Lian, Zhong Wang
|
2510.10313
|
Low-cost Pyranometer-Based ANN Approach for MPPT in Solar PV Systems
|
This article presents a study on the application of artificial neural networks (ANNs) for maximum power point tracking (MPPT) in photovoltaic (PV) systems using low-cost pyranometer sensors. The proposed approach integrates pyranometers, temperature sensors, and an ANN to estimate the duty cycle of a DC/DC converter, enabling the system to consistently operate at its maximum power point. The strategy was implemented in the local control of a Cuk converter and experimentally validated against the conventional Perturb and Observe (P&O) method. Results demonstrate that the ANN-based technique, leveraging affordable sensor technology, achieves accurate MPPT performance with reduced fluctuations, enhancing the responsiveness and efficiency of PV tracking systems.
| 2025-12-26
| 2025-12-29
|
[
"eess.SY",
"cs.SY"
] |
Luiz Fernando M. Arruda, Moises Ferber, Diego Greff
|
2512.21941
|
A Light Weight Neural Network for Automatic Modulation Classification in OFDM Systems
|
Automatic Modulation Classification (AMC) is a vital component in the development of intelligent and adaptive transceivers for future wireless communication systems. Existing statistically-based blind modulation classification methods for Orthogonal Frequency Division Multiplexing (OFDM) often fail to achieve the required accuracy and performance. Consequently, the modulation classification research community has shifted its focus toward deep learning techniques, which demonstrate promising performance, but come with increased computational complexity. In this paper, we propose a lightweight subcarrier-based modulation classification method for OFDM systems. In the proposed approach, a selected set of subcarriers in an OFDM frame is classified first, followed by the prediction of the modulation types for the remaining subcarriers based on the initial results. A Lightweight Neural Network (LWNN) is employed to identify the initially selected set of subcarriers, and its output is fed into a Recurrent Neural Network (RNN) as an embedded vector to predict the modulation schemes of the remaining subcarriers in the OFDM frame.
| 2025-12-26
| 2025-12-29
|
[
"eess.SP"
] |
Indiwara Nanayakkara, Dehan Jayawickrama, Dasuni Jayawardena, Vijitha R. Herath, Arjuna Madanayake
|
2511.02845
|
AI-Enhanced Real-Time Wi-Fi Sensing Through Single Transceiver Pair
|
The advancement of next-generation Wi-Fi technology heavily relies on sensing capabilities, which play a pivotal role in enabling sophisticated applications. In response to the growing demand for large-scale deployments, contemporary Wi-Fi sensing systems strive to achieve high-precision perception while maintaining minimal bandwidth consumption and antenna count requirements. Remarkably, various AI-driven perception technologies have demonstrated the ability to surpass the traditional resolution limitations imposed by radar theory. However, the theoretical underpinnings of this phenomenon have not been thoroughly investigated in existing research. In this study, we found that under hardware-constrained conditions, the performance gains brought by AI to Wi-Fi sensing systems primarily originate from two aspects: prior information and temporal correlation. Prior information enables the AI to generate plausible details based on vague input, while temporal correlation helps reduce the upper bound of sensing error. Building on these insights, we developed a real-time, AI-based Wi-Fi sensing and visualization system using a single transceiver pair, and designed experiments focusing on human pose estimation and indoor localization. The system operates in real time on commodity hardware, and experimental results confirm our theoretical findings.
| 2025-12-26
| 2025-12-29
|
[
"eess.SP",
"cs.AI",
"physics.ins-det"
] |
Yuxuan Liu, Chiya Zhang, Yifeng Yuan, Chunlong He, Weizheng Zhang, Gaojie Chen
|
2512.21955
|
Superradiant and dynamical spin-down of neutron stars with gravitational wave implications
|
Neutron stars such as pulsars and magnetars lose angular momentum primarily through electromagnetic dipole radiation, gravitational waves, $r$-mode oscillation, and also affected by fallback accretion processes. However, anomalous spin variations, particularly sudden enhanced spin-down rates, indicate additional spin-down mechanisms. We propose superradiant spin-down as a potential explanation for these events. By modelling the interplay between conventional and superradiant spin-down channels, we evaluate their impact on neutron star rotational evolution. We also discuss gravitational-wave emission produced by quadrupole deformation, $r$-mode oscillations, and axion-induced bosonic clouds around an isolated neutron star, highlighting their potential as distinct multimessenger probes in upcoming detectors.
| 2025-12-26
| 2025-12-29
|
[
"astro-ph.HE",
"astro-ph.SR",
"gr-qc"
] |
Indra Kumar Banerjee, Sandeep Chatterjee, Biswarup Das, Ujjal Kumar Dey
|
2512.21821
|
Optimal-Transport Stability of Inverse Point-Source Problems for Elliptic and Parabolic Equations
|
We establish quantitative global stability estimates, formulated in terms of optimal transport (OT) cost, for inverse point-source problems governed by elliptic and parabolic equations with spatially varying coefficients. The key idea is that the Kantorovich dual potential can be represented as a boundary functional of suitable adjoint solutions, thereby linking OT geometry with boundary observations. In the elliptic case, we construct complex geometric optics solutions that enforce prescribed pointwise constraints, whereas in the parabolic case we employ controllable adjoint solutions that transfer interior information to the boundary. Under mild regularity and separation assumptions, we obtain estimates of the form \[ \mathcal{T}_c(μ,ν) \le C\,\|u_1 - u_2\|_{L^2(\partialΩ)} \quad \text{and} \quad \mathcal{T}_c(μ,ν) \le C\,\|u_1 - u_2\|_{L^2(\partialΩ\times[0,T])}, \] where $μ$ and $ν$ are admissible point-source measures. These results provide a unified analytical framework connecting inverse source problems and optimal transport, and establish OT-based stability theory for inverse source problems governed by partial differential equations with spatially varying coefficients.
| 2025-12-26
| 2025-12-29
|
[
"math.NA",
"cs.NA",
"math.AP"
] |
Lingyun Qiu, Shenwen Yu
|
2512.22300
|
Evaluation of Turbulence Models and Boundary Conditions for Hybrid Ventilation in Reduced-scale Classroom Model
|
In this paper, we study the ventilation airflow in a model classroom, where exhaust fans throw out the used air, to replace it with outdoor air through open door. Hybrid ventilation, or mechanically assisted natural ventilation, of this kind is used as a retrofit design to reduce infection risk from airborne transmission. The air stream entering the door forms a jet-like flow, driven by the suction effect of exhaust fans. We compute the jet velocity using Reynolds averaged Navier Stokes (RANS) method and compare with velocity field measured using particle image velocimetry. Different turbulence models are found to match experimental data near the door, but they over-predict the peak jet velocity further downstream. There is minimal variation between the results obtained using different turbulence models. The computational results are found to be sensitive to inlet boundary conditions, whether the door entry is specified as a pressure inlet or velocity inlet. The geometry of the space outside the door also has a significant effect on the jet velocity. Changing the boundary condition takes the computational results closer to the experimental data; the velocity profiles computed with the extended domain being the closest to the measured peak velocity. Interestingly, the centerline velocity decay computed with the extended domain aligns well with the experimental data. The other cases, irrespective of turbulence model, show much lower decay rate that seem to align with wall jet scaling. This suggests that geometry and boundary conditions at the door is critical to predict the airflow in hybrid ventilation.
| 2025-12-26
| 2025-12-30
|
[
"physics.flu-dyn"
] |
Deep Narayan Singh, Lagoon Biswal, Girish Naik, Manaswita Bose, Krishnendu Sinha
|
2510.18821
|
Search Self-play: Pushing the Frontier of Agent Capability without Supervision
|
Reinforcement learning with verifiable rewards (RLVR) has become the mainstream technique for training LLM agents. However, RLVR highly depends on well-crafted task queries and corresponding ground-truth answers to provide accurate rewards, which requires significant human effort and hinders the scaling of RL processes, especially in agentic scenarios. Although a few recent works explore task synthesis methods, the difficulty of generated agentic tasks can hardly be controlled to provide effective RL training advantages. To achieve agentic RLVR with higher scalability, we explore self-play training for deep search agents, in which the learning LLM utilizes multi-turn search engine calling and acts simultaneously as both a task proposer and a problem solver. The task proposer aims to generate deep search queries with well-defined ground-truth answers and increasing task difficulty. The problem solver tries to handle the generated search queries and output the correct answer predictions. To ensure that each generated search query has accurate ground truth, we collect all the searching results from the proposer's trajectory as external knowledge, then conduct retrieval-augmentation generation (RAG) to test whether the proposed query can be correctly answered with all necessary search documents provided. In this search self-play (SSP) game, the proposer and the solver co-evolve their agent capabilities through both competition and cooperation. With substantial experimental results, we find that SSP can significantly improve search agents' performance uniformly on various benchmarks without any supervision under both from-scratch and continuous RL training setups. The code is at https://github.com/Qwen-Applications/SSP.
| 2025-12-26
| 2025-12-30
|
[
"cs.LG"
] |
Hongliang Lu, Yuhang Wen, Pengyu Cheng, Ruijin Ding, Jiaqi Guo, Haotian Xu, Chutian Wang, Haonan Chen, Xiaoxi Jiang, Guanjun Jiang
|
2512.21910
|
Fano Fibrations and Twisted Kähler-Einstein Metrics II: The Kähler-Ricci Flow
|
This is the second of two papers studying both the geometric structure of Fano fibrations and the application to Kähler-Ricci flows developing a singularity in finite time. We assume that the Kähler-Ricci flow on a compact Kähler manifold has a rational initial metric and develops a singularity in finite time such that the manifold admits a Fano fibration structure. Moreover, it is assumed that the volume form of the flow collapses uniformly at the rate of $C^{-1}(T-t)^{n-m} Ω\leq Ï(t)^n\leq C(T-t)^{n-m}Ω$. Under this setting, a diameter bound is obtained in any compact set away from singular fibres and the diameter of the fibres is proven to collapse at the optimal rate $\sqrt{T-t}$. Furthermore, several precise $C^0$-estimates are proven for the potential of the complex Monge-Ampere flow which involve the potentials of singular twisted Kähler-Einstein metrics on the base variety from part I. Finally, in the case of Kähler-Einstein Fano fibres, we deduce Type I scalar curvature in any compact set away from singular fibres and globally for a submersion.
| 2025-12-26
| 2025-12-29
|
[
"math.DG",
"math.CV"
] |
Alexander Bednarek
|
2512.12667
|
Open-World Deepfake Attribution via Confidence-Aware Asymmetric Learning
|
The proliferation of synthetic facial imagery has intensified the need for robust Open-World DeepFake Attribution (OW-DFA), which aims to attribute both known and unknown forgeries using labeled data for known types and unlabeled data containing a mixture of known and novel types. However, existing OW-DFA methods face two critical limitations: 1) A confidence skew that leads to unreliable pseudo-labels for novel forgeries, resulting in biased training. 2) An unrealistic assumption that the number of unknown forgery types is known *a priori*. To address these challenges, we propose a Confidence-Aware Asymmetric Learning (CAL) framework, which adaptively balances model confidence across known and novel forgery types. CAL mainly consists of two components: Confidence-Aware Consistency Regularization (CCR) and Asymmetric Confidence Reinforcement (ACR). CCR mitigates pseudo-label bias by dynamically scaling sample losses based on normalized confidence, gradually shifting the training focus from high- to low-confidence samples. ACR complements this by separately calibrating confidence for known and novel classes through selective learning on high-confidence samples, guided by their confidence gap. Together, CCR and ACR form a mutually reinforcing loop that significantly improves the model's OW-DFA performance. Moreover, we introduce a Dynamic Prototype Pruning (DPP) strategy that automatically estimates the number of novel forgery types in a coarse-to-fine manner, removing the need for unrealistic prior assumptions and enhancing the scalability of our methods to real-world OW-DFA scenarios. Extensive experiments on the standard OW-DFA benchmark and a newly extended benchmark incorporating advanced manipulations demonstrate that CAL consistently outperforms previous methods, achieving new state-of-the-art performance on both known and novel forgery attribution.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
Haiyang Zheng, Nan Pu, Wenjing Li, Teng Long, Nicu Sebe, Zhun Zhong
|
2512.20387
|
Generative Digital Twins: Vision-Language Simulation Models for Executable Industrial Systems
|
We propose a Vision-Language Simulation Model (VLSM) that unifies visual and textual understanding to synthesize executable FlexScript from layout sketches and natural-language prompts, enabling cross-modal reasoning for industrial simulation systems. To support this new paradigm, the study constructs the first large-scale dataset for generative digital twins, comprising over 120,000 prompt-sketch-code triplets that enable multimodal learning between textual descriptions, spatial structures, and simulation logic. In parallel, three novel evaluation metrics, Structural Validity Rate (SVR), Parameter Match Rate (PMR), and Execution Success Rate (ESR), are proposed specifically for this task to comprehensively evaluate structural integrity, parameter fidelity, and simulator executability. Through systematic ablation across vision encoders, connectors, and code-pretrained language backbones, the proposed models achieve near-perfect structural accuracy and high execution robustness. This work establishes a foundation for generative digital twins that integrate visual reasoning and language understanding into executable industrial simulation systems.
| 2025-12-26
| 2025-12-29
|
[
"cs.AI",
"cs.CL",
"cs.CV"
] |
YuChe Hsu, AnJui Wang, TsaiChing Ni, YuanFu Yang
|
2512.21867
|
DPAR: Dynamic Patchification for Efficient Autoregressive Visual Generation
|
Decoder-only autoregressive image generation typically relies on fixed-length tokenization schemes whose token counts grow quadratically with resolution, substantially increasing the computational and memory demands of attention. We present DPAR, a novel decoder-only autoregressive model that dynamically aggregates image tokens into a variable number of patches for efficient image generation. Our work is the first to demonstrate that next-token prediction entropy from a lightweight and unsupervised autoregressive model provides a reliable criterion for merging tokens into larger patches based on information content. DPAR makes minimal modifications to the standard decoder architecture, ensuring compatibility with multimodal generation frameworks and allocating more compute to generation of high-information image regions. Further, we demonstrate that training with dynamically sized patches yields representations that are robust to patch boundaries, allowing DPAR to scale to larger patch sizes at inference. DPAR reduces token count by 1.81x and 2.06x on Imagenet 256 and 384 generation resolution respectively, leading to a reduction of up to 40% FLOPs in training costs. Further, our method exhibits faster convergence and improves FID by up to 27.1% relative to baseline models.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
Divyansh Srivastava, Akshay Mehra, Pranav Maneriker, Debopam Sanyal, Vishnu Raj, Vijay Kamarshi, Fan Du, Joshua Kimball
|
2512.21826
|
Surrogate-Powered Inference: Regularization and Adaptivity
|
High-quality labeled data are essential for reliable statistical inference, but are often limited by validation costs. While surrogate labels provide cost-effective alternatives, their noise can introduce non-negligible bias. To address this challenge, we propose the surrogate-powered inference (SPI) toolbox, a unified framework that leverages both the validity of high-quality labels and the abundance of surrogates to enable reliable statistical inference. SPI comprises three progressively enhanced versions. Base-SPI integrates validated labels and surrogates through augmentation to improve estimation efficiency. SPI+ incorporates regularized regression to safely handle multiple surrogates, preventing performance degradation due to error accumulation. SPI++ further optimizes efficiency under limited validation budgets through an adaptive, multiwave labeling procedure that prioritizes informative subjects for labeling. Compared to traditional methods, SPI substantially reduces the estimation error and increases the power in risk factor identification. These results demonstrate the value of SPI in improving the reproducibility. Theoretical guarantees and extensive simulation studies further illustrate the properties of our approach.
| 2025-12-26
| 2025-12-29
|
[
"stat.ME"
] |
Jianmin Chen, Huiyuan Wang, Thomas Lumley, Xiaowu Dai, Yong Chen
|
2512.22106
|
Pruning as a Game: Equilibrium-Driven Sparsification of Neural Networks
|
Neural network pruning is widely used to reduce model size and computational cost. Yet, most existing methods treat sparsity as an externally imposed constraint, enforced through heuristic importance scores or training-time regularization. In this work, we propose a fundamentally different perspective: pruning as an equilibrium outcome of strategic interaction among model components. We model parameter groups such as weights, neurons, or filters as players in a continuous non-cooperative game, where each player selects its level of participation in the network to balance contribution against redundancy and competition. Within this formulation, sparsity emerges naturally when continued participation becomes a dominated strategy at equilibrium. We analyze the resulting game and show that dominated players collapse to zero participation under mild conditions, providing a principled explanation for pruning behavior. Building on this insight, we derive a simple equilibrium-driven pruning algorithm that jointly updates network parameters and participation variables without relying on explicit importance scores. This work focuses on establishing a principled formulation and empirical validation of pruning as an equilibrium phenomenon, rather than exhaustive architectural or large-scale benchmarking. Experiments on standard benchmarks demonstrate that the proposed approach achieves competitive sparsity-accuracy trade-offs while offering an interpretable, theory-grounded alternative to existing pruning methods.
| 2025-12-26
| 2025-12-29
|
[
"cs.AI"
] |
Zubair Shah, Noaman Khan
|
2410.01090
|
Parametrized Families of Resolvent Compositions
|
This paper presents an in-depth analysis of a parametrized version of the resolvent composition, an operation that combines a set-valued operator and a linear operator. We provide new properties and examples, and show that resolvent compositions can be interpreted as parallel compositions of perturbed operators. Additionally, we establish new monotonicity results, even in cases when the initial operator is not monotone. Finally, we derive asymptotic results regarding operator convergence, specifically focusing on graph-convergence and the $Ï$-Hausdorff distance.
| 2025-12-26
| 2025-12-30
|
[
"math.OC",
"math.FA"
] |
Diego J. Cornejo
|
2512.22335
|
Feature Learning with Multi-Stage Vision Transformers on Inter-Modality HER2 Status Scoring and Tumor Classification on Whole Slides
|
The popular use of histopathology images, such as hematoxylin and eosin (H&E), has proven to be useful in detecting tumors. However, moving such cancer cases forward for treatment requires accurate on the amount of the human epidermal growth factor receptor 2 (HER2) protein expression. Predicting both the lower and higher levels of HER2 can be challenging. Moreover, jointly analyzing H&E and immunohistochemistry (IHC) stained images for HER2 scoring is difficult. Although several deep learning methods have been investigated to address the challenge of HER2 scoring, they suffer from providing a pixel-level localization of HER2 status. In this study, we propose a single end-to-end pipeline using a system of vision transformers with HER2 status scoring on whole slide images of WSIs. The method includes patch-wise processing of H&E WSIs for tumor localization. A novel mapping function is proposed to correspondingly identify correlated IHC WSIs regions with malignant regions on H&E. A clinically inspired HER2 scoring mechanism is embedded in the pipeline and allows for automatic pixel-level annotation of 4-way HER2 scoring (0, 1+, 2+, and 3+). Also, the proposed method accurately returns HER2-negative and HER2-positive. Privately curated datasets were collaboratively extracted from 13 different cases of WSIs of H&E and IHC. A thorough experiment is conducted on the proposed method. Results obtained showed a good classification accuracy during tumor localization. Also, a classification accuracy of 0.94 and a specificity of 0.933 were returned for the prediction of HER2 status, scoring in the 4-way methods. The applicability of the proposed pipeline was investigated using WSIs patches as comparable to human pathologists. Findings from the study showed the usability of jointly evaluated H&E and IHC images on end-to-end ViTs-based models for HER2 scoring
| 2025-12-26
| 2025-12-30
|
[
"cs.CV",
"cs.AI"
] |
Olaide N. Oyelade, Oliver Hoxey, Yulia Humrye
|
2512.14795
|
A computational study of thermoelectric conversion in the PbSe$_{x}$Te$_{1-x}$ semiconductor alloys
|
The present theoretical study focuses on the structural, electronic and thermoelectric properties of PbTe, PbSe and their ternary alloys PbSe$_{x}$Te$_{1-x}$, using the density functional theory (DFT) by the full potential linearised augmented plane wave (FP-LAPW) method implemented in Wien2k code. Structural properties were performed by using the generalized gradient approximation of Perdew Burke and Ernzenhof (GGA-PBE) scheme. The results show that the calculated lattice parameters are in good agreement with theoretical data previously obtained. For electronic properties, we noticed that for all the compounds of PbSe$_{x}$Te$_{1-x}$, we have a direct band gap in L point. For thermoelectric properties, we used BoltzTraP2 code and Gibbs2 code. Our results show that the PbSe$_{x}$Te$_{1-x}$ compounds have reached a value of 2.55 for the figure of merit, which indicates that our material is a good thermoelectric candidate.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.mtrl-sci"
] |
M. Kaid Slimane, B. N. Brahmi, M. Bouchenaki, S. Bekhechi
|
2511.13540
|
Fairness-Aware Graph Representation Learning with Limited Demographic Information
|
Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair graph learning methods have been proposed in recent years. However, most of them assume full access to demographic information, a requirement rarely met in practice due to privacy, legal, or regulatory restrictions. To this end, this paper introduces a novel fair graph learning framework that mitigates bias in graph learning under limited demographic information. Specifically, we propose a mechanism guided by partial demographic data to generate proxies for demographic information and design a strategy that enforces consistent node embeddings across demographic groups. In addition, we develop an adaptive confidence strategy that dynamically adjusts each node's contribution to fairness and utility based on prediction confidence. We further provide theoretical analysis demonstrating that our framework, FairGLite, achieves provable upper bounds on group fairness metrics, offering formal guarantees for bias mitigation. Through extensive experiments on multiple datasets and fair graph learning frameworks, we demonstrate the framework's effectiveness in both mitigating bias and maintaining model utility.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG",
"cs.CY"
] |
Zichong Wang, Zhipeng Yin, Liping Yang, Jun Zhuang, Rui Yu, Qingzhao Kong, Wenbin Zhang
|
2509.18857
|
Optimal estimation for regression discontinuity design with binary outcomes
|
We develop a finite-sample optimal estimator for regression discontinuity design when the outcomes are bounded, including binary outcomes as the leading case. Our estimator achieves minimax mean squared error among linear shrinkage estimators with nonnegative weights when the regression function lies in a Lipschitz class. Although the original minimax problem involves an iterative noncovex optimization problem, we show that our estimator is obtained by solving a convex optimization problem. A key advantage of the proposed estimator is that the Lipschitz constant is its only tuning parameter. We also propose a uniformly valid inference procedure without a large-sample approximation. In a simulation exercise for small samples, our estimator exhibits smaller mean squared errors and shorter confidence intervals than those of conventional large-sample techniques. In an empirical multi-cutoff design in which the sample size for each cutoff is small, our method yields informative confidence intervals, in contrast to the leading large-sample approach.
| 2025-12-26
| 2025-12-29
|
[
"econ.EM",
"math.ST",
"stat.ME",
"stat.TH"
] |
Takuya Ishihara, Masayuki Sawada, Kohei Yata
|
2508.04440
|
StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning Fusion
|
Autoformalization aims to translate natural-language mathematical statements into a formal language. While LLMs have accelerated progress in this area, existing methods still suffer from low accuracy. We identify two key abilities for effective autoformalization: comprehensive mastery of formal-language domain knowledge, and reasoning capability of natural language problem understanding and informal-formal alignment. Without the former, a model cannot identify the correct formal objects; without the latter, it struggles to interpret real-world contexts and map them precisely into formal expressions. To address these gaps, we introduce ThinkingF, a data synthesis and training pipeline that improves both abilities. First, we construct two datasets: one by distilling and selecting large-scale examples rich in formal knowledge, and another by generating informal-to-formal reasoning trajectories guided by expert-designed templates. We then apply SFT and RLVR with these datasets to further fuse and refine the two abilities. The resulting 7B and 32B models exhibit both comprehensive formal knowledge and strong informal-to-formal reasoning. Notably, StepFun-Formalizer-32B achieves SOTA BEq@1 scores of 40.5% on FormalMATH-Lite and 26.7% on ProverBench, surpassing all prior general-purpose and specialized models.
| 2025-12-26
| 2025-12-29
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] |
Yutong Wu, Di Huang, Ruosi Wan, Yue Peng, Shijie Shang, Chenrui Cao, Lei Qi, Rui Zhang, Zidong Du, Jie Yan, Xing Hu
|
2502.21022
|
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity
|
This paper introduces the first fully unsupervised domain adaptation (UDA) framework for unsupervised anomaly detection (UAD). The performance of UAD techniques degrades significantly in the presence of a domain shift, difficult to avoid in a real-world setting. While UDA has contributed to solving this issue in binary and multi-class classification, such a strategy is ill-posed in UAD. This might be explained by the unsupervised nature of the two tasks, namely, domain adaptation and anomaly detection. Herein, we first formulate this problem that we call the two-fold unsupervised curse. Then, we propose a pioneering solution to this curse, considered intractable so far, by assuming that anomalies are rare. Specifically, we leverage clustering techniques to identify a dominant cluster in the target feature space. Posed as the normal cluster, the latter is aligned with the source normal features. Concretely, given a one-class source set and an unlabeled target set composed mostly of normal data and some anomalies, we fit the source features within a hypersphere while jointly aligning them with the features of the dominant cluster from the target set. The paper provides extensive experiments and analysis on common adaptation benchmarks for anomaly detection, demonstrating the relevance of both the newly introduced paradigm and the proposed approach. The code will be made publicly available.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG",
"cs.CV"
] |
Nesryne Mejri, Enjie Ghorbel, Anis Kacem, Pavel Chernakov, Niki Foteinopoulou, Djamila Aouada
|
2512.22084
|
A Frobenius-Optimal Projection for Enforcing Linear Conservation in Learned Dynamical Models
|
We consider the problem of restoring linear conservation laws in data-driven linear dynamical models. Given a learned operator $\widehat{A}$ and a full-rank constraint matrix $C$ encoding one or more invariants, we show that the matrix closest to $\widehat{A}$ in the Frobenius norm and satisfying $C^\top A = 0$ is the orthogonal projection $A^\star = \widehat{A} - C(C^\top C)^{-1}C^\top \widehat{A}$. This correction is uniquely defined, low rank and fully determined by the violation $C^\top \widehat{A}$. In the single-invariant case it reduces to a rank-one update. We prove that $A^\star$ enforces exact conservation while minimally perturbing the dynamics, and we verify these properties numerically on a Markov-type example. The projection provides an elementary and general mechanism for embedding exact invariants into any learned linear model.
| 2025-12-26
| 2025-12-29
|
[
"math.DS",
"cs.LG",
"cs.NA",
"math.NA"
] |
John M. Mango, Ronald Katende
|
2511.09348
|
A coupled finite element-virtual element method for thermomechanical analysis of electronic packaging structures
|
This study presents a finite element and virtual element (FE-VE) coupled method for thermomechanical analysis in electronic packaging structures. The approach partitions computational domains strategically, employing FEM for regular geometries to maximize computational efficiency and VEM for complex shapes to enhance geometric flexibility. Interface compatibility is maintained through coincident nodal correspondence, ensuring solution continuity across domain boundaries while reducing meshing complexity and computational overhead. Validation through electronic packaging applications demonstrates reasonable agreement with reference solutions and acceptable convergence characteristics across varying mesh densities. The method effectively captures thermal distributions and stress concentrations in multi-material systems, establishing a practical computational framework for electronic packaging analysis involving complex geometries. Source codes are available at https://github.com/yanpeng-gong/FeVeCoupled-ElectronicPackaging.
| 2025-12-26
| 2025-12-29
|
[
"math.NA",
"cs.CE",
"cs.NA"
] |
Yanpeng Gong, Sishuai Li, Yue Mei, Bingbing Xu, Fei Qin, Xiaoying Zhuang, Timon Rabczuk
|
2512.22104
|
Non-abelian soft radiation data for a celestial theory
|
Celestial holography posits that the long-distance behavior of gauge and gravity theories is dictated by two-dimensional conformal field theories defined on the celestial sphere. For non-abelian gauge theories, this proposal is verified, to all perturbative orders, by dipole color correlations in the infrared factor of non-abelian scattering amplitudes, which are given by a correlator of matrix-valued vertex operators in a free-boson theory on the sphere. Decades of high-order gauge-theory calculations have provided a number of further results that can be used to test and constrain a possible celestial theory: they include explicit expressions for soft emission currents up to three particles, and up to three loops for single soft emission. In this paper, we analyze this trove of data, appropriately translated in the celestial language, and we use them to extract information on the celestial theory. In particular, we show that all logarithms arising in the loop expansion of the single soft current can be reabsorbed in the scale choices for the $d$-dimensional coupling, casting some doubt on the need for a logarithmic celestial theory. We then note that the celestial OPEs suggested by the structure of multiple emission currents in collinear limits are never ambiguous, but involve coefficients depending on gluon energy fractions, which break holomorphic factorization, as well as associativity when double limits are taken. Strongly-ordered soft limits recover associativity, but suffer from ambiguities already discussed in earlier literature.
| 2025-12-26
| 2025-12-29
|
[
"hep-th",
"hep-ph"
] |
Lorenzo Magnea, Enrico Zunino
|
2512.21816
|
Delayed Choice Lorentz Transformations on a Qubit
|
A continuously monitored quantum bit (qubit) exhibits competition between unitary Hamiltonian dynamics and non-unitary measurement-collapse dynamics, which for diffusive measurements form an enlarged transformation group equivalent to the Lorentz group of spacetime. We leverage this equivalence to develop a four-dimensional generalization of the three-dimensional Bloch ball to visualize the state of a monitored qubit as the four-momentum of an effective classical charge affected by a stochastic electromagnetic force field. Unitary qubit dynamics generated by Hermitian Hamiltonians correspond to elliptic spatial rotations of this effective charge while non-unitary qubit dynamics generated by non-Hermitian Hamiltonians or stochastic measurement collapse correspond to hyperbolic Lorentz boosts. Notably, to faithfully emulate the stochastic qubit dynamics arising from continuous qubit measurement, the stochastic electromagnetic fields must depend on the velocity of the charge they are acting on. Moreover, continuous qubit measurements admit a dynamical delayed choice effect where a future experimental choice can appear to retroactively determine the type of past measurement backaction, so the corresponding point charge dynamics can also exhibit delayed choice Lorentz transformations in which a future experimental choice determines whether stochastic force fields are electric or magnetic in character long after they interact with the particle.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph"
] |
Lucas Burns, Sacha Greenfield, Justin Dressel
|
2512.21872
|
Compact Ca II K Brightenings Precede Solar Flares: A Dunn Solar Telescope Pilot Study
|
We present a uniform analysis of compact Ca II K (3934 Ã
) brightenings that occur near flare kernels and assess their value as short-lead indicators of solar flare onset. Using high-cadence imaging from the Rapid Oscillations in the Solar Atmosphere (ROSA) instrument at the Dunn Solar Telescope (DST), we examine eight flare sequences (seven C-class and one B-class) obtained between 2021 and 2025. Fixed, detector-coordinate regions of interest are used to generate mean-intensity light curves, which are detrended and smoothed to isolate impulsive brightenings. In every event, a compact Ca II K brightening is detected within or adjacent to the flaring region that peaks 10--45 min before the primary kernel and the corresponding rise in GOES 1--8 Ã
flux. The measured temporal offsets scale with the deprojected separation between the brightening and flare kernels, implying an apparent propagation speed of $\sim$30--35 km s$^{-1}$ that is consistent with chromospheric reconnection. Complementary Spectropolarimeter for Infrared and Optical Regions (SPINOR) spectropolarimetry for one event shows topological reconfiguration from closed to open or extended connectivity, supporting a reconnection-driven origin. These results demonstrate that compact Ca II K brightenings are reproducible, physically meaningful precursors to flare onset. Their simplicity and cadence make them attractive chromospheric indicators, and future work will evaluate their predictive skill alongside established UV/EUV and magnetic diagnostics.
| 2025-12-26
| 2025-12-29
|
[
"astro-ph.SR"
] |
Aman Priyadarshi M. Kumar, Juie Shetye, Sean G. Sellers, Damian J. Christian
|
2503.09626
|
Certainly Bot Or Not? Trustworthy Social Bot Detection via Robust Multi-Modal Neural Processes
|
Social bot detection is crucial for mitigating misinformation, online manipulation, and coordinated inauthentic behavior. While existing neural network-based detectors perform well on benchmarks, they struggle with generalization due to distribution shifts across datasets and frequently produce overconfident predictions for out-of-distribution accounts beyond the training data. To address this, we introduce a novel Uncertainty Estimation for Social Bot Detection (UESBD) framework, which quantifies the predictive uncertainty of detectors beyond mere classification. For this task, we propose Robust Multi-modal Neural Processes (RMNP), which aims to enhance the robustness of multi-modal neural processes to modality inconsistencies caused by social bot camouflage. RMNP first learns unimodal representations through modality-specific encoders. Then, unimodal attentive neural processes are employed to encode the Gaussian distribution of unimodal latent variables. Furthermore, to avoid social bots stealing human features to camouflage themselves thus causing certain modalities to provide conflictive information, we introduce an evidential gating network to explicitly model the reliability of modalities. The joint latent distribution is learned through the generalized product of experts, which takes the reliability of each modality into consideration during fusion. The final prediction is obtained through Monte Carlo sampling of the joint latent distribution followed by a decoder. Experiments on three real-world benchmarks show the effectiveness of RMNP in classification and uncertainty estimation, as well as its robustness to modality conflicts.
| 2025-12-26
| 2025-12-29
|
[
"cs.SI",
"cs.AI",
"cs.LG"
] |
Qi Wu, Yingguang Yang, hao liu, Hao Peng, Buyun He, Yutong Xia, Yong Liao
|
2512.21850
|
Chirality-selective topological magnon phase transition induced by interplay of anisotropic exchange interactions in honeycomb ferromagnet
|
A variety of distinct anisotropic exchange interactions commonly exist in one magnetic material due to complex crystal, magnetic and orbital symmetries. Here we investigate the effects of multiple anisotropic exchange interactions on topological magnon in a honeycomb ferromagnet, and find a chirality-selective topological magnon phase transition induced by a complicated interplay of Dzyaloshinsky-Moriya interaction (DMI) and pseudo-dipolar interaction (PDI), accompanied by the bulk gap close and reopen with chiral inversion. Moreover, this novel topological phase transition involves band inversion at high symmetry points $K$ and $K'$, which can be regarded as a pseudo-orbital reversal, i.e. magnon valley degree of freedom, implying a new manipulation corresponding to a sign change of the magnon thermal Hall conductivity. Indeed, it can be realized in 4$d$ or 5$d$ correlated materials with both spin-orbit coupling and orbital localized states, such as iridates and ruthenates, etc. This novel regulation may have potential applications on magnon devices and topological magnonics.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.str-el",
"cond-mat.mes-hall",
"cond-mat.mtrl-sci"
] |
Jin-Yu Ni, Xia-Ming Zheng, Peng-Tao Wei, Da-Yong Liu, Liang-Jian Zou
|
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