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2512.22358
|
Can high-$p_\perp$ theory and data constrain $η/s$?
|
Understanding the temperature dependence of the specific shear viscosity $(η/s)$ is crucial for characterizing the properties of the QCD matter produced in ultrarelativistic heavy-ion collisions. Since, low-$p_\perp$ theory and data are only weakly sensitive to the typical forms of $η/s(T)$, especially at high temperatures, we use high-$p_\perp$ data and theory to impose additional constraints on it. Our approach, based on dynamical radiative and collisional energy loss of high-$p_\perp$ particles, provides promising results in constraining the temperature dependence of $η/s$.
| 2025-12-26
| 2025-12-30
|
[
"hep-ph"
] |
Bithika Karmakar, Dusan Zigic, Igor Salom, Jussi Auvinen, Pasi Huovinen, Marko Djordjevic, Magdalena Djordjevic
|
2512.22330
|
Revisiting De Moivre-Laplace
|
We revisit the proof of the de Moivre--Laplace theorem, which is the ancestor of the central limit theorem for the binomial distribution. Our goal is to provide a proof that can be reasonably presented to undergraduate students within a basic course of probability theory. We follow the strategies presented in two classical references, the books of Breiman and Feller, but we replace the arguments involving series expansions of the logarithm or the exponential by the basic inequality $\exp(t)\geq 1+t$. This way we avoid completely the use of uniform convergence and power series. We also avoid using Stirling's formula, instead we use the exact formula for the Wallis integral. As a by product of the proof, we also obtain a non-asymptotic inequality linking the binomial and the Gaussian distributions.
| 2025-12-26
| 2025-12-30
|
[
"math.PR"
] |
Raphaël Cerf
|
2510.18802
|
Computational Foundations for Strategic Coopetition: Formalizing Interdependence and Complementarity
|
Coopetition refers to simultaneous cooperation and competition among actors who "cooperate to grow the pie and compete to split it up." Modern socio-technical systems are characterized by strategic coopetition in which actors concomitantly cooperate to create value and compete to capture it. While conceptual modeling languages such as i* provide rich qualitative representations of strategic dependencies, they lack mechanisms for quantitative analysis of dynamic trade-offs. Conversely, classical game theory offers mathematical rigor but strips away contextual richness. This technical report bridges this gap by developing computational foundations that formalize two critical dimensions of coopetition: interdependence and complementarity. We ground interdependence in i* structural dependency analysis, translating depender-dependee-dependum relationships into quantitative interdependence coefficients through a structured translation framework. We formalize complementarity following Brandenburger and Nalebuff's Added Value concept, modeling synergistic value creation with validated parameterization. We integrate structural dependencies with bargaining power in value appropriation and introduce a game-theoretic formulation where Nash Equilibrium incorporates structural interdependence. Validation combines comprehensive experimental testing comprising over 22,000 trials across power and logarithmic value function specifications, demonstrating functional form robustness, with empirical application to the Samsung-Sony S-LCD joint venture (2004-2011). This technical report serves as the foundational reference for a coordinated research program examining strategic coopetition in multi-agent systems, with companion work addressing trust dynamics, collective action, and reciprocity mechanisms.
| 2025-12-26
| 2025-12-29
|
[
"cs.MA",
"cs.AI",
"cs.SE"
] |
Vik Pant, Eric Yu
|
2512.20308
|
SpidR: Learning Fast and Stable Linguistic Units for Spoken Language Models Without Supervision
|
The parallel advances in language modeling and speech representation learning have raised the prospect of learning language directly from speech without textual intermediates. This requires extracting semantic representations directly from speech. Our contributions are threefold. First, we introduce SpidR, a self-supervised speech representation model that efficiently learns representations with highly accessible phonetic information, which makes it particularly suited for textless spoken language modeling. It is trained on raw waveforms using a masked prediction objective combined with self-distillation and online clustering. The intermediate layers of the student model learn to predict assignments derived from the teacher's intermediate layers. This learning objective stabilizes the online clustering procedure compared to previous approaches, resulting in higher quality codebooks. SpidR outperforms wav2vec 2.0, HuBERT, WavLM, and DinoSR on downstream language modeling benchmarks (sWUGGY, sBLIMP, tSC). Second, we systematically evaluate across models and layers the correlation between speech unit quality (ABX, PNMI) and language modeling performance, validating these metrics as reliable proxies. Finally, SpidR significantly reduces pretraining time compared to HuBERT, requiring only one day of pretraining on 16 GPUs, instead of a week. This speedup is enabled by the pretraining method and an efficient codebase, which allows faster iteration and easier experimentation. We open-source the training code and model checkpoints at https://github.com/facebookresearch/spidr.
| 2025-12-26
| 2025-12-29
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] |
Maxime Poli, Mahi Luthra, Youssef Benchekroun, Yosuke Higuchi, Martin Gleize, Jiayi Shen, Robin Algayres, Yu-An Chung, Mido Assran, Juan Pino, Emmanuel Dupoux
|
2512.12338
|
Shape descriptors of equilibrium states in a quantum lattice model with local multi-well potentials: A geometric analysis near the phase transitions in Sn$_2$P$_2$S$_6$ ferroelectric crystals
|
We analyze the equilibrium states of quantum lattice model with local multi-well potentials for Sn$_2$P$_2$S$_6$ ferroelectric crystals using the mean and Gaussian curvatures ($H$, $K$), curvedness ($C$) and shape index ($S$). From the energy gap, pressure and temperature variations of $H$, $K$, $C$ and $S$, we have reported the geometric construction of the free energy surfaces for the ferroelectric and paraelectric phases. Their behaviors are explicitly observed near the ferroelectric-paraelectric phase transitions. It is found that $H$, $C$ and $S$ display a cusp singularity at the criticality while $K$ converges to zero on both sides of the critical and tricritical points.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.mtrl-sci"
] |
S. Ãzüm, T. Akkurt, R. Erdem, N. Güçlü
|
2512.21836
|
Spectroscopic Characterization of Metallocene Single Crystals Grown by Physical Vapor Transport Method
|
High-quality metallocene single crystals with a low density of impurities and high homogeneity were prepared using the physical vapor transport method. These crystals were then characterized using various spectroscopic tools and X-ray diffraction. Laser-induced breakdown spectroscopy confirmed the presence of metal ions in each freshly grown sample despite all these crystals undergoing physical deformation with different lifetimes. X-ray diffraction analysis confirmed that all our metallocene single crystals retained a monoclinic structure at room temperature. The vibrational properties of our metallocene crystals were examined using Raman and Fourier-transform infrared spectroscopy. The inter- and intra-ring vibrational modes, along with additional modes associated with the crystalline form, were identified as inherent vibrational properties of our metallocene single crystals. Given the increasingly important role of metallocene in organic solar cells, organic light-emitting displays and molecular quantum systems, this research will enhance our understanding of the intrinsic physical properties of cleaner, more crystalline metallocene single crystals.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.mtrl-sci",
"cond-mat.soft"
] |
Ian B. Logue, Sandaruka Jayasooriya Arachchilage, Lance M. Griswold, Moses B. Gaither-Ganim, Lincoln W. Weber, Robyn Cook, Stephen Hofer, Praveena Satkunam, Dipanjan Mazumdar, Poopalasingam Sivakumar, Bumsu Lee
|
2512.22395
|
The Lieb-Robinson correlation function for long disordered transverse-field Ising chains
|
The transverse-field Ising model is useful for studying interacting qubit arrays. The Lieb--Robinson correlation function can be used to characterize the propagation of quantum information in Ising chains. Considerable work has been done to establish bounds on this correlation function in various circumstances. To actually calculate the value of the correlation function directly typically requires a state space which grows exponentially with system size, and so is intractable for all but relatively small systems. We employ a recently-developed method that enables direct calculation of the value of the Lieb--Robinson correlation function and which scales linearly with system size. This enables the computation for systems with many hundreds of qubits, revealing the propagation of quantum information down the chain. We extend this technique to the problem of Ising chains with randomly disordered coupling strengths. Increasing disorder causes localization of the quantum correlations and halts propagation of quantum information.
| 2025-12-26
| 2025-12-30
|
[
"quant-ph"
] |
Brendan J. Mahoney, Craig S. Lent
|
2512.22337
|
The Effectiveness of Approximate Regularized Replay for Efficient Supervised Fine-Tuning of Large Language Models
|
Although parameter-efficient fine-tuning methods, such as LoRA, only modify a small subset of parameters, they can have a significant impact on the model. Our instruction-tuning experiments show that LoRA-based supervised fine-tuning can catastrophically degrade model capabilities, even when trained on very small datasets for relatively few steps. With that said, we demonstrate that while the most straightforward approach (that is likely the most used in practice) fails spectacularly, small tweaks to the training procedure with very little overhead can virtually eliminate the problem. Particularly, in this paper we consider a regularized approximate replay approach which penalizes KL divergence with respect to the initial model and interleaves in data for next token prediction from a different, yet similar, open access corpus to what was used in pre-training. When applied to Qwen instruction-tuned models, we find that this recipe preserves general knowledge in the model without hindering plasticity to new tasks by adding a modest amount of computational overhead.
| 2025-12-26
| 2025-12-30
|
[
"cs.LG",
"cs.AI"
] |
Matthew Riemer, Erik Miehling, Miao Liu, Djallel Bouneffouf, Murray Campbell
|
2512.22311
|
Dihadron Transverse-Spin Asymmetries in Muon-Deuteron Deep-Inelastic Scattering
|
In 2022, the COMPASS collaboration performed semi-inclusive measurements of deep-inelastic muon-scattering on a transversely polarised deuteron (6LiD) target. From these data, transverse-spin-dependent dihadron asymmetries are extracted using pairs of oppositely charged hadrons. These asymmetries are directly sensitive to the quark transversity distributions and provide an independent handle on these fundamental quantities with respect to the Collins asymmetries measured in single-hadron production. The present results significantly improve upon the previous COMPASS deuteron measurements, which were the only available deuteron data worldwide, and reach a statistical precision comparable to that of the existing proton results from COMPASS. A small but nonzero asymmetry is observed at large Bjorken-x, consistent with theoretical expectations. A point-by-point extraction of the valence-quark transversity distributions yields, in particular, a substantially improved determination of the d-quark transversity. These measurements represent a major step towards a complete flavour mapping of the transverse-spin structure of the nucleon.
| 2025-12-26
| 2025-12-30
|
[
"hep-ex"
] |
G. D. Alexeev, M. G. Alexeev, C. Alice, A. Amoroso, V. Andrieux, V. Anosov, S. Asatryan, K. Augsten, W. Augustyniak, C. D. R. Azevedo, B. Badelek, R. Beck, J. Beckers, Y. Bedfer, V. Benesova, J. Bernhard, F. Bradamante, A. Bressan, W. -C . Chang, C. Chatterjee, M. Chiosso, S. -U. Chung, A. Cicuttin, M. L. Crespo, D. D'Ago, S. Dalla Torre, S. S. Dasgupta, S. Dasgupta, M. Dehpour, F. Delcarro, I. Denisenko, O. Yu. Denisov, S. V. Donskov, N. Doshita, Ch. Dreisbach, W. Dün nweber, R. R. Dusaev, D. Ecker, P. Faccioli, M. Faessler, M. Finger, M. Finger, H. Fischer, K. J. Flöthner, W. Florian, J. M. Friedrich, V. Frolov, L. G. Garcia Ordòñez, O. P. Gavrichtchouk, S. Gerassimov, J. Giarra, D. Giordan o, A. Grasso, A. Gridin, M. Grosse Perdekamp, B. Grube, M. Grüner, A. Guskov, P. Haas, D. von Harrach, M. Hoffmann, A. Hoghmrtsyan, N. d'Hose, C. -Y. Hsieh, S. Ishimoto, A. Ivanov, T. Iwata, V. Jary, E. Jelinkova, R. Joosten, E. KabuÃ, F. Kaspar, A. Kerbizi, B. Ketzer, G. V. Khaustov, T. Klasek, J. H. Koivuniemi, V. N. Kolosov, K. Kondo Horikawa, I. Konorov, A. Yu. Korzenev, A. M. Kotzinian, O. M. Kouznetsov, A. Koval, F. Kunne, K. Kurek, R. P. Kurjata, A. Kveton, K. Lavickova, S. Levorato, Y. -S. Lian, J. Lichtenstadt, P. -J. Lin, R. Longo, V. E. Lyubovitskij, A. Maggiora, N. Makke, G. K. Mallot, A. Maltsev, A. Martin, H. Marukyan, J. Marzec, J. MatouÅ¡ek, T. Matsuda, C. Menezes Pires, F. Metzge r, W. Meyer, M. Mikhasenko, E. Mitrofanov, D. Miura, Y. Miyachi, R. Molina, A. Movsisian, A. Moretti, A. Nagaytsev, D. Neyret, M. Niemiec, J. Nový, W. -D. Nowak, G. Nukazuka, A. G. Olshevsky, M. Ostrick, D. Panzieri, B. Parsamyan, S. P aul, H. Pekeler, J. -C. Peng, M. PeÅ¡ek, D. V. Peshekhonov, M. PeÅ¡ková, S. Platchkov, J. Pochodzalla, V. A. Polyakov, P. Pucci, C. Quintans, G. Reicherz, C. Riedl, D. I. Ryabchikov, A. Rychter, A. Rymbekova, V. D. Samoylenko, A. S andacz, S. Sarkar, I. A. Savin, G. Sbrizzai, H. Schmieden, A. Selyunin, S. Seriubin, L. Sinha, D. Spülbeck, A. Srnka, M. Stolarski, M. Sulc, H. Suzuki, S. Tessaro, F. Tessarotto, A. Thiel, F. Tosello, A. Townsend, V. Tskhay, B. Valino ti, B. M. Veit, J. F. C. A. Veloso, A. Vijayakumar, M. Virius, M. Wagner, S. Wallner, K. Zaremba, M. Zavertyaev, M. Zemko, E. Zemlyanichkina, M. Ziembicki
|
2512.14487
|
50 years of Yukhnovskii's critical point theory: its place in the constant flow of theoretical physics
|
Half a century ago, Ihor Yukhnovskii elaborated a method of studying the critical point of the three-dimensional Ising model based on a layer-by-layer integration in the space of collective variables. His method was an alternative to that based on the $\varepsilon$-expansion for which K. G. Wilson was awarded the Nobel Prize in Physics in 1982. However, Yukhnovskii's technique, which yielded similar results, provided even deeper insight into the nature of this phenomenon. At that time, we, professor's students, saw only this aspect of his theory. Later, I realized that the mentioned Yukhnovskii's work naturally fits into a more general context of the turbulent development of quantum field theory and statistical physics in the last quarter of the twentieth century. The aim of the present article is to look at the main aspects and the impact of Yukhnovskii's theory from this perspective.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.stat-mech"
] |
Yu. Kozitsky
|
2512.22014
|
HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness
|
Robustness in complex systems is of significant engineering and economic importance. However, conventional attack-based a posteriori robustness assessments incur prohibitive computational overhead. Recently, deep learning methods, such as Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs), have been widely employed as surrogates for rapid robustness prediction. Nevertheless, these methods neglect the complex higher-order correlations prevalent in real-world systems, which are naturally modeled as hypergraphs. Although Hypergraph Neural Networks (HGNNs) have been widely adopted for hypergraph learning, their topological expressive power has not yet reached the theoretical upper bound. To address this limitation, inspired by Graph Isomorphism Networks, this paper proposes a hypergraph-level Hypergraph Isomorphism Network framework. Theoretically, this approach is proven to possess an expressive power strictly equivalent to the Hypergraph Weisfeiler-Lehman test and is applied to predict hypergraph robustness. Experimental results demonstrate that while maintaining superior efficiency in training and prediction, the proposed method not only outperforms existing graph-based models but also significantly surpasses conventional HGNNs in tasks that prioritize topological structure representation.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG"
] |
Chengyu Tian, Wenbin Pei
|
2512.22338
|
Multi-Messenger Predictions for T Coronae Borealis: Probing Particle Acceleration in Novae
|
The MAGIC detection of near-TeV gamma-rays from the 2021 outburst of the recurrent nova RS Ophiuchi (RS Oph) has established it as a TeV-scale particle accelerator. However, the underlying production mechanism --\textit{hadronic} versus \textit{leptonic}-- remains uncertain due to the non-detection of coincident neutrinos at IceCube. Indeed, the neutrino flux predicted by the hadronic model for RS Oph was below IceCube sensitivity. T Coronae Borealis (T CrB), a nova similar to RS Oph, is anticipated to undergo an outburst soon. Being closer to Earth (0.8 kpc versus 2.45 kpc for RS Oph), T CrB is expected to yield a higher neutrino flux, making the upcoming outburst a once in a lifetime opportunity to test-and potentially detect-nova neutrinos. In this work, we present the first model-based estimates of the hadronic secondary fluxes from T CrB and assess their detectability with gamma-ray (LHAASO, Fermi-LAT, MAGIC, H.E.S.S., MACE, and HERD) and neutrino (IceCube and KM3NeT) telescopes. We adopt two proton-acceleration mechanisms: (i) an external shock (ES) driven mechanism at the interaction ($10^{13}$ cm) of nova ejecta and the red giant wind, and (ii) magnetic reconnection (MR) near the white dwarf surface ($10^{9}$ cm). The latter, arising deep inside the nova system, will fully absorb gamma-rays while allowing only neutrinos to escape. This could potentially produce neutrino signals hours before the ES origin photons or neutrinos-a unique temporal delay signature. For our benchmark ES model, gamma-rays are detectable across all facilities, while the neutrino detection prospect is poor. Only a tiny upper part of the ES model parameter space is above IceCube/KM3NeT sensitivity. In contrast, both observatories have significantly better prospects for detecting neutrinos in the MR scenario.
| 2025-12-26
| 2025-12-30
|
[
"astro-ph.HE",
"astro-ph.GA",
"hep-ph"
] |
Prantik Sarmah, Sovan Chakraborty, Xilu Wang
|
2512.22023
|
Fermionic domain-wall Skyrmions of QCD in a magnetic field
|
The ground state of low-energy QCD matter in strong magenetic fields is either a chiral soliton lattice (CSL), a periodic array of neutral pion domain walls (chiral solitons) perpendicular to the magnetic field, or domain-wall Skyrmion phase, in which Skyrmions are induced on top of the CSL. Previously found domain-wall Skyrmions are bosons with the baryon number two. In this paper, we show that the minimum domain-wall Skyrmions are fermions with the baryon number one; a bosonic domain-wall Skyrmion can be separated without cost of energy into two fermionic domain-wall Skyrmions attached on the opposite sides of a chiral soliton. The phase boundary between the CSL and domain-wall Skyrmion phases is unchanged. In the chiral limit, the CSL reduces to a linearly dependent neutral pion on the direction of the magnetic field, while fermionic domain-wall Skyrmions sit in an equal distance of a half period.
| 2025-12-26
| 2025-12-29
|
[
"hep-ph",
"hep-th",
"nucl-th"
] |
Patrick Copinger, Minoru Eto, Muneto Nitta, Zebin Qiu
|
2512.22353
|
Tableaux and orbit harmonics quotients for finite transformation monoids
|
We extend Grood's tableau construction of irreducible representations of the rook monoid and Steinberg's analogous result for the full transformation monoid. Our approach is characteristic-free and applies to any submonoid $\mathcal{M}(n)$ of the partial transformation monoid on an $n$-element set that contains the symmetric group. To achieve this, we introduce and study a functor from the category of rational representations of the monoid of $n \times n$ matrices to the category of finite dimensional representations of $\mathcal{M}(n)$. We establish two branching rules. Our main results describe graded module structures of orbit harmonics quotients for the rook, partial transformation, and full transformation monoids. This yields analogs of the Cauchy decomposition for polynomial rings in $n\times n$ variables.
| 2025-12-26
| 2025-12-30
|
[
"math.RT",
"math.CO"
] |
Mihalis Maliakas, Dimitra-Dionysia Stergiopoulou
|
2501.02214
|
Efficient estimation of average treatment effects with unmeasured confounding and proxies
|
Proximal causal inference provides a framework for estimating the average treatment effect (ATE) in the presence of unmeasured confounding by leveraging outcome and treatment proxies. Identification in this framework relies on the existence of a so-called bridge function. Standard approaches typically postulate a parametric specification for the bridge function, which is estimated in a first step and then plugged into an ATE estimator. However, this sequential procedure suffers from two potential sources of efficiency loss: (i) the difficulty of efficiently estimating a bridge function defined by an integral equation, and (ii) the failure to account for the correlation between the estimation steps. To overcome these limitations, we propose a novel approach that approximates the integral equation with increasing moment restrictions and jointly estimates the bridge function and the ATE. We show that, under suitable conditions, our estimator is efficient. Additionally, we provide a data-driven procedure for selecting the tuning parameter (i.e., the number of moment restrictions). Simulation studies reveal that the proposed method performs well in finite samples, and an application to the right heart catheterization dataset from the SUPPORT study demonstrates its practical value.
| 2025-12-26
| 2025-12-29
|
[
"stat.ME",
"econ.EM"
] |
Chunrong Ai, Jiawei Shan
|
2502.12765
|
Approximation results for weak solutions of stochastic partial differential equations
|
In probability theory, how to approximate the solution of a stochastic differential equation is an important topic. In Watanabe's classical textbook, by an approximation of the Wiener process, solutions of approximated equations converge to the solution of the stochastic differential equation in probability. In traditional approximation theorems, solutions do not contain the spatial variable. In recent years, stochastic partial differential equations have been playing major roles in probability theory. If the solution is a weak one with the spatial variable, we may not be able to directly apply these classical approximation results. In this work, we try to extend the approximation result to stochastic partial differential equations case. We show that in this case, the approximation result still holds.
| 2025-12-26
| 2025-12-29
|
[
"math.PR",
"math.AP"
] |
Xi Lin
|
2512.21898
|
Flexible Multitask Learning with Factorized Diffusion Policy
|
Multitask learning poses significant challenges due to the highly multimodal and diverse nature of robot action distributions. However, effectively fitting policies to these complex task distributions is often difficult, and existing monolithic models often underfit the action distribution and lack the flexibility required for efficient adaptation. We introduce a novel modular diffusion policy framework that factorizes complex action distributions into a composition of specialized diffusion models, each capturing a distinct sub-mode of the behavior space for a more effective overall policy. In addition, this modular structure enables flexible policy adaptation to new tasks by adding or fine-tuning components, which inherently mitigates catastrophic forgetting. Empirically, across both simulation and real-world robotic manipulation settings, we illustrate how our method consistently outperforms strong modular and monolithic baselines.
| 2025-12-26
| 2025-12-29
|
[
"cs.RO",
"cs.AI"
] |
Chaoqi Liu, Haonan Chen, Sigmund H. Høeg, Shaoxiong Yao, Yunzhu Li, Kris Hauser, Yilun Du
|
2512.22336
|
Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback
|
Symbolic world models (e.g., PDDL domains or executable simulators) are central to model-based planning, but training LLMs to generate such world models is limited by the lack of large-scale verifiable supervision. Current approaches rely primarily on static validation methods that fail to catch behavior-level errors arising from interactive execution. In this paper, we propose Agent2World, a tool-augmented multi-agent framework that achieves strong inference-time world-model generation and also serves as a data engine for supervised fine-tuning, by grounding generation in multi-agent feedback. Agent2World follows a three-stage pipeline: (i) A Deep Researcher agent performs knowledge synthesis by web searching to address specification gaps; (ii) A Model Developer agent implements executable world models; And (iii) a specialized Testing Team conducts adaptive unit testing and simulation-based validation. Agent2World demonstrates superior inference-time performance across three benchmarks spanning both Planning Domain Definition Language (PDDL) and executable code representations, achieving consistent state-of-the-art results. Beyond inference, Testing Team serves as an interactive environment for the Model Developer, providing behavior-aware adaptive feedback that yields multi-turn training trajectories. The model fine-tuned on these trajectories substantially improves world-model generation, yielding an average relative gain of 30.95% over the same model before training. Project page: https://agent2world.github.io.
| 2025-12-26
| 2025-12-30
|
[
"cs.AI",
"cs.CL"
] |
Mengkang Hu, Bowei Xia, Yuran Wu, Ailing Yu, Yude Zou, Qiguang Chen, Shijian Wang, Jiarui Jin, Kexin Li, Wenxiang Jiao, Yuan Lu, Ping Luo
|
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
|
2509.05723
|
Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy
|
LiDAR-Inertial Odometry (LIO) is a foundational technique for autonomous systems, yet its deployment on resource-constrained platforms remains challenging due to computational and memory limitations. We propose Super-LIO, a robust LIO system that demands both high performance and accuracy, ideal for applications such as aerial robots and mobile autonomous systems. At the core of Super-LIO is a compact octo-voxel-based map structure, termed OctVox, that limits each voxel to eight fused subvoxels, enabling strict point density control and incremental denoising during map updates. This design enables a simple yet efficient and accurate map structure, which can be easily integrated into existing LIO frameworks. Additionally, Super-LIO designs a heuristic-guided KNN strategy (HKNN) that accelerates the correspondence search by leveraging spatial locality, further reducing runtime overhead. We evaluated the proposed system using four publicly available datasets and several self-collected datasets, totaling more than 30 sequences. Extensive testing on both X86 and ARM platforms confirms that Super-LIO offers superior efficiency and robustness, while maintaining competitive accuracy. Super-LIO processes each frame approximately 73% faster than SOTA, while consuming less CPU resources. The system is fully open-source and plug-and-play compatible with a wide range of LiDAR sensors and platforms. The implementation is available at: https://github.com/Liansheng-Wang/Super-LIO.git
| 2025-12-26
| 2025-12-29
|
[
"cs.RO"
] |
Liansheng Wang, Xinke Zhang, Chenhui Li, Dongjiao He, Yihan Pan, Jianjun Yi
|
2512.22050
|
Non-reciprocal circular dichroism of ferro-rotational phonons in MnTiO$_{3}$
|
X-ray circular dichroism (XCD), defined as the difference in absorption or scattering intensity between X-rays of opposite polarizations, arises from the breaking of spatial inversion or time-reversal symmetry and is thus sensitive to chirality, magnetism, and their interplay. Non-reciprocal XCD, in which the dichroic response changes upon reversing the propagation direction of the probe, is generally forbidden in systems with both symmetries. Using resonant inelastic X-ray scattering, we identify circularly polarized phonons in ferro-rotational MnTiO$_3$, which we term ferro-rotational phonons. Their excitations provide a direct demonstration of non-reciprocal XCD in a system that globally preserves inversion and time-reversal symmetries. We propose that a condensate of these phonons, manifested as standing waves, underlies the ferro-rotational order in MnTiO$_3$. The interplay among photon helicity, phonon polarization, and the axial ferro-rotational order gives rise to the observed non-reciprocal circular dichroism.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.str-el",
"cond-mat.mtrl-sci"
] |
H. Y. Huang, G. Channagowdra, D. Banerjee, E. V. Komleva, J. Okamoto, C. T. Chen, M. Guennou, S. Johnston, S. V. Streltsov, C. Y. Mou, A. Fujimori, S-W. Cheong, D. J. Huang
|
2406.06804
|
Robustness to missing data: breakdown point analysis
|
Missing data is pervasive in econometric applications, and rarely is it plausible that the data are missing (completely) at random. This paper proposes a methodology for studying the robustness of results drawn from incomplete datasets. Selection is measured as the divergence from the distribution of complete observations to the distribution of incomplete observations. The breakdown point is defined as the minimal amount of selection needed to overturn a given result. Reporting point estimates and lower confidence intervals of the breakdown point is a simple, concise way to communicate the robustness of a result. An estimator of the breakdown point is proposed and shown root-n consistent and asymptotically normal. This estimator can be applied directly to conclusions drawn from any model identified with the generalized method of moments (GMM) that satisfies mild assumptions. Simulations demonstrate the finite sample performance of the breakdown point estimator on averages, linear regression, and logistic regression. The methodology is illustrated by estimating the breakdown point of conclusions drawn from several randomized controlled trails suffering from missing data due to attrition.
| 2025-12-26
| 2025-12-29
|
[
"econ.EM"
] |
Daniel Ober-Reynolds
|
2405.05512
|
Characteristic Learning for Provable One Step Generation
|
We propose the characteristic generator, a novel one-step generative model that combines the efficiency of sampling in Generative Adversarial Networks (GANs) with the stable performance of flow-based models. Our model is driven by characteristics, along which the probability density transport can be described by ordinary differential equations (ODEs). Specifically, we first estimate the underlying velocity field and use the Euler method to solve the probability flow ODE, generating discrete approximations of the characteristics. A deep neural network is then trained to fit these characteristics, creating a one-step map that pushes a simple Gaussian distribution to the target distribution. In the theoretical aspect, we provide a comprehensive analysis of the errors arising from velocity matching, Euler discretization, and characteristic fitting to establish a non-asymptotic convergence rate in the 2-Wasserstein distance under mild data assumptions. Crucially, we demonstrate that under a standard manifold assumption, this convergence rate depends only on the intrinsic dimension of data rather than the much larger ambient dimension, proving our model's ability to mitigate the curse of dimensionality. To our knowledge, this is the first rigorous convergence analysis for a flow-based one-step generative model. Experiments on both synthetic and real-world datasets demonstrate that the characteristic generator achieves high-quality and high-resolution sample generation with the efficiency of just a single neural network evaluation.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA",
"math.ST",
"stat.TH"
] |
Zhao Ding, Chenguang Duan, Yuling Jiao, Ruoxuan Li, Jerry Zhijian Yang, Pingwen 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
|
2512.21932
|
Volumes of foliations birationally bounded by algebraically integrable families
|
We prove that for log canonical foliations which are birationally bounded by algebraically integrable families, the set of their volumes satisfies the DCC. This answers a special case of a question posed by Cascini, Hacon, and Langer. As a key ingredient, we establish the deformation invariance of relative log canonical volumes for a family of weak semistable morphisms, which can be viewed as a relative version of the classical result proved by Hacon, McKernan, and Xu.
| 2025-12-26
| 2025-12-29
|
[
"math.AG"
] |
Zhixiu Fan
|
2512.23746
|
DEFT: Differentiable Automatic Test Pattern Generation
|
Modern IC complexity drives test pattern growth, with the majority of patterns targeting a small set of hard-to-detect (HTD) faults. This motivates new ATPG algorithms to improve test effectiveness specifically for HTD faults. This paper presents DEFT (Differentiable Automatic Test Pattern Generation), a new ATPG approach that reformulates the discrete ATPG problem as a continuous optimization task. DEFT introduces a mathematically grounded reparameterization that aligns the expected continuous objective with discrete fault-detection semantics, enabling reliable gradient-based pattern generation. To ensure scalability and stability on deep circuit graphs, DEFT integrates a custom CUDA kernel for efficient forward-backward propagation and applies gradient normalization to mitigate vanishing gradients. Compared to a leading commercial tool on two industrial benchmarks, DEFT improves HTD fault detection by 21.1% and 48.9% on average under the same pattern budget and comparable runtime. DEFT also supports practical ATPG settings such as partial assignment pattern generation, producing patterns with 19.3% fewer 0/1 bits while still detecting 35% more faults. These results indicate DEFT is a promising and effective ATPG engine, offering a valuable complement to existing heuristic.
| 2025-12-26
| 2026-01-01
|
[
"cs.SE"
] |
Wei Li, Yan Zou, Yixin Liang, José Moura, Shawn Blanton
|
2512.22069
|
Scaling Adversarial Training via Data Selection
|
Projected Gradient Descent (PGD) is a strong and widely used first-order adversarial attack, yet its computational cost scales poorly, as all training samples undergo identical iterative inner-loop optimization despite contributing unequally to robustness. Motivated by this inefficiency, we propose \emph{Selective Adversarial Training}, which perturbs only a subset of critical samples in each minibatch. Specifically, we introduce two principled selection criteria: (1) margin-based sampling, which prioritizes samples near the decision boundary, and (2) gradient-matching sampling, which selects samples whose gradients align with the dominant batch optimization direction. Adversarial examples are generated only for the selected subset, while the remaining samples are trained cleanly using a mixed objective. Experiments on MNIST and CIFAR-10 show that the proposed methods achieve robustness comparable to, or even exceeding, full PGD adversarial training, while reducing adversarial computation by up to $50\%$, demonstrating that informed sample selection is sufficient for scalable adversarial robustness.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG"
] |
Youran Ye, Dejin Wang, Ajinkya Bhandare
|
2512.22339
|
Gravitational waves from seesaw assisted collapsing domain walls
|
Spontaneous breaking of discrete symmetries like $Z_2$ leads to the formation of stable topological defects such as domain walls which, if allowed to dominate, can potentially be in conflict with cosmological observations. Incorporating explicit $Z_2$-breaking bias terms can lead to annihilation of such walls while also emitting stochastic gravitational wave (GW). We study the role of heavy right-handed neutrinos present in type-I seesaw origin of light neutrino masses to generate such bias term via quantum corrections. This offers interesting correlation among the seesaw scale, GW peak amplitude and peak frequency which can be probed at present and future experiments related to GW as well as precision measurements of the cosmic microwave background (CMB). In flavor symmetric UV complete scenarios with degenerate RHNs at leading order, such tiny coupling of RHNs to a $Z_2$-odd scalar can also lead to small mass splittings suitable for explaining the observed baryon asymmetry of the universe via resonant leptogenesis.
| 2025-12-26
| 2025-12-30
|
[
"hep-ph",
"astro-ph.CO"
] |
Debasish Borah, Indrajit Saha
|
2512.22074
|
Semiperfect rings with a Nakayama permutation: A survey of Double annihilator property and Size condition
|
For a semiperfect ring with essential socles, the Double annihilator property encodes that the top and socle have anti-isomorphic lattices of submodules, whereas the Size condition encodes that they are isomorphic as modules. Interest in both concepts, particularly for finite rings, was revived by coding theory, where they characterise QF rings and Frobenius rings, respectively. However, their shared origins date back to the work of T. Nakayama.
We study these concepts through the lens of the Nakayama permutation, an invariant initially used to define (quasi-)Frobenius rings. We propose semiperfect rings as the setting for this study, treating them as the natural generalisation of finite rings, because they possess the characteristic decomposition of unity preserved by projection onto a semisimple top. This allows us to extend the utility of the Nakayama permutation beyond the classical Artinian setting.
By analysing the Nakayama permutation in this broader context, we show that many classical properties of (quasi-)Frobenius rings are not exclusive to the finite case, but are special cases of the general behaviour of semiperfect rings with essential socles.
We illustrate these results using B. J. Müller's representation of semiperfect rings as rings of formal matrices. The clear description of socles and tops in this setting provides a straightforward method for constructing counterexamples, such as quasi-Frobenius rings that are not Frobenius.
| 2025-12-26
| 2025-12-29
|
[
"math.RA"
] |
Dominik Krasula
|
2510.02781
|
GCVAMD: A Modified CausalVAE Model for Causal Age-related Macular Degeneration Risk Factor Detection and Prediction
|
Age Related Macular Degeneration(AMD) has been one of the most leading causes of permanent vision impairment in ophthalmology. Though treatments, such as anti VEGF drugs or photodynamic therapies, were developed to slow down the degenerative process of AMD, there is still no specific cure to reverse vision loss caused by AMD. Thus, for AMD, detecting existence of risk factors of AMD or AMD itself within the patient retina in early stages is a crucial task to reduce the possibility of vision impairment. Apart from traditional approaches, deep learning based methods, especially attention mechanism based CNNs and GradCAM based XAI analysis on OCT scans, exhibited successful performance in distinguishing AMD retina from normal retinas, making it possible to use AI driven models to aid medical diagnosis and analysis by ophthalmologists regarding AMD. However, though having significant success, previous works mostly focused on prediction performance itself, not pathologies or underlying causal mechanisms of AMD, which can prohibit intervention analysis on specific factors or even lead to less reliable decisions. Thus, this paper introduces a novel causal AMD analysis model: GCVAMD, which incorporates a modified CausalVAE approach that can extract latent causal factors from only raw OCT images. By considering causality in AMD detection, GCVAMD enables causal inference such as treatment simulation or intervention analysis regarding major risk factors: drusen and neovascularization, while returning informative latent causal features that can enhance downstream tasks. Results show that through GCVAMD, drusen status and neovascularization status can be identified with AMD causal mechanisms in GCVAMD latent spaces, which can in turn be used for various tasks from AMD detection(classification) to intervention analysis.
| 2025-12-26
| 2025-12-29
|
[
"eess.IV",
"cs.CV"
] |
Daeyoung Kim
|
2508.05863
|
Bus Fleet Electrification Planning Through Logic-Based Benders Decomposition and Restriction Heuristics
|
To meet sustainability goals and regulatory requirements, transit agencies worldwide are planning partial and full transitions to electric bus fleets. This paper presents a comprehensive and computationally efficient multi-period optimization framework integrating the key decisions required to support such electrification initiatives. Our model is formulated as a two-stage integer program with integer subproblems. These two levels optimize, respectively, yearly fleet sizing and charging infrastructure investments, and hourly vehicle scheduling and charging operations. We develop an exact logic-based Benders decomposition algorithm enhanced by several acceleration techniques, including preprocessing, master problem strengthening, and efficient cut separation techniques applied to different relaxations of the operational problem. These accelerations achieve speedups of three orders of magnitude relative to a recently published logic-based Benders decomposition and provide new theoretical and practical insights into Benders cut selection. We also propose a heuristic tailored for long-term, citywide electrification planning. This approach imposes and progressively relaxes additional scheduling constraints derived from auxiliary problems. It delivers high-quality solutions with optimality gaps below 1% for instances an order of magnitude larger than those considered in prior work. We illustrate our model using real data from the Chicago public bus system, providing managerial insights into optimal investment and operational policies.
| 2025-12-26
| 2025-12-29
|
[
"math.OC"
] |
Robin Legault, Filipe Cabral, Xu Andy Sun
|
2112.05255
|
The dual approach to the $K(Ï, 1)$ conjecture
|
Dual presentations of Coxeter groups have recently led to breakthroughs in our understanding of affine Artin groups. In particular, they led to the proof of the $K(Ï, 1)$ conjecture and to the solution of the word problem. Will the "dual approach" extend to more general classes of Coxeter and Artin groups? In this paper, we describe the techniques used to prove the $K(Ï, 1)$ conjecture for affine Artin groups and we ask a series of questions that are mostly open beyond the spherical and affine cases.
| 2025-12-26
| 2025-12-30
|
[
"math.GR",
"math.AT",
"math.CO"
] |
Giovanni Paolini
|
2512.22305
|
PDx -- Adaptive Credit Risk Forecasting Model in Digital Lending using Machine Learning Operations
|
This paper presents PDx, an adaptive, machine learning operations (MLOps) driven decision system for forecasting credit risk using probability of default (PD) modeling in digital lending. While conventional PD models prioritize predictive accuracy during model development with complex machine learning algorithms, they often overlook continuous adaptation to changing borrower behaviour, resulting in static models that degrade over time in production and generate inaccurate default predictions. Many financial institutes also find it difficult transitioning ML models from development environment to production and maintaining their health. With PDx we aimed to addresses these limitations using a dynamic, end-to-end model lifecycle management approach that integrates continuous model monitoring, retraining, and validation through a robust MLOps pipeline. We introduced a dynamic champion-challenger framework for PDx to regularly update baseline models to recalibrate independent parameters with the latest data and select the best-performing model through out-of-time validation, ensuring resilience against data drift and changing credit risk patterns. Our empirical analysis shows that decision tree-based ensemble models consistently outperform others in classifying defaulters but require frequent updates to sustain performance. Linear models (e.g., logistic regression) and neural networks exhibit greater performance degradation. The study demonstrate with PDx we can mitigates value erosion for digital lenders, particularly in short-term, small-ticket loans, where borrower behavior shifts rapidly. We have validated the effectiveness of PDx using datasets from peer-to-peer lending, business loans, and auto loans, demonstrating its scalability and adaptability for modern credit risk forecasting.
| 2025-12-26
| 2025-12-30
|
[
"cs.LG"
] |
Sultan Amed, Chan Yu Hang, Sayantan Banerjee
|
2512.22112
|
The Lepton-Gluon Portal Beyond Lepto-Gluons
|
We explore models where single new exotic states interact with the Standard Model through an asymmetric Standard Model portal with couplings to at least one gluon and one lepton. We consider the complete set of effective operators up to dimension 6, and examine a few additional dimension 7 operators that contain interesting field content or potential collider signals. The lepton-gluon portal allows access to exotic states with an interesting range of SU(3) and SU(2) quantum numbers. Finally, we explore potential single-production modes and their phenomenological signatures at colliders.
| 2025-12-26
| 2025-12-29
|
[
"hep-ph"
] |
Linda M. Carpenter, Katherine Schwind
|
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
|
2403.17679
|
Shape Optimization of Geometrically Nonlinear Modal Coupling Coefficients: An Application to MEMS Gyroscopes
|
Micro- and nanoelectromechanical system (MEMS and NEMS) resonators can exhibit rich nonlinear dynamics as they are often operated at large amplitudes with high quality factors and possess a high mode density with a variety of nonlinear modal couplings. Their impact is strongly influenced by internal resonance conditions and by the strength of the modal coupling coefficients. On one hand, strong nonlinear couplings are of academic interest and promise novel device concepts. On the other hand, however, they have the potential to disturb the linear system behavior on which industrial devices such as gyroscopes and micro mirrors are based on. In either case, being able to optimize the coupling coefficients by design is certainly beneficial. A main source of nonlinear modal couplings are geometric nonlinearities. In this work, we apply node-based shape optimization to tune the geometrically nonlinear 3-wave coupling coefficients of a MEMS gyroscope. We demonstrate that individual coupling coefficients can be tuned over several orders of magnitude by shape optimization, while satisfying typical constraints on manufacturability and operability of the devices. The optimized designs contain unintuitive geometrical features far away from any solution an experienced human MEMS or NEMS designer could have thought of. Thus, this work demonstrates the power of shape optimization for tailoring the complex nonlinear dynamic properties of MEMS and NEMS resonators.
| 2025-12-26
| 2025-12-29
|
[
"cs.CE"
] |
Daniel Schiwietz, Marian Hörsting, Eva Maria Weig, Matthias Wenzel, Peter Degenfeld-Schonburg
|
2512.21837
|
Knowledge Reasoning of Large Language Models Integrating Graph-Structured Information for Pest and Disease Control in Tobacco
|
This paper proposes a large language model (LLM) approach that integrates graph-structured information for knowledge reasoning in tobacco pest and disease control. Built upon the GraphRAG framework, the proposed method enhances knowledge retrieval and reasoning by explicitly incorporating structured information from a domain-specific knowledge graph. Specifically, LLMs are first leveraged to assist in the construction of a tobacco pest and disease knowledge graph, which organizes key entities such as diseases, symptoms, control methods, and their relationships. Based on this graph, relevant knowledge is retrieved and integrated into the reasoning process to support accurate answer generation. The Transformer architecture is adopted as the core inference model, while a graph neural network (GNN) is employed to learn expressive node representations that capture both local and global relational information within the knowledge graph. A ChatGLM-based model serves as the backbone LLM and is fine-tuned using LoRA to achieve parameter-efficient adaptation. Extensive experimental results demonstrate that the proposed approach consistently outperforms baseline methods across multiple evaluation metrics, significantly improving both the accuracy and depth of reasoning, particularly in complex multi-hop and comparative reasoning scenarios.
| 2025-12-26
| 2025-12-29
|
[
"cs.CL"
] |
Siyu Li, Chenwei Song, Wan Zhou, Xinyi Liu
|
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
|
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
|
2512.22012
|
Generalized binomial edge ideals are Cartwright-Sturmfels
|
Binomial edge ideals associated to a simple graph G were introduced by Herzog and collaborators and, independently, by Ohtani. They became an ``instant classic" in combinatorial commutative algebra with more than 100 papers devoted to their investigation over the past 15 years. They exhibit many striking properties, including being radical and, moreover, Cartwright-Sturmfels. Using the fact that binomial edge ideals can be seen as ideals of 2-minors of a matrix of variables with two rows, generalized binomial edge ideals of 2-minors of matrices of m rows were introduced by Rauh and proved to be radical. The goal of this paper is to prove that generalized binomial edge ideals are Cartwright-Sturmfels. On the way we provide results on ideal constructions preserving the Cartwright-Sturmfels property. We also give examples and counterexamples to the Cartwright-Sturmfels property for higher minors.
| 2025-12-26
| 2025-12-29
|
[
"math.AC",
"math.CO"
] |
Aldo Conca, Emanuela De Negri, Volkmar Welker
|
2512.21909
|
A novel implementation of CCSD analytic gradients using Cholesky decomposition of the two-electron integrals and Abelian point-group symmetry
|
We present a novel and efficient implementation of coupled-cluster with singles and doubles (CCSD) analytic gradients that combines the Cholesky decomposition (CD) of electron-repulsion integrals with the exploitation of Abelian point-group symmetry. This approach is particularly effective for medium-sized and large symmetric molecular systems. The CD of two-electron integrals is performed using a symmetry-adapted two-step algorithm, while the derivatives of the Cholesky vectors are computed with respect to symmetry-adapted nuclear displacements and contracted on-the-fly with the CCSD density matrices. Geometry optimizations of symmetric systems with several hundreds of basis functions have been carried out to assess the efficiency of our implementation and to quantify the computational gain provided by the exploitation of point-group symmetry.
| 2025-12-26
| 2025-12-29
|
[
"physics.chem-ph"
] |
Luca Melega, Tommaso Nottoli, Jürgen Gauss, Filippo Lipparini
|
2502.08530
|
Residually Dominated Groups in Henselian Valued Fields of Equicharacteristic Zero
|
We introduce \emph{residually dominated groups} in pure henselian valued fields of equicharacteristic zero, as an analogue of stably dominated groups introduced by Hrushovski and Rideau-Kikuchi. We show that when $G$ is a residually dominated group, there is a finite-to-one group homomorphism from its connected component into a connected stably dominated group, and we study the functoriality and universality properties of this map. Moreover, we prove that residual domination is witnessed by a group homomorphism into a definable group in the residue field. In our proofs, we use the results of Montenegro, Onshuus, and Simon on groups definable in $\mathrm{NTP}_2$-theories that extend the theory of fields. Along the way, we also provide an algebraic characterization of residually dominated types, generalizing the work by Ealy, Haskell and Simon for stably dominated types in algebraically closed valued fields, and we study their properties.
| 2025-12-26
| 2025-12-29
|
[
"math.LO"
] |
Dicle Mutlu, Paul Z. Wang
|
2512.22114
|
(De)constructing Continuous Gauge Symmetries
|
A $(d+1)$-dimensional field theory with a periodic spatial dimension may be approximated by a $d$-dimensional theory with a truncated Kaluza-Klein tower of $k$ fields; as $k\to\infty$, one recovers the original $(d+1)$-dimensional theory. One may similarly expect that $\operatorname U(1)$-valued Maxwell theory may be approximated by $\mathbb Z_k$-valued gauge theory and that, as $k\to\infty$, one recovers the original Maxwell theory. However, this fails: the $k\to\infty$ limit of $\mathbb Z_k$-valued gauge theory is flat Maxwell theory with no local degrees of freedom. We instead construct field theories $\mathcal T_k$ such that, with appropriate matter couplings, the $k\to\infty$ limit does recover Maxwell theory in the absence of magnetic monopoles (but with possible Wilson loops), and show that $\mathcal T_k$ can be understood as Maxwell theory with the insertion of a certain nonlocal operator that projects out principal $\operatorname U(1)$-bundles that do not arise from principal $\mathbb Z_k$-bundles sectors (in particular, projecting out sectors with monopole charges).
| 2025-12-26
| 2025-12-29
|
[
"hep-th",
"math-ph",
"math.MP"
] |
Leron Borsten, Hyungrok Kim
|
2512.22070
|
Next-to-leading order QCD corrections to electromagnetic production and decay of fully charm tetraquarks
|
We investigate the electromagnetic properties of the fully charm tetraquark states, particularly incorporating contributions from internal gluon radiations. The paper first presents analytical expressions for the next-to-leading-order (NLO) QCD corrections to the decay amplitudes of fully charm tetraquarks into two photons. It is found that the QCD corrections are significant for the $J^{PC}=0^{++}$ fully charm tetraquark decay process, whereas they are relatively small for the $J^{PC}=2^{++}$ fully charm tetraquark decay process. Subsequently, by considering photon-photon fusion in ultra-peripheral high-energy collisions of protons and nuclei and in electron-positron collision processes, we provide theoretical predictions for the production cross sections of fully-charm tetraquark states. The results presented in this work regarding the electromagnetic production and decay of fully charm tetraquarks shall be tested in current and future experiments.
| 2025-12-26
| 2025-12-29
|
[
"hep-ph",
"hep-ex",
"hep-lat"
] |
Xinran Liu, Yefan Wang, Ruilin Zhu
|
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
|
2512.21915
|
Exploring the Heterogeneity of Tabular Data: A Diversity-aware Data Generator via LLMs
|
Tabular data generation has become increasingly essential for enabling robust machine learning applications, which require large-scale, high-quality data. Existing solutions leverage generative models to learn original data distributions. However, real-world data are naturally heterogeneous with diverse distributions, making it challenging to obtain a universally good model for diverse data generation. To address this limitation, we introduce Diversity-Aware Tabular data gEnerator (DATE), a framework that (i) prepares high-quality and distributionally distinct examples for in-context learning by effectively partitioning the original heterogeneous data into multiple diverse subsets; (ii) harnesses Large Language Models (LLMs) to explore the diversity of the partitioned distribution with decision tree reasoning as feedback, generating high-quality labeled data for each subset. However, the massive generated data inherently involves a trade-off between diversity and quality. To integrate this issue, existing solutions greedily select the validation-best data. However, we prove that the selection in heterogeneous settings does not possess the greedy-choice property, and design a Multi-Arm Bandit-based sampling algorithm that balances the diversity and quality of generated data. Extensive experiments on tabular classification and regression benchmarks demonstrate that DATE consistently outperforms state-of-the-art GAN-based and LLM-based methods. On average, DATE achieves a 23.75% reduction in error rate with just 100 generated data. Empirically, we demonstrate that data generated by DATE can improve the accuracy of Direct Preference Optimization (DPO) and enhance the reasoning capability of LLMs on the target data. Code is available at https://github.com/windblow32/DATE.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG",
"cs.DB"
] |
Yafeng Tang, Xiaoou Ding, Jianzhuo Du, Zishuo Yan, Zhuang Ma, Zheng Liang, Zekai Qian, Hongzhi Wang
|
2512.21902
|
Explainable Statute Prediction via Attention-based Model and LLM Prompting
|
In this paper, we explore the problem of automatic statute prediction where for a given case description, a subset of relevant statutes are to be predicted. Here, the term "statute" refers to a section, a sub-section, or an article of any specific Act. Addressing this problem would be useful in several applications such as AI-assistant for lawyers and legal question answering system. For better user acceptance of such Legal AI systems, we believe the predictions should also be accompanied by human understandable explanations. We propose two techniques for addressing this problem of statute prediction with explanations -- (i) AoS (Attention-over-Sentences) which uses attention over sentences in a case description to predict statutes relevant for it and (ii) LLMPrompt which prompts an LLM to predict as well as explain relevance of a certain statute. AoS uses smaller language models, specifically sentence transformers and is trained in a supervised manner whereas LLMPrompt uses larger language models in a zero-shot manner and explores both standard as well as Chain-of-Thought (CoT) prompting techniques. Both these models produce explanations for their predictions in human understandable forms. We compare statute prediction performance of both the proposed techniques with each other as well as with a set of competent baselines, across two popular datasets. Also, we evaluate the quality of the generated explanations through an automated counter-factual manner as well as through human evaluation.
| 2025-12-26
| 2025-12-29
|
[
"cs.CL"
] |
Sachin Pawar, Girish Keshav Palshikar, Anindita Sinha Banerjee, Nitin Ramrakhiyani, Basit Ali
|
2512.21997
|
Sample volume as a key design parameter in affinity-based biosensors
|
Affinity-based biosensors have become indispensable in modern diagnostics and health monitoring. While considerable research has focused on optimizing analyte transport and binding kinetics, a fundamental parameter - sample volume - remains largely underexplored in biosensor design. This is critical because biosensor performance depends on the absolute number of target molecules present, not solely their concentration, making volume a key consideration where sample availability is limited. To address this gap, we developed a tractable two-compartment mathematical model integrating simplified mass transport, Langmuir binding kinetics, and mass conservation under finite volume constraints. Validated against experimental measurements and numerical simulations, the model accurately predicts critical performance metrics including assay time and minimum required sample volume while achieving more than a 10,000-fold reduction in computational time compared to commercial simulation packages. Through systematic analysis, we derived quantitative design rules for biosensor optimization that explicitly account for measurement time and sample volume as primary decision variables. We validated this framework experimentally by optimizing flow rate parameters for a quartz crystal microbalance (QCM) biosensor and retrospectively applied it to enhance sensitivity of published biosensor designs. Released as open-source software, our model enables researchers to gain mechanistic insights, optimize device performance, and make informed design decisions tailored to specific healthcare contexts, including point-of-care testing and resource-constrained environments.
| 2025-12-26
| 2025-12-29
|
[
"q-bio.QM"
] |
Daan Beijersbergen, Jérôme Charmet
|
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
|
2512.22048
|
Classification and stability of black hole event horizon births: a contact geometry approach
|
A classical result by Penrose establishes that null geodesics generating a black hole event horizon can only intersect at their entrance to the horizon in ``crossover'' points. This points together with limit points of this set, namely caustics, form the so-called "crease set". Light rays enter into the horizon through the crease set, characterizing the latter as the birth of the horizon. A natural question in this context refers to the classification and stability of the structural possibilities of black hole crease sets. In this work we revisit the strategy adopted by Gadioux & Reall for such a classification in the setting of singularity theory in contact geometry. Specifically, in such contact geometry setting, the event horizon is identified as a component (not connected to null infinity) of a so-called ``BigFront''. The characterization of BigFronts as Legendrian projections of Legendrian submanifolds permits to classify the crease sets and ``cuspidal sets'' (or caustics in Penrose's terminology) by applying classical results established by V.I. Arnol'd. Here we refine the stability discussion presented by Gadioux & Reall of that connected component of the crease set that is not causally connected to null infinity and that constitutes the event horizon birth. In addition, we identify the existence of other components of the crease set that lie in the part of the BigFront that is causally connected to null infinity.
| 2025-12-26
| 2025-12-29
|
[
"gr-qc",
"math-ph",
"math.DG",
"math.MP"
] |
Oscar Meneses Rojas
|
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
|
2512.21811
|
A Story About Cohesion and Separation: Label-Free Metric for Log Parser Evaluation
|
Log parsing converts log messages into structured event templates, allowing for automated log analysis and reducing manual inspection effort. To select the most compatible parser for a specific system, multiple evaluation metrics are commonly used for performance comparisons. However, existing evaluation metrics heavily rely on labeled log data, which limits prior studies to a fixed set of datasets and hinders parser evaluations and selections in the industry. Further, we discovered that different versions of ground-truth used in existing studies can lead to inconsistent performance conclusions. Motivated by these challenges, we propose a novel label-free template-level metric, PMSS (parser medoid silhouette score), to evaluate log parser performance. PMSS evaluates both parser grouping and template quality with medoid silhouette analysis and Levenshtein distance within a near-linear time complexity in general. To understand its relationship with label-based template-level metrics, FGA and FTA, we compared their evaluation outcomes for six log parsers on the standard corrected Loghub 2.0 dataset. Our results indicate that log parsers achieving the highest PMSS or FGA exhibit comparable performance, differing by only 2.1% on average in terms of the FGA score; the difference is 9.8% for FTA. PMSS is also significantly (p<1e-8) and positively correlated to both FGA and FTA: the Spearman's rho correlation coefficient of PMSS-FGA and PMSS-FTA are respectively 0.648 and 0.587, close to the coefficient between FGA and FTA (0.670). We further extended our discussion on how to interpret the conclusions from different metrics, identifying challenges in using PMSS, and provided guidelines on conducting parser selections with our metric. PMSS provides a valuable evaluation alternative when ground-truths are inconsistent or labels are unavailable.
| 2025-12-26
| 2025-12-29
|
[
"cs.SE"
] |
Qiaolin Qin, Jianchen Zhao, Heng Li, Weiyi Shang, Ettore Merlo
|
2512.21938
|
Optimal Convergence Estimate of the Limit from Inverse Power Potential to Hard Sphere Boltzmann Equation
|
The inverse power potential $U(r)=r^{-1/s}, 0<s<1$, generates the Boltzmann kernel $B^{s}=|v-v_*|^{1-4s} b_s(θ)$ with an angular singularity as $θ\to 0$. Jang-Kepka-Nota-Velázquez (2023) proved the limit $B^{s}\to \frac14|v-v_*|$ as $s\to 0$, as well as weak convergence of solutions based on this kernel convergence. In this work we establish the following sharp quantitative estimate: $$ |b_s(θ)-\tfrac14| \le C\, s\,θ^{-2-2s}. $$ In particular, this sharp estimate yields the optimal $O(s)$ convergence rate for solutions of the homogeneous Boltzmann equation with large initial data in suitable Sobolev spaces; i.e., for any $t\in[0,T]$, we have $$f^s(t)=f^0(t)+O(s),$$ quantified by the $L^1_k$ norm for $k\ge 2.$
| 2025-12-26
| 2025-12-29
|
[
"math.AP"
] |
Zheng-Nan Hu, Jin Woo Jang, Zheng-An Yao, Yu-Long Zhou
|
2512.22073
|
Ferroelectricity in magnon Bose-Einstein condensate: non-reciprocal superfluidity, exceptional points and Majorana bosons
|
We investigate a ferroelectric instability of a magnon Bose-Einstein condensate, mediated by its interaction with an electric field through a geometric Aharonov-Casher (AC) phase. A distinct feature of the system is the positive feedback loop in which an electric field induces magnon orbital motion via the AC phase, generating electric polarization that in turn enhances the original field. Based on bosonic Bogoliubov-de Gennes (BdG) mean-field theory, we show that this feedback drives a spontaneous ferroelectric transition in the magnon superfluid, accompanied by a persistent magnon supercurrent. In the resulting ferroelectric phase, the quasiparticle excitation spectrum becomes nonreciprocal, reflecting spontaneous breaking of spatial inversion symmetry. At the critical point of the transition, the bosonic BdG Hamiltonian exhibits coalescence of both eigenvalues and eigenvectors, forming an exceptional point. The corresponding eigenvector is an equally weighted superposition of bosonic quasiparticle and quasihole states and is invariant under particle-hole transformation, allowing it to be interpreted as a bosonic analog of a Majorana fermion.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.mes-hall",
"cond-mat.quant-gas",
"cond-mat.supr-con"
] |
Kazuki Yamamoto, Takuto Kawakami, Mikito Koshino
|
2512.21907
|
SpatialBench: Can Agents Analyze Real-World Spatial Biology Data?
|
Spatial transcriptomics assays are rapidly increasing in scale and complexity, making computational analysis a major bottleneck in biological discovery. Although frontier AI agents have improved dramatically at software engineering and general data analysis, it remains unclear whether they can extract biological insight from messy, real-world spatial datasets. We introduce SpatialBench, a benchmark of 146 verifiable problems derived from practical spatial analysis workflows spanning five spatial technologies and seven task categories. Each problem provides a snapshot of experimental data immediately prior to an analysis step and a deterministic grader that evaluates recovery of a key biological result. Benchmark data on frontier models shows that base model accuracy remains low (20-38% across model families), with strong model-task and model-platform interactions. Harness design has a large empirical effect on performance, indicating that tools, prompts, control flow, and execution environment should be evaluated and improved as first-class objects. SpatialBench serves both as a measurement tool and a diagnostic lens for developing agents that can interact with real spatial datasets faithfully, transparently, and reproducibly.
| 2025-12-26
| 2025-12-29
|
[
"cs.AI"
] |
Kenny Workman, Zhen Yang, Harihara Muralidharan, Hannah Le
|
2512.22072
|
Rotationally invariant dynamical lattice regulators for Euclidean quantum field theories
|
We introduce a dynamical-lattice regulator (DLR) for Euclidean quantum field theories on a fixed hypercubic graph $Î\simeq \mathbb{Z}^d$, in which the embedding $x:Î\to \mathbb{R}^d$ is promoted to a dynamical field and integrated over subject to shape-regularity constraints. The total action is local on $Î$, gauge invariant, and depends on $x$ only through Euclidean invariants built from edge vectors (local metrics, volumes, etc.), hence the partition function is exactly covariant under the global Euclidean group SE(d) at any lattice spacing. The intended symmetry-restoring mechanism is not rigid global zero modes but short-range *local twisting* of the embedding that mixes local orientations; accordingly, our universality discussion is conditioned on a short-range geometry hypothesis (SR): after quotienting the global SE(d) modes, connected correlators of local geometric observables have correlation length O(1) in lattice units.
We prove Osterwalder-Schrader reflection positivity for the coupled system with embedding $x$ and generic gauge/matter fields $(U,Φ)$ in finite volume by treating $x$ as an additional multiplet of scalar fields on $Î$. Assuming (SR), integrating out $x$ at fixed cutoff yields a local Symanzik effective action in which geometry fluctuations generate only SO(d)-invariant irrelevant operators and finite renormalizations; in particular, in $d=4$ we recover the standard one-loop $β$-function in a scalar $Ï^4$ test theory. Finally, we describe a practical local Monte Carlo update and report $d=2$ proof-of-concept simulations showing a well-behaved geometry sector and a substantial reduction of axis-vs-diagonal cutoff artifacts relative to a fixed lattice at matched bare parameters.
| 2025-12-26
| 2025-12-29
|
[
"hep-lat",
"hep-th"
] |
Tsogtgerel Gantumur
|
2512.21235
|
RoboCade: Gamifying Robot Data Collection
|
Imitation learning from human demonstrations has become a dominant approach for training autonomous robot policies. However, collecting demonstration datasets is costly: it often requires access to robots and needs sustained effort in a tedious, long process. These factors limit the scale of data available for training policies. We aim to address this scalability challenge by involving a broader audience in a gamified data collection experience that is both accessible and motivating. Specifically, we develop a gamified remote teleoperation platform, RoboCade, to engage general users in collecting data that is beneficial for downstream policy training. To do this, we embed gamification strategies into the design of the system interface and data collection tasks. In the system interface, we include components such as visual feedback, sound effects, goal visualizations, progress bars, leaderboards, and badges. We additionally propose principles for constructing gamified tasks that have overlapping structure with useful downstream target tasks. We instantiate RoboCade on three manipulation tasks -- including spatial arrangement, scanning, and insertion. To illustrate the viability of gamified robot data collection, we collect a demonstration dataset through our platform, and show that co-training robot policies with this data can improve success rate on non-gamified target tasks (+16-56%). Further, we conduct a user study to validate that novice users find the gamified platform significantly more enjoyable than a standard non-gamified platform (+24%). These results highlight the promise of gamified data collection as a scalable, accessible, and engaging method for collecting demonstration data.
| 2025-12-26
| 2025-12-29
|
[
"cs.RO"
] |
Suvir Mirchandani, Mia Tang, Jiafei Duan, Jubayer Ibn Hamid, Michael Cho, Dorsa Sadigh
|
2512.22109
|
Index-Tracking Portfolio Construction and Rebalancing under Bayesian Sparse Modelling and Uncertainty Quantification
|
We study the construction and rebalancing of sparse index-tracking portfolios from an operational research perspective, with explicit emphasis on uncertainty quantification and implementability. The decision variables are portfolio weights constrained to sum to one; the aims are to track a reference index closely while controlling the number of names and the turnover induced by rebalancing. We cast index tracking as a high-dimensional linear regression of index returns on constituent returns, and employ a sparsity-inducing Laplace prior on the weights. A single global shrinkage parameter controls the trade-off between tracking error and sparsity, and is calibrated by an empirical-Bayes stochastic approximation scheme. Conditional on this calibration, we approximate the posterior distribution of the portfolio weights using proximal Langevin-type Markov chain Monte Carlo algorithms tailored to the budget constraint. This yields posterior uncertainty on tracking error, portfolio composition and prospective rebalancing moves. Building on these posterior samples, we propose rules for rebalancing that gate trades through magnitude-based thresholds and posterior activation probabilities, thereby trading off expected tracking error against turnover and portfolio size. A case study on tracking the S&P~500 index is carried out to showcase how our tools shape the decision process from portfolio construction to rebalancing.
| 2025-12-26
| 2025-12-29
|
[
"q-fin.CP",
"math.OC",
"q-fin.PM",
"stat.AP",
"stat.CO"
] |
Dimitrios Roxanas
|
2405.07375
|
Lie superalgebra invariants and almost classical knots
|
A virtual link is said to be almost classical (AC) if it has a homologically trivial representative in some thickened surface $Σ\times [0,1]$, where $Σ$ is a closed orientable surface. AC links provide a useful window for observing the geometric topology of virtual knots. Here we take a different approach and look at AC links through the lens of quantum topology. Two adjustments are needed to the existing theory. First, it is necessary to generalize the definition of AC to include virtual tangles and, in particular, virtual braids. Secondly, to distinguish AC and non-AC tangles, the additional structure of quantum supergroups is required. For each Lie superalgebra $\mathfrak{gl}(m|n)$, we define a pair of $U_q(\mathfrak{gl}(m|n))$ Reshetikhin-Turaev functors $Q^{m|n}$, $\widetilde{Q}^{m|n} \circ Zh$ on framed virtual tangles. Here $Zh$ denotes the Bar-Natan $Zh$ construction. These functors unify the Alexander polynomial (AP) of AC links and the generalized Alexander polynomial (GAP) of all virtual links into a single quantum model: $Q^{1|1}$ recovers the AP of an AC link and for any virtual link $K$, $\widetilde{Q}^{1|1}\circ Zh(K)$ is the 2-variable GAP. However, when $(m,n) \ne (1,1)$, these invariants are generally distinct from the AP and GAP. Furthermore, in contrast to the classical case, they are not determined by $m-n$. For example, there are virtual knots with trivial GAP but nontrivial $U_q(\mathfrak{gl}(2|2))$ and $U_q(\mathfrak{gl}(3|3))$ invariants.
Silver and Williams proved that the GAP vanishes on all AC links. Our main result is a generalization of this theorem to almost classical tangles and the $U_q(\mathfrak{gl}(m|n))$ Reshetikhin-Turaev functors. We prove that if $T$ is an almost classical tangle, then $\widetilde{Q}^{m|n}\circ Zh(T)$ is conjugate to $Q^{m|n}(T)$, with conjugation determined by an Alexander numbering of $T$.
| 2025-12-26
| 2025-12-30
|
[
"math.GT"
] |
Micah Chrisman, Anup Poudel
|
2508.02619
|
Impact of Non-Thermal Leptogenesis with Early Matter Domination on Gravitational Waves from First-order Phase Transition
|
We study the impact of non-thermal leptogenesis on the spectrum of gravitational waves (GWs) produced by a strong first-order phase transition in the early Universe. We consider a scenario in which a heavy scalar field, $Ï$, dominates the energy density of the early Universe and decays into heavy right-handed neutrinos (RHNs). The subsequent decay of RHNs generates a lepton asymmetry, which is partially converted into the observed baryon asymmetry via the sphaleron process. The $Ï$-dominated era and the entropy injection from the decays of $Ï$ and RHNs leave characteristic imprints on the GW spectrum, such as damping and modified frequency dependence, that distinguish it from the standard cosmological evolution. We identify the parameter space in which non-thermal leptogenesis is successful, leading to distinctive GW spectral features. We show that these GW signals can fall within the sensitivity ranges of future detectors such as ET, DECIGO and BBO. If observed, they would provide valuable insights into the thermal history and dynamics of the early Universe.
| 2025-12-26
| 2025-12-29
|
[
"hep-ph",
"astro-ph.CO"
] |
Dilip Kumar Ghosh, Anish Ghoshal, Koustav Mukherjee, Nimmala Narendra, Nobuchika Okada
|
2511.01443
|
Efficient Curvature-aware Graph Network
|
Graph curvature provides geometric priors for Graph Neural Networks (GNNs), enhancing their ability to model complex graph structures, particularly in terms of structural awareness, robustness, and theoretical interpretability. Among existing methods, Ollivier-Ricci curvature has been extensively studied due to its strong geometric interpretability, effectively characterizing the local geometric distribution between nodes. However, its prohibitively high computational complexity limits its applicability to large-scale graph datasets. To address this challenge, we propose a novel graph curvature measure--Effective Resistance Curvature--which quantifies the ease of message passing along graph edges using the effective resistance between node pairs, instead of the optimal transport distance. This method significantly outperforms Ollivier-Ricci curvature in computational efficiency while preserving comparable geometric expressiveness. Theoretically, we prove the low computational complexity of effective resistance curvature and establish its substitutability for Ollivier-Ricci curvature. Furthermore, extensive experiments on diverse GNN tasks demonstrate that our method achieves competitive performance with Ollivier-Ricci curvature while drastically reducing computational overhead.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG"
] |
Chaoqun Fei, Tinglve Zhou, Tianyong Hao, Yangyang Li
|
2512.11424
|
The Arf-Brown-Kervaire invariant on a lattice
|
We propose a lattice formulation of the Arf-Brown-Kervaire (ABK) invariant which takes values in $\mathbb{Z}_8$. Compared to the standard $\mathbb{Z}$-valued index, the ABK invariant is more involved in that it arises in Majorana fermion partition functions with reflection symmetry on two-dimensional non-orientable manifolds, and its definition contains an infinite sum over Dirac eigenvalues that requires proper regularization. We employ the massive Wilson Dirac operator, with and without domain-walls, on standard two-dimensional square lattices, and use its Pfaffian for the definition. Twisted boundary conditions and cross-caps, which reverse the orientation, are introduced to realize nontrivial topologies equipped with nontrivial $\mathrm{Pin}^{-}$ structures of Majorana fermions. We verify numerically (and partly analytically) that our formulation on a torus, Klein bottle, real projective plane (as well as its triple connected sum), and two types of Möbius strip reproduces the known values in continuum theory.
| 2025-12-26
| 2025-12-29
|
[
"hep-lat",
"cond-mat.mes-hall",
"hep-th",
"math-ph",
"math.MP"
] |
Sho Araki, Hidenori Fukaya, Tetsuya Onogi, Satoshi Yamaguchi
|
2512.21934
|
Double-Layered Silica-Engineered Fluorescent Nanodiamonds for Catalytic Generation and Quantum Sensing of Active Radicals
|
Fluorescent nanodiamonds (FNDs) hosting nitrogen-vacancy (NV) centers have attracted considerable attention for quantum sensing applications, particularly owing to notable advancements achieved in the field of weak magnetic signal detection in recent years. Here, we report a practical quantum-sensing platform for the controlled production and real-time monitoring of ultra-short-lived reactive free radicals using a double-layered silica modification strategy. An inner dense silica layer preserves the intrinsic properties of NV centers, while an outer porous silica layer facilitates efficient adsorption and stabilization of hydroxyl radicals and their precursor reactants. By doping this mesoporous shell with gadolinium (III) catalysts, we achieve sustained, light-free generation of hydroxyl radicals via catalytic water splitting, eliminating reliance on external precursors. The mechanism underlying this efficient radical generation is discussed in detail. The radical production is monitored in real time and in situ through spin-dependent T1 relaxometry of the NV centers, demonstrating stable and tunable radical fluxes, with concentration tunable across a continuous range from approximately 100 mM to molar levels by adjusting the catalyst condition. This study extends the technical application of nanodiamonds from relaxation sensing to the controlled synthesis of reactive free radicals, thereby providing robust experimental evidence to support the advancement of quantum sensing systems in intelligent manufacturing.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph",
"physics.chem-ph"
] |
Jia Su, Zenghao Kong, Fei Kong, Xing Liu, Linyu Zeng, Zhecheng Wang, Zijian Zeng, Jie Liu, Jihu Su, Junhua Yuan, Guosheng Shi, Fazhan Shi
|
2512.21963
|
Topological properties of generalized Markoff mod $p$ graphs
|
The generalized Markoff mod $p$ graph is defined via the equation $x^2+y^2+z^2=xyz+κ$ over the finite field $\mathbb{F}_p$ of prime order $p$. In this paper, we investigate the topological properties of the graph such as non-planarity, surface embeddability, and the existence of short cycles. Our approach is based on a systematic construction of $K_{3,3}$-subdivisions, integrating techniques from graph theory, computer algebra, and number theory.
| 2025-12-26
| 2025-12-29
|
[
"math.NT",
"math.CO"
] |
Shohei Satake, Yoshinori Yamasaki
|
2512.19443
|
D2Pruner: Debiased Importance and Structural Diversity for MLLM Token Pruning
|
Processing long visual token sequences poses a significant computational burden on Multimodal Large Language Models (MLLMs). While token pruning offers a path to acceleration, we find that current methods, while adequate for general understanding, catastrophically fail on fine-grained localization tasks. We attribute this failure to the inherent flaws of the two prevailing strategies: importance-based methods suffer from a strong positional bias, an inherent model artifact that distracts from semantic content, while diversity-based methods exhibit structural blindness, disregarding the user's prompt and spatial redundancy. To address this, we introduce D2Pruner, a framework that rectifies these issues by uniquely combining debiased importance with a structural pruning mechanism. Our method first secures a core set of the most critical tokens as pivots based on a debiased attention score. It then performs a Maximal Independent Set (MIS) selection on the remaining tokens, which are modeled on a hybrid graph where edges signify spatial proximity and semantic similarity. This process iteratively preserves the most important and available token while removing its neighbors, ensuring that the supplementary tokens are chosen to maximize importance and diversity. Extensive experiments demonstrate that D2Pruner has exceptional efficiency and fidelity. Applied to LLaVA-1.5-7B for general understanding tasks, it reduces FLOPs by 74.2\% while retaining 99.2\% of its original performance. Furthermore, in challenging localization benchmarks with InternVL-2.5-8B, it maintains 85.7\% performance at a 90\% token reduction rate, marking a significant advancement with up to 63. 53\% improvement over existing methods.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
Evelyn Zhang, Fufu Yu, Aoqi Wu, Zichen Wen, Ke Yan, Shouhong Ding, Biqing Qi, Linfeng Zhang
|
2512.22378
|
Towards Efficient Post-Training via Fourier-Driven Adapter Architectures
|
We propose a novel framework, termed Fourier-Activated Adapter (FAA), for parameter-efficient fine-tuning of large pre-trained language models. By incorporating random Fourier features into lightweight adapter modules, FAA decomposes intermediate representations into complementary low- and high-frequency components, enabling frequency-aware modulation of semantic information. This design allows the model to selectively emphasize informative frequency bands during adaptation while preserving the representational capacity of the frozen backbone. Extensive experiments on GLUE, E2E NLG, and instruction-tuning benchmarks demonstrate that FAA consistently achieves competitive or superior performance compared to existing parameter-efficient fine-tuning methods, while maintaining low computational and memory overhead. Ablation studies further verify the effectiveness of frequency-aware activation and adaptive weighting mechanisms, highlighting FAA as a robust and efficient approach for post-training large language models.
| 2025-12-26
| 2025-12-30
|
[
"cs.CL",
"cs.AI"
] |
Donggyun Bae, Jongil Park
|
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
|
2512.21959
|
Nonlocal Dirichlet problems involving the Logarithmic $p$-Laplacian
|
In this work, we show the existence of an unbounded sequence of minimax eigenvalues for the logarithmic $p$-Laplacian via the $\mathbb{Z}_2$-cohomological index of Fadell and Rabinowitz. As an application of these minimax eigenvalues and $p$-logarithmic Sobolev inequality proved in [4], we prove new existence results for nonlocal Dirichlet problems involving logarithmic $p$-Laplacian and nonlinearities with $p$-superlinear and subcritical growth at infinity.
| 2025-12-26
| 2025-12-29
|
[
"math.AP"
] |
Rakesh Arora, Hichem Hajaiej, Kanishka Perera
|
2512.21893
|
Evaluating Supervised Learning Approaches for Quantification of Quantum Entanglement
|
Quantum entanglement is a key resource in quantum computing and quantum information processing tasks. However, its quantification remains a major challenge since it cannot be directly extracted from physical observables. To address this issue, we study a few machine-learning based models to estimate the amount of entanglement in two-qubit as well as three-qubit systems. We use measurement outcomes as the input features and entanglement measures as the training labels. Our models predict entanglement without requiring the full state information. This demonstrates the potential of machine learning as an effcient and powerful tool for characterizing quantum entanglement
| 2025-12-26
| 2025-12-29
|
[
"quant-ph"
] |
Shruti Aggarwal, Trasha Gupta, R. K. Agrawal, S. Indu
|
2512.22365
|
Interstellar Interloper 3I/ATLAS: Nucleus Size, Photometry in RGB, Af(rho) and Antitail Structure Analysis
|
Interstellar comet 3I/ATLAS (C/2025 N1) exhibits an unusual, tightly collimated dust feature in the sunward hemisphere which has been widely described as an anti-tail. At the same time, precise constraints on the nucleus size have been derived from a combination of high-resolution imaging and non-gravitational dynamics. In this work I present a unified analysis that combines existing constraints on the nucleus radius with new ground-based RGB imaging of the dust anti-tail obtained with a 0.25 m telescope at the Toni Scarmato Astronomical Observatory (MPC L92).
| 2025-12-26
| 2025-12-30
|
[
"astro-ph.EP",
"astro-ph.GA",
"astro-ph.SR"
] |
Toni Scarmato
|
2511.02693
|
Model for charge carrier spectra in topological semimetals of the TaAs family
|
We propose a four-band model describing the electron energy spectra near the Weyl points in the topological semimetals of the TaAs family (TaAs, TaP, NbAs, NbP). This model takes into account the fact that these Weyl points result from the band-contact lines which would exist in the mirror-reflection planes of these materials if the spin-orbit interaction were absent in them. Within this model, we obtain conditions for the existence of the Weyl points, determine their positions in the Brillouin zone, and derive the explicit formula for dispersion of the bands along the straight line connecting the two close Weyl points with opposite topological charges. Using NbP as an example, the values of the parameters defining the model spectrum are found. The obtained results show that for the semimetals of the TaAs family, the charge-carriers spectrum in the vicinity of the two close Weyl points can be analyzed without complex band-structure calculations.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.mes-hall",
"cond-mat.mtrl-sci"
] |
G. P. Mikitik, Yu. V. Sharlai
|
2506.13464
|
Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study
|
Large language models (LLMs) have shown impressive capabilities across tasks such as mathematics, coding, and reasoning, yet their learning ability, which is crucial for adapting to dynamic environments and acquiring new knowledge, remains underexplored. In this work, we address this gap by introducing a framework inspired by cognitive psychology and education. Specifically, we decompose general learning ability into three distinct, complementary dimensions: Learning from Instructor (acquiring knowledge via explicit guidance), Learning from Concept (internalizing abstract structures and generalizing to new contexts), and Learning from Experience (adapting through accumulated exploration and feedback). We conduct a comprehensive empirical study across the three learning dimensions and identify several insightful findings, such as (i) interaction improves learning; (ii) conceptual understanding is scale-emergent and benefits larger models; and (iii) LLMs are effective few-shot learners but not many-shot learners. Based on our framework and empirical findings, we introduce a benchmark that provides a unified and realistic evaluation of LLMs' general learning abilities across three learning cognition dimensions. It enables diagnostic insights and supports evaluation and development of more adaptive and human-like models.
| 2025-12-26
| 2025-12-29
|
[
"cs.CL",
"cs.AI"
] |
Zhengyu Hu, Jianxun Lian, Zheyuan Xiao, Seraphina Zhang, Tianfu Wang, Nicholas Jing Yuan, Xing Xie, Hui Xiong
|
2512.21814
|
Stability for the inverse random potential scattering problem
|
This paper is concerned with an inverse random potential problem for the Schrödinger equation. The random potential is assumed to be a generalized Gaussian random function, whose covariance operator is a classical pseudo-differential operator. For the direct problem, the meromorphic continuation of the resolvent of the Schrödinger operator with rough potentials is investigated, which yields the well-posedness of the direct scattering problem and a Born series expansion. For the inverse problem, we derive a probabilistic stability estimate for determining the principle symbol of the covariance operator of the random potential. The stability result provides an estimate of the probability for an event when the principle symbol can be quantitatively determined by a single realization of the multi-frequency backscattered far-field pattern. The analysis employs the ergodicity theory and quantitative analytic continuation principle.
| 2025-12-26
| 2025-12-29
|
[
"math.AP"
] |
Tianjiao Wang, Xiang Xu, Yue Zhao
|
2512.22345
|
Extracting light-cone wave functions from covariant amplitudes: a detailed study in scalar field theory
|
We propose a conjectured formula that systematically maps covariant off-shell amplitudes to light-cone wave functions in scalar field theory. Through an explicit comparison at one-loop accuracy, we establish its equivalence to the light-cone perturbation theory series, thereby validating the conjecture at this order. Applying this formula, we efficiently re-derive wave functions from known covariant amplitudes, bypassing both the conceptual complexities of light-cone quantization and the technical challenges of perturbative calculations in this framework. In addition to simplifying computations, this approach opens new avenues for applications in gauge theories and deeper explorations of the fundamental equivalence between covariant and light-cone quantization.
| 2025-12-26
| 2025-12-30
|
[
"hep-th",
"hep-ph"
] |
Stéphane Munier
|
2512.21984
|
A Lightweight Multi-Scale Attention Framework for Real-Time Spinal Endoscopic Instance Segmentation
|
Real-time instance segmentation for spinal endoscopy is important for identifying and protecting critical anatomy during surgery, but it is difficult because of the narrow field of view, specular highlights, smoke/bleeding, unclear boundaries, and large scale changes. Deployment is also constrained by limited surgical hardware, so the model must balance accuracy and speed and remain stable under small-batch (even batch-1) training. We propose LMSF-A, a lightweight multi-scale attention framework co-designed across backbone, neck, and head. The backbone uses a C2f-Pro module that combines RepViT-style re-parameterized convolution (RVB) with efficient multi-scale attention (EMA), enabling multi-branch training while collapsing into a single fast path for inference. The neck improves cross-scale consistency and boundary detail using Scale-Sequence Feature Fusion (SSFF) and Triple Feature Encoding (TFE), which strengthens high-resolution features. The head adopts a Lightweight Multi-task Shared Head (LMSH) with shared convolutions and GroupNorm to reduce parameters and support batch-1 stability. We also release the clinically reviewed PELD dataset (61 patients, 610 images) with instance masks for adipose tissue, bone, ligamentum flavum, and nerve. Experiments show that LMSF-A is highly competitive (or even better than) in all evaluation metrics and much lighter than most instance segmentation methods requiring only 1.8M parameters and 8.8 GFLOPs, and it generalizes well to a public teeth benchmark. Code and dataset: https://github.com/hhwmortal/PELD-Instance-segmentation.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
Qi Lai, JunYan Li, Qiang Cai, Lei Wang, Tao Yan, XiaoKun Liang
|
2512.23744
|
Acoustic Black Holes in a Shock-Wave Exciton-Polariton Condensate
|
We demonstrate the spontaneous formation of acoustic black holes in exciton-polariton condensates triggered by discontinuous Riemann-type initial conditions. Starting from a quasi-conservative Gross-Pitaevskii model, we show that nonlinear dispersive shock waves naturally generate spatial regions where the local flow velocity exceeds the speed of sound, creating a self-induced transonic interface that functions as an acoustic horizon. Unlike previous schemes relying on externally engineered potentials or pump-loss landscapes, our approach reveals that the intrinsic nonlinear hydrodynamics of polariton fluids alone can lead to horizon formation. Using Whitham modulation theory and numerical simulations, we characterize the transition between subsonic and supersonic regimes and estimate the corresponding surface gravity and Hawking temperature. This mechanism opens a new route toward realizing polariton black holes and studying analogue gravitational effects, including Hawking-like emission, in Bose-Einstein quantum liquids.
| 2025-12-26
| 2026-01-01
|
[
"cond-mat.mes-hall",
"cond-mat.quant-gas",
"gr-qc"
] |
Junhui Cao, Jinling Wang, Kirill Bazarov, Chenqi Jin, Huijun Li, Anton Nalitov, Alexey Kavokin
|
2512.21958
|
Flow morphology and patterns in porous media convection: A persistent homology analysis
|
Convective mixing in porous media is crucial in both geophysical and industrial fields, spanning applications ranging from carbon dioxide sequestration to contaminant transport in groundwater. Key processes are affected by convective heat transport or diffusion of chemical species in porous formations. Intense convection flow and mixing create complex, dynamic patterns that are difficult to predict and measure. The present work focuses on the use of topological data analysis, in particular, the measures emerging from the growing field of persistent homology (PH), to quantify these patterns. These measures are objective and quantify structures across all temperature or concentration values simultaneously. These techniques, when applied to classical porous media setups, such as one-sided and Rayleigh-Bénard flow configurations, provide new insights into the system's structure, flow patterns, and macroscopic mixing properties. Using large datasets we make publicly available, comprising original simulations as well as those presented in previous works, we correlate the behaviour of the heat transport rate (quantified by the Nusselt number) with the evolution of the flow structures (quantified by the PH measures). Finally, we provide a detailed analysis of the flow evolution over a wide range of governing parameters, namely the Rayleigh-Darcy number and the domain size.
| 2025-12-26
| 2025-12-29
|
[
"physics.flu-dyn",
"physics.geo-ph"
] |
Marco De Paoli, Sergio Pirozzoli, Lou Kondic
|
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
|
2501.12742
|
$\mathbf{L}^p$-boundedness of the Bochner-Riesz operator
|
In this paper, we give a new approach to the Bochner-Riesz summability. As a result, we show that the Bochner-Riesz operator $\mathbf{S}^δ, 0<\Reδ<{1\over 2}$ is bounded on $\mathbf{L}^p(\mathbb{R}^n)$ for ${n-1\over 2n}\leq {1\over p}\leq{n+1\over 2n}$.
| 2025-12-26
| 2025-12-29
|
[
"math.CA"
] |
Zipeng Wang
|
2512.21900
|
Nucleon momentum distributions of complex nuclei from inclusive electron scattering
|
Nucleon momentum distributions (NMDs) reveal essential information about Fermi motion and short-range correlations (SRCs). In extracting NMDs from inclusive electron scattering data, theoretical analyses, such as the scaling analysis, are typically employed. For complex nuclei, consistently treating the excitation energy of the residual system is a complicated task, leading to discrepancies between existing extracted NMDs and ab initio calculations, particularly around the Fermi momentum $k_F$. To address this issue, we introduce an improved description of the excitation energy in the framework of the relativistic Fermi gas (RFG) model. With this treatment, the extracted NMDs of complex nuclei show better agreement with ab initio calculations across the low- and high-momentum range, especially around $k_F$, successfully reproducing both the behaviors of Fermi motion and SRCs. These results provide a new experimental perspective on the interplay between Fermi motion and SRCs in complex nuclei.
| 2025-12-26
| 2025-12-29
|
[
"nucl-th"
] |
Tongqi Liang, Dong Bai, Zhongzhou Ren
|
2512.21947
|
Notes on off-shell conformal integrals and correlation functions at five points
|
We study five-point off-shell conformal integrals and associated half-BPS correlation functions at the two-loop order in the 't Hooft coupling expansion in maximal supersymmetric Yang-Mills theory. We construct a basis of uniform transcendental, pure integrals, comprising six distinct topologies, through the method of diagonalizing leading singularities under the constraints of conformal invariance, which serve as basis integrals for conformally-symmetric observables at five points and two loops. By employing different conformal frame fixing choices, this integral basis can be mapped onto known two-loop four-massive-particle Feynman integral families. Subsequently, their integrated results are computed using the method of canonical differential equations and integration-by-parts reduction. We present for the first time the integrated results for the two-loop five-point half-BPS correlators, encompassing both maximal and non-maximal sectors, at symbol level.
| 2025-12-26
| 2025-12-29
|
[
"hep-th"
] |
Chia-Kai Kuo, Qinglin Yang
|
2509.20896
|
Deterministic Discrete Denoising
|
We propose a deterministic denoising algorithm for discrete-state diffusion models based on Markov chains. The generative reverse process is derandomized by introducing a variant of the herding algorithm with weakly chaotic dynamics, which induces deterministic discrete state transitions. Our approach is a direct replacement for the stochastic denoising process, requiring neither retraining nor continuous state embeddings. We demonstrate consistent improvements in both efficiency and sample quality on text and image generation tasks. Thus, this simple derandomization approach is expected to enhance the significance of discrete diffusion in generative modeling. Furthermore, our results reveal that deterministic reverse processes, well established in continuous diffusion, can also be effective in discrete state spaces.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG",
"nlin.CD"
] |
Hideyuki Suzuki, Hiroshi Yamashita
|
2512.21845
|
Scalable Class-Incremental Learning Based on Parametric Neural Collapse
|
Incremental learning often encounter challenges such as overfitting to new data and catastrophic forgetting of old data. Existing methods can effectively extend the model for new tasks while freezing the parameters of the old model, but ignore the necessity of structural efficiency to lead to the feature difference between modules and the class misalignment due to evolving class distributions. To address these issues, we propose scalable class-incremental learning based on parametric neural collapse (SCL-PNC) that enables demand-driven, minimal-cost backbone expansion by adapt-layer and refines the static into a dynamic parametric Equiangular Tight Frame (ETF) framework according to incremental class. This method can efficiently handle the model expansion question with the increasing number of categories in real-world scenarios. Additionally, to counteract feature drift in serial expansion models, the parallel expansion framework is presented with a knowledge distillation algorithm to align features across expansion modules. Therefore, SCL-PNC can not only design a dynamic and extensible ETF classifier to address class misalignment due to evolving class distributions, but also ensure feature consistency by an adapt-layer with knowledge distillation between extended modules. By leveraging neural collapse, SCL-PNC induces the convergence of the incremental expansion model through a structured combination of the expandable backbone, adapt-layer, and the parametric ETF classifier. Experiments on standard benchmarks demonstrate the effectiveness and efficiency of our proposed method. Our code is available at https://github.com/zhangchuangxin71-cyber/dynamic_ ETF2. Keywords: Class incremental learning; Catastrophic forgetting; Neural collapse;Knowledge distillation; Expanded model.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV",
"cs.LG"
] |
Chuangxin Zhang, Guangfeng Lin, Enhui Zhao, Kaiyang Liao, Yajun Chen
|
2512.21815
|
Few Tokens Matter: Entropy Guided Attacks on Vision-Language Models
|
Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, a measure of model uncertainty, is strongly correlated with the reliability of VLM. Prior entropy-based attacks maximize uncertainty at all decoding steps, implicitly assuming that every token contributes equally to generation instability. We show instead that a small fraction (about 20%) of high-entropy tokens, i.e., critical decision points in autoregressive generation, disproportionately governs output trajectories. By concentrating adversarial perturbations on these positions, we achieve semantic degradation comparable to global methods while using substantially smaller budgets. More importantly, across multiple representative VLMs, such selective attacks convert 35-49% of benign outputs into harmful ones, exposing a more critical safety risk. Remarkably, these vulnerable high-entropy forks recur across architecturally diverse VLMs, enabling feasible transferability (17-26% harmful rates on unseen targets). Motivated by these findings, we propose Entropy-bank Guided Adversarial attacks (EGA), which achieves competitive attack success rates (93-95%) alongside high harmful conversion, thereby revealing new weaknesses in current VLM safety mechanisms.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV",
"cs.LG"
] |
Mengqi He, Xinyu Tian, Xin Shen, Jinhong Ni, Shu Zou, Zhaoyuan Yang, Jing Zhang
|
2509.13688
|
CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion
|
Controllable, high-fidelity mesh editing remains a significant challenge in 3D content creation. Existing generative methods often struggle with complex geometries and fail to produce detailed results. We propose CraftMesh, a novel framework for high-fidelity generative mesh manipulation via Poisson Seamless Fusion. Our key insight is to decompose mesh editing into a pipeline that leverages the strengths of 2D and 3D generative models: we edit a 2D reference image, then generate a region-specific 3D mesh, and seamlessly fuse it into the original model. We introduce two core techniques: Poisson Geometric Fusion, which utilizes a hybrid SDF/Mesh representation with normal blending to achieve harmonious geometric integration, and Poisson Texture Harmonization for visually consistent texture blending. Experimental results demonstrate that CraftMesh outperforms state-of-the-art methods, delivering superior global consistency and local detail in complex editing tasks.
| 2025-12-26
| 2025-12-30
|
[
"cs.GR",
"cs.AI"
] |
James Jincheng, Yuxiao Wu, Youcheng Cai, Ligang Liu
|
2512.22029
|
LibContinual: A Comprehensive Library towards Realistic Continual Learning
|
A fundamental challenge in Continual Learning (CL) is catastrophic forgetting, where adapting to new tasks degrades the performance on previous ones. While the field has evolved with diverse methods, this rapid surge in diverse methodologies has culminated in a fragmented research landscape. The lack of a unified framework, including inconsistent implementations, conflicting dependencies, and varying evaluation protocols, makes fair comparison and reproducible research increasingly difficult. To address this challenge, we propose LibContinual, a comprehensive and reproducible library designed to serve as a foundational platform for realistic CL. Built upon a high-cohesion, low-coupling modular architecture, LibContinual integrates 19 representative algorithms across five major methodological categories, providing a standardized execution environment. Meanwhile, leveraging this unified framework, we systematically identify and investigate three implicit assumptions prevalent in mainstream evaluation: (1) offline data accessibility, (2) unregulated memory resources, and (3) intra-task semantic homogeneity. We argue that these assumptions often overestimate the real-world applicability of CL methods. Through our comprehensive analysis using strict online CL settings, a novel unified memory budget protocol, and a proposed category-randomized setting, we reveal significant performance drops in many representative CL methods when subjected to these real-world constraints. Our study underscores the necessity of resource-aware and semantically robust CL strategies, and offers LibContinual as a foundational toolkit for future research in realistic continual learning. The source code is available from \href{https://github.com/RL-VIG/LibContinual}{https://github.com/RL-VIG/LibContinual}.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG",
"cs.AI"
] |
Wenbin Li, Shangge Liu, Borui Kang, Yiyang Chen, KaXuan Lew, Yang Chen, Yinghuan Shi, Lei Wang, Yang Gao, Jiebo Luo
|
2512.22067
|
Random state comonads encode cellular automata evaluation
|
Cellular automata (CA) are quintessential ALife and ubiquitous in many studies of collective behaviour and emergence, from morphogenesis to social dynamics and even brain modelling. Recently, there has been an increased interest in formalising CA, theoretically through category theory and practically in terms of a functional programming paradigm. Unfortunately, these remain either in the realm of simple implementations lacking important practical features, or too abstract and conceptually inaccessible to be useful to the ALife community at large. In this paper, we present a brief and accessible introduction to a category-theoretical model of CA computation through a practical implementation in Haskell. We instantiate arrays as comonads with state and random generators, allowing stochastic behaviour not currently supported in other known implementations. We also emphasise the importance of functional implementations for complex systems: thanks to the Curry-Howard-Lambek isomorphism, functional programs facilitate a mapping between simulation, system rules or semantics, and categorical descriptions, which may advance our understanding and development of generalised theories of emergent behaviour. Using this implementation, we show case studies of four famous CA models: first Wolfram's CA in 1D, then Conway's game of life, Greenberg-Hasings excitable cells, and the stochastic Forest Fire model in 2D, and present directions for an extension to N dimensions. Finally, we suggest that the comonadic model can encode arbitrary topologies and propose future directions for a comonadic network.
| 2025-12-26
| 2025-12-29
|
[
"nlin.CG",
"cs.LO"
] |
Madalina I Sas, Julian H J Sutherland
|
2512.22086
|
Unlocking klockmannite: formation of colloidal quasi-2D CuSe nanocrystals and photo-physical properties arising from crystal anisotropy
|
Copper selenide is an exceptional quasi-layered monolithic material that exhibits both semiconducting and metallic properties in adjacent visible and near-infrared (NIR) spectral ranges. Here we introduce a thiol-free colloidal synthesis for generating quasi-2D klockmannite copper selenide nanocrystals via hot injection method, achieving shape control by tuning the injection temperature and precursor concentrations without any additional ligands. This approach produces large klockmannite nanosheets with lateral sizes from 200 nm to several micrometres, as well as uniform triangular nanoplatelets with sizes of 12-25 nm that are monocrystalline and display strong NIR plasmonic absorption. The spectral features of the anisotropic klockmannite phase in the NIR have been analysed using complex-scaled discrete dipole approximation (CSDDA) calculations, which reveal pronounced optical anisotropy and the emergence of hyperbolic regime. The combined effect of propagating and evanescent fields is regarded as the underlying reason of such modes in the hyperbolic domain. Finally, the ultrafast photophysical behaviour of the material in klockmannite phase is examined, including hot-hole cooling, trapping, and coherent phonons generation. Our findings emphasize the important role of the intrinsic crystal anisotropy in governing the physical properties of nanoscale klockmannite.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.mtrl-sci"
] |
Urvi Parekh, Nadiia Didukh, Samira Dabelstein, Ronja Piehler, Eugen Klein, Jivesh Kaushal, Tobias Korn, Stefan Lochbrunner, Christian Klinke, Stefan Scheel, Rostyslav Lesyuk
|
2512.22087
|
Context as a Tool: Context Management for Long-Horizon SWE-Agents
|
Agents based on large language models have recently shown strong potential on real-world software engineering (SWE) tasks that require long-horizon interaction with repository-scale codebases. However, most existing agents rely on append-only context maintenance or passively triggered compression heuristics, which often lead to context explosion, semantic drift, and degraded reasoning in long-running interactions. We propose CAT, a new context management paradigm that elevates context maintenance to a callable tool integrated into the decision-making process of agents. CAT formalizes a structured context workspace consisting of stable task semantics, condensed long-term memory, and high-fidelity short-term interactions, and enables agents to proactively compress historical trajectories into actionable summaries at appropriate milestones. To support context management for SWE-agents, we propose a trajectory-level supervision framework, CAT-GENERATOR, based on an offline data construction pipeline that injects context-management actions into complete interaction trajectories. Using this framework, we train a context-aware model, SWE-Compressor. Experiments on SWE-Bench-Verified demonstrate that SWE-Compressor reaches a 57.6% solved rate and significantly outperforms ReAct-based agents and static compression baselines, while maintaining stable and scalable long-horizon reasoning under a bounded context budget.
| 2025-12-26
| 2025-12-29
|
[
"cs.CL"
] |
Shukai Liu, Jian Yang, Bo Jiang, Yizhi Li, Jinyang Guo, Xianglong Liu, Bryan Dai
|
2512.22323
|
SpotEdit: Selective Region Editing in Diffusion Transformers
|
Diffusion Transformer models have significantly advanced image editing by encoding conditional images and integrating them into transformer layers. However, most edits involve modifying only small regions, while current methods uniformly process and denoise all tokens at every timestep, causing redundant computation and potentially degrading unchanged areas. This raises a fundamental question: Is it truly necessary to regenerate every region during editing? To address this, we propose SpotEdit, a training-free diffusion editing framework that selectively updates only the modified regions. SpotEdit comprises two key components: SpotSelector identifies stable regions via perceptual similarity and skips their computation by reusing conditional image features; SpotFusion adaptively blends these features with edited tokens through a dynamic fusion mechanism, preserving contextual coherence and editing quality. By reducing unnecessary computation and maintaining high fidelity in unmodified areas, SpotEdit achieves efficient and precise image editing.
| 2025-12-26
| 2025-12-30
|
[
"cs.CV",
"cs.AI"
] |
Zhibin Qin, Zhenxiong Tan, Zeqing Wang, Songhua Liu, Xinchao Wang
|
2512.21980
|
A 58-Addition, Rank-23 Scheme for General 3x3 Matrix Multiplication
|
This paper presents a new state-of-the-art algorithm for exact $3\times3$ matrix multiplication over general non-commutative rings, achieving a rank-23 scheme with only 58 scalar additions. This improves the previous best additive complexity of 60 additions without a change of basis. The result was discovered through an automated search combining ternary-restricted flip-graph exploration with greedy intersection reduction for common subexpression elimination. The resulting scheme uses only coefficients from $\{-1, 0, 1\}$, ensuring both efficiency and portability across arbitrary fields. The total scalar operation count is reduced from 83 to 81.
| 2025-12-26
| 2025-12-29
|
[
"cs.DS"
] |
A. I. Perminov
|
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
|
2512.22003
|
Cosmic-Ray-Constrained LSTM Model for Geomagnetic Storm Prediction
|
Geomagnetic storms (GSTs) driven by solar wind-magnetosphere coupling can severely disrupt technological systems, motivating the need for improved prediction accuracy and longer warning times. In this study, we develop a physics-informed Long Short-Term Memory (LSTM) model that incorporates cosmic-ray flux modulation as an additional precursor signal. As coronal mass ejection (CME)-driven disturbances propagate through the heliosphere, enhanced turbulence and magnetic-field compression reduce galactic cosmic-ray (GCR) flux measured by ground-based neutron monitors (Forbush decreases), providing early information that can precede near-Earth solar-wind signatures by 1--3 days. We integrate multi-source space-weather data, spanning 1995-2020, including cosmic-ray observations, solar wind plasma parameters, interplanetary magnetic-field data, and geomagnetic indices. Based on these data, we construct a 19-dimensional feature vector that includes flux background levels, decrease-related indicators, and inter-station correlation measures as model inputs. Employing a 50-unit LSTM architecture, the proposed model achieves root-mean-square errors (RMSE) of 5.106 nT, 8.315 nT, 10.854 nT, 12.883 nT, and 14.788 nT for 2-, 6-, 12-, 24-, and 48-hour predictions, respectively. Incorporating cosmic-ray information further improves 48-hour forecast skill by up to 25.84% (from 0.178 to 0.224). These results demonstrate the value of physics-informed deep learning and cosmic-ray precursors for advancing space-weather forecasting.
| 2025-12-26
| 2025-12-29
|
[
"astro-ph.SR",
"astro-ph.IM"
] |
Zongyuan Ge, Chenwaner Zhang, Wei Zhou, Hongyu Zeng, Guiping Zhou
|
2512.22403
|
Active Nonparametric Two-Sample Testing by Betting on Heterogeneous Data Sources
|
We study the problem of active nonparametric sequential two-sample testing over multiple heterogeneous data sources. In each time slot, a decision-maker adaptively selects one of $K$ data sources and receives a paired sample generated from that source for testing. The goal is to decide as quickly as possible whether the pairs are generated from the same distribution or not. The gain achieved by such adaptive sampling (in terms of smaller expected stopping time or larger error exponents) has been well-characterized for parametric models via Chernoff's adaptive MLE selection rule [1]. However, analogous results are not known for the case of nonparametric problems, such as two-sample testing, where we place no restrictions on the distributions.
Our main contribution is a general active nonparametric testing procedure that combines an adaptive source-selecting strategy within the testing-by-betting framework of [2] that works under minimal distributional assumptions. In each time slot, our scheme proceeds by selecting a source according to a probability that mixes exploitation, favoring sources with the largest empirical distinguishability, and exploration via a vanishing greedy strategy. The (paired) observations so collected are then used to update the "betting-wealth process", which is a stochastic process guaranteed to be a nonnegative martingale under the null. The procedure stops and rejects the null when the wealth process exceeds an appropriate threshold; an event that is unlikely under the null. We show that our test controls the type-I error at a prespecified level-$α$ under the null, and establish its power-one property and a bound on its expected sample size under the alternative. Our results provide a precise characterization of the improvements achievable by a principled adaptive sampling strategy over its passive analog.
| 2025-12-26
| 2025-12-30
|
[
"math.ST",
"cs.IT",
"math.IT",
"stat.TH"
] |
Chia-Yu Hsu, Shubhanshu Shekhar
|
2512.22306
|
Beyond Single Bugs: Benchmarking Large Language Models for Multi-Vulnerability Detection
|
Large Language Models (LLMs) have demonstrated significant potential in automated software security, particularly in vulnerability detection. However, existing benchmarks primarily focus on isolated, single-vulnerability samples or function-level classification, failing to reflect the complexity of real-world software where multiple interacting vulnerabilities often coexist within large files. Recent studies indicate that LLMs suffer from "count bias" and "selection bias" in multi-label tasks, yet this has not been rigorously quantified in the domain of code security. In this work, we introduce a comprehensive benchmark for Multi-Vulnerability Detection across four major languages: C, C++, Python, and JavaScript. We construct a dataset of 40,000 files by systematically injecting controlled counts of vulnerabilities (1, 3, 5, and 9) into long-context code samples (7.5k-10k tokens) sourced from CodeParrot. We evaluate five state-of-the-art LLMs, including GPT-4o-mini, Llama-3.3-70B, and the Qwen-2.5 series. Our results reveal a sharp degradation in performance as vulnerability density increases. While Llama-3.3-70B achieves near-perfect F1 scores (approximately 0.97) on single-vulnerability C tasks, performance drops by up to 40% in high-density settings. Notably, Python and JavaScript show distinct failure modes compared to C/C++, with models exhibiting severe "under-counting" (Recall dropping to less than 0.30) in complex Python files.
| 2025-12-26
| 2025-12-30
|
[
"cs.CR",
"cs.AI"
] |
Chinmay Pushkar, Sanchit Kabra, Dhruv Kumar, Jagat Sesh Challa
|
2511.04526
|
Generalizing Goodstein's theorem and Cichon's independence proof
|
We generalize Goodstein's theorem (Goodstein 1944) and Cichon's independence proof (Cichon 1983) to $Î ^1_1-\mathrm{CA}_0$ using results from (Wilken 2026). The method is generalizable to stronger notation systems that provide unique terms for ordinals and enjoy Bachmann property.
| 2025-12-26
| 2025-12-29
|
[
"math.LO"
] |
Gunnar Wilken
|
2512.22105
|
Learning Association via Track-Detection Matching for Multi-Object Tracking
|
Multi-object tracking aims to maintain object identities over time by associating detections across video frames. Two dominant paradigms exist in literature: tracking-by-detection methods, which are computationally efficient but rely on handcrafted association heuristics, and end-to-end approaches, which learn association from data at the cost of higher computational complexity. We propose Track-Detection Link Prediction (TDLP), a tracking-by-detection method that performs per-frame association via link prediction between tracks and detections, i.e., by predicting the correct continuation of each track at every frame. TDLP is architecturally designed primarily for geometric features such as bounding boxes, while optionally incorporating additional cues, including pose and appearance. Unlike heuristic-based methods, TDLP learns association directly from data without handcrafted rules, while remaining modular and computationally efficient compared to end-to-end trackers. Extensive experiments on multiple benchmarks demonstrate that TDLP consistently surpasses state-of-the-art performance across both tracking-by-detection and end-to-end methods. Finally, we provide a detailed analysis comparing link prediction with metric learning-based association and show that link prediction is more effective, particularly when handling heterogeneous features such as detection bounding boxes. Our code is available at \href{https://github.com/Robotmurlock/TDLP}{https://github.com/Robotmurlock/TDLP}.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
Momir AdžemoviÄ
|
2511.22460
|
An Efficient Embedding Based Ad Retrieval with GPU-Powered Feature Interaction
|
In large-scale advertising recommendation systems, retrieval serves as a critical component, aiming to efficiently select a subset of candidate ads relevant to user behaviors from a massive ad inventory for subsequent ranking and recommendation. The Embedding-Based Retrieval (EBR) methods modeled by the dual-tower network are widely used in the industry to maintain both retrieval efficiency and accuracy. However, the dual-tower model has significant limitations: the embeddings of users and ads interact only at the final inner product computation, resulting in insufficient feature interaction capabilities. Although DNN-based models with both user and ad as input features, allowing for early-stage interaction between these features, are introduced in the ranking stage to mitigate this issue, they are computationally infeasible for the retrieval stage. To bridge this gap, this paper proposes an efficient GPU-based feature interaction for the dual-tower network to significantly improve retrieval accuracy while substantially reducing computational costs. Specifically, we introduce a novel compressed inverted list designed for GPU acceleration, enabling efficient feature interaction computation at scale. To the best of our knowledge, this is the first framework in the industry to successfully implement Wide and Deep in a retrieval system. We apply this model to the real-world business scenarios in Tencent Advertising, and experimental results demonstrate that our method outperforms existing approaches in offline evaluation and has been successfully deployed to Tencent's advertising recommendation system, delivering significant online performance gains. This improvement not only validates the effectiveness of the proposed method, but also provides new practical guidance for optimizing large-scale ad retrieval systems.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG",
"cs.IR"
] |
Yifan Lei, Jiahua Luo, Tingyu Jiang, Bo Zhang, Lifeng Wang, Dapeng Liu, Zhaoren Wu, Haijie Gu, Huan Yu, Jie Jiang
|
2512.22312
|
Necessary and sufficient conditions for high dimensional Central Limit Theorem under moment conditions
|
High dimensional central limit theorems (the CLTs) have been extensively studied in recent years under a variety of sufficient moment conditions connecting the dimension growth rate with the tail decay rate. In this article, we investigate whether the existing moment conditions are also necessary under the independence of the components. We consider four exhaustive classes, viz. when underlying random variables (I) have all polynomial moments, (II) have some polynomial moment of order higher than two, (III) have only second moment but no polynomial moment higher than two exists, and (IV) have infinite second moment, but belong to the domain of attraction of normal distribution. We find the optimal growth rate of the dimension with respect to sample size in the high dimensional CLTs over hyper-rectangles. More precisely, we derive necessary and sufficient moment conditions for the validity of the the CLT over hyper-rectangles in each of the four regimes listed above, showing that the CLT may hold under much weaker conditions compared to those considered in the existing literature.
| 2025-12-26
| 2025-12-30
|
[
"math.PR"
] |
Debraj Das, Soumendra Lahiri
|
2512.22384
|
Nonequilibrium QCD in heavy-ion collisions: Kinetic theory and jet modifications during the initial stages
|
This thesis focuses on how jets are modified by the nonequilibrium quark-gluon plasma during the initial stages in heavy-ion collisions. Its influence on their propagation is typically encoded in a single medium function, the dipole cross section. Its small distance behavior is characterized by the jet quenching parameter $\hat q$, and we obtain its numerical value throughout the pre-equilibrium stage, finding values comparable in magnitude to the earlier Glasma stage. We also compute the more general elastic collision kernel, obtained by Fourier transforming the dipole cross section. This constitutes an important step to facilitate the understanding of jet-medium interactions during the initial stages in heavy-ion collisions. Additionally, we improve QCD kinetic theory simulations by employing a more realistic (HTL) screening mechanism to incorporate medium effects, which we compare with simpler screening mechanisms. An expanding plasma realized in the initial stages of heavy-ion collisions exhibits a significantly reduced maximum anisotropy and reduced specific shear viscosity $η/s$ when using the improved screening prescription. Moreover, we investigate the gluon splitting rates, which are typically obtained using an isotropic model for the collision kernel. Going beyond that approximation, we find that the splitting rates obtained from the nonequilibrium anisotropic kernel differ significantly both in magnitude and in their qualitative time evolution. We further identify a novel type of weak-coupling attractor, which can be observed in the ratio of the jet quenching parameter and pressure ratio, and is obtained by extrapolating to vanishing coupling. This improved kinetic theory description and novel limiting attractors contribute towards a more realistic modeling of the nonequilibrium QCD plasma and its equilibration and hydrodynamization process during the initial stages.
| 2025-12-26
| 2025-12-30
|
[
"hep-ph",
"nucl-th"
] |
Florian Lindenbauer
|
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