Published: 2026-02-12 06:20:23
Authors: Bin Huang, Xun Yu, Yikun Zhang, Yi Zhang, Yang Chen, Qiegen Liu
Categories: cs.CV, cs.AI
Abstract:
Low-dose computed tomography (LDCT) reconstruction is fundamentally challenged by severe noise and compromised data fidelity under reduced radiation exposure. Most existing methods operate either in the image or post-log projection domain, which fails to fully exploit the rich structural information in pre-log measurements while being highly susceptible to noise. The requisite logarithmic transformation critically amplifies noise within these data, imposing exceptional demands on reconstruction precision. To overcome these challenges, we propose PLOT-CT, a novel framework for Pre-Log vOronoi decomposiTion-assisted CT generation. Our method begins by applying Voronoi decomposition to pre-log sinograms, disentangling the data into distinct underlying components, which are embedded in separate latent spaces. This explicit decomposition significantly enhances the model's capacity to learn discriminative features, directly improving reconstruction accuracy by mitigating noise and preserving information inherent in the pre-log domain. Extensive experiments demonstrate that PLOT-CT achieves state-of-the-art performance, attaining a 2.36dB PSNR improvement over traditional methods at the 1e4 incident photon level in the pre-log domain.
Published: 2026-02-12 06:17:12
Authors: Weida Li, Yaoliang Yu, Bryan Kian Hsiang Low
Categories: cs.LG
Abstract:
We revisit the use of probabilistic values, which include the well-known Shapley and Banzhaf values, to rank features for explaining the local predicted values of decision trees. The quality of feature rankings is typically assessed with the insertion and deletion metrics. Empirically, we observe that co-optimizing these two metrics is closely related to a joint optimization that selects a subset of features to maximize the local predicted value while minimizing it for the complement. However, we theoretically show that probabilistic values are generally unreliable for solving this joint optimization. Therefore, we explore deriving feature rankings by directly optimizing the joint objective. As the backbone, we propose TreeGrad, which computes the gradients of the multilinear extension of the joint objective in $O(L)$ time for decision trees with $L$ leaves; these gradients include weighted Banzhaf values. Building upon TreeGrad, we introduce TreeGrad-Ranker, which aggregates the gradients while optimizing the joint objective to produce feature rankings, and TreeGrad-Shap, a numerically stable algorithm for computing Beta Shapley values with integral parameters. In particular, the feature scores computed by TreeGrad-Ranker satisfy all the axioms uniquely characterizing probabilistic values, except for linearity, which itself leads to the established unreliability. Empirically, we demonstrate that the numerical error of Linear TreeShap can be up to $10^{15}$ times larger than that of TreeGrad-Shap when computing the Shapley value. As a by-product, we also develop TreeProb, which generalizes Linear TreeShap to support all probabilistic values. In our experiments, TreeGrad-Ranker performs significantly better on both insertion and deletion metrics. Our code is available at https://github.com/watml/TreeGrad.
Published: 2026-02-12 06:06:58
Authors: Yousuf Choudhary, Tosiron Adegbija
Categories: cs.AR, cs.ET
Abstract:
Antiferromagnetic Tunnel Junctions (AFMTJs) offer picosecond switching and high integration density for in-memory computing, but their ultrafast dynamics and low tunnel magnetoresistance (TMR) make state-of-the-art MRAM interfaces unreliable. This work develops a device-circuit co-designed read/write interface optimized for AFMTJ behavior. Using a calibrated SPICE AFMTJ model as a baseline, we identify the limitations of conventional drivers and propose an asymmetric pulse driver (PD) for deterministic picosecond switching and a self-timed sense amplifier (STSA) with dynamic trip-point tuning for low-TMR sensing. Our experiments using SPICE and Monte Carlo evaluations demonstrate that the proposed circuits preserve AFMTJ latency and energy benefits while achieving robust read/write yield under realistic PVT and 3D integration parasitics, outperforming standard MRAM front-ends under the same conditions.
Published: 2026-02-12 05:53:51
Authors: Fei Xu, Cheng Ye, Jie OuYang, Ziqiang Wu, Haoze Chen, An Hua, Meifeng Gao, Qiandong Zhang, Minghan Li, Feilong Li, Yajun Miao, Wei Qi
Categories: cs.CR
Abstract:
The security foundation of blockchain system relies primarily on classical cryptographic methods and consensus algorithms. However, the advent of quantum computing poses a significant threat to conventional public-key cryptosystems based on computational hardness assumptions. In particular, Shor's algorithm can efficiently solve discrete logarithm and integer factorization problems in polynomial time, thereby undermining the immutability and security guarantees of existing systems. Moreover, current Practical Byzantine Fault Tolerance (PBFT) protocols, widely adopted in consortium blockchains, suffer from high communication overhead and limited efficiency when coping with dynamic node reconfigurations, while offering no intrinsic protection against quantum adversaries.
To address these challenges, we propose QDBFT, a quantum-secured dynamic consensus algorithm, with two main contributions: first,we design a primary node automatic rotation mechanism based on a consistent hash ring to enable consensus under dynamic membership changes, ensuring equitable authority distribution; second, we integrate Quantum Key Distribution (QKD) networks to provide message authentication for inter-node communication, thereby achieving information-theoretic security in the consensus process. Experimental evaluations demonstrate that QDBFT achieves performance comparable to traditional PBFT while delivering strong resilience against quantum attacks, making it a promising solution for future quantum-secure decentralized infrastructures.
Published: 2026-02-12 05:32:06
Authors: Noriaki Arima, Mamoru Doi, Shigeyuki Sako, Yuu Niino, Ryou Ohsawa, Nozomu Tominaga, Masaomi Tanaka, Michael Richmond, Shinsuke Abe, Naoto Kobayashi, Sohei Kondo, Yuki Mori, Ko Arimatsu, Toshihiro Kasuga, Shin-ichiro Okumura, Jun-ichi Watanabe, Takuya Yamashita
Categories: astro-ph.EP, astro-ph.HE, astro-ph.IM
Abstract:
Recent time-domain surveys have revealed rapid transients that evolve on timescales of $\lesssim 10$ days, expanding the transient population into the short-duration regime. The transient search on even shorter timescales, particularly those lasting only seconds or less, remains a largely unexplored frontier. Very short-duration optical transients could serve as potential counterparts to millisecond-duration fast radio bursts (FRBs), providing clues to their origins. However, the optical search for transients on such short timescales has been limited primarily by instrumental constraints. Here we report the discovery of an optical transient candidate (TMG20200322) with a duration of $\lesssim 2$~s by wide-field video observations in the direction of the Earth's shadow. TMG20200322 was detected in just two consecutive images of 1-second exposure time, with its shape becoming elongated in the second frame. PSF shape variability analysis of field stars reveals that such an elongated PSF cannot be explained by atmospheric fluctuations. We investigate the potential origins of TMG20200322 in two scenarios: meteoroid impact flashes on near-Earth asteroids (NEAs) and head-on meteors in the Earth's atmosphere. None of the scenarios provides a satisfactory explanation for this transient. We derive a sky-projected rate of the TMG20200322 event of $R_{\mathrm{trans}} = (3.4 \times 10^{-2})^{+0.13}_{-0.028}$~deg$^{-2}$~day$^{-1}$ and an upper limit on second-timescale transients with durations of $1~\mathrm{s} \leq τ\lesssim 15~\mathrm{s}$ of $R_{\mathrm{trans}} \lesssim 0.10$~deg$^{-2}$~day$^{-1}$ for the non-detection case. We highlight that continuous monitoring observations in the direction of the Earth's shadow could be a key strategy to unveil a new population of optical transients on timescales of seconds or less.
Published: 2026-02-12 05:28:06
Authors: Yuhang Liu, Fengpeng An, Guang Luo, Wei Wang, Wei Wei, Xuesong Zhang, Dixiao Lu, Xiaohao Yin
Categories: physics.ins-det, hep-ex
Abstract:
We present a detailed characterization of the thermal neutron sensitive transparent glass scintillator SG101, benchmarked against the conventional LiF ZnS(Ag)based scintillator EJ426. The detection efficiency, energy resolution, and pulse shape discrimination (PSD) performance ofSG101 were evaluated under AmBe neutron irradiation. When coupled with organic scintillators(EJ200 or EJ276),the SG101 EJ200 system achieves a figure of merit (FOM) of 3.81 for thermal neutron/gamma separation, while the SG101 EJ276 configuration resolves three distinct particle populations gamma rays, fast neutrons, and thermal neutrons with FOM values of 3.46 and2.21, respectively. Correlation analysis reveals that the number of fast thermal neutron coincidence events significantly exceeds the accidental background, and the count of gamma fast thermal neutron triple-coincidence events is also far higher than the expected accidental rate, confirming significant physical correlations for both event types within a 100 us time window. These results demonstrate that SG101 is a promising candidate for applications requiring high-efficiency thermal neutron detection and precise event tagging coupling with a scintillator with PSD approach
Published: 2026-02-12 05:22:30
Authors: Welington de Oliveira, Johannes O. Royset
Categories: math.OC
Abstract:
This work presents a unified framework that combines global approximations with locally built models to handle challenging nonconvex and nonsmooth composite optimization problems, including cases involving extended real-valued functions. We show that near-stationary points of the approximating problems converge to stationary points of the original problem under suitable conditions. Building on this, we develop practical algorithms that use tractable convex master programs derived from local models of the approximating problems. The resulting double-loop structure improves global approximations while adapting local models, providing a flexible and implementable approach for a wide class of composite optimization problems. It also lays the groundwork for new algorithmic developments in this domain.
Published: 2026-02-12 05:08:49
Authors: Yujie Gu, Richeng Jin, Zhaoyang Zhang, Huaiyu Dai
Categories: cs.LG, cs.AI
Abstract:
It is commonly believed that gradient compression in federated learning (FL) enjoys significant improvement in communication efficiency with negligible performance degradation. In this paper, we find that gradient compression induces sharper loss landscapes in federated learning, particularly under non-IID data distributions, which suggests hindered generalization capability. The recently emerging Sharpness Aware Minimization (SAM) effectively searches for a flat minima by incorporating a gradient ascent step (i.e., perturbing the model with gradients) before the celebrated stochastic gradient descent. Nonetheless, the direct application of SAM in FL suffers from inaccurate estimation of the global perturbation due to data heterogeneity. Existing approaches propose to utilize the model update from the previous communication round as a rough estimate. However, its effectiveness is hindered when model update compression is incorporated. In this paper, we propose FedSynSAM, which leverages the global model trajectory to construct synthetic data and facilitates an accurate estimation of the global perturbation. The convergence of the proposed algorithm is established, and extensive experiments are conducted to validate its effectiveness.
Published: 2026-02-12 04:49:26
Authors: Hui Wang, Rui Wang, Daichi Sugiyama, J. S. Tsai
Categories: quant-ph
Abstract:
We experimentally studied the switching off processes in the double-resonator coupler superconducting quantum circuit.In both frequency and time-domain, we observed the variation of qubit-qubit effective coupling by tuning qubits'frequencies. According to the measurement results, by just shifting qubits' frequencies smaller than 50 MHz, the effective qubit-qubit coupling strength can be tuned from switching off point to two qubit gate point (effective coupling larger than 5 MHz) in double-resonator superconducting quantum circuit. The double-resonator coupler superconducting quantum circuit has the advantage of simple fabrications, introducing less flux noises, reducing occupancy of dilution refrigerator cables, which might supply a promising platform for future large-scale superconducting quantum processors.
Published: 2026-02-12 04:33:37
Authors: Tianhe Lin, Ziwei Xiong, Baoyuan Ou, Yingjie Qin, Lai Xu, Xiaocheng Zhong, Yao Hu, Zhiyong Wang, Tao Zhou, Yubin Xu, Di Wu
Categories: cs.IR
Abstract:
Modeling ultra-long user behavior sequences is pivotal for capturing evolving and lifelong interests in modern recommendation systems. However, deploying such models in real-time industrial environments faces a strict "Latency Wall", constrained by two distinct bottlenecks: the high I/O latency of retrieving massive user histories and the quadratic computational complexity of standard attention mechanisms. To break these bottlenecks, we present LASER, a full-stack optimization framework developed and deployed at Xiaohongshu (RedNote). Our approach tackles the challenges through two complementary innovations: (1) System efficiency: We introduce SeqVault, a unified schema-aware serving infrastructure for long user histories. By implementing a hybrid DRAM-SSD indexing strategy, SeqVault reduces retrieval latency by 50% and CPU usage by 75%, ensuring millisecond-level access to full real-time and life-cycle user histories. (2) Algorithmic efficiency: We propose a Segmented Target Attention (STA) mechanism to address the computational overhead. Motivated by the inherent sparsity of user interests, STA employs a sigmoid-based gating strategy that acts as a silence mechanism to filter out noisy items. Subsequently, a lightweight Global Stacked Target Attention (GSTA) module refines these compressed segments to capture cross-segment dependencies without incurring high computational costs. This design performs effective sequence compression, reducing the complexity of long-sequence modeling while preserving critical signals. Extensive offline evaluations demonstrate that LASER consistently outperforms state-of-the-art baselines. In large-scale online A/B testing serving over 100 million daily active users, LASER achieved a 2.36% lift in ADVV and a 2.08% lift in revenue, demonstrating its scalability and significant commercial impact.
Published: 2026-02-12 04:31:16
Authors: Xiaowei Wang, Yize Chen, Yue Chen
Categories: math.OC
Abstract:
Electric vehicles (EVs) are expanding rapidly, driven by the proposal to comply with global emission reduction targets. However, EV adoption in cold regions is hindered by degraded battery performance at low temperatures, which necessitates effective battery thermal management. Hence, this work proposes a novel online EV charging control strategy, incorporating battery thermal management for cold environments. We first build queue models for both battery charging and thermal dynamics. Then, we formulate an optimization problem, which allows us to coordinate battery charging and heating through maintaining queue stability. To solve the problem, we develop an online control algorithm within the theoretical framework of Lyapunov optimization. Note that our online method is prediction-free and independent of any assumed modeling of uncertainty. We also characterize both the feasibility and optimality of the proposed control approach. Numerical results based on real-world data corroborate our theoretical findings and demonstrate the effectiveness and robustness of our control method through comparisons.
Published: 2026-02-12 04:27:53
Authors: David Vokrouhlický, David Nesvorný, William F. Bottke
Categories: astro-ph.EP
Abstract:
We use our home catalog of the asteroid proper elements to study the Karin family. The hierarchical clustering method provides formal identification with 3,863 members, but this set also includes objects from the neighboring Koronis2 and Kuitaisi families, as well as interlopers originating from the much older Koronis family. By tracking the trajectories of cluster objects backward in time, we identified 2,161 asteroids whose orbits converged with that of their parent body (832) Karin at $5.72\pm 0.09$ My ago ($95$\% C.L.). This method of calculating the family's age is based on a novel convergence metric that is directly related to the velocities at which fragments were ejected from (832) Karin. We analyze the extent to which members $\leq 1.5$ km in diameter had drifted in semimajor axis due to Yarkovsky thermal forces and find it reflects the tilt of their rotation poles away from the ecliptic, recording the influence of the YORP torque. Karin's size frequency distribution in the $\simeq(0.8-3)$ km range follows a power-law with a cumulative slope index $-3.20\pm 0.01$. Removing members of the Karin family from the original group, we examine the Koronis2 family, whose members are associated with (158) Koronis. We find it difficult for large members of the Koronis2 family to converge with the orbit of (158) Koronis within its previously estimated age of $7.6$ My. Achieving such convergence would require the Koronis2 family to be older than $10$ My, but our result must be verified with a direct numerical approach in the future.
Published: 2026-02-12 04:25:39
Authors: Fanqi Shen, Enhong Yang, Jiahe Li, Junru Hong, Xiaoran Pan, Zhizhang Yuan, Meng Li, Yang Yang
Categories: cs.LG
Abstract:
Brain Foundation Models (BFMs) are transforming neuroscience by enabling scalable and transferable learning from neural signals, advancing both clinical diagnostics and cutting-edge neuroscience exploration. Their emergence is powered by large-scale clinical recordings, particularly electroencephalography (EEG) and intracranial EEG, which provide rich temporal and spatial representations of brain dynamics. However, despite their rapid proliferation, the field lacks a unified understanding of existing methodologies and a standardized evaluation framework. To fill this gap, we map the benchmark design space along two axes: (i) from the model perspective, we organize BFMs under a self-supervised learning (SSL) taxonomy; and (ii) from the dataset perspective, we summarize common downstream tasks and curate representative public datasets across clinical and human-centric neurotechnology applications. Building on this consolidation, we introduce Brain4FMs, an open evaluation platform with plug-and-play interfaces that integrates 15 representative BFMs and 18 public datasets. It enables standardized comparisons and analysis of how pretraining data, SSL strategies, and architectures affect generalization and downstream performance, guiding more accurate and transferable BFMs. The code is available at https://anonymous.4open.science/r/Brain4FMs-85B8.
Published: 2026-02-12 04:23:13
Authors: Seung-Yeal Ha, Xinyu Wang, Fanqin Zeng
Categories: math.DS
Abstract:
We study finite-time flocking for an infinite set of Cucker-Smale particles with sublinear velocity coupling under fixed and switching sender networks. For this, we use a component-wise diameter framework and exploit sub-linear dissipation mechanisms, and derive sufficient conditions for finite-time flocking equipped with explicit alignment-time estimate. For a fixed sender network, we establish component-wise finite-time flocking results under both integrable and non-integrable communication weights. When communication weight function is non-integrable, finite-time flocking is guaranteed for any bounded initial configuration. We further extend the flocking analysis to switching sender networks and show that finite-time flocking persists under mild assumptions on the cumulative influence of time-varying sender weights. The proposed framework is also applicable to both finite and infinite systems, and it yields alignment-time estimates that do not depend on the number of agents.
Published: 2026-02-12 04:18:56
Authors: Jakkapat Seeyangnok, Udomsilp Pinsook
Categories: cond-mat.supr-con
Abstract:
We present a comprehensive first-principles investigation of the structural, electronic, vibrational, and superconducting properties of halogen-functionalized Mo2YX2 (Y = C, N; X = F, Cl, Br, I) MXene monolayers. Density functional theory and density functional perturbation theory calculations reveal that, among the halogenated systems considered, only Br- and I-functionalized Mo2C monolayers are dynamically stable, as confirmed by positive definite phonon spectra throughout the Brillouin zone. Electronic structure calculations show metallic behavior with states near the Fermi level dominated by Mo d orbitals with pronounced electronic density of states, providing favorable conditions for strong electron-phonon coupling (EPC). The resulting EPC constants place both systems in the strong coupling regime, yielding superconducting transition temperatures of Tc = 13.1 K for Mo2CBr2 and Tc = 18.1 K for Mo2CI2 within the Allen-Dynes formalism. Notably, halogen functionalization itself plays a crucial role in enhancing superconductivity in Mo2C, which has Tc = 7.2 K, leading to a substantial increase in the superconducting transition temperature compared with pristine Mo2C through strengthened electron-phonon coupling. Furthermore, we demonstrate that superconductivity in these systems is highly tunable via carrier doping and biaxial tensile strain. Electron doping significantly enhances EPC and raises Tc up to 21.7 K for Mo2CBr2 and 21.3 K for Mo2CI2. Our results identify halogen-functionalized Mo2C MXenes as mechanically robust, phonon mediated two dimensional superconductors and highlight carrier doping as an effective strategy for optimizing their superconducting performance.
Published: 2026-02-12 04:05:47
Authors: Yiming Zhou, Kaiping Xue, Enhong Chen
Categories: cs.DC
Abstract:
In decentralized networks, nodes cannot ensure that their shared information will be securely preserved by their neighbors, making privacy vulnerable to inference by curious nodes. Adding calibrated random noise before communication to satisfy differential privacy offers a proven defense; however, most existing methods are tailored to specific downstream tasks and lack a general, protocol-level privacy-preserving solution. To bridge this gap, we propose Differentially Private Perturbed Push-Sum (DPPS), a lightweight differential privacy protocol for decentralized communication. Since protocol-level differential privacy introduces the unique challenge of obtaining the sensitivity for each communication round, DPPS introduces a novel sensitivity estimation mechanism that requires each node to compute and broadcast only one scalar per round, enabling rigorous differential privacy guarantees. This design allows DPPS to serve as a plug-and-play, low-cost privacy-preserving solution for downstream applications built on it. To provide a concrete instantiation of DPPS and better balance the privacy-utility trade-off, we design PartPSP, a privacy-preserving decentralized algorithm for non-convex optimization that integrates a partial communication mechanism. By partitioning model parameters into local and shared components and applying DPPS only to the shared parameters, PartPSP reduces the dimensionality of consensus data, thereby lowering the magnitude of injected noise and improving optimization performance. We theoretically prove that PartPSP converges under non-convex objectives and, with partial communication, achieves better optimization performance under the same privacy budget. Experimental results validate the effectiveness of DPPS's privacy-preserving and demonstrate that PartPSP outperforms existing privacy-preserving decentralized optimization algorithms.
Published: 2026-02-12 03:57:14
Authors: Yukihiro Okamoto, Marián Poppr
Categories: math.SG, math.GT
Abstract:
In the unit cotangent bundle of $\mathbb{R}^3$, we consider loops of Legendrian tori arising as families of the unit conormal bundles of smooth knots in $\mathbb{R}^3$. In this paper, using the cord algebra of knots, we give a topological method to compute the monodromy on the Legendrian contact homology in degree $0$ induced by those loops. As an application, we obtain an infinite family of non-contractible loops of Legendrian tori which are contractible in the space of smoothly embedded tori.
Published: 2026-02-12 03:51:30
Authors: Kai Qi, Guoxiang Wang, Xiang Shen, Yixiao Gao
Categories: physics.optics
Abstract:
In this paper, we propose a switchable high-Q light absorber based on a reconfigurable metasurface enabled by a lowloss phase-change material (PCM). By leveraging the coupling between guided-mode resonance and Fabry-Perot modes, mediated by the phase-transition dynamics of the embedded PCM, the resonance Q factor can be actively tuned. This allows the system to switch from a perfect dark state, governed by the physics of bound states in the continuum, to a critically coupled resonance with a finite Q factor. Consequently, the metasurface exhibits perfect absorption in the amorphous state and a reflection-dominated response in the crystalline state. The proposed metasurface holds significant potential for diverse nanophotonic applications, including photodetection and thermal emission control.
Published: 2026-02-12 03:41:34
Authors: Ying He, Dong-Hong Wu, Sheng Jin
Categories: astro-ph.EP
Abstract:
Warm Jupiters-giant exoplanets with orbital periods between 10 and 200 days-exhibit a broad range of eccentricities and are often accompanied by nearby low-mass planets. Understanding the origins of their orbital architectures requires examining both their migration histories and subsequent dynamical interactions. In this study, we perform extensive N-body simulations to explore how distant giant planet perturbers affect the eccentricity evolution of warm Jupiters and the role of nearby super-Earth companions in mediating these interactions. We find that while distant perturbers can induce large-amplitude eccentricity oscillations in warm Jupiters via the von Zeipel-Lidov-Kozai mechanism, the presence of nearby super-Earth companions often suppresses these variations via strong dynamical coupling. This mechanism naturally leads to a bimodal eccentricity distribution: warm Jupiters with nearby companions tend to maintain low eccentricities, whereas those without exhibit significantly broader eccentricity distributions. We show that reproducing the observed eccentricity distribution of warm Jupiters lacking nearby companions is most naturally explained if a substantial fraction of distant perturbers occupy dynamically extreme orbits, either with large mutual inclinations or high orbital eccentricities. These results support a scenario in which warm Jupiters experience substantial post-disk dynamical evolution, shaped jointly by distant perturbers and nearby companions.
Published: 2026-02-12 03:31:19
Authors: Li He, Qiang Qu, He Zhao, Stephen Wan, Dadong Wang, Lina Yao, Tongliang Liu
Categories: cs.LG
Abstract:
Reinforcement Learning from Human Feedback (RLHF) has advanced alignment capabilities significantly but remains hindered by two core challenges: \textbf{reward hacking} and \textbf{stable optimization}. Current solutions independently address these issues through separate regularization strategies, specifically a KL-divergence penalty against a supervised fine-tuned model ($π_0$) to mitigate reward hacking, and policy ratio clipping towards the current policy ($π_t$) to promote stable alignment. However, the implicit trade-off arising from simultaneously regularizing towards both $π_0$ and $π_t$ remains under-explored. In this paper, we introduce a unified regularization approach that explicitly balances the objectives of preventing reward hacking and maintaining stable policy updates. Our simple yet principled alignment objective yields a weighted supervised fine-tuning loss with a superior trade-off, which demonstrably improves both alignment results and implementation complexity. Extensive experiments across diverse benchmarks validate that our method consistently outperforms RLHF and online preference learning methods, achieving enhanced alignment performance and stability.
Published: 2026-02-12 03:22:05
Authors: Yupeng Li, Ben Chen, Mingyue Cheng, Zhiding Liu, Xuxin Zhang, Chenyi Lei, Wenwu Ou
Categories: cs.IR
Abstract:
E-commerce search serves as a central interface, connecting user demands with massive product inventories and plays a vital role in our daily lives. However, in real-world applications, it faces challenges, including highly ambiguous queries, noisy product texts with weak semantic order, and diverse user preferences, all of which make it difficult to accurately capture user intent and fine-grained product semantics. In recent years, significant advances in large language models (LLMs) for semantic representation and contextual reasoning have created new opportunities to address these challenges. Nevertheless, existing e-commerce search datasets still suffer from notable limitations: queries are often heuristically constructed, cold-start users and long-tail products are filtered out, query and product texts are anonymized, and most datasets cover only a single stage of the search pipeline. Collectively, these issues constrain research on LLM-based e-commerce search. To address these challenges, we construct and release KuaiSearch. To the best of our knowledge, it is the largest e-commerce search dataset currently available. KuaiSearch is built upon real user search interactions from the Kuaishou platform, preserving authentic user queries and natural-language product texts, covering cold-start users and long-tail products, and systematically spanning three key stages of the search pipeline: recall, ranking, and relevance judgment. We conduct a comprehensive analysis of KuaiSearch from multiple perspectives, including products, users, and queries, and establish benchmark experiments across several representative search tasks. Experimental results demonstrate that KuaiSearch provides a valuable foundation for research on real-world e-commerce search.
Published: 2026-02-12 03:19:44
Authors: Renan Favero, Lily Elefteriadou
Categories: cs.ET, cs.LG
Abstract:
Autonomous shuttles (AS) are fully autonomous transit vehicles with operating characteristics distinct from conventional autonomous vehicles (AV). Developing dedicated car-following models for AS is critical to understanding their traffic impacts; however, few studies have calibrated such models with field data. More advanced machine learning (ML) techniques have not yet been applied to AS trajectories, leaving the potential of ML for capturing AS dynamics unexplored and constraining the development of dedicated AS models. Furthermore, there is a lack of a unified framework for systematically evaluating and comparing the performance of car-following models to replicate real trajectories. Existing car-following studies often rely on disparate metrics, which limit reproducibility and performance comparability.
This study addresses these gaps through two main contributions: (1) the calibration of a diverse set of car-following models using real-world AS trajectory data, including eight machine learning algorithms and two physics-based models; and (2) the introduction of a multi-criteria evaluation framework that integrates measures of prediction accuracy, trajectory stability, and statistical similarity, which provides a generalizable methodology for a systematic assessment of car-following models.
Results indicated that the proposed calibrated XGBoost model achieved the best overall performance. Sequential model type, such as LSTM and CNN, captured long-term positional stability but were less responsive to short-term dynamics. LSTM and CNN captured long-term positional stability but were less responsive to short-term dynamics. Traditional models (IDM, ACC) and kernel methods showed lower accuracy and stability than most ML models tested.
Published: 2026-02-12 03:19:04
Authors: Hong Su
Categories: cs.AI
Abstract:
Learning internal reasoning processes is crucial for developing AI systems capable of sustained adaptation in dynamic real-world environments. However, most existing approaches primarily emphasize learning task-specific outputs or static knowledge representations, while overlooking the continuous refinement of internal reasoning structures, action scheduling policies, and learning mechanisms themselves. In this paper, we propose a human-inspired continuous learning framework that unifies reasoning, action, reflection, and verification within a sequential reasoning model enhanced by parallel learning. The framework explicitly treats internal thinking processes as primary learning objects. It systematically records internal reasoning trajectories and environmental interactions as structured learning material, enabling the system to optimize not only task-level content but also the organization, scheduling, and evolution of reasoning activities. This design realizes learning alongside processing, allowing cognitive structures to improve during execution. Furthermore, the framework supports controlled replacement of predefined logic with learned procedures and introduces a hierarchical learning-to-learn mechanism that jointly adapts task-level parameters and learning strategies. As a result, the system progressively evolves its internal cognitive architecture while preserving operational stability. Experimental results on a temperature sensor abnormality detection task show that incorporating internal-process learning reduces average runtime by 23.9%.
Published: 2026-02-12 03:05:10
Authors: Dajun Lin, Brian Baker, Rajesh Menon
Categories: physics.optics, physics.app-ph
Abstract:
Volumetric lithography offers a path to scalable fabrication of complex three-dimensional (3D) micro- and nanoscale architectures, yet existing approaches are limited by quasi-two-dimensional exposure physics or slow serial writing. We present a single-exposure volumetric fabrication strategy that enables creation of ultrahigh-aspect-ratio 3D structures with 6 um minimum features. An inverse-designed volumetric (holographic) phase mask generates an extended-depth-of-field intensity distribution inside a photoresist volume while preserving high transverse resolution, enabling uniform polymerization of the full volume in a single exposure. With exposure times of approximately 20 s, we fabricate lattices, Penrose tilings, and micromechanical elements with feature sizes down to 6 um over volumes up to 800 x 800 x 720 um^3, achieving aspect ratios exceeding 120:1. Quantitative analysis of capillary flow in hollow lattices demonstrates controlled fluid transport with an effective capillary transport coefficient of 176.3 um/(ms)^(1/2). In situ nanoindentation-based micro-compression reveals that the printed 3D hexagonal close-packed lattices exhibit a well-defined linear elastic regime with an effective Young's modulus of 5.7 GPa, followed by progressive buckling and densification characteristic of mechanically robust cellular architectures. Overlapping, tilted and multi-mask exposures further enable quasi-3D complex geometries with potential for reconfigurability. This approach establishes a new regime of high-throughput volumetric fabrication.
Published: 2026-02-12 03:04:54
Authors: Ryuji Matsuo, Hailong Liu, Toshihiro Hiraoka, Takahiro Wada
Categories: cs.HC
Abstract:
Level 3 automated driving systems (ADS) have attracted significant attention and are being commercialized. A level 3 ADS prompts the driver to take control by issuing a request to intervene (RtI) when its operational design domains (ODD) are exceeded. However, complex traffic situations can cause drivers to perceive multiple potential triggers of RtI simultaneously, causing hesitation or confusion during take-over. Therefore, drivers need to clearly understand the ADS's system limitations to ensure safe take-over. This study proposes a voice-based educational human machine interface~(HMI) for providing RtI trigger cues and reason to help drivers understand ADS's system limitations. The results of a between-group experiment using a driving simulator showed that incorporating effective trigger cues and reason into the RtI was related to improved driver comprehension of the ADS's system limitations. Moreover, most participants, instructed via the proposed method, could proactively take over control of the ADS in cases where RtI fails; meanwhile, their number of collisions was lower compared with the other RtI HMI conditions. Therefore, using the proposed method to continually enhance the driver's understanding of the system limitations of ADS through the proposed method is associated with safer and more effective real-time interactions with ADS.
Published: 2026-02-12 02:58:19
Authors: Zhaodong Chu, Carter Fox, Zixin Zhai, Haihua Liu, Priti Yadav, Bing Lv, Yue Li, Thomas E Gage, Jun Xiao, Haidan Wen
Categories: cond-mat.mtrl-sci, cond-mat.mes-hall
Abstract:
Understanding how low-dimensional ferroelectrics respond to ultrafast excitation at nanoscales is essential for controlling energy flow and mechanical functionality in next-generation polar materials and devices. Here, we report spatiotemporally resolved structural dynamics in the van der Waals ferroelectric NbOI2 using combined ultrafast electron microscopy and diffraction. Above-band-gap photoexcitation rapidly screens the in-plane polarization and heats the lattice, launching three coherent acoustic phonons: two transverse shear modes and one longitudinal breathing mode. The transverse mode that shears the layers perpendicular to the in-plane polar axis dominates over that along the polar axis, reflecting anisotropic coupling between polarization and strain. Spatially resolved measurements further reveal spatially correlated heterogeneity in the mode amplitudes and lifetimes. Regions dominated by a single shear mode exhibit significantly longer lifetime of acoustic oscillations than that of multimode regions, suggesting that acoustic phonon-phonon scattering is a major source of decoherence. Our results provide a microscopic understanding of polarization-strain coupling and spatially heterogeneous energy dissipation in van der Waals ferroelectrics under ultrafast excitation.
Published: 2026-02-12 02:51:59
Authors: Zhenlong Yuan, Xiangyan Qu, Jing Tang, Rui Chen, Lei Sun, Ruidong Chen, Hongwei Yu, Chengxuan Qian, Xiangxiang Chu, Shuo Li, Yuyin Zhou
Categories: cs.CV
Abstract:
Multimodal Large Language Models have shown promising capabilities in bridging visual and textual reasoning, yet their reasoning capabilities in Open-Vocabulary Human-Object Interaction (OV-HOI) are limited by cross-modal hallucinations and occlusion-induced ambiguity. To address this, we propose \textbf{ImagineAgent}, an agentic framework that harmonizes cognitive reasoning with generative imagination for robust visual understanding. Specifically, our method innovatively constructs cognitive maps that explicitly model plausible relationships between detected entities and candidate actions. Subsequently, it dynamically invokes tools including retrieval augmentation, image cropping, and diffusion models to gather domain-specific knowledge and enriched visual evidence, thereby achieving cross-modal alignment in ambiguous scenarios. Moreover, we propose a composite reward that balances prediction accuracy and tool efficiency. Evaluations on SWIG-HOI and HICO-DET datasets demonstrate our SOTA performance, requiring approximately 20\% of training data compared to existing methods, validating our robustness and efficiency.
Published: 2026-02-12 02:46:01
Authors: Sen Wang, Yirong Yang, Jooho Lee, Grant M. Stevens, Adam S. Wang
Categories: physics.med-ph, eess.IV
Abstract:
Quantitative imaging is an important feature of spectral X-ray and CT systems, especially photon-counting CT (PCCT) imaging systems, which is achieved through material decomposition (MD) using spectral measurements. In this work, we present a novel framework that makes the PCCT imaging chain end-to-end differentiable (differentiable PCCT), with which we can leverage quantitative information in the image domain to enable cross-domain learning and optimization for upstream models. Specifically, the material decomposition from maximum-likelihood estimation (MLE) was made differentiable based on the Implicit Function Theorem and inserted as a layer into the imaging chain for end-to-end optimization. This framework allows for an automatic and adaptive solution of a wide range of imaging tasks, ultimately achieving quantitative imaging through computation rather than manual intervention. The end-to-end training mechanism effectively avoids the need for direct-domain training or supervision from intermediate references as models are trained using quantitative images. We demonstrate its applicability in two representative tasks: correcting detector energy bin drift and training an object scatter correction network using cross-domain reference from quantitative material images.
Published: 2026-02-12 01:56:22
Authors: Xingyu Wang
Categories: math.AP
Abstract:
In this work, we study a matrix-valued Allen-Cahn equation with a Saint Venant-Kirchhoff potential $F(\mathbf{A})=\frac{1}{4}\|\mathbf{A}\mathbf{A}^\top-\mathbf{I}\|^2$. Our approach employs the modulated energy method together with weak convergence methods for nonlinear partial differential equations. This avoids the subtle spectrum analysis of the linearized operator at the so-called quasi-minimal orbits as well as the construction of asymptotic expansion. Moreover, it relaxes the assumption on the admissible initial data, which exhibits a phase transition along an initial interface. As a byproduct, we construct a weak solution to the limiting harmonic heat flow system with both minimal pair and Neumann-type boundary conditions across the interface.
Published: 2026-02-12 01:50:05
Authors: Kazuki Haishima, Kyohei Suzuki, Konstantinos Slavakis
Categories: cs.LG
Abstract:
This paper presents a novel method for recovering sparse vectors from linear models corrupted by Poisson noise. The contribution is twofold. First, an operator defined via the external division of two Bregman proximity operators is introduced to promote sparse solutions while mitigating the estimation bias induced by classical $\ell_1$-norm regularization. This operator is then embedded into the already established NoLips algorithm, replacing the standard Bregman proximity operator in a plug-and-play manner. Second, the geometric structure of the proposed external-division operator is elucidated through two complementary reformulations, which provide clear interpretations in terms of the primal and dual spaces of the Poisson inverse problem. Numerical tests show that the proposed method exhibits more stable convergence behavior than conventional Kullback-Leibler (KL)-based approaches and achieves significantly superior performance on synthetic data and an image restoration problem.
Published: 2026-02-12 01:46:26
Authors: Yuan Gao, Xiao-Yun Wang, Xiang Liu
Categories: nucl-th, hep-th
Abstract:
In this work, the production mechanisms of the hyperon resonances $Λ(1405)$ and $Λ(1520)$ in the $π^- p$ scattering are investigated within an effective Lagrangian approach incorporating Regge trajectories. By including contributions from $t$-channel $K^*$ and $u$-channel $Σ$ exchanges, we perform global fits to the total and differential cross sections for $π^{-} p \rightarrow KΛ(1405)$ and $π^{-} p \rightarrow KΛ(1520)$. The results show good agreement with available experimental data. For the total cross section of $Λ(1405)$ production, the $u$-channel contribution is dominant, whereas the $t$-channel contribution plays the primary role in $Λ(1520)$ production. Furthermore, the differential cross sections of the two processes exhibit distinctly different shapes, reflecting their distinct underlying reaction mechanisms. An analysis based on the constituent counting rule indicates that $Λ(1520)$ is consistent with a conventional three-quark configuration, while $Λ(1405)$ shows a clear deviation, suggesting a more exotic structure. Owing to the large branching ratio of $Λ^* \to πΣ$, the Dalitz process $π^{-} p \rightarrow K Λ^{*} \rightarrow K πΣ$ is also calculated. Our results demonstrate that reconstructing $Λ^*$ via the $KπΣ$ final state is experimentally feasible. This study provides important theoretical insights into the production dynamics of these hyperon resonances, and suggests future high-precision measurements of the $t$-distribution at large momentum transfer at facilities such as AMBER, J-PARC, HIKE, and HIAF, which can further clarify their reaction mechanisms and structural properties.
Published: 2026-02-12 01:01:38
Authors: Ye Yu, Yifan Zhou, Yi Chen, Pedro Soto, Wenjie Xiong, Meng Li
Categories: cs.CR
Abstract:
Generative large language models (LLMs) have revolutionized multiple domains. Modern LLMs predominantly rely on an autoregressive decoding strategy, which generates output tokens sequentially and employs a key-value cache (KV cache) to avoid redundant computation. However, the widespread deployment of LLMs has raised serious privacy concerns, as users are feeding all types of data into the model, motivating the development of secure inference frameworks based on fully homomorphic encryption (FHE). A major limitation of existing FHE-based frameworks is their inability to effectively integrate the KV cache, resulting in prohibitively high latency for autoregressive decoding. In this paper, we propose Cachemir, a KV Cache Accelerated Homomorphic Encrypted LLM Inference Regime to overcome this limitation. Cachemir comprises three key technical contributions: 1) a set of novel HE packing algorithms specifically designed to leverage the computational advantages of the KV cache; 2) an interleaved replicated packing algorithm to efficiently compute the vector-matrix multiplications that result from using the KV cache in Transformer linear layers; and 3) an augmented bootstrapping placement strategy that accounts for the KV cache to minimize bootstrapping cost. We demonstrate that Cachemir achieves $48.83\times$ and $67.16\times$ speedup over MOAI (ICML'25) and THOR (CCS'25) respectively on CPU and consumes less than 100 seconds on GPU to generate an output token for Llama-3-8B.
Published: 2026-02-12 00:54:22
Authors: Yun-Cheng Li, Sen Lei, Heng-Chao Li, Ke Li
Categories: cs.CV
Abstract:
Semantic Change Detection (SCD) aims to detect and categorize land-cover changes from bi-temporal remote sensing images. Existing methods often suffer from blurred boundaries and inadequate temporal modeling, limiting segmentation accuracy. To address these issues, we propose a Dual-Branch Framework for Semantic Change Detection with Boundary and Temporal Awareness, termed DBTANet. Specifically, we utilize a dual-branch Siamese encoder where a frozen SAM branch captures global semantic context and boundary priors, while a ResNet34 branch provides local spatial details, ensuring complementary feature representations. On this basis, we design a Bidirectional Temporal Awareness Module (BTAM) to aggregate multi-scale features and capture temporal dependencies in a symmetric manner. Furthermore, a Gaussian-smoothed Projection Module (GSPM) refines shallow SAM features, suppressing noise while enhancing edge information for boundary-aware constraints. Extensive experiments on two public benchmarks demonstrate that DBTANet effectively integrates global semantics, local details, temporal reasoning, and boundary awareness, achieving state-of-the-art performance.
Published: 2026-02-12 00:41:01
Authors: Tao Zhang, Song Xia, Ye Wang, Qin Jin
Categories: cs.RO
Abstract:
Robot imitation learning is often hindered by the high cost of collecting large-scale, real-world data. This challenge is especially significant for low-cost robots designed for home use, as they must be both user-friendly and affordable. To address this, we propose the EasyMimic framework, a low-cost and replicable solution that enables robots to quickly learn manipulation policies from human video demonstrations captured with standard RGB cameras. Our method first extracts 3D hand trajectories from the videos. An action alignment module then maps these trajectories to the gripper control space of a low-cost robot. To bridge the human-to-robot domain gap, we introduce a simple and user-friendly hand visual augmentation strategy. We then use a co-training method, fine-tuning a model on both the processed human data and a small amount of robot data, enabling rapid adaptation to new tasks. Experiments on the low-cost LeRobot platform demonstrate that EasyMimic achieves high performance across various manipulation tasks. It significantly reduces the reliance on expensive robot data collection, offering a practical path for bringing intelligent robots into homes. Project website: https://zt375356.github.io/EasyMimic-Project/.
Published: 2026-02-12 00:39:23
Authors: Kainat Yasmeen, Shobha Sundar Ram
Categories: eess.SP
Abstract:
Electromagnetic wave propagation through complex inhomogeneous walls introduces significant distortions to through-wall radar signatures. Estimation of wall thickness, dielectric, and conductivity profiles may enable wall effects to be deconvolved from target scattering. We propose to use deep neural networks (DNNs) to estimate wall characteristics from broadband scattered electric fields on the same side of the wall as the transmitter. We demonstrate that both single deep artificial and convolutional neural networks and dual networks involving generative adversarial networks are capable of performing the highly nonlinear regression operation of electromagnetic inverse scattering for wall characterization. These networks are trained with simulation data generated from full wave solvers and validated on both simulated and real wall data with approximately 95% accuracy.
Published: 2026-02-12 00:38:29
Authors: Ying Wai Lee
Categories: math.NT, math.DS, math.PR
Abstract:
Two longest-run statistics are studied: the longest run of a fixed value and the longest run over all values. Under quantitative mixing and exponential cylinder estimates for constant words, a general theorem is proved. Quantitative almost-sure logarithmic growth is obtained, and eventual two-sided bounds with double-logarithmic error terms are established. For continued-fraction partial quotients, explicit centring constants and double-logarithmic error bounds are derived for both statistics.
Published: 2026-02-12 00:32:41
Authors: Ying Wai Lee
Categories: math.DS, math.NT, math.PR
Abstract:
The number of distinct symbols appearing in digit expansions generated by full-branch affine countable iterated function systems is studied whose branch weights are regularly varying. The Hausdorff dimensions of the exceptional sets in which the distinct-digit count grows at a positive linear rate or at a prescribed sublinear rate are determined. The resulting dimension laws exhibit a sharp phase transition: imposing any positive linear rate forces the dimension to collapse to a value determined solely by the tail index, whereas under a broad class of sublinear growth rates, the exceptional sets retain full Hausdorff dimension.
Published: 2026-02-11 23:53:15
Authors: Nghia Nguyen, Tianjiao Ding, René Vidal
Categories: cs.LG, cs.CV
Abstract:
Interpretable-by-design models are gaining traction in computer vision because they provide faithful explanations for their predictions. In image classification, these models typically recover human-interpretable concepts from an image and use them for classification. Sparse concept recovery methods leverage the latent space of vision-language models to represent image embeddings as a sparse combination of concept embeddings. However, because such methods ignore the hierarchical structure of concepts, they can produce correct predictions with explanations that are inconsistent with the hierarchy. In this work, we propose Hierarchical Concept Embedding \& Pursuit (HCEP), a framework that induces a hierarchy of concept embeddings in the latent space and uses hierarchical sparse coding to recover the concepts present in an image. Given a hierarchy of semantic concepts, we construct a corresponding hierarchy of concept embeddings and, assuming the correct concepts for an image form a rooted path in the hierarchy, derive desirable conditions for identifying them in the embedded space. We show that hierarchical sparse coding reliably recovers hierarchical concept embeddings, whereas vanilla sparse coding fails. Our experiments on real-world datasets demonstrate that HCEP outperforms baselines in concept precision and recall while maintaining competitive classification accuracy. Moreover, when the number of samples is limited, HCEP achieves superior classification accuracy and concept recovery. These results show that incorporating hierarchical structures into sparse coding yields more reliable and interpretable image classification models.
Published: 2026-02-11 23:38:58
Authors: Shuyao Wu, Delong Li, Zhonghao Zhang
Categories: econ.GN
Abstract:
Urban parks play a vital role in delivering various essential ecosystem services that significantly contribute to the well-being of urban populations. However, there is quite a limited understanding of how people value these ecosystem services differently. Here, we investigated the relationships among nine ecosystem service demands in urban parks across China using a large-scale survey with 20,075 responses and a point-allotment experiment. We found particularly high preferences for air purification and recreation services at the expense of other services among urban residents in China. These preferences were further reflected in three distinct demand bundles: air purification-dominated, recreation-dominated, and balanced demands. Each bundle delineated a typical group of people with different representative characteristics. Socio-economic and environmental factors, such as environmental interest and vegetation coverage, were found to significantly influence the trade-off intensity among service demands. These results underscore the necessity for tailored urban park designs that address diverse service demands with the aim of enhancing the quality of urban life in China and beyond sustainably.
Published: 2026-02-11 23:20:14
Authors: Carolina Brás, Soufiane Ben Haddou, Thijs P. Kuipers, Laura Alvarez-Florez, R. Nils Planken, Fleur V. Y. Tjong, Connie Bezzina, Ivana Išgum
Categories: cs.CV, cs.AI
Abstract:
The anisotropic nature of short-axis (SAX) cardiovascular magnetic resonance imaging (CMRI) limits cardiac shape analysis. To address this, we propose to leverage near-isotropic, higher resolution computed tomography angiography (CTA) data of the heart. We use this data to train a single neural implicit function to jointly represent cardiac shapes from CMRI at any resolution. We evaluate the method for the reconstruction of right ventricle (RV) and myocardium (MYO), where MYO simultaneously models endocardial and epicardial left-ventricle surfaces. Since high-resolution SAX reference segmentations are unavailable, we evaluate performance by extracting a 4-chamber (4CH) slice of RV and MYO from their reconstructed shapes. When compared with the reference 4CH segmentation masks from CMRI, our method achieved a Dice similarity coefficient of 0.91 $\pm$ 0.07 and 0.75 $\pm$ 0.13, and a Hausdorff distance of 6.21 $\pm$ 3.97 mm and 7.53 $\pm$ 5.13 mm for RV and MYO, respectively. Quantitative and qualitative assessment demonstrate the model's ability to reconstruct accurate, smooth and anatomically plausible shapes, supporting improvements in cardiac shape analysis.
Published: 2026-02-11 23:19:45
Authors: Haolin Li, Michael Coblenz
Categories: cs.SE
Abstract:
Debugging is a central yet complex activity in software engineering. Prior studies have documented debugging strategies and tool usage, but little theory explains how experienced developers reason about bugs in large, real-world codebases. We conducted a qualitative study using a grounded theory approach. We observed seven professional developers and five professional live-coding streamers working on 17 debugging tasks in their own codebases, capturing diverse contexts of debugging. We theorize debugging as a structured, iterative diagnostic process in which programmers update a mental model of the system to guide information gathering. Developers gather information by alternating between navigation and execution strategies, employing forward and backward tracing modes of reasoning and adapting these approaches according to codebase context, complexity, and familiarity. Developers also gather external resources to complement code-based evidence, with their experience enabling them to systematically construct a mental model. We contribute a grounded theory of professional debugging that surfaces the human-centered dimensions of the practice, with implications for tool design and software engineering education.
Published: 2026-02-11 23:03:12
Authors: Zitian Qiu, Yunjie Gu
Categories: eess.SY
Abstract:
As the growing penetration of inverter-based resources (IBRs) in modern power systems, the system strength is decreasing. Due to the inherent difference in short-circuit capacity contributions of synchronous generators and inverters, the short-circuit ratio is not a one-size-fit-all metric to assess the system strength. Following the distinct dynamic behavior of the IBR in small- and large-signal disturbance, the system strength is separated accordingly. To address the large-signal system strength assessment, a control type-dependent metric, Power Margin Ratio (PMR), is proposed in this paper. PMR is defined as the ratio between the maximum power that can be injected to the system without causing any instability and the nominal power of the IBR. It can be obtained via power flow calculation with a modified algorithm. The theoretical foundation of PMR is established from the viewpoint of dynamical systems. PMR is identical to SCR for the single-plant-infinite-bus system, while presents advancement for multi-infeed power systems. Comprehensive case studies and discussions have validated that PMR reveals the large-signal system strength from a static perspective.
Published: 2026-02-11 23:01:16
Authors: Davide Zenatti, Patanjali Kambhampati
Categories: cond-mat.mes-hall, physics.optics
Abstract:
Despite three decades of experimental study, optical gain in colloidal quantum dots still lacks a microscopic theory capable of explaining gain thresholds approaching one exciton per dot, their size dependence, or the anomalously small effective stimulated-emission cross sections observed across materials. Existing descriptions treat quantum dots as effective two-level systems comprised of an exciton and a biexciton, attributing gain thresholds to biexciton Auger recombination. This assumption is inconsistent with state-resolved optical pumping experiments and basic spectroscopic constraints. Here we present a microscopic theory of optical gain explicitly anchored in the Einstein relations governing absorption and stimulated emission. Within this framework, gain is determined by a spectral balance between stimulated emission from single excitons and excited-state absorption into biexcitonic manifolds, rather than by biexciton lifetimes. Using a spin-boson description of excitons coupled to a lattice bath, we show that gain thresholds and effective gain cross sections are controlled by the interplay of biexciton stabilization and exciton-lattice dressing. The theory unifies disparate materials by quantitatively explaining all longstanding gain phenomenology in CdSe quantum dots and predicts a continuous crossover to effective four-level, near-thresholdless gain in dynamically disordered lattices such as perovskite quantum dots.
Published: 2026-02-11 22:50:25
Authors: Xingyu Li, Huasheng Xie, Lai Wei, Zhengxiong Wang
Categories: physics.plasm-ph
Abstract:
Standard reduced models often fail to adequately describe the complex geometric response of tokamak plasmas to strong toroidal rotation. In this work, we present VEQ-R, a computationally efficient spectral solver designed to calculate fixed-boundary equilibria with arbitrary toroidal flow. In contrast to computationally intensive grid-based codes, our model employs a 12-parameter shifted Chebyshev spectral expansion to explicitly resolve radial variations in high-order shaping profiles--such as dynamic elongation and triangularity. This capability allows the solver to accurately capture differential flux surface distortions (non-rigid effects) even in challenging sonic regimes ($M \sim 1.0$). By synergizing this compact variational formulation with a novel ``Matrix-Kernel'' acceleration technique, we transform the problem into pre-computed algebraic matrix operations. This approach achieves convergence in approximately 5 ms, maintaining exceptional geometric fidelity compared to high-resolution benchmarks while balancing speed and accuracy. Our analysis reveals that rotation-induced flux compression leads to a monotonic decrease in the core safety factor $q_0$, pushing it dangerously close to unity--a structural deformation mechanism effectively captured by this approximate yet robust solver.
Published: 2026-02-11 22:24:33
Authors: David Pardoe, Neil Daftary, Miro Furtado, Aditya Aiyer, Yu Wang, Liuqing Li, Tao Song, Lars Hertel, Young Jin Yun, Senthil Radhakrishnan, Zhiwei Wang, Tommy Li, Khai Tran, Ananth Nagarajan, Ali Naqvi, Yue Zhang, Renpeng Fang, Avi Romascanu, Arjun Kulothungun, Deepak Kumar, Praneeth Boda, Fedor Borisyuk, Ruoyan Wang
Categories: cs.LG
Abstract:
Click-through rate (CTR) prediction is fundamental to online advertising systems. While Deep Learning Recommendation Models (DLRMs) with explicit feature interactions have long dominated this domain, recent advances in generative recommenders have shown promising results in content recommendation. However, adapting these transformer-based architectures to ads CTR prediction still presents unique challenges, including handling post-scoring contextual signals, maintaining offline-online consistency, and scaling to industrial workloads. We present CADET (Context-Conditioned Ads Decoder-Only Transformer), an end-to-end decoder-only transformer for ads CTR prediction deployed at LinkedIn. Our approach introduces several key innovations: (1) a context-conditioned decoding architecture with multi-tower prediction heads that explicitly model post-scoring signals such as ad position, resolving the chicken-and-egg problem between predicted CTR and ranking; (2) a self-gated attention mechanism that stabilizes training by adaptively regulating information flow at both representation and interaction levels; (3) a timestamp-based variant of Rotary Position Embedding (RoPE) that captures temporal relationships across timescales from seconds to months; (4) session masking strategies that prevent the model from learning dependencies on unavailable in-session events, addressing train-serve skew; and (5) production engineering techniques including tensor packing, sequence chunking, and custom Flash Attention kernels that enable efficient training and serving at scale. In online A/B testing, CADET achieves a 11.04\% CTR lift compared to the production LiRank baseline model, a hybrid ensemble of DCNv2 and sequential encoders. The system has been successfully deployed on LinkedIn's advertising platform, serving the main traffic for homefeed sponsored updates.
Published: 2026-02-11 22:16:20
Authors: Tomer Gafni, Garud Iyengar, Assaf Zeevi
Categories: stat.ML, cs.LG
Abstract:
We consider an online learning problem in environments with multiple change points. In contrast to the single change point problem that is widely studied using classical "high confidence" detection schemes, the multiple change point environment presents new learning-theoretic and algorithmic challenges. Specifically, we show that classical methods may exhibit catastrophic failure (high regret) due to a phenomenon we refer to as endogenous confounding. To overcome this, we propose a new class of learning algorithms dubbed Anytime Tracking CUSUM (ATC). These are horizon-free online algorithms that implement a selective detection principle, balancing the need to ignore "small" (hard-to-detect) shifts, while reacting "quickly" to significant ones. We prove that the performance of a properly tuned ATC algorithm is nearly minimax-optimal; its regret is guaranteed to closely match a novel information-theoretic lower bound on the achievable performance of any learning algorithm in the multiple change point problem. Experiments on synthetic as well as real-world data validate the aforementioned theoretical findings.
Published: 2026-02-11 22:10:54
Authors: Antonio Jiménez-Pastor, Sonia L. Rueda
Categories: math.AC, math.OA, math.SP
Abstract:
The correspondence between commutative rings of ordinary differential operators (ODOs) and algebraic curves was established by Burchnall and Chaundy, Krichever and Mumford, among many others. To make this correspondence computationally effective, in this paper we aim to compute the defining ideals of spectral curves, Burchnall-Chaundy (BC) ideals. We provide an algorithm to compute a Gröbner basis of a BC ideal. The point of departure is the computation of the finite set of generators of a maximal commutative ring of ODOs, which was implemented by the authors in the package dalgebra of SageMath. The algorithm to compute BC ideals has been also implemented in dalgebra.
The differential Galois theory of the corresponding spectral problems, linear differential equations with parameters, would benefit from the computation on this prime ideal, generated by constant coefficient polynomials. In particular, we prove the primality of the differential ideal generated by a BC ideal, after extending the coefficient field. This is a fundamental result to develop Picard-Vessiot theory for spectral problems.
Published: 2026-02-11 22:03:37
Authors: Hormoz Shahrzad, Niharika Gajawell, Kaitlin Maile, Manish Saggar, Risto Miikkulainen
Categories: cs.NE
Abstract:
Evolutionary search is well suited for large-scale biophysical brain modeling, where many parameters with nonlinear interactions and no tractable gradients need to be optimized. Standard evolutionary approaches achieve an excellent fit to MRI data; however, among many possible such solutions, it finds ones that overfit to individual subjects and provide limited predictive power. This paper investigates whether guiding evolution with biological knowledge can help. Focusing on whole-brain Dynamic Mean Field (DMF) models, a baseline where 20 parameters were shared across the brain was compared against a heterogeneous formulation where different sets of 20 parameters were used for the seven canonical brain regions. The heterogeneous model was optimized using four strategies: optimizing all parameters at once, a curricular approach following the hierarchy of brain networks (HICO), a reversed curricular approach, and a randomly shuffled curricular approach. While all heterogeneous strategies fit the data well, only curricular approaches generalized to new subjects. Most importantly, only HICO made it possible to use the parameter sets to predict the subjects' behavioral abilities as well. Thus, by guiding evolution with biological knowledge about the hierarchy of brain regions, HICO demonstrated how domain knowledge can be harnessed to serve the purpose of optimization in real-world domains.
Published: 2026-02-11 21:53:18
Authors: Md Tanvir Rouf Shawon, Mohammad Sabik Irbaz, Hadeel R. A. Elyazori, Keerti Reddy Resapu, Yili Lin, Vladimir Franzuela Cardenas, Farrokh Alemi, Kevin Lybarger
Categories: cs.CL
Abstract:
Objective: This paper introduces a patient simulator designed to enable scalable, automated evaluation of healthcare conversational agents. The simulator generates realistic, controllable patient interactions that systematically vary across medical, linguistic, and behavioral dimensions, allowing annotators and an independent AI judge to assess agent performance, identify hallucinations and inaccuracies, and characterize risk patterns across diverse patient populations. Methods: The simulator is grounded in the NIST AI Risk Management Framework and integrates three profile components reflecting different dimensions of patient variation: (1) medical profiles constructed from electronic health records in the All of Us Research Program; (2) linguistic profiles modeling variation in health literacy and condition-specific communication patterns; and (3) behavioral profiles representing empirically observed interaction patterns, including cooperation, distraction, and adversarial engagement. We evaluated the simulator's effectiveness in identifying errors in an AI decision aid for antidepressant selection. Results: We generated 500 conversations between the patient simulator and the AI decision aid across systematic combinations of five linguistic and three behavioral profiles. Human annotators assessed 1,787 medical concepts across 100 conversations, achieving high agreement (F1=0.94, \k{appa}=0.73), and the LLM judge achieved comparable agreement with human annotators (F1=0.94, \k{appa}=0.78; paired bootstrap p=0.21). The simulator revealed a monotonic degradation in AI decision aid performance across the health literacy spectrum: rank-one concept retrieval accuracy increased from 47.9% for limited health literacy to 69.1% for functional and 81.6% for proficient.
Published: 2026-02-11 21:48:09
Authors: Christian Z. Pratt, Kyle J. Ray, James P. Crutchfield
Categories: cond-mat.stat-mech, cond-mat.supr-con, cs.AR, cs.ET, nlin.CD
Abstract:
Smartphones, laptops, and data centers are CMOS-based technologies that ushered our world into the information age of the 21st century. Despite their advantages for scalable computing, their implementations come with surprisingly large energetic costs. This challenge has revitalized scientific and engineering interest in energy-efficient information-processing designs. One current paradigm -- dynamical computing -- controls the location and shape of minima in potential energy landscapes that are connected to a thermal environment. The landscape supports distinguishable metastable energy minima that serve as a system's mesoscopic memory states. Information is represented by microstate distributions. Dynamically manipulating the memory states then corresponds to information processing. This framing provides a natural description of the associated thermodynamic transformations and required resources. Appealing to bifurcation theory, a computational protocol in the metastable regime can be analyzed by tracking the evolution of fixed points in the state space. We illustrate the paradigm's capabilities by performing 1-bit and 2-bit computations with double-well and quadruple-well potentials, respectively. These illustrate how dynamical computing can serve as a basis for designing universal logic gates and investigating their out-of-equilibrium thermodynamic performance.