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Collaborative learning environments that support students' problem solving have been shown to promote better decision-making, greater academic achievement, and more reasonable argumentation about controversial issues. In this research, we developed a technology-based critical discussion platform to support middle school students' argumentation, with a focus on evidence-based reasoning and perspective taking. A feasibility study was conducted to examine the patterns of group interaction and individual students' contributions to the critical discussion and their perceptions of the critical discussion activity. We found that more students used text-based communications than audio, but students who used audio collaborated with each other more frequently. In addition, student engagement in argumentative discourse varied greatly across groups as well as individuals. At the end of the discussion, most groups provided a solution that integrated both sides of the controversial issue. Survey and interview results suggest an overall positive experience with this technology-supported critical discussion activity. Using the insights from our research, we develop a conceptual dialogue analysis framework that identifies relevant skills under the argumentation and collaboration dimensions. In this report, we discuss our design considerations, feasibility study results, and implications of engaging students in computer-supported collaborative argumentation.

Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine-tuned models often leads to degraded performance due to overlapping instruction-following components. Task Arithmetic (TA), which combines task vectors derived from fine-tuning, enables multi-task learning and task forgetting but struggles to isolate task-specific knowledge from general instruction-following behavior. To address this, we propose Layer-Aware Task Arithmetic (LATA), a novel approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. By amplifying task-relevant layers and attenuating instruction-following layers, LATA improves task learning and forgetting performance while preserving overall model utility. Experiments on multiple benchmarks, including WikiText-2, GSM8K, and HumanEval, demonstrate that LATA outperforms existing methods in both multi-task learning and selective task forgetting, achieving higher task accuracy and alignment with minimal degradation in output quality. Our findings highlight the importance of layer-wise analysis in disentangling task-specific and general-purpose knowledge, offering a robust framework for efficient model merging and editing.

The complex application scenarios have raised critical requirements for precise and generalizable gaze estimation methods. Recently, the pre-trained CLIP has achieved remarkable performance on various vision tasks, but its potentials have not been fully exploited in gaze estimation. In this paper, we propose a novel CLIP-driven Dual Feature Enhancing Network (CLIP-DFENet), which boosts gaze estimation performance with the help of CLIP under a novel `main-side' collaborative enhancing strategy. Accordingly, a Language-driven Differential Module (LDM) is designed on the basis of the CLIP's text encoder to reveal the semantic difference of gaze. This module could empower our Core Feature Extractor with the capability of characterizing the gaze-related semantic information. Moreover, a Vision-driven Fusion Module (VFM) is introduced to strengthen the generalized and valuable components of visual embeddings obtained via CLIP's image encoder, and utilizes them to further improve the generalization of the features captured by Core Feature Extractor. Finally, a robust Double-head Gaze Regressor is adopted to map the enhanced features to gaze directions. Extensive experimental results on four challenging datasets over within-domain and cross-domain tasks demonstrate the discriminability and generalizability of our CLIP-DFENet.

The geometric distances of active galactic nuclei (AGNs) are challenging to measure because of their exceptionally compact structure yet vast cosmic distances. A combination of spectroastrometry and reverberation mapping (SARM) of broad-line regions (BLRs) constitutes a novel means to probe the geometric distance of AGNs, which has recently become practically feasible owing to successful interferometric observations with VLTI/GRAVITY. Here, we perform SARM analysis of four nearby quasars: Mrk 509, PDS 456, 3C 273, and NGC 3783. Results for the former two are reported for the first time and the latter two are revisited using our improved BLR dynamical modeling that includes the radial-dependent responsivity of BLRs. This allows us to self-consistently account for the emissivity weighting of the BLR in spectroastrometry and responsivity weighting in reverberation mapping. We obtain angular-diameter distances of the four quasars, from which we derive a Hubble constant of $H_0=69_{-10}^{+12}\,\rm km\,s^{-1}\,Mpc^{-1}$. Although this consititutes a large uncertainty for a measurement of $H_0$, it is anticipated that the precision will improve to a competitive level once a greater number of AGNs are accessible following the upgrade of GRAVITY in the near future. From SARM analysis, the black hole masses of the four quasars are also measured with the statistical uncertainty ranging from 0.06 to 0.23 dex, consistent with the correlations between black hole masses and properties of the host bulges., Comment: 21 pages, 14 figures, 4 tables; submitted to ApJ; comments welcome

Given the interpretability, accuracy, and stability of numerical weather prediction (NWP) models, current operational weather forecasting relies heavily on the NWP approach. In the past two years, the rapid development of Artificial Intelligence (AI) has provided an alternative solution for medium-range (1-10 days) weather forecasting. Bi et al. (2023) (hereafter Bi23) introduced the first AI-based weather prediction (AIWP) model in China, named Pangu-Weather, which offers fast prediction without compromising accuracy. In their work, Bi23 made notable claims regarding its effectiveness in extreme weather predictions. However, this claim lacks persuasiveness because the extreme nature of the two tropical cyclones (TCs) examples presented in Bi23, namely Typhoon Kong-rey and Typhoon Yutu, stems primarily from their intensities rather than their moving paths. Their claim may mislead into another meaning which is that Pangu-Weather works well in predicting unusual typhoon paths, which was not explicitly analyzed. Here, we reassess Pangu-Weather's ability to predict extreme TC trajectories from 2020-2024. Results reveal that while Pangu-Weather overall outperforms NWP models in predicting tropical cyclone (TC) tracks, it falls short in accurately predicting the rarely observed sudden-turning tracks, such as Typhoon Khanun in 2023. We argue that current AIWP models still lag behind traditional NWP models in predicting such rare extreme events in medium-range forecasts.

In integrable spin chains, the spectral problem can be solved by the method of Bethe ansatz, which transforms the problem of diagonalization of the Hamiltonian into the problem of solving a set of algebraic equations named Bethe equations. In this work, we systematically investigate the spin-$s$ XXX chain with twisted and open boundary conditions using the rational $Q$-system, which is a powerful tool to solve Bethe equations. We establish basic frameworks of the rational $Q$-system and confirm its completeness numerically in both cases. For twisted boundaries, we investigate the polynomiality conditions of the rational $Q$-system and derive physical conditions for singular solutions of Bethe equations. For open boundaries, we uncover novel phenomena such as hidden symmetries and magnetic strings under specific boundary parameters. Hidden symmetries lead to the appearance of extra degeneracies in the Hilbert space, while the magnetic string is a novel type of exact string configuration, whose length depends on the boundary magnetic fields. These findings, supported by both analytical and numerical evidences, offer new insights into the interplay between symmetries and boundary conditions., Comment: 41 pages, 5 figures

Pedestrian detection in intelligent transportation systems has made significant progress but faces two critical challenges: (1) insufficient fusion of complementary information between visible and infrared spectra, particularly in complex scenarios, and (2) sensitivity to illumination changes, such as low-light or overexposed conditions, leading to degraded performance. To address these issues, we propose PedDet, an adaptive spectral optimization complementarity framework specifically enhanced and optimized for multispectral pedestrian detection. PedDet introduces the Multi-scale Spectral Feature Perception Module (MSFPM) to adaptively fuse visible and infrared features, enhancing robustness and flexibility in feature extraction. Additionally, the Illumination Robustness Feature Decoupling Module (IRFDM) improves detection stability under varying lighting by decoupling pedestrian and background features. We further design a contrastive alignment to enhance intermodal feature discrimination. Experiments on LLVIP and MSDS datasets demonstrate that PedDet achieves state-of-the-art performance, improving the mAP by 6.6% with superior detection accuracy even in low-light conditions, marking a significant step forward for road safety. Code will be available at https://github.com/AIGeeksGroup/PedDet.

TianQin is a future space-based gravitational wave observatory targeting the frequency window of $10^{-4}$ Hz $\sim 1$ Hz. A large variety of gravitational wave sources are expected in this frequency band, including the merger of massive black hole binaries, the inspiral of extreme/intermediate mass ratio systems, stellar-mass black hole binaries, Galactic compact binaries, and so on. TianQin will consist of three Earth orbiting satellites on nearly identical orbits with orbital radii of about $10^5$ km. The satellites will form a normal triangle constellation whose plane is nearly perpendicular to the ecliptic plane. The TianQin project has been progressing smoothly following the ``0123" technology roadmap. In step ``0", the TianQin laser ranging station has been constructed and it has successfully ranged to all the five retro-reflectors on the Moon. In step ``1", the drag-free control technology has been tested and demonstrated using the TianQin-1 satellite. In step ``2", the inter-satellite laser interferometry technology will be tested using the pair of TianQin-2 satellites. The TianQin-2 mission has been officially approved and the satellites will be launched around 2026. In step ``3", i.e., the TianQin-3 mission, three identical satellites will be launched around 2035 to form the space-based gravitational wave detector, TianQin, and to start gravitational wave detection in space., Comment: 45 pages, 3 figures

The traditional heat-load generation pattern of combined heat and power generators has become a problem leading to renewable energy source (RES) power curtailment in cold regions, motivating the proposal of a planning model for alternative heat sources. The model aims to identify non-dominant capacity allocation schemes for heat pumps, thermal energy storage, electric boilers, and combined storage heaters to construct a Pareto front, considering both economic and sustainable objectives. The integration of various heat sources from both generation and consumption sides enhances flexibility in utilization. The study introduces a novel optimization algorithm, the adaptive multi-objective Bayesian optimization (AMBO). Compared to other widely used multi-objective optimization algorithms, AMBO eliminates predefined parameters that may introduce subjectivity from planners. Beyond the algorithm, the proposed model incorporates a noise term to account for inevitable simulation deviations, enabling the identification of better-performing planning results that meet the unique requirements of cold regions. What's more, the characteristics of electric-thermal coupling scenarios are captured and reflected in the operation simulation model to make sure the simulation is close to reality. Numerical simulation verifies the superiority of the proposed approach in generating a more diverse and evenly distributed Pareto front in a sample-efficient manner, providing comprehensive and objective planning choices., Comment: 11 pages, 11 figures

We investigate the shadows and optical appearances of a new type of regular black holes (BHs) with a Minkowski core under different spherical accretion. These BHs are constructed by modifying the Newtonian potential based on the minimum observable length in the Generalized Uncertainty Principle (GUP). They correspond one-to-one with the traditional regular BHs with a de-Sitter (dS) core (such as Bardeen/Hayward BHs), characterized by quantum gravity effect parameter ($\alpha_0$) and spacetime deformation factor ($n$). We find that the characteristic parameters give rise to some novel observable features. For these new BHs, the shadow radius, photon sphere radius, and total observed intensity increase with the increase of $\alpha_0$ but decrease with the increase of $n$. Under different spherical accretion, the shadow and photon sphere radius are identical, but the total observed intensity under the static spherical accretion is greater than that under the infalling spherical accretion. In addition, we find that these regular BHs with different cores show differences in shadows and optical appearances, especially under the static spherical accretion. Compared with Bardeen BH, the new BH has a smaller total observed intensity, dimmer maximum luminosity, and smaller shadow and photon sphere radius. The larger $\alpha_0$ leads to more significant differences, and a similar trend is also seen in the comparison with Hayward BH. Under the infalling spherical accretion, these regular BHs with different cores only have slight differences in total observed intensity, shadow and photon sphere radius, which become more obvious when $\alpha_0$ is relatively large. It suggests that the unique spacetime features of these regular BHs with different cores can be distinguished through astronomical observations.

Recent advancements in controllable expressive speech synthesis, especially in text-to-speech (TTS) models, have allowed for the generation of speech with specific styles guided by textual descriptions, known as style prompts. While this development enhances the flexibility and naturalness of synthesized speech, there remains a significant gap in understanding how these models handle vague or abstract style prompts. This study investigates the potential gender bias in how models interpret occupation-related prompts, specifically examining their responses to instructions like "Act like a nurse". We explore whether these models exhibit tendencies to amplify gender stereotypes when interpreting such prompts. Our experimental results reveal the model's tendency to exhibit gender bias for certain occupations. Moreover, models of different sizes show varying degrees of this bias across these occupations., Comment: NAACL 2025 Findings

Despite their impressive capacities, Large language models (LLMs) often struggle with the hallucination issue of generating inaccurate or fabricated content even when they possess correct knowledge. In this paper, we extend the exploration of the correlation between hidden-state prediction changes and output factuality into a deeper, token-wise level. Based on the insights , we propose cross-layer Entropy eNhanced Decoding (END), a decoding method that mitigates hallucinations without requiring extra training. END leverages inner probability changes across layers to individually quantify the factual knowledge required for each candidate token, and adjusts the final predicting distribution to prioritize tokens with higher factuality. Experiments on both hallucination and QA benchmarks demonstrate that END significantly enhances the truthfulness and informativeness of generated content while maintaining robust QA accuracy. Moreover, our work provides a deeper perspective on understanding the correlations between inherent knowledge and output factuality., Comment: NAACL 2025 Findings

The rapid advancement of large language models (LLMs) has opened new possibilities for their adoption as evaluative judges. This paper introduces Themis, a fine-tuned LLM judge that delivers sophisticated context-aware evaluations. We provide a comprehensive overview of the development pipeline for Themis, highlighting its scenario-dependent evaluation prompts and two novel methods for controlled instruction generation. These designs enable Themis to effectively distill evaluative skills from teacher models, while retaining flexibility for continuous development. We introduce two human-labeled benchmarks for meta-evaluation, demonstrating that Themis can achieve high alignment with human preferences in an economical manner. Additionally, we explore insights into the LLM-as-a-judge paradigm, revealing nuances in performance and the varied effects of reference answers. Notably, we observe that pure knowledge distillation from strong LLMs, though common, does not guarantee performance improvement through scaling. We propose a mitigation strategy based on instruction-following difficulty. Furthermore, we provide practical guidelines covering data balancing, prompt customization, multi-objective training, and metric aggregation. We aim for our method and findings, along with the fine-tuning data, benchmarks, and model checkpoints, to support future research and development in this area., Comment: accepted at WWW'25 (Industrial Track), extended version

We present BreezyVoice, a Text-to-Speech (TTS) system specifically adapted for Taiwanese Mandarin, highlighting phonetic control abilities to address the unique challenges of polyphone disambiguation in the language. Building upon CosyVoice, we incorporate a $S^{3}$ tokenizer, a large language model (LLM), an optimal-transport conditional flow matching model (OT-CFM), and a grapheme to phoneme prediction model, to generate realistic speech that closely mimics human utterances. Our evaluation demonstrates BreezyVoice's superior performance in both general and code-switching contexts, highlighting its robustness and effectiveness in generating high-fidelity speech. Additionally, we address the challenges of generalizability in modeling long-tail speakers and polyphone disambiguation. Our approach significantly enhances performance and offers valuable insights into the workings of neural codec TTS systems.

We extract the equation of state for hot quark matter from a holographic $2+1$ flavor QCD model, which could form the core of a stable compact star. By adding a thin hadron shell, a new type of hybrid star is constructed. With the temperature serving as a parameter, the EoS varies and we obtain stable stars with the maximum mass of around 23 to 30 solar masses, and the compactness around $0.1$. The I-Love-Q-C relations are further discussed, and compared with the neutron star cases. These compact stars are candidates for black hole mimickers, which could be observed by gravitational waves and distinguished by properties like nonzero tidal Love number and electromagnetic signals., Comment: 17 pages, 8 figures, 1 table

The Mamba model, utilizing a structured state-space model (SSM), offers linear time complexity and demonstrates significant potential. Vision Mamba (ViM) extends this framework to vision tasks by incorporating a bidirectional SSM and patch embedding, surpassing Transformer-based models in performance. While model quantization is essential for efficient computing, existing works have focused solely on the original Mamba model and have not been applied to ViM. Additionally, they neglect quantizing the SSM layer, which is central to Mamba and can lead to substantial error propagation by naive quantization due to its inherent structure. In this paper, we focus on the post-training quantization (PTQ) of ViM. We address the issues with three core techniques: 1) a k-scaled token-wise quantization method for linear and convolutional layers, 2) a reparameterization technique to simplify hidden state quantization, and 3) a factor-determining method that reduces computational overhead by integrating operations. Through these methods, the error caused by PTQ can be mitigated. Experimental results on ImageNet-1k demonstrate only a 0.8-1.2\% accuracy degradation due to PTQ, highlighting the effectiveness of our approach.

With the rapid advancement of Multimodal Large Language Models (MLLMs), numerous evaluation benchmarks have emerged. However, comprehensive assessments of their performance across diverse industrial applications remain limited. In this paper, we introduce MME-Industry, a novel benchmark designed specifically for evaluating MLLMs in industrial settings.The benchmark encompasses 21 distinct domain, comprising 1050 question-answer pairs with 50 questions per domain. To ensure data integrity and prevent potential leakage from public datasets, all question-answer pairs were manually crafted and validated by domain experts. Besides, the benchmark's complexity is effectively enhanced by incorporating non-OCR questions that can be answered directly, along with tasks requiring specialized domain knowledge. Moreover, we provide both Chinese and English versions of the benchmark, enabling comparative analysis of MLLMs' capabilities across these languages. Our findings contribute valuable insights into MLLMs' practical industrial applications and illuminate promising directions for future model optimization research., Comment: 9 pages,2 figures

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing reasoning behaviors. However, it encounters challenges such as poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks. To support the research community, we open-source DeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama.

Embedding quantum dot circuits into microwave cavities has emerged as a novel platform for controlling photon emission statistics by electrical means. With such a model, we reveal previously undefined quantum phase transitions in electrically driven lasing regimes by breaking the photon gain-loss balance condition. For one-photon interaction, the scaling theory indicates that the system undergoes a continuous phase transition from thermal to coherent photon emissions, consistent with conventional laser physics. Going beyond this, a hidden discontinuous quantum phase transition from superbunched to coherent states in two-photon processes, accompanied by the bistability within a mean-field theory, is predicted. Our prediction, along with its extension to multiphoton processes, represents a key step towards accessing lasing phase transitions.

The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 700 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi25., Comment: Part of the MaCVi 2025 workshop

Developers often insert temporary "print" or "log" instructions into their code to help them better understand runtime behavior, usually when the code is not behaving as they expected. Despite the fact that such monitoring instructions, or "ad-hoc logs," are so commonly used by developers, there is almost no existing literature that studies developers' practices in how they use them. This paucity of knowledge of the use of these ephemeral logs may be largely due to the fact that they typically only exist in the developers' local environments and are removed before they commit their code to their revision control system. In this work, we overcome this challenge by observing that developers occasionally mistakenly forget to remove such instructions before committing, and then they remove them shortly later. Additionally, we further study such developer logging practices by watching and analyzing live-streamed coding videos. Through these empirical approaches, we study where, how, and why developers use ad-hoc logs to better understand their code and its execution. We collect 27 GB of accidental commits that removed 548,880 ad-hoc logs in JavaScript from GitHub Archive repositories to provide the first large-scale dataset and empirical studies on ad-hoc logging practices. Our results reveal several illuminating findings, including a particular propensity for developers to use ad-hoc logs in asynchronous and callback functions. Our findings provide both empirical evidence and a valuable dataset for researchers and tool developers seeking to enhance ad-hoc logging practices, and potentially deepen our understanding of developers' practices towards understanding of software's runtime behaviors., Comment: Accepted at MSR 2025

Nanobubbles are typical nanodefects commonly existing in two-dimensional (2D) van der Waals materials such as transition metal dioxides, especially after their transfer from growth substrate to target substrates. These nanobubbles, though tiny, may significantly alter the local electric, optoelectronic, thermal, or mechanical properties of 2D materials and therefore are rather detrimental to the constructed devices. However, there is no post-processing method so far that can effectively eliminate nanobubbles in 2D materials after their fabrication and transfer, which has been a major obstacle in the development of 2D material based devices. Here, we propose a principle, called laser optothermal nanobomb (LOTB), that can effectively flatten nanobubbles in 2D materials through a dynamic process of optothermally induced phase transition and stress-pulling effect in nanobubbles. Operation of LOTB on monolayer molybdenum disulfide (1L-MoS2) films shows that the surface roughness can be reduced by more than 70% on a time scale of ~50 ms, without damage to the intrinsic property of 1L-MoS2 as validated by micro-nano photoluminescence and Raman spectroscopy. Moreover, a dual-beam cascaded LOTB and a multi-shot LOTB strategies are proposed to increase the flattened area and processing effect, showing the potential of LOTB for fast nanodefect repairing in the mass production of van der Waals materials and devices., Comment: 20 pages, 4 figures, research article

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

Recent baryon acoustic oscillation (BAO) measurements from the Dark Energy Spectroscopic Instrument (DESI) collaboration, combined with the cosmic microwave background (CMB) and type Ia supernovae (SNe Ia) observations, suggest a preference for dynamical dark energy (DDE) with $w_0>-1$ and $w_a<0$. Given the cosmological origin of fast radio bursts (FRBs), the combination of their dispersion measures and host galaxy redshifts makes localized FRBs a valuable tool for probing cosmology. Using an updated sample of 92 localized FRBs, along with DESI BAO, PlantheonPlus and CMB data, we constrain the dark energy (DE) equation of state (EoS) under the Chevallier-Polarski-Linder (CPL) parameterization. We find that even without incorporating CMB data, DDE remains preferred with $w_0 = -0.855 ^{+0.084}_{-0.084}$ and $w_a = -1.174^{+0.462}_{-0.491}$ at a confidence level of $\sim2.5 \sigma$. A joint analysis constrains these to be $w_0 = -0.784^{+0.064}_{-0.064}$ and $w_a = -0.872^{+0.269}_{-0.278}$, showing a discrepancy with $\Lambda$CDM at a $\sim3.1\sigma$ level. Furthermore, using localized FRBs alone, we estimate the Hubble constant $H_0$ to be $69.04^{+2.30}_{-2.07}$ and $75.61^{+2.23}_{-2.07} \, \rm km \, s^{-1} \, Mpc^{-1}$, assuming the Galactic electron density models to be NE2001 (Cordes \& Lazio) and YMW16 (Yao et al.), respectively. Thus, accurate accounting of the Galactic dispersion measure is crucial for resolving the Hubble tension with FRBs. Future BAO measurements, next-generation CMB experiments, and more localized FRBs will further constrain the DE EoS and the cosmological parameters., Comment: 15 pages, 5 figures, and 3 tables, accepted for publication in ApJ

We theoretically consider disorder and temperature effects on the integer quantum Hall effect (IQHE) using a variety of distinct and complementary analytical and numerical techniques. In particular, we address simple, physical, and experimentally relevant questions: How does disorder and/or temperature affect the IQHE plateau width? Does the plateau width increase or decrease with disorder and/or temperature? What happens to the peak in the longitudinal conductance with increasing disorder/temperature? Does the longitudinal conductance obey any universal scaling property? Is there "floating" with increasing disorder and/or decreasing magnetic field? Can disorder destroy the IQHE? Is there an IQHE to localization transition? What is the Landau level dependence of the plateau width? Our detailed theory provides answers to these and other related experimentally relevant questions. We discuss our results in the context of existing experimental results and suggest future experiments arising from our work. A key finding is that disorder and temperature are intrinsically connected in affecting IQHE, and there is an intricate interplay between them leading to nonmonotonicity in how the IQHE plateau width behaves as a function of increasing disorder. Both must be considered on an equal footing in understanding IQHE experiments., Comment: 24 pages, 18 figures

The James Webb Space Telescope (JWST) has recently conducted observations of massive galaxies at high redshifts, revealing a notable anomaly in their star formation efficiency (SFE). Motivated by the recent identification of three $\sim 10^{6}M_\odot$ dark star candidates, we investigate whether dark stars can be the origin of the SFE excess. It turns out that the excess can be reproduced by a group of dark stars with $M \gtrsim 10^{3}\, \rm M_{\odot}$, because of their domination in generating primary UV radiation in high-redshift galaxies. The genesis of these dark stars is attributed to the capture of Weakly Interacting Massive Particles (WIMPs) within a mass range of tens of GeV to a few TeV. However, if the top-heavy initial mass function of dark stars holds up to $\sim 10^{5}M_\odot$, the relic black holes stemming from their collapse would be too abundant to be consistent with the current observations of Massive Compact Halo Objects (MACHOs). We thus suggest that just a small fraction of SFE excess may be contributed by the very massive dark stars and the majority likely originated from other reasons such as the Population III stars in view of their rather similar UV radiation efficiencies., Comment: 8+8 pages including Appendix, 10 figures, ApJ, in press, references added

The inflated radii of hot Jupiters have been explored by various theoretical mechanisms. By connecting planetary thermal evolution models with the observed properties of hot Jupiters using hierarchical Bayesian models, a theoretical parameter called the heating efficiency has been introduced to describe the heating of the interiors of these planets. Previous studies have shown that the marginal distribution of this heating efficiency parameter has a single-peak distribution along the planetary equilibrium temperature (Teq). Since the foundation of these Bayesian inference models are the observed properties of hot Jupiters, there must be a corresponding feature in the observed data that leads to the inferred single-peak distribution of the heating efficiency. This study aims to find the underlying cause of the single-peak heating efficiency distribution without relying on specific theoretical models. By analyzing the relationship between different observed physical properties, we obtained a similar single-peak distribution of the radius expansion efficiency of hot Jupiters along Teq, which can be explained by the correlation with the stellar effective temperature. However, a detailed investigation suggests that this single-peak distribution is actually the result of straightforward physical processes. Specifically, the increase in heating efficiency can be attributed to the increase in incident stellar flux, while the decrease in heating efficiency can be attributed to the rise in gravitational binding energy associated with the increase in planetary mass., Comment: 15 pages, 7 figures, accepted for publication in AJ

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

Learning policies in simulation and transferring them to the real world has become a promising approach in dexterous manipulation. However, bridging the sim-to-real gap for each new task requires substantial human effort, such as careful reward engineering, hyperparameter tuning, and system identification. In this work, we present a system that leverages low-level skills to address these challenges for more complex tasks. Specifically, we introduce a hierarchical policy for in-hand object reorientation based on previously acquired rotation skills. This hierarchical policy learns to select which low-level skill to execute based on feedback from both the environment and the low-level skill policies themselves. Compared to learning from scratch, the hierarchical policy is more robust to out-of-distribution changes and transfers easily from simulation to real-world environments. Additionally, we propose a generalizable object pose estimator that uses proprioceptive information, low-level skill predictions, and control errors as inputs to estimate the object pose over time. We demonstrate that our system can reorient objects, including symmetrical and textureless ones, to a desired pose., Comment: website: https://dexhier.github.io

Defect-free single atom array in optical tweezers is a promising platform for scalable quantum computing, quantum simulation, and quantum metrology. Extending single-species array to mixed-species one promise to offer new possibilities. In our recent proof of principle realization of defect-free two-dimensional assembly of mixed-species $^{85}$Rb ($^{87}$Rb) atom arrays [C. Sheng et al.\href{https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.128.083202}{{\color{blue} Phys. Rev. Lett. 128, 083202(2022)}}], the filling fractions were limited by the imperfect transfer of atoms and the occurrence of logjams during the atom rearrangement. In order to scale up the size of defect-free mixed-species atom array, we scale up the tweezer array and improve the atom transfer, and upgrade the heuristic heteronuclear algorithm so as to facilitate multiple rearrangement cycles. Consequently, we successfully create defect-free atom arrays with 120 mixed-species single atoms. The corresponding filling fraction and defect-free probability are improved to be 98.6(1)\% and 14(2)\%, respectively. It is anticipated that the enhanced algorithm can be extended to other combinations of atomic species, and this mixed-species atom array is readily for studies of many-body physics, quantum error correction, and quantum metrology., Comment: 8 pages, 5 figures

Phase change materials (PCMs) play a pivotal role in the development of advanced thermal devices due to their reversible phase transitions, which drastically modify their thermal and optical properties. In this study, we present an effective dynamic thermal transistor with an asymmetric design that employs distinct PCMs, vanadium dioxide (VO2) and germanium antimony telluride (GST), on either side of the gate terminal, which is the center of the control unit of the near-field thermal transistor. This asymmetry introduces unique thermal modulation capabilities, taking control of thermal radiation in the near-field regime. VO2 transitions from an insulating to a metallic state, while GST undergoes a reversible switch between amorphous (aGST) and crystalline (cGST) phases, each inducing substantial changes in thermal transport properties. By strategically combining these materials, the transistor exhibits enhanced functionality, dynamically switching between states of absorbing and releasing heat by tuning the temperature of gate. This gate terminal not only enables active and efficient thermal management but also provides effective opportunities for manipulating heat flow in radiative thermal circuits. Our findings highlight the potential of such asymmetrically structured thermal transistors in advancing applications across microelectronics, high-speed data processing, and sustainable energy systems, where precise and responsive thermal control is critical for performance and efficiency., Comment: 10 pages, 5 figures

Boosting has emerged as a useful machine learning technique over the past three decades, attracting increased attention. Most advancements in this area, however, have primarily focused on numerical implementation procedures, often lacking rigorous theoretical justifications. Moreover, these approaches are generally designed for datasets with fully observed data, and their validity can be compromised by the presence of missing observations. In this paper, we employ semiparametric estimation approaches to develop boosting prediction methods for data with missing responses. We explore two strategies for adjusting the loss functions to account for missingness effects. The proposed methods are implemented using a functional gradient descent algorithm, and their theoretical properties, including algorithm convergence and estimator consistency, are rigorously established. Numerical studies demonstrate that the proposed methods perform well in finite sample settings.

Pulsar timing arrays (PTAs) are designed to detect nanohertz-frequency gravitational waves (GWs). Since GWs are anticipated from cosmic strings, PTAs offer a viable approach to testing their existence. We present the results of the first Bayesian search for gravitational-wave bursts from cosmic string cusps (GWCS) using the third PPTA data release for 30 millisecond pulsars. In this data collection, we find no evidence for GWCS signals. We compare a model with a GWCS signal to one with only noise, including a common spatially uncorrelated red noise (CURN), and find that our data is more consistent with the noise-only model.We then establish upper limits on the strain amplitude of GWCS at the pulsar term, based on the analysis of 30 millisecond pulsars, after finding no compelling evidence. We find the addition of a CURN with different spectral indices into the noise model has a negligible impact on the upper limits. And the upper limit range of the amplitude of the pulsar-term GWCS is concentrated between 10^{-12} and 10^{-11}. Finally, we set upper limits on the amplitude of GWCS events, parametrized by width and event epoch, for a single pulsar PSR J1857+0943. Moreover, we derive upper limit on the cosmic string tension as a function of burst width and compare it with previous results.

This paper presents a novel framework for importance-aware adaptive data transmission, designed specifically for real-time computer vision (CV) applications where task-specific fidelity is critical. An importance-weighted mean square error (IMSE) metric is introduced, assigning data importance based on bit positions within pixels and semantic relevance within visual segments, thus providing a task-oriented measure of reconstruction quality.To minimize IMSE under the total power constraint, a data-importance-aware waterfilling approach is proposed to optimally allocate transmission power according to data importance and channel conditions. Simulation results demonstrate that the proposed approach significantly outperforms margin-adaptive waterfilling and equal power allocation strategies, achieving more than $7$ dB and $10$ dB gains in normalized IMSE at high SNRs ($> 10$ dB), respectively. These results highlight the potential of the proposed framework to enhance data efficiency and robustness in real-time CV applications, especially in bandwidth-limited and resource-constrained environments., Comment: Accepted in IEEE ICC2025

By analyzing $4.5$ fb$^{-1}$ of $e^{+}e^{-}$ collision data accumulated with the BESIII detector at center-of-mass energies ranging from $4599.53$ MeV to $4698.82$ MeV, we report the measurement of the absolute branching fraction (BF) of the inclusive decay $\Lambda_{c}^{+} \to K_{S}^{0} X$ using the double-tag technique. The result is $\mathcal{B}(\Lambda_{c}^{+} \to K_{S}^{0} X)=(10.9\pm0.2\pm0.1)\%$, where the first uncertainty is statistical and the second is systematic. This result indicates that there are still undiscovered decay channels containing $K_{S}^{0}$ in the final state with a combined BF of $(3.1\pm0.4)\%$. The BF of the inclusive decay $\Lambda_{c}^{+} \to \overline{K}^{0} / K^{0} X$ is calculated to be $\mathcal{B}(\Lambda_{c}^{+} \to \overline{K}^{0} / K^{0} X)=(21.8 \pm0.4 \pm0.2 \pm1.1)\%$, where the third uncertainty accounts for a possible difference between $\mathcal{B}(\Lambda_{c}^{+} \to K_{S}^{0} X)$ and $\mathcal{B}(\Lambda_{c}^{+} \to K_{L}^{0} X)$. The result is in agreement with the prediction of the statistical isospin model.

Microresonator-based Kerr frequency combs ("Kerr microcombs") constitute chip-scale frequency combs of broad spectral bandwidth and repetition rate ranging from gigahertz to terahertz. An appealing application exploiting microcombs' coherence and large repetition rate is microwave and millimeter-wave generation. Latest endeavor applying two-point optical frequency division (OFD) on photonic-chip-based microcombs has created microwaves with exceptionally low phase noise. Nevertheless, microcomb-based OFD still requires extensive active locking, additional lasers, and external RF or microwave sources, as well as sophisticated initiation. Here we demonstrate a simple and entirely passive (no active locking) architecture, which incorporates an optoelectronic oscillator (OEO) and symphonizes a coherent microcomb and a low-noise microwave spontaneously. Our OEO microcomb leverages state-of-the-art integrated chip devices including a high-power DFB laser, a broadband silicon Mach-Zehnder modulator, an ultralow-loss silicon nitride microresonator, and a high-speed photodetector. Each can be manufactured in large volume with low cost and high yield using established CMOS and III-V foundries. Our system synergizes a microcomb of 10.7 GHz repetition rate and an X-band microwave with phase noise of $-$97/$-$126/$-$130 dBc/Hz at 1/10/100 kHz Fourier frequency offset, yet does not demand active locking, additional lasers, and external RF or microwave sources. With potential to be fully integrated, our OEO microcomb can become an invaluable technology and building block for microwave photonics, radio-over-fiber, and optical communication.

The heavy fermion (HF) state of $d$-electron systems is of great current interest since it exhibits various exotic phases and phenomena that are reminiscent of the Kondo effect in $f$-electron HF systems. Here, we present a combined infrared spectroscopy and first-principles band structure calculation study of the $3d$-electron HF compound YFe$_2$Ge$_2$. The infrared response exhibits several charge-dynamical hallmarks of HF and a corresponding scaling behavior that resemble those of the $f$-electron HF systems. In particular, the low-temperature spectra reveal a dramatic narrowing of the Drude response along with the appearance of a hybridization gap ($\Delta \sim$ 50 meV) and a strongly enhanced quasiparticle effective mass. Moreover, the temperature dependence of the infrared response indicates a crossover around $T^{\ast} \sim$ 100 K from a coherent state at low temperature to a quasi-incoherent one at high temperature. Despite of these striking similarities, our band structure calculations suggest that the mechanism underlying the HF behavior in YFe$_2$Ge$_2$ is distinct from the Kondo scenario of the $f$-electron HF compounds and even from that of the $d$-electron iron-arsenide superconductor KFe$_2$As$_2$. For the latter, the HF state is driven by orbital-selective correlations due to a strong Hund's coupling. Instead, for YFe$_2$Ge$_2$ the HF behavior originates from the band flatness near the Fermi level induced by the combined effects of kinetic frustration from a destructive interference between the direct Fe-Fe and indirect Fe-Ge-Fe hoppings, band hybridization involving Fe $3d$ and Y $4d$ electrons, and electron correlations. This highlights that rather different mechanisms can be at the heart of the HF state in $d$-electron systems., Comment: 11 pages, 7 figures

The nonlinearity of the conformal group is an essential factor that ruins the global conformal invariance for interacting material fields. In this paper we attempt to track such nonlinearity from spacetime transformations to spinor representations. To this end we rederive the spinor representation by generalizing the linear fractional transformation from two dimensions to four dimensions via replacing complex numbers with biquaternions. To check the effect of the nonlinearity we apply the translations and special conformal transformations (SCTs) to Dirac spinors in certain interactions. These two transformations do not lead to nonlinear terms in Yukawa term, but do in vector-spinor interaction. And the nonlinear terms would definitely cause $CP$ violation., Comment: 7 pages

We consider the problem of learning to exploit learning algorithms through repeated interactions in games. Specifically, we focus on the case of repeated two player, finite-action games, in which an optimizer aims to steer a no-regret learner to a Stackelberg equilibrium without knowledge of its payoffs. We first show that this is impossible if the optimizer only knows that the learner is using an algorithm from the general class of no-regret algorithms. This suggests that the optimizer requires more information about the learner's objectives or algorithm to successfully exploit them. Building on this intuition, we reduce the problem for the optimizer to that of recovering the learner's payoff structure. We demonstrate the effectiveness of this approach if the learner's algorithm is drawn from a smaller class by analyzing two examples: one where the learner uses an ascent algorithm, and another where the learner uses stochastic mirror ascent with known regularizer and step sizes.

Using first-principles electronic structure calculation, we demonstrate the spin dissipation process in bulk Fe by orbital excitations within the energy bands of pure spin character. The variation of orbitals in the intraband transitions provides an efficient channel to convert spin to orbital angular momentum with spin-orbit interaction. This mechanism dominates the Gilbert damping of Fe below room temperature. The theoretical prediction is confirmed by the ferromagnetic resonance experiment performed on single-crystal Fe(001) films. A significant thickness-dependent damping oscillation is found at low temperature induced by the quantum well states of the corresponding energy bands. Our findings not only explain the microscopic nature of the recently reported ultralow damping of Fe-based alloys, but also help for the understanding of the transport and dissipation process of orbital currents.

In recent years, Diffusion Models (DMs) have demonstrated significant advances in the field of image generation. However, according to current research, DMs are vulnerable to backdoor attacks, which allow attackers to control the model's output by inputting data containing covert triggers, such as a specific patch or phrase. Existing defense strategies are well equipped to thwart such attacks through backdoor detection and trigger inversion because previous attack methods are constrained by limited input spaces and triggers defined by low-dimensional features. To bridge these gaps, we propose Gungnir, a novel method that enables attackers to activate the backdoor in DMs through hidden style triggers within input images. Our approach proposes using stylistic features as triggers for the first time and implements backdoor attacks successfully in image2image tasks by utilizing Reconstructing-Adversarial Noise (RAN) and Short-Term-Timesteps-Retention (STTR) of DMs. Meanwhile, experiments demonstrate that our method can easily bypass existing defense methods. Among existing DM main backdoor defense frameworks, our approach achieves a 0\% backdoor detection rate (BDR). Our codes are available at https://github.com/paoche11/Gungnir.

We derive a heuristic nonlinear interference model for 4D probabilistic shaping considering the polarization and time correlation of the 4D symbols. We demonstrate an average SNR prediction gap from split-step Fourier simulations of 0.15~dB., Comment: 3 pages, 4 figures

Large language models (LLMs) excel in tasks that require complex linguistic abilities, such as reference disambiguation and metaphor recognition/generation. Although LLMs possess impressive capabilities, their internal mechanisms for processing and representing linguistic knowledge remain largely opaque. Previous work on linguistic mechanisms has been limited by coarse granularity, insufficient causal analysis, and a narrow focus. In this study, we present a systematic and comprehensive causal investigation using sparse auto-encoders (SAEs). We extract a wide range of linguistic features from six dimensions: phonetics, phonology, morphology, syntax, semantics, and pragmatics. We extract, evaluate, and intervene on these features by constructing minimal contrast datasets and counterfactual sentence datasets. We introduce two indices-Feature Representation Confidence (FRC) and Feature Intervention Confidence (FIC)-to measure the ability of linguistic features to capture and control linguistic phenomena. Our results reveal inherent representations of linguistic knowledge in LLMs and demonstrate the potential for controlling model outputs. This work provides strong evidence that LLMs possess genuine linguistic knowledge and lays the foundation for more interpretable and controllable language modeling in future research.

The representation disparity between visual generation and understanding imposes a critical gap in integrating these capabilities into a single framework. To bridge this gap, we introduce UniTok, a discrete visual tokenizer that encodes fine-grained details for generation while also capturing high-level semantics for understanding. Despite recent studies have shown that these objectives could induce loss conflicts in training, we reveal that the underlying bottleneck stems from limited representational capacity of discrete tokens. We address this by introducing multi-codebook quantization, which divides vector quantization with several independent sub-codebooks to expand the latent feature space, while avoiding training instability caused by overlarge codebooks. Our method significantly raises the upper limit of unified discrete tokenizers to match or even surpass domain-specific continuous tokenizers. For instance, UniTok achieves a remarkable rFID of 0.38 (versus 0.87 for SD-VAE) and a zero-shot accuracy of 78.6% (versus 76.2% for CLIP) on ImageNet. Our code is available at https://github.com/FoundationVision/UniTok.

To maximize palm oil yield and quality, it is essential to harvest palm fruit at the optimal maturity stage. This project aims to develop an automated computer vision system capable of accurately classifying palm fruit images into five ripeness levels. We employ deep Convolutional Neural Networks (CNNs) to classify palm fruit images based on their maturity stage. A shallow CNN serves as the baseline model, while transfer learning and fine-tuning are applied to pre-trained ResNet50 and InceptionV3 architectures. The study utilizes a publicly available dataset of over 8,000 images with significant variations, which is split into 80\% for training and 20\% for testing. The proposed deep CNN models achieve test accuracies exceeding 85\% in classifying palm fruit maturity stages. This research highlights the potential of deep learning for automating palm fruit ripeness assessment, which can contribute to optimizing harvesting decisions and improving palm oil production efficiency.

Projected entangled-pair states (PEPS) have proven effective in capturing chiral spin liquid ground states, yet the presence of long-range ``gossamer'' correlation tails raises concerns about their ability to accurately describe bulk gaps. Here, we address this challenge and demonstrate that PEPS can reliably characterize gapped bulk excitations in chiral topological phases. Using a variational principle for excited states within a local mode approximation, we establish that correlation functions decaying faster than $r^{-2}$ are not necessarily related to gapless modes and thus long-range ``gossamer'' correlation tails in chiral PEPS do not contradict the presence of a bulk gap. This framework is validated in the spin-$\frac{1}{2}$ Kitaev model with a chiral term, where PEPS yields excitation gaps that agree well with exact solutions. Extending our approach to the $\mathbb{Z}_3$ Kitaev model, we present compelling evidence for its chiral ground state and accurately resolve its gapped excitations. These findings thus solidify PEPS as a powerful tool for studying both ground and excited states in chiral topological systems, thereby bridging a key gap in the understanding of their bulk properties., Comment: 4 figures in 6 pages

The exploration of the surrounding world and the universe is an important theme in the legacy of humankind. The detection of gravitational waves is adding a new dimension to this grand effort. What are the fundamental physical laws governing the dynamics of the universe? What is the fundamental composition of the universe? How has the universe evolved in the past and how will it evolve in the future? These are the basic questions that press for answers. The space-based gravitational wave detector TianQin will tune in to gravitational waves in the millihertz frequency range ($10^{-4} \sim 1$ Hz, to be specific), opening a new gravitational wave spectrum window to explore many of the previously hidden sectors of the universe. TianQin will discover many astrophysical systems, populating the universe at different redshifts: some will be of new types that have never been detected before, some will have very high signal-to-noise ratios, and some will have very high parameter estimation precision. The plethora of information collected will bring us to new fronts on which to search for the breaking points of general relativity, the possible violation of established physical laws, the signature of possible new gravitational physics and new fundamental fields, and to improve our knowledge on the expansion history of the universe. In this white paper, we highlight the advances that TianQin can bring to fundamental physics and cosmology., Comment: 113 pages

SISSO (sure-independence screening and sparsifying operator) is an artificial intelligence (AI) method based on symbolic regression and compressed sensing widely used in materials science research. SISSO++ is its C++ implementation that employs MPI and OpenMP for parallelization, rendering it well-suited for high-performance computing (HPC) environments. As heterogeneous hardware becomes mainstream in the HPC and AI fields, we chose to port the SISSO++ code to GPUs using the Kokkos performance-portable library. Kokkos allows us to maintain a single codebase for both Nvidia and AMD GPUs, significantly reducing the maintenance effort. In this work, we summarize the necessary code changes we did to achieve hardware and performance portability. This is accompanied by performance benchmarks on Nvidia and AMD GPUs. We demonstrate the speedups obtained from using GPUs across the three most time-consuming parts of our code.

Diffuse speckle contrast analysis (DSCA), also called speckle contrast optical spectroscopy(SCOS), has emerged as a groundbreaking optical imaging technique for tracking dynamic biological processes, including blood flow and tissue perfusion. Recent advancements in single-photon avalanche diode (SPAD) cameras have unlocked exceptional capabilities in sensitivity, time resolution, and high frame rate imaging. Despite this, the application of large-format SPAD arrays in speckle contrast analysis is still relatively uncommon. In this study, we introduce a pioneering use of a large format SPAD camera for DSCA. By harnessing the camera's high temporal resolution and photon detection efficiency, we significantly enhance the accuracy and robustness of speckle contrast measurements. Our experimental results demonstrate the system's remarkable ability to capture rapid temporal variations over a broad field of view, enabling detailed spatiotemporal analysis. Through simulations, phantom experiments, and in vivo studies, we validate the approach's potential for a wide range of biomedical applications, such as cuff occlusion tests and functional tissue monitoring. This work highlights the transformative impact of large SPAD cameras on DSCA, paving the way for new breakthroughs in optical imaging.