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The V-based kagome superconductors $A$V$_3$Sb$_5$ ($A$ = K, Rb, and Cs) host charge density wave (CDW) and a topological nontrivial band structure, thereby provide a great platform to study the interplay of superconductivity (SC), CDW, frustration, and topology. Here, we report ultralow-temperature thermal conductivity measurements on CsV$_3$Sb$_5$ and Ta-doped Cs(V$_{0.86}$Ta$_{0.14}$)$_3$Sb$_5$ and scanning tunneling microscopy (STM) measurements on CsV$_3$Sb$_5$. The finite residual linear term of thermal conductivity at zero magnetic field suggests the existence of a residual density of states (DOS) in the superconducting state of CsV$_3$Sb$_5$. This is supported by the observation of non-zero conductance at zero bias in STM spectrum at an electronic temperature of 90 mK. However, in Cs(V$_{0.86}$Ta$_{0.14}$)$_3$Sb$_5$, which does not have CDW order, there is no evidence for residual DOS. These results show the importance of CDW order for the residual DOS, and a nodal $s$-wave gap or residual Fermi arc may be the origin of the residual DOS in such an unusual multiband kagome superconductor, CsV$_3$Sb$_5$., Comment: A small part of the contents overlaps with arXiv:2102.08356

Excitation energy transfer between photosynthetic light-harvesting complexes is vital for highly efficient primary photosynthesis. Controlling this process is the key for advancing the emerging artificial photosynthetic systems. Here, we experimentally demonstrate the enhanced excitation energy transfer between photosynthetic light-harvesting 2 complexes (LH2) mediated through the Fabry-Perot optical microcavity. Using intensity-dependent pump-probe spectroscopy, we analyse the exciton-exciton annihilation (EEA) due to inter-LH2 energy transfer. Comparing EEA in LH2 within cavity samples and the bare LH2 films, we observe enhanced EEA in cavities indicating improved excitation energy transfer via coupling to a common cavity mode. Surprisingly, the effect remains even in the weak coupling regime. The enhancement is attributed to the additional connectivity between LH2s introduced by the resonant optical microcavity. Our results suggest that optical microcavities can be a strategic tool for modifying excitation energy transfer between molecular complexes, offering a promising approach towards efficient artificial light harvesting.

When deployed in dynamic environments, AI agents will inevitably encounter challenges that exceed their individual capabilities. Leveraging assistance from expert agents-whether human or AI-can significantly enhance safety and performance in such situations. However, querying experts is often costly, necessitating the development of agents that can efficiently request and utilize expert guidance. In this paper, we introduce a fundamental coordination problem called Learning to Yield and Request Control (YRC), where the objective is to learn a strategy that determines when to act autonomously and when to seek expert assistance. We consider a challenging practical setting in which an agent does not interact with experts during training but must adapt to novel environmental changes and expert interventions at test time. To facilitate empirical research, we introduce YRC-Bench, an open-source benchmark featuring diverse domains. YRC-Bench provides a standardized Gym-like API, simulated experts, evaluation pipeline, and implementation of competitive baselines. Towards tackling the YRC problem, we propose a novel validation approach and investigate the performance of various learning methods across diverse environments, yielding insights that can guide future research.

We utilized archived data from the Five-hundred-meter Aperture Spherical Radio Telescope (FAST) to analyze the single-pulse profile morphology of PSR J1935$+$1616 (B1933$+$16). The results show that PSR J1935$+$1616 exhibits significant micropulses as well as various changes in single-pulse profile morphology. In the FAST archived data, a total of 969 single pulses with microstructure were identified, accounting for 9.69$\%$ of the total pulse sample, with characteristic widths of $127.63^{+70.74}_{-46.25}$ $\mu$s. About half of these pulses display quasiperiodic micropulses, with a periodicity of 231.77 $\pm$ 9.90 $\mu$s. Among the 520 single pulses with quasiperiodic microstructure, 208 also exhibit quasiperiodicity in circular polarization, with a characteristic period of $244.70^{+45.66}_{-21.05}$ $\mu$s. The micropulse characteristic width in circular polarization is 106.52 $\pm$ 46.14 $\mu$s. Compared to normal pulses, the relative energy (E/) of single pulse with microstructure follows a double Gaussian distribution, while that of normal pulses follows a single Gaussian distribution. Based on the intensity of the leading and trailing components in the single-pulse profile morphology of PSR J1935+1616, we classified the pulses into four morphological modes (A, B, C, and D). The relative energy distribution of pulses in mode A is significantly different from the others, following a double Gaussian distribution, while the relative energy distributions in modes B, C, and D follow a single Gaussian distribution. Our study also suggests a possible correlation between micropulses and single-pulse profile morphology. Single pulse with micropulses are most likely to occur in mode A, while their occurrence is least likely in mode D.

Generalizing policies to unseen scenarios remains a critical challenge in visual reinforcement learning, where agents often overfit to the specific visual observations of the training environment. In unseen environments, distracting pixels may lead agents to extract representations containing task-irrelevant information. As a result, agents may deviate from the optimal behaviors learned during training, thereby hindering visual generalization.To address this issue, we propose the Salience-Invariant Consistent Policy Learning (SCPL) algorithm, an efficient framework for zero-shot generalization. Our approach introduces a novel value consistency module alongside a dynamics module to effectively capture task-relevant representations. The value consistency module, guided by saliency, ensures the agent focuses on task-relevant pixels in both original and perturbed observations, while the dynamics module uses augmented data to help the encoder capture dynamic- and reward-relevant representations. Additionally, our theoretical analysis highlights the importance of policy consistency for generalization. To strengthen this, we introduce a policy consistency module with a KL divergence constraint to maintain consistent policies across original and perturbed observations.Extensive experiments on the DMC-GB, Robotic Manipulation, and CARLA benchmarks demonstrate that SCPL significantly outperforms state-of-the-art methods in terms of generalization. Notably, SCPL achieves average performance improvements of 14\%, 39\%, and 69\% in the challenging DMC video hard setting, the Robotic hard setting, and the CARLA benchmark, respectively.Project Page: https://sites.google.com/view/scpl-rl.

We describe a novel approach to subsystem decoherence without the usual tracing-out of the environment. The subsystem of focus is described entirely by a pure state evolving non-unitarily along a single classical trajectory of its environment. The approach is deduced from the exact factorization framework for arbitrary systems of electrons and nuclei. The non-unitarity of the electronic dynamics arises exclusively from non-adiabatic correlations between electrons and nuclei. We demonstrate that the approach correctly describes the coherence gain and the subsequent decoherence for the example of a nuclear trajectory passing through an avoided crossing, the prototypical case where single-trajectory Ehrenfest dynamics fails to produce decoherence. We further demonstrate that the essential difference between unitary and non-unitary electronic dynamics shows up in the time constants characterising the short-time regime., Comment: 6 pages, 2 figures

While Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by incorporating external knowledge, they still face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. We present ParetoRAG, an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle. By decomposing paragraphs into sentences and dynamically re-weighting core content while preserving contextual coherence, ParetoRAG achieves dual improvements in both retrieval precision and generation quality without requiring additional training or API resources. This framework has been empirically validated across various datasets, LLMs, and retrievers.

The development of intelligent robots seeks to seamlessly integrate them into the human world, providing assistance and companionship in daily life and work, with the ultimate goal of achieving human-robot symbiosis. To realize this vision, robots must continuously learn and evolve through consistent interaction and collaboration with humans, while humans need to gradually develop an understanding of and trust in robots through shared experiences. However, training and testing algorithms directly on physical robots involve substantial costs and safety risks. Moreover, current robotic simulators fail to support real human participation, limiting their ability to provide authentic interaction experiences and gather valuable human feedback. In this paper, we introduce SymbioSim, a novel human-in-the-loop robotic simulation platform designed to enable the safe and efficient development, evaluation, and optimization of human-robot interactions. By leveraging a carefully designed system architecture and modules, SymbioSim delivers a natural and realistic interaction experience, facilitating bidirectional continuous learning and adaptation for both humans and robots. Extensive experiments and user studies demonstrate the platform's promising performance and highlight its potential to significantly advance research on human-robot symbiosis.

Federated Distillation (FD) has emerged as a popular federated training framework, enabling clients to collaboratively train models without sharing private data. Public Dataset-Assisted Federated Distillation (PDA-FD), which leverages public datasets for knowledge sharing, has become widely adopted. Although PDA-FD enhances privacy compared to traditional Federated Learning, we demonstrate that the use of public datasets still poses significant privacy risks to clients' private training data. This paper presents the first comprehensive privacy analysis of PDA-FD in presence of an honest-but-curious server. We show that the server can exploit clients' inference results on public datasets to extract two critical types of private information: label distributions and membership information of the private training dataset. To quantify these vulnerabilities, we introduce two novel attacks specifically designed for the PDA-FD setting: a label distribution inference attack and innovative membership inference methods based on Likelihood Ratio Attack (LiRA). Through extensive evaluation of three representative PDA-FD frameworks (FedMD, DS-FL, and Cronus), our attacks achieve state-of-the-art performance, with label distribution attacks reaching minimal KL-divergence and membership inference attacks maintaining high True Positive Rates under low False Positive Rate constraints. Our findings reveal significant privacy risks in current PDA-FD frameworks and emphasize the need for more robust privacy protection mechanisms in collaborative learning systems., Comment: 14 pages, 10 figures

Biphoton sources that use room-temperature or hot atoms are valuable for real-world applications in long-distance quantum communication and photonic quantum computation. The heralded single photons produced by biphoton sources using the double-$\Lambda$ spontaneous four-wave mixing (SFWM) process offer advantages of narrow linewidth, stable frequency, and tunable linewidth -- qualities not found in other types of biphoton sources. In this study, we investigated a hot-atom SFWM double-$\Lambda$ biphoton source. We discovered that, under the condition counterintuitive to the present theory, heralded single photons of the source enhanced their generation rate by a factor of 3.6, heralding probability by a factor of 3.0, temporal width by 2.1, and spectral brightness by a factor of 10. These unexpected findings led us to propose a new theoretical framework for a previously unexplored physical mechanism. Our proposed theory effectively explains the observed results. Traditionally, similar spectral brightness (SB) from atom-based sources resulted in a lower signal-to-background ratio (SBR) than crystal- or chip-based biphoton sources, mainly due to poorer heralding probabilities. In our work, we experimentally demonstrated that the SBR improved by a factor of 4.8 while maintaining a comparable SB. As a result, the SBR performance of the atom-based biphoton source is now on par with that of crystal- or chip-based sources. This research introduces a new tuning parameter for double-$\Lambda$ SFWM biphoton sources, enhances our understanding of biphoton generation, and opens new avenues for improving the performance of these sources., Comment: 8 pages, 5 figures, 2 tables

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

Accurate acquisition of surface meteorological conditions at arbitrary locations holds significant importance for weather forecasting and climate simulation. Due to the fact that meteorological states derived from satellite observations are often provided in the form of low-resolution grid fields, the direct application of spatial interpolation to obtain meteorological states for specific locations often results in significant discrepancies when compared to actual observations. Existing downscaling methods for acquiring meteorological state information at higher resolutions commonly overlook the correlation with satellite observations. To bridge the gap, we propose Satellite-observations Guided Diffusion Model (SGD), a conditional diffusion model pre-trained on ERA5 reanalysis data with satellite observations (GridSat) as conditions, which is employed for sampling downscaled meteorological states through a zero-shot guided sampling strategy and patch-based methods. During the training process, we propose to fuse the information from GridSat satellite observations into ERA5 maps via the attention mechanism, enabling SGD to generate atmospheric states that align more accurately with actual conditions. In the sampling, we employed optimizable convolutional kernels to simulate the upscale process, thereby generating high-resolution ERA5 maps using low-resolution ERA5 maps as well as observations from weather stations as guidance. Moreover, our devised patch-based method promotes SGD to generate meteorological states at arbitrary resolutions. Experiments demonstrate SGD fulfills accurate meteorological states downscaling to 6.25km.

This paper introduces PFlow-VC, a conditional flow matching voice conversion model that leverages fine-grained discrete pitch tokens and target speaker prompt information for expressive voice conversion (VC). Previous VC works primarily focus on speaker conversion, with further exploration needed in enhancing expressiveness (such as prosody and emotion) for timbre conversion. Unlike previous methods, we adopt a simple and efficient approach to enhance the style expressiveness of voice conversion models. Specifically, we pretrain a self-supervised pitch VQVAE model to discretize speaker-irrelevant pitch information and leverage a masked pitch-conditioned flow matching model for Mel-spectrogram synthesis, which provides in-context pitch modeling capabilities for the speaker conversion model, effectively improving the voice style transfer capacity. Additionally, we improve timbre similarity by combining global timbre embeddings with time-varying timbre tokens. Experiments on unseen LibriTTS test-clean and emotional speech dataset ESD show the superiority of the PFlow-VC model in both timbre conversion and style transfer. Audio samples are available on the demo page https://speechai-demo.github.io/PFlow-VC/., Comment: Accepted by ICASSP 2025

Well-designed prompts are crucial for enhancing Large language models' (LLMs) reasoning capabilities while aligning their outputs with task requirements across diverse domains. However, manually designed prompts require expertise and iterative experimentation. While existing prompt optimization methods aim to automate this process, they rely heavily on external references such as ground truth or by humans, limiting their applicability in real-world scenarios where such data is unavailable or costly to obtain. To address this, we propose Self-Supervised Prompt Optimization (SPO), a cost-efficient framework that discovers effective prompts for both closed and open-ended tasks without requiring external reference. Motivated by the observations that prompt quality manifests directly in LLM outputs and LLMs can effectively assess adherence to task requirements, we derive evaluation and optimization signals purely from output comparisons. Specifically, SPO selects superior prompts through pairwise output comparisons evaluated by an LLM evaluator, followed by an LLM optimizer that aligns outputs with task requirements. Extensive experiments demonstrate that SPO outperforms state-of-the-art prompt optimization methods, achieving comparable or superior results with significantly lower costs (e.g., 1.1% to 5.6% of existing methods) and fewer samples (e.g., three samples). The code is available at https://github.com/geekan/MetaGPT.

Flagellar motors enable bacteria to navigate their environments by switching rotation direction in response to external cues with high sensitivity. Previous work suggested that ultrasensitivity of the flagellar motor originates from conformational spread, in which subunits of the switching complex are strongly coupled to their neighbors as in an equilibrium Ising model. However, dynamic single-motor measurements indicated that rotation switching is driven out of equilibrium, and the mechanism for this dissipative driving remains unknown. Here, based on recent cryo-EM structures, we propose that local mechanical torques on motor subunits can affect their conformation dynamics. This gives rise to a tug of war between stator-associated subunits, which produces cooperative, non-equilibrium switching responses without requiring nearest-neighbor interactions. Since subunits are effectively coupled at a distance, we call this mechanism ``Global Mechanical Coupling." Our model makes a qualitatively new prediction that the motor response cooperativity grows with the number of stators driving rotation. Re-analyzing published motor dose-response curves in varying load conditions, we find tentative experimental evidence for this prediction. Finally, we show that operating out of equilibrium enables motors to achieve high cooperativity with faster responses compared to equilibrium motors. Our results suggest a general role for mechanics in sensitive chemical regulation., Comment: 9 pages, 5 figures, plus supporting information

Metallic glasses are promising materials with unique mechanical and thermal properties, but their atomic-scale dynamics remain challenging to understand. In this work, we develop a unified approach to investigate the glass transition and structural relaxation in CoCrNi, Zr46Cu46Al8, Zr50Cu40Al10, and Zr64.13Cu15.75Ni10.12Al10 metallic glasses. Molecular dynamics (MD) simulation is employed to analyze the radial distribution function at different temperatures and accurately determine the glass transition temperature. We then combine this temperature with the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to predict the temperature dependence of the structural relaxation time, tau_alpha(T). By connecting $\tau_\alpha(T)$ to the diffusion constant, the ECNLE predictions of tau_alpha(T) can be compared with those calculated from MD simulations or estimated based on the diffusion constant. By combining atomistic simulation with force-level statistical mechanics, our multiscale approach offers deeper insights into relaxation dynamics and diffusion across various timescales. The relationship between the glass transition and the liquidus temperature is elucidated. This study enhances understanding of the glassy dynamics and properties in complex amorphous materials., Comment: 7 pages, 5 figures, accepted for publication in Computational Materials Science

Synchronization among a group of active agents is ubiquitous in nature. Although synchronization based on direct interactions between agents described by the Kuramoto model is well understood, the other general mechanism based on indirect interactions among agents sharing limited resources are less known. Here, we propose a minimal thermodynamically consistent model for the altruistic resource-sharing (ARS) mechanism wherein resources are needed for individual agent to advance but a more advanced agent has a lower competence to obtain resources. We show that while differential competence in ARS mechanism provides a negative feedback leading to synchronization it also breaks detailed balance and thus requires additional energy dissipation besides the cost of driving individual agents. By solving the model analytically, our study reveals a general tradeoff relation between the total energy dissipation rate and the two key performance measures of the system: average speed and synchronization accuracy. For a fixed dissipation rate, there is a distinct speed-accuracy Pareto front traversed by the scarcity of resources: scarcer resources lead to slower speed but more accurate synchronization. Increasing energy dissipation eases this tradeoff by pushing the speed-accuracy Pareto front outwards. The connections of our work to realistic biological systems such as the KaiABC system in cyanobacterial circadian clock and other theoretical results based on thermodynamic uncertainty relation are also discussed., Comment: main text 6 pages, 3 figures; SI 10 pages, 4 figures

Constructing a no-fly zone (NFZ) is a straightforward and effective way to facilitate the coexistence of unmanned aerial vehicles (drones) and existing systems (typically satellite systems). However, there has been little work on understanding the optimal design of such NFZs. In the absence of this design, one invariably ends up overestimating this region, hence significantly limiting the allowed airspace for the drones. To optimize the volume of the NFZ, we formulate this task as a variational problem and utilize the calculus of variations to rigorously obtain the NFZ as a function of the antenna pattern of victim receivers and the spatial distribution of drones. This approach parallels the matched filter design in the sense that the NFZ extends in directions where the antenna gain and/or the density of drones is high. Numerical simulations demonstrate the effectiveness of our optimal design compared to the known baselines in reducing the volume of the NFZ without compromising the protective performance., Comment: 5 pages, 3 figures, correspondence articles

Few studies quantify how traffic management dynamically reshapes modal split and emission-exposure outcomes over pollution severities. This paper proposes a novel day-to-day assignment model integrating exposure cost, which includes exposure perception and emissions-dispersion-exposure algorithm. Numerical experiments reveal that and various levels of traffic-related measures have an air pollution scenario-dependent effect on the MT system. In light pollution scenarios, vehicle restrictions and reduced fares for buses or ridesharing help lower car usage and reduce emissions and exposure. However, under heavy pollution, higher-level restrictions and ridesharing fares paradoxically increase travelers' exposure by 18% and 6.3%, respectively, due to modal shift. Furthermore, timely pollution information updates could plausibly encourage healthier travel. This paper also proposes practical strategies for both routine and emergency traffic management, considering the trade-offs among travel cost, emission, and exposure, and emphasizes the need for measures tailored to different air pollution contexts to offer deeper insights for urban traffic policies.

Recent generalizable fault diagnosis researches have effectively tackled the distributional shift between unseen working conditions. Most of them mainly focus on learning domain-invariant representation through feature-level methods. However, the increasing numbers of unseen domains may lead to domain-invariant features contain instance-level spurious correlations, which impact the previous models' generalizable ability. To address the limitations, we propose the Fourier-based Augmentation Reconstruction Network, namely FARNet.The methods are motivated by the observation that the Fourier phase component and amplitude component preserve different semantic information of the signals, which can be employed in domain augmentation techniques. The network comprises an amplitude spectrum sub-network and a phase spectrum sub-network, sequentially reducing the discrepancy between the source and target domains. To construct a more robust generalized model, we employ a multi-source domain data augmentation strategy in the frequency domain. Specifically, a Frequency-Spatial Interaction Module (FSIM) is introduced to handle global information and local spatial features, promoting representation learning between the two sub-networks. To refine the decision boundary of our model output compared to conventional triplet loss, we propose a manifold triplet loss to contribute to generalization. Through extensive experiments on the CWRU and SJTU datasets, FARNet demonstrates effective performance and achieves superior results compared to current cross-domain approaches on the benchmarks.

Despite the rise of recent neural networks in machine translation, those networks do not work well if the training data is insufficient. In this paper, we proposed an approach for machine translation in low-resource languages such as Vietnamese-Chinese. Our proposed method leveraged the power of the multilingual pre-trained language model (mBART) and both Vietnamese and Chinese monolingual corpus. Firstly, we built an early bird machine translation model using the bilingual training dataset. Secondly, we used TF-IDF technique to select sentences from the monolingual corpus which are the most related to domains of the parallel dataset. Finally, the first model was used to synthesize the augmented training data from the selected monolingual corpus for the translation model. Our proposed scheme showed that it outperformed 8% compared to the transformer model. The augmented dataset also pushed the model performance., Comment: Technical report of VLSP 2022 NMT; The first two authors contributed equally to this work

Large language models (LLMs) such as OpenAI's o1 have demonstrated remarkable abilities in complex reasoning tasks by scaling test-time compute and exhibiting human-like deep thinking. However, we identify a phenomenon we term underthinking, where o1-like LLMs frequently switch between different reasoning thoughts without sufficiently exploring promising paths to reach a correct solution. This behavior leads to inadequate depth of reasoning and decreased performance, particularly on challenging mathematical problems. To systematically analyze this issue, we conduct experiments on three challenging test sets and two representative open-source o1-like models, revealing that frequent thought switching correlates with incorrect responses. We introduce a novel metric to quantify underthinking by measuring token efficiency in incorrect answers. To address underthinking, we propose a decoding strategy with thought switching penalty TIP that discourages premature transitions between thoughts, encouraging deeper exploration of each reasoning path. Experimental results demonstrate that our approach improves accuracy across challenging datasets without requiring model fine-tuning. Our findings contribute to understanding reasoning inefficiencies in o1-like LLMs and offer a practical solution to enhance their problem-solving capabilities.

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieved from a knowledge base. However, its effectiveness is fundamentally constrained by the reliability of both the retriever and the knowledge base. In real-world scenarios, imperfections in these components often lead to the retrieval of noisy, irrelevant, or misleading counterfactual information, ultimately undermining the trustworthiness of RAG systems. To address this challenge, we propose Robust Fine-Tuning (RbFT), a method designed to enhance the resilience of LLMs against retrieval defects through two targeted fine-tuning tasks. Experimental results demonstrate that RbFT significantly improves the robustness of RAG systems across diverse retrieval conditions, surpassing existing methods while maintaining high inference efficiency and compatibility with other robustness techniques.

We present an interactive and explainable automated coaching assistant called CueTip for a variant of pool/billiards. CueTip's novelty lies in its combination of three features: a natural-language interface, an ability to perform contextual, physics-aware reasoning, and that its explanations are rooted in a set of predetermined guidelines developed by domain experts. We instrument a physics simulator so that it generates event traces in natural language alongside traditional state traces. Event traces lend themselves to interpretation by language models, which serve as the interface to our assistant. We design and train a neural adaptor that decouples tactical choices made by CueTip from its interactivity and explainability allowing it to be reconfigured to mimic any pool playing agent. Our experiments show that CueTip enables contextual query-based assistance and explanations while maintaining the strength of the agent in terms of win rate (improving it in some situations). The explanations generated by CueTip are physically-aware and grounded in the expert rules and are therefore more reliable.

We report the spectroscopic identification of three brown dwarf candidates -- o005_s41280, o006_s00089, and o006_s35616 -- discovered in the RUBIES using James Webb Space Telescope (JWST) Near-Infrared Spectrograph (NIRSpec) PRISM/CLEAR spectroscopy. We fit these sources with multiple substellar atmosphere models and present the atmospheric parameters, including effective temperature ($T_\mathrm{eff}$), surface gravity, and other derived properties. The results suggest that o005_s41280 and o006_s35616, with $T_\mathrm{eff}$ in the ranges of 2100--2300 K and 1800--2000 K, are likely L dwarfs, while o006_s00089, with $T_\mathrm{eff} < 1000$ K, is consistent with a late T dwarf classification. The best-fit model spectra provide a reasonable match to the observed spectra. However, distinct residuals exist in the $Y$, $J$, and $H$ bands for the two L dwarf candidates, particularly for o006_s35616. Incorporating the extinction parameter into the fitting process can significantly reduce these residuals. The distance estimates indicate that these candidates are about 2 kpc away. The analysis of the color-color diagram using multiple JWST NIRcam photometry suggests that cooler T dwarfs, such as o006_s00089, overlap with little red dots (LRDs), while hotter L dwarfs, like o005_s41280 and o006_s35616, tend to contaminate the high-redshift galaxy cluster. These findings suggest a brown dwarf contamination rate of approximately 0.1% in extragalactic deep field surveys, with L dwarfs being more frequently detected than cooler T and Y dwarfs., Comment: 17 pages, 8 figures, accepted for publication in ApJ

This paper explores opportunities and challenges for data-driven advocacy to support home care workers, an often overlooked group of low-wage, frontline health workers. First, we investigate what data to collect and how to collect it in ways that preserve privacy and avoid burdening workers. Second, we examine how workers and advocates could use collected data to strengthen individual and collective advocacy efforts. Our qualitative study with 11 workers and 15 advocates highlights tensions between workers' desires for individual and immediate benefits and advocates' preferences to prioritize more collective and long-term benefits. We also uncover discrepancies between participants' expectations for how data might transform advocacy and their on-the-ground experiences collecting and using real data. Finally, we discuss future directions for data-driven worker advocacy, including combining different kinds of data to ameliorate challenges, leveraging advocates as data stewards, and accounting for workers' and organizations' heterogeneous goals., Comment: Accepted to CHI 2025

Large-scale text-to-image (T2I) diffusion models have achieved remarkable generative performance about various concepts. With the limitation of privacy and safety in practice, the generative capability concerning NSFW (Not Safe For Work) concepts is undesirable, e.g., producing sexually explicit photos, and licensed images. The concept erasure task for T2I diffusion models has attracted considerable attention and requires an effective and efficient method. To achieve this goal, we propose a CE-SDWV framework, which removes the target concepts (e.g., NSFW concepts) of T2I diffusion models in the text semantic space by only adjusting the text condition tokens and does not need to re-train the original T2I diffusion model's weights. Specifically, our framework first builds a target concept-related word vocabulary to enhance the representation of the target concepts within the text semantic space, and then utilizes an adaptive semantic component suppression strategy to ablate the target concept-related semantic information in the text condition tokens. To further adapt the above text condition tokens to the original image semantic space, we propose an end-to-end gradient-orthogonal token optimization strategy. Extensive experiments on I2P and UnlearnCanvas benchmarks demonstrate the effectiveness and efficiency of our method., Comment: 24 pages, 15 figures

The Josephson effect is a macroscopic quantum tunneling phenomenon in a system with superfluid property, when it is split into two parts by a barrier. Here, we examine the Josephson effect in a driven-dissipative supersolid realized by coupling Bose-Einstein condensates to an optical ring cavity. We show that the spontaneous breaking of spatial translation symmetry in supersolid makes the location of the splitting barrier have a significant influence on the Josephson effect. Remarkably, for the same splitting barrier, depending on its location, two different types of DC Josephson currents are found in the supersolid phase (compared to only one type found in the superfluid phase). Thus, we term it a bi-Josephson effect. We examine the Josephson relationships and critical Josephson currents in detail, revealing that the emergence of supersolid order affects these two types of DC Josephson currents differently -- one is enhanced, while the other is suppressed. The findings of this work unveil unique Josephson physics in the supersolid phase, and show new opportunities to build novel Josephson devices with supersolids.

We introduce Qwen2.5-1M, a series of models that extend the context length to 1 million tokens. Compared to the previous 128K version, the Qwen2.5-1M series have significantly enhanced long-context capabilities through long-context pre-training and post-training. Key techniques such as long data synthesis, progressive pre-training, and multi-stage supervised fine-tuning are employed to effectively enhance long-context performance while reducing training costs. To promote the use of long-context models among a broader user base, we present and open-source our inference framework. This framework includes a length extrapolation method that can expand the model context lengths by at least four times, or even more, without additional training. To reduce inference costs, we implement a sparse attention method along with chunked prefill optimization for deployment scenarios and a sparsity refinement method to improve precision. Additionally, we detail our optimizations in the inference engine, including kernel optimization, pipeline parallelism, and scheduling optimization, which significantly enhance overall inference performance. By leveraging our inference framework, the Qwen2.5-1M models achieve a remarkable 3x to 7x prefill speedup in scenarios with 1 million tokens of context. This framework provides an efficient and powerful solution for developing applications that require long-context processing using open-source models. The Qwen2.5-1M series currently includes the open-source models Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, as well as the API-accessed model Qwen2.5-Turbo. Evaluations show that Qwen2.5-1M models have been greatly improved in long-context tasks without compromising performance in short-context scenarios. Specifically, the Qwen2.5-14B-Instruct-1M model significantly outperforms GPT-4o-mini in long-context tasks and supports contexts eight times longer.

Perception is crucial for autonomous driving, but single-agent perception is often constrained by sensors' physical limitations, leading to degraded performance under severe occlusion, adverse weather conditions, and when detecting distant objects. Multi-agent collaborative perception offers a solution, yet challenges arise when integrating heterogeneous agents with varying model architectures. To address these challenges, we propose STAMP, a scalable task- and model-agnostic, collaborative perception pipeline for heterogeneous agents. STAMP utilizes lightweight adapter-reverter pairs to transform Bird's Eye View (BEV) features between agent-specific and shared protocol domains, enabling efficient feature sharing and fusion. This approach minimizes computational overhead, enhances scalability, and preserves model security. Experiments on simulated and real-world datasets demonstrate STAMP's comparable or superior accuracy to state-of-the-art models with significantly reduced computational costs. As a first-of-its-kind task- and model-agnostic framework, STAMP aims to advance research in scalable and secure mobility systems towards Level 5 autonomy. Our project page is at https://xiangbogaobarry.github.io/STAMP and the code is available at https://github.com/taco-group/STAMP., Comment: Paper is accepted by ICLR 2025

We present GreenAuto, an end-to-end automated platform designed for sustainable AI model exploration, generation, deployment, and evaluation. GreenAuto employs a Pareto front-based search method within an expanded neural architecture search (NAS) space, guided by gradient descent to optimize model exploration. Pre-trained kernel-level energy predictors estimate energy consumption across all models, providing a global view that directs the search toward more sustainable solutions. By automating performance measurements and iteratively refining the search process, GreenAuto demonstrates the efficient identification of sustainable AI models without the need for human intervention.

This paper introduces the open-source dataset WanJuanSiLu, designed to provide high-quality training corpora for low-resource languages, thereby advancing the research and development of multilingual models. To achieve this, we have developed a systematic data processing framework tailored for low-resource languages. This framework encompasses key stages such as data extraction, corpus cleaning, content deduplication, security filtering, quality evaluation, and theme classification. Through the implementation of this framework, we have significantly improved both the quality and security of the dataset, while maintaining its linguistic diversity. As of now, data for all five languages have been fully open-sourced. The dataset can be accessed at https://opendatalab.com/applyMultilingualCorpus, and GitHub repository is available at https://github.com/opendatalab/WanJuan3.0

Driving World Models (DWMs) have become essential for autonomous driving by enabling future scene prediction. However, existing DWMs are limited to scene generation and fail to incorporate scene understanding, which involves interpreting and reasoning about the driving environment. In this paper, we present a unified Driving World Model named HERMES. We seamlessly integrate 3D scene understanding and future scene evolution (generation) through a unified framework in driving scenarios. Specifically, HERMES leverages a Bird's-Eye View (BEV) representation to consolidate multi-view spatial information while preserving geometric relationships and interactions. We also introduce world queries, which incorporate world knowledge into BEV features via causal attention in the Large Language Model (LLM), enabling contextual enrichment for understanding and generation tasks. We conduct comprehensive studies on nuScenes and OmniDrive-nuScenes datasets to validate the effectiveness of our method. HERMES achieves state-of-the-art performance, reducing generation error by 32.4% and improving understanding metrics such as CIDEr by 8.0%. The model and code will be publicly released at https://github.com/LMD0311/HERMES., Comment: Work in progress. The code will be available at https://github.com/LMD0311/HERMES

Continual learning requires machine learning models to continuously acquire new knowledge in dynamic environments while avoiding the forgetting of previous knowledge. Prompt-based continual learning methods effectively address the issue of catastrophic forgetting through prompt expansion and selection. However, existing approaches often suffer from low accuracy in prompt selection, which can result in the model receiving biased knowledge and making biased predictions. To address this issue, we propose the Multiple Queries with Multiple Keys (MQMK) prompt matching paradigm for precise prompt selection. The goal of MQMK is to select the prompts whose training data distribution most closely matches that of the test sample. Specifically, Multiple Queries enable precise breadth search by introducing task-specific knowledge, while Multiple Keys perform deep search by representing the feature distribution of training samples at a fine-grained level. Experiments show that MQMK enhances the prompt matching rate by over 30% in challenging scenarios and achieves state-of-the-art performance on three widely adopted continual learning benchmarks. Once this paper is accepted, we will release the code.

Multimodal Large Language Models (MLLMs) have shown significant advancements, providing a promising future for embodied agents. Existing benchmarks for evaluating MLLMs primarily utilize static images or videos, limiting assessments to non-interactive scenarios. Meanwhile, existing embodied AI benchmarks are task-specific and not diverse enough, which do not adequately evaluate the embodied capabilities of MLLMs. To address this, we propose EmbodiedEval, a comprehensive and interactive evaluation benchmark for MLLMs with embodied tasks. EmbodiedEval features 328 distinct tasks within 125 varied 3D scenes, each of which is rigorously selected and annotated. It covers a broad spectrum of existing embodied AI tasks with significantly enhanced diversity, all within a unified simulation and evaluation framework tailored for MLLMs. The tasks are organized into five categories: navigation, object interaction, social interaction, attribute question answering, and spatial question answering to assess different capabilities of the agents. We evaluated the state-of-the-art MLLMs on EmbodiedEval and found that they have a significant shortfall compared to human level on embodied tasks. Our analysis demonstrates the limitations of existing MLLMs in embodied capabilities, providing insights for their future development. We open-source all evaluation data and simulation framework at https://github.com/thunlp/EmbodiedEval.

Deep high-dispersion spectroscopy of Galactic photoionized gaseous nebulae, mainly planetary nebulae and HII regions, has revealed numerous emission lines. As a key step of spectral analysis, identification of emission lines hitherto has mostly been done manually, which is a tedious task, given that each line needs to be carefully checked against huge volumes of atomic transition/spectroscopic database to reach a reliable assignment of identity. Using Python, we have developed a line-identification code PyEMILI, which is a significant improvement over the Fortran-based package EMILI introduced ~20 years ago. In our new code PyEMILI, the major shortcomings in EMILI's line-identification technique have been amended. Moreover, the atomic transition database utilized by PyEMILI was adopted from Atomic Line List v3.00b4 but greatly supplemented with theoretical transition data from the literature. The effective recombination coefficients of the CII, OII, NII and NeII nebular lines are collected from the literature to form a subset of the atomic transition database to aid identification of faint optical recombination lines in the spectra of PNe and HII regions. PyEMILI is tested using the deep, high-dispersion spectra of two Galactic PNe, Hf2-2 and IC418, and gives better results of line identification than EMILI does. We also ran PyEMILI on the optical spectrum of a late-type [WC11] star UVQS J060819.93-715737.4 recently discovered in the Large Magellanic Cloud, and our results agree well with the previous manual identifications. The new identifier PyEMILI is applicable to not only emission-line nebulae but also emission stars, such as Wolf-Rayet stars., Comment: Accepted for publication in ApJS; 34 pages including 2 figures and 10 tables. The PyEMILI code as well as the atomic transition database has been publicly available on GitHub, Zenodo and PyPI. This paper describes the PyEMILI code in thorough details (the criteria, methodology and technology developed for line identification), and provides an overview of emission line formation

We consider the constrained sampling problem where the goal is to sample from a target distribution on a constrained domain. We propose skew-reflected non-reversible Langevin dynamics (SRNLD), a continuous-time stochastic differential equation with skew-reflected boundary. We obtain non-asymptotic convergence rate of SRNLD to the target distribution in both total variation and 1-Wasserstein distances. By breaking reversibility, we show that the convergence is faster than the special case of the reversible dynamics. Based on the discretization of SRNLD, we propose skew-reflected non-reversible Langevin Monte Carlo (SRNLMC), and obtain non-asymptotic discretization error from SRNLD, and convergence guarantees to the target distribution in 1-Wasserstein distance. We show better performance guarantees than the projected Langevin Monte Carlo in the literature that is based on the reversible dynamics. Numerical experiments are provided for both synthetic and real datasets to show efficiency of the proposed algorithms., Comment: 30 pages, 9 figures

The Electron-Ion Collider (EIC) is an upcoming accelerator facility aimed at exploring the properties of quarks and gluons in nucleons and nuclei, shedding light on their structure and dynamics. The inaugural experimental apparatus, ePIC (electron-Proton and Ion Collider), is designed as a general purpose detector to address the NAS/NSAC physics program at the EIC. The wider EIC community is strongly supporting a second interaction region and associated second detector to enhance the full science program. In this study, we evaluate how the second interaction region and detector can be complementary to ePIC. The pre-conceptual layout of an interaction region for the second detector offers a secondary focus that provides better forward detector acceptance at scattering angles near $\theta \sim 0$~mrad, which can specifically enhance the exclusive, tagging, and diffractive physics program. This article presents an analysis of a tagging program using the second interaction region layout with incoherent diffractive vector meson production. The potential for the second interaction region to provide improved vetoing capabilities for incoherent events to elucidate the coherent diffractive cross-section is evaluated. The capability to access the coherent diffractive cross-section is of prime importance for studying the spatial imaging of nucleons and nuclei., Comment: 9 pages, 6 figures, 4 tables

The antiferromagnetic Ising model on a triangular lattice (AFIT) exemplifies the most classical frustration system, arising from its triangular geometry that prevents all interactions from being simultaneously satisfied. Understanding geometric frustration in AFIT is crucial for advancing our knowledge of materials science and complex phases of matter. Here, we present a simple platform to study AFIT by arranging cylindrical magnets in vertical cavities of a triangular lattice, where magnets can slide along the cavity axis and stabilize either at the bottom or at the top of the cavity, analogous to the bistability of the Ising spin. The strong interactions of the magnets and the unique growing process allow the frustrated behavior and its ground state configurations to be directly observed. Notably, we observe a curved stripe phase, which is exotic to the Ising model. An effective thermalization process is developed to minimize the interaction energy, facilitating the evolution of various magnetic states, thereby visually realizing the ground state antiferromagnetic Ising model. Theoretical simulations and machine learning are performed concurrently to reveal the ground state and its evolution under effective thermal fluctuations, which are remarkably consistent with experimental results. Our system provides a unique platform to study frustrated systems and pave the way for future explorations in complex geometries., Comment: 4 figures

Testing ultra-large microservices-based FinTech systems presents significant challenges, including restricted access to production environments, complex dependencies, and stringent security constraints. We propose SandBoxFuzz, a scalable grey-box fuzzing technique that addresses these limitations by leveraging aspect-oriented programming and runtime reflection to enable dynamic specification mining, generating targeted inputs for constrained environments. SandBoxFuzz also introduces a log-based coverage mechanism, seamlessly integrated into the build pipeline, eliminating the need for runtime coverage agents that are often infeasible in industrial settings. SandBoxFuzz has been successfully deployed to Ant Group's production line and, compared to an initial solution built on a state-of-the-art fuzzing framework, it demonstrates superior performance in their microservices software. SandBoxFuzz achieves a 7.5% increase in branch coverage, identifies 1,850 additional exceptions, and reduces setup time from hours to minutes, highlighting its effectiveness and practical utility in a real-world industrial environment. By open-sourcing SandBoxFuzz, we provide a practical and effective tool for researchers and practitioners to test large-scale microservices systems.

This paper investigates an adaptive sliding-mode control for an integrated UAV autopilot and guidance system. First, a two-dimensional mathematical model of the system is derived by considering the incorporated lateral dynamics and relative kinematics of the UAV and its potential target of attack. Then, a sliding surface is derived utilizing the zero-effort miss distance. An adaptive twisting sliding mode (ATSMC) algorithm is applied to the integrated system. Simulation and comparisons have been accomplished. The results show our proposed design performs well in interception precision, even with high nonlinearity, uncertainties, disturbances, and abrupt changes in the target's movement, thanks to the adaptation strategy., Comment: in Proceedings of the 2025 International Conference on Energy, Infrastructure and Environmental Research (EIER2025)

We employ the SU(n)_k Wess-Zumino-Witten (WZW) model in conformal field theory to construct lattice wave functions in both one and two dimensions. It is unveiled that these wave functions can be reinterpreted as parton states, which enables efficient conversion to matrix product states such that many physical properties can be evaluated directly. In one dimension, these wave functions describe critical spin chains whose universality classes are in one-to-one correspondence with the WZW models used in the construction. In two dimensions, our constructions yield model wave functions for chiral spin liquids, and we show how to find all topological sectors of them in a systematic way. Using the null vectors of Kac-Moody algebras, parent Hamiltonians of the SU(3)_k series are derived. The SU(3)_k chiral spin liquids are lattice analogs of non-Abelian spin-singlet fractional quantum Hall states, and the k = 2 member hosts Fibonacci anyons., Comment: 14 pages, 6 figures

Online medical consultation (OMC) restricts doctors to gathering patient information solely through inquiries, making the already complex sequential decision-making process of diagnosis even more challenging. Recently, the rapid advancement of large language models has demonstrated a significant potential to transform OMC. However, most studies have primarily focused on improving diagnostic accuracy under conditions of relatively sufficient information, while paying limited attention to the "inquiry" phase of the consultation process. This lack of focus has left the relationship between "inquiry" and "diagnosis" insufficiently explored. In this paper, we first extract real patient interaction strategies from authentic doctor-patient conversations and use these strategies to guide the training of a patient simulator that closely mirrors real-world behavior. By inputting medical records into our patient simulator to simulate patient responses, we conduct extensive experiments to explore the relationship between "inquiry" and "diagnosis" in the consultation process. Experimental results demonstrate that inquiry and diagnosis adhere to the Liebig's law: poor inquiry quality limits the effectiveness of diagnosis, regardless of diagnostic capability, and vice versa. Furthermore, the experiments reveal significant differences in the inquiry performance of various models. To investigate this phenomenon, we categorize the inquiry process into four types: (1) chief complaint inquiry; (2) specification of known symptoms; (3) inquiry about accompanying symptoms; and (4) gathering family or medical history. We analyze the distribution of inquiries across the four types for different models to explore the reasons behind their significant performance differences. We plan to open-source the weights and related code of our patient simulator at https://github.com/LIO-H-ZEN/PatientSimulator.

To address the singularity problem in black hole physics, Penrose proposed the cosmic censorship conjecture . However, whether this conjecture holds in different types of black holes, especially for the Strong Cosmic Censorship Conjecture (SCCC), remains an issue worth further exploration. This study investigates the Yang-Mills field modified RN black hole, which provides new possibilities for exploring the Strong Cosmic Censorship Conjecture due to its unique nonlinear dynamical properties. In this work, the perturbation equations of two scalar fields for the Yang-Mills field modified RN black hole are derived. By combining the WKB method and the Prony method, the quasi-normal modes frequencies of the perturbations are analyzed. Additionally, constraints on the charge-to-mass ratio of the charged and massive scalar field are obtained using the Weak Gravity Conjecture (WGC), and it is shown that the Yang-Mills field modified RN black hole satisfies the SCCC conditions in the extreme state. The study reveals that the Yang-Mills field charge $q_{YM}$ significantly affects the damping effect and gravitational blueshift effect of the perturbations, causing the RN black hole in the extreme state to transition from violating the SCCC to satisfying it. Furthermore, when the charge-to-mass ratio $q/m$ of the charged and massive scalar field satisfies the WGC condition, the scalar field perturbations destabilize the Cauchy horizon of the Yang-Mills field modified RN black hole in the extreme state, thus confirming the applicability of the SCCC under extreme conditions. This study highlights the key role of nonlinear fields in singularity theory and gravitational dynamics, providing important support for the completeness of general relativity and offering new insights into the theoretical research of black holes in extreme states.