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The concept of skyrmions was introduced as early as the 1960s by Tony Skyrme. The topologically protected configuration embedded in skyrmions has prompted some investigations into their fundamental properties and versatile applications, sparking interest and guiding ongoing development. The topological protection associated with skyrmions was initially observed in systems with interactions. It is widely believed that skyrmions are stable yet relevant confirmation and empirical research remains limited. A pertinent question is whether skyrmion configurations formed by single-particle wave functions also exhibit topological stability. In this study, we affirm this hypothesis by investigating the effects of local decoherence. We analytically and numerically demonstrate the topological resilience of skyrmions and occurrence of transition points of skyrmion numbers in local decoherence of three typical decoherence channels. On the other hand, we show that these qualities are independent of the initial state. From the numerical results, we verify that inhomogeneous but continuous decoherence channels also adhere to the same behaviors and hold topological stability of skyrmions as homogeneous decoherence channels. These properties of skyrmions contribute to further applications in various areas including communication and imaging., Comment: 19 pages

The Psycho Frame, a sophisticated system primarily used in Universal Century (U.C.) series mobile suits for NEWTYPE pilots, has evolved as an integral component in harnessing the latent potential of mental energy. Its ability to amplify and resonate with the pilot's psyche enables real-time mental control, creating unique applications such as psychomagnetic fields and sensory-based weaponry. This paper presents the development of a novel robotic control system inspired by the Psycho Frame, combining electroencephalography (EEG) and deep learning for real-time control of robotic systems. By capturing and interpreting brainwave data through EEG, the system extends human cognitive commands to robotic actions, reflecting the seamless synchronization of thought and machine, much like the Psyco Frame's integration with a Newtype pilot's mental faculties. This research demonstrates how modern AI techniques can expand the limits of human-machine interaction, potentially transcending traditional input methods and enabling a deeper, more intuitive control of complex robotic systems.

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

HOPS 87 is a Class 0 protostellar core known to harbor an extremely young bipolar outflow and a hot corino. We report the discovery of localized, chemically rich regions near the bases of the two-lobe bipolar molecular outflow in HOPS 87 containing molecules such as H$_2$CO, $^{13}$CS, H$_2$S, OCS, and CH$_3$OH, the simplest complex organic molecule (COM). The locations and kinematics suggest that these localized features are due to jet-driven shocks rather than being part of the hot corino region encasing the protostar. The COM compositions of the molecular gas in these jet-localized regions are relatively simpler than those in the hot corino zone. We speculate that this simplicity is due to either the liberation of ice with a less complex chemical history or the effects of shock chemistry. Our study highlights the dynamic interplay between the protostellar bipolar outflow, disk, inner core environment, and the surrounding medium, contributing to our understanding of molecular complexity in solar-like young stellar objects., Comment: 16 pages, 6+2 figures, accepted by ApJ

We present high-spatial-resolution (0.7 to 1.0 arcsec) submillimeter observations of continuum and molecular lines of CH3OCHO, CH3OCH3, and H2CCO toward 11 high-mass star-forming regions using the Atacama Large Millimetre/submillimetre Array (ALMA). A total of 19 separate cores from 9 high-mass star-forming regions are found to be line-rich, including high-, intermediate-, and low-mass line-rich cores. The three molecules are detected in these line-rich cores. We map the emission of CH3OCHO, CH3OCH3, and H2CCO in 9 high-mass star-forming regions. The spatial distribution of the three molecules is very similar and concentrated in the areas of intense continuum emission. We also calculate the rotation temperatures, column densities, and abundances of CH3OCHO, CH3OCH3, and H2CCO under the local thermodynamic equilibrium (LTE) assumption. The abundances relative to H2 and CH3OH, and line widths of the three molecules are significantly correlated. The abundances relative to H2, temperatures and line widths of the three molecules tend to be higher in cores with higher mass and outflows detected. The possible chemical links of the three molecules are discussed., Comment: 35 pages, 9 figures

Neural implicit representations have revolutionized dense multi-view surface reconstruction, yet their performance significantly diminishes with sparse input views. A few pioneering works have sought to tackle the challenge of sparse-view reconstruction by leveraging additional geometric priors or multi-scene generalizability. However, they are still hindered by the imperfect choice of input views, using images under empirically determined viewpoints to provide considerable overlap. We propose PVP-Recon, a novel and effective sparse-view surface reconstruction method that progressively plans the next best views to form an optimal set of sparse viewpoints for image capturing. PVP-Recon starts initial surface reconstruction with as few as 3 views and progressively adds new views which are determined based on a novel warping score that reflects the information gain of each newly added view. This progressive view planning progress is interleaved with a neural SDF-based reconstruction module that utilizes multi-resolution hash features, enhanced by a progressive training scheme and a directional Hessian loss. Quantitative and qualitative experiments on three benchmark datasets show that our framework achieves high-quality reconstruction with a constrained input budget and outperforms existing baselines.

The scarcity of accessible medical image data poses a significant obstacle in effectively training deep learning models for medical diagnosis, as hospitals refrain from sharing their data due to privacy concerns. In response, we gathered a diverse dataset named MedImgs, which comprises over 250,127 images spanning 61 disease types and 159 classes of both humans and animals from open-source repositories. We propose a Leapfrog Latent Consistency Model (LLCM) that is distilled from a retrained diffusion model based on the collected MedImgs dataset, which enables our model to generate real-time high-resolution images. We formulate the reverse diffusion process as a probability flow ordinary differential equation (PF-ODE) and solve it in latent space using the Leapfrog algorithm. This formulation enables rapid sampling without necessitating additional iterations. Our model demonstrates state-of-the-art performance in generating medical images. Furthermore, our model can be fine-tuned with any custom medical image datasets, facilitating the generation of a vast array of images. Our experimental results outperform those of existing models on unseen dog cardiac X-ray images. Source code is available at https://github.com/lskdsjy/LeapfrogLCM., Comment: Total 16 pages including 5 figures and 36 references

Fast radio bursts (FRBs) are high-energy, short-duration phenomena in radio astronomy. Identifying their host galaxies can provide insights into their mysterious origins. In this paper, we introduce a novel approach to identifying potential host galaxies in three-dimensional space. We use FRB 20190425A and GW190425 as an example to illustrate our method. Recently, due to spatial and temporal proximity, the potential association of GW190425 with FRB 20190425A has drawn attention, leading to the identification of a likely host galaxy, UGC 10667, albeit without confirmed kilonova emissions. We search for the host galaxy of FRB 20190425A with a full CHIME localization map. Regardless of the validity of the association between GW190425 and FRB 20190425A, we identify an additional potential host galaxy (SDSS J171046.84+212732.9) from the updated GLADE galaxy catalog, supplementing the importance of exploring the new volume. We employed various methodologies to determine the most probable host galaxy of GW190424 and FRB 20190425A, including a comparison of galaxy properties and constraints on their reported observation limits using various Kilonova models. Our analysis suggests that current observations do not definitively identify the true host galaxy. Additionally, the Kilonova models characterized by a gradual approach to their peak are contradicted by the observational upper limits of both galaxies. Although the absence of optical emission detection raises doubts, it does not definitively disprove the connection between GW and FRB., Comment: 19 pages, 10 figures, 3 tables

Cooperative grasping and transportation require effective coordination to complete the task. This study focuses on the approach leveraging force-sensing feedback, where robots use sensors to detect forces applied by others on an object to achieve coordination. Unlike explicit communication, it avoids delays and interruptions; however, force-sensing is highly sensitive and prone to interference from variations in grasping environment, such as changes in grasping force, grasping pose, object size and geometry, which can interfere with force signals, subsequently undermining coordination. We propose multi-agent reinforcement learning (MARL) with ternary force representation, a force representation that maintains consistent representation against variations in grasping environment. The simulation and real-world experiments demonstrate the robustness of the proposed method to changes in grasping force, object size and geometry as well as inherent sim2real gap.

Asteroseismology has emerged as a powerful tool to unravel the intricate relationships between evolved stars and their planetary systems. In this study, we leverage this technique to investigate the evolutionary stages of five exoplanet host stars, each exhibiting solar-like oscillations. Building on our previous work that identified two host stars as red clump and red giant branch (RGB) stars, this study focuses on a new and broader sample. By precisely measuring asteroseismic parameters such as the period spacing of dipole gravity modes ($\Delta\Pi_{1}$), we provide definitive confirmation of these stars' evolutionary states as subgiants or RGB stars. These results are not only crucial for understanding the internal structures of evolved stars but also for predicting the eventual fate of their planetary companions, which may face engulfment as their host stars expand. This research highlights the profound role of asteroseismology in advancing our knowledge of planetary system evolution and opens new pathways for exploring how stellar evolution impacts planetary survival. Our findings set the stage for future studies on the dynamic fates of exoplanets, providing key insights into the intricate processes of stellar and planetary evolution.

SrRuO3, a 4d transition metal oxide, has gained significant interest due to its topological states in both momentum space (Weyl points) and real space (skyrmions). However, probing topological states in ultrathin SrRuO3 faces challenges such as the metal-insulator transition and questioned existence of skyrmions due to possible superposition of opposite anomalous Hall effect (AHE) signals. To address these issues, we investigate ultrathin SrRuO3/SrIrO3 heterostructures and their AHE and topological Hall effect (THE). Our results reveal metallized ultrathin SrRuO3 down to the monolayer limit with an AHE signal. ARPES measurements confirm the metallic and topological band structure of ultrathin SrRuO3. Furthermore, the AHE sign remains negative over a wide thickness range, where THE is still observed. This observation excludes the two-channel explanation of THE and provides evidence for the existence of skyrmions in ultrathin SrRuO3.

Recent advances on Multi-modal Large Language Models have demonstrated that high-resolution image input is crucial for model capabilities, especially for fine-grained tasks. However, high-resolution images lead to a quadratic increase in the number of visual tokens input into LLMs, resulting in significant computational costs. Current work develop visual token compression methods to achieve efficiency improvements, often at the expense of performance. We argue that removing visual redundancy can simultaneously improve both efficiency and performance. We build a coarse-to-fine visual token compression method, with a vision-guided sampler for compressing redundant regions with low information density, and a text-guided sampler for selecting visual tokens that are strongly correlated with the user instructions.With these two modules, the proposed FocusLLaVA achieves improvements in both efficiency and performance. We validate the effectiveness of our approach on a wide range of evaluation datasets.

Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of raw data in centralized methods. However, these FL methods adopt offline learning which may yield subpar performance, when concept drift occurs, i.e., distributions of historical and future data vary. Online learning can detect concept drift during model training, thus more applicable to TFF. Nevertheless, the existing federated online learning method for TFF fails to efficiently solve the concept drift problem and causes tremendous computing and communication overhead. Therefore, we propose a novel method named Resource-Efficient Federated Online Learning (REFOL) for TFF, which guarantees prediction performance in a communication-lightweight and computation-efficient way. Specifically, we design a data-driven client participation mechanism to detect the occurrence of concept drift and determine clients' participation necessity. Subsequently, we propose an adaptive online optimization strategy, which guarantees prediction performance and meanwhile avoids meaningless model updates. Then, a graph convolution-based model aggregation mechanism is designed, aiming to assess participants' contribution based on spatial correlation without importing extra communication and computing consumption on clients. Finally, we conduct extensive experiments on real-world datasets to demonstrate the superiority of REFOL in terms of prediction improvement and resource economization.

Fast Radio Bursts (FRBs) are highly energetic millisecond-duration astrophysical phenomena typically categorized as repeaters or non-repeaters. However, observational limitations may lead to misclassifications, suggesting a larger proportion of repeaters than currently identified. In this study, we leverage unsupervised machine learning techniques to classify FRBs using data from the CHIME/FRB catalog, including both the first catalog and a recent repeater catalog. By employing Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and clustering algorithms (k-means and HDBSCAN), we successfully segregate repeaters and non-repeaters into distinct clusters, identifying over 100 potential repeater candidates. Our analysis reveals several empirical relations within the clusters, including the ${\rm log \,}\Delta t_{sc} - {\rm log \,}\Delta t_{rw}$, ${\rm log \,}\Delta t_{sc} - {\rm log \,}T_B$, and $r - \gamma$ correlations, which provide new insights into the physical properties and emission mechanisms of FRBs. This study demonstrates the effectiveness of unsupervised learning in classifying FRBs and identifying potential repeaters, paving the way for more precise investigations into their origins and applications in cosmology. Future improvements in observational data and machine learning methodologies are expected to further enhance our understanding of FRBs., Comment: 17 pages, 8 pages, 3 tables

Achieving high levels of safety and reliability in autonomous driving remains a critical challenge, especially due to occlusion and limited perception ranges in standalone systems. Cooperative perception among vehicles offers a promising solution, but existing research is hindered by datasets with a limited number of agents. Scaling up the number of cooperating agents is non-trivial and introduces significant computational and technical hurdles that have not been addressed in previous works. To bridge this gap, we present Wireless enHanced Autonomous vehicles with Large number of Engaged agentS (WHALES), a dataset generated using CARLA simulator that features an unprecedented average of 8.4 agents per driving sequence. In addition to providing the largest number of agents and viewpoints among autonomous driving datasets, WHALES records agent behaviors, enabling cooperation across multiple tasks. This expansion allows for new supporting tasks in cooperative perception. As a demonstration, we conduct experiments on agent scheduling task, where the ego agent selects one of multiple candidate agents to cooperate with, optimizing perception gains in autonomous driving. The WHALES dataset and codebase can be found at https://github.com/chensiweiTHU/WHALES.

We model a spin-phase transition in a two-dimensional square array, or a lateral superlattice, of quantum rings in an external perpendicular homogeneous magnetic field. The electron system is placed in a circular cylindrical far-infrared photon cavity with a single circularly symmetric photon mode. Our numerical results reveal that the spin ordering of the two-dimensional electron gas in each quantum ring can be influenced or controlled by the electron-photon coupling strength and the energy of the photons. The Coulomb interaction between the electrons is described by a spin-density functional approach, but the para- and the diamagnetic electron-photon interactions are modeled via a configuration interaction formalism in a truncated many-body Fock-space, which is updated in each iteration step of the density functional approach. In the absence of external electromagnetic pulses this spin-phase transition is replicated in the orbital magnetization of the rings. The spin-phase transition can be suppressed by a strong electron-photon interaction. In addition, fluctuations in the spin configuration are found in dynamical calculations, where the system is excited by a time-dependent scheme specially fit for emphasizing the diamagnetic electron-photon interaction., Comment: RevTeX - pdfLaTeX, 11 pages with 9 included pdf figures

The PID controller currently occupies a prominent position as the most prevalent control architecture, which has achieved groundbreaking success across extensive implications. However, its parameters online regulation remains a formidable challenge. The majority of existing theories hinge on the linear constant system structure, contemplating only Single-Input, Single-Output (SISO) scenarios. Restricted research has been conducted on the intricate PID control problem within high-dimensional, Multi-Input, Multi-Output (MIMO) nonlinear systems that incorporate disturbances. This research, providing insights on the velocity form of nonlinear system, aims to bolster the controller's robustness. It establishes a quantitative metric to assess the robustness of high-dimensional PID controller, elucidates the pivotal theory regarding robustness's impact on error exponential convergence, and introduces a localized compensation strategy to optimize the robustness indicator. Guided by these theoretical insights, we exploit a robust high-dimensional PID (RH-PID) controller without the crutch of oversimplifying assumptions. Experimental results demonstrate the controller's commendable exponential stabilization efficacy and the controller exhibits exceptional robustness under the robust indicator's guidance. Notably, the robust convergence indicator can also effectively evaluate the comprehensive performance., Comment: 12 pages, 11 figures

With the increasing sophistication of Advanced Persistent Threats (APTs), the demand for effective detection and mitigation strategies and methods has escalated. Program execution leaves traces in the system audit log, which can be analyzed to detect malicious activities. However, collecting and analyzing large volumes of audit logs over extended periods is challenging, further compounded by insufficient labeling that hinders their usability. Addressing these challenges, this paper introduces SAGA (Synthetic Audit log Generation for APT campaigns), a novel approach for generating find-grained labeled synthetic audit logs that mimic real-world system logs while embedding stealthy APT attacks. SAGA generates configurable audit logs for arbitrary duration, blending benign logs from normal operations with malicious logs based on the definitions the MITRE ATT\&CK framework. Malicious audit logs follow an APT lifecycle, incorporating various attack techniques at each stage. These synthetic logs can serve as benchmark datasets for training machine learning models and assessing diverse APT detection methods. To demonstrate the usefulness of synthetic audit logs, we ran established baselines of event-based technique hunting and APT campaign detection using various synthetic audit logs. In addition, we show that a deep learning model trained on synthetic audit logs can detect previously unseen techniques within audit logs.

To reduce computational overhead while maintaining model performance, model pruning techniques have been proposed. Among these, structured pruning, which removes entire convolutional channels or layers, significantly enhances computational efficiency and is compatible with hardware acceleration. However, existing pruning methods that rely solely on image features or gradients often result in the retention of redundant channels, negatively impacting inference efficiency. To address this issue, this paper introduces a novel pruning method called Feature-Gradient Pruning (FGP). This approach integrates both feature-based and gradient-based information to more effectively evaluate the importance of channels across various target classes, enabling a more accurate identification of channels that are critical to model performance. Experimental results demonstrate that the proposed method improves both model compactness and practicality while maintaining stable performance. Experiments conducted across multiple tasks and datasets show that FGP significantly reduces computational costs and minimizes accuracy loss compared to existing methods, highlighting its effectiveness in optimizing pruning outcomes. The source code is available at: https://github.com/FGP-code/FGP.

In this paper, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose an adaptive cross-layer transmission mechanism that dynamically adjusts CRC, channel coding, and retransmission schemes based on the importance of semantic information. This ensures that important information is prioritized under poor transmission conditions. To verify the aforementioned framework, we also design an end-to-end adaptive panoramic video semantic transmission (APVST) network that leverages a deep joint source-channel coding (Deep JSCC) structure and attention mechanism, integrated with a latitude adaptive module that facilitates adaptive semantic feature extraction and variable-length encoding of panoramic videos. The proposed CLESC is also applicable to the transmission of other modal data. Simulation results demonstrate that the proposed CLESC effectively achieves compatibility and adaptation between semantic communication and traditional communication systems, improving both transmission efficiency and channel adaptability. Compared to traditional cross-layer transmission schemes, the CLESC framework can reduce bandwidth consumption by 85% while showing significant advantages under low signal-to-noise ratio (SNR) conditions.

Observations have revealed unique temperature profiles in hot Jupiter atmospheres. We propose that the energy transport by vertical mixing could lead to such thermal features. In our new scenario, strong absorbers, TiO and VO are not necessary. Vertical mixing could be naturally excited by atmospheric circulation or internal gravity wave breaking. We perform radiative transfer calculations by taking into account the vertical mixing driven energy transport. The radiative equilibrium (RE) is replaced by radiative-mixing equilibrium (RME). We investigate how the mixing strength, $K_{\rm zz}$, affects the atmospheric temperature-pressure profile. Strong mixing can heat the lower atmosphere and cool the upper atmosphere. This effect has important effects on the atmosphere thermal features that would form without mixing. In certain circumstances, it can induce temperature inversions in scenarios where the temperature monotonically increases with increasing pressure under conditions of lower thermal band opacity. Temperature inversions show up as $K_{\rm zz}$ increases with altitude due to shear interaction with the convection layer. The atmospheric thermal structure of HD~209458b can be well fitted with $K_{\rm zz} \propto (P/1\ {\rm bar})^{-1/2}\ {\rm cm}^{2} \ {\rm s}^{-1}$. Our findings suggest vertical mixing promotes temperature inversions and lowers $K_{\rm zz}$ estimates compared to prior studies. Incorporating chemical species into vertical mixing will significantly affect the thermal profile due to their temperature sensitivity., Comment: 12 pages, 6 figure. Accepted in ApJ

Social media data is inherently rich, as it includes not only text content, but also users, geolocation, entities, temporal information, and their relationships. This data richness can be effectively modeled using heterogeneous information networks (HINs) as it can handle multiple types of nodes and relationships, allowing for a comprehensive representation of complex interactions within social data. Meta-path-based methods use the sequences of relationships between different types of nodes in an HIN to capture the diverse and rich relationships within the social networks. However, the performance of social event detection methods is highly sensitive to the selection of meta-paths and existing meta-path based detectors either rely on human efforts or struggle to determining the effective meta-path set for model training and evaluation. In order to automatically discover the most important meta-paths, we propose a simple, yet effective, end-to-end Learning To Sample (LTS) framework for meta-path searching. Specifically, we build graphs that contain not only user profiles, textual content, and details about entities, but also the intricate relationships among them. The prioritized meta-paths, based on their importance, are sampled from the maintained distribution and their features are constructed before feeding into the social event detector. After picking up the top-ranked meta-paths, we streamline the exponential increment of meta-path combinations into a finite set of highly influential ones. The chosen meta-paths, along with their respective weights, are then used to train our social event detection model. As an alternative to social event detector training, we further propose an extra non-parametric evaluation process in order to determine the importance of each meta-path, which can further guide the paths sampling during model training.

In the context of quantum electrodynamics, the decay of false vacuum leads to the production of electron-positron pair, a phenomenon known as the Schwinger effect. In practical experimental scenarios, producing a pair requires an extremely strong electric field, thus suppressing the production rate and making this process very challenging to observe. Here we report an experimental investigation, in a cold-atom quantum simulator, of the effect of the background field on pair production from the infinite-mass vacuum in a $1+1$D $\mathrm{U}(1)$ lattice gauge theory. The ability to tune the background field allows us to study pair production in a large production rate regime. Furthermore, we find that the energy spectrum of the time-evolved observables in the zero mass limit displays excitation peaks analogous to bosonic modes in the Schwinger model. Our work opens the door to quantum-simulation experiments that can controllably tune the production of pairs and manipulate their far-from-equilibrium dynamics.

Let $\Sigma$ and $\Sigma'$ be two refinements of a fan $\Sigma_0$ and $f \colon X_{\Sigma} \dashrightarrow X_{\Sigma'}$ be the birational map induced by $X_{\Sigma} \rightarrow X_{\Sigma_0} \leftarrow X_{\Sigma'}$. We show that the graph closure $\overline{\Gamma}_f$ is a not necessarily normal toric variety and we give a combinatorial criterion for its normality. In contrast to it, for $f$ being a toric flop/flip, we show that the scheme-theoretic fiber product $X:=X_{\Sigma}\mathop{\times}\limits_{X_{\Sigma_0}}X_{\Sigma'}$ is in general not toric, though it is still irreducible and $X_{\rm red} = \overline{\Gamma}_f$. A complete numerical criterion to ensure $X = X_{\rm red}$ is given for 3-folds, which is fulfilled when $X_\Sigma$ has at most terminal singularities. In this case, we further conclude that $X$ is normal.

Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of surface changes between multi-temporal remote sensing images, detailing the categories, locations, and dynamics of changed objects (e.g., additions or disappearances). Many current methods attempt to leverage the long-sequence understanding and reasoning capabilities of multimodal large language models (MLLMs) for this task. However, without comprehensive data support, these approaches often alter the essential feature transmission pathways of MLLMs, disrupting the intrinsic knowledge within the models and limiting their potential in RSICC. In this paper, we propose a novel model, CCExpert, based on a new, advanced multimodal large model framework. Firstly, we design a difference-aware integration module to capture multi-scale differences between bi-temporal images and incorporate them into the original image context, thereby enhancing the signal-to-noise ratio of differential features. Secondly, we constructed a high-quality, diversified dataset called CC-Foundation, containing 200,000 image pairs and 1.2 million captions, to provide substantial data support for continue pretraining in this domain. Lastly, we employed a three-stage progressive training process to ensure the deep integration of the difference-aware integration module with the pretrained MLLM. CCExpert achieved a notable performance of $S^*_m=81.80$ on the LEVIR-CC benchmark, significantly surpassing previous state-of-the-art methods. The code and part of the dataset will soon be open-sourced at https://github.com/Meize0729/CCExpert.

Operating atom-interferometer gyroscopes outside a laboratory environment is challenging primarily owing to the instability of laser systems. To enhance the thermal stability of free-space laser systems, a compact laser system using fiber lasers and all-quartz-jointed optical modules was developed for a dual-atom-interferometer gyroscope. Millimeter-scale optical elements jointed on quartz plates with identical quartz supports, ensure laser power stability and facilitate component upgrades. The primary diode laser was locked to the modulation transfer spectrum of Rb atoms, and Raman lasers were phase-locked to the primary laser. Frequencies for repumping, blow-away, and detection lasers were adjusted with acousto-optic modulators. At room temperature, laser power fluctuation was under 1:1000, polarization extinction ratio exceeded 30 dB, frequency fluctuation was below 91 kHz, and phase noise reached to -100 dBc/Hz @ 1 kHz. The optical modules were tested at 5--50 $^{\circ}$C and applied to a dual-atom-interferometer gyroscope. The fringe contrast was tested over the temperature range. The proposed system paves the way for promoting field applications of atom-interferometer sensors., Comment: 11 pages, 9 figures

The $C\!P$ asymmetry and branching fraction of the CKM-suppressed decay $B^+\!\to J\mskip -3mu/\mskip -2mu\psi\,\pi^+$ are precisely measured relative to the favoured decay $B^+\!\to J\mskip -3mu/\mskip -2mu\psi\,K^+$, using a sample of proton-proton collision data corresponding to an integrated luminosity of $5.4~\mathrm{fb}^{-1}$ recorded at center-of-mass energy of $13~\mathrm{TeV}$ during 2016--2018. The results of the $C\!P$ asymmetry difference and branching fraction ratio are \begin{align*} \Delta\mathcal{A}^{C\!P} &\equiv \mathcal{A}^{C\!P}(B^+ \to J\mskip -3mu/\mskip -2mu\psi\,\pi^+) - \mathcal{A}^{C\!P}(B^+ \to J\mskip -3mu/\mskip -2mu\psi\,K^+) = (1.29 \pm 0.49 \pm 0.08) \times 10^{-2}, \end{align*} \begin{equation*} \mathcal{R}_{\pi/K} \equiv \frac{\mathcal{B}(B^+ \!\to J\mskip -3mu/\mskip -2mu\psi\,\pi^+)}{\mathcal{B}(B^+ \!\to J\mskip -3mu/\mskip -2mu\psi\,K^+)} = (3.852 \pm 0.022 \pm 0.018) \times 10^{-2}. \end{equation*} where the first uncertainties are statistical and the second systematic. A combination with previous LHCb results based on data collected at $7$ and $8~\mathrm{TeV}$ in 2011 and 2012 yields $\Delta\mathcal{A}^{C\!P} = (1.42 \pm 0.43 \pm 0.08) \times 10^{-2}$ and $\mathcal{R}_{\pi/K} = (3.846 \pm 0.018 \pm 0.018) \times 10^{-2}$. The combined $\Delta\mathcal{A}^{C\!P}$ value deviates from zero by 3.2 standard deviations, providing the first evidence for direct $C\!P$ violation in the amplitudes of beauty decays to charmonium final states., Comment: 18 pages, 2 figures, no conference or journal information All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1623/ (LHCb public pages)

Quantum secure direct communication (QSDC) enables the message sender to directly send secure messages to the receiver through the quantum channel without keys. Device-independent (DI) and measurement-device-independent (MDI) QSDC protocols can enhance QSDC's practical security in theory. DI QSDC requires extremely high global detection efficiency and has quite low secure communication distance. DI and MDI QSDC both require high-quality entanglement. Current entanglement sources prepare entangled photon pairs with low efficiency, largely reducing their practical communication efficiency. In the paper, we propose a single-photon-based receiver-device-independent (RDI) QSDC protocol. It only relies on the trusted single-photon source, which is nearly on-demand under current technology, and treats all the receiving devices in both communication parties as ``black-boxes''. The parties ensure the message security only from the observed statistics. We develop a numerical method to simulate its performance in practical noisy communication situation. RDI QSDC provides the same security level as MDI QSDC. Compared with DI and MDI QSDC, RDI QSDC has some advantages. First, it uses the single-photon source and single-photon measurement, which makes it obtain the practical communication efficiency about 3415 times of that in DI QSDC and easy to implement. The whole protocol is feasible with current technology. Second, it has higher photon loss robustness and noise tolerance than DI QSDC, which enables it to have a secure communication distance about 26 times of that in DI QSDC. Based on above features, the RDI QSDC protocol makes it possible to achieve highly-secure and high-efficient QSDC in the near future.

In laser-driven indirect inertial confinement fusion (ICF), a hohlraum--a cavity constructed from high-Z materials--serves the purpose of converting laser energy into thermal x-ray energy. This process involves the interaction of low-density ablated plasmas, which can give rise to weakly collisional shock waves characterized by a Knudsen number $K_n$ on the order of 1. The Knudsen number serves as a metric for assessing the relative importance of collisional interactions. Preliminary experimental investigations and computational simulations have demonstrated that the kinetic effects associated with weakly collisional shock waves significantly impact the efficiency of the implosion process. Therefore, a comprehensive understanding of the physics underlying weakly collisional shock waves is essential. This research aims to explore the formation and fundamental structural properties of weakly collisional shock waves within a hohlraum, as well as the phenomena of ion mixing and ion separation in multicomponent plasmas. Weakly collisional shocks occupy a transition regime between collisional shock waves ($K_n \ll 1$) and collisionless shock waves ($K_n \gg 1$), thereby exhibiting both kinetic effects and hydrodynamic behavior. These shock waves are primarily governed by an electrostatic field, which facilitates significant electrostatic sheath acceleration and ion reflection acceleration. The differentiation of ions occurs due to the varying charge-to-mass ratios of different ion species in the presence of electrostatic field, resulting in the separation of ion densities, velocities, temperatures and concentrations. The presence of weakly collisional shock waves within the hohlraum is expected to affect the transition of laser energy and the overall efficiency of the implosion process.

Efficient deployment of large language models, particularly Mixture of Experts (MoE), on resource-constrained platforms presents significant challenges, especially in terms of computational efficiency and memory utilization. The MoE architecture, renowned for its ability to increase model capacity without a proportional increase in inference cost, greatly reduces the token generation latency compared with dense models. However, the large model size makes MoE models inaccessible to individuals without high-end GPUs. In this paper, we propose a high-throughput MoE batch inference system, that significantly outperforms past work. MoE-Lightning introduces a novel CPU-GPU-I/O pipelining schedule, CGOPipe, with paged weights to achieve high resource utilization, and a performance model, HRM, based on a Hierarchical Roofline Model we introduce to help find policies with higher throughput than existing systems. MoE-Lightning can achieve up to 10.3x higher throughput than state-of-the-art offloading-enabled LLM inference systems for Mixtral 8x7B on a single T4 GPU (16GB). When the theoretical system throughput is bounded by the GPU memory, MoE-Lightning can reach the throughput upper bound with 2-3x less CPU memory, significantly increasing resource utilization. MoE-Lightning also supports efficient batch inference for much larger MoEs (e.g., Mixtral 8x22B and DBRX) on multiple low-cost GPUs (e.g., 2-4 T4).

We report the detection of an extreme stellar prominence eruption on the M dwarf LAMOST J044431.62+235627.9, observed through time-domain H$\alpha$ spectroscopy with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). This prominence eruption was accompanied by a superflare lasting over 160.4 minutes. The H$\alpha$ line profile exhibits significant blue-wing enhancement during the impulsive phase and near the flare peak, with a projected bulk blueshift velocity of $-228\pm11$~km~s$^{-1}$ and a maximum blueshift velocity reaching $-605\pm15$~km~s$^{-1}$. Velocity analysis of the eruptive prominence at various heights above the stellar surface indicates that some of the projected ejection velocities along the line of sight exceed the corresponding escape velocities, suggesting a potential coronal mass ejection (CME). The equivalent width (EW) of the H$\alpha$ blue-wing enhancement in this eruption appears to be the largest observed to date and is comparable to the EW of the H$\alpha$ line profile during the quiescent phase of the host star. We performed a two-cloud modeling for the prominence and the associated flare, which suggests that the eruptive prominence has a mass ranging from $1.6 \times 10^{19}~\text{g}$ to $7.2 \times 10^{19}~\text{g}$. More importantly, the mass ratio of the erupting prominence to its host star is the largest among all reported stellar prominence eruptions/CMEs., Comment: Accepted by ApJL

Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, impacts, opportunities, and risks of GenAI systems. The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements themselves. This four-level approach differs from the way measurement is typically done in ML, where researchers and practitioners appear to jump straight from background concepts to measurement instruments, with little to no explicit systematization in between. As well as surfacing assumptions, thereby making it easier to understand exactly what the resulting measurements do and do not mean, this framework has two important implications for evaluating evaluations: First, it can enable stakeholders from different worlds to participate in conceptual debates, broadening the expertise involved in evaluating GenAI systems. Second, it brings rigor to operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements., Comment: NeurIPS 2024 Workshop on Evaluating Evaluations (EvalEval)

An angular analysis of the $B_s^0 \rightarrow \phi e^+e^-$ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of $9\,{\rm fb}^{-1}$ at centre-of-mass energies of 7, 8 and $13\,{\rm TeV}$. The analysis is performed in the very low dielectron invariant mass-squared region between $0.0009$ and $0.2615\,{\rm GeV}^2\!/c^4$. The longitudinal polarisation fraction of the $\phi$ meson is measured to be less than $11.5\%$ at $90\%$ confidence level. The $A_{\mathrm{T}}^{\mathcal{R}e C\!P}$ observable, which is related to the lepton forward-backward asymmetry, is measured to be $0.116 \pm 0.155 \pm 0.006$, where the first uncertainty is statistical and the second systematic. The transverse asymmetries, $A_{\mathrm{T}}^{(2)}$ and $A_{\mathrm{T}}^{\mathcal{I}m C\!P}$ , which are sensitive to the virtual photon polarisation, are found to be $-0.045 \pm 0.235 \pm 0.014$ and $0.002 \pm 0.247 \pm 0.016$, respectively. The results are consistent with Standard Model predictions., Comment: 21 pages, 4 figures. All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3433/ (LHCb public pages)

Recent developments in Deep learning based Joint Source-Channel Coding (DeepJSCC) have demonstrated impressive capabilities within wireless semantic communications system. However, existing DeepJSCC methodologies exhibit limited generalization ability across varying channel conditions, necessitating the preparation of multiple models. Optimal performance is only attained when the channel status during testing aligns precisely with the training channel status, which is very inconvenient for real-life applications. In this paper, we introduce a novel DeepJSCC framework, termed Prompt JSCC (PJSCC), which incorporates a learnable prompt to implicitly integrate the physical channel state into the transmission system. Specifically, the Channel State Prompt (CSP) module is devised to generate prompts based on diverse SNR and channel distribution models. Through the interaction of latent image features with channel features derived from the CSP module, the DeepJSCC process dynamically adapts to varying channel conditions without necessitating retraining. Comparative analyses against leading DeepJSCC methodologies and traditional separate coding approaches reveal that the proposed PJSCC achieves optimal image reconstruction performance across different SNR settings and various channel models, as assessed by Peak Signal-to-Noise Ratio (PSNR) and Learning-based Perceptual Image Patch Similarity (LPIPS) metrics. Furthermore, in real-world scenarios, PJSCC shows excellent memory efficiency and scalability, rendering it readily deployable on resource-constrained platforms to facilitate semantic communications.

The ability to automatically build 3D digital twins of plants from images has countless applications in agriculture, environmental science, robotics, and other fields. However, current 3D reconstruction methods fail to recover complete shapes of plants due to heavy occlusion and complex geometries. In this work, we present a novel method for 3D reconstruction of agricultural crops based on optimizing a parametric model of plant morphology via inverse procedural modeling. Our method first estimates depth maps by fitting a neural radiance field and then employs Bayesian optimization to estimate plant morphological parameters that result in consistent depth renderings. The resulting 3D model is complete and biologically plausible. We validate our method on a dataset of real images of agricultural fields, and demonstrate that the reconstructions can be used for a variety of monitoring and simulation applications., Comment: Preprint

The first measurement of $\phi(1020)$ meson production in fixed-target $p$Ne collisions at $\sqrt{s_{NN}}=68.5$ GeV is presented. The $\phi(1020)$ mesons are reconstructed in their $K^{+}K^{-}$ decay in a data sample consisting of proton collisions on neon nuclei at rest, corresponding to an integrated luminosity of $21.7 \pm 1.4$ nb$^{-1}$, collected by the LHCb detector at CERN. The $\phi(1020)$ production cross-section in the centre-of-mass rapidity range of $-1.8

Integrating autonomous contact-based robotic characterization into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose a self-supervised convolutional neural network with a spatially differentiable loss function, incorporating shape priors to refine predictions of optimal robot contact poses for semiconductor characterization. This network improves valid pose generation by 20.0%, relative to existing models. We demonstrate our network's performance by driving a 4-degree-of-freedom robot to characterize photoconductivity at 3,025 predicted poses across a gradient of perovskite compositions, achieving throughputs over 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals regions of inhomogeneity. With this self-supervised deep learning-driven robotic system, we enable high-precision and reliable automation of contact-based characterization techniques at high throughputs, thereby allowing the measurement of previously inaccessible yet important semiconductor properties for self-driving laboratories.

The cold atom qubit platform emerges as an attractive choice for the next stage of quantum computation research, where a special family of synthetic analytical pulses has considerably improved the experimental performance of Controlled-PHASE Rydberg blockade gates in recent studies. The success of Controlled-PHASE Rydberg blockade gates triggers the intriguing question of whether the two-qubit Rydberg blockade gate SWAP gate exists. Via investigating the transition linkage structure, we provide a definitive answer to this question and establish the method of fast SWAP Rydberg blockade gates with synthetic continuously-modulated driving. These gate protocols use careful analysis to properly generate coherent population transfer and phase accumulation of the wave function in the atom-laser interaction process. They can adapt to finite Rydberg blockade strengths and bear considerable resistance some major adverse effects such as laser fluctuations. Further examinations reveal that we can anticipate satisfying performances of the method with currently available experimental techniques in relevant research areas., Comment: 8 pages, 8 figures

Electroencephalography (EEG) plays a crucial role in the diagnosis of various neurological disorders. However, small hospitals and clinics often lack advanced EEG signal analysis systems and are prone to misinterpretation in manual EEG reading. This study proposes an innovative hybrid artificial intelligence (AI) system for automatic interpretation of EEG background activity and report generation. The system combines deep learning models for posterior dominant rhythm (PDR) prediction, unsupervised artifact removal, and expert-designed algorithms for abnormality detection. For PDR prediction, 1530 labeled EEGs were used, and the best ensemble model achieved a mean absolute error (MAE) of 0.237, a root mean square error (RMSE) of 0.359, an accuracy of 91.8% within a 0.6Hz error, and an accuracy of 99% within a 1.2Hz error. The AI system significantly outperformed neurologists in detecting generalized background slowing (p = 0.02; F1: AI 0.93, neurologists 0.82) and demonstrated improved focal abnormality detection, although not statistically significant (p = 0.79; F1: AI 0.71, neurologists 0.55). Validation on both an internal dataset and the Temple University Abnormal EEG Corpus showed consistent performance (F1: 0.884 and 0.835, respectively; p = 0.66), demonstrating generalizability. The use of large language models (LLMs) for report generation demonstrated 100% accuracy, verified by three other independent LLMs. This hybrid AI system provides an easily scalable and accurate solution for EEG interpretation in resource-limited settings, assisting neurologists in improving diagnostic accuracy and reducing misdiagnosis rates., Comment: Example code available at https://github.com/tcs211/AI_EEEG_REPORT

Audio-driven portrait animation has made significant advances with diffusion-based models, improving video quality and lipsync accuracy. However, the increasing complexity of these models has led to inefficiencies in training and inference, as well as constraints on video length and inter-frame continuity. In this paper, we propose JoyVASA, a diffusion-based method for generating facial dynamics and head motion in audio-driven facial animation. Specifically, in the first stage, we introduce a decoupled facial representation framework that separates dynamic facial expressions from static 3D facial representations. This decoupling allows the system to generate longer videos by combining any static 3D facial representation with dynamic motion sequences. Then, in the second stage, a diffusion transformer is trained to generate motion sequences directly from audio cues, independent of character identity. Finally, a generator trained in the first stage uses the 3D facial representation and the generated motion sequences as inputs to render high-quality animations. With the decoupled facial representation and the identity-independent motion generation process, JoyVASA extends beyond human portraits to animate animal faces seamlessly. The model is trained on a hybrid dataset of private Chinese and public English data, enabling multilingual support. Experimental results validate the effectiveness of our approach. Future work will focus on improving real-time performance and refining expression control, further expanding the applications in portrait animation. The code is available at: https://github.com/jdh-algo/JoyVASA.

This study employs numerical simulations to explore the relationship between the dynamical instability of planetary systems and the uniformity of planetary masses within the system, quantified by the Gini index. Our findings reveal a significant correlation between system stability and mass uniformity. Specifically, planetary systems with higher mass uniformity demonstrate increased stability, particularly when they are distant from first-order mean motion resonances (MMRs). In general, for non-resonant planetary systems with a constant total mass, non-equal mass systems are less stable than equal mass systems for a given spacing in units of mutual Hill radius. This instability may arise from the equipartition of the total random energy, which can lead to higher eccentricities in smaller planets, ultimately destabilizing the system. This work suggests that the observed mass uniformity within multi-planet systems detected by \textit{Kepler} may result from a combination of survival bias and ongoing dynamical evolution processes., Comment: 9 pages, 5 figures, accepted to The Astronomical Journal

The Atacama Large Millimeter/submillimeter Array Survey of Orion Planck Galactic Cold Clumps (ALMASOP) reveals complex nested morphological and kinematic features of molecular outflows through the CO (J = 2 - 1) and SiO (J = 5 - 4) emission. We characterize the jet and outflow kinematics of the ALMASOP sample in four representative sources (HOPS 10, 315, 358, and G203.21-11.20W2) through channel maps and position-velocity diagrams (PVDs) parallel and transverse to the outflow axes. The combined CO and SiO emission exhibits the coexistence of the conventional extremely-high-velocity (EHV) jets and shell-like low-velocity (LV) cavity walls and new features. More complex, nested bubble-like and filamentary structures in the images and channel maps, triangle-shaped regions near the base of the parallel PVDs, and regions composed of rhombus/oval shapes in the transverse PVDs, are also evident. Such features find natural explanations within the bubble structure of the unified model of jet, wind, and ambient medium. The reverse shock cavity is revealed on the PVD base regions, and other features naturally arise within the dynamic postshock region of magnetic interaction. The finer nested shells observed within the compressed wind region reveal previously unnoticed shocked emission between the jet and the conventional large cavity walls. These pseudopulse-produced filamentary features connect to the jet-like knotty blobs, creating an impression of episodicity in mass ejection. SiO emission is enhanced downstream of the reverse shock boundary, with jet-like excitation conditions. Combined, these observed features reveal the extended structures induced by the magnetic interplay between a jet-bearing magnetized wide-angle wind and its ambient magnetized surrounding medium., Comment: 27 pages, 11 figures, accepted by ApJ

Since the initial discovery of two-dimensional van der Waals (vdW) materials, significant effort has been made to incorporate the three properties of magnetism, band structure topology, and strong electron correlations $-$ to leverage emergent quantum phenomena and expand their potential applications. However, the discovery of a single vdW material that intrinsically hosts all three ingredients has remained an outstanding challenge. Here we report the discovery of a Kondo-interacting topological antiferromagnet in the vdW 5$f$ electron system UOTe. It has a high antiferromagnetic (AFM) transition temperature of 150 K, with a unique AFM configuration that breaks the combined parity and time reversal ($PT$) symmetry in an even number of layers while maintaining zero net magnetic moment. Our angle-resolved photoemission spectroscopy (ARPES) measurements reveal Dirac bands near the Fermi level, which combined with our theoretical calculations demonstrate UOTe as an AFM Dirac semimetal. Within the AFM order, we observed the presence of the Kondo interaction, as evidenced by the emergence of a 5$f$ flat band near the Fermi level below 100 K and hybridization between the Kondo band and the Dirac band. Our density functional theory calculations in its bilayer form predict UOTe as a rare example of a fully-compensated AFM Chern insulator., Comment: Accepted to Advanced Materials

We show that there exists an algorithm that takes as input two closed, simply connected, topological 4-manifolds and decides whether or not these 4-manifolds are homeomorphic. In particular, we explain in detail how closed, simply connected, topological 4-manifolds can be naturally represented by a Kirby diagram consisting only of 2-handles. This representation is used as input for our algorithm. Along the way, we develop an algorithm to compute the Kirby-Siebenmann invariant of a closed, simply connected, topological 4-manifold from any of its Kirby diagrams and describe an algorithm that decides whether or not two intersection forms are isometric. In a slightly different direction, we discuss the decidability of the stable classification of smooth manifolds with more general fundamental groups. Here we show that there exists an algorithm that takes as input two closed, oriented, smooth 4-manifolds with fundamental groups isomorphic to a finite group with cyclic Sylow 2-subgroup, an infinite cyclic group, or a group of geometric dimension at most 3 (in the latter case we additionally assume that the universal covers of both 4-manifolds are not spin), and decides whether or not these two 4-manifolds are orientation-preserving stably diffeomorphic., Comment: 24 pages, 1 Figure

By combining the femtoscopic interferometry and the optical deblurring algorithm, we implement a novel method to image the source formed in HICs, while the interaction strength between the particle pair can be simultaneously determined. The spatial distribution of the emission source has been reconstructed for protons ($p$) and antiprotons ($\bar{p}$) from the respective $pp$ and $\bar{p}\bar{p}$ correlation functions in Au+Au collisions at $\sqrt{S_{\rm NN}}=200$ GeV. Within experimental uncertainties, protons and antiprotons share the same freeze-out distribution showing higher density in the center compared to the widely assumed Gaussian shape. The results evidence the matter-antimatter symmetry in coordinate space at the moment of freeze-out before the nucleons are fully randomized in the collisions.

Quasi-two-dimensional averievites with triangle-kagome-triangle trilayers are of interest due to their rich structural and magnetic transitions and strong spin frustration that are expected to host quantum spin liquid ground state with suitable substitution or doping. Herein, we report growth of bulk single crystals of averievite CsClCu5V2O10 with dimensions of several millimeters on edge in order to (1) address the open question whether the room temperature crystal structure is P-3m1, P-3, P21/c or else, (2) to elucidate the nature of phase transitions, and (3) to study direction-dependent physical properties. Single-crystal-to-single-crystal structural transitions at ~305 K and ~127 K were observed in the averievite CsClCu5V2O10 single crystals. The nature of the transition at ~305 K, which was reported as P-3m1-P21/c transition, was found to be a structural transition from high temperature P-3m1 to low temperature P-3 by combining variable temperature synchrotron X-ray single crystal and high-resolution powder diffraction. In-plane and out-of-plane magnetic susceptibility and heat capacity measurements confirm a first-order transition at 305 K, a structural transition at 127 K and an antiferromagnetic transition at 24 K. These averievites are thus ideal model systems for a deeper understanding of structural transitions and magnetism., Comment: 7 figures

Arrays of single atoms trapped in optical tweezers are increasingly recognized as a promising platform for scalable quantum computing. In both the fault-tolerant and NISQ eras, the ability to individually control qubits is essential for the efficient execution of quantum circuits. Time-division multiplexed control schemes based on atom shuttling or beam scanning have been employed to build programmable neutral atom quantum processors, but achieving high-rate, highly parallel gate operations remains a challenge. Here, we propose a fiber array architecture for atom quantum computing capable of fully independent control of individual atoms. The trapping and addressing lasers for each individual atom are emitted from the same optical waveguide, enabling robust control through common-mode suppression of beam pointing noise. Using a fiber array, we experimentally demonstrate the trapping and independent control of ten single atoms in two-dimensional optical tweezers, achieving individually addressed single-qubit gate with an average fidelity of 0.9966(3). Moreover, we perform simultaneous arbitrary single-qubit gate on four randomly selected qubits, resulting in an average fidelity of 0.9961(4). Our work paves the way for time-efficient execution of quantum algorithms on neutral atom quantum computers., Comment: 12 pages

Robust grasping represents an essential task in robotics, necessitating tactile feedback and reactive grasping adjustments for robust grasping of objects. Previous research has extensively combined tactile sensing with grasping, primarily relying on rule-based approaches, frequently neglecting post-grasping difficulties such as external disruptions or inherent uncertainties of the object's physics and geometry. To address these limitations, this paper introduces an human-demonstration-based adaptive grasping policy base on tactile, which aims to achieve robust gripping while resisting disturbances to maintain grasp stability. Our trained model generalizes to daily objects with seven different sizes, shapes, and textures. Experimental results demonstrate that our method performs well in dynamic and force interaction tasks and exhibits excellent generalization ability.

Solving constrained multi-objective optimization problems (CMOPs) is a challenging task. While many practical algorithms have been developed to tackle CMOPs, real-world scenarios often present cases where the constraint functions are unknown or unquantifiable, resulting in only binary outcomes (feasible or infeasible). This limitation reduces the effectiveness of constraint violation guidance, which can negatively impact the performance of existing algorithms that rely on this approach. Such challenges are particularly detrimental for algorithms employing the epsilon-based method, as they hinder effective relaxation of the feasible region. To address these challenges, this paper proposes a novel algorithm called DRMCMO based on the detection region method. In DRMCMO, detection regions dynamic monitor feasible solutions to enhance convergence, helping the population escape local optima. Additionally, these regions collaborate with the neighbor pairing strategy to improve population diversity within narrow feasible areas. We have modified three existing test suites to serve as benchmark test problems for CMOPs with binary constraints(CMOP/BC) and conducted comprehensive comparative experiments with state-of-the-art algorithms on these test suites and real-world problems. The results demonstrate the strong competitiveness of DRMCMO against state-of-the-art algorithms. Given the limited research on CMOP/BC, our study offers a new perspective for advancing this field.

In this survey we summarize the current state of known orthogonality relations for the $q$ and $q^{-1}$-symmetric and dual subfamilies of the Askey--Wilson polynomials in the $q$-Askey scheme. These polynomials are the continuous dual $q$ and $q^{-1}$-Hahn polynomials, the $q$ and $q^{-1}$-Al-Salam--Chihara polynomials, the continuous big $q$ and $q^{-1}$-Hermite polynomials and the continuous $q$ and $q^{-1}$-Hermite polynomials and their dual counterparts which are connected with the big $q$-Jacobi polynomials, the little $q$-Jacobi polynomials and the $q$ and $q^{-1}$-Bessel polynomials. The $q^{-1}$-symmetric polynomials in the $q$-Askey scheme satisfy an indeterminate moment problem, satisfying an infinite number of orthogonality relations for these polynomials. Among the infinite number of orthogonality relations for the $q^{-1}$-symmetric families, we attempt to summarize those currently known. These fall into several classes, including continuous orthogonality relations and infinite discrete (including bilateral) orthogonality relations. Using symmetric limits, we derive a new infinite discrete orthogonality relation for the continuous big $q^{-1}$-Hermite polynomials. Using duality relations, we explore orthogonality relations for and from the dual families associated with the $q$ and $q^{-1}$-symmetric subfamilies of the Askey--Wilson polynomials. In order to give a complete description of the convergence properties for these polynomials, we provide the large degree asymptotics using the Darboux method for these polynomials. In order to apply the Darboux method, we derive a generating function with two free parameters for the $q^{-1}$-Al-Salam--Chihara polynomials which has natural limits to the lower $q^{-1}$-symmetric families.