arXiv subjects

Weiwei Chen

Publications and source records attributed to Weiwei Chen.

At least 19 recordsLinked to original sources

arXiv2026

The EDD Radio Astronomy Backend Framework

Modern digital radio astronomy receivers produce increasingly wide-bandwidth, high bit-rate data streams that necessitate the development of flexible, scalable, and maintainable backend processing and recording systems. Historically, such backend instrumentation has been tightly coupled to telescope observing modes, limiting reuse between observatories and science cases. We present the Effelsberg Direct Digitisation (EDD) backend framework, a software-defined architecture for constructing real-time radio astronomy backends on commodity off-the-shelf computing infrastructure. We describe its design, implementation, supported observing modes, and operational deployments. EDD separates a common core framework from plugin-provided observing capabilities. The core provides orchestration, telescope interfaces, pipeline lifecycle management, monitoring, and deployment tooling, while plugins implement processing pipelines for specific observing modes. The framework is designed to support both single-dish and interferometric instruments through site-specific configuration and plugin selection. EDD currently supports spectroscopy and spectropolarimetry, pulsar timing and searching, baseband recording, very long baseline interferometry, correlation, and beamforming. Operational deployments include the Effelsberg 100-m telescope, the SKA-MPI prototype dish, the Thai National Radio Telescope, and the ARGOS interferometric prototype array. By separating common services, observing-mode plugins, and site-specific configuration, it allows backend capabilities to be deployed across heterogeneous telescope environments and provides a community resource for broadband radio astronomy instrumentation.

astro-ph.IM↗

arXiv2026

The first MeerKAT S-band globular cluster pulsar survey

Globular clusters are efficient factories of recycled pulsars, but searches toward high-dispersion-measure (DM) clusters can be strongly limited near 1 GHz by dispersive smearing and interstellar scattering. We present the first MeerKAT S-band (nu ~ 2.4 GHz) pulsar survey of 14 globular clusters. High time- and frequency-resolution observations were searched using segmented acceleration and jerk techniques, followed by candidate folding and targeted folding with available timing ephemerides. We re-detected 39 known pulsars and discovered four new millisecond pulsars in Glimpse-C01: J1848-0129C, D, E, and F. Multi-epoch follow-up enabled preliminary Keplerian orbital fits for J1848-0129C and J1848-0129D. J1848-0129C is an eclipsing MSP in a ~5 d orbit, placing it among long-period eclipsing systems known as huntsman binaries, while J1848-0129D is in a ~3.4 d nearly circular orbit with a massive white-dwarf companion of about 1 solar mass. Two Glimpse-C01 pulsars show large DM offsets from the cluster average. Comparison with other Galactic globular clusters indicates that intracluster DM spreads tend to increase with foreground DM, implying that narrow DM search windows may be sub-optimal for high-DM clusters. From detections and non-detections, we infer a practical single-epoch MeerKAT S-band tied-array detectability scale of about 10-20 microJy. These results demonstrate the value of high-frequency searches for pulsars in strongly dispersed and scattered cluster environments.

astro-ph.HE↗

arXiv2026

Cross-Layer Anomalous Hall Transport driven by N\'eel-Vector rotating in the Altermagnet candidate V2Te2O

In van der Waals (vdW) materials, weak interlayer coupling generally suppresses vertical dispersion, reinforcing the conventional paradigm that in-plane transport dominates over cross-layer channels. Here, using first-principles calculations and magnetic symmetry analyses, we uncover a giant, symmetry-unlocked cross-layer anomalous Hall conductivity (AHC) in the vdW altermagnet V2Te2O. In the magnetic ground state with Neel vector N//z, horizontal mirror symmetry protects a spin-polarized nodal chain near the Fermi level and strictly enforces zero anomalous Hall response. Tilting the Neel vector explicitly breaks this mirror protection, allowing spin-orbit coupling to gap the nodal chain and activate a sharp cross-layer Hall response. When the Neel vector is rotated into the in-plane configuration (N//x), cross-layer orbital hybridization generates intensive Berry curvature hotspots, boosting the cross-layer component of AHC {\sigma}_{yz} to approximately 255 S/cm, which exceeds in-plane component {\sigma}_{xy} by nearly two orders of magnitude. Furthermore, varying the azimuthal angle systematically redistributes the anomalous Hall response, enabling full directional control of transverse transport. Our findings demonstrate a highly sensitive cross-layer anomalous Hall switch activated by low-barrier spin canting, offering promising avenues for directional tensor selection and low-power multi-axial vdW spintronics.

cond-mat.mtrl-sci↗

arXiv2026

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions

Ensuring food safety represents a critical public health challenge, particularly when inspection resources are limited and regional sampling data are sparse. This study proposes a Transformer-based framework capable of forecasting fine-grained, city-level food safety risks by unifying over 11 million inspection records with supplemental demographic, economic, and environmental indicators extracted from the Statistical Yearbook. A three-stage pretraining design leverages partial supervision from the Wilson interval (capturing both safety and risk rankings), together with semi-supervised label refinement, to effectively utilize historical records even when local sample sizes are insufficient. Experimental evaluations on data from 2022 show that the proposed approach outperforms baselines significantly. A subsequent field experiment in collaboration with the Zhejiang Provincial Administration for Market Regulation further demonstrates improved detection rates and more efficient allocation of inspection resources compared to a manually developed plan. Observations of regulatory decision-making reveal a threshold-based heuristic employed by inspectors, hinting that additional training or decision-support interfaces could further enhance the impact of AI-generated risk scores. Overall, these findings underscore that a rigorous integration of large-scale public inspection data, Wilson interval-based confidence modeling, and advanced deep learning can facilitate earlier and more granular identification of food safety threats. By reducing reliance on reactive measures alone, the proposed framework has the potential to advance proactive, data-driven oversight of the global food supply.

cs.AI↗

arXiv2026

Task-Oriented Multimodal Edge Intelligence via Integrated Sensing-Communication-Computation

Integrated sensing, communication, and computation (ISCC) has recently emerged as a unified framework for enabling edge intelligence. However, existing ISCC designs predominantly rely on single-modal sensing, which is inherently vulnerable to occlusions, environmental uncertainties, and modality-specific failures, leading to degraded robustness in real-world deployments. This motivates the need for multi-modal ISCC, yet its design remains insufficiently explored. Compared with the single-modal case, multi-modal ISCC is more challenging because heterogeneous modalities enlarge data dimensionality and tighten communication/computation/energy budgets, while inter-modal correlations further complicate performance characterization. To address these challenges, we propose a task-oriented multi-modal ISCC framework that integrates device-side feature extraction with edge-side joint multi-modal inference. A central component of our approach is the maximal coding rate reduction (MCR^2) criterion, which enables each device to learn compact and discriminative task-relevant features, offering clear advantages over conventional cross-entropy-based extractors. We further leverage MCR^2 as a principled metric for edge-side sensing evaluation. On this basis, we formulate a sensing accuracy maximization problem under delay and resource constraints and develop an efficient block coordinate descent (BCD) algorithm after transforming the problem into a more tractable equivalent form. Focusing on a human activity recognition task, we conduct extensive experiments on publicly available datasets to evaluate the performance of the proposed ISCC framework. The results demonstrate that our approach consistently outperforms three baseline schemes under limited resource conditions.

eess.SP↗

arXiv2026

Toward Efficient Sensing in Multi-Device ISCC by Removing Frequency Domain Redundancy

Integrated sensing, communication, and computation (ISCC) is envisioned as a key enabler for intelligent services in future wireless networks. However, in multi-device ISCC systems, directly offloading full orthogonal frequency division multiplexing (OFDM) sensing data to the edge may incur excessive overhead, thereby limiting sensing performance under practical resource constraints. In this paper, we propose a subcarrier selection-based sensing framework for multi-device ISCC systems, where frequency-domain redundancy in OFDM sensing data is removed during local preprocessing to reduce sensing data transmission and processing overhead. Based on the proposed framework, we establish analytical models for sensing accuracy, delay, and energy consumption, and formulate a sensing accuracy maximization problem under practical resource constraints. To solve this problem, we develop an alternating direction method of multipliers (ADMM)-based algorithm. Experiments on commodity wireless devices validate the effectiveness of the proposed framework and show that it consistently outperforms three baseline schemes under various resource constraints.

eess.SP↗

arXiv2026

Harvesting AI Computation at the Edge via Generic Approximation

With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge. These chips are typically specialized for structured neural network (NN) processing and are designed to meet peak workload demands. However, they are often underutilized and suffer from considerable computational waste due to temporal or spatial redundancy in processing. Conversely, general-purpose processing engines at the edge may struggle with compute-intensive tasks such as signal processing and complex numerical operations because of stringent resource constraints. To address this imbalance, we propose a framework that harvests unused AI computation resources using general-purpose approximation techniques. The core idea is to automatically convert traditional computing tasks into neural network models via a representative neural architecture search (NAS) method. These approximate versions of general-purpose tasks are then deployed on AI engines during their idle periods. Specifically, we introduce a runtime scheduler that offloads these tasks to AI chips without compromising the performance of primary AI workloads, thereby alleviating the burden on general-purpose processors. Experiments on a representative AIoT processor show that our proposed AI computation harvesting strategy delivers substantial performance improvements across a set of edge processing tasks.

cs.AR↗

arXiv2026

ASAP: A Disaggregated and Asynchronous Inference System for MoE Prefill

Mixture-of-Experts (MoE) models have become the de facto standard for scaling large language models. To maintain computational efficiency, modern MoE serving systems typically employ a hybrid parallelism strategy, combining Data Parallelism (DP) for attention stages with Expert Parallelism (EP) for MoE stages. However, this design necessitates frequent global synchronization barriers between attention DP groups and experts. In online serving, significant variance in request arrival rates and sequence lengths inherently leads to DP imbalance, causing severe synchronization stalls that degrade Time-to-First-Token (TTFT) and system throughput. We present ASAP, an asynchronous inference system specifically designed to accelerate the prefill phase of MoE models. ASAP disaggregates the attention and MoE stages and implements a fully asynchronous execution pipeline. This is achieved through a suite of specialized asynchronous communication primitives and four coordinated optimizations across request scheduling and model execution, which collectively dismantle global synchronization barriers. We implement and evaluate ASAP on CloudMatrix384 super-nodes, demonstrating that it improves SLO-compliant prefill throughput by 90% compared to state-of-the-art synchronous serving solutions.

cs.DC↗

arXiv2026

Machine Learning Methods for Studying Latent Neural Activity Dynamics

Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations of neurons. In this paper, we provide a comprehensive survey that outlines the trajectory of Latent Variable Models (LVMs) from early state-space models to more recent deep generative models. We organize the literature into three closely related domains: (1) Single-Region Latent Dynamics, which includes models such as linear dynamical systems to more complex dynamics represented by Recurrent Neural Networks (RNNs) and Neural Ordinary Differential Equations (ODEs); (2) Multi-Region Communication, which employs probabilistic as well as subspace methods to study how information is transferred across different brain areas considering synaptic propagation delays and network connectivity; and (3) Behavior-Aligned Modeling, which seeks to disentangle neural activity related to task performance from other internal states via supervised or contrastive learning. This survey also includes large-scale neural foundation models, such as Transformers and diffusion models, that rely on large-scale pre-training for optimal performance across subjects. Finally, we conclude and discuss benchmarks, evaluation criteria, and open challenges, such as the ability to identify causal links or directionality of communication, to facilitate future research for bridging interpretable brain dynamics with reliable neural decoding.

cs.LG↗

arXiv2026

Flash-WAM: Modality-Aware Distillation for World Action Models

World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation benchmarks but requiring tens of denoising steps, a cost that precludes real-time control. Step distillation has emerged as the natural remedy, but off-the-shelf methods break down in the joint video-action setting because video and action streams use different SNR-shifted noise schedules and reach training with substantially different marginal noise distributions, an asymmetry that single-modality distillation methods cannot accommodate. We introduce \textbf{Flash-WAM}, a modality-aware step-distillation framework inspired by consistency distillation that selects the consistency function for each modality to match its noise regime: a linear-gradient-scaling parametrization for the action stream's low-noise regime, paired with a variance-preserving parametrization for the video stream's high-noise regime, grounded in a structural analysis of the consistency-function family that characterizes the achievable gradient scaling under the consistency boundary condition. Instantiated on LingBot-VA, Flash-WAM compresses inference to a single step in each modality. On RoboTwin 2.0, this reduces per-chunk latency from $8.1$ seconds to $348$ ms on NVIDIA L40S, a $23{\times}$ speedup that enables real-time inference. Flash-WAM preserves task success on simulation benchmarks ($85.5\%$ RoboTwin 2.0, $95.7\%$ LIBERO) and substantially recovers real-world performance ($60\%$ average on a Unitree G1 humanoid robot), while naive consistency distillation drops to $24\%$ at the same step budget.

cs.LG↗

arXiv2026

PhyWorld: Physics-Faithful World Model for Video Generation

World simulators can provide safe and scalable environments for training Physical AI systems before real-world deployment. Large video generation models are emerging as a promising basis for such simulators because they can generate diverse and realistic visual futures. However, using them as world simulators requires physically faithful video continuations, namely, generated videos that preserve the physical state implied by the conditioning input, and evolve in ways consistent with basic physical principles. We propose PhyWorld, a video generation world model designed to produce temporally coherent and physically faithful scene continuations through two-stage post-training. In the first stage, we improve video-to-video continuation with flow matching fine-tuning, encouraging stable visual attributes and coherent motion dynamics across frames. In the second stage, we align generated dynamics with physical principles using Direct Preference Optimization (DPO) over physics preference pairs, guiding the model toward outputs with higher physical plausibility. To evaluate PhyWorld, we use both standard video-quality benchmarks and a dedicated physical-faithfulness benchmark with per-law scoring. Experiments show that PhyWorld improves video consistency, achieving an average score of 0.769 on VBench compared with 0.756 or below for state-of-the-art baselines. PhyWorld also improves physical plausibility, reaching an average score of 3.09 on our physical-faithfulness benchmark compared with 2.99 for the strongest baseline. These results suggest that post-training large video generation models with continuation and physics-preference signals can make them more effective world simulators for Physical AI.

cs.CV↗

arXiv2026

Human Cognition in Machines: A Unified Perspective of World Models

This report of world models distinguishes prior works by the cognitive functions they innovate. Many works claim an almost human-like cognitive capability in their world models. To evaluate these claims requires a proper grounding in first principles from human and machine cognition theory. In moving towards human-like world models we present a conceptual unified framework for world models that fully incorporates all the cognitive functions (i.e., memory, perception, language, reasoning, imagining, motivation, and metacognition) and identify gaps in existing research as a guide for future states of the art. In particular, we find that motivation (especially intrinsic motivation) and metacognition remain drastically under-researched, and we propose concrete directions to address these gaps informed by active inference and global workspace theory. We also introduce epistemic world models, a new category encompassing agent frameworks for scientific discovery that operate over structured knowledge. Our taxonomy, applied to video, embodied, and epistemic world models, suggests research directions where prior taxonomies have not.

cs.RO↗

arXiv2026

A joint MeerKAT and Parkes view of Omega Centauri: New TRAPUM Searches and Pulsar Timing

Millisecond pulsars (MSPs) are powerful probes of globular clusters (GCs), tracing stellar evolution, cluster dynamics, and the local gravitational potential. We investigate the MSP population in GC Omega Centauri. We perform Fourier-domain acceleration and jerk searches on MeerKAT observations, and carry out pulsar timing using MeerKAT and Parkes Murriyang data spanning 2021-2025. We fold Fermi LAT and NICER photons using updated radio ephemerides to search for high-energy pulsations. We discover a new isolated MSP, PSR J1326-4728S (hereafter S), with a spin period of 4.538 ms and a dispersion measure of 96.24 cm$^3$pc. We update the orbital parameters of all known binary systems, with those of I, N, and Q differing significantly from previous estimates, and obtain new timing solutions for G, H, and K. Pulsars B, G, H, K, and L exhibit black widow-like properties, I, N and Q are found in wider binaries, with N and Q having >0.2 M$_\odot$ companions, and N showing a significant orbital eccentricity (e=0.093). Significant spin period derivatives are measured for eight pulsars and interpreted as arising from the cluster gravitational potential. No pulsed high-energy emission is detected from individual pulsars. The inferred line-of-sight accelerations are consistent with a King-model gravitational potential. While our measurements are insensitive to an intermediate-mass black hole with mass 10$^3$-10$^4$ M$_\odot$, they place an upper limit of <10$^5$ M$_\odot$ at 90% confidence. The high fraction of isolated MSPs and black widows systems, and possibly the eccentricity of N, are difficult to reconcile with MSP population predictions based solely on encounter rates. Instead, these properties likely reflect the complex evolutionary history of Omega Centauri, with part of its MSP population having formed in denser environments than the one observed today.

astro-ph.HE↗

arXiv2026

Strain Engineering of Intrinsic Anomalous Hall and Nernst Effects in Altermagnetic MnTe at Realistic Doping Levels

Hexagonal MnTe has emerged as a prototypical g-wave altermagnet, hosting time-reversal symmetry breaking in momentum space despite a vanishing net magnetization. While this symmetry breaking theoretically allows for an intrinsic anomalous Hall effect, experimentally observed signals have remained weak. In this work, we investigate the origin of this suppression and demonstrate a strategy to amplify anomalous transport responses within the experimentally accessible doping regime. Using a $\bm{k}\cdot\bm{p}$ effective model, we reveal that near the valence band maximum, which corresponds to the energy window relevant for typical hole doping ($\sim10^{19}cm^{-3}$), the intrinsic Hall effect is suppressed due to a symmetry-enforced cancellation of opposing Berry curvature contributions. We propose that breaking the crystalline symmetry via volume-conserving biaxial strain lifts this cancellation, resulting in a significant enhancement of the anomalous Hall conductivity by orders of magnitude. This strain-induced Fermi surface distortion also amplifies the anomalous Nernst effect. Furthermore, the analysis of the spin texture confirms that these strain-enabled anomalous transport signatures emerge while preserving the zero net magnetization.

cond-mat.mtrl-sci↗

arXiv2025

SFusion: Energy and Coding Fusion for Ultra-Robust Low-SNR LoRa Networks

LoRa has become a cornerstone for city-wide IoT applications due to its long-range, low-power communication. It achieves extended transmission by spreading symbols over multiple samples, with redundancy controlled by the Spreading Factor (SF), and further error resilience provided by Forward Error Correction (FEC). However, practical limits on SF and the separation between signal-level demodulation and coding-level error correction in conventional LoRa PHY leave it vulnerable under extremely weak signals - common in city-scale deployments. To address this, we present SFusion, a software-based coding framework that jointly leverages signal-level aggregation and coding-level redundancy to enhance LoRa's robustness. When signals fall below the decodable threshold, SFusion encodes a quasi-SF(k +m) symbol using 2^m SFk symbols to boost processing gain through energy accumulation. Once partial decoding becomes feasible with energy aggregation, an opportunistic decoding strategy directly combines IQ signals across symbols to recover errors. Extensive evaluations show that SFusion achieves up to 15dB gain over SF12 and up to 13dB improvement over state-of-the-art solutions.

cs.NI↗

arXiv2025

LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior

Simulating consumer decision-making is vital for designing and evaluating marketing strategies before costly real-world deployment. However, post-event analyses and rule-based agent-based models (ABMs) struggle to capture the complexity of human behavior and social interaction. We introduce an LLM-powered multi-agent simulation framework that models consumer decisions and social dynamics. Building on recent advances in large language model simulation in a sandbox environment, our framework enables generative agents to interact, express internal reasoning, form habits, and make purchasing decisions without predefined rules. In a price-discount marketing scenario, the system delivers actionable strategy-testing outcomes and reveals emergent social patterns beyond the reach of conventional methods. This approach offers marketers a scalable, low-risk tool for pre-implementation testing, reducing reliance on time-intensive post-event evaluations and lowering the risk of underperforming campaigns.

cs.AI↗

arXiv2025

\textit{Ab initio} studies of influence of periodic-direction electric fields on spin lifetime and spin diffusion length and the validation of an \textit{ab initio} matrix-drift-diffusion model

Recently, we developed an \textit{ab initio} approach of spin lifetime (\tau_{s}) and spin diffusion length (l_{s}) in solids [Phys. Rev. Lett. 135, 046705 (2025)], based on a density-matrix master equation with quantum treatment of electron scattering processes. In this work, we extend the method to include the drift term due to an electric field along a periodic direction, implemented using a Wannier-representation-based covariant derivative. We employ this approach to investigate the electric-field effect on \tau_{s} and l_{s} of monolayer WSe_{2}, bulk GaAs, bulk GaN, and graphene-h-BN heterostructure. We find that an electric field reduces \tau_{s} of GaAs, due to the induced D'yakonov-Perel'-type spin relaxation. In GaN and graphene-h-BN, \tau_{s} is significantly affected, partly because the electric field generates an effective magnetic field corresponding to the k-derivative of Rashba spin-orbit (magnetic) field. Our results show that l_{s} can be significantly enhanced or suppressed by a moderate downstream or upstream field respectively. While the standard drift-diffusion model performs well for WSe_{2}, it can introduce large errors of the electric-field-induced changes of l_{s} in GaAs, GaN and graphene-h-BN. Our proposed \textit{ab initio} matrix-drift-diffusion model improves results for GaAs and GaN, but still fails for graphene-h-BN. Thus, to accurately capture the influence of electric fields on l_{s} in realistic materials, it is necessary to go beyond the drift-diffusion model and adopt a microscopic \textit{ab initio} methodology. Moreover, in graphene-h-BN, we find that the field-induced changes of \tau_{s} and l_{s} are not only governed by the drift term in the master equation, but are also significantly affected by the electric-field modification of the equilibrium density matrix away from Fermi-Dirac distribution function.

cond-mat.mtrl-sci↗

arXiv2025

Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs

Vision-Language-Action (VLA) models have emerged as powerful generalist policies for robotic control, yet their performance scaling across model architectures and hardware platforms, as well as their associated power budgets, remain poorly understood. This work presents an evaluation of five representative VLA models -- spanning state-of-the-art baselines and two newly proposed architectures -- targeting edge and datacenter GPU platforms. Using the LIBERO benchmark, we measure accuracy alongside system-level metrics, including latency, throughput, and peak memory usage, under varying edge power constraints and high-performance datacenter GPU configurations. Our results identify distinct scaling trends: (1) architectural choices, such as action tokenization and model backbone size, strongly influence throughput and memory footprint; (2) power-constrained edge devices exhibit non-linear performance degradation, with some configurations matching or exceeding older datacenter GPUs; and (3) high-throughput variants can be achieved without significant accuracy loss. These findings provide actionable insights when selecting and optimizing VLAs across a range of deployment constraints. Our work challenges current assumptions about the superiority of datacenter hardware for robotic inference.

cs.AI↗