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Jie Zheng

Publications and source records attributed to Jie Zheng.

At least 19 recordsLinked to original sources

Testing $f(Q)$ Gravity with DESI DR2 and Strong-Lensing Time Delays

Symmetric teleparallel gravity provides an alternative description of gravitation in which non-metricity replaces curvature and torsion. Its extension through $f(Q)$ gravity offers a different geometric description of the late-time expansion of the Universe and its accelerated phase. In this work, we investigate two $f(Q)$ models, a normalized power-law model and a square-root exponential model, and test their ability to describe the late-time expansion history. We constrain the model parameters through Markov chain Monte Carlo analyses using Cosmic Chronometer measurements, DESI DR2 baryon acoustic oscillations, strong-lensing time-delay observations, and three Type Ia supernova compilations, Pantheon$^+$, Union 3.0, and DES Y5. We compare both models with the flat $Λ$CDM model using the minimum $χ^2$, Akaike information criterion, and Bayesian information criterion. The square-root exponential model provides a better statistical fit than $Λ$CDM for the combinations of Cosmic Chronometer, DESI DR2, and strong-lensing time-delay data with Pantheon$^+$ and Union 3.0, with improvements in both the goodness of fit and information criteria. The normalized power-law model remains statistically competitive with $Λ$CDM for the supernova-inclusive combinations, although the information criteria do not favor its additional parameter. We also determine the transition redshift from cosmic deceleration to acceleration for both models, obtaining consistent values across the different dataset combinations. The transition redshifts agree with observational estimates of the cosmic acceleration epoch. Overall, our results support $f(Q)$ gravity as a viable alternative to $Λ$CDM for explaining the late-time accelerated expansion of the Universe without requiring a cosmological constant.

gr-qc

Revisiting the equation of state of dark energy from DESI BAO with SNe Ia and CMB

The Dark Energy Spectroscopic Instrument (DESI) measurements of baryon acoustic oscillations (BAO) have recently shown a mild preference for dynamical dark energy over the standard $\rm Λ$CDM model. In this paper, we analyze the $w_0w_a$CDM model using DESI BAO DR2, Pantheon+ SNe Ia, and Planck 2018 CMB distance prior data. To examine how different parts of the data affect the apparent deviation from $\rm Λ$CDM, we adopt two complementary redshift-cut strategies, dividing the dataset into $z z_{\rm cut}$ subsamples. We find that the most noticeable shifts occur when BAO and SNe Ia data in the redshift range $z\sim0.4$--$0.8$ are included, reaching a significance of about $\sim2σ$. Within this framework, the inclusion of higher-redshift measurements progressively weakens this deviation and brings the constraints closer to the $\rm Λ$CDM expectation. Moreover, the information criteria show no statistically significant preference between the $w_0w_a$CDM and $\rm Λ$CDM models, while the Bayesian information criterion consistently favors the $\rm Λ$CDM model. In addition, a parameter-shift consistency test reveals no statistically significant tension between complementary redshift subsamples. Within the DESI BAO DR2, Pantheon+, and CMB distance prior framework adopted here, these results do not provide statistically robust evidence favoring the $w_0w_a$CDM model over $\rm Λ$CDM. They instead indicate that the apparent parameter shifts under different redshift cuts may be affected by statistical fluctuations and by the limited precision and number of datapoints in the current subsamples. Our analysis provides a complementary redshift-dependent diagnostic for assessing how the inferred cosmological constraints vary under different redshift selections.

astro-ph.CO

Evolution-Aware MSA Reasoning for Subsampling via Factor Graphs

Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoidable under limited token budgets. Existing strategies, including random selection, identity-based filtering, and diversity-driven sampling, are effective heuristics, yet provide limited control over the evolutionary signals retained in the subset. In this work, we recast MSA subsampling as an explicit optimization problem, where key evolutionary measures, including query identity and diversity, are treated as controllable objectives. Building on this view, we introduce AP-REASONER, an Affinity-Propagation-based factor-graph approach. With evolution-aware unary factors, exemplar-consistency factors, and two control knobs, AP-REASONER performs factor-graph reasoning through message passing to infer a fixed-budget MSA subset. Experiments on long-range contact prediction and conformational ensemble prediction show that AP-REASONER outperforms baseline subsamplers on structure-sensitive downstream tasks and enables controllable recovery of alternative protein conformations. These results highlight the value of modeling MSA subsampling as a controllable optimization problem, where factor-graph reasoning offers an effective alternative to heuristic selection.

cs.LG

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Large Language Models (LLMs) show promise for code compilation tasks, but applying them to runtime performance tuning is difficult due to complex microarchitectural effects and noisy runtime measurements. We present AutoPass, a multi-agent framework for compiler performance tuning that uses compiler and runtime evidence to guide LLM-generated optimization decisions. Rather than treating the compiler as a black box like prior auto-tuning schemes, AutoPass opens up the compiler to the LLM, enabling it to query compiler-internal optimization states and analyze the intermediate representation to orchestrate compiler options. The search process iteratively refines optimization configurations using measured runtime feedback to diagnose regressions and guide latency-improving edits. AutoPass operates in an inference-only, training-free setting and requires no offline training or task-specific fine-tuning, making it readily applicable to new benchmarks and platforms. We implement AutoPass on the LLVM compiler and evaluate it on server-grade x86-64 and embedded ARM64 systems. AutoPass outperforms expert-tuned heuristics and classical autotuning methods, achieving geometric-mean speedups of 1.043x and 1.117x over LLVM -O3 on x86-64 and ARM64, respectively.

cs.SE

AstroSkyFlow: an astronomical sky image flow simulator for time domain survey validation and machine learning

Modern time-domain optical surveys produce massive data volumes that require robust, high-fidelity simulated datasets for developing and validating automated pipelines and machine-learning models. We present AstroSkyFlow, a modular sky-image simulator that generates on-demand, time-dependent flux variations and models the full observing stack, from celestial sources and atmospheric effects to sensor response. Given a simulated observing schedule, AstroSkyFlow produces multi-epoch, time-series images with realistic noise and variability. Compared to real observational data, AstroSkyFlow reproduces noise characteristics and point spread function properties more accurately than the widely used SkyMaker simulator. In addition, AstroSkyFlow successfully recovers injected photometric and motion signals, such as exoplanet transits and asteroid trails. AstroSkyFlow enables the generation of labeled, high-fidelity datasets essential for training machine-learning pipelines and conducting rigorous injection-recovery tests for analysis pipelines for next-generation time-domain surveys.

astro-ph.IM

An Automated Photometric Pipeline for the 80cm Xizang University Telescope

Processing astronomical data can take up a significant amount of researchers' time. The 80cm telescope at Xizang University is currently in its trial operation phase; however, it lacks a data processing program, which makes efficient handling of the data it generates an urgent concern. To address this issue, we have developed an automatic pipeline for processing photometric data and extracting light curves using Python 3. This pipeline has several advantages, including high speed, ease of use, and modularity. The differential photometric accuracy of this pipeline is comparable to that of data processing programs used by other similar telescopes. This development effectively overcomes the limitations of manually processing data, providing efficient and reliable support for future studies of variable stars. The pipeline has already been integrated into the telescope's operational system.

astro-ph.IM

CosFly-Track: A Large-Scale Multi-Modal Dataset for UAV Visual Tracking via Multi-Constraint Trajectory Optimization

Recent aerial vision-language navigation (VLN) datasets have grown rapidly, but they primarily address goal-oriented navigation to static destinations, leaving UAV visual tracking -- continuously following a moving target while maintaining visibility -- largely without dedicated training data. We introduce CosFlyTrack, a large-scale multi-modal dataset and scalable generation pipeline for UAV visual tracking in urban environments. The dataset provides approximately 12,000 expert and perturbed UAV trajectories generated from 6,000 pedestrian paths, comprising 2.4 million timesteps (approximately 334 hours) with seven aligned data channels: RGB, metric depth, semantic segmentation, six-degree-of-freedom drone pose, target state with visibility flag, bilingual (Chinese-English) instructions, and trajectory-pair metadata. To generate high-quality expert trajectories, we develop MuCO, a multi-constraint optimizer that plans directly in continuous three-dimensional space with BVH-accelerated collision and visibility queries, jointly enforcing target visibility, viewpoint quality, collision avoidance, smoothness, and kinematic feasibility, avoiding the discretization artifacts and post-hoc smoothing of grid-based planners. Fine-tuning experiments on seven vision-language models show that CosFlyTrack improves tracking performance to 78.3 to 95.6 percent SR@1 meter, a 53 to 69 percentage point gain over zero-shot baselines, supporting the dataset as a training resource for dynamic target-following agents. The dataset is publicly available at https://huggingface.co/datasets/AutelRobotics/CosFly; evaluation scripts and pre-trained checkpoints are hosted at https://huggingface.co/AutelRobotics/CosFly-Track.

cs.RO

CosFly: Plan in the Matrix, Fly in the World

We present CosFly, a box-structured planning and multimodal simulation pipeline for aerial tracking, together with CosFly-Track, a large-scale UAV dataset for dynamic target tracking across diverse environments including urban centers, highways, rural landscapes, forests, and coastal towns. In our current implementation on CARLA, CosFly provides a modular 7-step construction pipeline that converts complex 3D worlds into structured obstacle representations for planning, then projects the resulting trajectories back into multi-modal sensor data -- including RGB images, high-precision depth maps, and semantic segmentation masks -- paired with natural language navigation instructions. A key feature is the support for configurable fixed-FOV zoom levels (one FOV setting drawn per trajectory and held constant throughout), enabling simulation of various focal lengths through camera-intrinsic adjustments. The pipeline covers the complete workflow from 3D map export through grid simplification, pedestrian and drone trajectory planning, multi-modal rendering with 6-DOF pose annotations, quality inspection, and teacher-student caption generation. We analyze two trajectory-planning paradigms for aerial target tracking: a conventional two-stage pipeline with front-end candidate generation and backend refinement, and a direct gradient-based formulation that optimizes multiple tracking constraints in a single objective. The public CosFly-Track release contains 250 validated trajectories and approximately 100,000 rendered images with complete 6-DOF drone pose annotations (position x, y, z and orientation yaw, pitch, roll). Together, the pipeline and dataset establish a scalable foundation for aerial-ground collaborative research, supporting dynamic target tracking, UAV navigation, and multi-modal perception across diverse environments.

cs.RO

The Stellar Abundances and Galactic Evolution Survey (SAGES). V. The First Data Release of the DDO51 Band

We present the first public data release of DDO51 band from the Stellar Abundances and Galactic Evolution Survey (SAGES), based on Nanshan One-meter Wide-field Telescope (NOWT) observations obtained between 2023 September and 2024 January. This release initiates the DDO51-band component of the survey, covering $\sim$ 2,500 deg$^2$ of the northern sky and including more than 10 million sources. The DDO51 filter is centered near the \ion{Mg}{1}~$b$ triplet and the adjacent MgH feature, offering sensitivity to stellar surface gravity. The data reduction pipeline incorporates an improved astrometric solution anchored to Gaia DR3 and a photometric calibration strategy tied to synthetic photometry from Gaia XP spectra. These procedures yield a point-source depth of $\sim$18.9 mag at S/N$\sim$10 and an internal photometric precision $\approx$6-7 mmag at the bright end. A preliminary color--color analysis using Gaia broadband photometry confirms the expected sensitivity of the DDO51 band to stellar surface gravity, demonstrating a clear photometric separation between dwarf and giant sequences for late-type stars. This dataset, when combined with existing SAGES photometry in other bands, provides a crucial tool for disentangling the substructures of the Milky Way. All data products from this release upon publication will be available.

astro-ph.SR

Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale

Large-scale ranking systems depend on thousands of features derived from user behavior across multiple time horizons. Typically requires model retraining -- resulting in long iteration cycles (3--6 months), substantial GPU resource consumption, and limited rollout throughput. We introduce Intelligent Elastic Feature Fading (IEFF), a production infrastructure system that enables retrain-free feature efficiency rollouts by elastically controlling feature coverage and distribution at serving time. IEFF supports incremental feature coverage adjustments while models adapt through recurring training, eliminating dependencies on explicit retraining cycles. The system incorporates strict safety guardrails, reversibility mechanisms, and comprehensive monitoring to ensure stability at scale. Across multiple production use cases, IEFF accelerates efficiency-related rollouts by 5$\times$, eliminates retraining-related GPU overhead, and enables faster capacity recycling. Extensive offline and online experiments demonstrate that gradual feature fading prevents 50--55\% of online performance degradation compared to abrupt feature removal, while maintaining stable model behavior. These results establish elastic, system-level feature fading as a practical and scalable approach for managing feature efficiency in modern industrial ranking systems.

cs.IR

Multiple Consistent 2D-3D Mappings for Robust Zero-Shot 3D Visual Grounding

Zero-shot 3D Visual Grounding (3DVG) is a critical capability for open-world embodied AI. However, existing methods are fundamentally bottlenecked by the poor quality of open-vocabulary 3D proposals, suffering from inaccurate categories and imprecise geometries, as well as the spatial redundancy of exhaustive multi-view reasoning. To address these challenges, we propose MCM-VG, a novel framework that achieves robust zero-shot 3DVG by explicitly establishing Multiple Consistent 2D-3D Mappings. Instead of passively relying on noisy 3D segments, MCM-VG enforces 2D-3D consistency across three fundamental dimensions to achieve precise target localization and reliable reasoning. First, a Semantic Alignment module corrects category mismatches via LLM-driven query parsing and coarse-to-fine 2D-3D matching. Second, an Instance Rectification module leverages VLM-guided 2D segmentations to reconstruct missing targets, back-projecting these reliable visual priors to establish accurate 3D geometries. Finally, to eliminate spatial redundancy, a Viewpoint Distillation module clusters 3D camera directions to extract optimal frames. By pairing these optimal RGB frames with Bird's Eye View maps into concise visual prompt sets, we formulate the final target disambiguation as a multiple-choice reasoning task for Vision-Language Models. Extensive evaluations on ScanRefer and Nr3D benchmarks demonstrate that MCM-VG sets a new state-of-the-art for zero-shot 3D visual grounding. Remarkably, it achieves 62.0\% and 53.6\% in Acc@0.25 and Acc@0.5 on ScanRefer, outperforming previous baselines by substantial margins of 6.4\% and 4.0\%.

cs.CV

Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap

Vision-and-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) represents a pivotal challenge in embodied artificial intelligence, focused on enabling UAVs to interpret high-level human commands and execute long-horizon tasks in complex 3D environments. This paper provides a comprehensive and structured survey of the field, from its formal task definition to the current state of the art. We establish a methodological taxonomy that charts the technological evolution from early modular and deep learning approaches to contemporary agentic systems driven by large foundation models, including Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, and the emerging integration of generative world models with VLA architectures for physically-grounded reasoning. The survey systematically reviews the ecosystem of essential resources simulators, datasets, and evaluation metrics that facilitates standardized research. Furthermore, we conduct a critical analysis of the primary challenges impeding real-world deployment: the simulation-to-reality gap, robust perception in dynamic outdoor settings, reasoning with linguistic ambiguity, and the efficient deployment of large models on resource-constrained hardware. By synthesizing current benchmarks and limitations, this survey concludes by proposing a forward-looking research roadmap to guide future inquiry into key frontiers such as multi-agent swarm coordination and air-ground collaborative robotics.

cs.RO

Density-aware Soft Context Compression with Semi-Dynamic Compression Ratio

Soft context compression reduces the computational workload of processing long contexts in LLMs by encoding long context into a smaller number of latent tokens. However, existing frameworks apply uniform compression ratios, failing to account for the extreme variance in natural language information density. While adopting a density-aware dynamic compression ratio seems intuitive, empirical investigations reveal that models struggle intrinsically with operations parameterized by input dependent, continuous structural hyperparameters. To resolve this pitfall, we introduce Semi-Dynamic Context Compression framework. Our approach features a Discrete Ratio Selector, which predicts a compression target based on intrinsic information density and quantizes it to a predefined set of discrete compression ratios. It is efficiently jointly trained with the compressor on synthetic data, with the summary lengths as a proxy to create labels for compression ratio prediction. Extensive evaluations confirm that our density-aware framework, utilizing mean pooling as the backbone, consistently outperforms static baselines, establishing a robust Pareto frontier for context compression techniques. Our code, data and model weights are available at https://github.com/yuyijiong/semi-dynamic-context-compress

cs.CL

Spin-Based True Random Number Generation Enabled by Voltage-Amplified Quantum Fluctuations

We investigate spin quantum-fluctuation effects that originate from the Heisenberg uncertainty principle during the dynamical cycle of disentanglement, entanglement, and re-disentanglement between itinerant electrons and localized magnetic moments mediated by the s-d exchange interaction. Beyond conventional deterministic spin-transfer torque, we analyze an intrinsic mechanism that transfers spin quantum fluctuations to a nanomagnet. By extending the Landau-Lifshitz-Gilbert equation to incorporate both quantum and thermal stochastic fields, we identify a temperature regime in which quantum fluctuations dominate the magnetization dynamics. We further show that voltage-controlled magnetic anisotropy exponentially amplifies spin quantum fluctuations, enabling binary readout through magnetoresistance in magnetic tunnel junctions. These findings provide a microscopic framework for fluctuation-driven spin dynamics and outline a device-level pathway toward spin-based quantum true random number generation.

cond-mat.mes-hall

From Digital Twins to World Models:Opportunities, Challenges, and Applications for Mobile Edge General Intelligence

The rapid evolution toward 6G and beyond communication systems is accelerating the convergence of digital twins and world models at the network edge. Traditional digital twins provide high-fidelity representations of physical systems and support monitoring, analysis, and offline optimization. However, in highly dynamic edge environments, they face limitations in autonomy, adaptability, and scalability. This paper presents a systematic survey of the transition from digital twins to world models and discusses its role in enabling edge general intelligence (EGI). First, the paper clarifies the conceptual differences between digital twins and world models and highlights the shift from physics-based, centralized, and system-centric replicas to data-driven, decentralized, and agent-centric internal models. This discussion helps readers gain a clear understanding of how this transition enables more adaptive, autonomous, and resource-efficient intelligence at the network edge. The paper reviews the design principles, architectures, and key components of world models, including perception, latent state representation, dynamics learning, imagination-based planning, and memory. In addition, it examines the integration of world models and digital twins in wireless EGI systems and surveys emerging applications in integrated sensing and communications, semantic communication, air-ground networks, and low-altitude wireless networks. Finally, this survey provides a systematic roadmap and practical insights for designing world-model-driven edge intelligence systems in wireless and edge computing environments. It also outlines key research challenges and future directions toward scalable, reliable, and interoperable world models for edge-native agentic AI.

cs.AI

The Spectroscopic and Photometric Study of a Star Cluster Sample in Andromeda Halo

Halo star clusters serve as vital tracers for the formation and evolution of the Andromeda galaxy. In this work, we present physical parameters for 29 M31 halo star clusters, derived from a combination of spectroscopic and photometric data. Low-resolution spectra were acquired using the BFOSC spectrograph on the NAOC Xinglong 2.16-m telescope. For the photometric analysis, we utilized uSC and vSAGE bands from the SAGE survey, complemented by archival data from GALEX(NUV, FUV), PAN-STARRS(grizy) and the 2MASS(JHK). Ages and metallicities were determined via ULySS (Vazdekis et al. and pegase-hr) SSP model and the Bruzual & Charlot (2003) (BC03) stellar population synthesis models. The derived parameters show good agreement with literature values. Notably, for three of these clusters, this study represents the first combined photometric and spectroscopic analysis.

astro-ph.GA

Low-cost cross-correlation noise setup for measuring the Boltzmann constant and the elementary charge

We present a low-cost experimental setup to measure the Boltzmann constant (kB) and the elementary charge (e) through thermal and shot noise, achieving relative accuracies of up to 1%. The system utilizes a cost-effective ADC module integrated with a carefully selected low-noise audio operational amplifier, resulting in a simple and compact circuit design that better satisfies experimental requirements. Furthermore, the approach employs a dual-channel sampling method to measure cross-correlation noise, thereby significantly reducing the uncorrelated background noise of the device and improving the signal-to-noise ratio. The noise measurement methodology offers an intuitive demonstration of microscopic fluctuations, making it a valuable teaching tool. In addition, the experimental setup employs standard electronic components, is low cost and robust, making it well-suited for widespread adoption in university and even high school laboratories.

physics.ed-ph

Spectral Dataset of Stripped-Envelope Supernovae from the Tsinghua Supernova Group

The extent of envelope stripping in the progenitor stars is directly reflected in the diversity of spectral features observed in stripped-envelope supernovae (SESNe). Through extensive spectral observation and analysis, we aim to clarify the statistical differences between the subclasses of SESNe. The Tsinghua Supernova group obtained 249 optical spectra of 62 SESNe during the years from 2010 to 2020, covering phases from $-$16 to over 190 days relative to maximum light. Most spectra were obtained during the photospheric phases after the supernova explosion. For each spectrum, the pseudo-equivalent widths (pEWs) and blueshift velocities of principal lines were measured. We further investigated the common spectral features by analysing their velocity and strength correlations across all subtypes. We identify the feature near 6200~Å in SNe Ib as H$\mathrmα$ through comparison with SNe IIb and Ic, which resolves inconsistent literature interpretations. Our finding reveals prevalent residual hydrogen in SNe Ib, further supporting a continuous stripping sequence from SNe IIb to Ib. We observe a trend in increasing velocity among different subtypes of stripped-envelope SNe, with SNe IIb exhibiting the lowest line velocities, followed by Ib, Ic, and Ic-BL. Typically, the O~I lines in SNe Ic/Ic-BL are stronger than those seen in SNe IIb/Ib. In nebular phases, the [Ca II] emission dominates over [O I] in SNe IIb/Ib while [O I] is stronger in SNe Ic, including the He-rich SN 2016coi. This spectral dichotomy implies that progenitors of SNe Ic (BL) have more massive CO cores and hence higher initial masses.

astro-ph.HE