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Cheng Li

Publications and source records attributed to Cheng Li.

At least 55 records · Page 3Linked to original sources

Anisotropic Secondary Bias of Dark Matter Haloes in a $Λ$CDM Universe

Secondary bias is the dependence of halo clustering on properties beyond halo mass. Using the $z=0$ TNG300-1-Dark simulation, we study anisotropic secondary bias (ASB): the variation of secondary bias with direction relative to the halo major axis. We first use ordinary, orientation-averaged secondary bias (OSB) as a baseline to compare three environmental manifestations: halo-environment alignment, outer matter anisotropy, and tidal anisotropy. Matching tidal anisotropy suppresses much of the OSB, whereas matching halo-environment alignment or outer matter anisotropy does not. ASB behaves differently. It is weak for formation time, concentration, and triaxiality, but strong for both spin definitions and minor-to-major axis ratio; slowly rotating and more elongated haloes are more strongly aligned with filamentary structure. Matching halo-environment alignment substantially reduces the spin- and shape-dependent ASB signals, whereas matching tidal anisotropy or the outer matter axis ratio leaves them largely intact. Halo definition has little impact on ASB, yet strongly affects low-mass spin bias: including unbound particles can move dense-environment haloes with low bound-particle spin into the high all-particle-spin sample. These results clarify which clustering signals are associated with halo-environment alignment, matter anisotropy, or tidal anisotropy, and which are sensitive to halo definition.

astro-ph.CO↗

Galaxy populations in groups and clusters-II. Conditional luminosity functions at redshifts from z~1 to z~0

Using DESI SV3 spectroscopic group centrals and HSC photometric data, we measure conditional luminosity functions (CLFs) of central and satellite galaxies for red and blue populations in dark matter haloes spanning $M_h\sim10^{12}- 10^{15}M_{\odot}$ and $0<z<1$. HSC depth permits measurements to $M_r \approx -15$ at $0.2 \leqslant z < 0.5$ and $M_r \approx -17$ at $0.5 \leqslant z < 1.0$. We find satellite CLFs evolve weakly over $0<z<1$. Blue satellite CLFs are well described by a single Schechter function across halo masses and redshifts, with a nearly constant slope of $-1.25\lesssim α\lesssim -1.2$. In contrast, red satellite CLFs exhibit a pronounced faint-end upturn in all halo mass and redshift bins, with little evolution in the faint-end slope ($-1.8\lesssim α_f\lesssim -1.7$). The low-mass red sequence was therefore already established in clusters/groups by $z\sim1$. The lack of faint-end-slope evolution favors models where the steep upturn originates from early formation processes at $z\gtrsim2$, rather than environmental quenching after infall. Satellite characteristic magnitudes and central galaxy luminosities fade with time. Red central galaxies are consistent with passive evolution, whereas blue-central luminosity evolution is dominated by ongoing star formation. Satellites evolve more rapidly than predicted by simple stellar population models, highlighting environmental effects. Satellite quenched fractions as a function of stellar mass exhibit a minimum at $M_{*} \sim 10^9M_{\odot}$ that is consistent across halo masses and redshifts. We discuss possible interpretations of these results and their implications for galaxy formation and evolution.

astro-ph.GA↗

Electrochemical and thermal control of continuous phase transitions in P2-NaxNi1/3Mn2/3O2

Sodium layered oxides often undergo phase transformations involving ordering or disordering of Na+ upon desodiation, i.e., when cycled as a battery electrode. Accurately characterizing these phases is crucial for understanding functional properties, such as chemical diffusivity. In this work, we reveal that Na+-vacancy (dis)ordering in a layered oxide is intrinsically coupled to symmetry-changing phase transformations of the host structure. We examine the low-symmetry orthorhombic unit cell of P2-NaxNi1/3Mn2/3O2 (NNM) using both neutron and X-ray diffraction. Specifically, special sodium stoichiometries (x = 2/3 and 1/2) exhibit concomitant Na+-vacancy ordering and an orthorhombic distortion from the parent hexagonal unit cell. We then demonstrate that electrochemical desodiation drives symmetry-changing transformations in NNM that are linked to Na+-vacancy (dis)ordering, with evidence of second-order behavior observed near x = 2/3. Variable-temperature synchrotron X-ray diffraction further clarifies the coupling between Na+-vacancy disordering and orthorhombic-to-hexagonal phase transitions in NNM. Surprisingly, the temperature-driven phase transitions at x = 2/3 and 1/2 differ in character, appearing second-order and first-order, respectively. Our analysis of the phase transitions in NNM has fundamental consequences for sodium chemical diffusivity in the vicinity of the ordered phases and leads to design principles for modifying phase transition behavior in the broader class of intercalation electrodes.

cond-mat.mtrl-sci↗

Efficient and Privacy Aware Edge Cloud Collaborative Inference for Large Language Models

On-device LLM inference faces a trilemma of response latency, limited hardware resources and user privacy. Full cloud inference delivers strong computing power but exposes user prompts and dialogue data, while standalone on-device inference is unfeasible for most consumer and embedded edge devices. This paper presents a privacy-centric edge-cloud collaborative LLM inference framework built on endpoint-authenticated KV cache. Local endpoints handle input preprocessing, embedding computation, adaptive feature optimization, KV cache authentication, speculative decoding and low-dimensional model head calculation, while the cloud conducts authenticated decoder inference, KV cache management, token verification and high-dimensional vocabulary projection. Endpoints fuse partial outputs, apply language-adaptive masking and sample target tokens. All transmitted data and truncated logits are quantized and AES-GCM encrypted for privacy, with core lightweight modules, draft parameters and cache access policies kept local to avoid leakage. The framework supports heterogeneous devices including CPU-only, GPU-equipped and embedded devices via optimized streaming, batching and quantized ONNX deployment. Evaluations demonstrate that the framework reduces per-token latency by up to 46.1\% and downlink payloads by up to 67.4\% over baseline split inference, retaining comparable performance to full cloud inference.

cs.CR↗

The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results

This paper presents a review of the second LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aims to advance unified image restoration under diverse real-world degradation conditions, including blur, low-light, haze, rain, and snow. It provides a common benchmark for evaluating the restoration accuracy, robustness, and generalization capability of models across multiple degradation categories within a unified framework. The competition attracted 158 registered participants, and 20 teams were included in the final ranking after their submitted results were successfully reproduced and verified. This report provides a comprehensive analysis of the submitted solutions and corresponding results, highlighting recent advances in real-world all-in-one image restoration. The summarized methods and empirical findings reveal effective design strategies and establish an updated benchmark for future research in real-world low-level vision.

cs.CV↗

CSST large-scale structure analysis pipeline: IV. Cosmic Voids Identified from Galaxy Group Samples as Probes of the Large-scale Structure

Because groups are directly associated with halos, they allow for considerably simpler theoretical modeling than approaches based on individual galaxies. We therefore propose to use voids identified in galaxy group catalogs, referred to as group-voids, to investigate the cosmic large-scale structure (LSS). Using the reference mock galaxy redshift survey (MGRS) designed for the Chinese Space-station Survey Telescope (CSST), we build two galaxy group catalogs representing ideal and realistic scenarios, derived from galaxy samples with 100\% and roughly 30\% spectroscopic redshift completeness, respectively. We then identify voids in these two mock group catalogs, as well as in the underlying halo catalog, and measure two void statistics, the void size function (VSF) and the void density profile, within five redshift intervals spanning $z=0$ to $1.0$. We compare the statistics obtained from two kinds of voids: those defined by galaxy groups (group-voids) and those defined by dark matter halos (halo-voids). In the void-finding process, we adopt the brightest central galaxy (BCG) as the group center to improve the accuracy of the inferred void centers. Our analysis shows that void statistics derived from group-voids with spectroscopic redshift completeness of at least 40\% can faithfully reproduce the corresponding statistics from halo-voids. Even when the redshift completeness of galaxies falls to as low as 30\%, we can still reliably describe group-voids via halo-voids by incorporating a redshift error term. This indicates that group-voids are a promising tool for probing LSS and offer a valuable complement to standard void studies, which is especially advantageous for emulator-based methods.

astro-ph.CO↗

Extracting nuclear charge radii from binding energies: a single-parameter empirical formula with structural corrections

Nuclear binding energies and charge radii stem from the same underlying physics: saturation, isospin dependence, shell structure, and deformation. Binding-energy data therefore provide a natural constraint for charge-radius modeling. We propose a one-parameter charge-radius formula ($\mathrm{BECR}_\mathrm{1p}$) that combines binding-energy correlations with local structural corrections. On a curated set of 893 experimental charge radii, the macroscopic BECR term alone reproduces the leading charge-radius scale with a root-mean-square deviation (RMSD) of 0.0345 fm; adding shell, odd--even, finite-size, and deformation corrections further reduces the RMSD of BECR1p to 0.0138 fm. An anisotropic kernel ridge regression (AKRR) applied to the residuals further lowers the leave-one-out cross-validation RMSD to about 0.0081 fm. We use the formula to predict charge radii for 11205 nuclei across the nuclear chart.

nucl-th↗

Mapping Dust Attenuation at Kiloparsec Scales. IV. A Dust-model Interpretation of Attenuation Curves in Nearby Galaxies

In this fourth paper on kiloparsec-scale dust attenuation, we ask whether the empirical trends found in Papers I--III can be translated into effective dust properties. Using attenuation curves for 2487 high-continuum-S/N spaxels in 91 SwiM v4.2 galaxies, we construct a grid of uniform-screen dust models composed of astronomical silicate and graphite grains with MRN-like size distributions. We fit the normalized attenuation-curve shape to constrain model parameters and then use the attenuation amplitude to estimate the model-dependent dust mass surface density. The inferred dust masses, compositions, and small-grain fractions are therefore effective quantities defined within the adopted attenuation model. The fitted models reproduce the main attenuation-curve variations and provide a direct bridge to Papers I--III: within this model, the relative 2175Å bump sequence maps mainly onto the effective fraction of small graphitic/carbonaceous grains, while the NUV-slope sequence maps onto the effective small-silicate grain fraction and total silicate mass fraction. Non-SF regions have higher dust mass surface densities but lower dust-to-stellar mass ratios than SF regions, separating absolute dust content from dust content per unit stellar mass. Regions with larger specific H$α$ surface brightness have larger dust-to-stellar mass ratios but lower inferred small-grain fractions, especially lower small-silicate fractions. In non-SF regions this quantity is interpreted as ionized-gas emission per unit stellar mass rather than as a direct sSFR. These model-dependent trends support a picture in which local dust processing changes the relative abundance of small grains and thereby shapes the attenuation-curve variations found across the series.

astro-ph.GA↗

Analytical penetration probability including the centrifugal potential: An improved Buck--Merchant--Perez model for alpha-decay half-lives

We derive a closed-form, non-perturbative WKB penetration formula for alpha-decay that explicitly incorporates the centrifugal potential within the Buck--Merchant--Perez (BMP) cluster model. The centrifugal term is shown to enhance the hindrance by effectively enlarging the barrier width: it pushes the outer turning point outward and, via the Bohr--Sommerfeld quantization condition, shifts the inner turning point inward. Building on this analytical result, we further develop an improved BMP model in which the nuclear potential depth is expressed as a unified four-parameter formula that simultaneously encodes shell corrections, odd-even pairing effects, and orbital-angular-momentum dependence. For 534 ground-state-to-ground-state alpha decays spanning Z = 60--118, the root-mean-square deviation of log base 10 T1/2 is reduced to 0.267, representing a 57% improvement over the original constant-depth BMP model (0.615), with robust performance for both favored (0.188) and unfavored (0.398) transitions. The framework is further applied to predict the half-lives of hitherto-unmeasured nuclei in the region Z = 117--120, providing quantitative benchmarks for future experimental investigations.

nucl-th↗

Post-starburst Galaxies with Active Galactic Nucleus: Properties and Evolutionary Sequences

Post-starburst (PSB) galaxies, identified by strong Balmer absorption and weak nebular emission, provide a key laboratory for studying rapid quenching. Using the final data release of the SDSS-IV MaNGA survey, we follow the traditional PSB selection criteria of Chen et al. (2019) and develop a new method to identify regions that simultaneously exhibit PSB features and nuclear activities (AGN-PSBs). Our final sample comprises 48 AGN-PSBs, 92 central PSBs (CPSBs), 89 ring-like PSBs (RPSBs), and 828 irregular PSBs (IPSBs). We find the global and spatially resolved properties of CPSBs and RPSBs are consistent with the results of Chen et al. (2019). In this work, we focus on the properties of AGN-PSBs, comparing them with CPSBs, RPSBs, and control galaxies. Similar to CPSBs and RPSBs, AGN-PSBs show positive $\mathrm{D}_{n}4000$ gradients relative to negative $\mathrm{D}_{n}4000$ gradients of their controls, which indicates younger stellar populations in the central region than that in the outskirt. Among the three sub-types, high-mass CPSBs (H-CPSBs, with $\log(M_{*}/M_{\odot})>9.5$) display the highest incidence of merger remnants and gas--star kinematic misalignment, consistent with a merger/interaction-dominated origin. AGN-PSBs and RPSBs, however, show lower and comparable fractions of merger remnants and gas--star kinematic misalignment, favoring less violent external mechanisms. Based on radial profiles of mass-weighted age and $V_{\rm star}/σ_{\rm star}$, we suggest that RPSBs can evolve into AGN-PSBs, whereas H-CPSBs likely follow a distinct evolutionary pathway. The existence of RPSBs and IPSBs also indicates that AGN feedback is not a necessary condition for the formation of PSB.

astro-ph.GA↗

LEOSTP: A Spatio-Temporal Traffic Prediction Framework for LEO Satellite Networks

With the evolution of next-generation mobile communication networks and the commercial boom of Low Earth Orbit (LEO) satellites, globally covered satellite networks are gradually becoming a crucial infrastructure for massive user access and seamless connectivity. Accurate traffic prediction is crucial for maintaining the quality of service (QoS) and resource allocation efficiency in satellite networks. However, existing methods struggle to effectively address the three major challenges of LEO networks: highly complex temporal dynamics caused by satellite cross-regional movement, multivariate dependencies in multi-satellite collaboration, and strong spatial heterogeneity driven by user distribution, human activity intensity, and local geographic environments. In this article, we propose a LEO Satellite Traffic Predictor (LEOSTP) framework, a diffusion model-based end-to-end model that forecasts future satellite traffic by jointly leveraging historical traffic patterns and contextual characteristics of the corresponding service regions. The framework consists of two core modules: 1) The general traffic feature extractor module combines the diffusion process with a Transformer architecture to model the multi-scale temporal features of the traffic itself. 2) The external condition encoder module integrates geographic semantic information such as population distribution, point-of-interest (POI) distribution, and local time into the prediction process through a Transformer-based encoder. In this way, the model captures the deep correlation between the external environment and traffic dynamics. Experimental results based on large-scale simulated constellation data show that LEOSTP significantly outperforms traditional statistical models such as ARIMA and SVR, and classical sequence models including LSTM and Transformer, in prediction accuracy.

cs.IT↗

Episodic Star Formation -- I. Overview and Scatter of the Star-Forming Main Sequence

Episodic star formation cycles in both high- and low-redshift galaxies have gained more and more evidence. This paper aims to understand the detailed physical processes behind such behaviors and investigate how such an episodic star-forming scenario can explain the scatter in star-formation rate (SFR) of star-forming main-sequence galaxies. This is achieved through tracing back in time the history of z=0 star-forming central galaxies in the TNG100 simulation over the past 7-8 Gyrs. As the first paper in this series, we provide an overview of the episodic star formation history. We find that two branches of star formation typically develop during each episode: while one branch happens in heavily metal-enriched gas in the centers of galaxies, a secondary branch starts in lower-metallicity regions at galaxy outskirts where fresh gas first arrives, and gradually progresses to inner regions of galaxies. Additionally, the temporal variation in the SFR at galaxy outskirts is more significant than that at centers. As a consequence, the metallicities in both gas and young stars exhibit remarkably different distributions between SFR peaks and valleys. The resulting temporal SFR fluctuation within individual galaxies has an average of ~ 0.2 dex, while the intrinsic differentiation between (the historical mean of) galaxies is ~ 0.15 dex. These two together can well account for the scatter in SFR of ~ 0.25 dex as observed for z=0 star-forming main-sequence galaxies.

astro-ph.GA↗

What is the True HI Gas Content in Massive Quiescent Galaxies in the Local Universe?

While massive quiescent galaxies are known to be poor in atomic hydrogen (HI), their true HI content remains poorly constrained due to the limited sensitivity and morphological biases of existing surveys. We present deep HI observations using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) for a representative sample of 78 low-redshift massive quiescent galaxies, selected by stellar mass ($M_\ast > 10^{10} M_\odot$), color (NUV$-r > 5$), and specific star formation rate ($\rm \log sSFR < -11\ yr^{-1}$). Our observations reach a remarkable sensitivity of $\log(M_{HI}/M_\ast) = -2.6$ for 55 targets and $\log(M_{HI}/M_\ast) = -3.2$ for 23 targets. We find that one-third of the sample follows the HI scaling relation derived from previous surveys, while the remaining two-thirds exhibit significantly lower HI content. The HI mass fraction shows no clear correlation with specific star formation rate, NUV$-r$ color index, stellar surface mass density, and concentration. Our FAST sample shows remarkable similarity to the $ATLAS^{3D}$ sample which only includes early-type galaxies, both in its high fraction of HI-poor galaxies and its high satellite fraction among HI-poor galaxies. These results suggest that while both early-type morphology and environment may contribute to the extreme HI deficiency, neither factor alone fully explains the observed gas depletion, indicating that additional physical mechanisms must be responsible for the extreme HI deficiency prevalent in massive quiescent galaxies.

astro-ph.GA↗

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiving increasing attention. LLM unlearning, which aims to remove the influence of specific data while preserving overall model utility, is becoming an important research area. One of the mainstream unlearning classes is optimization-based methods, which achieve forgetting directly through fine-tuning, exemplified by Negative Preference Optimization (NPO). However, NPO's effectiveness is limited by its inherent lack of explicit positive preference signals. Attempts to introduce such signals by constructing preferred responses often necessitate domain-specific knowledge or well-designed prompts, fundamentally restricting their generalizability. In this paper, we shift the focus to the distribution-level, directly targeting the next-token probability distribution instead of entire responses, and derive a novel unlearning algorithm termed \textbf{Di}stribution \textbf{P}reference \textbf{O}ptimization (DiPO). We show that the requisite preference distribution pairs for DiPO, which are distributions over the model's output tokens, can be constructed by selectively amplifying or suppressing the model's high-confidence output logits, thereby effectively overcoming NPO's limitations. We theoretically prove the consistency of DiPO's loss function with the desired unlearning direction. Extensive experiments demonstrate that DiPO achieves a strong trade-off between model utility and forget quality. Notably, DiPO attains the highest forget quality on the TOFU benchmark, and maintains leading scalability and sustainability in utility preservation on the MUSE benchmark.

cs.LG↗

A muon scattering tomography system based on high spatial resolution scintillating detector

Cosmic ray muon scattering tomography (MST) is an imaging technique that utilizes muon scattering in matter to inspect high-Z materials non-destructively, without requiring an artificial radiation source. This method offers significant potential for applications in border security and long-term monitoring of nuclear materials. In this study, we developed a high-precision plastic-scintillator-based position-sensitive detector with a spatial resolution of 0.09 times the strip pitch. A fully functional, full-scale imaging system was then constructed using four layers of such XY position-sensitive detectors, each with an effective area of 53 cm x 53 cm. This paper details the following key contributions: the Geant4-simulated design and optimization of the imaging system, the fabrication, assembly, and testing of the detectors, and an evaluation of the imaging performance of the completed system.

physics.ins-det↗

CentroidKV: Efficient Long-Context LLM Inference via KV Cache Clustering

Large language models (LLMs) with extended context windows have become increasingly prevalent for tackling complex tasks. However, the substantial Key-Value (KV) cache required for long-context LLMs poses significant deployment challenges. Existing approaches either discard potentially critical information needed for future generations or offer limited efficiency gains due to high computational overhead. In this paper, we introduce CentroidKV, a simple yet effective framework for online KV cache clustering. Our approach is based on the observation that key states exhibit high similarity along the sequence dimension. To enable efficient clustering, we divide the sequence into chunks and propose Chunked Soft Matching, which employs an alternating partition strategy within each chunk and identifies clusters based on similarity. CentroidKV then merges the KV cache within each cluster into a single centroid. Additionally, we provide a theoretical analysis of the computational complexity and the optimality of the intra-chunk partitioning strategy. Extensive experiments across various models and long-context benchmarks demonstrate that CentroidKV achieves up to 75% reduction in KV cache memory usage while maintaining comparable model performance. Moreover, with minimal computational overhead, CentroidKV accelerates the decoding stage of inference by up to $1.92\times$ and increases the serving throughput by up to $4\times$.

cs.CL↗

LLM-Enabled NWDAF: A Step Toward AI-Native 6G Network Intelligence

The Network Data Analytics Function (NWDAF) is central to enabling zero-touch network management in fifth-generation (5G) networks by supporting real-time analytics and closed-loop automation. Despite its critical role, open-source NWDAF implementations remain limited in scope and accessibility. In this paper, we develop an open-source NWDAF, compatible with the open-source core network Free5GC, that collects network data via subscriptions to Network Functions (NFs), and also includes an integrated Large Language Model (LLM) interface that enables natural language interaction with human operators. The interface processes user intents, encodes them using a semantic embedding model, and maps them to one of seven predefined intent categories to trigger analytics queries or event subscription commands. This architecture abstracts the complexity of traditional interfaces, allowing non-expert users to manage network analytics and subscriptions with ease. The system supports Access and Management Function (AMF) and Session Management Function (SMF) event subscriptions, real-time monitoring, and analytics retrieval via Prometheus, all accessible through a conversational interface. By bridging AI-driven intent recognition with standardized network analytics, our implementation enhances operator usability and provides a foundation towards AI-native 6G networks. The source code and datasets generated during the current study are available in the github repository, https://github.com/HenokDanielbfg/testbed.

cs.NI↗

Two Integral Sliding-Window Inequalities for Maximal Convolutions

We prove two sliding-window inequalities for maximal convolutions. The first concerns the multiplicative maximal convolution. If $f$ and $g$ are nonnegative continuous functions on $[0,A]$ and $[0,B]$, respectively, define \[ h(x)=\max_{\substack{0\le u\le A\\0\le x-u\le B}} f(u)g(x-u),\qquad 0\le x\le A+B. \] Then there exists a window $[a,a+B]$ of length $B$ such that \[ \frac1B\int_a^{a+B}h(x)\,dx\ge \left(\frac1A\int_0^A f(x)\,dx\right) \left(\frac1B\int_0^B g(x)\,dx\right). \] The second concerns the additive maximal convolution. Let $f$ and $g$ be nonnegative continuous functions on $[0,C]$, and define \[ H(x)=\max_{\substack{0\le u\le C\\0\le x-u\le C}}\{f(u)+g(x-u)\},\qquad 0\le x\le 2C. \] Then, for every $p\ge1$, there exists a window $[a,a+C]$ of length $C$ such that \[ \left(\int_a^{a+C}H(x)^p\,dx\right)^{1/p} \ge \left(\int_0^C f(x)^p\,dx\right)^{1/p} + \left(\int_0^C g(x)^p\,dx\right)^{1/p}. \] We also record discrete analogues. The main point is that, in one-dimensional maximal-convolution settings, certain global Brunn--Minkowski or Prékopa--Leindler type phenomena admit natural sliding-window localizations.

math.FA↗