The Eaton--Moret\'o Conjecture Holds True for \(p\)-Solvable Groups
In this paper we prove that the Eaton--Moret\'{o} conjecture holds true for all $p$-solvable groups.
math.RT↗
arXiv subjects
Fang Zhou
Publications and source records attributed to Fang Zhou.
Economic Distance Structures Urban Mobility in 109 U.S. Cities
Urban mobility promises social integration, yet daily movement is systematically constrained by socioeconomic hierarchies. Introducing "economic distance"--the continuous income gap between origin and destination--as a unified lens, we analyze large-scale mobility records across 109 U.S. cities to reveal how urban flows are structured. We identify a universal structural boundary: flows concentrate intensely within a narrow economic distance of 0.25 quantiles, defining the effective "economic radius" of routine mobility. This boundary exhibits profound asymmetry; upward mobility faces a uniform structural ceiling across cities, whereas downward mobility drives cross-city heterogeneity. Mechanistically, the boundary is physically anchored by meso-scale residential clustering but is further tightened by an independent economic-distance friction, validated via gravity modeling. These interactions yield four distinct mobility regimes, with "affluent-confined" systems exhibiting the strongest stratification. These findings establish economic distance as a fundamental, asymmetric, and multi-scale filter shaping urban inequality, offering new theoretical grounds for interventions targeting structural barriers to cross-class interaction.
physics.soc-ph↗
Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors
Zero-shot anomaly detection aims to identify defects in arbitrary novel domains; however, existing models assume that the auxiliary data contains a rich diversity of anomalies, neglecting the far more complex and unpredictable variations in real-world target domains. This study introduces DIVE, the first approach to investigate the scenario of limited auxiliary anomaly priors and resolve the resulting substantial performance degradation. Through a shallow-and-deep text embedding injection strategy during visual encoding, DIVE learns to abstract generic anomaly concepts shared across the auxiliary training domain and diverse target domains. Moreover, we propose a disentanglement mechanism to tackle the suboptimal alignment between visual embeddings entangled with object semantics and object-agnostic textual prompts. Experiments demonstrate that, under the setting of limited anomaly patterns in auxiliary data, DIVE outperforms SOTA baselines by up to 16.2% and 28.5% on two classification metrics, and 23.4%, 24.1%, and 47.0% on three segmentation metrics, in terms of average performance across twelve datasets. Furthermore, it maintains highly competitive performance when auxiliary data exhibits sufficient anomaly diversity.
cs.CV↗
Design of a mission to measure the shape and substructure of the 511 keV gamma-ray line from the center of the Milky Way
The 511 keV electron-positron annihilation feature near the galactic center has been detected for more than half a century, yet its origin remains a mystery. In this paper, we describe a concept for a balloon-borne 511 keV $\gamma$-ray mission called the 511-Spectrometer Mission. The mission will use Transition-Edge Sensor (TES) arrays with thick metal absorbers that are thermally coupled to the TES. The strength of the approach is a projected energy resolution of 200 eV Full Width Half Maximum (FWHM) at 511 keV, enabling detailed studies of the shape and substructure of the 511 keV emission from the galactic center region. A first mission equipped with 8,192 $\gamma$-ray detectors and a fully active shield and collimator could detect the galactic center with ~35 $\sigma$ statistical significance. We present the mission concept as well as first results obtained with a prototype detector equipped with $1.35\times1.35\times2$ mm$^{3}$ Bi absorbers. The detector has a quantum efficiency of 15% for 511 keV photons in photoelectric effect interactions. In tests with a $^{137}$Cs source, these prototype detectors show an energy resolution of 525 eV FWHM at 662 keV. We end with a discussion of follow-up missions that use coded mask imaging, or use concentrating or focusing optics to scrutinize the sources of 511 keV $\gamma$-rays on smaller angular scales.
astro-ph.IM↗
ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection
Existing Chinese toxic content detection methods mainly target sentence-level classification but often fail to provide readable and contiguous toxic evidence spans. We propose \textbf{ToxiTrace}, an explainability-oriented method for BERT-style encoders with three components: (1) \textbf{CuSA}, which refines encoder-derived saliency cues into fine-grained toxic spans with lightweight LLM guidance; (2) \textbf{GCLoss}, a gradient-constrained objective that concentrates token-level saliency on toxic evidence while suppressing irrelevant activations; and (3) \textbf{ARCL}, which constructs sample-specific contrastive reasoning pairs to sharpen the semantic boundary between toxic and non-toxic content. Experiments show that ToxiTrace improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent, human-readable explanations. We have released the model at https://huggingface.co/ArdLi/ToxiTrace.
cs.CL↗
UPDA: Unsupervised Progressive Domain Adaptation for No-Reference Point Cloud Quality Assessment
While no-reference point cloud quality assessment (NR-PCQA) approaches have achieved significant progress over the past decade, their performance often degrades substantially when a distribution gap exists between the training (source domain) and testing (target domain) data. However, to date, limited attention has been paid to transferring NR-PCQA models across domains. To address this challenge, we propose the first unsupervised progressive domain adaptation (UPDA) framework for NR-PCQA, which introduces a two-stage coarse-to-fine alignment paradigm to address domain shifts. At the coarse-grained stage, a discrepancy-aware coarse-grained alignment method is designed to capture relative quality relationships between cross-domain samples through a novel quality-discrepancy-aware hybrid loss, circumventing the challenges of direct absolute feature alignment. At the fine-grained stage, a perception fusion fine-grained alignment approach with symmetric feature fusion is developed to identify domain-invariant features, while a conditional discriminator selectively enhances the transfer of quality-relevant features. Extensive experiments demonstrate that the proposed UPDA effectively enhances the performance of NR-PCQA methods in cross-domain scenarios, validating its practical applicability. The code is available at https://github.com/yokeno1/UPDA-main.
eess.IV↗
Long-term Monitoring of Kernel and Hardware Events to Understand Latency Variance
This paper presents our experience to understand latency variance caused by kernel and hardware events, which are often invisible at the application level. For this purpose, we have built VarMRI, a tool chain to monitor and analyze those events in the long term. To mitigate the "big data" problem caused by long-term monitoring, VarMRI selectively records a subset of events following two principles: it only records events that are affecting the requests recorded by the application; it records coarse-grained information first and records additional information only when necessary. Furthermore, VarMRI introduces an analysis method that is efficient on large amount of data, robust on different data set and against missing data, and informative to the user. VarMRI has helped us to carry out a 3,000-hour study of six applications and benchmarks on CloudLab. It reveals a wide variety of events causing latency variance, including interrupt preemption, Java GC, pipeline stall, NUMA balancing etc.; simple optimization or tuning can reduce tail latencies by up to 31%. Furthermore, the impacts of some of these events vary significantly across different experiments, which confirms the necessity of long-term monitoring.
cs.PF↗
ReLKD: Inter-Class Relation Learning with Knowledge Distillation for Generalized Category Discovery
Generalized Category Discovery (GCD) faces the challenge of categorizing unlabeled data containing both known and novel classes, given only labels for known classes. Previous studies often treat each class independently, neglecting the inherent inter-class relations. Obtaining such inter-class relations directly presents a significant challenge in real-world scenarios. To address this issue, we propose ReLKD, an end-to-end framework that effectively exploits implicit inter-class relations and leverages this knowledge to enhance the classification of novel classes. ReLKD comprises three key modules: a target-grained module for learning discriminative representations, a coarse-grained module for capturing hierarchical class relations, and a distillation module for transferring knowledge from the coarse-grained module to refine the target-grained module's representation learning. Extensive experiments on four datasets demonstrate the effectiveness of ReLKD, particularly in scenarios with limited labeled data. The code for ReLKD is available at https://github.com/ZhouF-ECNU/ReLKD.
cs.CV↗
ERCache: An Efficient and Reliable Caching Framework for Large-Scale User Representations in Meta's Ads System
The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and strict service-level agreements (SLAs). Previous research efforts have focused on optimizing model inference but have overlooked a critical question: is it necessary to perform user model inference for every ad request in large-scale social networks? To address this question and these challenges, we first analyze user access patterns at Meta and find that most user model inferences occur within a short timeframe. T his observation reveals a triangular relationship among model complexity, embedding freshness, and service SLAs. Building on this insight, we designed, implemented, and evaluated ERCache, an efficient and robust caching framework for large-scale user representations in ads recommendation systems on social networks. ERCache categorizes cache into direct and failover types and applies customized settings and eviction policies for each model, effectively balancing model complexity, embedding freshness, and service SLAs, even considering the staleness introduced by caching. ERCache has been deployed at Meta for over six months, supporting more than 30 ranking models while efficiently conserving computational resources and complying with service SLA requirements.
cs.IR↗
The role of susceptible individuals in spreading dynamics
Exploring the internal mechanism of information spreading is critical for understanding and controlling the process. Traditional spreading models often assume individuals play the same role in the spreading process. In reality, however, individuals' diverse characteristics contribute differently to the spreading performance, leading to a heterogeneous infection rate across the system. To investigate network spreading dynamics under heterogeneous infection rates, we integrate two individual-level features -- influence (i.e., the ability to influence neighbors) and susceptibility (i.e., the extent to be influenced by neighbors) -- into the independent cascade model. Our findings reveal significant differences in spreading performance under heterogeneous and constant infection rates, with traditional structural centrality metrics proving more effective in the latter scenario. Additionally, we take the constant and heterogeneous infection rates into a state-of-the-art maximization algorithm, the well-known TIM algorithm, and find the seeds selected by heterogeneous infection rates are more dispersed compared to those under constant rates. Lastly, we find that both individuals' influence and susceptibility are vital to the spreading performance. Strikingly, susceptible individuals are particularly important to spreading when information is disseminated by social celebrities. By integrating influence and susceptibility into the spreading model, we gain a more profound understanding of the underlying mechanisms driving information spreading.
cs.SI↗
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
Effective user representations are pivotal in personalized advertising. However, stringent constraints on training throughput, serving latency, and memory, often limit the complexity and input feature set of online ads ranking models. This challenge is magnified in extensive systems like Meta's, which encompass hundreds of models with diverse specifications, rendering the tailoring of user representation learning for each model impractical. To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models. SUM leverages a few designated upstream user models to synthesize user embeddings from massive amounts of user features with advanced modeling techniques. These embeddings then serve as inputs to downstream online ads ranking models, promoting efficient representation sharing. To adapt to the dynamic nature of user features and ensure embedding freshness, we designed SUM Online Asynchronous Platform (SOAP), a latency free online serving system complemented with model freshness and embedding stabilization, which enables frequent user model updates and online inference of user embeddings upon each user request. We share our hands-on deployment experiences for the SUM framework and validate its superiority through comprehensive experiments. To date, SUM has been launched to hundreds of ads ranking models in Meta, processing hundreds of billions of user requests daily, yielding significant online metric gains and improved infrastructure efficiency.
cs.IR↗
Cooperative Network Learning for Large-Scale and Decentralized Graphs
Graph research, the systematic study of interconnected data points represented as graphs, plays a vital role in capturing intricate relationships within networked systems. However, in the real world, as graphs scale up, concerns about data security among different data-owning agencies arise, hindering information sharing and, ultimately, the utilization of graph data. Therefore, establishing a mutual trust mechanism among graph agencies is crucial for unlocking the full potential of graphs. Here, we introduce a Cooperative Network Learning (CNL) framework to ensure secure graph computing for various graph tasks. Essentially, this CNL framework unifies the local and global perspectives of GNN computing with distributed data for an agency by virtually connecting all participating agencies as a global graph without a fixed central coordinator. Inter-agency computing is protected by various technologies inherent in our framework, including homomorphic encryption and secure transmission. Moreover, each agency has a fair right to design or employ various graph learning models from its local or global perspective. Thus, CNL can collaboratively train GNN models based on decentralized graphs inferred from local and global graphs. Experiments on contagion dynamics prediction and traditional graph tasks (i.e., node classification and link prediction) demonstrate that our CNL architecture outperforms state-of-the-art GNNs developed at individual sites, revealing that CNL can provide a reliable, fair, secure, privacy-preserving, and global perspective to build effective and personalized models for network applications. We hope this framework will address privacy concerns in graph-related research and integrate decentralized graph data structures to benefit the network research community in cooperation and innovation.
cs.LG↗
A generalized simplicial model and its application
Higher-order structures, consisting of more than two individuals, provide a new perspective to reveal the missed non-trivial characteristics under pairwise networks. Prior works have researched various higher-order networks, but research for evaluating the effects of higher-order structures on network functions is still scarce. In this paper, we propose a framework to quantify the effects of higher-order structures (e.g., 2-simplex) and vital functions of complex networks by comparing the original network with its simplicial model. We provide a simplicial model that can regulate the quantity of 2-simplices and simultaneously fix the degree sequence. Although the algorithm is proposed to control the quantity of 2-simplices, results indicate it can also indirectly control simplexes more than 2-order. Experiments on spreading dynamics, pinning control, network robustness, and community detection have shown that regulating the quantity of 2-simplices changes network performance significantly. In conclusion, the proposed framework is a general and effective tool for linking higher-order structures with network functions. It can be regarded as a reference object in other applications and can deepen our understanding of the correlation between micro-level network structures and global network functions.
physics.soc-ph↗
Quasi two-dimensional nature of high-Tc superconductivity in iron-based (Li,Fe)OHFeSe
The intercalated iron selenide (Li,Fe)OHFeSe has a strongly layered structure analogous to the quasi two-dimensional (2D) bismuth cuprate superconductors, and exhibits both high-temperature (Tc) and topological superconductivity. However, the issue of its superconductivity dimensionality has not yet been fully investigated so far. Here we report that the quasi-2D superconductivity features, including the high anisotropy γ = 151 and the associated quasi-2D vortices, are also revealed for (Li,Fe)OHFeSe, based on systematic experiments of the electrical transport and magnetization and model fittings. Thus, we establish a new vortex phase diagram for (Li,Fe)OHFeSe, which delineates an emergent quasi-2D vortex-liquid state, and a subsequent vortex-solid dimensional crossover from a pancake-like to a three-dimensional state with decreasing temperature and magnetic field. Furthermore, we find that all the quasi-2D characteristics revealed here for the high-Tc iron selenide superconductor are very similar to those reported for the high-Tc bismuth cuprate superconductors.
cond-mat.supr-con↗
Contactless Electrocardiogram Monitoring with Millimeter Wave Radar
The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring.
eess.SP↗
When Six Degrees of Separation Meets Online Social Networks: How Low Can the Degree Be?
The proposal of Six Degrees of Separation makes people realize that the world is not as big as we imagined. Even if the world's population now exceeds 7 billion, two strangers can still get in touch through a limited intermediary. When online social networks have taken the world by storm, can people connect through shorter distances? This issue is worth thinking about. This paper describes Six Degrees of Separation and the limitations of this theory in practical applications. Combined with online social networks, the paper analyzes the development and change of the degrees of connection between people. Finally, the paper considers the actual coverage of online social networks and other issues, and rationalizes the possible degrees of the future Six Degrees of Separation.
cs.SI↗
Fast Heterogeneous Federated Learning with Hybrid Client Selection
Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL). However, the large variance of the model updates aggregated from the randomly-selected unrepresentative subsets directly slows the FL convergence. We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction. Simple yet effective schemes are designed to improve the clustering effect and control the effect fluctuation, therefore, generating the client subset with certain representativeness of sampling. Theoretically, we demonstrate the improvement of the proposed scheme in variance reduction. We also present the tighter convergence guarantee of the proposed method thanks to the variance reduction. Experimental results confirm the exceed efficiency of our scheme compared to alternatives.
cs.LG↗
A disorder-sensitive emergent vortex phase identified in high-Tc superconductor (Li,Fe)OHFeSe
The magneto-transport properties are systematically measured under c-direction fields up to 33 T for a series of single-crystal films of intercalated iron-selenide superconductor (Li,Fe)OHFeSe. The film samples with varying degree of disorder are grown hydrothermally. We observe a magnetic-field-enhanced shoulder-like feature in the mixed state of the high-Tc (Li,Fe)OHFeSe films with weak disorder, while the feature fades away in the films with enhanced disorder. The irreversibility field is significantly suppressed to lower temperatures with the appearance of the shoulder feature. Based on the experiment and model analysis, we establish a new vortex phase diagram for the weakly disordered high-Tc (Li,Fe)OHFeSe, which features an emergent dissipative vortex phase intermediate between the common vortex glass and liquid phases. The reason for the emergence of this intermediate vortex state is further discussed based on related experiments and models.
cond-mat.supr-con↗