arXiv subjects

Ping Zhang

Publications and source records attributed to Ping Zhang.

At least 271 records · Page 15Linked to original sources

arXiv2024

Nonreciprocal interactions in crowd dynamics: investigating the impact of moving threats on pedestrian speed preferences

Nonreciprocal interaction crowd systems, such as human-human, human-vehicle, and human-robot systems, often have serious impacts on pedestrian safety and social order. A more comprehensive understanding of these systems is needed to optimize system stability and efficiency. Despite the importance of these interactions, empirical research in this area remains limited. Thus, in our study we explore this underresearched area, focusing on scenarios where nonreciprocity plays a critical role, such as mass stabbings, which pose a substantial risk to public safety. We conducted the first experiments on this system and analysed high-accuracy data obtained from these experiments. The extent of the direct threat zone is determined by the speed of the moving threat and the radius of danger occurrence. We further categorize potential threats into direct, adjacent, and rear-view zones, quantifying the level of threat for pedestrians. Our study revealed that a pedestrian's desired velocity correlated positively with potential threat intensity, increasing until near the direct threat zone. An emerging steady state is observed when escape routes are blocked by moving threats. This deviation affects the density-velocity relationship, making it distinct from the general relationship. This deviation signifies unique pedestrian behaviour in the presence of moving threats. Additionally, the rate of change in the angle for pedestrian motion in various desired directions is synchronized. This indicates the emergence of collective intelligence in nonreciprocal interaction crowd systems. As a result, our study may constitute a pioneering step towards understanding nonreciprocal interactions in crowd systems through laboratory experiments. These findings may enhance pedestrian safety and inform not only government crowd management strategies but also individual self-protection measures.

physics.soc-ph↗

arXiv2024

Towards 6G Digital Twin Channel Using Radio Environment Knowledge Pool

The digital twin channel (DTC) is crucial for 6G wireless autonomous networks as it replicates the wireless channel fading states in 6G air interface transmissions. It is well known that the physical environment influences channels. A key task for accurately twinning channels in complex 6G scenarios is establishing precise relationships between the environment and the channels. In this article, the radio environment knowledge pool (REKP) is proposed, with its core function being to construct and store as much knowledge between the environment and channels as possible. Firstly, the research progress related to DTC is summarized, and a comparative analysis of these achievements on key indicators in digital twin is conducted, proposing the challenges faced in knowledge construction. Secondly, instructions on how to construct and update REKP are given. Then, a typical case is presented to demonstrate the great potential of REKP in enabling DTC. Finally, how to utilize REKP to address open issues in the 6G wireless communication system is discussed, including enhancing performance, reducing costs, and keeping a trustworthy DTC.

eess.SP↗

arXiv2024

Periodicity of sub-pulses in a radio pulsar

Pulsars are known to manifest complex phenomena, such as nulling, sub-pulse drifting, and periodicity. Within the purview of this investigation, we have harnessed the wavelet analysis technique to scrutinize the multifaceted periodicities and sub-pulse drifting characteristics exhibited by PSR J1926-0652, discovered by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Our analysis draws upon the rich dataset acquired from the FAST ultra-wide-bandwidth receiver (UWB), meticulously examining pulse attributes encompassing an entire pulse. It is notable that the pulse apex recurrently manifests approximately every 17.11 times the pulsar's period P, individual sub-pulses exhibit a drifting phenomenon, with a phase decrement of approximately $1.04^\circ$ over each P. Intriguingly, the central phase of each sub-pulse track gradually increments over temporal evolution. Furthermore, the relative offset distribution between successive sub-pulse tracks emanating from the trailing and leading components remains comparatively stable, with a central tendency of approximately $\sim$6.87$\pm$2.56 P. Most notably, derived from the outcomes of wavelet analysis, we ascertain a negative correlation of -0.98 between the periods of sub-pulses and their drifting rates, alongside the intrinsic period of sub-pulses identified at 28.14 seconds.

astro-ph.HE↗

arXiv2024

KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs

Treatment effect estimation (TEE) is the task of determining the impact of various treatments on patient outcomes. Current TEE methods fall short due to reliance on limited labeled data and challenges posed by sparse and high-dimensional observational patient data. To address the challenges, we introduce a novel pre-training and fine-tuning framework, KG-TREAT, which synergizes large-scale observational patient data with biomedical knowledge graphs (KGs) to enhance TEE. Unlike previous approaches, KG-TREAT constructs dual-focus KGs and integrates a deep bi-level attention synergy method for in-depth information fusion, enabling distinct encoding of treatment-covariate and outcome-covariate relationships. KG-TREAT also incorporates two pre-training tasks to ensure a thorough grounding and contextualization of patient data and KGs. Evaluation on four downstream TEE tasks shows KG-TREAT's superiority over existing methods, with an average improvement of 7% in Area under the ROC Curve (AUC) and 9% in Influence Function-based Precision of Estimating Heterogeneous Effects (IF-PEHE). The effectiveness of our estimated treatment effects is further affirmed by alignment with established randomized clinical trial findings.

cs.LG↗

arXiv2024

Jiagu: Optimizing Serverless Computing Resource Utilization with Harmonized Efficiency and Practicability

Current serverless platforms struggle to optimize resource utilization due to their dynamic and fine-grained nature. Conventional techniques like overcommitment and autoscaling fall short, often sacrificing utilization for practicability or incurring performance trade-offs. Overcommitment requires predicting performance to prevent QoS violation, introducing trade-off between prediction accuracy and overheads. Autoscaling requires scaling instances in response to load fluctuations quickly to reduce resource wastage, but more frequent scaling also leads to more cold start overheads. This paper introduces Jiagu, which harmonizes efficiency with practicability through two novel techniques. First, pre-decision scheduling achieves accurate prediction while eliminating overheads by decoupling prediction and scheduling. Second, dual-staged scaling achieves frequent adjustment of instances with minimum overhead. We have implemented a prototype and evaluated it using real-world applications and traces from the public cloud platform. Our evaluation shows a 54.8% improvement in deployment density over commercial clouds (with Kubernetes) while maintaining QoS, and 81.0%--93.7% lower scheduling costs and a 57.4%--69.3% reduction in cold start latency compared to existing QoS-aware schedulers in research work.

cs.DC↗

arXiv2024

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis

Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires an early diagnosis with high uncertainty from the clinician. Our aim is to explore the design requirements for AI systems that can support clinical experts in making better decisions for the early diagnosis of sepsis. The study begins with a formative study investigating why clinical experts abandon an existing AI-powered Sepsis predictive module in their electrical health record (EHR) system. We argue that a human-centered AI system needs to support human experts in the intermediate stages of a medical decision-making process (e.g., generating hypotheses or gathering data), instead of focusing only on the final decision. Therefore, we build SepsisLab based on a state-of-the-art AI algorithm and extend it to predict the future projection of sepsis development, visualize the prediction uncertainty, and propose actionable suggestions (i.e., which additional laboratory tests can be collected) to reduce such uncertainty. Through heuristic evaluation with six clinicians using our prototype system, we demonstrate that SepsisLab enables a promising human-AI collaboration paradigm for the future of AI-assisted sepsis diagnosis and other high-stakes medical decision making.

cs.HC↗

arXiv2024

Quasi-neutral limit of Nernst-Planck-Navier-Stokes system

In this paper, we investigate the quasi-neutral limit of Nernst-Planck-Navier-Stokes system in a smooth bounded domain $Ω$ of $\mathbb{R}^d$ for $d=2,3,$ with ``electroneutral boundary conditions" and well-prepared data. We first prove by using modulated energy estimate that the solution sequence converges to the limit system in the norm of $L^\infty((0,T);L^2(Ω))$ for some positive time $T.$ In order to justify the limit in a stronger norm, we need to construct both the initial layers and weak boundary layers in the approximate solutions.

math.AP↗

arXiv2024

Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder

Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups with distinct characteristics. This limitation hinders their ability to provide accurate estimations and treatment recommendations for specific subgroups. In this study, we introduce a novel neural network-based framework, named SubgroupTE, which incorporates subgroup identification and treatment effect estimation. SubgroupTE identifies diverse subgroups and simultaneously estimates treatment effects for each subgroup, improving the treatment effect estimation by considering the heterogeneity of treatment responses. Comparative experiments on synthetic data show that SubgroupTE outperforms existing models in treatment effect estimation. Furthermore, experiments on a real-world dataset related to opioid use disorder (OUD) demonstrate the potential of our approach to enhance personalized treatment recommendations for OUD patients.

cs.LG↗

arXiv2024

Semantic Arithmetic Coding using Synonymous Mappings

Recent semantic communication methods explore effective ways to expand the communication paradigm and improve the system performance of the communication systems. Nonetheless, the common problem of these methods is that the essence of semantics is not explicitly pointed out and directly utilized. A new epistemology suggests that synonymy, which is revealed as the fundamental feature of semantics, guides the establishment of the semantic information theory from a novel viewpoint. Building on this theoretical basis, this paper proposes a semantic arithmetic coding (SAC) method for semantic lossless compression using intuitive semantic synonymy. By constructing reasonable synonymous mappings and performing arithmetic coding procedures over synonymous sets, SAC can achieve higher compression efficiency for meaning-contained source sequences at the semantic level and thereby approximate the semantic entropy limits. Experimental results on edge texture map compression show an evident improvement in coding efficiency using SAC without semantic losses, compared to traditional arithmetic coding, which demonstrates its effectiveness.

cs.IT↗

arXiv2024

Semantic Huffman Coding using Synonymous Mapping

Semantic communication stands out as a highly promising avenue for future developments in communications. Theoretically, source compression coding based on semantics can achieve lower rates than Shannon entropy. This paper introduces a semantic Huffman coding built upon semantic information theory. By incorporating synonymous mapping and synonymous sets, semantic Huffman coding can achieve shorter average code lengths. Furthermore, we demonstrate that semantic Huffman coding theoretically have the capability to approximate semantic entropy. Experimental results indicate that, under the condition of semantic lossless, semantic Huffman coding exhibits clear advantages in compression efficiency over classical Huffman coding.

cs.IT↗

arXiv2024

A Mathematical Theory of Semantic Communication: Overview

Semantic communication initiates a new direction for future communication. In this paper, we aim to establish a systematic framework of semantic information theory (SIT). First, we propose a semantic communication model and define the synonymous mapping to indicate the critical relationship between semantic information and syntactic information. Based on this core concept, we introduce the measures of semantic information, such as semantic entropy $H_s(\tilde{U})$, up/down semantic mutual information $I^s(\tilde{X};\tilde{Y})$ $(I_s(\tilde{X};\tilde{Y}))$, semantic capacity $C_s=\max_{p(x)}I^s(\tilde{X};\tilde{Y})$, and semantic rate-distortion function $R_s(D)=\min_{p(\hat{x}|x):\mathbb{E}d_s(\tilde{x},\hat{\tilde{x}})\leq D}I_s(\tilde{X};\hat{\tilde{X}})$. Furthermore, we prove three coding theorems of SIT, that is, the semantic source coding theorem, semantic channel coding theorem, and semantic rate-distortion coding theorem. We find that the limits of information theory are extended by using synonymous mapping, that is, $H_s(\tilde{U})\leq H(U)$, $C_s\geq C$ and $R_s(D)\leq R(D)$. All these works composite the basis of semantic information theory. In summary, the theoretic framework proposed in this paper is a natural extension of classic information theory and may reveal great performance potential for future communication.

cs.IT↗

arXiv2024

Ultra-broadband near-field Josephson microwave microscopy

Advanced microwave technologies constitute the foundation of a wide range of modern sciences, including quantum computing, microwave photonics, spintronics, etc. To facilitate the design of chip-based microwave devices, there is an increasing demand for state-of-the-art microscopic techniques capable of characterizing the near-field microwave distribution and performance. In this work, we integrate Josephson junctions onto a nano-sized quartz tip, forming a highly sensitive microwave mixer on-tip. This allows us to conduct spectroscopic imaging of near-field microwave distributions with high spatial resolution. Leveraging its microwave-sensitive characteristics, our Josephson microscope achieves a broad detecting bandwidth of up to 200 GHz with remarkable frequency and intensity sensitivities. Our work emphasizes the benefits of utilizing the Josephson microscope as a real-time, non-destructive technique to advance integrated microwave electronics.

physics.app-ph↗

arXiv2024

Pragmatic Communication in Multi-Agent Collaborative Perception

Collaborative perception allows each agent to enhance its perceptual abilities by exchanging messages with others. It inherently results in a trade-off between perception ability and communication costs. Previous works transmit complete full-frame high-dimensional feature maps among agents, resulting in substantial communication costs. To promote communication efficiency, we propose only transmitting the information needed for the collaborator's downstream task. This pragmatic communication strategy focuses on three key aspects: i) pragmatic message selection, which selects task-critical parts from the complete data, resulting in spatially and temporally sparse feature vectors; ii) pragmatic message representation, which achieves pragmatic approximation of high-dimensional feature vectors with a task-adaptive dictionary, enabling communicating with integer indices; iii) pragmatic collaborator selection, which identifies beneficial collaborators, pruning unnecessary communication links. Following this strategy, we first formulate a mathematical optimization framework for the perception-communication trade-off and then propose PragComm, a multi-agent collaborative perception system with two key components: i) single-agent detection and tracking and ii) pragmatic collaboration. The proposed PragComm promotes pragmatic communication and adapts to a wide range of communication conditions. We evaluate PragComm for both collaborative 3D object detection and tracking tasks in both real-world, V2V4Real, and simulation datasets, OPV2V and V2X-SIM2.0. PragComm consistently outperforms previous methods with more than 32.7K times lower communication volume on OPV2V. Code is available at github.com/PhyllisH/PragComm.

cs.CV↗

arXiv2024

SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification

Precise estimation of treatment effects is crucial for evaluating intervention effectiveness. While deep learning models have exhibited promising performance in learning counterfactual representations for treatment effect estimation (TEE), a major limitation in most of these models is that they treat the entire population as a homogeneous group, overlooking the diversity of treatment effects across potential subgroups that have varying treatment effects. This limitation restricts the ability to precisely estimate treatment effects and provide subgroup-specific treatment recommendations. In this paper, we propose a novel treatment effect estimation model, named SubgroupTE, which incorporates subgroup identification in TEE. SubgroupTE identifies heterogeneous subgroups with different treatment responses and more precisely estimates treatment effects by considering subgroup-specific causal effects. In addition, SubgroupTE iteratively optimizes subgrouping and treatment effect estimation networks to enhance both estimation and subgroup identification. Comprehensive experiments on the synthetic and semi-synthetic datasets exhibit the outstanding performance of SubgroupTE compared with the state-of-the-art models on treatment effect estimation. Additionally, a real-world study demonstrates the capabilities of SubgroupTE in enhancing personalized treatment recommendations for patients with opioid use disorder (OUD) by advancing treatment effect estimation with subgroup identification.

cs.LG↗

arXiv2024

Gevrey well-posedness of quasi-linear hyperbolic Prandtl equations

We study the hyperbolic version of the Prandtl system derived from the hyperbolic Navier-Stokes system with no-slip boundary condition. Compared to the classical Prandtl system, the quasi-linear terms in the hyperbolic Prandtl equation leads to an additional instability mechanism. To overcome the loss of derivatives in all directions in the quasi-linear term, we introduce a new auxiliary function for the well-posedness of the system in an anisotropic Gevrey space which is Gevrey class $\frac 32$ in the tangential variable and is analytic in the normal variable.

math.AP↗

arXiv2024

Semantic Communications with Explicit Semantic Base for Image Transmission

Semantic communications, aiming at ensuring the successful delivery of the meaning of information, are expected to be one of the potential techniques for the next generation communications. However, the knowledge forming and synchronizing mechanism that enables semantic communication systems to extract and interpret the semantics of information according to the communication intents is still immature. In this paper, we propose a semantic image transmission framework with explicit semantic base (Seb), where Sebs are generated and employed as the knowledge shared between the transmitter and the receiver with flexible granularity. To represent images with Sebs, a novel Seb-based reference image generator is proposed to generate Sebs and then decompose the transmitted images. To further encode/decode the residual information for precise image reconstruction, a Seb-based image encoder/decoder is proposed. The key components of the proposed framework are optimized jointly by end-to-end (E2E) training, where the loss function is dedicatedly designed to tackle the problem of nondifferentiable operation in Seb-based reference image generator by introducing a gradient approximation mechanism. Extensive experiments show that the proposed framework outperforms state-of-art works by 0.5 - 1.5 dB in peak signal-to-noise ratio (PSNR) w.r.t. different signal-to-noise ratios (SNR).

eess.IV↗

arXiv2024

A Statistical Study of Solar White-Light Flares Observed by the White-light Solar Telescope of the Lyman-alpha Solar Telescope on the Advanced Space-based Solar Observatory (ASO-S/LST/WST) at 360 nm

Solar white-light flares (WLFs) are those accompanied by brightenings in the optical continuum or integrated light. The White-light Solar Telescope (WST), as an instrument of the Lyman-alpha Solar Telescope (LST) on the Advanced Space-based Solar Observatory (ASO-S), provides continuous solar full-disk images at 360 nm, which can be used to study WLFs. We analyze 205 major flares above M1.0 from October 2022 to May 2023 and identify 49 WLFs at 360 nm from WST observations, i.e. with an occurrence rate of 23.9%. The percentages of WLFs for M1 - M4 (31 out of 180), M5 - M9 (11 out of 18), and above X1 (7 for all) flares are 17.2%, 61.1%, and 100%, respectively, namely the larger the flares, the more likely they are WLFs at 360 nm. We further analyze 39 WLFs among the identified WLFs and investigate their properties such as white-light enhancement, duration, and brightening area. It is found that the relative enhancement of the white-light emission at 360 nm is mostly (>90%) less than 30% and the mean enhancement is 19.4%. The WLFs' duration at 360 nm is mostly (>80%) less than 20 minutes and its mean is 10.3 minutes. The brightening area at 360 nm is mostly (>75%) less than 500 arcsecond2 and the median value is 225. We find that there exist good correlations between the white-light enhancement/duration/area and the peak soft X-ray (SXR) flux of the flare, with correlation coefficients of 0.68, 0.58, and 0.80, respectively. In addition, the white-light emission in most WLFs peaks around the same time as the temporal derivative of SXR flux as well as the hard X-ray emission at 20 - 50 keV, indicative of Neupert effect. It is also found that the limb WLFs are more likely to have a greater enhancement, which is consistent with numerical simulations.

astro-ph.SR↗

arXiv2024

Monolayer Fe$_{3}$GaX$_{2}$ (X=I, Br, Sb): high-temperature two-dimensional magnets and a novel partially ordered spin state

We systematically investigated the effects of charge doping and strain on monolayer Fe$_3$GaTe$_2$, and proposed three new novel two-dimensional magnetic materials: monolayer Fe$_3$GaX$_2$ (X=I, Br, Sb). We found that both strain and charge doping can tune the magnetic interactions, and the tuning by charge doping is more significant. Differential charge analysis revealed that the doped charges predominantly accumulate around Te atoms. Based on this insight, we introduced Fe$_{3}$GaI$_{2}$, Fe$_{3}$GaBr$_{2}$, and Fe$_{3}$GaSb$_{2}$ monolayers. The Fe$_{3}$GaI$_{2}$ and Fe$_{3}$GaBr$_{2}$ monolayers contain I and Br atoms rather than Te atoms, emulate electron-doped Fe$_{3}$GaTe$_{2}$ monolayer, resulting in notably high T$_c$ values of 867 K and 844 K, respectively. In contrast, the Fe$_3$GaSb$_2$ monolayer mimics hole-doped Fe$_{3}$GaTe$_{2}$ monolayer, presents a mix of FM and antiferromagnetic interactions, manifesting a distinctive partially ordered magnetic state. Our study demonstrates that substitution atoms based on the charge-doping effect offer a promising approach for predicting new magnetic materials. The proposed Fe$_{3}$GaI$_{2}$, Fe$_{3}$GaBr$_{2}$, and Fe$_{3}$GaSb$_{2}$ monolayers hold great potential for spintronics applications, and may stimulate the pursuit of new types of spin liquid.

cond-mat.mtrl-sci↗