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Researcher profile

Ping Wang

Ping Wang contributes to research discovery and scholarly infrastructure.

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Artificial IntelligenceComputation and LanguageMachine LearningComputer VisionNetworking and Internet ArchitectureComputer Science and Game Theoryhep-phastro-ph.HEInformation Theorymath.ITeess.SPeess.SYInformation Retrievalphysics.soc-phastro-ph.EPastro-ph.SRComputational Engineering, Finance, and Sciencecond-mat.dis-nncond-mat.mes-hallCryptography and SecurityDiscrete Mathematicseess.IVhep-exmath.OCMultiagent Systemsnlin.AOphysics.ao-phphysics.chem-phquant-phRoboticsSocial and Information NetworksSystems and Control

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Published work

42 published item(s)

preprint2026arXiv

Brightest GRB flare observed in GRB 221009A: bridge the last gap between flare and prompt emission in GRB

Flares are usually observed during the afterglow phase of Gamma-Ray Bursts (GRBs) in soft X-ray, optical and radio bands, but rarely in gamma-ray band. Despite the extraordinary brightness, GECAM-C has accurately measured both the bright prompt emission and flare emission of GRB 221009A without instrumental effects, offering a good opportunity to study the relation between them. In this work, we present a comprehensive analysis of flare emission of GRB 221009A, which is composed of a series of flares. Among them, we identify an exceptionally bright flare with a record-breaking isotropic energy $E_{\rm iso} = 1.82 \times 10^{53}$ erg of GRB flares. It exhibits the highest peak energy ever detected in GRB flares, $E_{\rm peak} \sim 300$ keV, making it a genuine gamma-ray flare. It also shows rapid rise and decay timescales, significantly shorter than those of typical X-ray flares observed in soft X-ray or optical band, but comparable to those observed in prompt emissions. Despite these exceptional properties, the flare shares several common properties with typical GRB flares. We note that this is the first observation of a GRB flare in the keV-MeV band with sufficiently high temporal resolution and high statistics, which bridges the last gap between prompt emission and flare.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Deep Probabilistic Unfolding for Quantized Compressive Sensing

We propose a deep probabilistic unfolding model to address the classical quantized compressive sensing problem that leverages an unfolding framework to enhance the reconstruction accuracy and efficiency. Unlike previous unfolding methods that apply L2 projection to measurements, we derive a closed-form, numerically stable likelihood gradient projection, which allows the model to respect the true quantization physics, turning the hard quantization constraint into a soft probabilistic guidance. Furthermore, an efficient, dual-domain Mamba module is specifically designed to dynamically capture and fuse the multi-scale local and global features, ensuring the interactions between the distant but correlated regions. Extensive experiments demonstrate the state-of-the-art performance of the proposed method over previous works, which is capable of promoting the application of quantized compressive sensing in real life.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Finite-Size Gradient Transport in Large Language Model Pretraining: From Cascade Size to Intensive Transport Efficiency

We introduce a finite-size gradient-transport framework for real language-model training, based on five observables $(D,z,β,δ,v_{\mathrm{rel}})$ that separate cascade size, duration, absolute transport, and intensive transport efficiency. We analyze direct raw-gradient measurements from Pico-LM across four scales and 125 aligned steps, together with a five-scale Pythia companion dataset built from 153 aligned checkpoint-difference update fields. The same algebraic closure holds in both families, and both share a near-unity cascade-size backbone, but they occupy distinct transport regimes: Pico-LM shows positive duration scaling and negative intensive-efficiency scaling, whereas Pythia remains near the $D=1$ baseline with only weak positive efficiency scale dependence. Randomized-field controls give nearly matched null floors in the intensive and duration channels, indicating that the contrast reflects different real departures from a shared null skeleton rather than different null calibrations. The families also differ in stepwise power-law compressibility: Pico-LM retains clean duration and efficiency power laws, whereas Pythia preserves the size backbone but shows weaker one-slope compressibility in those channels. External performance associations are correspondingly channel-level, carried mainly by $v_{\mathrm{rel}}$ and normalized cascade duration, while $D(t)$ acts as a shared size backbone without a significant exponent-level performance association. These results support a reusable transport measurement framework without claiming a universal fixed point or a first-principles derivation of neural scaling laws.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity

Uncrewed aerial vehicles (UAVs) are increasingly deployed in complex networked environments, yet the joint optimization of multi-UAV motion control and connectivity remains a fundamental challenge. In this paper, we study a multi-UAV system operating in an integrated terrestrial and non-terrestrial network (ITNTN) comprising terrestrial base stations and high-altitude platform stations (HAPS). We consider a three-dimensional (3D) aerial highway scenario where UAVs must adapt their motion to ensure collision avoidance, efficient traffic flow, and reliable communication under dynamic and partially observable conditions. We first model the problem as a hierarchical multi-objective partially observable Markov decision process (H-MO-POMDP), capturing the coupling between control and communication objectives. Based on this formulation, we propose a large language model (LLM)-driven hierarchical multi-rate control framework. At the global level, an LLM-based controller on the HAPS performs long-term planning for load balancing and handover decisions. At the local level, each UAV employs a hybrid controller that integrates a slow-timescale LLM for high-level spatial reasoning with a reinforcement learning agent for faster UAV-to-infrastructure (U2I) communication and motion control. We further develop a high-fidelity 3D simulation platform by integrating the gym-pybullet-drones environment with 3GPP-compliant RF/THz channel models. Numerical results demonstrate that the proposed framework significantly outperforms state-of-the-art baselines, achieving a 14% increase in transportation efficiency and a 25% improvement in telecommunication throughput. Additionally, it achieves a 23% reduction in physical collision rates, demonstrating strong handover stability and zero-shot generalization in dynamic scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

PulseMind: A Multi-Modal Medical Model for Real-World Clinical Diagnosis

Recent advances in medical multi-modal models focus on specialized image analysis like dermatology, pathology, or radiology. However, they do not fully capture the complexity of real-world clinical diagnostics, which involve heterogeneous inputs and require ongoing contextual understanding during patient-physician interactions. To bridge this gap, we introduce PulseMind, a new family of multi-modal diagnostic models that integrates a systematically curated dataset, a comprehensive evaluation benchmark, and a tailored training framework. Specifically, we first construct a diagnostic dataset, MediScope, which comprises 98,000 real-world multi-turn consultations and 601,500 medical images, spanning over 10 major clinical departments and more than 200 sub-specialties. Then, to better reflect the requirements of real-world clinical diagnosis, we develop the PulseMind Benchmark, a multi-turn diagnostic consultation benchmark with a four-dimensional evaluation protocol comprising proactiveness, accuracy, usefulness, and language quality. Finally, we design a training framework tailored for multi-modal clinical diagnostics, centered around a core component named Comparison-based Reinforcement Policy Optimization (CRPO). Compared to absolute score rewards, CRPO uses relative preference signals from multi-dimensional com-parisons to provide stable and human-aligned training guidance. Extensive experiments demonstrate that PulseMind achieves competitive performance on both the diagnostic consultation benchmark and public medical benchmarks.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SongSage: A Large Musical Language Model with Lyric Generative Pre-training

Large language models have achieved significant success in various domains, yet their understanding of lyric-centric knowledge has not been fully explored. In this work, we first introduce PlaylistSense, a dataset to evaluate the playlist understanding capability of language models. PlaylistSense encompasses ten types of user queries derived from common real-world perspectives, challenging LLMs to accurately grasp playlist features and address diverse user intents. Comprehensive evaluations indicate that current general-purpose LLMs still have potential for improvement in playlist understanding. Inspired by this, we introduce SongSage, a large musical language model equipped with diverse lyric-centric intelligence through lyric generative pretraining. SongSage undergoes continual pretraining on LyricBank, a carefully curated corpus of 5.48 billion tokens focused on lyrical content, followed by fine-tuning with LyricBank-SFT, a meticulously crafted instruction set comprising 775k samples across nine core lyric-centric tasks. Experimental results demonstrate that SongSage exhibits a strong understanding of lyric-centric knowledge, excels in rewriting user queries for zero-shot playlist recommendations, generates and continues lyrics effectively, and performs proficiently across seven additional capabilities. Beyond its lyric-centric expertise, SongSage also retains general knowledge comprehension and achieves a competitive MMLU score. We will keep the datasets inaccessible due to copyright restrictions and release the SongSage and training script to ensure reproducibility and support music AI research and applications, the datasets release plan details are provided in the appendix.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

On thermodynamically consistent quasiparticle model at finite chemical potential

We explore the quasiparticle model at finite chemical potential related to Ru-Keng Su's distinguished contributions to the topic. Besides, we discuss recent developments in the model, and in particular, one argues that the effective mass of the quasiparticle might attain a specific form as a function of momentum, in addition to its dependence on temperature and chemical potential. Unlike the approaches based on the properties of underlying symmetry or renormalization group, the momentum dependence emerges as a special solution to an integro-differential equation resulting from the underlying thermodynamic consistency. Moreover, this special solution to the problem is shown to be more general than previously explored in the literature. Instead of fitting to the lattice QCD data at vanishing chemical potential, in this work, we adopt a ``bottom-up'' approach by assuming some analytic ansatzes that are manifestly thermodynamically consistent. The remaining physical quantities are subsequently derived, and possible implications are also addressed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction

Joint medical relation extraction refers to extracting triples, composed of entities and relations, from the medical text with a single model. One of the solutions is to convert this task into a sequential tagging task. However, in the existing works, the methods of representing and tagging the triples in a linear way failed to the overlapping triples, and the methods of organizing the triples as a graph faced the challenge of large computational effort. In this paper, inspired by the tree-like relation structures in the medical text, we propose a novel scheme called Bidirectional Tree Tagging (BiTT) to form the medical relation triples into two two binary trees and convert the trees into a word-level tags sequence. Based on BiTT scheme, we develop a joint relation extraction model to predict the BiTT tags and further extract medical triples efficiently. Our model outperforms the best baselines by 2.0\% and 2.5\% in F1 score on two medical datasets. What's more, the models with our BiTT scheme also obtain promising results in three public datasets of other domains.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data

The Internet of Things (IoT) has been continuously rising in the past few years, and its potentials are now more apparent. However, transient data generation and limited energy resources are the major bottlenecks of these networks. Besides, minimum delay and other conventional quality of service measurements are still valid requirements to meet. An efficient caching policy can help meet the standard quality of service requirements while bypassing IoT networks' specific limitations. Adopting deep reinforcement learning (DRL) algorithms enables us to develop an effective caching scheme without the need for any prior knowledge or contextual information. In this work, we propose a DRL-based caching scheme that improves the cache hit rate and reduces energy consumption of the IoT networks, in the meanwhile, taking data freshness and limited lifetime of IoT data into account. To better capture the regional-different popularity distribution, we propose a hierarchical architecture to deploy edge caching nodes in IoT networks. The results of comprehensive experiments show that our proposed method outperforms the well-known conventional caching policies and an existing DRL-based solution in terms of cache hit rate and energy consumption of the IoT networks by considerable margins.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Sequence Tagging based Framework for Few-Shot Relation Extraction

Relation Extraction (RE) refers to extracting the relation triples in the input text. Existing neural work based systems for RE rely heavily on manually labeled training data, but there are still a lot of domains where sufficient labeled data does not exist. Inspired by the distance-based few-shot named entity recognition methods, we put forward the definition of the few-shot RE task based on the sequence tagging joint extraction approaches, and propose a few-shot RE framework for the task. Besides, we apply two actual sequence tagging models to our framework (called Few-shot TPLinker and Few-shot BiTT), and achieves solid results on two few-shot RE tasks constructed from a public dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

An Ultra-Reliable Low-Latency Non-Binary Polar Coded SCMA Scheme

The joint transmission scheme of polar codes and sparse code multiple access (SCMA) has been regarded as a promising technology for future wireless communication systems. However, most of the existing polar-coded SCMA (PC-SCMA) systems suffer from high latency caused by the feedback iteration and list decoding. In addition, the error performance of PC-SCMA systems is unsatisfactory for ultra-reliable transmission. Inspired by the compelling benefits of non-binary polar codes, in this paper, we design a non-binary polar-coded SCMA (NB-PC-SCMA) system with a free order matching strategy to address the issues of delay and reliability. Specifically, we first formulate a joint factor graph for NB-PC-SCMA and propose a non-binary successive cancellation list (NB-SCL) and damping based joint iterative detection and decoding (NSD-JIDD) multiuser receiver to improve the BER and latency performance. Then, a lazy-search based NB-SCL (L-NB-SCL) decoding is proposed to reduce the computational complexity by simplifying the path search pattern of the list decoder. After that, we modify the update of user nodes for SCMA detection to improve the convergence error and finally propose the improved NSD-JIDD (ISD-JIDD) algorithm, which can avoid redundant operations by exploiting L-NB-SCL decoding. Simulation results show that the proposed NB-PC-SCMA system achieves better bit error rate (BER) performance and considerable latency gain when compared to its counterparts. In particular, the proposed ISD-JIDD can achieve similar BER performance of NSD-JIDD with less complexity.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes

Question Answering (QA) in clinical notes has gained a lot of attention in the past few years. Existing machine reading comprehension approaches in clinical domain can only handle questions about a single block of clinical texts and fail to retrieve information about multiple patients and their clinical notes. To handle more complex questions, we aim at creating knowledge base from clinical notes to link different patients and clinical notes, and performing knowledge base question answering (KBQA). Based on the expert annotations available in the n2c2 dataset, we first created the ClinicalKBQA dataset that includes around 9K QA pairs and covers questions about seven medical topics using more than 300 question templates. Then, we investigated an attention-based aspect reasoning (AAR) method for KBQA and analyzed the impact of different aspects of answers (e.g., entity, type, path, and context) for prediction. The AAR method achieves better performance due to the well-designed encoder and attention mechanism. From our experiments, we find that both aspects, type and path, enable the model to identify answers satisfying the general conditions and produce lower precision and higher recall. On the other hand, the aspects, entity and context, limit the answers by node-specific information and lead to higher precision and lower recall.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation

Unsupervised pre-training has been proven as an effective approach to boost various downstream tasks given limited labeled data. Among various methods, contrastive learning learns a discriminative representation by constructing positive and negative pairs. However, it is not trivial to build reasonable pairs for a segmentation task in an unsupervised way. In this work, we propose a novel unsupervised pre-training framework that avoids the drawback of contrastive learning. Our framework consists of two principles: unsupervised over-segmentation as a pre-train task using mutual information maximization and boundary-aware preserving learning. Experimental results on two benchmark medical segmentation datasets reveal our method's effectiveness in improving segmentation performance when few annotated images are available.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Event Detection Explorer: An Interactive Tool for Event Detection Exploration

Event Detection (ED) is an important task in natural language processing. In the past few years, many datasets have been introduced for advancing ED machine learning models. However, most of these datasets are under-explored because not many tools are available for people to study events, trigger words, and event mention instances systematically and efficiently. In this paper, we present an interactive and easy-to-use tool, namely ED Explorer, for ED dataset and model exploration. ED Explorer consists of an interactive web application, an API, and an NLP toolkit, which can help both domain experts and non-experts to better understand the ED task. We use ED Explorer to analyze a recent proposed large-scale ED datasets (referred to as MAVEN), and discover several underlying problems, including sparsity, label bias, label imbalance, and debatable annotations, which provide us with directions to improve the MAVEN dataset. The ED Explorer can be publicly accessed through http://edx.leafnlp.org/. The demonstration video is available here https://www.youtube.com/watch?v=6QPnxPwxg50.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Insight-HXMT dedicated 33-day observation of SGR J1935+2154 I. Burst Catalog

Magnetars are neutron stars with extreme magnetic field and sometimes manifest as soft gamma-ray repeaters (SGRs). SGR J1935+2154 is one of the most prolific bursters and the first confirmed source of fast radio burst (i.e. FRB 200428). Encouraged by the discovery of the first X-ray counterpart of FRB, Insight-Hard X-ray Modulation Telescope (Insight-HXMT) implemented a dedicated 33-day long ToO observation of SGR J1935+2154 since April 28, 2020. With the HE, ME, and LE telescopes, Insight-HXMT provides a thorough monitoring of burst activity evolution of SGR J1935+2154, in a very broad energy range (1-250 keV) with high temporal resolution and high sensitivity, resulting in a unique valuable data set for detailed studies of SGR J1935+2154. In this work, we conduct a comprehensive analysis of this observation including detailed burst search, identification and temporal analyses. After carefully removing false triggers, we find a total of 75 bursts from SGR J1935+2154, out of which 70 are single-pulsed. The maximum burst rate is about 56 bursts/day. Both the burst duration and the waiting time between two successive bursts follow log-normal distributions, consistent with previous studies. We also find that bursts with longer duration (some are multi-pulsed) tend to occur during the period with relatively high burst rate. There is no correlation between the waiting time and the fluence or duration of either the former or latter burst. It also seems that there is no correlation between burst duration and hardness ratio, in contrast to some previous reports. In addition, we do not find any X-ray burst associated with any reported radio bursts except for FRB 200428.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Insight-HXMT dedicated 33-day observation of SGR J1935+2154 II. Burst Spectral Catalog

Since April 28, 2020, Insight-HXMT has implemented a dedicated observation on the magnetar SGR J1935+2154. Thanks to the wide energy band (1-250 keV) and high sensitivity of Insight-HXMT, we obtained 75 bursts from SGR J1935+2154 during a month-long activity episode after the emission of FRB 200428. Here, we report the detailed time-integrated spectral analysis of these bursts and the statistical distribution of the spectral parameters. We find that for 15%(11/75) of SGR J1935+2154 bursts, the CPL model is preferred, and most of them occurred in the latter part of this active epoch. In the cumulative fluence distribution, we find that the fluence of bursts in our sample is about an order of magnitude weaker than that of Fermi/GBM, but follows the same power law distribution. Finally, we find a burst with similar peak energy to the time-integrated spectrum of the X-ray burst associated with FRB 200428 (FRB 200428-Associated Burst), but the low energy index is harder.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Localization Distillation for Dense Object Detection

Knowledge distillation (KD) has witnessed its powerful capability in learning compact models in object detection. Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement. In this paper, by reformulating the knowledge distillation process on localization, we present a novel localization distillation (LD) method which can efficiently transfer the localization knowledge from the teacher to the student. Moreover, we also heuristically introduce the concept of valuable localization region that can aid to selectively distill the semantic and localization knowledge for a certain region. Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and localization knowledge distillation is more important and efficient than semantic knowledge for distilling object detectors. Our distillation scheme is simple as well as effective and can be easily applied to different dense object detectors. Experiments show that our LD can boost the AP score of GFocal-ResNet-50 with a single-scale 1x training schedule from 40.1 to 42.1 on the COCO benchmark without any sacrifice on the inference speed. Our source code and trained models are publicly available at https://github.com/HikariTJU/LD

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data

Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between the global and local objectives, making the FL model slow to converge. In this paper, we proposed Optimal Aggregation algorithm for better aggregation, which finds out the optimal subset of local updates of participating nodes in each global round, by identifying and excluding the adverse local updates via checking the relationship between the local gradient and the global gradient. Then, we proposed a Probabilistic Node Selection framework (FedPNS) to dynamically change the probability for each node to be selected based on the output of Optimal Aggregation. FedPNS can preferentially select nodes that propel faster model convergence. The unbiasedness of the proposed FedPNS design is illustrated and the convergence rate improvement of FedPNS over the commonly adopted Federated Averaging (FedAvg) algorithm is analyzed theoretically. Experimental results demonstrate the effectiveness of FedPNS in accelerating the FL convergence rate, as compared to FedAvg with random node selection.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PINO-MBD: Physics-informed Neural Operator for Solving Coupled ODEs in Multi-body Dynamics

In multi-body dynamics, the motion of a complicated physical object is described as a coupled ordinary differential equation system with multiple unknown solutions. Engineers need to constantly adjust the object to meet requirements at the design stage, where a highly efficient solver is needed. The rise of machine learning-based partial differential equation solvers can meet this need. These solvers can be classified into two categories: approximating the solution function (Physics-informed neural network) and learning the solution operator (Neural operator). The recently proposed physics-informed neural operator (PINO) gains advantages from both categories by embedding physics equations into the loss function of a neural operator. Following this state-of-art concept, we propose the physics-informed neural operator for coupled ODEs in multi-body dynamics (PINO-MBD), which learns the mapping between parameter spaces and solution spaces. Once PINO-MBD is trained, only one forward pass of the network is required to obtain the solutions for a new instance with different parameters. To handle the difficulty that coupled ODEs contain multiple solutions (instead of only one in normal PDE problems), two new physics embedding methods are also proposed. The experimental results on classic vehicle-track coupled dynamics problem show state-of-art performance not only on solutions but also the first and second derivatives of solutions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications

Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Ambient Backscatter-Assisted Wireless-Powered Relaying

Internet-of-Things (IoT) is featured with low-power communications among a massive number of ubiquitously-deployed and energy-constrained electronics, e.g., sensors and actuators. To cope with the demand, wireless-powered cooperative relaying emerges as a promising communication paradigm to extend data transmission coverage and solve energy scarcity for the IoT devices. In this paper, we propose a novel hybrid relaying strategy by combining wireless-powered communication and ambient backscattering functions to improve applicability and performance of data transfer. In particular, the hybrid relay can harvest energy from radio frequency (RF) signals and use the energy for active transmission. Alternatively, the hybrid relay can choose to perform ambient backscattering of incident RF signals for passive transmission. To efficiently utilize the ambient RF resource, we design mode selection protocols to coordinate between the active and passive relaying in circumstances with and without instantaneous channel gain. With different mode selection protocols, we characterize the success probability and ergodic capacity of a dual-hop relaying system with the hybrid relay in the field of randomly located ambient transmitters. The analytical and the numerical results demonstrate the effectiveness of the mode selection protocols in adapting the hybrid relaying into the network environment and reveal the impacts of system parameters on the performance gain of the hybrid relaying. As applications of our analytical framework which is computationally tractable, we formulate optimization problems based on the derived expressions to optimize the system parameters with different objectives. The optimal solutions exhibit a tradeoff between the maximum energy efficiency and target success probability.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Few-shot Image Classification with Multi-Facet Prototypes

The aim of few-shot learning (FSL) is to learn how to recognize image categories from a small number of training examples. A central challenge is that the available training examples are normally insufficient to determine which visual features are most characteristic of the considered categories. To address this challenge, we organize these visual features into facets, which intuitively group features of the same kind (e.g. features that are relevant to shape, color, or texture). This is motivated from the assumption that (i) the importance of each facet differs from category to category and (ii) it is possible to predict facet importance from a pre-trained embedding of the category names. In particular, we propose an adaptive similarity measure, relying on predicted facet importance weights for a given set of categories. This measure can be used in combination with a wide array of existing metric-based methods. Experiments on miniImageNet and CUB show that our approach improves the state-of-the-art in metric-based FSL.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Millimeter Wave MIMO based Depth Maps for Wireless Virtual and Augmented Reality

Augmented and virtual reality systems (AR/VR) are rapidly becoming key components of the wireless landscape. For immersive AR/VR experience, these devices should be able to construct accurate depth perception of the surrounding environment. Current AR/VR devices rely heavily on using RGB-D depth cameras to achieve this goal. The performance of these depth cameras, however, has clear limitations in several scenarios, such as the cases with shiny objects, dark surfaces, and abrupt color transition among other limitations. In this paper, we propose a novel solution for AR/VR depth map construction using mmWave MIMO communication transceivers. This is motivated by the deployment of advanced mmWave communication systems in future AR/VR devices for meeting the high data rate demands and by the interesting propagation characteristics of mmWave signals. Accounting for the constraints on these systems, we develop a comprehensive framework for constructing accurate and high-resolution depth maps using mmWave systems. In this framework, we developed new sensing beamforming codebook approaches that are specific for the depth map construction objective. Using these codebooks, and leveraging tools from successive interference cancellation, we develop a joint beam processing approach that can construct high-resolution depth maps using practical mmWave antenna arrays. Extensive simulation results highlight the potential of the proposed solution in building accurate depth maps. Further, these simulations show the promising gains of mmWave based depth perception compared to RGB-based approaches in several important use cases.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Quantum nonlinear spectroscopy of single nuclear spins

Nonlinear spectroscopy is widely used for studying physical systems. Conventional nonlinear optical spectroscopy and magnetic resonance spectroscopy, which use classical probes such as electromagnetic waves, can only access certain types of correlations in a quantum system. The idea of quantum nonlinear spectroscopy was recently proposed to use quantum probes such as entangled photons to achieve sensitivities and resolutions beyond the classical limits. It is shown that quantum sensing can extract arbitrary types and orders of correlations in a quantum system by first quantum-entangling a sensor and the object and then measuring the sensor. Quantum sensing has been applied to achieve nuclear magnetic resonance (NMR) of single atoms and the second-order correlation spectroscopy has been adopted to enhance the spectral resolution. However, quantum nonlinear spectroscopy (i.e., the measurement of higher-order correlations) of single nuclear spins is still elusive. Here we demonstrate the extraction of fourth-order correlations of single nuclear spins that cannot be measured in conventional nonlinear spectroscopy, using sequential weak measurement via an atomic quantum sensor, namely, a nitrogen-vacancy center in diamond. We show that the quantum nonlinear spectroscopy provides fingerprint features to identify different types of objects, such as Gaussian noises, random-phased AC fields, and quantum spins, which would be indistinguishable in second-order correlations. The measured fourth-order correlation unambiguously differentiates a single nuclear spin and a random-phased AC field. This work constitutes an initial step toward the application of higher-order correlations to quantum sensing, to examining the quantum foundation (by, e.g., higher-order Leggett-Garg inequality), and to studying quantum many-body physics.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment

Entity alignment (EA) is the task to discover entities referring to the same real-world object from different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs. The majority of the existing embeddings-based entity alignment methods embed entities and relations into a vector space based on relation triples of KGs for local alignment. As these methods insufficiently consider the multiple relations between entities, the structure information of KGs has not been fully leveraged. In this paper, we propose a novel framework based on Relation-aware Graph Attention Networks to capture the interactions between entities and relations. Our framework adopts the self-attention mechanism to spread entity information to the relations and then aggregate relation information back to entities. Furthermore, we propose a global alignment algorithm to make one-to-one entity alignments with a fine-grained similarity matrix. Experiments on three real-world cross-lingual datasets show that our framework outperforms the state-of-the-art methods.

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preprint2021arXiv

Restoring the top-of-atmosphere reflectance during solar eclipses: a proof of concept with the UV Absorbing Aerosol Index measured by TROPOMI

During a solar eclipse the solar irradiance reaching the top-of-atmosphere (TOA) is reduced in the Moon shadow. The solar irradiance is commonly measured by Earth observation satellites before the start of the solar eclipse and is not corrected for this reduction, which results in a decrease of the computed TOA reflectances. Consequently, air quality products that are derived from TOA reflectance spectra, such as the ultraviolet (UV) Absorbing Aerosol Index (AAI), are distorted or undefined in the shadow of the Moon. The availability of air quality satellite data in the penumbral and antumbral shadow during solar eclipses, however, is of particular interest to users studying the atmospheric response to solar eclipses. Given the time and location of a point on the Earth's surface, we explain how to compute the obscuration during a solar eclipse taking into account wavelength-dependent solar limb darkening. With the calculated obscuration fractions, we restore the TOA reflectances and the AAI in the penumbral shadow during the annular solar eclipses on 26 December 2019 and 21 June 2020 measured by the TROPOMI/S5P instrument. In the corrected products, the signature of the Moon shadow disappeared, but only if wavelength-dependent solar limb darkening is taken into account. We conclude that the correction method of this paper can be used to detect real AAI rising phenomena during a solar eclipse and has the potential to restore any other product that is derived from TOA reflectance spectra. This would resolve the solar eclipse anomalies in satellite air quality measurements and would allow for studying the effect of the eclipse obscuration on the composition of the Earth's atmosphere from space.

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preprint2021arXiv

Sea quark contributions to the electromagnetic form factors of $Σ$ hyperons

We study the sea quark contributions to the electromagnetic form factors of $Σ$ baryons with nonlocal chiral effective theory. Both octet and decuplet intermediate states are included in the one loop calculation. $G_{Σ^{-}}^{u}$ and $G_{Σ^{+}}^{d}$ could be priority observables for the examination of sea quark contributions to baryon structure because these quantities are much larger than the strange form factors of nucleon. It will be less difficult for lattice simulation to determine the sign of these pure sea quark contributions unambiguously. In $Σ^0$, the light sea quark form factors $G_{Σ^{0}}^{u}$ and $G_{Σ^{0}}^{d}$ are identical. Since the light sea quark form factors in proton are different, it will be more meaningful to compare lattice result of the light sea quark form factors in $Σ^0$ with that obtained from effective field theory.

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preprint2020arXiv

A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection

Unsupervised aspect detection (UAD) aims at automatically extracting interpretable aspects and identifying aspect-specific segments (such as sentences) from online reviews. However, recent deep learning-based topic models, specifically aspect-based autoencoder, suffer from several problems, such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest. To tackle these challenges, in this paper, we first propose a self-supervised contrastive learning framework and an attention-based model equipped with a novel smooth self-attention (SSA) module for the UAD task in order to learn better representations for aspects and review segments. Secondly, we introduce a high-resolution selective mapping (HRSMap) method to efficiently assign aspects discovered by the model to aspects of interest. We also propose using a knowledge distilling technique to further improve the aspect detection performance. Our methods outperform several recent unsupervised and weakly supervised approaches on publicly available benchmark user review datasets. Aspect interpretation results show that extracted aspects are meaningful, have good coverage, and can be easily mapped to aspects of interest. Ablation studies and attention weight visualization also demonstrate the effectiveness of SSA and the knowledge distilling method.

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preprint2020arXiv

Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks

As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of "block mining" by solving a cryptographic puzzle. Under the circumstance of limited local computing resources, e.g., mobile devices, it is natural for rational miners, i.e., consensus nodes, to offload computational tasks for proof of work to the cloud/fog computing servers. Therefore, we focus on the trading between the cloud/fog computing service provider and miners, and propose an auction-based market model for efficient computing resource allocation. In particular, we consider a proof-of-work based blockchain network. Due to the competition among miners in the blockchain network, the allocative externalities are particularly taken into account when designing the auction mechanisms. Specifically, we consider two bidding schemes: the constant-demand scheme where each miner bids for a fixed quantity of resources, and the multi-demand scheme where the miners can submit their preferable demands and bids. For the constant-demand bidding scheme, we propose an auction mechanism that achieves optimal social welfare. In the multi-demand bidding scheme, the social welfare maximization problem is NP-hard. Therefore, we design an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we show that our proposed auction mechanisms with the two bidding schemes can efficiently maximize the social welfare of the blockchain network and provide effective strategies for the cloud/fog computing service provider.

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preprint2020arXiv

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks

Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces.

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preprint2020arXiv

GPView: a program for wave function analysis and visualization

In this manuscript, we will introduce a recently developed program GPView, which can be used for wave function analysis and visualization. The wave function analysis module can calculate and generate 3D cubes for various types of molecular orbitals and electron density of electronic excited states, such as natural orbitals, natural transition orbitals, natural difference orbitals, hole-particle density, detachment-attachment density and transition density. The visualization module of GPView can display molecular and electronic (iso-surfaces) structures. It is also able to animate single trajectories of molecular dynamics and non-adiabatic excited state molecular dynamics using the data stored in existing files. There are also other utilities to extract and process the output of quantum chemistry calculations. The GPView provides full graphic user interface (GUI), so it very easy to use. It is available from website \href{http://life-tp.com/gpview}{http://life-tp.com/gpview}.

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preprint2020arXiv

Hadronic cross section of $e^+e^-$ annihilation at bottomonium energy region

The Born cross section and dressed cross section of $e^+e^-$ to $b\bar{b}$ and the total hadronic cross section in $e^+e^-$ annihilation in the bottomonium energy region are calculated based on the Rb values measured by the BaBar and Belle experiments. The data are used to calculate the vacuum polarization factors in the bottomonium energy region, and to determine the resonant parameters of the vector bottomonium(-like) states, the Y(10750), Upsilon(5S), and Upsilon(6S).

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preprint2020arXiv

Mechanism Design for Wireless Powered Spatial Crowdsourcing Networks

Wireless power transfer (WPT) is a promising technology to prolong the lifetime of the sensors and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing framework which consists of two mutually dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the spatial crowdsourcing platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, the workers may have an incentive to misreport its real working location to improve its utility, which causes adverse effects to the spatial crowdsourcing platform. To address this issue, we present three strategyproof deployment mechanisms for the spatial crowdsourcing platform to place a mobile base station, e.g., vehicle or robot, which is responsible for transferring the wireless power and collecting the crowdsourced data. As the benchmark, we first apply the classical median mechanism and evaluate its worst-case performance. Then, we design a conventional strategyproof deployment mechanism to improve the expected utility of the spatial crowdsourcing platform under the condition that the workers' locations follow a known geographical distribution. For a more general case with only the historical location data available, we propose a deep learning based strategyproof deployment mechanism to maximize the spatial crowdsourcing platform's utility. Extensive experimental results based on synthetic and real-world datasets reveal the effectiveness of the proposed framework in allocating tasks and charging power to workers while avoiding the dishonest worker's manipulation.

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preprint2020arXiv

Optimal Pricing of Internet of Things: A Machine Learning Approach

Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.

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preprint2020arXiv

Performance Limits of Differential Power Processing

This paper investigates the performance limits of differential power processing (DPP) and presents quantitative and systematic design guidelines for the selection and comparison of DPP topologies. A stochastic model is developed to evaluate the expected power losses of a variety of DPP topologies with probabilistic load distribution. The expected losses of several DPP topologies are derived and compared against traditional dc-dc converters to reveal their performance limits. The impacts of the load distribution and load scale on the expected losses are investigated. The theoretical models are verified with SPICE simulations and experimental results.

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preprint2020arXiv

Sea quark contributions to nucleon electromagnetic form factors with the nonlocal chiral effective Lagrangian

The sea quark contributions to the nucleon electromagnetic form factors from up, down and strange quarks are studied with the nonlocal chiral effective Lagrangian. Both octet and decuplet intermediate states are included in the one loop calculation. Compared with the strange form factors, though their signs are the same, the absolute value of the light quark form factors are much larger. For both electric and magnetic form factors, the contribution from $d$ quark is larger than that from $u$ quark. The current lattice data for the light-sea quark form factors are between our sea quark results for $u$ and $d$.

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preprint2020arXiv

Shipper Cooperation in Stochastic Drone Delivery: A Dynamic Bayesian Game Approach

With the recent technological innovation, unmanned aerial vehicles, known as drones, have found numerous applications including package and parcel delivery for shippers. Drone delivery offers benefits over conventional ground-based vehicle delivery in terms of faster speed, lower cost, more environment-friendly, and less manpower needed. However, most of existing studies on drone delivery planning and scheduling focus on a single shipper and ignore uncertainty factors. As such, in this paper, we consider a scenario that multiple shippers can cooperate to minimize their drone delivery cost. We propose the Bayesian Shipper Cooperation in Stochastic Drone Delivery (BCoSDD) framework. The framework is composed of three functions, i.e., package assignment, shipper cooperation formation and cost management. The uncertainties of drone breakdown and misbehavior of cooperative shippers are taken into account by using multistage stochastic programming optimization and dynamic Bayesian coalition formation game. We conduct extensive performance evaluation of the BCoSDD framework by using customer locations from Solomon benchmark suite and a real Singapore logistics industry. As a result, the framework can help the shippers plan and schedule their drone delivery effectively.

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preprint2020arXiv

Structural properties and average tapping time on scale-free graphs with smallest diameter

In this paper, we propose a class of graphs $G^{\star}(m,t)$ and first study some structural properties, such as, average degree, on them. The results show that (1) graphs $G^{\star}(m,t)$ have density feature because of their average degrees proportional to time step $t$ not to a constant in the large graph size limit, (2) graphs $G^{\star}(m,t)$ obey the power-law distribution with exponent equal to $2$, which is rarely found in most previous scale-free models, (3) graphs $G^{\star}(m,t)$ display small-world property in terms of ultra-small diameter and higher clustering coefficient, and (4) graphs $G^{\star}(m,t)$ possess disassortative structure with respect to Pearson correlation coefficient smaller than zero. In addition, we consider the trapping problem on the proposed graphs $G^{\star}(m,t)$ and then find that they all have more optimal trapping efficiency by means of their own average trapping time achieving the theoretical lower bound, a phenomenon that is seldom observed in existing scale-free models. We conduct extensive simulations that are consistent with our theoretical analysis.

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preprint2020arXiv

Text-to-SQL Generation for Question Answering on Electronic Medical Records

Electronic medical records (EMR) contain comprehensive patient information and are typically stored in a relational database with multiple tables. Effective and efficient patient information retrieval from EMR data is a challenging task for medical experts. Question-to-SQL generation methods tackle this problem by first predicting the SQL query for a given question about a database, and then, executing the query on the database. However, most of the existing approaches have not been adapted to the healthcare domain due to a lack of healthcare Question-to-SQL dataset for learning models specific to this domain. In addition, wide use of the abbreviation of terminologies and possible typos in questions introduce additional challenges for accurately generating the corresponding SQL queries. In this paper, we tackle these challenges by developing a deep learning based TRanslate-Edit Model for Question-to-SQL (TREQS) generation, which adapts the widely used sequence-to-sequence model to directly generate the SQL query for a given question, and further performs the required edits using an attentive-copying mechanism and task-specific look-up tables. Based on the widely used publicly available electronic medical database, we create a new large-scale Question-SQL pair dataset, named MIMICSQL, in order to perform the Question-to-SQL generation task in healthcare domain. An extensive set of experiments are conducted to evaluate the performance of our proposed model on MIMICSQL. Both quantitative and qualitative experimental results indicate the flexibility and efficiency of our proposed method in predicting condition values and its robustness to random questions with abbreviations and typos.

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preprint2020arXiv

Toward an Automated Auction Framework for Wireless Federated Learning Services Market

In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this paper, we propose an auction based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.

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preprint2020arXiv

Trapping problem on star-type graphs with applications

The trapping problem on graph (or network) as a typical focus of great interest has attracted more attention from various science fields, including applied mathematics and theoretical computer science, in the past. Here, we first study this problem on an arbitrary graph and obtain the closed-form formula for calculating the theoretical lower bound of average trapping time ($ATT$), a quantity that evaluates trapping efficiency of graph in question, using methods from spectral graph theory. The results show that the choice of the trap's location has a significant influence on determining parameter $ATT$. As a result, we consider the problem on star-type graphs, a special graph family which will be introduced shortly, with a single trap $θ$ and then derive using probability generating functions the exact solution to quantity $ATT$. Our results suggest that all star-type graphs have most optimal trapping efficiency by achieving the corresponding theoretical lower bounds of $ATT$. More importantly, we further find that a given graph is most optimal only if its underlying structure is star-type when considering the trapping problem. At meantime, we also provide the upper bounds for $ATT$ of several graphs in terms of well-known Holder inequality, some of which are sharp. By using all the consequences obtained, one may be able to design better control scheme for complex networks from respect of trapping efficiency, to some extent, which are in well agreement with many other previous thoughts.

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preprint2019arXiv

An ensemble of random graphs with identical degree distribution

Degree distribution, or equivalently called degree sequence, has been commonly used to be one of most significant measures for studying a large number of complex networks with which some well-known results have been obtained. By contrast, in this paper, we report a fact that two arbitrarily chosen networks with identical degree distribution can have completely different other topological structure, such as diameter, spanning trees number, pearson correlation coefficient, and so forth. Besides that, for a given degree distribution (as power-law distribution with exponent $γ=3$ discussed here), it is reasonable to ask how many network models with such a constraint we can have. To this end, we generate an ensemble of this kind of random graphs with $P(k)\sim k^{-γ}$ ($γ=3$), denoted as graph space $\mathcal{N}(p,q,t)$ where probability parameters $p$ and $q$ hold on $p+q=1$, and indirectly show the cardinality of $\mathcal{N}(p,q,t)$ seems to be large enough in the thermodynamics limit, i.e., $N\rightarrow\infty$, by varying values of $p$ and $q$. From the theoretical point of view, given an ultrasmall constant $p_{c}$, perhaps only graph model $N(1,0,t)$ is small-world and other are not in terms of diameter. And then, we study spanning trees number on two deterministic graph models and obtain both upper bound and lower bound for other members. Meanwhile, for arbitrary $p(\neq1)$, we prove that graph model $N(p,q,t)$ does go through two phase transitions over time, i.e., starting by non-assortative pattern and then suddenly going into disassortative region, and gradually converging to initial place (non-assortative point). Among of them, one "null" graph model is built.

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