arXiv subjects

Yan Zhang

Publications and source records attributed to Yan Zhang.

At least 559 records · Page 31Linked to original sources

arXiv2020

Top-K Influential Nodes in Social Networks: A Game Perspective

Influence maximization, the fundamental of viral marketing, aims to find top-$K$ seed nodes maximizing influence spread under certain spreading models. In this paper, we study influence maximization from a game perspective. We propose a Coordination Game model, in which every individual makes its decision based on the benefit of coordination with its network neighbors, to study information propagation. Our model serves as the generalization of some existing models, such as Majority Vote model and Linear Threshold model. Under the generalized model, we study the hardness of influence maximization and the approximation guarantee of the greedy algorithm. We also combine several strategies to accelerate the algorithm. Experimental results show that after the acceleration, our algorithm significantly outperforms other heuristics, and it is three orders of magnitude faster than the original greedy method.

cs.AI↗

arXiv2020

Short-term Load Forecasting with Dense Average Network

As an important part of the power system, power load forecasting directly affects the national economy. The data shows that improving the load forecasting accuracy by 0.01% can save millions of dollars for the power industry. Therefore, improving the accuracy of power load forecasting has always been the pursuing goals for a power system. Based on this goal, this paper proposes a novel connection, the dense average connection, in which the outputs of all preceding layers are averaged as the input of the next layer in a feed-forward fashion. Based on dense average connection , we construct the dense average network for power load forecasting. The predictions of the proposed model for two public datasets are better than those of existing methods. On this basis, we use the ensemble method to further improve the accuracy of the model. To verify the reliability of the model predictions, the robustness is analyzed and verified by adding input disturbances. The experimental results show that the proposed model is effective and robust for power load forecasting.

cs.LG↗

arXiv2020

Calibration and performance of the neutron detector onboard of the DAMPE mission

The DArk Matter Particle Explorer (DAMPE), one of the four space-based scientific missions within the framework of the Strategic Pioneer Program on Space Science of the Chinese Academy of Sciences, has been successfully launched on Dec. 17th 2015 from Jiuquan launch center. One of the most important scientific goals of DAMPE is to search for the evidence of dark matter indirectly by measuring the spectrum of high energy cosmic-ray electrons. The neutron detector, one of the four sub-payloads of DAMPE, is designed to distinguish high energy electrons from hadron background by measuring the secondary neutrons produced in the shower. In this paper, a comprehensive introduction of the neutron detector is presented, including the design, the calibration and the performance. The analysis with simulated data and flight data indicates a powerful proton rejection capability of the neutron detector, which plays an essential role for TeV electron identification of DAMPE.

physics.ins-det↗

arXiv2020

Environmental tests of the HXI spectrometer for the ASO-S mission

The advanced space-based solar observatory (ASO-S) is the first Chinese solar mission which is scheduled to be launched in the end of 2021. It is designed to study the magnetic field, the solar coronal mass ejections the solar flares as well as their relationships. ASO-S includes three scientific payloads, the Full-disk vector MagnetoGraph (FMG), the Lyman-alpha Solar Telescope (LST) and the Hard X-ray Imager (HXI). As a key part on ASO-S, HXI will improve our understanding on the solar flares during the 25th solar maximum. The HXI is composed of three instruments, the collimator (HXI-C), the spectrometer (HXI-S) and the electrical control box (HXI-E). This paper describes the procedure and results of the environmental tests including the mechanical and thermal which were done on the qualification model of the HXI-S. The functional tests of the spectrometer are further carried out, and the results show that the detector could operate normally. 10

physics.ins-det↗

arXiv2020

FSPool: Learning Set Representations with Featurewise Sort Pooling

Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem. We introduce a pooling method for sets of feature vectors based on sorting features across elements of the set. This can be used to construct a permutation-equivariant auto-encoder that avoids this responsibility problem. On a toy dataset of polygons and a set version of MNIST, we show that such an auto-encoder produces considerably better reconstructions and representations. Replacing the pooling function in existing set encoders with FSPool improves accuracy and convergence speed on a variety of datasets.

cs.LG↗

arXiv2020

Deep Set Prediction Networks

Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our model is able to auto-encode point sets, predict the set of bounding boxes of objects in an image, and predict the set of attributes of these objects.

cs.LG↗

arXiv2020

Generating 3D People in Scenes without People

We present a fully automatic system that takes a 3D scene and generates plausible 3D human bodies that are posed naturally in that 3D scene. Given a 3D scene without people, humans can easily imagine how people could interact with the scene and the objects in it. However, this is a challenging task for a computer as solving it requires that (1) the generated human bodies to be semantically plausible within the 3D environment (e.g. people sitting on the sofa or cooking near the stove), and (2) the generated human-scene interaction to be physically feasible such that the human body and scene do not interpenetrate while, at the same time, body-scene contact supports physical interactions. To that end, we make use of the surface-based 3D human model SMPL-X. We first train a conditional variational autoencoder to predict semantically plausible 3D human poses conditioned on latent scene representations, then we further refine the generated 3D bodies using scene constraints to enforce feasible physical interaction. We show that our approach is able to synthesize realistic and expressive 3D human bodies that naturally interact with 3D environment. We perform extensive experiments demonstrating that our generative framework compares favorably with existing methods, both qualitatively and quantitatively. We believe that our scene-conditioned 3D human generation pipeline will be useful for numerous applications; e.g. to generate training data for human pose estimation, in video games and in VR/AR. Our project page for data and code can be seen at: \url{https://vlg.inf.ethz.ch/projects/PSI/}.

cs.CV↗

arXiv2020

Ramsey-biased spectroscopy of superconducting qubits under dispersion

We proposed a spectroscopic method that extends Ramsey's atomic spectroscopy to detect the transition frequency of a qubit fabricated on a superconducting circuit. The method uses a multi-interval train of qubit biases to implement an alternate resonant and dispersive couplings to an incident probe field. The consequent absorption spectrum of the qubit has a narrower linewidth at its transition frequency than that obtained from constantly biasing the qubit to resonance while the middle dispersive evolution incurs only a negligible shift in detected frequency. Modeling on transmon qubits, we find that the linewidth reduction reaches 23% and Ramsey fringes are simultaneously suppressed at extreme duration ratio of dispersion over resonance for a double-resonance scheme. If the scheme is augmented by an extra resonance segment, a further 37% reduction can be achieved.

quant-ph↗

arXiv2020

On the Performance of Dual-Hop Systems over Mixed FSO/mmWave Fading Channels

Free-space optical (FSO) links are considered as a cost-efficient way to fill the backhaul/fronthaul connectivity gap between millimeter wave (mmWave) access networks and optical fiber based central networks. In this paper, we investigate the end-to-end performance of dual-hop mixed FSO/mmWave systems to address this combined use. The FSO link is modeled as a Gamma-Gamma fading channel using both heterodyne detection and indirect modulation/direct detection with pointing error impairments, while the mmWave link experiences the fluctuating two-ray fading. Under the assumption of both amplify-and-forward and decode-and-forward relaying, we derive novel closed-form expressions for the outage probability, average bit error probability (BER), ergodic capacity, effective capacity in terms of bivariate Fox's $H$-functions. Additionally, we discuss the diversity gain and provide other important engineering insights based on the high signal-to-noise-ratio analysis of the outage probability and the average BER. Finally, all our analytical results are verified using Monte Carlo simulations.

cs.IT↗

arXiv2020

Satirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets

Satirical news detection is an important yet challenging task to prevent spread of misinformation. Many feature based and end-to-end neural nets based satirical news detection systems have been proposed and delivered promising results. Existing approaches explore comprehensive word features from satirical news articles, but lack semantic metrics using word vectors for tweet form satirical news. Moreover, the vagueness of satire and news parody determines that a news tweet can hardly be classified with a binary decision, that is, satirical or legitimate. To address these issues, we collect satirical and legitimate news tweets, and propose a semantic feature based approach. Features are extracted by exploring inconsistencies in phrases, entities, and between main and relative clauses. We apply game-theoretic rough set model to detect satirical news, in which probabilistic thresholds are derived by game equilibrium and repetition learning mechanism. Experimental results on the collected dataset show the robustness and improvement of the proposed approach compared with Pawlak rough set model and SVM.

cs.CL↗

arXiv2020

Frontal Low-rank Random Tensors for Fine-grained Action Segmentation

Fine-grained action segmentation in long untrimmed videos is an important task for many applications such as surveillance, robotics, and human-computer interaction. To understand subtle and precise actions within a long time period, second-order information (e.g. feature covariance) or higher is reported to be effective in the literature. However, extracting such high-order information is considerably non-trivial. In particular, the dimensionality increases exponentially with the information order, and hence gaining more representation power also increases the computational cost and the risk of overfitting. In this paper, we propose an approach to representing high-order information for temporal action segmentation via a simple yet effective bilinear form. Specifically, our contributions are: (1) From the multilinear perspective, we derive a bilinear form of low complexity, assuming that the three-way tensor has low-rank frontal slices. (2) Rather than learning the tensor entries from data, we sample the entries from different underlying distributions, and prove that the underlying distribution influences the information order. (3) We employed our bilinear form as an intermediate layer in state-of-the-art deep neural networks, enabling to represent high-order information in complex deep models effectively and efficiently. Our experimental results demonstrate that the proposed bilinear form outperforms the previous state-of-the-art methods on the challenging temporal action segmentation task. One can see our project page for data, model and code: \url{https://vlg.inf.ethz.ch/projects/BilinearTCN/}.

cs.LG↗

arXiv2020

SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints

Since the PointNet was proposed, deep learning on point cloud has been the concentration of intense 3D research. However, existing point-based methods usually are not adequate to extract the local features and the spatial pattern of a point cloud for further shape understanding. This paper presents an end-to-end framework, SK-Net, to jointly optimize the inference of spatial keypoint with the learning of feature representation of a point cloud for a specific point cloud task. One key process of SK-Net is the generation of spatial keypoints (Skeypoints). It is jointly conducted by two proposed regulating losses and a task objective function without knowledge of Skeypoint location annotations and proposals. Specifically, our Skeypoints are not sensitive to the location consistency but are acutely aware of shape. Another key process of SK-Net is the extraction of the local structure of Skeypoints (detail feature) and the local spatial pattern of normalized Skeypoints (pattern feature). This process generates a comprehensive representation, pattern-detail (PD) feature, which comprises the local detail information of a point cloud and reveals its spatial pattern through the part district reconstruction on normalized Skeypoints. Consequently, our network is prompted to effectively understand the correlation between different regions of a point cloud and integrate contextual information of the point cloud. In point cloud tasks, such as classification and segmentation, our proposed method performs better than or comparable with the state-of-the-art approaches. We also present an ablation study to demonstrate the advantages of SK-Net.

cs.CV↗

arXiv2020

A complete optical model for liquid-scintillator detectors

Liquid scintillator (LS) is widely used in various neutrino oscillation experiments, in particular, the reactor neutrino experiments. The complex absorption and re-emission processes of optical photons are known to be an important source of the non-linear and non-uniform response of LS detectors. Precise simulation of light propagation in LS is highly desirable to model the detector response and reduce the systematic errors. In this paper, we develop a novel optical model which can completely deal with the competitive photon absorption and subsequent re-emission processes of the LS components. It allows to directly plug in the laboratory measurements of the LS components to model any LS composition. Extensive measurements have been performed to obtain the essential optical parameters for this model. We validate the model with a bench-top experiment featuring a small LS volume. Furthermore, we demonstrate that for any given detector geometry, this model provides the capability of optimizing the LS recipe to maximize the light collection. It is valuable for designing future LS-based detectors and improving the agreement between Monte Carlo and data for current neutrino experiments.

physics.ins-det↗

arXiv2020

Estimation of genome size using k-mer frequencies from corrected long reads

The third-generation long reads sequencing technologies, such as PacBio and Nanopore, have great advantages over second-generation Illumina sequencing in de novo assembly studies. However, due to the inherent low base accuracy, third-generation sequencing data cannot be used for k-mer counting and estimating genomic profile based on k-mer frequencies. Thus, in current genome projects, second-generation data is also necessary for accurately determining genome size and other genomic characteristics. We show that corrected third-generation data can be used to count k-mer frequencies and estimate genome size reliably, in replacement of using second-generation data. Therefore, future genome projects can depend on only one sequencing technology to finish both assembly and k-mer analysis, which will largely decrease sequencing cost in both time and money. Moreover, we present a fast light-weight tool kmerfreq and use it to perform all the k-mer counting tasks in this work. We have demonstrated that corrected third-generation sequencing data can be used to estimate genome size and developed a new open-source C/C++ k-mer counting tool, kmerfreq, which is freely available at https://github.com/fanagislab/kmerfreq.

q-bio.GN↗

arXiv2020

The gamma-ray and Optical Variability Analysis of the BL Lac Object 3FGL J0449.4-4350

We have assembled the historical light curves of the BL Lac Object 3FGL J0449.4-4350 at optical and gamma-ray bands, the time spanning about 10 years, analyzed the periodic variability of the light curves by using four different methods (Lomb-Scargle periodogram, REDFIT38, Jurkevich and DACF). We detected a marginally possible quasi-periodic oscillation (QPO) of ~450 days. Assuming it originates from the helical motion jet in a supermassive binary black hole (SMBBH) system undergoing major merger, we estimate the primary black hole mass M~7.7*10^{9} M_sun. To explore the origin of the gamma-ray, we investigated the optical-gamma-ray correlations using discrete correlation function (DCF) method, and found that the correlation between the two bands is very significant. This strong correlation tends to imply lepton self-synchro-Compton (LSSC) model to produce the gamma-ray.

astro-ph.HE↗

arXiv2020

RF-Rhythm: Secure and Usable Two-Factor RFID Authentication

Passive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user's tapping rhythm. In addition to verifying the RFID card's identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user's secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero.

eess.SP↗

arXiv2020

Transfer Reinforcement Learning under Unobserved Contextual Information

In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a demonstrator agent that has access to a context-aware policy and can generate transition and reward data based on that policy. These data constitute the experience of the demonstrator. Then, the goal is to transfer this experience, excluding the underlying contextual information, to a learner agent that does not have access to the environmental context, so that they can learn a control policy using fewer samples. It is well known that, disregarding the causal effect of the contextual information, can introduce bias in the transition and reward models estimated by the learner, resulting in a learned suboptimal policy. To address this challenge, in this paper, we develop a method to obtain causal bounds on the transition and reward functions using the demonstrator's data, which we then use to obtain causal bounds on the value functions. Using these value function bounds, we propose new Q learning and UCB-Q learning algorithms that converge to the true value function without bias. We provide numerical experiments for robot motion planning problems that validate the proposed value function bounds and demonstrate that the proposed algorithms can effectively make use of the data from the demonstrator to accelerate the learning process of the learner.

cs.LG↗

arXiv2020

Semantic Change Pattern Analysis

Change detection is an important problem in vision field, especially for aerial images. However, most works focus on traditional change detection, i.e., where changes happen, without considering the change type information, i.e., what changes happen. Although a few works have tried to apply semantic information to traditional change detection, they either only give the label of emerging objects without taking the change type into consideration, or set some kinds of change subjectively without specifying semantic information. To make use of semantic information and analyze change types comprehensively, we propose a new task called semantic change pattern analysis for aerial images. Given a pair of co-registered aerial images, the task requires a result including both where and what changes happen. We then describe the metric adopted for the task, which is clean and interpretable. We further provide the first well-annotated aerial image dataset for this task. Extensive baseline experiments are conducted as reference for following works. The aim of this work is to explore high-level information based on change detection and facilitate the development of this field with the publicly available dataset.

cs.CV↗