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Tian Wang

Publications and source records attributed to Tian Wang.

At least 109 records · Page 6Linked to original sources

Control of Andreev bound states using superconducting phase texture

Andreev bound states with opposite phase-inversion asymmetries are observed in local and non-local tunneling spectra at the two ends of a superconductor-semiconductor-superconductor planar Josephson junction in the presence of a perpendicular magnetic field. Spectral signatures agree with a theoretical model, yielding a physical picture in which phase textures in superconducting leads localize and control the position of Andreev bound states in the junction, demonstrating a simple means of controlling the position and size of Andreev states within a planar junction.

cond-mat.supr-con↗

Bounds for the distribution of the Frobenius traces associated to products of non-CM elliptic curves

Let $g \geq 1$ be an integer and let $A/\mathbb{Q}$ be an abelian variety that is isogenous over $\mathbb{Q}$ to %the product $E_1 \times \ldots \times E_g$ of elliptic curves $E_1/\mathbb{Q}$, $\ldots$, $E_g/\mathbb{Q}$, without complex multiplication and pairwise non-isogenous over $\overline{\mathbb{Q}}$. a product of $g$ elliptic curves defined over $\mathbb{Q}$, pairwise non-isogenous over $\overline{\mathbb{Q}}$ and each without complex multiplication. %pairwise non-isogenous over $\overline{\mathbb{Q}}$. For an integer $t$ and a positive real number $x$, denote by $π_A(x, t)$ the number of primes $p \leq x$, of good reduction for %the abelian variety $A$, for which the Frobenius trace $a_{1, p}(A)$ associated to the reduction of $A$ modulo $p$ equals $t$. Assuming the Generalized Riemann Hypothesis for Dedekind zeta functions, we prove that $π_A(x, 0) \ll_A x^{1 - \frac{1}{3 g+1 }}/(\log x)^{1 - \frac{2}{3 g+1}}$ and $π_A(x, t) \ll_A x^{1 - \frac{1}{3 g + 2}}/(\log x)^{1 - \frac{2}{3 g + 2}}$ if $t \neq 0$. These bounds largely improve upon recent ones obtained for $g = 2$ by H. Chen, N. Jones, and V. Serban, and may be viewed as generalizations to arbitrary $g$ of the bounds obtained for $g=1$ by M.R. Murty, V.K. Murty, and N. Saradha, combined with a refinement in the power of $\log x$ by D. Zywina. Under the same assumptions, we also prove the existence of a density one set of primes $p$ satisfying $|a_{1, p}(A)|>p^{\frac{1}{3 g + 1} - \varepsilon}$ for any fixed $\varepsilon>0$.

math.NT↗

Bi-level Doubly Variational Learning for Energy-based Latent Variable Models

Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process based on maximum likelihood estimate that requires sampling from two intractable distributions. In this paper, we propose Bi-level doubly variational learning (BiDVL), which is based on a new bi-level optimization framework and two tractable variational distributions to facilitate learning EBLVMs. Particularly, we lead a decoupled EBLVM consisting of a marginal energy-based distribution and a structural posterior to handle the difficulties when learning deep EBLVMs on images. By choosing a symmetric KL divergence in the lower level of our framework, a compact BiDVL for visual tasks can be obtained. Our model achieves impressive image generation performance over related works. It also demonstrates the significant capacity of testing image reconstruction and out-of-distribution detection.

cs.CV↗

Delving into the Estimation Shift of Batch Normalization in a Network

Batch normalization (BN) is a milestone technique in deep learning. It normalizes the activation using mini-batch statistics during training but the estimated population statistics during inference. This paper focuses on investigating the estimation of population statistics. We define the estimation shift magnitude of BN to quantitatively measure the difference between its estimated population statistics and expected ones. Our primary observation is that the estimation shift can be accumulated due to the stack of BN in a network, which has detriment effects for the test performance. We further find a batch-free normalization (BFN) can block such an accumulation of estimation shift. These observations motivate our design of XBNBlock that replace one BN with BFN in the bottleneck block of residual-style networks. Experiments on the ImageNet and COCO benchmarks show that XBNBlock consistently improves the performance of different architectures, including ResNet and ResNeXt, by a significant margin and seems to be more robust to distribution shift.

cs.CV↗

DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval

Many previous methods on text-based person retrieval tasks are devoted to learning a latent common space mapping, with the purpose of extracting modality-invariant features from both visual and textual modality. Nevertheless, due to the complexity of high-dimensional data, the unconstrained mapping paradigms are not able to properly catch discriminative clues about the corresponding person while drop the misaligned information. Intuitively, the information contained in visual data can be divided into person information (PI) and surroundings information (SI), which are mutually exclusive from each other. To this end, we propose a novel Deep Surroundings-person Separation Learning (DSSL) model in this paper to effectively extract and match person information, and hence achieve a superior retrieval accuracy. A surroundings-person separation and fusion mechanism plays the key role to realize an accurate and effective surroundings-person separation under a mutually exclusion constraint. In order to adequately utilize multi-modal and multi-granular information for a higher retrieval accuracy, five diverse alignment paradigms are adopted. Extensive experiments are carried out to evaluate the proposed DSSL on CUHK-PEDES, which is currently the only accessible dataset for text-base person retrieval task. DSSL achieves the state-of-the-art performance on CUHK-PEDES. To properly evaluate our proposed DSSL in the real scenarios, a Real Scenarios Text-based Person Reidentification (RSTPReid) dataset is constructed to benefit future research on text-based person retrieval, which will be publicly available.

cs.CV↗

Throughput Maximization of UAV Networks

In this paper we study the deployment of multiple unmanned aerial vehicles (UAVs) to form a temporal UAV network for the provisioning of emergent communications to affected people in a disaster zone, where each UAV is equipped with a lightweight base station device and thus can act as an aerial base station for users. Unlike most existing studies that assumed that a UAV can serve all users in its communication range, we observe that both computation and communication capabilities of a single lightweight UAV are very limited, due to various constraints on its size, weight, and power supply. Thus, a single UAV can only provide communication services to a limited number of users. We study a novel problem of deploying $K$ UAVs in the top of a disaster area such that the sum of the data rates of users served by the UAVs is maximized, subject to that (i) the number of users served by each UAV is no greater than its service capacity; and (ii) the communication network induced by the $K$ UAVs is connected. We then propose a $\frac{1-1/e}{\lfloor \sqrt{K} \rfloor}$-approximation algorithm for the problem, improving the current best result of the problem by five times (the best approximation ratio so far is $\frac{1-1/e}{5( \sqrt{K} +1)}$), where $e$ is the base of the natural logarithm. We finally evaluate the algorithm performance via simulation experiments. Experimental results show that the proposed algorithm is very promising. Especially, the solution delivered by the proposed algorithm is up to 12% better than those by existing algorithms.

cs.DC↗

Personalized Embedding-based e-Commerce Recommendations at eBay

Recommender systems are an essential component of e-commerce marketplaces, helping consumers navigate massive amounts of inventory and find what they need or love. In this paper, we present an approach for generating personalized item recommendations in an e-commerce marketplace by learning to embed items and users in the same vector space. In order to alleviate the considerable cold-start problem present in large marketplaces, item and user embeddings are computed using content features and multi-modal onsite user activity respectively. Data ablation is incorporated into the offline model training process to improve the robustness of the production system. In offline evaluation using a dataset collected from eBay traffic, our approach was able to improve the Recall@k metric over the Recently-Viewed-Item (RVI) method. This approach to generating personalized recommendations has been launched to serve production traffic, and the corresponding scalable engineering architecture is also presented. Initial A/B test results show that compared to the current personalized recommendation module in production, the proposed method increases the surface rate by $\sim$6\% to generate recommendations for 90\% of listing page impressions.

cs.IR↗

The First Step Towards Modeling Unbreakable Malware

Constructing stealthy malware has gained increasing popularity among cyber attackers to conceal their malicious intent. Nevertheless, the constructed stealthy malware still fails to survive the reverse engineering by security experts. Therefore, this paper modeled a type of malware with an "unbreakable" security attribute-unbreakable malware (UBM), and made a systematical probe into this new type of threat through modeling, method analysis, experiments, evaluation and anti-defense capacity tests. Specifically, we first formalized the definition of UBM and analyzed its security attributes, put forward two core features that are essential for realizing the "unbreakable" security attribute, and their relevant tetrad for evaluation. Then, we worked out and implemented four algorithms for constructing UBM, and verified the "unbreakable" security attribute based on our evaluation of the abovementioned two core features. After that, the four verified algorithms were employed to construct UBM instances, and by analyzing their volume increment and anti-defense capacity, we confirmed real-world applicability of UBM. Finally, to address the new threats incurred by UBM to the cyberspace, this paper explored some possible defense measures, with a view to establishing defense systems against UBM attacks.

cs.CR↗

Accelerating temporal action proposal generation via high performance computing

Temporal action recognition always depends on temporal action proposal generation to hypothesize actions and algorithms usually need to process very long video sequences and output the starting and ending times of each potential action in each video suffering from high computation cost. To address this, based on boundary sensitive network we propose a new temporal convolution network called Multipath Temporal ConvNet (MTN), which consists of two parts i.e. Multipath DenseNet and SE-ConvNet. In this work, one novel high performance ring parallel architecture based on Message Passing Interface (MPI) is further introduced into temporal action proposal generation, which is a reliable communication protocol, in order to respond to the requirements of large memory occupation and a large number of videos. Remarkably, the total data transmission is reduced by adding a connection between multiple computing load in the newly developed architecture. It is found that, compared to the traditional Parameter Server architecture, our parallel architecture has higher efficiency on temporal action detection task with multiple GPUs, which is suitable for dealing with the tasks of temporal action proposal generation, especially for large datasets of millions of videos. We conduct experiments on ActivityNet-1.3 and THUMOS14, where our method outperforms other state-of-art temporal action detection methods with high recall and high temporal precision. In addition, a time metric is further proposed here to evaluate the speed performance in the distributed training process.

cs.CV↗

Double-Layer Game Based Wireless Charging Scheduling for Electric Vehicles

Wireless charging technology provides a solution to the insufficient battery life of electric vehicles (EVs). However, the conflict of interests between wireless charging lanes (WCLs) and EVs is difficult to resolve. In the day-ahead electricity market, considering the revenue of WCLs caused by the deviation between actual electricity sales and pre-purchased electricity, as well as endurance and traveling experience of EVs, this paper proposes a charging scheduling algorithm based on a double-layer game model. In lower layer, the potential game is used to model the multi-vehicle game of vehicle charging planning. A shortest path algorithm based on the three-way greedy strategy is designed to solve in dynamic charging sequence problem, and the improved particle swarm optimization algorithm are used to solve the variable ordered potential game. In the upper layer, the reverse Stackelberg game is adopted to harmonize the cost of wireless charging lanes and electric vehicles. As the leader, WCLs stimulate EVs to carry out reasonable charing action by electricity price regulation. As the follower, EVs make the best charging decisions for a given electricity price. An iteration algorithm is designed to ensure the Nash equilibrium convergence of this game. The simulation results show that the double-layer game model proposed in this paper can effectively suppress the deviation between the actual electricity sales and the pre-sale of the charging lane caused by the disorderly charging behavior of the vehicle, and ensure the high endurance and traveling experience of EVs.

eess.SY↗

Multi-Kernel Capsule Network for Schizophrenia Identification

Objective: Schizophrenia seriously affects the quality of life. To date, both simple (linear discriminant analysis) and complex (deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. Methods: To overcome the aforementioned drawbacks, we proposed a multi-kernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match with partition sizes of brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of widely-used dropout strategy in deep learning, we developed vector dropout in the capsule layer to prevent overfitting of the model. Results: The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. Conclusion: MKCapsnet is promising for schizophrenia identification. Significance: Our study not only proposed a multi-kernel capsule network but also provided useful information in the parameter setting, which is informative for further studies using a capsule network for neurophysiological signal classification.

cs.LG↗

AED-Net: An Abnormal Event Detection Network

It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a self-supervised framework, abnormal event detection network (AED-Net), which is composed of PCAnet and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, PCAnet is trained to extract high-level semantics of crowd's situation. Next, kPCA,a one-class classifier, is trained to determine anomaly of the scene. In contrast to some prevailing deep learning methods,the framework is completely self-supervised because it utilizes only video sequences in a normal situation. Experiments of global and local abnormal event detection are carried out on UMN and UCSD datasets, and competitive results with higher EER and AUC compared to other state-of-the-art methods are observed. Furthermore, by adding local response normalization (LRN) layer, we propose an improvement to original AED-Net. And it is proved to perform better by promoting the framework's generalization capacity according to the experiments.

cs.CV↗

Wireless Charging Lane Deployment in Urban Areas Considering Traffic Light and Regional Energy Supply-Demand Balance

In this paper, to optimize the Wireless Charging Lane (WCL) deployment in urban areas, we focus on installation cost reduction while achieving regional balance of energy supply and demand, as well as vehicle continuous operability issues. In order to explore the characteristics of energy demand in various regions of the city, we first analyze the daily driving trajectory of taxis in different regions and find that the daily energy demand fluctuates to different degrees in different regions. Then, we establish the WCL power supply model to obtain the wireless charging supply situation in line with the real urban traffic condition, which is the first work considering the influence of traffic lights on charging situation. To ensure minimum deployment cost and to coordinate the contradiction between regional energy supply-demand balance and overall supply-demand matching, we formulate optimization problems ensuring the charge-energy consumption ratio of vehicles. In addition, we rank the priority of WCL efficiency to reduce the complexity of solution and solve the Mixed Integer NonLinear Programming (MINLP) problem to determine deployment plan. Compared with the baseline, the proposed method in this paper has significantly improved the effect.

math.OC↗

HE-SLAM: a Stereo SLAM System Based on Histogram Equalization and ORB Features

In the real-life environments, due to the sudden appearance of windows, lights, and objects blocking the light source, the visual SLAM system can easily capture the low-contrast images caused by over-exposure or over-darkness. At this time, the direct method of estimating camera motion based on pixel luminance information is infeasible, and it is often difficult to find enough valid feature points without image processing. This paper proposed HE-SLAM, a new method combining histogram equalization and ORB feature extraction, which can be robust in more scenes, especially in stages with low-contrast images. Because HE-SLAM uses histogram equalization to improve the contrast of images, it can extract enough valid feature points in low-contrast images for subsequent feature matching, keyframe selection, bundle adjustment, and loop closure detection. The proposed HE-SLAM has been tested on the popular datasets (such as KITTI and EuRoc), and the real-time performance and robustness of the system are demonstrated by comparing system runtime and the mean square root error (RMSE) of absolute trajectory error (ATE) with state-of-the-art methods like ORB-SLAM2.

cs.RO↗

Region based Ensemble Learning Network for Fine-grained Classification

As an important research topic in computer vision, fine-grained classification which aims to recognition subordinate-level categories has attracted significant attention. We propose a novel region based ensemble learning network for fine-grained classification. Our approach contains a detection module and a module for classification. The detection module is based on the faster R-CNN framework to locate the semantic regions of the object. The classification module using an ensemble learning method, which trains a set of sub-classifiers for different semantic regions and combines them together to get a stronger classifier. In the evaluation, we implement experiments on the CUB-2011 dataset and the result of experiments proves our method s efficient for fine-grained classification. We also extend our approach to remote scene recognition and evaluate it on the NWPU-RESISC45 dataset.

cs.CV↗

Evidence of topological superconductivity in planar Josephson junctions

Majorana zero modes are quasiparticle states localized at the boundaries of topological superconductors that are expected to be ideal building blocks for fault-tolerant quantum computing. Several observations of zero-bias conductance peaks measured in tunneling spectroscopy above a critical magnetic field have been reported as experimental indications of Majorana zero modes in superconductor/semiconductor nanowires. On the other hand, two dimensional systems offer the alternative approach to confine Ma jorana channels within planar Josephson junctions, in which the phase difference ϕ between the superconducting leads represents an additional tuning knob predicted to drive the system into the topological phase at lower magnetic fields. Here, we report the observation of phase-dependent zero-bias conductance peaks measured by tunneling spectroscopy at the end of Josephson junctions realized on a InAs/Al heterostructure. Biasing the junction to ϕ ~ π significantly reduces the critical field at which the zero-bias peak appears, with respect to ϕ = 0. The phase and magnetic field dependence of the zero-energy states is consistent with a model of Majorana zero modes in finite-size Josephson junctions. Besides providing experimental evidence of phase-tuned topological superconductivity, our devices are compatible with superconducting quantum electrodynamics architectures and scalable to complex geometries needed for topological quantum computing.

cond-mat.mes-hall↗

The free energy of biomembrane and nerve excitation and the role of anesthetics

In the electromechanical theory of nerve stimulation, the nerve impulse consists of a traveling region of solid membrane in a liquid environment. Therefore, the free energy necessary to stimulate a pulse is directly related to the free energy difference necessary to induce a phase transition in the nerve membrane. It is a function of temperature and pressure, and it is sensitively dependent on the presence of anesthetics which lower melting transitions. We investigate the free energy difference of solid and liquid membrane phases under the influence of anesthetics. We calculate stimulus-response curves of electromechanical pulses and compare them to measured stimulus-response profiles in lobster and earthworm axons. We also compare them to stimulus-response experiments on human median nerve and frog sciatic nerve published in the literature.

physics.bio-ph↗

Attention-based Mixture Density Recurrent Networks for History-based Recommendation

The goal of personalized history-based recommendation is to automatically output a distribution over all the items given a sequence of previous purchases of a user. In this work, we present a novel approach that uses a recurrent network for summarizing the history of purchases, continuous vectors representing items for scalability, and a novel attention-based recurrent mixture density network, which outputs each component in a mixture sequentially, for modelling a multi-modal conditional distribution. We evaluate the proposed approach on two publicly available datasets, MovieLens-20M and RecSys15. The experiments show that the proposed approach, which explicitly models the multi-modal nature of the predictive distribution, is able to improve the performance over various baselines in terms of precision, recall and nDCG.

cs.LG↗