arXiv subjects

Xiaoyu Zhang

Publications and source records attributed to Xiaoyu Zhang.

229 records · Page 13Linked to original sources

arXiv2019

Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions

Video surveillance can be significantly enhanced by using both top-view data, e.g., those from drone-mounted cameras in the air, and horizontal-view data, e.g., those from wearable cameras on the ground. Collaborative analysis of different-view data can facilitate various kinds of applications, such as human tracking, person identification, and human activity recognition. However, for such collaborative analysis, the first step is to associate people, referred to as subjects in this paper, across these two views. This is a very challenging problem due to large human-appearance difference between top and horizontal views. In this paper, we present a new approach to address this problem by exploring and matching the subjects' spatial distributions between the two views. More specifically, on the top-view image, we model and match subjects' relative positions to the horizontal-view camera in both views and define a matching cost to decide the actual location of horizontal-view camera and its view angle in the top-view image. We collect a new dataset consisting of top-view and horizontal-view image pairs for performance evaluation and the experimental results show the effectiveness of the proposed method.

cs.CV↗

arXiv2019

Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements

Combining data content with visual embellishments, infographics can effectively deliver messages in an engaging and memorable manner. Various authoring tools have been proposed to facilitate the creation of infographics. However, creating a professional infographic with these authoring tools is still not an easy task, requiring much time and design expertise. Therefore, these tools are generally not attractive to casual users, who are either unwilling to take time to learn the tools or lacking in proper design expertise to create a professional infographic. In this paper, we explore an alternative approach: to automatically generate infographics from natural language statements. We first conducted a preliminary study to explore the design space of infographics. Based on the preliminary study, we built a proof-of-concept system that automatically converts statements about simple proportion-related statistics to a set of infographics with pre-designed styles. Finally, we demonstrated the usability and usefulness of the system through sample results, exhibits, and expert reviews.

cs.HC↗

arXiv2019

Socially Aware Kalman Neural Networks for Trajectory Prediction

Trajectory prediction is a critical technique in the navigation of robots and autonomous vehicles. However, the complex traffic and dynamic uncertainties yield challenges in the effectiveness and robustness in modeling. We purpose a data-driven approach socially aware Kalman neural networks (SAKNN) where the interaction layer and the Kalman layer are embedded in the architecture, resulting in a class of architectures with huge potential to directly learn from high variance sensor input and robustly generate low variance outcomes. The evaluation of our approach on NGSIM dataset demonstrates that SAKNN performs state-of-the-art on prediction effectiveness in a relatively long-term horizon and significantly improves the signal-to-noise ratio of the predicted signal.

cs.RO↗

arXiv2019

Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks

With the rapid development of fashion market, the customers' demands of customers for fashion recommendation are rising. In this paper, we aim to investigate a practical problem of fashion recommendation by answering the question "which item should we select to match with the given fashion items and form a compatible outfit". The key to this problem is to estimate the outfit compatibility. Previous works which focus on the compatibility of two items or represent an outfit as a sequence fail to make full use of the complex relations among items in an outfit. To remedy this, we propose to represent an outfit as a graph. In particular, we construct a Fashion Graph, where each node represents a category and each edge represents interaction between two categories. Accordingly, each outfit can be represented as a subgraph by putting items into their corresponding category nodes. To infer the outfit compatibility from such a graph, we propose Node-wise Graph Neural Networks (NGNN) which can better model node interactions and learn better node representations. In NGNN, the node interaction on each edge is different, which is determined by parameters correlated to the two connected nodes. An attention mechanism is utilized to calculate the outfit compatibility score with learned node representations. NGNN can not only be used to model outfit compatibility from visual or textual modality but also from multiple modalities. We conduct experiments on two tasks: (1) Fill-in-the-blank: suggesting an item that matches with existing components of outfit; (2) Compatibility prediction: predicting the compatibility scores of given outfits. Experimental results demonstrate the great superiority of our proposed method over others.

cs.IR↗

arXiv2019

Revisiting Persistence of Chemical Reaction Networks through Lyapunov Function Partial Differential Equations

In this paper, we propose a novel technique, referred to as the Lyapunov Function PDEs technique, to diagnose persistence of chemical reaction networks with mass-action kinetics. The technique allows that every network is attached to a Lyapuonv function PDE and a boundary condition whose solutions are expected to be Lyapunov functions for the network. By means of solution of the PDEs, either in the forms of itself or its time derivative, some checkable criteria are proposed for persistence of network systems. These criteria show high validity in proving that neither non-semilocking boundary points nor semilocking boundary non-equilibrium points (with additional conditions included) in mass-action systems are $ω$-$limit$ points. Further, we prove that a class of networks, called $\mathcal{W}_{I}$-endotactic networks that are regardless of values of the system parameters, a set larger than endotactic networks but smaller than $w_{I}$-endotactic networks, also give rise to persistent systems if the networks are $1$-dimensional. Although part of our results are covered by existing ones, part of them are still new. The proposed Lyapunov Function PDEs technique allows us to check persistence of mass action systems in an alternative corner, and is exhibiting large potential.

math.DS↗

arXiv2018

SSGAN: Secure Steganography Based on Generative Adversarial Networks

In this paper, a novel strategy of Secure Steganograpy based on Generative Adversarial Networks is proposed to generate suitable and secure covers for steganography. The proposed architecture has one generative network, and two discriminative networks. The generative network mainly evaluates the visual quality of the generated images for steganography, and the discriminative networks are utilized to assess their suitableness for information hiding. Different from the existing work which adopts Deep Convolutional Generative Adversarial Networks, we utilize another form of generative adversarial networks. By using this new form of generative adversarial networks, significant improvements are made on the convergence speed, the training stability and the image quality. Furthermore, a sophisticated steganalysis network is reconstructed for the discriminative network, and the network can better evaluate the performance of the generated images. Numerous experiments are conducted on the publicly available datasets to demonstrate the effectiveness and robustness of the proposed method.

cs.CV↗

arXiv2018

Special L-values and Selmer groups of Siegel modular forms of genus 2

Let $p$ be an odd prime, $N$ a square-free odd positive integer prime to $p$, $π$ a $p$-ordinary cohomological irreducible cuspidal automorphic representation of $\mathrm{GSp}_4(\mathbb{A}_\mathbb{Q})$ of principal level $N$ and Iwahori level at $p$. Using a $p$-integral version of Rallis inner product formula and modularity theorems for $\mathrm{GSp}_{4/\mathbb{Q}}$ and $\mathrm{U}_{4/\mathbb{Q}}$, we establish an identity between the $p$-part of the critical value at $1$ of the degree $5$ $L$-function of $π$ twisted by the non-trivial quadratic Dirichlet character $ξ$ associated to the extension $\mathbb{Q}(\sqrt{-N})/\mathbb{Q}$ and the $p$-part of the Selmer group of the degree $5$ Galois representation associated to $π$ twisted by $ξ$, under certain conditions on the residual Galois representation.

math.NT↗

arXiv2018

Selmer groups of symmetric powers of ordinary modular Galois representations

Let $p$ be a fixed odd prime number, $μ$ be a Hida family over the Iwasawa algebra of one variable, $ρ_μ$ its Galois representation, $\mathbb{Q}_\infty/\mathbb{Q}$ the $p$-cyclotomic tower and $S$ the variable of the cyclotomic Iwasawa algebra. We compare, for $n\leq 4$ and under certain assumptions, the characteristic power series $L(S)$ of the dual of Selmer groups $\mathrm{Sel}(\mathbb{Q}_{\infty},\mathrm{Sym}^{2n}\otimes\mathrm{det}^{-n}ρ_μ)$ to certain congruence ideals. The case $n=1$ has been treated by H.Hida. In particular, we express the first term of the Taylor expansion at the trivial zero $S=0$ of $L(S)$ in terms of an $\mathcal{L}$-invariant and a congruence number. We conjecture the non-vanishing of this $\mathcal{L}$-invariant; this implies therefore that these Selmer groups are cotorsion. We also show that our $\mathcal{L}$-invariants coincide with Greenberg's $\mathcal{L}$-invariants calculated by R.Harron and A.Jorza.

math.NT↗

arXiv2017

High-efficiency spontaneous terahertz emission from electrically-excited plasmons in AlGaN/GaN two-dimensional electron gas

The advance of terahertz science and technology yet lays wait for the breakthrough in high-efficiency and high-power solid-state terahertz sources applicable at room temperature. Plasmon in two-dimensional electron gas (2DEG) has long been pursued as a type of promising active medium for terahertz emitters. However, a high wall-plug efficiency sufficient for high-power operation has not been achieved. Here we report spontaneous terahertz emission tunable in a frequency range from 0.3 to 2.5 THz through electrical excitation of plasmon or plasmon-polariton modes in a grating-gate coupled AlGaN/GaN 2DEG at 7 K. A wall-plug efficiency ranging from $10^{-6}$ to $10^{-1}$ is found by tuning the energy (4.5 eV to 0.5 eV) of tunneling electrons between the grating gates and the 2DEG. The total emission power, albeit below 15 nW, is already detectable by using a direct terahertz field-effect transistor detector based on the same type of 2DEG. The findings indicate that both high-efficiency and high-power terahertz plasmon-polariton emission could be obtained by a proper injection of tunneling electrons with low energy and high current density.

cond-mat.mes-hall↗

arXiv2016

A Scalable Framework for CSI Feedback in FDD Massive MIMO via DL Path Aligning

Unlike the time-division duplexing (TDD) systems, the downlink (DL) and uplink (UL) channels are not reciprocal anymore in the case of frequency-division duplexing (FDD). However, some long-term parameters, e.g. the time delays and angles of arrival (AoAs) of the channel paths, still enjoy reciprocity. In this paper, by efficiently exploiting the aforementioned limited reciprocity, we address the DL channel state information (CSI) feedback in a practical wideband massive multiple-input multiple-output (MIMO) system operating in the FDD mode. With orthogonal frequency-division multiplexing (OFDM) waveform and assuming frequency-selective fading channels, we propose a scalable framework for the DL pilots design, DL CSI acquisition, and the corresponding CSI feedback in the UL. In particular, the base station (BS) can transmit the FFT-based pilots with the carefully-selected phase shifts. Then the user can rely on the so-called time-domain aggregate channel (TAC) to derive the feedback of reduced imensionality according to either its own knowledge about the statistics of the DL channels or the instruction from the serving BS. We demonstrate that each user can just feed back one scalar number per DL channel path for the BS to recover the DL CSIs. Comprehensive numerical results further corroborate our designs.

cs.IT↗

arXiv2016

Flexible Pilot Contamination Mitigation with Doppler PSD Alignment

Pilot contamination in the uplink (UL) can severely degrade the channel estimation quality at the base station (BS) in a massive multi-input multi-output (MIMO) system. Thus, it is critical to explore all possible avenues to enable more orthogonal resources for the users to transmit non-interfering UL pilots. In conventional designs, pilot orthogonality typically assumes constant channel gains over time, which limits the amount of orthogonal resources in the case of time-selective channels. To circumvent this constraint, in this paper, we show how to enable orthogonal multiplexing of pilots in the case of Doppler fading by aligning the power spectrum densities (PSD) of different users. From the derived PSD aligning rules, we can see multiple users can be sounded simultaneously without creating/suffering pilot contamination even when these users are experiencing time-varying channels. Furthermore, we provide analytical formulas characterizing the channel estimation mean square error (MSE) performance. Computer simulations further confirm us the PSD alignment can serve as one important decontamination mechanism for the UL pilots in massive MIMO.

cs.IT↗

arXiv2016

Pilot Decontamination via PDP Alignment

In this paper, we look into the issue of intra-cell uplink (UL) pilot orthogonalization and schemes for mitigating the inter-cell pilot contamination with a realistic massive multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system model. First, we show how to align the power-delay profiles (PDP) of different users served by one BS so that the pilots sent within one common OFDM symbol are orthogonal. From the derived aligning rule, we see much more users can be sounded in the same OFDM symbol as their channels are sparse in time. Second, in the case of massive MIMO, we show how PDP alignment can help to alleviate the pilot contamination due to inter-cell interference. We demonstrate that, by utilizing the fact that different paths in time are associated with different angles of arrival (AoA), the pilot contamination can be significantly reduced through aligning the PDPs of the users served by different BSs appropriately. Computer simulations further convince us PDP aligning can serve as the new baseline design philosophy for the UL pilots in massive MIMO.

cs.IT↗

arXiv2016

A Self-Paced Regularization Framework for Multi-Label Learning

In this paper, we propose a novel multi-label learning framework, called Multi-Label Self-Paced Learning (MLSPL), in an attempt to incorporate the self-paced learning strategy into multi-label learning regime. In light of the benefits of adopting the easy-to-hard strategy proposed by self-paced learning, the devised MLSPL aims to learn multiple labels jointly by gradually including label learning tasks and instances into model training from the easy to the hard. We first introduce a self-paced function as a regularizer in the multi-label learning formulation, so as to simultaneously rank priorities of the label learning tasks and the instances in each learning iteration. Considering that different multi-label learning scenarios often need different self-paced schemes during optimization, we thus propose a general way to find the desired self-paced functions. Experimental results on three benchmark datasets suggest the state-of-the-art performance of our approach.

cs.LG↗