arXiv subjects

Shuo Yang

Publications and source records attributed to Shuo Yang.

At least 361 records · Page 20Linked to original sources

arXiv2021

CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results

As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Recently, a large-scale face anti-spoofing dataset, CelebA-Spoof which comprised of 625,537 pictures of 10,177 subjects has been released. It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects. This paper reports methods and results in the CelebA-Spoof Challenge 2020 on Face AntiSpoofing which employs the CelebA-Spoof dataset. The model evaluation is conducted online on the hidden test set. A total of 134 participants registered for the competition, and 19 teams made valid submissions. We will analyze the top ranked solutions and present some discussion on future work directions.

cs.CV↗

arXiv2021

DeeperForensics Challenge 2020 on Real-World Face Forgery Detection: Methods and Results

This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection. The challenge employs the DeeperForensics-1.0 dataset, one of the most extensive publicly available real-world face forgery detection datasets, with 60,000 videos constituted by a total of 17.6 million frames. The model evaluation is conducted online on a high-quality hidden test set with multiple sources and diverse distortions. A total of 115 participants registered for the competition, and 25 teams made valid submissions. We will summarize the winning solutions and present some discussions on potential research directions.

cs.CV↗

arXiv2021

The ANTARES Astronomical Time-Domain Event Broker

We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts. With the advent of large-format CCDs on wide-field imaging telescopes, time-domain surveys now routinely discover tens of thousands of new events each night, more than can be evaluated by astronomers alone. The ANTARES event broker will process alerts, annotating them with catalog associations and filtering them to distinguish customizable subsets of events. We describe the data model of the system, the overall architecture, annotation, implementation of filters, system outputs, provenance tracking, system performance, and the user interface.

astro-ph.IM↗

arXiv2020

LDG approximation of large deformations of prestrained plates

A reduced model for large deformations of prestrained plates consists of minimizing a second order bending energy subject to a nonconvex metric constraint. The former involves the second fundamental form of the middle plate and the later is a restriction on its first fundamental form. We discuss a formal derivation of this reduced model along with an equivalent formulation that makes it amenable computationally. We propose a local discontinuous Galerkin (LDG) finite element approach that hinges on the notion of reconstructed Hessian. We design discrete gradient flows to minimize the ensuing nonconvex problem and to find a suitable initial deformation. We present several insightful numerical experiments, some of practical interest, and assess various computational aspects of the approximation process.

math.NA↗

arXiv2020

SMOT: Single-Shot Multi Object Tracking

We present single-shot multi-object tracker (SMOT), a new tracking framework that converts any single-shot detector (SSD) model into an online multiple object tracker, which emphasizes simultaneously detecting and tracking of the object paths. Contrary to the existing tracking by detection approaches which suffer from errors made by the object detectors, SMOT adopts the recently proposed scheme of tracking by re-detection. We combine this scheme with SSD detectors by proposing a novel tracking anchor assignment module. With this design SMOT is able to generate tracklets with a constant per-frame runtime. A light-weighted linkage algorithm is then used for online tracklet linking. On three benchmarks of object tracking: Hannah, Music Videos, and MOT17, the proposed SMOT achieves state-of-the-art performance.

cs.CV↗

arXiv2020

Secure Your Intention: On Notions of Pre-Opacity in Discrete-Event Systems

This paper investigates an important informationflow security property called opacity in partially-observed discrete-event systems. We consider the presence of a passive intruder (eavesdropper) that knows the dynamic model of the system and can use the generated information-flow to infer some "secret" of the system. A system is said to be opaque if it always holds the plausible deniability for its secret. Existing notions of opacity only consider secret either as currently visiting some secret states or as having visited some secret states in the past. In this paper, we investigate information-flow security from a new angle by considering the secret of the system as the intention to execute some particular behavior of importance in the future. To this end, we propose a new class of opacity called pre-opacity that characterizes whether or not the intruder can predict the visit of secret states a certain number of steps ahead before the system actually does so. Depending the prediction task of the intruder, we propose two specific kinds of pre-opacity called K-step instant pre-opacity and K-step trajectory pre-opacity to specify this concept. For each notion of pre-opacity, we provide a necessary and sufficient condition as well as an effective verification algorithm. The complexity for the verification of pre-opacity is exponential in the size of the system as we show that pre-opacity is inherently PSPACE-hard. Finally, we generalize our setting to the case where the secret intention of the system is modeled as executing a particular sequence of events rather than visiting a secret state.

eess.SY↗

arXiv2020

Secure-by-Construction Optimal Path Planning for Linear Temporal Logic Tasks

In this paper, we investigate the problem of planning an optimal infinite path for a single robot to achieve a linear temporal logic (LTL) task with security guarantee. We assume that the external behavior of the robot, specified by an output function, can be accessed by a passive intruder (eavesdropper). The security constraint requires that the intruder should never infer that the robot was started from a secret location. We provide a sound and complete algorithmic procedure to solve this problem. Our approach is based on the construction of the twin weighted transition systems (twin-WTS) that tracks a pair of paths having the same observation. We show that the security-aware path planning problem can be effectively solved based on graph search techniques in the product of the twin-WTS and the Büchi automaton representing the LTL formula. The complexity of the proposed planning algorithm is polynomial in the size of the system model. Finally, we illustrate our algorithm by a simple robot planning example.

eess.SY↗

arXiv2020

Lattice model constructions for gapless domain walls between topological phases

Domain walls between different topological phases are one of the most interesting phenomena that reveal the non-trivial bulk properties of topological phases. Very recently, gapped domain walls between different topological phases have been intensively studied. In this paper, we systematically construct a large class of lattice models for gapless domain walls between twisted and untwisted gauge theories with arbitrary finite group $G$. As simple examples, we numerically study several finite groups(including both Abelian and non-Abelian finite group such as $S_3$) in $2$D using the state-of-the-art loop optimization of tensor network renormalization algorithm. We also propose a physical mechanism for understanding the gapless nature of these particular domain wall models. Finally, by taking advantage of the classification and construction of twisted gauge theories using group cohomology theory, we generalize such constructions into arbitrary dimensions, which might provide us a systematical way to understand gapless domain walls and topological quantum phase transitions.

cond-mat.str-el↗

arXiv2020

High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification

Occluded person re-identification (ReID) aims to match occluded person images to holistic ones across dis-joint cameras. In this paper, we propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. At first, we use a CNN backbone and a key-points estimation model to extract semantic local features. Even so, occluded images still suffer from occlusion and outliers. Then, we view the local features of an image as nodes of a graph and propose an adaptive direction graph convolutional (ADGC)layer to pass relation information between nodes. The proposed ADGC layer can automatically suppress the message-passing of meaningless features by dynamically learning di-rection and degree of linkage. When aligning two groups of local features from two images, we view it as a graph matching problem and propose a cross-graph embedded-alignment (CGEA) layer to jointly learn and embed topology information to local features, and straightly predict similarity score. The proposed CGEA layer not only take full use of alignment learned by graph matching but also re-place sensitive one-to-one matching with a robust soft one. Finally, extensive experiments on occluded, partial, and holistic ReID tasks show the effectiveness of our proposed method. Specifically, our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.

cs.CV↗

arXiv2020

An Automated Framework for Board-level Trojan Benchmarking

Economic and operational advantages have led the supply chain of printed circuit boards (PCBs) to incorporate various untrusted entities. Any of the untrusted entities are capable of introducing malicious alterations to facilitate a functional failure or leakage of secret information during field operation. While researchers have been investigating the threat of malicious modification within the scale of individual microelectronic components, the possibility of a board-level malicious manipulation has essentially been unexplored. In the absence of standard benchmarking solutions, prospective countermeasures for PCB trust assurance are likely to utilize homegrown representation of the attacks that undermines their evaluation and does not provide scope for comparison with other techniques. In this paper, we have developed the first-ever benchmarking solution to facilitate an unbiased and comparable evaluation of countermeasures applicable to PCB trust assurance. Based on a taxonomy tailored for PCB-level alterations, we have developed high-level Trojan models. From these models, we have generated a custom pool of board-level Trojan designs of varied complexity and functionality. We have also developed a tool-flow for automatically inserting these Trojans into various PCB designs and generate the Trojan benchmarks (i.e., PCB designs with Trojan). The tool-based Trojan insertion facilitate a comprehensive evaluation against large number of diverse Trojan implementations and application of data mining for trust verification. Finally, with experimental measurements from a fabricated PCB, we analyze the stealthiness of the Trojan designs.

cs.CR↗

arXiv2020

Predicting quantum many-body dynamics with transferable neural networks

Machine learning (ML) architectures such as convolutional neural networks (CNNs) have garnered considerable recent attention in the study of quantum many-body systems. However, advanced ML approaches such as transfer learning have seldom been applied to such contexts. Here we demonstrate that a simple recurrent unit (SRU) based efficient and transferable sequence learning framework is capable of learning and accurately predicting the time evolution of one-dimensional (1D) Ising model with simultaneous transverse and parallel magnetic fields, as quantitatively corroborated by relative entropy measurements and magnetization between the predicted and exact state distributions. At a cost of constant computational complexity, a larger many-body state evolution was predicted in an autoregressive way from just one initial state, without any guidance or knowledge of any Hamiltonian. Our work paves the way for future applications of advanced ML methods in quantum many-body dynamics only with knowledge from a smaller system.

cond-mat.stat-mech↗

arXiv2020

Adaptive Semantic-Visual Tree for Hierarchical Embeddings

Merchandise categories inherently form a semantic hierarchy with different levels of concept abstraction, especially for fine-grained categories. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make predictions less ambiguous. However, previous studies of fine-grained image retrieval primarily focus on semantic similarities or visual similarities. In a real application, merely using visual similarity may not satisfy the need of consumers to search merchandise with real-life images, e.g., given a red coat as a query image, we might get a red suit in recall results only based on visual similarity since they are visually similar. But the users actually want a coat rather than suit even the coat is with different color or texture attributes. We introduce this new problem based on photoshopping in real practice. That's why semantic information are integrated to regularize the margins to make "semantic" prior to "visual". To solve this new problem, we propose a hierarchical adaptive semantic-visual tree (ASVT) to depict the architecture of merchandise categories, which evaluates semantic similarities between different semantic levels and visual similarities within the same semantic class simultaneously. The semantic information satisfies the demand of consumers for similar merchandise with the query while the visual information optimizes the correlations within the semantic class. At each level, we set different margins based on the semantic hierarchy and incorporate them as prior information to learn a fine-grained feature embedding. To evaluate our framework, we propose a new dataset named JDProduct, with hierarchical labels collected from actual image queries and official merchandise images on an online shopping application. Extensive experimental results on the public CARS196 and CUB-

cs.CV↗

arXiv2020

Construction and classification of point group symmetry protected topological phases in 2D interacting fermionic systems

The construction and classification of symmetry-protected topological (SPT) phases in interacting bosonic and fermionic systems have been intensively studied in the past few years. Very recently, a complete classification and construction of space group SPT phases were also proposed for interacting bosonic systems. In this paper, we attempt to generalize this classification and construction scheme systematically into interacting fermion systems. In particular, we construct and classify point group SPT phases for 2D interacting fermion systems via lower-dimensional block-state decorations. We discover several intriguing fermionic SPT states that can only be realized in interacting fermion systems (i.e., not in free-fermion or bosonic SPT systems). Moreover, we also verify the recently conjectured crystalline equivalence principle for 2D interacting fermion systems. Finally, the potential experimental realization of these new classes of point group SPT phases in 2D correlated superconductors is addressed.

cond-mat.str-el↗

arXiv2019

Dynamical symmetry in quantum dissipative models

We show that the dynamical symmetry exists in dissipative quantum many-body systems. Under constraints on both Hamiltonian and dissipation parts, the time evolution of particular observables can be symmetric between repulsive and attractive interactions in the Hubbard model, or symmetric between ferromagnetic and anti-ferromagnetic interactions in the Ising model with external fields. We present a theorem to determine the existence of the dynamical symmetry in dissipative systems. This theorem is also responsible for the symmetry of steady states, even without the constraint on the initial state. We demonstrate the applications of our theorem with numerical simulations using tensor network algorithms.

cond-mat.str-el↗

arXiv2019

Alpha Decay to Doubly Magic Core in Quartetting Wave Function Approach

We present a microscopic calculation of $α$-cluster formation in heavy nuclei $^{104}$Te ($α$+$^{100}$Sn), $^{212}$Po ($α$+$^{208}$Pb) and their neighbors $^{102}$Sn, $^{102}$Te, $^{210}$Pb and $^{210}$Po by using the quartetting wave function approach. Improving the local density approximation, the shell structure of the core nucleus is considered, and the center-of-mass (c.o.m.) effective potential for the quartet is obtained self-consistently from the shell model wavefunctions. The $α$-cluster formation and decay probabilities are obtained by solving the bound-state of the c.o.m. motion of the quartet and the scattering state of the formed $α$-cluster in the Gurvitz approach. Striking shell effects on the $α$-cluster formation probabilities are analyzed for magic numbers 50, 82 and 126. The computed $α$-decay half-lives of these special nuclei are compared with the newest experimental data.

nucl-th↗

arXiv2019

Design and Implementation of a Three-Link Brachiation Robot with Optimal Control Based Trajectory Tracking Controller

This paper reports the design and implementation of a three-link brachiation robot. The robot is able to travel along horizontal monkey bars using continuous arm swings. We build a full order dynamics model for the robot and formulate each cycle of robot swing motion as an optimal control problem. The iterative Linear Quadratic Regulator (iLQR) algorithm is used to find the optimal control strategy during one swing. We select suitable robot design parameters by comparing the cost of robot motion generated by the iLQR algorithm for different robot designs. In particular, using this approach we show the importance of having a body link and low inertia arms for efficient brachiation. Further, we propose a trajectory tracking controller that combines a cascaded PID controller and an input-output linearization controller to enable the robot to track desired trajectory precisely and reject external disturbance during brachiation. Experiments on the simulated robot and the real robot demonstrate that the robot can robustly swing between monkey bars with same or different spacing of handholds.

eess.SY↗

arXiv2019

State Estimation for Legged Robots Using Contact-Centric Leg Odometry

Our goal is to send legged robots into challenging, unstructured terrains that wheeled systems cannot traverse. Moreover, precise estimation of the robot's position and orientation in rough terrain is especially difficult. To address this problem, we introduce a new state estimation algorithm which we term Contact-Centric Leg Odometry (COCLO). This new estimator uses a Square Root Unscented Kalman Filter (SR-UKF) to fuse multiple proprioceptive sensors available on a legged robot. In contrast to IMU-centric filtering approaches, COCLO formulates prediction and measurement models according to the contact status of legs. Additionally, COCLO has an indirect measurement model using joint velocities to estimate the robot body velocity. In rough terrain, when IMUs suffer from large amounts of noise, COCLO's contact-centric approach outperforms previous IMU-centric methods. To demonstrate improved state estimation accuracy, we compare COCLO with Visual Inertial Navigation System (VINS), a state-of-the-art visual inertial odometry, in three different environments: flat ground, ramps, and stairs. COCLO achieves better estimation precision than VINS in all three environments and is robust to unstable motion. Finally, we also show that COCLO and a modified VINS can work in tandem to improve each other's performance.

eess.SY↗

arXiv2019

Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space

Quadratic regression involves modeling the response as a (generalized) linear function of not only the features $x^{j_1}$ but also of quadratic terms $x^{j_1}x^{j_2}$. The inclusion of such higher-order "interaction terms" in regression often provides an easy way to increase accuracy in already-high-dimensional problems. However, this explodes the problem dimension from linear $O(p)$ to quadratic $O(p^2)$, and it is common to look for sparse interactions (typically via heuristics). In this paper, we provide a new algorithm - Interaction Hard Thresholding (IntHT) which is the first one to provably accurately solve this problem in sub-quadratic time and space. It is a variant of Iterative Hard Thresholding; one that uses the special quadratic structure to devise a new way to (approx.) extract the top elements of a $p^2$ size gradient in sub-$p^2$ time and space. Our main result is to theoretically prove that, in spite of the many speedup-related approximations, IntHT linearly converges to a consistent estimate under standard high-dimensional sparse recovery assumptions. We also demonstrate its value via synthetic experiments. Moreover, we numerically show that IntHT can be extended to higher-order regression problems, and also theoretically analyze an SVRG variant of IntHT.

cs.LG↗