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Yong Wang

Publications and source records attributed to Yong Wang.

At least 451 records · Page 25Linked to original sources
arXiv2021

Probabilistic Process Algebra for True Concurrency

The well-known process algebras, such as CCS, ACP and $π$-calculus, capture the interleaving concurrency based on bisimilarity semantics. We did some work on truly concurrent process algebras, such as CTC, APTC and $π_{tc}$ , capture the true concurrency based on truly concurrent bisimilarities, such as pomset bisimilarity, step bisimilarity, history-preserving (hp-) bisimilarity and hereditary history-preserving (hhp-) bisimilarity. Truly concurrent process algebras are generalizations of the corresponding traditional process algebras. In this book, we introduce probabilism into truly concurrent process algebras, based on the work on probabilistic process algebra.

cs.LO↗
arXiv2021

DeHumor: Visual Analytics for Decomposing Humor

Despite being a critical communication skill, grasping humor is challenging -- a successful use of humor requires a mixture of both engaging content build-up and an appropriate vocal delivery (e.g., pause). Prior studies on computational humor emphasize the textual and audio features immediately next to the punchline, yet overlooking longer-term context setup. Moreover, the theories are usually too abstract for understanding each concrete humor snippet. To fill in the gap, we develop DeHumor, a visual analytical system for analyzing humorous behaviors in public speaking. To intuitively reveal the building blocks of each concrete example, DeHumor decomposes each humorous video into multimodal features and provides inline annotations of them on the video script. In particular, to better capture the build-ups, we introduce content repetition as a complement to features introduced in theories of computational humor and visualize them in a context linking graph. To help users locate the punchlines that have the desired features to learn, we summarize the content (with keywords) and humor feature statistics on an augmented time matrix. With case studies on stand-up comedy shows and TED talks, we show that DeHumor is able to highlight various building blocks of humor examples. In addition, expert interviews with communication coaches and humor researchers demonstrate the effectiveness of DeHumor for multimodal humor analysis of speech content and vocal delivery.

cs.CL↗
arXiv2021

From Chaos to Pseudo-Randomness: A Case Study on the 2D Coupled Map Lattice

Applying chaos theory for secure digital communications is promising and it is well acknowledged that in such applications the underlying chaotic systems should be carefully chosen. However, the requirements imposed on the chaotic systems are usually heuristic, without theoretic guarantee for the resultant communication scheme. Among all the primitives for secure communications, it is well-accepted that (pseudo) random numbers are most essential. Taking the well-studied two-dimensional coupled map lattice (2D CML) as an example, this paper performs a theoretical study towards pseudo-random number generation with the 2D CML. In so doing, an analytical expression of the Lyapunov exponent (LE) spectrum of the 2D CML is first derived. Using the LEs, one can configure system parameters to ensure the 2D CML only exhibits complex dynamic behavior, and then collect pseudo-random numbers from the system orbits. Moreover, based on the observation that least significant bit distributes more evenly in the (pseudo) random distribution, an extraction algorithm E is developed with the property that, when applied to the orbits of the 2D CML, it can squeeze uniform bits. In implementation, if fixed-point arithmetic is used in binary format with a precision of $z$ bits after the radix point, E can ensure that the deviation of the squeezed bits is bounded by $2^{-z}$ . Further simulation results demonstrate that the new method not only guide the 2D CML model to exhibit complex dynamic behavior, but also generate uniformly distributed independent bits. In particular, the squeezed pseudo random bits can pass both NIST 800-22 and TestU01 test suites in various settings. This study thereby provides a theoretical basis for effectively applying the 2D CML to secure communications.

cs.CR↗
arXiv2021

Explore missing flow dynamics by physics-informed deep learning: the parameterised governing systems

Gaining and understanding the flow dynamics have much importance in a wide range of disciplines, e.g. astrophysics, geophysics, biology, mechanical engineering and biomedical engineering. As a reliable way in practice, especially for turbulent flows, regional flow information such as velocity and its statistics, can be measured experimentally. Due to the poor fidelity or other technical limitations, some information may not be resolved in a region of interest. On the other hand, detailed flow features are described by the governing equations, e.g. the Navier-Stokes equations for viscous fluid, and can be resolved numerically, which is heavily dependent on the capability of either computing resources or modelling. Alternatively, we address this problem by employing the physics-informed deep learning, and treat the governing equations as a parameterised constraint to recover the missing flow dynamics. We demonstrate that with limited data, no matter from experiment or others, the flow dynamics in the region where the required data is missing or not measured, can be reconstructed with the parameterised governing equations. Meanwhile, a richer dataset, with spatial distribution of the control parameter (e.g. eddy viscosity of turbulence modellings), can be obtained. The method provided in this paper may shed light on data-driven scale-adaptive turbulent structure recovering and understanding of complex fluid physics, and can be extended to other parameterised governing systems beyond fluid mechanics.

physics.flu-dyn↗
arXiv2021

Codazzi tensors and the quasi-statistical structure associated to affine connections on three-dimensional Lorentzian Lie groups

In this paper, we classify three-dimensional Lorentzian Lie groups on which Ricci tensors associated to Bott connections, canonical connections and Kobayashi-Nomizu connections are Codazzi tensors associated to these connections. We also classify three-dimensional Lorentzian Lie group with the quasi-statistical structure associated to Bott connections, canonical connections and Kobayashi-Nomizu connections.

math.DG↗
arXiv2021

ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning

This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively. We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches.

cs.CL↗
arXiv2021

iQUANT: Interactive Quantitative Investment Using Sparse Regression Factors

The model-based investing using financial factors is evolving as a principal method for quantitative investment. The main challenge lies in the selection of effective factors towards excess market returns. Existing approaches, either hand-picking factors or applying feature selection algorithms, do not orchestrate both human knowledge and computational power. This paper presents iQUANT, an interactive quantitative investment system that assists equity traders to quickly spot promising financial factors from initial recommendations suggested by algorithmic models, and conduct a joint refinement of factors and stocks for investment portfolio composition. We work closely with professional traders to assemble empirical characteristics of "good" factors and propose effective visualization designs to illustrate the collective performance of financial factors, stock portfolios, and their interactions. We evaluate iQUANT through a formal user study, two case studies, and expert interviews, using a real stock market dataset consisting of 3000 stocks times 6000 days times 56 factors.

cs.HC↗
arXiv2021

Boundary Layer Solution of the Boltzmann Equation for Diffusive Reflection Boundary Conditions in Half-space

We study steady Boltzmann equation in half-space, which arises in the Knudsen boundary layer problem, with diffusive reflection boundary conditions. Under certain admissible conditions and the source term decaying exponentially, we establish the existence of boundary layer solution for both linear and nonlinear Boltzmann equation in half-space with diffusive reflection boundary condition in $L^\infty_{x,v}$ when the far-field Mach number of the Maxwellian is zero. The continuity and the spacial decay of the solution are obtained. The uniqueness is established under some constraint conditions.

math.AP↗
arXiv2021

Fast Evaporation Enabled Ultrathin Polymeric Coatings on Nanoporous Substrates for Highly Permeable Membranes

Membranes derived from ultrathin polymeric films are promising to meet fast separations, but currently available approaches to produce polymer films with greatly reduced thicknesses on porous supports still faces challenges. Here, defect-free ultrathin polymer covering films (UPCFs) are realized by a facile general approach of rapid solvent evaporation. By fast evaporating dilute polymer solutions, we realize ultrathin coating (~30 nm) of porous substrates exclusively on the top surface, forming UPCFs with a block copolymer of polystyrene-block-poly(2-vinyl pyridine) at room temperature or a homopolymer of poly(vinyl alcohol) (PVA) at elevated temperatures. With subsequent selective swelling to the block copolymer and crosslinking to PVA, the resulting bi-layered composite structures serve as highly permeable membranes delivering ~2-10 times higher permeability in ultrafiltration and pervaporation applications than state-of-the-art separation membranes with similar rejections and selectivities. This work opens up a new, facile avenue for the controllable fabrication of ultrathin coatings on porous substrates, which shows great potentials in membrane-based separations and other areas.

cond-mat.soft↗
arXiv2021

Highly Anisotropic Excitons and Multiple Phonon Bound States in a Van der Waals Antiferromagnetic Insulator

Two-dimensional semiconducting systems, such as quantum wells and transition metal dichalcogenides, are the foundations to investigate low dimensional light-matter interactions. To date, the study of elementary photoexcitation, namely the exciton, in 2D semiconductors with intrinsic magnetic order remains a challenge due to the lack of suitable material platforms. Here, we report an observation of excitons coupled to zigzag antiferromagnetic order in the layered antiferromagnetic insulator NiPS3 using both photoluminescence (PL) and optical reflection spectroscopy. The exciton exhibits a linewidth as narrow as ~350 ueV with near unity linear polarization in the PL spectrum. As the thicknesses of samples is reduced from five layers to bilayers, the PL intensity is drastically suppressed and eventually vanishes in monolayers, consistent with the calculated bandgap being highly indirect for both bilayer and monolayer. We observed strong linear dichroism (LD) over a broad spectra range, which shares the same optical anisotropy axis, being locked to the zigzag direction, as the exciton PL. Both LD and the degree of linear polarization in the exciton PL decrease as the temperature increases and become negligible above the Neel temperature. These observations suggest both optical quantities are probes of the symmetry breaking magnetic order parameter. In addition, a sharp resonance in the LD spectrum is observed with an energy near the exciton PL. There exist over ten exciton-A1g phonon bound states on its high energy side, which likely result from the strong modulation of the ligand-to-metal charge transfer energy by strong electron-lattice interactions. Our work establishes NiPS3 as a new 2D platform for exploring magneto-exciton physics with strong correlations, as well as a building block for 2D heterostructures for engineering physical phenomena with time reversal symmetry breaking.

cond-mat.mes-hall↗
arXiv2021

Affine connections and Gauss-Bonnet theorems in the Heisenberg group

In this paper, we compute sub-Riemannian limits of Gaussian curvature associated to two kinds of Schouten-Van Kampen affine connections and the adapted connection for a Euclidean $C^2$-smooth surface in the Heisenberg group away from characteristic points and signed geodesic curvature associated to two kinds of Schouten-Van Kampen affine connections and the adapted connection for Euclidean $C^2$-smooth curves on surfaces. We get Gauss-Bonnet theorems associated to two kinds of Schouten-Van Kampen affine connections in the Heisenberg group.

math.DG↗
arXiv2021

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation

Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation. In particular, we take one step further and exploit the feature distances from prototypes that provide richer information than mere prototypes. Specifically, we use it to estimate the likelihood of pseudo labels to facilitate online correction in the course of training. Meanwhile, we align the prototypical assignments based on relative feature distances for two different views of the same target, producing a more compact target feature space. Moreover, we find that distilling the already learned knowledge to a self-supervised pretrained model further boosts the performance. Our method shows tremendous performance advantage over state-of-the-art methods. We will make the code publicly available.

cs.CV↗
arXiv2021

A Visual Analytics Approach to Facilitate the Proctoring of Online Exams

Online exams have become widely used to evaluate students' performance in mastering knowledge in recent years, especially during the pandemic of COVID-19. However, it is challenging to conduct proctoring for online exams due to the lack of face-to-face interaction. Also, prior research has shown that online exams are more vulnerable to various cheating behaviors, which can damage their credibility. This paper presents a novel visual analytics approach to facilitate the proctoring of online exams by analyzing the exam video records and mouse movement data of each student. Specifically, we detect and visualize suspected head and mouse movements of students in three levels of detail, which provides course instructors and teachers with convenient, efficient and reliable proctoring for online exams. Our extensive evaluations, including usage scenarios, a carefully-designed user study and expert interviews, demonstrate the effectiveness and usability of our approach.

cs.HC↗
arXiv2020

Affine Ricci solitons of three-dimensional Lorentzian Lie groups

In this paper, we classify affine Ricci solitons associated to canonical connections and Kobayashi-Nomizu connections and perturbed canonical connections and perturbed Kobayashi-Nomizu connections on three-dimensional Lorentzian Lie groups with some product structure.

math.DG↗