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Shuang Zhao

Publications and source records attributed to Shuang Zhao.

At least 37 records · Page 2Linked to original sources
arXiv2023

Signature of Topological Semimetal in Harmonic-honeycomb ReO3

Transition-metal honeycomb compounds are capturing scientific attention due to their distinctive electronic configurations, underscored by the triangular-lattice spin-orbit coupling and competition between multiple interactions, paving the way for potential manifestations of phenomena such as Dirac semimetal, superconductivity, and quantum spin liquid states. These compounds can undergo discernible pressure-induced alterations in their crystallographic and electronic paradigms, as exemplified by our high-pressure (HP) synthesis and exploration of the honeycomb polymorph of ReO3 (P6322). This HP-P6322 polymorph bears a phase transition from P6322 to P63/mmc upon cooling around Tp = 250 K, as evidenced by the evolution of temperature-dependent magnetization (M-T curves), cell dimension, and conductivity initiated by an inherent bifurcation of the oxygen position in the ab plane. Insightful analysis of its band structure positions suggests this HP-P6322 polymorph being a plausible candidate for Dirac semimetal properties. This phase transition evokes anomalies in the temperature-dependent variation of paramagnetism (non-linearity) and a crossover from semiconductor to temperature-independent metal, showing a temperature independent conductivity behavior below ~200 K. Under increasing external pressure, both the Tp and resistance of this HP-polymorph is slightly magnetic-field dependent and undergo a "V"-style evolution (decreasing and then increasing) before becoming pressure independent up to 20.2 GPa. Theoretical calculations pinpoint this anionic disorder as a probable catalyst for the decrement in the conductive efficiency and muted temperature-dependent conductivity response.

cond-mat.mtrl-sci↗
arXiv2023

On regular 2-path Hamiltonian graphs

Kronk introduced the $l$-path hamiltonianicity of graphs in 1969. A graph is $l$-path Hamiltonian if every path of length not exceeding $l$ is contained in a Hamiltonian cycle. We have shown that if $P=uvz$ is a 2-path of a 2-connected, $k$-regular graph on at most $2k$ vertices and $G - V(P)$ is connected, then there must exist a Hamiltonian cycle in $G$ that contains the 2-path $P$. In this paper, we characterize a class of graphs that illustrate the sharpness of the bound $2k$. Additionally, we show that by excluding the class of graphs, both 2-connected, $k$-regular graphs on at most $2k + 1$ vertices and 3-connected, $k$-regular graphs on at most $3k-6$ vertices satisfy that there is a Hamiltonian cycle containing the 2-path $P$ if $G\setminus V(P)$ is connected.

math.CO↗
arXiv2023

DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction

Click-Through Rate (CTR) prediction is a pivotal task in product and content recommendation, where learning effective feature embeddings is of great significance. However, traditional methods typically learn fixed feature representations without dynamically refining feature representations according to the context information, leading to suboptimal performance. Some recent approaches attempt to address this issue by learning bit-wise weights or augmented embeddings for feature representations, but suffer from uninformative or redundant features in the context. To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA. DELTA contains two key components: (I) conscious truncation module (CTM), which utilizes curriculum learning to apply adaptive truncation on attention weights to select the most critical feature in the context; (II) explicit embedding optimization (EEO), which applies an auxiliary task during training that directly and independently propagates the gradient from the loss layer to the embedding layer, thereby optimizing the embedding explicitly via linear feature crossing. Extensive experiments on five challenging CTR datasets demonstrate that DELTA achieves new state-of-art performance among current CTR methods.

cs.IR↗
arXiv2023

A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images

Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients. However, most of the existing methods overlook the essential domain knowledge required for skin disease diagnosis. A novel multi-task model, namely DermImitFormer, is proposed to fill this gap by imitating dermatologists' diagnostic procedures and strategies. Through multi-task learning, the model simultaneously predicts body parts and lesion attributes in addition to the disease itself, enhancing diagnosis accuracy and improving diagnosis interpretability. The designed lesion selection module mimics dermatologists' zoom-in action, effectively highlighting the local lesion features from noisy backgrounds. Additionally, the presented cross-interaction module explicitly models the complicated diagnostic reasoning between body parts, lesion attributes, and diseases. To provide a more robust evaluation of the proposed method, a large-scale clinical image dataset of skin diseases with significantly more cases than existing datasets has been established. Extensive experiments on three different datasets consistently demonstrate the state-of-the-art recognition performance of the proposed approach.

cs.CV↗
arXiv2023

Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for AI-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62,000 samples generated by coarse-grained molecular dynamics (CGMD). Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e., RNN, LSTM, and Transformer) and structural deep learning models (i.e., GCN, GAT, and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.

q-bio.BM↗
arXiv2023

PSDR-Room: Single Photo to Scene using Differentiable Rendering

A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance. Staging such a scene is time-consuming and requires both artistic and technical skills. In this work, we propose PSDR-Room, a system allowing to optimize lighting as well as the pose and materials of individual objects to match a target image of a room scene, with minimal user input. To this end, we leverage a recent path-space differentiable rendering approach that provides unbiased gradients of the rendering with respect to geometry, lighting, and procedural materials, allowing us to optimize all of these components using gradient descent to visually match the input photo appearance. We use recent single-image scene understanding methods to initialize the optimization and search for appropriate 3D models and materials. We evaluate our method on real photographs of indoor scenes and demonstrate the editability of the resulting scene components.

cs.CV↗
arXiv2023

SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering

Model-based reinforcement learning (MBRL) is recognized with the potential to be significantly more sample-efficient than model-free RL. How an accurate model can be developed automatically and efficiently from raw sensory inputs (such as images), especially for complex environments and tasks, is a challenging problem that hinders the broad application of MBRL in the real world. In this work, we propose a sensing-aware model-based reinforcement learning system called SAM-RL. Leveraging the differentiable physics-based simulation and rendering, SAM-RL automatically updates the model by comparing rendered images with real raw images and produces the policy efficiently. With the sensing-aware learning pipeline, SAM-RL allows a robot to select an informative viewpoint to monitor the task process. We apply our framework to real world experiments for accomplishing three manipulation tasks: robotic assembly, tool manipulation, and deformable object manipulation. We demonstrate the effectiveness of SAM-RL via extensive experiments. Videos are available on our project webpage at https://sites.google.com/view/rss-sam-rl.

cs.RO↗
arXiv2023

High sensitivity silicon carbide divacancy-based thermometer

Color centers in silicon carbide have become potentially versatile quantum sensors. Particularly, wide temperature range temperature sensing has been realized in recent years. However, the sensitivity is limited due to the short dephasing time of the color centers. In this work, we realize a high sensitivity silicon carbide divacancy-based thermometer using the thermal Carr-Purcell-Meiboom-Gill (TCPMG) method. First, the zero field splitting D of PL6 divacancy as a function of temperature is measured with a linear slope of -99.7 kHz/K. The coherence times of TCPMG pulses linearly increase with the pulse number and the longest coherence time is about 21 us, which is ten times larger than dephasing time. The corresponding temperature sensing sensitivity is 13.4 mK/Hz1/2, which is about 15 times higher than previous results. Finally, we monitor the laboratory temperature variations for 24 hours using the TCMPG pulse. The experiments pave the way for the applications of silicon carbide-based high sensitivity thermometer in the semiconductor industry, biology, and materials sciences.

quant-ph↗
arXiv2022

Physics-Based Inverse Rendering using Combined Implicit and Explicit Geometries

Mathematically representing the shape of an object is a key ingredient for solving inverse rendering problems. Explicit representations like meshes are efficient to render in a differentiable fashion but have difficulties handling topology changes. Implicit representations like signed-distance functions, on the other hand, offer better support of topology changes but are much more difficult to use for physics-based differentiable rendering. We introduce a new physics-based inverse rendering pipeline that uses both implicit and explicit representations. Our technique enjoys the benefit of both representations by supporting both topology changes and differentiable rendering of complex effects such as environmental illumination, soft shadows, and interreflection. We demonstrate the effectiveness of our technique using several synthetic and real examples.

cs.GR↗
arXiv2022

Maliva: Using Machine Learning to Rewrite Visualization Queries Under Time Constraints

We consider data-visualization systems where a middleware layer translates a frontend request to a SQL query to a backend database to compute visual results. We study the problem of answering a visualization request within a limited time constraint due to the responsiveness requirement. We explore the optimization options of rewriting an original query by adding hints and/or doing approximations so that the total time is within the time constraint. We develop a novel middleware solution called Maliva based on machine learning (ML) techniques. It applies the Markov Decision Process (MDP) model to decide how to rewrite queries and uses training instances to learn an agent to make a sequence of decisions judiciously for an online request. We give a full specification of the technique, including how to construct an MDP model, how to train an agent, and how to use approximating rewrite options. Our experiments on both real and synthetic datasets show that Maliva performs significantly better than a baseline solution that does not do any rewriting, in terms of both the probability of serving requests interactively and query execution time.

cs.DB↗
arXiv2021

Statistical Feature-based Personal Information Detection in Mobile Network Traffic

With the popularity of smartphones, mobile applications (apps) have penetrated the daily life of people. Although apps provide rich functionalities, they also access a large amount of personal information simultaneously. As a result, privacy concerns are raised. To understand what personal information the apps collect, many solutions are presented to detect privacy leaks in apps. Recently, the traffic monitoring-based privacy leak detection method has shown promising performance and strong scalability. However, it still has some shortcomings. Firstly, it suffers from detecting the leakage of personal information with obfuscation. Secondly, it cannot discover the privacy leaks of undefined type. Aiming at solving the above problems, a new personal information detection method based on traffic monitoring is proposed in this paper. In this paper, statistical features of personal information are designed to depict the occurrence patterns of personal information in the traffic, including local patterns and global patterns. Then a detector is trained based on machine learning algorithms to discover potential personal information with similar patterns. Since the statistical features are independent of the value and type of personal information, the trained detector is capable of identifying various types of privacy leaks and obfuscated privacy leaks. As far as we know, this is the first work that detects personal information based on statistical features. Finally, the experimental results show that the proposed method could achieve better performance than the state-of-the-art.

cs.CR↗
arXiv2021

Motion Acoustic Flow Field: Motion Estimation for Blob Targets in Active Sonar Echograph of Harbor Environments

Motion feature is of great significance for blob targets recognition, behavior analysis and threat estimation in active sonar echographs. Hence, it is desirable to access the space-time variation of echo intensity on each spatial-temporal resolution cell from the sonar echographs sequence. Then the subtle motion information of the potential blob targets can be accurately characterized. This idea has been conduced in optical image sequences by solving an motion optical flow field (MOFF) function. Nonetheless, due to the sparkle of the sonar echograph sequences, and strong interferences caused by wake and cavitation noise of fast-moving ship in harbor environments, the constraints underlying the traditional motion optical flow function that is couples the brightness constancy constant along time dimension of each echo intensity points and the motion field spatial smoothness does not hold in our case. Hence, this paper presents a new motion acoustic flow field (MAFF) function and its solving strategy to accurately characterize the subtle motion information of blob targets in active sonar echographs of harbor environments. Experiments on a series of cooperative targets in real-world harbor environments demonstrate the efficacy of our proposed MAFF.

physics.ao-ph↗
arXiv2021

Matching forcing polynomial of generalized Petersen graph GP(n, 2)

Harary et al. and Klein and Randic proposed the forcing number of a perfect matching in mathematics and chemistry, respectively. In detail, the forcing number of a perfect matching M of a graph G is the smallest cardinality of subsets of M that are contained in no other perfect matchings of G. The author and cooperators defined the forcing polynomial of G as the count polynomial for perfect matchings with the same forcing number of G, from which the average forcing number, forcing spectrum, and the maximum and minimum forcing numbers of G can be obtained. Up to now, a few papers have been considered on matching forcing problem of non-plane non-bipartite graphs. In this paper, we investigate the forcing polynomials of generalized Petersen graphs GP(n, 2) for n = 5, 6, . . . , 15, which is a typical class of non-plane non-bipartite graph.

math.CO↗
arXiv2021

Existence and Nonlinear Stability of Steady-States to Outflow Problem for the Full Two-Phase Flow

The outflow problem for the viscous full two-phase flow model in a half line is investigated in the present paper. The existence, uniqueness and nonlinear stability of the steady-state are shown respectively corresponding to the supersonic, sonic or subsonic state at far field. This is different from the outflow problem for the isentropic Navier-Stokes equations, where there is no steady-state for the subsonic state. Furthermore, we obtain either exponential time decay rates for the supersonic state or algebraic time decay rates for supersonic and sonic states in weighted Sobolev spaces.

math.AP↗