arXiv subjects

Yao Wang

Publications and source records attributed to Yao Wang.

At least 235 records · Page 13Linked to original sources

arXiv2022

Profit-Driven Team Grouping in Social Networks

In this paper, we investigate the profit-driven team grouping problem in social networks. We consider a setting in which people possess different skills, and the compatibility between these individuals is captured by a social network. Moreover, there is a collection of tasks, where each task requires a specific set of skills and yields a profit upon completion. Individuals may collaborate with each other as \emph{teams} to accomplish a set of tasks. We aim to find a group of teams to maximize the total profit of the tasks that they can complete. Any feasible grouping must satisfy the following conditions: (i) each team possesses all the skills required by the task assigned to it, (ii) individuals belonging to the same team are socially compatible, and (iii) no individual is overloaded. We refer to this as the \textsc{TeamGrouping} problem. We analyze the computational complexity of this problem and then propose a linear program-based approximation algorithm to address it and its variants. Although we focus on team grouping, our results apply to a broad range of optimization problems that can be formulated as cover decomposition problems.

cs.SI↗

arXiv2022

Copula approach to exchange-correlation hole in many-electron systems with strong correlations

Electronic correlation is a fundamental topic in many-electron systems. To characterize this correlation, one may introduce the concept of exchange-correlation hole. In this paper, we first briefly revisit its definition and relation to electron and geminal densities, followed by their intimate relations to copula functions in probability theory and statistics. We then propose a copula-based approach to estimate the exchange-correlation hole from the electron density. It is anticipated that the proposed scheme would become a promising ingredient towards the future development of strongly correlated electronic structure calculations.

physics.chem-ph↗

arXiv2022

Prevailing charge order in overdoped cuprates beyond the superconducting dome

The extremely overdoped cuprates are generally considered to be Fermi liquid metals without exotic orders, whereas the underdoped cuprates harbor intertwined states. Contrary to this conventional wisdom, using Cu $L_3$ edge and O $K$ edge resonant x-ray scattering, we reveal a charge order (CO) in overdoped La$_{2-x}$Sr$_x$CuO$_4$ (0.35 $\leq$ x $\leq$ 0.6) beyond the superconducting dome. This CO has a periodicity of $\sim$ 6 lattice units with correlation lengths of $\sim 3 - 20$ lattice units. It shows similar in-plane momentum and polarization dependence and dispersive excitations as the CO of underdoped cuprates, but its maximum intensity differs along the c-direction and persists up to 300 K. This CO cannot be explained by either the Fermi surface instability or the doped Hubbard model and its origin remains to be understood. Our results suggest that CO is prevailing in the overdoped metallic regime and superconductivity emerges out of the CO phase upon decreasing hole carriers.

cond-mat.supr-con↗

arXiv2022

Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients

Unrecognized patient deterioration can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images or comprehensive laboratory tests. This is infeasible for telehealth solutions and highlights a gap in deterioration prediction models that are based on minimal data, which can be recorded at a large scale in any clinic, nursing home, or even at the patient's home. In this study, we propose and develop a prognostic model that predicts if a patient will experience deterioration in the forthcoming 3-24 hours. The model sequentially processes routine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c) temperature. The model is also provided with basic patient information, including sex, age, vaccination status, vaccination date, and status of obesity, hypertension, or diabetes. We train and evaluate the model using data collected from 37,006 COVID-19 patients at NYU Langone Health in New York, USA. The model achieves an area under the receiver operating characteristic curve (AUROC) of 0.808-0.880 for 3-24 hour deterioration prediction. We also conduct occlusion experiments to evaluate the importance of each input feature, where the results reveal the significance of continuously monitoring the variations of the vital signs. Our results show the prospect of accurate deterioration forecast using a minimum feature set that can be relatively easily obtained using wearable devices and self-reported patient information.

cs.LG↗

arXiv2022

Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance

Deep learning has made great strides for object detection in images. The detection accuracy and computational cost of object detection depend on the spatial resolution of an image, which may be constrained by both the camera and storage considerations. Compression is often achieved by reducing either spatial or amplitude resolution or, at times, both, both of which have well-known effects on performance. Detection accuracy also depends on the distance of the object of interest from the camera. Our work examines the impact of spatial and amplitude resolution, as well as object distance, on object detection accuracy and computational cost. We develop a resolution-adaptive variant of YOLOv5 (RA-YOLO), which varies the number of scales in the feature pyramid and detection head based on the spatial resolution of the input image. To train and evaluate this new method, we created a dataset of images with diverse spatial and amplitude resolutions by combining images from the TJU and Eurocity datasets and generating different resolutions by applying spatial resizing and compression. We first show that RA-YOLO achieves a good trade-off between detection accuracy and inference time over a large range of spatial resolutions. We then evaluate the impact of spatial and amplitude resolutions on object detection accuracy using the proposed RA-YOLO model. We demonstrate that the optimal spatial resolution that leads to the highest detection accuracy depends on the 'tolerated' image size. We further assess the impact of the distance of an object to the camera on the detection accuracy and show that higher spatial resolution enables a greater detection range. These results provide important guidelines for choosing the image spatial resolution and compression settings predicated on available bandwidth, storage, desired inference time, and/or desired detection range, in practical applications.

cs.CV↗

arXiv2022

Complex Organic Molecules Formation in Cold Cores on Stochastically Heated Grains

We investigate the roles of stochastic grain heating in the formation of complex organic molecules (COMs) in cold cores, where COMs have been detected. Two different types of grain-size distributions are used in the chemical models. The first one is the MRN distribution, and the second one considers grain coagulation to study its effects on the chemical evolution in these environments. The macroscopic Monte Carlo method is used to perform the two-phase chemical model simulations. We find that (1) grain coagulation can affect certain gas-phase species, such as CO$_2$ and N$_2$H$^+$, in the cold core environments, which can be attributed to the volatile precursors originating from the small grains with temperature fluctuations; (2) grains with radii around 4.6 $\times$ 10$^{-3}$ $μ$m contribute most to the production of COMs on dust grains under cold core conditions, while few species can be formed on even smaller grains with radii less than 2 $\times$ 10$^{-3}$ $μ$m; (3) COMs formed on stochastically heated grains could help explain the observed abundances of gas-phase COMs in cold cores.

astro-ph.GA↗

arXiv2022

Learning to Predict on Octree for Scalable Point Cloud Geometry Coding

Octree-based point cloud representation and compression have been adopted by the MPEG G-PCC standard. However, it only uses handcrafted methods to predict the probability that a leaf node is non-empty, which is then used for entropy coding. We propose a novel approach for predicting such probabilities for geometry coding, which applies a denoising neural network to a "noisy" context cube that includes both neighboring decoded voxels as well as uncoded voxels. We further propose a convolution-based model to upsample the decoded point cloud at a coarse resolution on the decoder side. Integration of the two approaches significantly improves the rate-distortion performance for geometry coding compared to the original G-PCC standard and other baseline methods for dense point clouds. The proposed octree-based entropy coding approach is naturally scalable, which is desirable for dynamic rate adaptation in point cloud streaming systems.

eess.IV↗

arXiv2022

Improved Image Classification with Token Fusion

In this paper, we propose a method using the fusion of CNN and transformer structure to improve image classification performance. In the case of CNN, information about a local area on an image can be extracted well, but there is a limit to the extraction of global information. On the other hand, the transformer has an advantage in relatively global extraction, but has a disadvantage in that it requires a lot of memory for local feature value extraction. In the case of an image, it is converted into a feature map through CNN, and each feature map's pixel is considered a token. At the same time, the image is divided into patch areas and then fused with the transformer method that views them as tokens. For the fusion of tokens with two different characteristics, we propose three methods: (1) late token fusion with parallel structure, (2) early token fusion, (3) token fusion in a layer by layer. In an experiment using ImageNet 1k, the proposed method shows the best classification performance.

cs.CV↗

arXiv2022

Detect and Approach: Close-Range Navigation Support for People with Blindness and Low Vision

People with blindness and low vision (pBLV) experience significant challenges when locating final destinations or targeting specific objects in unfamiliar environments. Furthermore, besides initially locating and orienting oneself to a target object, approaching the final target from one's present position is often frustrating and challenging, especially when one drifts away from the initial planned path to avoid obstacles. In this paper, we develop a novel wearable navigation solution to provide real-time guidance for a user to approach a target object of interest efficiently and effectively in unfamiliar environments. Our system contains two key visual computing functions: initial target object localization in 3D and continuous estimation of the user's trajectory, both based on the 2D video captured by a low-cost monocular camera mounted on in front of the chest of the user. These functions enable the system to suggest an initial navigation path, continuously update the path as the user moves, and offer timely recommendation about the correction of the user's path. Our experiments demonstrate that our system is able to operate with an error of less than 0.5 meter both outdoor and indoor. The system is entirely vision-based and does not need other sensors for navigation, and the computation can be run with the Jetson processor in the wearable system to facilitate real-time navigation assistance.

cs.CV↗

arXiv2022

Magnetic Excitations in Strained Infinite-layer Nickelate PrNiO2

Strongly correlated materials often respond sensitively to the external perturbations. In the recently discovered superconducting infinite-layer nickelates, the superconducting transition temperature can be dramatically enhanced via only ~1% compressive strain-tuning enabled by substrate design. However, the root of such enhancement remains elusive. While the superconducting pairing mechanism is still not settled, magnetic Cooper pairing - similar to the cuprates has been proposed. Using resonant inelastic x-ray scattering, we investigate the magnetic excitations in infinite-layer PrNiO2 thin films for different strain conditions. The magnon bandwidth of PrNiO2 shows only marginal response to strain-tuning, in sharp contrast to the striking enhancement of the superconducting transition temperature Tc in the doped superconducting samples. These results suggest the enhancement of Tc is not mediated by spin excitations and thus provide important empirics for the understanding of superconductivity in infinite-layer nickelates.

cond-mat.supr-con↗

arXiv2022

VisRecall: Quantifying Information Visualisation Recallability via Question Answering

Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. In this work, we propose a question-answering paradigm to study visualisation recallability and present VisRecall - a novel dataset consisting of 200 visualisations that are annotated with crowd-sourced human (N = 305) recallability scores obtained from 1,000 questions of five question types. Furthermore, we present the first computational method to predict recallability of different visualisation elements, such as the title or specific data values. We report detailed analyses of our method on VisRecall and demonstrate that it outperforms several baselines in overall recallability and FE-, F-, RV-, and U-question recallability. Our work makes fundamental contributions towards a new generation of methods to assist designers in optimising visualisations.

cs.HC↗

arXiv2022

Coherent excitation energy transfer in model photosynthetic reaction center: Effects of non-Markovian quantum environment

Excitation energy transfer (EET) and electron transfer (ET) are crucially involved in photosynthetic processes. In reality, the photosynthetic reaction center constitutes an open quantum system of EET and ET, which manifests an interplay of pigments, solar light and phonon baths. So far theoretical studies have been mainly based on master equation approaches in the Markovian condition. The non-Markovian environmental effect, which may play a crucial role, has not been sufficiently considered. In this work, we propose a mixed dynamic approach to investigate this open system. The influence of phonon bath is treated via the exact dissipaton equation of motion (DEOM) while that of photon bath is via the Lindblad master equation. Specifically, we explore the effect of non-Markovian quantum phonon bath on the coherent transfer dynamics and its manipulation on the current-voltage behavior. Distinguished from the results of completely Markovian Lindblad equation and those adopting classical environment description, the mixed DEOM-Lindblad simulations exhibit transfer coherence up to a few hundreds femtoseconds and the related environmental manipulation effect on current. These non-Markovian quantum coherent effects be extended tomore complex and realistic systems and be helpful to the design of organic photovoltaic devices.

quant-ph↗

arXiv2022

A statistical quasi-particles thermofield theory with Gaussian environments: System-bath entanglement theorem for nonequilibrium correlation functions

For open quantum systems, the Gaussian environmental dissipative effect can be represented by statistical quasi-particles, namely, dissipatons. We exploit this fact to establish the dissipaton thermofield theory. The resulting generalized Langevin dynamics of absorptive and emissive thermofield operators are effectively noise-resolved. The system-bath entanglement theorem is then readily followed between an important class of nonequilibrium steady-state correlation functions. All these relations are validated numerically. A simple corollary is the transport current expression, which exactly recovers the result obtained from the nonequilibrium Green's function formalism.

cond-mat.stat-mech↗

arXiv2022

An original model for multi-target learning of logical rules for knowledge graph reasoning

Large-scale knowledge graphs provide structured representations of human knowledge. However, as it is impossible to collect all knowledge, knowledge graphs are usually incomplete. Reasoning based on existing facts paves a way to discover missing facts. In this paper, we study the problem of learning logical rules for reasoning on knowledge graphs for completing missing factual triplets. Learning logical rules equips a model with strong interpretability as well as the ability to generalize to similar tasks. We propose a model able to fully use training data which also considers multi-target scenarios. In addition, considering the deficiency in evaluating the performance of models and the quality of mined rules, we further propose two novel indicators to help with the problem. Experimental results empirically demonstrate that our model outperforms state-of-the-art methods on five benchmark datasets. The results also prove the effectiveness of the indicators.

cs.AI↗

arXiv2022

Nonequilibrium work distributions in quantum impurity system-bath mixing processes

The fluctuation theorem, where the central quantity is the work distribution, is an important characterization of nonequilibrium thermodynamics. In this work, based on the dissipaton-equation-of-motion theory, we develop an exact method to evaluate the work distributions in quantum impurity system-bath mixing processes, in the presence of non-Markovian and strong couplings. Our results not only precisely reproduce the Jarzynski equality and Crooks relation, but also reveal rich information on large deviation. The numerical demonstrations are carried out with a spin-boson model system.

cond-mat.stat-mech↗

arXiv2022

Enhanced superconductivity by near-neighbor attraction in the doped Hubbard model

Recent experiment has unveiled an anomalously strong electron-electron attraction in one-dimensional copper-oxide chain Ba$_{2-x}$Sr$_x$CuO$_{3+δ}$. While the near-neighbor electron attraction $V$ in the one-dimensional extended Hubbard chain has been examined recently, its effect in the Hubbard model beyond the one-dimensional chain remains unclear. We report a density-matrix renormalization group study of the extended Hubbard model on long four-leg cylinders on the square lattice. We find that the near-neighbor electron attraction $V$ can notably enhance the long-distance superconducting correlations while simultaneously suppressing the charge-density-wave correlations. Specifically, for a modestly strong electron attraction, the superconducting correlations become dominant over the CDW correlations with a Luttinger exponent $K_{sc}\sim 1$ and strong divergent superconducting susceptibility. Our results provide a promising way to realize long-range superconductivity in the doped Hubbard model in two dimensions. The relevance of our numerical results to cuprate materials is also discussed.

cond-mat.str-el↗

arXiv2022

Experimental Quantum Simulation of Dynamic Localization on Curved Photonic Lattices

Dynamic localization, which originates from the phenomena of particle evolution suppression under an externally applied AC electric field, has been simulated by suppressed light evolution in periodically-curved photonic arrays. However, experimental studies on their quantitative dynamic transport properties and application for quantum information processing are rare. Here we fabricate one-dimensional and hexagonal two-dimensional arrays, both with sinusoidal curvature. We successfully observe the suppressed single-photon evolution patterns, and for the first time measure the variances to study their transport properties. For one-dimensional arrays, the measured variances match both the analytical electric field calculation and the quantum walk Hamiltonian engineering approach. For hexagonal arrays, as anisotropic effective couplings in four directions are mutually dependent, the analytical approach suffers, while quantum walk conveniently incorporates all anisotropic coupling coefficients in the Hamiltonian and solves its exponential as a whole, yielding consistent variances with our experimental results. Furthermore, we implement a nearly complete localization to show that it can preserve both the initial injection and the wave-packet after some evolution, acting as a memory of a flexible time scale in integrated photonics. We demonstrate a useful quantum simulation of dynamic localization for studying their anisotropic transport properties, and a promising application of dynamic localization as a building block for quantum information processing in integrated photonics.

quant-ph↗

arXiv2022

Correlated driving-and-dissipation equation for non-Condon spectroscopy with the Herzberg-Teller vibronic coupling

Correlated driving-and-dissipation equation (CODDE) is an optimized complete second-order quantum dissipation approach, which is originally concerned with the reduced system dynamics only. However, one can actually extract the hybridized bath dynamics from CODDE with the aid of dissipaton-equation-of-motion theory, a statistical quasi-particle quantum dissipation formalism. Treated as an one{dissipaton theory, CODDE is successfully extended to deal with the Herzberg-Teller vibronic couplings in dipole-field interactions. Demonstrations will be carried out on the non-Condon spectroscopies of a model dimer system.

physics.chem-ph↗