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Chao Wang

Publications and source records attributed to Chao Wang.

At least 451 records · Page 25Linked to original sources
arXiv2023

Highly structured turbulence in high-mass star formation: an evolved infrared dark cloud G35.20-0.74 N

Context. Massive stars are generally believed to form in supersonic turbulent environment. However, recent observations have challenged this traditional view. High spatial and spectral resolution observations of the Orion Molecular Cloud and an infrared dark cloud G35.39 show a resolution-dependent turbulence, and that high-mass stars are forming exclusively in subsonic to transonic cores in those clouds. These studies demand a re-evaluation of the role of the turbulence in massive star formation. Aims. We aim to study the turbulence in a typical massive star-forming region G35.20-0.74 N with a sufficient spatial resolution to resolve the thermal Jeans length, and a spectral resolution to resolve the thermal linewidth. Methods. We use the ALMA dust continuum emission to resolve fragmentation, JVLA 1.2 cm continuum to trace ionized gas, and JVLA NH3 (1,1) to (7,7) inversion transition lines to trace linewidth, temperature, and dynamics. We fit those lines and remove line broadening due to channel width, thermal pressure, and velocity gradient to obtain a clean map of intrinsic turbulence. Results. We find that (1) the turbulence in G35.20 is overall supersonic, with mean and median Mach numbers 3.7 and 2.8, respectively. (2) Mach number decreases from 6-7 at 0.1 pc scale to <3 towards the central cores at 0.01 pc scale. (3) The central ALMA cores appear to be decoupled form the host filament, evident by an opposite velocity gradient and significantly reduced turbulence. Because of intense star formation activities in G35.20 (as compared to the relatively young and quiescent IRDC G35.39), the supersonic turbulence is likely replenished by protostellar outflows. G35.20 is, thus, representative of an evolved form of IRDC G35.39. More observations of a sample of IRDCs are highly demanded to further investigate the role of turbulence in initial conditions for the massive star formation.

astro-ph.GA↗
arXiv2023

Observation of non-superconducting phase changes in LuH$_{2\pm\text{x}}$N$_y$

The recent report of near-ambient superconductivity in nitrogen doped lutetium hydride has triggered a worldwide fanaticism and raised major questions about the latest claims. An intriguing phenomenon of color changes in pressurized samples from blue to pink to red was observed and correlated with the claimed superconducting transition, but the origin and underlying physics of these color changes have yet to be elucidated. Here we report synthesis and characterization of high-purity nitrogen doped lutetium hydride LuH$_{2\pm\text{x}}$N$_y$ with the same structure and composition as in the main phase of near-ambient superconductor1. We find a new purple phase of LuH$_{2\pm\text{x}}$N$_y$ between blue and pink phase, and reveal that the sample color changes likely stem from pressure-driven redistribution of nitrogen and its interaction with the LuH$_2$ framework. No superconducting transition is found in all blue, purple, pink and red phases at temperatures 1.8-300 K and pressures 0-30 GPa. Instead, we identify a notable temperature-induced resistance anomaly of structural and/or electronic origin in LuH$_{2\pm\text{x}}$N$_y$, which is most pronounced in the pink phase and may have been erroneously interpreted as a sign of superconducting transition. This work establishes key benchmarks for nitrogen doped lutetium hydrides, allowing an in-depth understanding of the novel pressure-induced phase changes.

cond-mat.supr-con↗
arXiv2023

Chip-Integrated Metasurface Full-Stokes Polarimetric Imaging Sensor

Polarimetric imaging has a wide range of applications for uncovering features invisible to human eyes and conventional imaging sensors. Compact, fast, cost-effective and accurate full-Stokes polarimetric imaging sensors are highly desirable in many applications, which, however, remain elusive due to fundamental material limitations. Here we present a Metasurface-based Full-Stokes Polarimetric Imaging sensor (MetaPolarIm) realized by integrating an ultrathin (~600 nm) metasurface polarization filter array (MPFA) onto a visible imaging sensor with CMOS compatible fabrication processes. The MPFA is featured with broadband dielectric-metal hybrid chiral metasurfaces and double-layer nanograting polarizers. This chip-integrated polarimetric imaging sensor enables single-shot full-Stokes imaging (speed limited by the CMOS imager) with the most compact form factor, record high measurement accuracy, dual-color operation (green and red) and a full angle of view up to 40 degrees. MetaPolarIm holds great promise to enable transformative applications in autonomous vision, industry inspection, space exploration, medical imaging and diagnosis.

physics.optics↗
arXiv2023

Weight-sharing Supernet for Searching Specialized Acoustic Event Classification Networks Across Device Constraints

Acoustic Event Classification (AEC) has been widely used in devices such as smart speakers and mobile phones for home safety or accessibility support. As AEC models run on more and more devices with diverse computation resource constraints, it became increasingly expensive to develop models that are tuned to achieve optimal accuracy/computation trade-off for each given computation resource constraint. In this paper, we introduce a Once-For-All (OFA) Neural Architecture Search (NAS) framework for AEC. Specifically, we first train a weight-sharing supernet that supports different model architectures, followed by automatically searching for a model given specific computational resource constraints. Our experimental results showed that by just training once, the resulting model from NAS significantly outperforms both models trained individually from scratch and knowledge distillation (25.4% and 7.3% relative improvement). We also found that the benefit of weight-sharing supernet training of ultra-small models comes not only from searching but from optimization.

cs.SD↗
arXiv2023

Understanding the Uncertainty Loop of Human-Robot Interaction

Recently the field of Human-Robot Interaction gained popularity, due to the wide range of possibilities of how robots can support humans during daily tasks. One form of supportive robots are socially assistive robots which are specifically built for communicating with humans, e.g., as service robots or personal companions. As they understand humans through artificial intelligence, these robots will at some point make wrong assumptions about the humans' current state and give an unexpected response. In human-human conversations, unexpected responses happen frequently. However, it is currently unclear how such robots should act if they understand that the human did not expect their response, or even showing the uncertainty of their response in the first place. For this, we explore the different forms of potential uncertainties during human-robot conversations and how humanoids can, through verbal and non-verbal cues, communicate these uncertainties.

cs.HC↗
arXiv2023

Aggregate Analytic Window Query over Spatial Data

Analytic window query is a commonly used query in the relational databases. It answers the aggregations of data over a sliding window. For example, to get the average prices of a stock for each day. However, it is not supported in the spatial databases. Because the spatial data are not in a one-dimension space, there is no straightforward way to extend the original analytic window query to spatial databases. But these queries are useful and meaningful. For example, to find the average number of visits for all the POIs in the circle with a fixed radius for each POI as the centre. In this paper, we define the aggregate analytic window query over spatial data and propose algorithms for grid index and tree-index. We also analyze the complexity of the algorithms to prove they are efficient and practical.

cs.DB↗
arXiv2023

Support Aggregate Analytic Window Function over Large Data by Spilling

Analytic function, also called window function, is to query the aggregation of data over a sliding window. For example, a simple query over the online stock platform is to return the average price of a stock of the last three days. These functions are commonly used features in SQL databases. They are supported in most of the commercial databases. With the increasing usage of cloud data infra and machine learning technology, the frequency of queries with analytic window functions rises. Some analytic functions only require const space in memory to store the state, such as SUM, AVG, while others require linear space, such as MIN, MAX. When the window is extremely large, the memory space to store the state may be too large. In this case, we need to spill the state to disk, which is a heavy operation. In this paper, we proposed an algorithm to manipulate the state data in the disk to reduce the disk I/O to make spill available and efficienct. We analyze the complexity of the algorithm with different data distribution.

cs.DB↗
arXiv2023

Interactive Trajectory Planner for Mandatory Lane Changing in Dense Non-Cooperative Traffic

When the traffic stream is extremely congested and surrounding vehicles are not cooperative, the mandatory lane changing can be significantly difficult. In this work, we propose an interactive trajectory planner, which will firstly attempt to change lanes as long as safety is ensured. Based on receding horizon planning, the ego vehicle can abort or continue changing lanes according to surrounding vehicles' reactions. We demonstrate the performance of our planner in extensive simulations with eight surrounding vehicles, initial velocity ranging from 0.5 to 5 meters per second, and bumper to bumper gap ranging from 4 to 10 meters. The ego vehicle with our planner can change lanes safely and smoothly. The computation time of the planner at every step is within 10 milliseconds in most cases on a laptop with 1.8GHz Intel Core i7-10610U.

cs.RO↗
arXiv2023

Direct effect of solvent viscosity on the physical mass transfer for wavy film flow in a packed column

The interphase mass transfer plays a critical role in determining the height of packed column used in the absorption process. In a recent experiment (Song D. Ind. Eng. Chem. Res. 2018, 57, 718), the direct impact of viscosity ($μ_L$) on the physical mass transfer coefficient ($k_L$) was observed to be higher in a packed column as compared to the wetted wall column. We offer a plausible mechanism involving the wavy film and eddy enhanced mass transfer in a packed column to explain the underlying physics via analytical and numerical studies. The analytically derived mass transfer coefficient matches well with experimental observation in a packed column. The countercurrent flow simulations in a packed column with both uniform and wavy films also confirm this behavior. The predicted $k_L$ shows steep variation with $μ_L$ for a wavy film than a uniform film and further confirms the proposed theory. A similar relation ($k_L \propto μ_L^{-0.38}$) for a wavy film is also observed in theoretical, experimental, and numerical studies.

physics.flu-dyn↗
arXiv2023

Resonance contributions from $χ_{c0}$ in the charmless three-body hadronic $B$ meson decays

Within the framework of perturbative QCD factorization, we investigate the nonfactorizable contributions to these factorization-forbidden Quasi-two-body decays $B_{(s)}\rightarrow hχ_{c0}\rightarrow hπ^+π^-(K^+K^-)$ with $ h=π, K$. We compare our predicted branching ratios for the $B_{(s)}\rightarrow Kχ_{c0}\rightarrow Kπ^+π^-(K^+K^-)$ decay with available experiment data as well as predictions by other theoretical studies. The branching ratios of these decays are consistent with data and other theoretical predictions. In the Cabibbo-suppressed decays $B_{(s)}\rightarrow hχ_{c0}\rightarrow hπ^+π^-(K^+K^-)$ with $h=\bar{K}^0,π$, however, the values of the branching ratios are the order of $10^{-7}$ and $10^{-8}$. The ratio $R_{χ_{c0}}$ between the decay $B^+\rightarrow π^+χ_{c0}\rightarrow π^+π^+π^-$ and $B^+\rightarrow K^+χ_{c0}\rightarrow K^+π^+π^-$ and the distribution of branching ratios for different decay modes in invariant mass are considered in this work.

hep-ph↗
arXiv2023

Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search

The construction of machine learning models involves many bi-level multi-objective optimization problems (BL-MOPs), where upper level (UL) candidate solutions must be evaluated via training weights of a model in the lower level (LL). Due to the Pareto optimality of sub-problems and the complex dependency across UL solutions and LL weights, an UL solution is feasible if and only if the LL weight is Pareto optimal. It is computationally expensive to determine which LL Pareto weight in the LL Pareto weight set is the most appropriate for each UL solution. This paper proposes a bi-level multi-objective learning framework (BLMOL), coupling the above decision-making process with the optimization process of the UL-MOP by introducing LL preference $r$. Specifically, the UL variable and $r$ are simultaneously searched to minimize multiple UL objectives by evolutionary multi-objective algorithms. The LL weight with respect to $r$ is trained to minimize multiple LL objectives via gradient-based preference multi-objective algorithms. In addition, the preference surrogate model is constructed to replace the expensive evaluation process of the UL-MOP. We consider a novel case study on multi-task graph neural topology search. It aims to find a set of Pareto topologies and their Pareto weights, representing different trade-offs across tasks at UL and LL, respectively. The found graph neural network is employed to solve multiple tasks simultaneously, including graph classification, node classification, and link prediction. Experimental results demonstrate that BLMOL can outperform some state-of-the-art algorithms and generate well-representative UL solutions and LL weights.

cs.NE↗
arXiv2023

Explainable Human-Robot Training and Cooperation with Augmented Reality

The current spread of social and assistive robotics applications is increasingly highlighting the need for robots that can be easily taught and interacted with, even by users with no technical background. Still, it is often difficult to grasp what such robots know or to assess if a correct representation of the task is being formed. Augmented Reality (AR) has the potential to bridge this gap. We demonstrate three use cases where AR design elements enhance the explainability and efficiency of human-robot interaction: 1) a human teaching a robot some simple kitchen tasks by demonstration, 2) the robot showing its plan for solving novel tasks in AR to a human for validation, and 3) a robot communicating its intentions via AR while assisting people with limited mobility during daily activities.

cs.HC↗
arXiv2023

A User Interface for Sense-making of the Reasoning Process while Interacting with Robots

As knowledge graph has the potential to bridge the gap between commonsense knowledge and reasoning over actionable capabilities of mobile robotic platforms, incorporating knowledge graph into robotic system attracted increasing attention in recent years. Previously, graph visualization has been used wildly by developers to make sense of knowledge representations. However, due to lacking the link between abstract knowledge of the real-world environment and the robot's actions, transitional visualization tools are incompatible for expert-user to understand, test, supervise and modify the graph-based reasoning system with the embodiment of the robots. Therefore, we developed an interface which enables robotic experts to send commands to the robot in natural language, then interface visualizes the procedures of the robot mapping the command to the functions for querying in the commonsense knowledge database, links the result to the real world instances in a 3D map and demonstrate the execution of the robot from the first-person perspective of the robot. After 3 weeks of usage of the system by robotic experts in their daily development, some feedback was collected, which provides insight for designing such systems.

cs.HC↗
arXiv2023

Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection

Copy-move forgery is a manipulation of copying and pasting specific patches from and to an image, with potentially illegal or unethical uses. Recent advances in the forensic methods for copy-move forgery have shown increasing success in detection accuracy and robustness. However, for images with high self-similarity or strong signal corruption, the existing algorithms often exhibit inefficient processes and unreliable results. This is mainly due to the inherent semantic gap between low-level visual representation and high-level semantic concept. In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation. Our detection method expands the traditional works on two aspects: 1) we introduce the bag-of-visual-words model into this field for the first time, may meaning a new perspective of forensic study; 2) we propose a word-to-phrase feature description and matching pipeline, covering the spatial structure and visual saliency information of digital images. Extensive experimental results show the superior performance of our framework over state-of-the-art algorithms in overcoming the related problems caused by the semantic gap.

cs.CV↗
arXiv2023

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- XV. Steady Accretion from Global Collapse to Core Feeding in Massive Hub-filament System SDC335

We present ALMA Band-3/7 observations towards "the Heart" of a massive hub-filament system (HFS) SDC335, to investigate its fragmentation and accretion. At a resolution of $\sim0.03$ pc, 3 mm continuum emission resolves two massive dense cores MM1 and MM2, with $383(^{+234}_{-120})$ $M_\odot$ (10-24% mass of "the Heart") and $74(^{+47}_{-24})$ $M_\odot$, respectively. With a resolution down to 0.01 pc, 0.87 mm continuum emission shows MM1 further fragments into six condensations and multi-transition lines of H$_2$CS provide temperature estimation. The relation between separation and mass of condensations at a scale of 0.01 pc favors turbulent Jeans fragmentation where the turbulence seems to be scale-free rather than scale-dependent. We use the H$^{13}$CO$^+$ (1-0) emission line to resolve the complex gas motion inside "the Heart" in position-position-velocity space. We identify four major gas streams connected to large-scale filaments, inheriting the anti-clockwise spiral pattern. Along these streams, gas feeds the central massive core MM1. Assuming an inclination angle of $45(\pm15)^{\circ}$ and a H$^{13}$CO$^+$ abundance of $5(\pm3)\times10^{-11}$, the total mass infall rate is estimated to be $2.40(\pm0.78)\times10^{-3}$ $M_\odot$ yr$^{-1}$, numerically consistent with the accretion rates derived from the clump-scale spherical infall model and the core-scale outflows. The consistency suggests a continuous, near steady-state, and efficient accretion from global collapse, therefore ensuring core feeding. Our comprehensive study of SDC335 showcases the detailed gas kinematics in a prototypical massive infalling clump and calls for further systematic and statistical analyses in a large sample.

astro-ph.GA↗
arXiv2022

Generative Graph Neural Networks for Link Prediction

Inferring missing links or detecting spurious ones based on observed graphs, known as link prediction, is a long-standing challenge in graph data analysis. With the recent advances in deep learning, graph neural networks have been used for link prediction and have achieved state-of-the-art performance. Nevertheless, existing methods developed for this purpose are typically discriminative, computing features of local subgraphs around two neighboring nodes and predicting potential links between them from the perspective of subgraph classification. In this formalism, the selection of enclosing subgraphs and heuristic structural features for subgraph classification significantly affects the performance of the methods. To overcome this limitation, this paper proposes a novel and radically different link prediction algorithm based on the network reconstruction theory, called GraphLP. Instead of sampling positive and negative links and heuristically computing the features of their enclosing subgraphs, GraphLP utilizes the feature learning ability of deep-learning models to automatically extract the structural patterns of graphs for link prediction under the assumption that real-world graphs are not locally isolated. Moreover, GraphLP explores high-order connectivity patterns to utilize the hierarchical organizational structures of graphs for link prediction. Our experimental results on all common benchmark datasets from different applications demonstrate that the proposed method consistently outperforms other state-of-the-art methods. Unlike the discriminative neural network models used for link prediction, GraphLP is generative, which provides a new paradigm for neural-network-based link prediction.

cs.SI↗
arXiv2022

Hydrodynamics of countercurrent flows in a structured packed column: effects of initial wetting and dynamic contact angle

Computational countercurrent flow investigation in the structured packed column is a multiscale problem. Multiphase flow studies using volume of fluid (VOF) method in the representative elementary unit (REU) of the packed column can insight into the local hydrodynamics such as interfacial area, film thickness, etc. The interfacial area dictates the mass transfer in absorption process and thereby overall efficiency of column. Impacts of solvent's physical properties, liquid loads and static contact angle (SCA) on the interfacial area were examined earlier. In the present study, the dynamic contact angle (DCA) was used to explore the impact of contact angle hysteresis on the interfacial area. DCA has more pronounced impact on the interfacial area (10%) for aqueous solvent of 0.10M Sodium hydroxide (NaOH). The interfacial area shows undulation and does not achieve the pseudo-steady state. In contrary, the interfacial area gets a net pseudo-steady value for the aqueous solvent having 40% monoethanolamine (MEA) by weight. The wetting hysteresis was also explored via simulations conducted with initially dry and wetted sheets. For 0.10M NaOH aqueous solvent, the initially wetted sheets lead to slightly higher value of the interfacial area (10%) as compared to the initially dry sheets at the same liquid load and DCA. As expected, wetting hysteresis reduces with increasing liquid loads. On the other hand, wetting hysteresis is not significant for 40% MEA aqueous solvent which might be lower surface tension and higher viscosity. Overall, the effect of the dynamic contact angle is not pronounced as compared to those found in a flat surface.

physics.flu-dyn↗
arXiv2022

Precision calculations of $B_{d, s} \to π, K$ decay form factors in soft-collinear effective theory

We improve QCD calculations of the $B_{d, s} \to π, K$ form factors at large hadronic recoil by implementing the next-to-leading-logarithmic resummation for the obtained leading-power light-cone sum rules in the soft-collinear effective theory (SCET) framework. Additionally, we endeavour to investigate a variety of the subleading-power contributions to these heavy-to-light form factors at ${\cal O}(α_s^0)$, by including the higher-order terms in the heavy-quark expansion of the hard-collinear quark propagator, by evaluating the desired effective matrix element of the next-to-leading-order term in the ${\rm SCET_{I}}$ representation of the weak transition current, by taking into account the off-light-cone contributions of the two-body heavy-quark effective theory matrix elements as well as the three-particle higher-twist corrections from the subleading bottom-meson light-cone distribution amplitudes, and by computing the twist-five and twist-six four-body higher-twist effects with the aid of the factorization approximation. Having at our disposal the SCET sum rules for the exclusive $B$-meson decay form factors, we further explore in detail numerical implications of the newly computed subleading-power corrections by employing the three-parameter model for both the leading-twist and higher-twist $B$-meson distribution amplitudes. Taking advantage of the customary Bourrely-Caprini-Lellouch parametrization for the semileptonic $B_{d, s} \to π, K$ form factors, we then determine the correlated numerical results for the interesting series coefficients, by carrying out the simultaneous fit of the exclusive $B$-meson decay form factors to both the achieved SCET sum rule predictions and the available lattice QCD results.

hep-ph↗