arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 613 records · Page 34Linked to original sources

arXiv2021

TRIE: End-to-End Text Reading and Information Extraction for Document Understanding

Since real-world ubiquitous documents (e.g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic. Most existing works decouple the problem into two separate tasks, (1) text reading for detecting and recognizing texts in images and (2) information extraction for analyzing and extracting key elements from previously extracted plain text. However, they mainly focus on improving information extraction task, while neglecting the fact that text reading and information extraction are mutually correlated. In this paper, we propose a unified end-to-end text reading and information extraction network, where the two tasks can reinforce each other. Specifically, the multimodal visual and textual features of text reading are fused for information extraction and in turn, the semantics in information extraction contribute to the optimization of text reading. On three real-world datasets with diverse document images (from fixed layout to variable layout, from structured text to semi-structured text), our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy.

cs.CV↗

arXiv2021

Selective observation of surface and bulk bands in polar WTe2 by laser-based spin- and angle-resolved photoemission spectroscopy

The electronic state of WTe2, a candidate of type-II Weyl semimetal, is investigated by using laser-based spin- and angle-resolved photoemission spectroscopy (SARPES). We prepare the pair of WTe2 samples, one with (001) surface and the other with (00-1) surface, by "sandwich method", and measure the band structures of each surface separately. The Fermi arcs are observed on both surfaces. We identify that the Fermi arcs on the two surfaces are both originating from surface states. We further find a surface resonance band, which connects with the Fermi-arc band, forming a Dirac-cone-like band dispersion. Our results indicate that the bulk electron and hole bands are much closer in momentum space than band calculations.

cond-mat.mtrl-sci↗

arXiv2021

On the Formation of Hydrogen Peroxide in Water Microdroplets

Recent reports on the formation of hydrogen peroxide (H$_2$O$_2$) in water microdroplets produced via pneumatic spraying or capillary condensation have garnered significant attention. How covalent bonds in water could break under such conditions challenges our textbook understanding of physical chemistry and the water substance. While there is no definitive answer, it has been speculated that ultrahigh electric fields at the air-water interface are responsible for this chemical transformation. Here, we resolve this mystery via a comprehensive experimental investigation of H$_2$O$_2$ formation in (i) water microdroplets sprayed over a range of liquid flow-rates, the (shearing) air flow rates, and the air composition (ii) water microdroplets condensed on hydrophobic substrates formed via hot water or humidifier under controlled air composition. Specifically, we assessed the contributions of the evaporative concentration and shock waves in sprays and the effects of trace O$_3$(g) on the H$_2$O$_2$ formation. Glovebox experiments revealed that the H$_2$O$_2$ formation in water microdroplets was most sensitive to the air-borne ozone (O$_3$) concentration. In the absence of O$_3$(g), we could not detect H$_2$O$_2$(aq) in sprays or condensates (detection limit: 250 nM. In contrast, microdroplets exposed to atmospherically relevant O$_3$(g) concentration (10-100 ppb) formed 2-30 $μ$M H$_2$O$_2$(aq); increasing the gas-liquid surface area, mixing, and contact duration increased H$_2$O$_2$(aq) concentration. Thus, the mystery is resolved - the water surface facilitates the O$_3$(g) mass transfer, which is followed by the chemical transformation of O$_3$(aq) into H$_2$O$_2$(aq). These findings should also help us understand the implications of this chemistry in natural and applied contexts.

cond-mat.soft↗

arXiv2021

Prior Attention Network for Multi-Lesion Segmentation in Medical Images

The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. Convolutional neural networks (CNNs) based on the coarse-to-fine strategy have been widely used in this field. However, multi-lesion segmentation remains to be challenging due to the uncertainty in size, contrast, and high interclass similarity of tissues. In addition, the commonly adopted cascaded strategy is rather demanding in terms of hardware, which limits the potential of clinical deployment. To address the problems above,we propose a novel Prior Attention Network (PANet) that follows the coarse-to-fine strategy to perform multi-lesion segmentation in medical images. The proposed network achieves the two steps of segmentation in a single network by inserting lesion-related spatial attention mechanism in the network. Further, we also propose the intermediate supervision strategy for generating lesion-related attention to acquire the regions of interest (ROIs), which accelerates the convergence and obviously improves the segmentation performance. We have investigated the proposed segmentation framework in two applications: 2D segmentation of multiple lung infections in lung CT slices and 3D segmentation of multiple lesions in brain MRIs. Experimental results show that in both 2D and 3D segmentation tasks our proposed network achieves better performance with less computational cost compared with cascaded networks. The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks. The source code is available at: https://github.com/hsiangyuzhao/PANet.

eess.IV↗

arXiv2021

Spin-affected reflexive and stretching separation of off-center droplet collision

Recent studies have demonstrated the significant roles of droplet self-spin motion in affecting the head-on collision of binary droplets. In this paper, we present a computational study by using the Volume-of-Fluid (VOF) method to investigate the spin-affected droplet separation of off-center collisions, which are more probable in reality and phenomenologically richer than head-on collisions. Different separation modes are identified through a parametric study with varying spinning speed and impact parameter. A prominent finding is that increasing the droplet spinning speed tends to suppress the reflexive separation and to promote the stretching separation. Physically, the reflexive separation is suppressed because the increased rotational energy reduces the excessive reflexive kinetic energy within the droplet, which is the cause for the droplet reflexive separation. The stretching separation is promoted because the increased droplet angular momentum enhances the local stretching flow within the droplet, which tends to separate the droplet. The roles of orbital angular momentum and spin angular momentum in affecting the droplet separation are further substantiated by studying the collision between two spinning droplets with either the same or opposite chirality. In addition, a theoretical model based on conservation laws is proposed to qualitatively describe the boundaries of coalescence-separation transition influenced by droplet self-spin motion.

physics.flu-dyn↗

arXiv2021

The air-water interface of condensed water microdroplets does not produce H$_2$O$_2$

Recent reports on the production of hydrogen peroxide (H$_2$O$_2$) on the surface of condensed water microdroplets without the addition of catalysts or additives have sparked significant interest. The underlying mechanism is speculated to be ultrahigh electric fields at the air-water interface; smaller droplets present higher interfacial area and produce higher (detectable) H$_2$O$_2$ yields. Herein, we present an alternative explanation for these experimental observations. We compare H$_2$O$_2$ production in water microdroplets condensed from vapor produced via (i) heating water to 50-70 °C and (ii) ultrasonic humidification (as exploited in the original report). Water microdroplets condensed after heating do not show any enhancement in the H$_2$O$_2$ level in comparison to the bulk water, regardless of droplet size or the substrate wettability. In contrast, those condensed after ultrasonic humidification produce significantly higher H$_2$O$_2$ quantities. We conclude that the ultrasonication of water contributes to the H$_2$O$_2$ production, not droplet interfacial effects.

cond-mat.soft↗

arXiv2021

WIMP Dark Matter Hidden behind its Companion

The WIMP dark matter (DM) hypothesis now is in an awkward position, owing to the stronger and stronger exclusion from DM direct detection. In this article we design a mechanism to evade this constraint.The idea is simple. DM has a companion, and they are both charged under the DM protecting symmetry G; they admit the trilinear coupling DM-DM-companion, so the latter provides a portal to the standard model (SM) via, for instance, the coupling to Higgs doublet.Then, DM semi-annihilates into the companion to arrive correct relic density, without leaving DM-nucleon scattering signal. The idea can be realized for ZN symmetric models with N >2.We stress that this mechanism has the characteristics of co-annihilation, and as a matter of fact its effect becomes necessary near or above the TeV region. This means that it may be difficult to detect our dark matter directly or indirectly.

hep-ph↗

arXiv2021

Hi-Phy: A Benchmark for Hierarchical Physical Reasoning

Reasoning about the behaviour of physical objects is a key capability of agents operating in physical worlds. Humans are very experienced in physical reasoning while it remains a major challenge for AI. To facilitate research addressing this problem, several benchmarks have been proposed recently. However, these benchmarks do not enable us to measure an agent's granular physical reasoning capabilities when solving a complex reasoning task. In this paper, we propose a new benchmark for physical reasoning that allows us to test individual physical reasoning capabilities. Inspired by how humans acquire these capabilities, we propose a general hierarchy of physical reasoning capabilities with increasing complexity. Our benchmark tests capabilities according to this hierarchy through generated physical reasoning tasks in the video game Angry Birds. This benchmark enables us to conduct a comprehensive agent evaluation by measuring the agent's granular physical reasoning capabilities. We conduct an evaluation with human players, learning agents, and heuristic agents and determine their capabilities. Our evaluation shows that learning agents, with good local generalization ability, still struggle to learn the underlying physical reasoning capabilities and perform worse than current state-of-the-art heuristic agents and humans. We believe that this benchmark will encourage researchers to develop intelligent agents with advanced, human-like physical reasoning capabilities. URL: https://github.com/Cheng-Xue/Hi-Phy

cs.AI↗

arXiv2021

Efficient Byzantine-Resilient Stochastic Gradient Desce

Distributed Learning often suffers from Byzantine failures, and there have been a number of works studying the problem of distributed stochastic optimization under Byzantine failures, where only a portion of workers, instead of all the workers in a distributed learning system, compute stochastic gradients at each iteration. These methods, albeit workable under Byzantine failures, have the shortcomings of either a sub-optimal convergence rate or high computation cost. To this end, we propose a new Byzantine-resilient stochastic gradient descent algorithm (BrSGD for short) which is provably robust against Byzantine failures. BrSGD obtains the optimal statistical performance and efficient computation simultaneously. In particular, BrSGD can achieve an order-optimal statistical error rate for strongly convex loss functions. The computation complexity of BrSGD is O(md), where d is the model dimension and m is the number of machines. Experimental results show that BrSGD can obtain competitive results compared with non-Byzantine machines in terms of effectiveness and convergence.

cs.DC↗

arXiv2021

GIPA: General Information Propagation Algorithm for Graph Learning

Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation. In this paper, we present a new graph attention neural network, namely GIPA, for attributed graph data learning. GIPA consists of three key components: attention, feature propagation and aggregation. Specifically, the attention component introduces a new multi-layer perceptron based multi-head to generate better non-linear feature mapping and representation than conventional implementations such as dot-product. The propagation component considers not only node features but also edge features, which differs from existing GNNs that merely consider node features. The aggregation component uses a residual connection to generate the final embedding. We evaluate the performance of GIPA using the Open Graph Benchmark proteins (ogbn-proteins for short) dataset. The experimental results reveal that GIPA can beat the state-of-the-art models in terms of prediction accuracy, e.g., GIPA achieves an average test ROC-AUC of $0.8700\pm 0.0010$ and outperforms all the previous methods listed in the ogbn-proteins leaderboard.

cs.LG↗

arXiv2021

Metallic nature and site-selective magnetic collapse in iron oxide Fe4O5 at the extreme conditions of Earth's deep interior

Properties of iron oxides at the extreme conditions are of essential importance in condensed matter physics and Geophysics. The recent discovery of a new type of iron oxide, Fe4O5, at high pressure and high temperature of Earth's deep interior attracts great interests. In this paper, we report the electronic structure and the magnetic properties of Fe4O5 predicted by the density functional theory plus dynamic mean field theory (DFT+DMFT) approach. We find that Fe4O5 stays metallic from ambient pressure to high pressure. The magnetic moments of iron atoms at the three different crystallographic positions of Fe4O5 undergo position-dependent collapse as being compressed. Such site-selective magnetic moment collapse originates from the shift of energy levels and the consequent charge transfer among the Fe-3d orbits under compression.

cond-mat.str-el↗

arXiv2021

Site-selective magnetic moment collapse in compressed Fe5O6

Iron oxide is one of the most important components in Earth's mantle. Recent discovery of the stable presence of Fe5O6 at Earth's mantle environment stimulates significant interests in the understanding of this new category of iron oxides. In this paper, we report the electronic structure and magnetic properties of Fe5O6 calculated by the density functional theory plus dynamic mean field theory (DFT+DMFT) approach. Our calculations indicate that Fe5O6 is a conductor at the ambient pressure with dominant Fe-3d density of states at the Fermi level. The magnetic moments of iron atoms at three non-equivalent crystallographic sites in Fe5O6 collapse at significantly different rate under pressure. Such site-selective collapse of magnetic moments originates from the shifting of energy levels and the consequent charge transfer among the Fe-3d orbits when Fe5O6 is being compressed. Our simulations suggest that there could be high conductivity and volume contraction in Fe5O6 at high pressure, which may induce anomalous features in seismic velocity, energy exchange, and mass distribution at the deep interior of Earth.

cond-mat.mtrl-sci↗

arXiv2021

Exact mathematical formulas for wall-heat flux in compressible turbulent channel flows

In this paper, several exact expressions for the mean heat flux at the wall ($q_w$) for the compressible turbulent channel flows are derived by using the internal energy equation or the total energy equation. Two different routes, including the FIK method and the RD method, can be applied. The direct numerical simulations data of compressible channel flows at different Reynolds and Mach numbers verify the correctness of the derived formulas. Discussions related to the different energy equations, and different routes are carried out, and we may arrive at the conclusion that most of the formulas derived in the present work are just mathematically ones which generally are lack of clear physical interpretation. They can be used to estimate $q_w$, but might not be suitable for explore the underlying physics.

physics.flu-dyn↗

arXiv2021

Bulk dissipation in the quantum anomalous Hall effect

Even at the lowest accessible temperatures, measurements of the quantum anomalous Hall (QAH) effect have indicated the presence of parasitic dissipative conduction channels. There is no consensus whether parasitic conduction is related to processes in the bulk or along the edges. Here, we approach this problem by comparing transport measurements of Hall bar and Corbino geometry devices fabricated from Cr-doped (BiSb)$_2$Te$_3$. We identify bulk conduction as the dominant source of dissipation at all values of temperature and in-plane electric field. Furthermore, we observe identical breakdown phenomenology in both geometries, indicating that breakdown of the QAH phase is a bulk process. The methodology developed in this study could be used to identify dissipative conduction mechanisms in new QAH materials, ultimately guiding material development towards realization of the QAH effect at higher temperatures.

cond-mat.mes-hall↗

arXiv2021

Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking

The target representation learned by convolutional neural networks plays an important role in Thermal Infrared (TIR) tracking. Currently, most of the top-performing TIR trackers are still employing representations learned by the model trained on the RGB data. However, this representation does not take into account the information in the TIR modality itself, limiting the performance of TIR tracking. To solve this problem, we propose to distill representations of the TIR modality from the RGB modality with Cross-Modal Distillation (CMD) on a large amount of unlabeled paired RGB-TIR data. We take advantage of the two-branch architecture of the baseline tracker, i.e. DiMP, for cross-modal distillation working on two components of the tracker. Specifically, we use one branch as a teacher module to distill the representation learned by the model into the other branch. Benefiting from the powerful model in the RGB modality, the cross-modal distillation can learn the TIR-specific representation for promoting TIR tracking. The proposed approach can be incorporated into different baseline trackers conveniently as a generic and independent component. Furthermore, the semantic coherence of paired RGB and TIR images is utilized as a supervised signal in the distillation loss for cross-modal knowledge transfer. In practice, three different approaches are explored to generate paired RGB-TIR patches with the same semantics for training in an unsupervised way. It is easy to extend to an even larger scale of unlabeled training data. Extensive experiments on the LSOTB-TIR dataset and PTB-TIR dataset demonstrate that our proposed cross-modal distillation method effectively learns TIR-specific target representations transferred from the RGB modality. Our tracker outperforms the baseline tracker by achieving absolute gains of 2.3% Success, 2.7% Precision, and 2.5% Normalized Precision respectively.

cs.CV↗

arXiv2021

Measurement of the relative Quantum Efficiency of Hamamatsu model R5912-20MOD photomultiplier tubes at liquid argon temperature

The model R5912-20MOD photomultiplier tube(PMT) is made for cryogenic application by Hamamatsu. In this paper, we report on the measurement of relative quantum efficiency (QE) of this model PMT at liquid argon(LAr) temperature. Furthermore, a specially designed setup and relevant test method are introduced. The relative QE is measured in visible wavelengths with the PMT emerged in high purity nitrogen atmosphere. The results show that the change of QE at LAr temperature is within about 5% compared with room temperature around 420 nm. However, the QE increases about 10% in the shorter wavelength range and decreases significantly after 550 nm.

physics.ins-det↗

arXiv2021

Visualization of the strain-induced topological phase transition in a quasi-one-dimensional superconductor TaSe3

Control of the phase transition from topological to normal insulators can allow for an on/off switching of spin current. While topological phase transitions have been realized by elemental substitution in semiconducting alloys, such an approach requires the preparation of materials with various compositions, thus it is quite far from a feasible device application, which demands a reversible operation. Here we use angle-resolved photoemission spectroscopy (ARPES) and spin-resolved ARPES to visualize the strain-driven band structure evolution of the quasi-1D superconductor TaSe3. We demonstrate that it undergoes reversible strain-induced topological phase transitions from a strong topological insulator phase with spin-polarized, quasi-1D topological surface states, to topologically trivial semimetal and band insulating phases. The quasi-1D superconductor TaSe3 provides a suitable platform for engineering the topological spintronics, for example as an on/off switch for spin current robust against impurity scattering.

cond-mat.mtrl-sci↗

arXiv2021

WASE: Learning When to Attend for Speaker Extraction in Cocktail Party Environments

In the speaker extraction problem, it is found that additional information from the target speaker contributes to the tracking and extraction of the target speaker, which includes voiceprint, lip movement, facial expression, and spatial information. However, no one cares for the cue of sound onset, which has been emphasized in the auditory scene analysis and psychology. Inspired by it, we explicitly modeled the onset cue and verified the effectiveness in the speaker extraction task. We further extended to the onset/offset cues and got performance improvement. From the perspective of tasks, our onset/offset-based model completes the composite task, a complementary combination of speaker extraction and speaker-dependent voice activity detection. We also combined voiceprint with onset/offset cues. Voiceprint models voice characteristics of the target while onset/offset models the start/end information of the speech. From the perspective of auditory scene analysis, the combination of two perception cues can promote the integrity of the auditory object. The experiment results are also close to state-of-the-art performance, using nearly half of the parameters. We hope that this work will inspire communities of speech processing and psychology, and contribute to communication between them. Our code will be available in https://github.com/aispeech-lab/wase/.

eess.AS↗