arXiv subjects

Jing Chen

Publications and source records attributed to Jing Chen.

At least 235 records · Page 13Linked to original sources

arXiv2020

Towards a Verified Model of the Algorand Consensus Protocol in Coq

The Algorand blockchain is a secure and decentralized public ledger based on pure proof of stake rather than proof of work. At its core it is a novel consensus protocol with exactly one block certified in each round: that is, the protocol guarantees that the blockchain does not fork. In this paper, we report on our effort to model and formally verify the Algorand consensus protocol in the Coq proof assistant. Similar to previous consensus protocol verification efforts, we model the protocol as a state transition system and reason over reachable global states. However, in contrast to previous work, our model explicitly incorporates timing issues (e.g., timeouts and network delays) and adversarial actions, reflecting a more realistic environment faced by a public blockchain. Thus far, we have proved asynchronous safety of the protocol: two different blocks cannot be certified in the same round, even when the adversary has complete control of message delivery in the network. We believe that our model is sufficiently general and other relevant properties of the protocol such as liveness can be proved for the same model.

cs.CR↗

arXiv2020

Universality of the dynamic characteristic relationship of electron correlation in the two-photon double ionization process of a helium-like system

Universality of the dynamic characteristic relationship between the characteristic time $t_c$ and the two-electron Coulomb interaction energy $\overline{V}_{12}$ of the ground state in the two-photon double ionization process is investigated through changing the parameters of the two-electron atomic system and the corresponding laser conditions. The numerical results show that the product $t_{c}\overline{V}_{12}$ keeps constant around 4.1 in the cases of changing the nucleus charge, the electron charge, the electron mass, and changing simultaneously the nucleus charge and the electron charge. These results demonstrate that the dynamic characteristic relationship in the two-photon double ionization process is universal. This work sheds more light on the the dynamic characteristic relationship in ultrafast processes and may find its application in the measurement of attosecond pulses.

physics.atom-ph↗

arXiv2020

Bayesian Auctions with Efficient Queries

Generating good revenue is one of the most important problems in Bayesian auction design, and many (approximately) optimal dominant-strategy incentive compatible (DSIC) Bayesian mechanisms have been constructed for various auction settings. However, most existing studies do not consider the complexity for the seller to carry out the mechanism. It is assumed that the seller knows "each single bit" of the distributions and is able to optimize perfectly based on the entire distributions. Unfortunately, this is a strong assumption and may not hold in reality: for example, when the value distributions have exponentially large supports or do not have succinct representations. In this work we consider, for the first time, the query complexity of Bayesian mechanisms. We only allow the seller to have limited oracle accesses to the players' value distributions, via quantile queries and value queries. For a large class of auction settings, we prove logarithmic lower-bounds for the query complexity for any DSIC Bayesian mechanism to be of any constant approximation to the optimal revenue. For single-item auctions and multi-item auctions with unit-demand or additive valuation functions, we prove tight upper-bounds via efficient query schemes, without requiring the distributions to be regular or have monotone hazard rate. Thus, in those auction settings the seller needs to access much less than the full distributions in order to achieve approximately optimal revenue.

cs.GT↗

arXiv2020

Spectral-change enhancement with prior SNR for the hearing impaired

A previous signal processing algorithm that aimed to enhance spectral changes (SCE) over time showed benefit for hearing-impaired (HI) listeners to recognize speech in background noise. In this work, the previous SCE was manipulated to perform on target-dominant segments, rather than treating all frames equally. Instantaneous signal-to-noise ratios (SNRs) were calculated to determine whether the segments should be processed. Initially, the ideal SNR calculated by the knowledge of premixed signals was introduced to the previous SCE algorithm (SCE-iSNR). Speech intelligibility (SI) and clarity preference were measured for 12 HI listeners in steady speech-spectrum noise (SSN) and six-talk speech (STS) maskers, respectively. The results showed the SCE-iSNR algorithm improved SI significantly for both maskers at high signal-to-masker ratios (SMRs) and for STS masker at low SMRs, while processing effect on speech quality was small. Secondly, the estimated SNR obtained from real mixtures was used, resulting in another SCE-eSNR. SI and subjective rating on naturalness and speech quality were tested for 7 HI subjects. The SCE-eSNR algorithm showed improved SI for SSN masker at high SMRs and for STS masker at low SMRs, as well as better naturalness and speech quality for STS masker. The limitations of applying the algorithms are discussed.

eess.AS↗

arXiv2020

HEU Emotion: A Large-scale Database for Multi-modal Emotion Recognition in the Wild

The study of affective computing in the wild setting is underpinned by databases. Existing multimodal emotion databases in the real-world conditions are few and small, with a limited number of subjects and expressed in a single language. To meet this requirement, we collected, annotated, and prepared to release a new natural state video database (called HEU Emotion). HEU Emotion contains a total of 19,004 video clips, which is divided into two parts according to the data source. The first part contains videos downloaded from Tumblr, Google, and Giphy, including 10 emotions and two modalities (facial expression and body posture). The second part includes corpus taken manually from movies, TV series, and variety shows, consisting of 10 emotions and three modalities (facial expression, body posture, and emotional speech). HEU Emotion is by far the most extensive multi-modal emotional database with 9,951 subjects. In order to provide a benchmark for emotion recognition, we used many conventional machine learning and deep learning methods to evaluate HEU Emotion. We proposed a Multi-modal Attention module to fuse multi-modal features adaptively. After multi-modal fusion, the recognition accuracies for the two parts increased by 2.19% and 4.01% respectively over those of single-modal facial expression recognition.

cs.CV↗

arXiv2020

Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology

The widespread popularity of unmanned aerial vehicles enables an immense amount of power lines inspection data to be collected. How to employ massive inspection data especially the visible images to maintain the reliability, safety, and sustainability of power transmission is a pressing issue. To date, substantial works have been conducted on the analysis of power lines inspection data. With the aim of providing a comprehensive overview for researchers who are interested in developing a deep-learning-based analysis system for power lines inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future research. Following the typical procedure of inspection data analysis, we categorize current works in this area into component detection and fault diagnosis. For each aspect, the techniques and methodologies adopted in the literature are summarized. Some valuable information is also included such as data description and method performance. Further, an in-depth discussion of existing deep-learning-related analysis methods in power lines inspection is proposed. Finally, we conclude the paper with several research trends for the future of this area, such as data quality problems, small object detection, embedded application, and evaluation baseline.

cs.CV↗

arXiv2020

The effect of Coulomb field on laser-induced ultrafast imaging methods

By performing a joint theoretical and experimental investigation on the high-order above-threshold ionization (HATI) spectrum, the dominant role of the 3rd-return-recollision trajectories in the region near the cutoff due to the ionic Coulomb field is identified. This invalidates the key assumption adopted in the conventional laser-induced electron diffraction (LIED) approach that the 1st-returnrecollision trajectories dominate the spectrum according to strong field approximation (SFA). Our results show that the incident (return) electron beams produced by the 1st and 3rd returns possess distinct characteristics of beam energy, beam diameter and temporal evolution law due to the influence of Coulomb field, and therefore the extracted results in the LIED will be altered if the significance of the 3rd-return-recollision trajectories is properly considered in the analysis. Such Coulomb field effect should be taken into account in all kinds of laser-induced imaging schemes based on recollision.

physics.atom-ph↗

arXiv2020

Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching

Decision-making in dense traffic scenarios is challenging for automated vehicles (AVs) due to potentially stochastic behaviors of other traffic participants and perception uncertainties (e.g., tracking noise and prediction errors, etc.). Although the partially observable Markov decision process (POMDP) provides a systematic way to incorporate these uncertainties, it quickly becomes computationally intractable when scaled to the real-world large-size problem. In this paper, we present an efficient uncertainty-aware decision-making (EUDM) framework, which generates long-term lateral and longitudinal behaviors in complex driving environments in real-time. The computation complexity is controlled to an appropriate level by two novel techniques, namely, the domain-specific closed-loop policy tree (DCP-Tree) structure and conditional focused branching (CFB) mechanism. The key idea is utilizing domain-specific expert knowledge to guide the branching in both action and intention space. The proposed framework is validated using both onboard sensing data captured by a real vehicle and an interactive multi-agent simulation platform. We also release the code of our framework to accommodate benchmarking.

cs.RO↗

arXiv2020

Strong field frustrated double ionization of argon atoms

Using a three-dimensional semiclassical method, we theoretically investigate frustrated double ionization (FDI) of Ar atoms subjected to strong laser fields. The double-hump photoelectron momentum distribution generated from FDI observed in a recent experiment [S. Larimian et al., Phys. Rev. Research 2, 013021 (2020)] is reproduced by our simulation. We confirm that the observed spectrum is due to recollision. The laser intensity dependence of FDI is investigated. We reveal that the doubly excited states of Ar atoms and excited states of Ar+ are the dominant pathways for producing FDI at relatively low and high intensities, respectively. Our work demonstrates that at modest intensities, FDI is a general strong-field physical process accompanied with nonsequential double ionization and it is an important consequence of recollision.

physics.atom-ph↗

arXiv2020

Low-Energy Interference Structure with Attosecond Temporal Resolution

Accessing precisely to the phase variation of electronic wave-packet (EWP) provides unprecedented spatiotemporal information of microworld. A radial interference pattern at near-zero energy has been widely observed in experiments of strong-field photoionization. However, the underlying physical picture of this interference pattern is still under debate. Here we report an experimental and theoretical investigation of this low-energy interference structure (LEIS) in mid-infrared laser fields. We clarify that the LEIS arises due to the soft-recollision mechanism, which was previously found to play a pivotal role in producing the pronounced low-energy structure. Specifically, the LEIS is induced by the interference between direct and soft-recollision EWPs launched within a 1/18 laser-cycle time scale in our experiments. Moreover, the observation of LEIS is independent of laser wavelength and specific atomic targets. Our result opens a promising new avenue for retrieving the structure and dynamics of EWPs in atoms and molecules with attosecond time resolution.

physics.atom-ph↗

arXiv2020

Weak cosmic censorship conjecture for Kerr-Taub-NUT black hole with test scalar field and particle

Motivated by the recent researches of black holes with NUT charge, we investigate the validity of the weak cosmic censorship conjecture for Kerr-Taub-NUT black hole with a test massive scalar field and a test particle, respectively. For the scalar field scattering gedanken experiment, we consider an infinitesimal time interval process. The result shows that both extremal and near-extremal Kerr-Taub-NUT black holes cannot be over-spun. For the test particle thought experiment, the study suggests that extremal Kerr-Taub-NUT black hole cannot be over-spun; while near-extremal Kerr-Taub-NUT black hole can be over-spun. By comparing the two methods, the results indicate the time interval for particles crossing the black hole horizon might be important for consideration of the weak cosmic censorship conjecture.

gr-qc↗

arXiv2019

Imaging the geometrical structure of complex symmetric molecules by above-threshold ionization spectrum in an IR+XUV laser field

Based on the frequency-domain theory, we put forward a method of imaging the complex symmetrical polyatomic molecules by using IR+XUV two-color laser fields. Although the wave function of a polyatomic molecule, such as SF6, is quite complex in momentum space, it can be simplified by several terms in the region of high value of momentum, where the destructive interference fringes from these terms carry the information of the molecular structure. In this work, we find that, since the XUV laser field may increases the kinetic energy of the photoelectron dramatically in the above-threshold ionization (ATI) process of a molecule in an IR+XUV two-color laser field, the information of the molecular structure can be obtained by analyzing the interference fringes of the ATI spectrum in the high-energy region, where the simplified wave function of the molecule plays a dominant role in this part of the ATI spectrum. This method provides a unique accessible route towards imaging polyatomic molecules from a frequency perspective.

physics.optics↗

arXiv2019

A Heterogeneous Graphical Model to Understand User-Level Sentiments in Social Media

Social Media has seen a tremendous growth in the last decade and is continuing to grow at a rapid pace. With such adoption, it is increasingly becoming a rich source of data for opinion mining and sentiment analysis. The detection and analysis of sentiment in social media is thus a valuable topic and attracts a lot of research efforts. Most of the earlier efforts focus on supervised learning approaches to solve this problem, which require expensive human annotations and therefore limits their practical use. In our work, we propose a semi-supervised approach to predict user-level sentiments for specific topics. We define and utilize a heterogeneous graph built from the social networks of the users with the knowledge that connected users in social networks typically share similar sentiments. Compared with the previous works, we have several novelties: (1) we incorporate the influences/authoritativeness of the users into the model, 2) we include comment-based and like-based user-user links to the graph, 3) we superimpose multiple heterogeneous graphs into one thereby allowing multiple types of links to exist between two users.

cs.LG↗

arXiv2019

Lithospheric delamination beneath the southern Puna plateau resolved by local earthquake tomography

We present a local earthquake tomography to illuminate the crustal and uppermost mantle structure beneath the southern Puna plateau and to test the delamination hypothesis. Vp and Vp/Vs ratios were obtained using travel time variations recorded by 75 temporary seismic stations between 2007 and 2009. In the upper crust, prominent low Vp anomalies are found beneath the main volcanic centers, indicating the presence of magma and melt beneath the southern Puna plateau. In the lowlands to the southeast of the Puna plateau, below the Sierras Pampeanas, a high Vp body is observed in the crust. Beneath the Moho at around 90 km depth, a strong high Vp anomaly is detected just west of the giant backarc Cerro Galan Ignimbrite caldera with the robustness of this feature being confirmed by multiple synthetic tests. This high velocity body can be interpreted as a delaminated block of lower crust and uppermost mantle lithosphere under the southern Puna plateau. The low velocities in the crust can be interpreted as having been induced by the delamination event that triggered the rise of fluids and melts into the crust and induced the high topography in this part of the plateau. The tomography also reveals low velocity anomalies that link arc magnetism at the Ojos del Salado volcanic center with slab seismicity clusters at depths of about 100 and 150 km and support fluid fluxing in the mantle wedge due to dehydration reaction within the subducted slab.

physics.geo-ph↗

arXiv2019

How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions

We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting MQR dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. We provide human annotations for a subset of the dataset as a quality estimate. When moving from ill-formed to well-formed questions, the question quality improves by an average of 45 points across three aspects. We train sequence-to-sequence neural models on the constructed dataset and obtain an improvement of 13.2% in BLEU-4 over baseline methods built from other data resources. We release the MQR dataset to encourage research on the problem of question rewriting.

cs.CL↗

arXiv2019

Deep Surface Normal Estimation with Hierarchical RGB-D Fusion

The growing availability of commodity RGB-D cameras has boosted the applications in the field of scene understanding. However, as a fundamental scene understanding task, surface normal estimation from RGB-D data lacks thorough investigation. In this paper, a hierarchical fusion network with adaptive feature re-weighting is proposed for surface normal estimation from a single RGB-D image. Specifically, the features from color image and depth are successively integrated at multiple scales to ensure global surface smoothness while preserving visually salient details. Meanwhile, the depth features are re-weighted with a confidence map estimated from depth before merging into the color branch to avoid artifacts caused by input depth corruption. Additionally, a hybrid multi-scale loss function is designed to learn accurate normal estimation given noisy ground-truth dataset. Extensive experimental results validate the effectiveness of the fusion strategy and the loss design, outperforming state-of-the-art normal estimation schemes.

cs.CV↗

arXiv2019

Maximin-Aware Allocations of Indivisible Goods

We study envy-free allocations of indivisible goods to agents in settings where each agent is unaware of the goods allocated to other agents. In particular, we propose the maximin aware (MMA) fairness measure, which guarantees that every agent, given the bundle allocated to her, is aware that she does not envy at least one other agent, even if she does not know how the other goods are distributed among other agents. We also introduce two of its relaxations and discuss their egalitarian guarantee and existence. Finally, we present a polynomial-time algorithm, which computes an allocation that approximately satisfies MMA or its relaxations. Interestingly, the returned allocation is also 1/2 -approximate EFX when all agents have subadditive valuations, which improves the algorithm in [Plaut and Roughgarden, SODA 2018].

cs.GT↗

arXiv2019

Hybrid Purification and Sampling Approach for Thermal Quantum Systems

We propose an algorithm which combines the beneficial aspects of two different methods for studying finite-temperature quantum systems with tensor networks. One approach is the ancilla method, which gives high-precision results but scales poorly at low temperatures. The other method is the minimally entangled typical thermal state (METTS) sampling algorithm which scales better than the ancilla method at low temperatures and can be parallelized, but requires many samples to converge to a precise result. Our proposed hybrid of these two methods purifies physical sites in a small central spatial region with partner ancilla sites, sampling the remaining sites using the METTS algorithm. Observables measured within the purified cluster have much lower sample variance than in the METTS approach, while sampling the sites outside the cluster reduces their entanglement and the computational cost of the algorithm. The sampling steps of the algorithm remain straightforwardly parallelizable. The hybrid approach also solves an important technical issue with METTS that makes it difficult to benefit from quantum number conservation. By studying S=1 Heisenberg ladder systems, we find the hybrid method converges more quickly than both the ancilla and METTS algorithms at intermediate temperatures and for systems with higher entanglement.

cond-mat.str-el↗