arXiv subjects

Jing Chen

Publications and source records attributed to Jing Chen.

At least 253 records · Page 14Linked to original sources

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↗

arXiv2019

Insulator-metal transition and quasi-flat-band of Shastry-Sutherland lattice

Insulator-metal transition is investigated self-consistently on the frustrated Shastry-Sutherland lattice in the framework of Slave-Boson mean-field theory. Due to the presence of quasi-flat band structure characteristic, the system displays a spin-density-wave (SDW) insulating phase at the weak doping levels, which is robust against frustration, and it will be transited into an SDW metallic phase at high doping levels. As further increasing the doping, the temperature or the frustration on the diagonal linking bonds, the magnetic order $m$ will be monotonically suppressed, resulting in the appearance of a paramagnetic metallic phase. Although the Fermi surface of the SDW metallic phase may be immersed by temperature, the number of mobile charges is robust against temperature at weak doping levels.

cond-mat.str-el↗

arXiv2019

Substrate Integrated Bragg Waveguide: an Octave-bandwidth Single-mode Functional Transmission-Line for Millimeter-Wave and Terahertz Applications

We demonstrate an air-core single-mode hollow waveguide that uses Bragg reflector structures in place of the vertical metal walls of the standard rectangular waveguide or via holes of the so-called substrate integrated waveguide. The high-order modes in the waveguide are substantially suppressed by a modal-filtering effect, making the waveguide operate in the fundamental mode over more than one octave. Numerical simulations show that the propagation loss of the proposed waveguide can be lower than that of classic hollow metallic rectangular waveguides at terahertz frequencies, benefiting from a significant reduction in Ohmic loss. To facilitate fabrication and characterization, a proof-of-concept 20 to 45 GHz waveguide is demonstrated, which verifies the properties and advantages of the proposed waveguide. A zero group-velocity dispersion point is observed at near the middle of the operating band. This work offers a step towards a novel hybrid transmission-line medium that can be used in a variety of functional components for broadband millimeter-wave and terahertz applications.

physics.app-ph↗

arXiv2019

Small Profits and Quick Returns: An Incentive Mechanism Design for IoT-based Crowdsourcing under Continuous Platform Competition

Crowdsourcing can be applied to the Internet-of-Things (IoT) systems to provide more scalable and efficient services to support various tasks. As the driving force of crowdsourcing is the interaction among participants, various incentive mechanisms have been proposed to attract and retain a sufficient number of participants to provide a sustainable crowdsourcing service. However, there exist some gaps between the modeled entities or markets in the existing works and those in reality: 1) \textit{dichotomous} task valuation and workers' punctuality, and 2) crowdsourcing service market \textit{monopolized} by a platform. To bridge those gaps of such impractical assumption, we model workers' heterogeneous punctuality behavior and task depreciation over time. Based on those models, we propose an Expected Social Welfare Maximizing (ESWM) mechanism that aims to maximize the expected social welfare by attracting and retaining more participants in the long-term, i.e., multiple rounds of crowdsourcing. In the evaluation, we modeled the continuous competition between the ESWM and one of the existing works in both short-term and long-term scenarios. Simulation results show that the ESWM mechanism achieves higher expected social welfare and platform utility than the benchmark by attracting and retaining more participants. Moreover, we prove that the ESWM mechanism achieves the desirable economic properties: individual rationality, budget-balance, computational efficiency, and truthfulness.

cs.DC↗

arXiv2019

Finite-Temperature Charge Dynamics and the Melting of the Mott Insulator

The Mott insulator is the quintessential strongly correlated electronic state. We obtain complete insight into the physics of the two-dimensional Mott insulator by extending the slave-fermion (holon-doublon) description to finite temperatures. We first benchmark its predictions against state-of-the-art quantum Monte Carlo simulations, demonstrating quantitative agreement. Qualitatively, the short-ranged spin fluctuations both induce holon-doublon bound states and renormalize the charge sector to form the Hubbard bands. The Mott gap is understood as the charge gap renormalized downwards by these spin fluctuations. As temperature increases, the Mott gap closes before the charge gap, causing a pseudogap regime to appear naturally during the melting of the Mott insulator.

cond-mat.str-el↗

arXiv2019

Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor

Planning safe trajectories for autonomous vehicles in complex urban environments is challenging since there are numerous semantic elements (such as dynamic agents, traffic lights and speed limits) to consider. These semantic elements may have different mathematical descriptions such as obstacle, constraint and cost. It is non-trivial to tune the effects from different combinations of semantic elements for a stable and generalizable behavior. In this paper, we propose a novel unified spatio-temporal semantic corridor (SSC) structure, which provides a level of abstraction for different types of semantic elements. The SSC consists of a series of mutually connected collision-free cubes with dynamical constraints posed by the semantic elements in the spatio-temporal domain. The trajectory generation problem then boils down to a general quadratic programming (QP) formulation. Thanks to the unified SSC representation, our framework can generalize to any combination of semantic elements. Moreover, our formulation provides a theoretical guarantee that the entire trajectory is safe and constraint-satisfied, by using the convex hull and hodograph properties of piecewise Bezier curve parameterization. We also release the code of our method to accommodate benchmarking.

cs.RO↗

arXiv2019

Quantum dynamics of atomic Rydberg excitation in strong laser fields

Neutral atoms have been observed to survive intense laser pulses in high Rydberg states with surprisingly large probability. Only with this Rydberg-state excitation (RSE) included is the picture of intense-laser-atom interaction complete. Various mechanisms have been proposed to explain the underlying physics. However, neither one can explain all the features observed in experiments and in time-dependent Schrödinger equation (TDSE) simulations. Here we propose a fully quantum-mechanical model based on the strong-field approximation (SFA). It well reproduces the intensity dependence of RSE obtained by the TDSE, which exhibits a series of modulated peaks. They are due to recapture of the liberated electron and the fact that the pertinent probability strongly depends on the position and the parity of the Rydberg state. We also present measurements of RSE in xenon at 800 nm, which display the peak structure consistent with the calculations.

physics.atom-ph↗

arXiv2019

A Books Recommendation Approach Based on Online Bookstore Data

In the era of information explosion, facing complex information, it is difficult for users to choose the information of interest, and businesses also need detailed information on ways to let the ad stand out. By this time, it is recommended that a good way. We firstly by using random interviews, simulations, asking experts, summarizes methods outlined the main factors affecting the scores of books that users drew. In order to further illustrate the impact of these factors, we also by combining the AHP consistency test, then fuzzy evaluation method, empowered each factor, influencing factors and the degree of influence come. For the second question, predict user evaluation of the listed books from the predict annex. First, given the books Annex labels, user data extraction scorebooks and mathematical analysis of data obtained from SPSS user preferences and then use software to nearest neighbor analysis to result in predicted value.

cs.DB↗

arXiv2019

Assessment of Urban Ecological Service value used in Urban Rail Transit Project

Ecosystem services refer to the ones human beings often obtain from the natural environment ecosystem. In order to solve the problem of environmental degradation, based on the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST model), this paper makes innovation by adding the urban module that was not in the previous models, which can better deal with the evaluation of ecosystem services in urban scenarios.

cs.OH↗

arXiv2019

To save the Environmental degradation: Please pay for ecosystem services!

Project planners should consider the monetary value of ecosystem services in the regional service category and add it to the cost calculation of the project as environmental cost. Managers should monitor and regularly maintain the ecological environment within the project area to maintain the benefits of environmental costs.With regard to the problem of predicting how the model changes over time, we use LSTM to realize dynamic evaluation. The analysis that has been made shows that the monetary value of urban ecological services has increased. The environment is still being damaged. Through neural network memory and learning, the problem can be found, and the model can be modified in time.

cs.CY↗

arXiv2019

SmartSeed: Smart Seed Generation for Efficient Fuzzing

Fuzzing is an automated application vulnerability detection method. For genetic algorithm-based fuzzing, it can mutate the seed files provided by users to obtain a number of inputs, which are then used to test the objective application in order to trigger potential crashes. As shown in existing literature, the seed file selection is crucial for the efficiency of fuzzing. However, current seed selection strategies do not seem to be better than randomly picking seed files. Therefore, in this paper, we propose a novel and generic system, named SmartSeed, to generate seed files towards efficient fuzzing. Specifically, SmartSeed is designed based on a machine learning model to learn and generate high-value binary seeds. We evaluate SmartSeed along with American Fuzzy Lop (AFL) on 12 open-source applications with the input formats of mp3, bmp or flv. We also combine SmartSeed with different fuzzing tools to examine its compatibility. From extensive experiments, we find that SmartSeed has the following advantages: First, it only requires tens of seconds to generate sufficient high-value seeds. Second, it can generate seeds with multiple kinds of input formats and significantly improves the fuzzing performance for most applications with the same input format. Third, SmartSeed is compatible to different fuzzing tools. In total, our system discovers more than twice unique crashes and 5,040 extra unique paths than the existing best seed selection strategy for the evaluated 12 applications. From the crashes found by SmartSeed, we discover 16 new vulnerabilities and have received their CVE IDs.

cs.CR↗

arXiv2019

Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network

Anticipating possible behaviors of traffic participants is an essential capability of autonomous vehicles. Many behavior detection and maneuver recognition methods only have a very limited prediction horizon that leaves inadequate time and space for planning. To avoid unsatisfactory reactive decisions, it is essential to count long-term future rewards in planning, which requires extending the prediction horizon. In this paper, we uncover that clues to vehicle behaviors over an extended horizon can be found in vehicle interaction, which makes it possible to anticipate the likelihood of a certain behavior, even in the absence of any clear maneuver pattern. We adopt a recurrent neural network (RNN) for observation encoding, and based on that, we propose a novel vehicle behavior interaction network (VBIN) to capture the vehicle interaction from the hidden states and connection feature of each interaction pair. The output of our method is a probabilistic likelihood of multiple behavior classes, which matches the multimodal and uncertain nature of the distant future. A systematic comparison of our method against two state-of-the-art methods and another two baseline methods on a publicly available real highway dataset is provided, showing that our method has superior accuracy and advanced capability for interaction modeling.

cs.RO↗

arXiv2019

Approximately Maximizing the Broker's Profit in a Two-sided Market

We study how to maximize the broker's (expected) profit in a two-sided market, where she buys items from a set of sellers and resells them to a set of buyers. Each seller has a single item to sell and holds a private value on her item, and each buyer has a valuation function over the bundles of the sellers' items. We consider the Bayesian setting where the agents' values are independently drawn from prior distributions, and aim at designing dominant-strategy incentive-compatible (DSIC) mechanisms that are approximately optimal. Production-cost markets, where each item has a publicly-known cost to be produced, provide a platform for us to study two-sided markets. Briefly, we show how to covert a mechanism for production-cost markets into a mechanism for the broker, whenever the former satisfies cost-monotonicity. This reduction holds even when buyers have general combinatorial valuation functions. When the buyers' valuations are additive, we generalize an existing mechanism to production-cost markets in an approximation-preserving way. We then show that the resulting mechanism is cost-monotone and thus can be converted into an 8-approximation mechanism for two-sided markets.

cs.GT↗

arXiv2019

Adaptive DDoS attack detection method based on multiple-kernel learning

Distributed denial of service (DDoS) attacks have caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the inter-class mean with a gradient ascent and reducing the intra-class variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple kernel learning (SMKL) models with two characteristics including inter-class mean squared difference growth (M-SMKL) and intra-class variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

cs.CR↗