arXiv subjects

Cheng Chen

Publications and source records attributed to Cheng Chen.

At least 379 records · Page 21Linked to original sources

arXiv2018

Late Delivery of Nitrogen to Earth

Atmospheric nitrogen may be a necessary ingredient for the habitability of a planet since its presence helps to prevent water loss from a planet. The present day nitrogen isotopic ratio, $^{15}$N/$^{14}$N, in the Earth's atmosphere is a combination of the primitive Earth's ratio and the ratio that might have been delivered in comets and asteroids. Asteroids have a nitrogen isotopic ratio that is close to the Earth's. This indicates either a similar formation environment to the Earth or that the main source of nitrogen was delivery by asteroids. However, according to geological records, the Earth's atmosphere could have been enriched in $^{15}$N during the Archean era. Comets have higher a $^{15}$N/$^{14}$N ratio than the current atmosphere of the Earth and we find that about $5\%$ $\sim$ $10\%$ of nitrogen in the atmosphere of the Earth may have been delivered by comets to explain the current Earth's atmosphere or the enriched $^{15}$N Earth's atmosphere. We model the evolution of the radii of the snow lines of molecular nitrogen and ammonia in a protoplanetary disk and find that both have radii that put them farther from the Sun than the main asteroid belt. With an analytic secular resonance model and N--body simulations we find that the $ν_8$ apsidal precession secular resonance with Neptune, which is located in the Kuiper belt, is a likely origin for the nitrogen-delivering comets that impact the Earth.

astro-ph.EP↗

arXiv2018

Anomalous quantum Griffiths singularity in ultrathin crystalline lead films

We study the magnetic field induced superconductor-metal transition (SMT) in ultrathin crystalline Pb films. By performing ultralow temperature transport measurement, the divergent critical exponent as an indication of quantum Griffiths singularity (QGS) is observed when approaching zero temperature quantum critical point. Distinctively, the phase boundary of SMT exhibits an anomalous behavior in low temperature regime that the onset critical field decreases with decreasing temperatures, which distinguishes our observation from earlier reports of QGS in various two-dimensional superconductors. We demonstrate that this observed anomalous phase boundary has its origin from the superconducting fluctuations in ultrathin Pb films. Our findings reveal a novel aspect of the QGS of SMT in two-dimentional superconducting systems with anomalous phase boundary.

cond-mat.supr-con↗

arXiv2018

A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising

In online display advertising, guaranteed contracts and real-time bidding (RTB) are two major ways to sell impressions for a publisher. Despite the increasing popularity of RTB, there is still half of online display advertising revenue generated from guaranteed contracts. Therefore, simultaneously selling impressions through both guaranteed contracts and RTB is a straightforward choice for a publisher to maximize its yield. However, deriving the optimal strategy to allocate impressions is not a trivial task, especially when the environment is unstable in real-world applications. In this paper, we formulate the impression allocation problem as an auction problem where each contract can submit virtual bids for individual impressions. With this formulation, we derive the optimal impression allocation strategy by solving the optimal bidding functions for contracts. Since the bids from contracts are decided by the publisher, we propose a multi-agent reinforcement learning (MARL) approach to derive cooperative policies for the publisher to maximize its yield in an unstable environment. The proposed approach also resolves the common challenges in MARL such as input dimension explosion, reward credit assignment, and non-stationary environment. Experimental evaluations on large-scale real datasets demonstrate the effectiveness of our approach.

cs.AI↗

arXiv2018

Sourcerer's Apprentice and the study of code snippet migration

On the worldwide web, not only are webpages connected but source code is too. Software development is becoming more accessible to everyone and the licensing for software remains complicated. We need to know if software licenses are being maintained properly throughout their reuse and evolution. This motivated the development of the Sourcerer's Apprentice, a webservice that helps track clone relicensing, because software typically employ software licenses to describe how their software may be used and adapted. But most developers do not have the legal expertise to sort out license conflicts. In this paper we put the Apprentice to work on empirical studies that demonstrate there is much sharing between StackOverflow code and Python modules and Python documentation that violates the licensing of the original Python modules and documentation: software snippets shared through StackOverflow are often being relicensed improperly to CC-BY-SA 3.0 without maintaining the appropriate attribution. We show that many snippets on StackOverflow are inappropriately relicensed by StackOverflow users, jeopardizing the status of the software built by companies and developers who reuse StackOverflow snippets.

cs.SE↗

arXiv2018

Detection of bosonic mode as a signature of magnetic excitation in one unit cell FeSe on SrTiO3

We report an in situ scanning tunneling spectroscopy study of one-unit-cell (1-UC) FeSe film on SrTiO3(001) (STO) substrate. In quasiparticle density of states, bosonic excitation mode characterized by the "dip-hump" structure is detected outside the larger superconducting gap with energy comparable with phonon and spin resonance modes in heavily electron-doped iron selenides. Statistically, the excitation mode, which is intimately correlated with superconductivity, shows an anticorrelation with pairing strength and yields an energy scale upper-bounded by twice the superconducting gap coinciding with the characteristics of magnetic resonance in cuprates and iron-based superconductors. The local response of tunneling spectra to magnetically different Se defects all exhibits the induced in-gap quasiparticle bound states, indicating an unconventional sign-reversing pairing. These results support the magnetic nature of the excitation mode and possibly reveal a signature of electron-magnetic-excitation coupling in high-temperature superconductivity of 1-UC FeSe/STO.

cond-mat.supr-con↗

arXiv2018

Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

Convolutional networks (ConvNets) have achieved great successes in various challenging vision tasks. However, the performance of ConvNets would degrade when encountering the domain shift. The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions. Given that annotating the medical data is especially expensive, the supervised transfer learning approaches are not quite optimal. In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations. Specifically, our model is based on a dilated fully convolutional network for pixel-wise prediction. Moreover, we build a plug-and-play domain adaptation module (DAM) to map the target input to features which are aligned with source domain feature space. A domain critic module (DCM) is set up for discriminating the feature space of both domains. We optimize the DAM and DCM via an adversarial loss without using any target domain label. Our proposed method is validated by adapting a ConvNet trained with MRI images to unpaired CT data for cardiac structures segmentations, and achieved very promising results.

cs.CV↗

arXiv2018

Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation

In spite of the compelling achievements that deep neural networks (DNNs) have made in medical image computing, these deep models often suffer from degraded performance when being applied to new test datasets with domain shift. In this paper, we present a novel unsupervised domain adaptation approach for segmentation tasks by designing semantic-aware generative adversarial networks (GANs). Specifically, we transform the test image into the appearance of source domain, with the semantic structural information being well preserved, which is achieved by imposing a nested adversarial learning in semantic label space. In this way, the segmentation DNN learned from the source domain is able to be directly generalized to the transformed test image, eliminating the need of training a new model for every new target dataset. Our domain adaptation procedure is unsupervised, without using any target domain labels. The adversarial learning of our network is guided by a GAN loss for mapping data distributions, a cycle-consistency loss for retaining pixel-level content, and a semantic-aware loss for enhancing structural information. We validated our method on two different chest X-ray public datasets for left/right lung segmentation. Experimental results show that the segmentation performance of our unsupervised approach is highly competitive with the upper bound of supervised transfer learning.

cs.CV↗

arXiv2018

Design of a Non-vacuum-cooling Compact Scientific CCD Camera

CCD was born in Bell Laboratories in 1969 and has been widely used in various fields. Its ultra-low noise and high quantum efficiency make it work well in particle physics, high energy physics, nuclear physics and astrophysics. Nowadays, more and more CCD cameras have been developed for medical diagnosis, scientific experiments, aerospace, military exploration and other fields. For the wide range of CCD cameras, a Non-vacuum-cooling compact (NVCC) scientific CCD camera has been developed, including FPGA-based low noise clock and bias driver circuit, data acquisition circuit, STM32-based temperature control design. At the same time, the readout noise of the imaging system is studied emphatically. The scheme to generate the CCD clock and the bias driving circuit through ultralow noise LDOs is proposed. The camera was tested in a variety of environments, and the test results show that the system can run at a maximum rate of 5M pixels/s and readout noise is as low as 9.29e^- when the CCD readout speed is 500K pixels/s. Finally, a series of stability tests were carried out on the camera system.

physics.ins-det↗

arXiv2018

RPC Considered Harmful: Fast Distributed Deep Learning on RDMA

Deep learning emerges as an important new resource-intensive workload and has been successfully applied in computer vision, speech, natural language processing, and so on. Distributed deep learning is becoming a necessity to cope with growing data and model sizes. Its computation is typically characterized by a simple tensor data abstraction to model multi-dimensional matrices, a data-flow graph to model computation, and iterative executions with relatively frequent synchronizations, thereby making it substantially different from Map/Reduce style distributed big data computation. RPC, commonly used as the communication primitive, has been adopted by popular deep learning frameworks such as TensorFlow, which uses gRPC. We show that RPC is sub-optimal for distributed deep learning computation, especially on an RDMA-capable network. The tensor abstraction and data-flow graph, coupled with an RDMA network, offers the opportunity to reduce the unnecessary overhead (e.g., memory copy) without sacrificing programmability and generality. In particular, from a data access point of view, a remote machine is abstracted just as a "device" on an RDMA channel, with a simple memory interface for allocating, reading, and writing memory regions. Our graph analyzer looks at both the data flow graph and the tensors to optimize memory allocation and remote data access using this interface. The result is up to 25 times speedup in representative deep learning benchmarks against the standard gRPC in TensorFlow and up to 169% improvement even against an RPC implementation optimized for RDMA, leading to faster convergence in the training process.

cs.DC↗

arXiv2018

Spatial mapping and analysis of aerosols during a forest fire using computational mobile microscopy

Forest fires are a major source of particulate matter (PM) air pollution on a global scale. The composition and impact of PM are typically studied using only laboratory instruments and extrapolated to real fire events owing to a lack of analytical techniques suitable for field-settings. To address this and similar field test challenges, we developed a mobile-microscopy and machine-learning-based air quality monitoring platform called c-Air, which can perform air sampling and microscopic analysis of aerosols in an integrated portable device. We tested its performance for PM sizing and morphological analysis during a recent forest fire event in La Tuna Canyon Park by spatially mapping the PM. The result shows that with decreasing distance to the fire site, the PM concentration increases dramatically, especially for particles smaller than 2 microns. Image analysis from the c-Air portable device also shows that the increased PM is comparatively strongly absorbing and asymmetric, with an aspect ratio of 0.5-0.7. These PM features indicate that a major portion of the PM may be open-flame-combustion-generated element carbon soot-type particles. This initial small-scale experiment shows that c-Air has some potential for forest fire monitoring.

physics.ins-det↗

arXiv2018

GARDENIA: A Domain-specific Benchmark Suite for Next-generation Accelerators

This paper presents the Graph Analytics Repository for Designing Next-generation Accelerators (GARDENIA), a benchmark suite for studying irregular algorithms on massively parallel accelerators. Existing generic benchmarks for accelerators have mainly focused on high performance computing (HPC) applications with limited control and data irregularity, while available graph analytics benchmarks do not apply state-of-the-art algorithms and/or optimization techniques. GARDENIA includes emerging irregular applications in big-data and machine learning domains which mimic massively multithreaded commercial programs running on modern large-scale datacenters. Our characterization shows that GARDENIA exhibits irregular microarchitectural behavior which is quite different from structured workloads and straightforward-implemented graph benchmarks.

cs.PF↗

arXiv2017

Fast simulation of the CEPC detector with Delphes

Fast simulation tools are highly appreciated in particle physics phenomenology studies, especially in the exploration of the physics potential of future experimental facilities. The Circular Electron Positron Collider is a proposed Higgs and Z factory that can precisely measure the Higgs boson properties and the electroweak precision observables. A fast-simulation toolkit dedicated to the CEPC detector has been developed using Delphes. The comparison shows that this fast simulation tool is highly consistent with the full simulation, on a set of benchmark distributions. Therefore, we recommend this fast simulation toolkit for CEPC phenomenological investigations.

hep-ex↗

arXiv2017

Ultrafast, highly-sensitive infrared photodetectors based on two-dimensional oxyselenide crystals

Infrared detection and sensing is deeply embedded in modern technology and human society and its development has always been benefitting from the discovery of new photoelectric response materials. The rise of two-dimensional (2D) materials, thanks to their distinct electronic structure, extreme dimensional confinement and strong light-matter interactions, provides new material platform for next-generation infrared photodetection. Ideal infrared detectors should have fast respond, high sensitivity and air-stability, which is rare to meet at the same time for all existing 2D materials, either graphene, transition metal dichalcogenide or black phosphorous. Herein we demonstrate a new infrared photodetector based on 2D Bi2O2Se crystals, whose main characteristics are superb in the whole 2D family: high sensitivity of ~65 A/W at 1200 nm and ultrafast intrinsic photoresponse of ~1 ps at room temperature. Such great performance is attributed to the suitable electronic bandgap and high carrier mobility of 2D oxyselenide. With additional merits of mass production, excellent stability and flexibility, 2D oxyselenide detectors should open new avenues in highly-sensitive, high-speed, low-cost, flexible infrared photodetection and imaging.

cond-mat.mtrl-sci↗

arXiv2017

$H \rightarrow e^+ e^- $ at CEPC: ISR effect with MadGraph

The Circular Electron Positron Collider (CEPC) is a future Higgs factory proposed by the Chinese high energy physics community. It will operate at a center-of-mass energy of 240-250 GeV. The CEPC will accumulate an integrated luminosity of 5 ab$^{\rm{-1}}$ in ten years' operation. With GEANT4-based full simulation samples for CEPC, Higgs boson decaying into electron pair is studied at the CEPC. The upper limit of ${\cal B}(H \rightarrow e^+ e^-)$ could reach 0.024\% at 95\% confidence level. The signal process is generated by MadGraph, with Initial State Radiation (ISR) implemented, as a first step to adjust MadGraph for a electron positron Collider.

hep-ph↗

arXiv2017

Observation of "broad" d-wave Feshbach resonances with a triplet structure

High partial-wave ($l\ge2$) Feshbach resonance (FR) in an ultracold mixture of $^{85}$Rb-$^{87}$Rb atoms is investigated experimentally aided by a partial-wave insensitive analytic multichannel quantum-defect theory (MQDT). Two "broad" resonances from coupling between d-waves in both the open and closed channels are observed and characterized. One of them shows a fully resolved triplet structure with splitting ratio well explained by the perturbation to the closed channel due to interatomic spin-spin interaction. These tunable "broad" d-wave resonances, especially the one in the lowest-energy open channel, could find important applications in simulating d-wave coupling dominated many-body systems. In addition, we find that there is generally a time and temperature requirement, associated with tunneling through the angular momentum barrier, to establish and observe resonant coupling in nonzero partial waves.

cond-mat.quant-gas↗

arXiv2017

Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation

Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web. To alleviate the cold start problem, previous approaches have incorporated various additional sources of information into traditional matrix factorization models. These upgraded models, however, achieve only "marginal" enhancements on the performance of personalized recommendation. Therefore, inspired by the recent development of deep-semantic modeling, we propose a hybrid deep-semantic matrix factorization (HDMF) model to further improve the performance of tag-aware personalized recommendation by integrating the techniques of deep-semantic modeling, hybrid learning, and matrix factorization. Experimental results show that HDMF significantly outperforms the state-of-the-art baselines in tag-aware personalized recommendation, in terms of all evaluation metrics, e.g., its mean reciprocal rank (resp., mean average precision) is 1.52 (resp., 1.66) times as high as that of the best baseline.

cs.IR↗

arXiv2017

Competitive Resource Allocation in HetNets: the Impact of Small-cell Spectrum Constraints and Investment Costs

Heterogeneous wireless networks with small-cell deployments in licensed and unlicensed spectrum bands are a promising approach for expanding wireless connectivity and service. As a result, wireless service providers (SPs) are adding small-cells to augment their existing macro-cell deployments. This added flexibility complicates network management, in particular, service pricing and spectrum allocations across macro- and small-cells. Further, these decisions depend on the degree of competition among SPs. Restrictions on shared spectrum access imposed by regulators, such as low power constraints that lead to small-cell deployments, along with the investment cost needed to add small cells to an existing network, also impact strategic decisions and market efficiency. If the revenue generated by small-cells does not cover the investment cost, then there will be no deployment even if it increases social welfare. We study the implications of such spectrum constraints and investment costs on resource allocation and pricing decisions by competitive SPs, along with the associated social welfare. Our results show that while the optimal resource allocation taking constraints and investment into account can be uniquely determined, adding those features with strategic SPs can have a substantial effect on the equilibrium market structure.

cs.NI↗

arXiv2017

The Impact of Small-Cell Bandwidth Requirements on Strategic Operators

Small-cell deployment in licensed and unlicensed spectrum is considered to be one of the key approaches to cope with the ongoing wireless data demand explosion. Compared to traditional cellular base stations with large transmission power, small-cells typically have relatively low transmission power, which makes them attractive for some spectrum bands that have strict power regulations, for example, the 3.5GHz band [1]. In this paper we consider a heterogeneous wireless network consisting of one or more service providers (SPs). Each SP operates in both macro-cells and small-cells, and provides service to two types of users: mobile and fixed. Mobile users can only associate with macro-cells whereas fixed users can connect to either macro- or small-cells. The SP charges a price per unit rate for each type of service. Each SP is given a fixed amount of bandwidth and splits it between macro- and small-cells. Motivated by bandwidth regulations, such as those for the 3.5Gz band, we assume a minimum amount of bandwidth has to be set aside for small-cells. We study the optimal pricing and bandwidth allocation strategies in both monopoly and competitive scenarios. In the monopoly scenario the strategy is unique. In the competitive scenario there exists a unique Nash equilibrium, which depends on the regulatory constraints. We also analyze the social welfare achieved, and compare it to that without the small-cell bandwidth constraints. Finally, we discuss implications of our results on the effectiveness of the minimum bandwidth constraint on influencing small-cell deployments.

cs.IT↗