arXiv subjects

Jie Lin

Publications and source records attributed to Jie Lin.

At least 145 records · Page 8Linked to original sources

arXiv2018

Atomic process of oxidative etching in monolayer molybdenum disulfide

The microscopic process of oxidative etching of two-dimensional molybdenum disulfide (2D MoS2) at an atomic scale is investigated using a correlative TEM-etching study. MoS2 flakes on graphene TEM grids are precisely tracked and characterized by TEM before and after the oxidative etching. This allows us to determine the structural change with an atomic resolution on the edges of the domains, of well-oriented triangular pits and along the grain boundaries. We observe that the etching mostly starts from the open edges, grain boundaries and pre-existing atomic defects. A zigzag Mo edge is assigned as the dominant termination of the triangular pits, and profound terraces and grooves are observed on the etched edges. Based on the statistical TEM analysis, we reveal possible routes for the kinetics of the oxidative etching in 2D MoS2, which should also be applicable for other 2D transition metal dichalcogenide materials like MoSe2 and WS2.

cond-mat.mtrl-sci↗

arXiv2018

Simple security analysis of phase-matching measurement-device-independent quantum key distribution

Variations of phase-matching measurement-device-independent quantum key distribution (PM-MDI QKD) protocols have been investigated before, but it was recently discovered that this type of protocol (under the name of twin-field QKD) can beat the linear scaling of the repeaterless bound on secret key rate capacity. We propose a variation of PM-MDI QKD protocol, which reduces the sifting cost and uses non-phase-randomized coherent states as test states. We provide a security proof in the infinite key limit. Our proof is conceptually simple and gives tight key rates. We obtain an analytical key rate formula for the loss-only scenario, confirming the square root scaling and also showing the loss limit. We simulate the key rate for realistic imperfections and show that PM-MDI QKD can overcome the repeaterless bound with currently available technology.

quant-ph↗

arXiv2018

Characteristics of Two-episode Emission Patterns in {\em Fermi} Long Gamma-Ray Bursts

Two-episode emission components separated by quiescent gaps in the prompt emission of gamma-ray bursts (GRBs) have been observed in the {\em Swift} era, but there is a lack of spectral information due to the narrow energy band of the {\em Swift}/Burst Alert Telescope. In this paper, a systematic analysis of the spectral and temporal properties of the prompt emission of 101 {\em Fermi}/Gamma-ray Burst Monitor detected long GRBs show the existence of two-episode emission components in the light curves, with quiescent times of up to hundreds of seconds. We focus on investigating the differences of those two emission episodes. We find that the light curves of the two emission components exhibit different behavior, e.g., a soft emission component that either precedes or follows the main prompt emission or that the intensity of the two emission episodes are comparable with each other. No statistically significant correlation in the duration of the two emission episodes can be claimed. We define a new parameter $\varepsilon$ as the ratio of the peak flux of the first and second emission episodes and find that a higher $\varepsilon$ corresponds to a larger fluence. The preferred spectral model in our analysis is a cutoff power-law model for most GRBs. The distribution of $E_p$ for episodes I and II range from tens of keV to 1000 keV with a lognormal fit and there are no significant differences between them. Moreover, we do not find significant relationships between $\varepsilon$ and $E_p$ for the two emission episodes. Those results suggest that these two-episode emission components likely share the same physical origin.

astro-ph.HE↗

arXiv2018

Optimal segregation of proteins: phase transitions and symmetry breaking

Asymmetric segregation of key proteins at cell division -- be it a beneficial or deleterious protein -- is ubiquitous in unicellular organisms and often considered as an evolved trait to increase fitness in a stressed environment. Here, we provide a general framework to describe the evolutionary origin of this asymmetric segregation. We compute the population fitness as a function of the protein segregation asymmetry $a$, and show that the value of $a$ which optimizes the population growth manifests a phase transition between symmetric and asymmetric partitioning phases. Surprisingly, the nature of phase transition is different for the case of beneficial proteins as opposed to proteins which decrease the single-cell growth rate. Our study elucidates the optimization problem faced by evolution in the context of protein segregation, and motivates further investigation of asymmetric protein segregation in biological systems.

q-bio.PE↗

arXiv2018

First Electromagnetic Pulse Associated with a Gravitational-Wave Event: Profile, Duration, and Delay

We study the first electromagnetic pulse after the gravitational wave chirp signal, focusing on the profile and duration. It is found that the light curve, especially the steep decay (SD) phase, can be very different by adopting different viewing angle $θ_{\rm view}$ on the jet shell. For an on-axis jet with a power-law radiation spectrum, the observed flux in the SD is proportional to $t_{\rm{obs}}^{-2-β}$ with $β$ being the spectral index and $t_{\rm{obs}}$ being the observer time. Here, $t_{\rm{obs}}=0$ is set at the observed time of the jet ejected from the central engine. The SD may become steep by increasing $θ_{\rm view}$. We also study the bolometric luminosity $L$ from a jet shell with a non-power-law radiation spectrum. For an on-axis jet, $L{\propto}t_{\rm{obs}}^{-3}$ is found in the SD. However, the SD is steeper than $L{\propto}t_{\rm{obs}}^{-3}$ for the radiation from an off-axis jet. The higher value of $θ_{\rm view}$ is, the steeper of SD would be. Then, we suggest that the SD phase can be used to discriminate an off-axis jet from an on-axis jet. The reason for above behaviors is discussed. In addition, we find that the duration of first electromagnetic pulse is close to its peak time, especially for $θ_{\rm{view}}\sim20^\circ$. This result is consistent with that found in GW~170817/GRB~170817A. Thus, the jet corresponding to the prompt emission of GRB~170817A should be immediately ejected after the merger. Our results also reveal that the duration of the first electromagnetic pulse can provide the information of the time to search gravitational waves.

astro-ph.HE↗

arXiv2018

Microscopic processes controlling the Herschel-Bulkley exponent

The flow curve of various yield stress materials is singular as the strain rate vanishes, and can be characterized by the so-called Herschel-Bulkley exponent $n=1/β$. A mean-field approximation due to Hebraud and Lequeux (HL) assumes mechanical noise to be Gaussian, and leads to $β=2$ in rather good agreement with observations. Here we prove that the improved mean-field model where the mechanical noise has fat tails instead leads to $β=1$ with logarithmic correction. This result supports that HL is not a suitable explanation for the value of $β$, which is instead significantly affected by finite dimensional effects. From considerations on elasto-plastic models and on the limitation of speed at which avalanches of plasticity can propagate, we argue that $β=1+1/(d-d_f)$ where $d_f$ is the fractal dimension of avalanches and $d$ the spatial dimension. Measurements of $d_f$ then supports that $β\approx 2.1$ and $β\approx 1.7$ in two and three dimensions respectively. We discuss theoretical arguments leading to approximations of $β$ in finite dimensions.

cond-mat.soft↗

arXiv2018

Modeling cell size regulation: From single-cell level statistics to molecular mechanisms and population level effects

Most microorganisms regulate their cell size. We review here some of the mathematical formulations of the problem of cell size regulation. We focus on coarse-grained stochastic models and the statistics they generate. We review the biologically relevant insights obtained from these models. We then describe cell cycle regulation and their molecular implementations, protein number regulation, and population growth, all in relation to size regulation. Finally, we discuss several future directions for developing understanding beyond phenomenological models of cell size regulation.

q-bio.CB↗

arXiv2017

State Transitions in Ultracompact Neutron Star LMXBs: towards the Low Luminosity Limit

Luminosity of X-ray spectral state transitions in black hole and neutron star X-ray binaries can put constraint on the critical mass accretion rate between accretion regimes. Previous studies indicate that the hard-to-soft spectral state transitions in some ultracompact neutron star LMXBs have the lowest luminosity. With X-ray monitoring observations in the past decade, we were able to identify state transitions towards the lowest luminosity limit in 4U 0614+091, 2S 0918-549 and 4U 1246-588. By analysing corresponding X-ray pointed observations with the Swift/XRT and the RXTE/PCA, we found no hysteresis of state transitions in these sources, and determined the critical mass accretion rate in the range of 0.002 - 0.04 $\dot{\rm M}_{\rm Edd}$ and 0.003 - 0.05 $\dot{\rm M}_{\rm Edd}$ for the hard-to-soft and the soft-to-hard transition, respectively, by assuming a neutron star mass of 1.4 solar masses. This range is comparable to the lowest transition luminosity measured in black hole X-ray binaries, indicating the critical mass accretion rate is not affected by the nature of the surface of the compact stars. Our result does not support the Advection-Dominated Accretion Flow (ADAF) model which predicts that the critical mass accretion rate in neutron star systems is an order of magnitude lower if same viscosity parameters are taken. The low transition luminosity and insignificant hysteresis in these ultracompact X-ray binaries provide further evidence that the transition luminosity is likely related to the mass in the disc.

astro-ph.HE↗

arXiv2017

End-to-End Video Classification with Knowledge Graphs

Video understanding has attracted much research attention especially since the recent availability of large-scale video benchmarks. In this paper, we address the problem of multi-label video classification. We first observe that there exists a significant knowledge gap between how machines and humans learn. That is, while current machine learning approaches including deep neural networks largely focus on the representations of the given data, humans often look beyond the data at hand and leverage external knowledge to make better decisions. Towards narrowing the gap, we propose to incorporate external knowledge graphs into video classification. In particular, we unify traditional "knowledgeless" machine learning models and knowledge graphs in a novel end-to-end framework. The framework is flexible to work with most existing video classification algorithms including state-of-the-art deep models. Finally, we conduct extensive experiments on the largest public video dataset YouTube-8M. The results are promising across the board, improving mean average precision by up to 2.9%.

cs.CV↗

arXiv2017

The effects of stochasticity at the single-cell level and cell size control on the population growth

Establishing a quantitative connection between the population growth rate and the generation times of single cells is a prerequisite for understanding evolutionary dynamics of microbes. However, existing theories fail to account for the experimentally observed correlations between mother-daughter generation times that are unavoidable when cell size is controlled for - which is essentially always the case. Here, we study population-level growth in the presence of cell size control and corroborate our theory using experimental measurements of single-cell growth rates. We derive a closed formula for the population growth rate and demonstrate that it only depends on the single-cell growth rate variability, not other sources of stochasticity. Our work provides an evolutionary rationale for the narrow growth rate distributions often observed in nature: when single-cell growth rates are less variable but have a fixed mean, the population will exhibit an enhanced population growth rate, as long as the correlations between the mother and daughter cells' growth rates are not too strong.

q-bio.PE↗

arXiv2017

Energy-efficient Analytics for Geographically Distributed Big Data

Big data analytics on geographically distributed datasets (across data centers or clusters) has been attracting increasing interests from both academia and industry, but also significantly complicates the system and algorithm designs. In this article, we systematically investigate the geo-distributed big-data analytics framework by analyzing the fine-grained paradigm and the key design principles. We present a dynamic global manager selection algorithm (GMSA) to minimize energy consumption cost by fully exploiting the system diversities in geography and variation over time. The algorithm makes real-time decisions based on the measurable system parameters through stochastic optimization methods, while achieving the performance balances between energy cost and latency. Extensive trace-driven simulations verify the effectiveness and efficiency of the proposed algorithm. We also highlight several potential research directions that remain open and require future elaborations in analyzing geo-distributed big data.

cs.DC↗

arXiv2017

Entanglement and its relationship to classical dynamics

We present an analysis of the entangling quantum kicked top focusing on the few qubit case and the initial condition dependence of the time-averaged entanglement $S_Q$ for spin-coherent states. We show a very strong connection between the classical phase space and the initial condition dependence of $S_Q$ even for the extreme case of two spin-$1/2$ qubits. This correlation is not related directly to chaos in the classical dynamics. We introduce a measure of the behavior of a classical trajectory which correlates far better with the entanglement and show that the maps of classical and quantum initial-condition dependence are both organized around the symmetry points of the Hamiltonian. We also show clear (quasi-)periodicity in entanglement as a function of number of kicks and of kick strength.

quant-ph↗

arXiv2017

Mean-field description of plastic flow in amorphous solids

Failure and flow of amorphous materials are central to various phenomena including earthquakes and landslides. There is accumulating evidence that the yielding transition between a flowing and an arrested phase is a critical phenomenon, but the associated exponents are not understood, even at a mean-field level where the validity of popular models is debated. Here we solve a mean-field model that captures the broad distribution of the mechanical noise generated by plasticity, whose behavior is related to biased Lévy flights near an absorbing boundary. We compute the exponent $θ$ characterising the density of shear transformation $P(x)\sim x^θ$, where $x$ is the stress increment beyond which they yield. We find that after an isotropic thermal quench, $θ=1/2$. However, $θ$ depends continuously on the applied shear stress, this dependence is not monotonic, and its value at the yield stress is not universal. The model rationalizes previously unexplained observations, and captures reasonably well the value of exponents in three dimensions. Values of exponents in four dimensions are accurately predicted. These results support that it is the true mean-field model that applies in large dimension, and raise fundamental questions on the nature of the yielding transition.

cond-mat.soft↗

arXiv2017

Pruning Convolutional Neural Networks for Image Instance Retrieval

In this work, we focus on the problem of image instance retrieval with deep descriptors extracted from pruned Convolutional Neural Networks (CNN). The objective is to heavily prune convolutional edges while maintaining retrieval performance. To this end, we introduce both data-independent and data-dependent heuristics to prune convolutional edges, and evaluate their performance across various compression rates with different deep descriptors over several benchmark datasets. Further, we present an end-to-end framework to fine-tune the pruned network, with a triplet loss function specially designed for the retrieval task. We show that the combination of heuristic pruning and fine-tuning offers 5x compression rate without considerable loss in retrieval performance.

cs.CV↗

arXiv2017

Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text

The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% out of 650 participants using released video and audio features. Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text. The newly introduced text data is termed as YouTube-8M-Text. We present a classification framework for the joint use of text, visual and audio features, and conduct an extensive set of experiments to quantify the benefit that this additional mode brings. The inclusion of text yields state-of-the-art results, e.g. 86.7% GAP on the YouTube-8M-Text validation dataset.

cs.CV↗

arXiv2017

Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge

We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and finally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.

cs.CV↗

arXiv2017

Factorization of arithmetic automorphic periods

In this paper, we prove that the arithmetic automorphic periods for $GL_{n}$ over a CM field factorize through the infinite places. This generalizes a conjecture of Shimura in 1983, and is predicted by the Langlands correspondence between automorphic representations and motives.

math.NT↗

arXiv2017

An automorphic variant of the Deligne conjecture

In this paper we introduce an automorphic variant of the Deligne conjecture for tensor product of two motives over a quadratic imaginary field. On one hand, we define some motivic periods and rewrite the Deligne conjecture in terms of these periods. On the other hand, we define the automorphic analogue of these motivic periods and then give a purely automorphic variant of the Deligne conjecture. At last, we introduce some known results of this automorphic variant.

math.NT↗