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Kai Zhao

Kai Zhao appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Computer Visionnucl-thmath.APmath.DSDistributed, Parallel, and Cluster Computingphysics.soc-phSocial and Information NetworksComputation and LanguageBiological PhysicsBiomoleculescond-mat.mtrl-sciData Structures and Algorithmshep-thHuman-Computer InteractionMachine Learningmath.COmath.PRmath.SGnucl-exphysics.data-anphysics.opticsquant-phQuantitative MethodsSoftware Engineering

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Published work

33 published item(s)

preprint2026arXiv

EdgeFM: Efficient Edge Inference for Vision-Language Models

Vision-language models (VLMs) have demonstrated strong applicability in edge industrial applications, yet their deployment remains severely constrained by requirements for deterministic low latency and stable execution under resource limitations. Existing frameworks either rely on bloated general-purpose designs or force developers into opaque, hardware-specific closed-source ecosystems, leading to hardware lock-in limitation and poor cross-platform adaptability. Observing that modern AI agents can efficiently search and tune configurations to generate highly optimized low-level kernels for standard LLM operators, we propose EdgeFM, a lightweight, agent-driven VLM/LLM inference framework tailored for cross-platform industrial edge deployment. EdgeFM removes non-essential features to reduce single-request latency, and encapsulates agent-tuned kernel optimizations as a modular library of reusable skills. By allowing direct invocation of these skills rather than waiting for closed-source implementations, it effectively closes the performance gap long dominated by proprietary toolchains. The framework natively supports mainstream platforms including x86 and NVIDIA Orin SoCs, and represents the first end-to-end VLA deployment on the domestic Horizon Journey platform, enhancing cross-platform portability. In most cases, it yields clearly better inference performance than conventional vendor-specific toolchains, achieving up to 1.49 times speedup over TensorRT-Edge-LLM on the NVIDIA Orin platform. Experimental results show that EdgeFM delivers favorable end-to-end inference performance, providing an open-source, production-grade solution for diverse edge industrial scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards

Large-scale Chinese spelling correction (CSC) remains critical for real-world text processing, yet existing LLMs and supervised methods lack robustness to novel errors and rely on costly annotations. We introduce CEC-Zero, a zero-supervision reinforcement learning framework that addresses this by enabling LLMs to correct their own mistakes. CEC-Zero synthesizes errorful inputs from clean text, computes cluster-consensus rewards via semantic similarity and candidate agreement, and optimizes the policy with PPO. It outperforms supervised baselines by 10--13 F$_1$ points and strong LLM fine-tunes by 5--8 points across 9 benchmarks, with theoretical guarantees of unbiased rewards and convergence. CEC-Zero establishes a label-free paradigm for robust, scalable CSC, unlocking LLM potential in noisy text pipelines.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ContrastMask: Contrastive Learning to Segment Every Thing

Partially-supervised instance segmentation is a task which requests segmenting objects from novel unseen categories via learning on limited seen categories with annotated masks thus eliminating demands of heavy annotation burden. The key to addressing this task is to build an effective class-agnostic mask segmentation model. Unlike previous methods that learn such models only on seen categories, in this paper, we propose a new method, named ContrastMask, which learns a mask segmentation model on both seen and unseen categories under a unified pixel-level contrastive learning framework. In this framework, annotated masks of seen categories and pseudo masks of unseen categories serve as a prior for contrastive learning, where features from the mask regions (foreground) are pulled together, and are contrasted against those from the background, and vice versa. Through this framework, feature discrimination between foreground and background is largely improved, facilitating learning of the class-agnostic mask segmentation model. Exhaustive experiments on the COCO dataset demonstrate the superiority of our method, which outperforms previous state-of-the-arts.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Geometric Synthesis: A Free lunch for Large-scale Palmprint Recognition Model Pretraining

Palmprints are private and stable information for biometric recognition. In the deep learning era, the development of palmprint recognition is limited by the lack of sufficient training data. In this paper, by observing that palmar creases are the key information to deep-learning-based palmprint recognition, we propose to synthesize training data by manipulating palmar creases. Concretely, we introduce an intuitive geometric model which represents palmar creases with parameterized Bézier curves. By randomly sampling Bézier parameters, we can synthesize massive training samples of diverse identities, which enables us to pretrain large-scale palmprint recognition models. Experimental results demonstrate that such synthetically pretrained models have a very strong generalization ability: they can be efficiently transferred to real datasets, leading to significant performance improvements on palmprint recognition. For example, under the open-set protocol, our method improves the strong ArcFace baseline by more than 10\% in terms of TAR@1e-6. And under the closed-set protocol, our method reduces the equal error rate (EER) by an order of magnitude.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Learning an Efficient Multimodal Depth Completion Model

With the wide application of sparse ToF sensors in mobile devices, RGB image-guided sparse depth completion has attracted extensive attention recently, but still faces some problems. First, the fusion of multimodal information requires more network modules to process different modalities. But the application scenarios of sparse ToF measurements usually demand lightweight structure and low computational cost. Second, fusing sparse and noisy depth data with dense pixel-wise RGB data may introduce artifacts. In this paper, a light but efficient depth completion network is proposed, which consists of a two-branch global and local depth prediction module and a funnel convolutional spatial propagation network. The two-branch structure extracts and fuses cross-modal features with lightweight backbones. The improved spatial propagation module can refine the completed depth map gradually. Furthermore, corrected gradient loss is presented for the depth completion problem. Experimental results demonstrate the proposed method can outperform some state-of-the-art methods with a lightweight architecture. The proposed method also wins the championship in the MIPI2022 RGB+TOF depth completion challenge.

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preprint2022arXiv

Nonlinear semigroup approach to Hamilton-Jacobi equations -- A toy model

In this paper, we discuss the existence and multiplicity problem of viscosity solution to the Hamilton-Jacobi equation $$h(x,d_x u)+λ(x)u=c,\quad x\in M,$$ where $M$ is a closed manifold and $λ:M\rightarrow\mathbb{R}$ changes signs on $M$, via nonlinear semigroup method. It turns out that a bifurcation phenomenon occurs when parameter $c$ strides over the critical value. As an application of the main result, we analyse the structure of the set of viscosity solutions of an one-dimensional example in detail.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets

Today's scientific high performance computing (HPC) applications or advanced instruments are producing vast volumes of data across a wide range of domains, which introduces a serious burden on data transfer and storage. Error-bounded lossy compression has been developed and widely used in scientific community, because not only can it significantly reduce the data volumes but it can also strictly control the data distortion based on the use-specified error bound. Existing lossy compressors, however, cannot offer ultra-fast compression speed, which is highly demanded by quite a few applications or use-cases (such as in-memory compression and online instrument data compression). In this paper, we propose a novel ultra-fast error-bounded lossy compressor, which can obtain fairly high compression performance on both CPU and GPU, also with reasonably high compression ratios. The key contributions are three-fold: (1) We propose a novel, generic ultra-fast error-bounded lossy compression framework -- called UFZ, by confining our design to be composed of only super-lightweight operations such as bitwise and addition/subtraction operation, still keeping a certain high compression ratio. (2) We implement UFZ on both CPU and GPU and optimize the performance according to their architectures carefully. (3) We perform a comprehensive evaluation with 6 real-world production-level scientific datasets on both CPU and GPU. Experiments show that UFZ is 2~16X as fast as the second-fastest existing error-bounded lossy compressor (either SZ or ZFP) on CPU and GPU, with respect to both compression and decompression.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Variational attraction of the KAM torus for the conformally symplectic system

For the conformally symplectic system \[ \left\{ \begin{aligned} \dot{q}&=H_p(q,p),\quad(q,p)\in T^*\mathbb{T}^n\\ \dot p&=-H_q(q,p)-λp, \quad λ>0 \end{aligned} \right. \] with a positive definite Hamiltonian, we discuss the variational significance of invariant Lagrangian graphs and explain how the KAM torus impacts the $W^{1,\infty}-$convergence speed of the Lax-Oleinik semigroup.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Res2Net: A New Multi-scale Backbone Architecture

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The localization of quantum random walks on sierpinski gaskets

We consider the discrete time quantum random walks on a Sierpinski gasket. We study the hitting probability as the level of fractal goes to infinity in terms of their localization exponents $β_w$ , total variation exponents $δ_w$ and relative entropy exponents $η_w$ . We define and solve the amplitude Green functions recursively when the level of the fractal graph goes to infinity. We obtain exact recursive formulas for the amplitude Green functions, based on which the hitting probabilities and expectation of the first-passage time are calculated. Using the recursive formula with the aid of Monte Carlo integration, we evaluate their numerical values. We also show that when the level of the fractal graph goes to infinity, with probability 1, the quantum random walks will return to origin, i.e., the quantum walks on Sierpinski gasket are recurrent.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Time-periodic solutions of contact Hamilton-Jacobi equations on the circle

We are concerned with the existence and multiplicity of nontrivial time-periodic viscosity solutions to \[ \partial_t w(x,t) + H( x,\partial_x w(x,t),w(x,t) )=0,\quad (x,t)\in \mathbb{S} \times [0,+\infty). \] We find that there are infinitely many nontrivial time-periodic viscosity solutions with different periods when $\frac{\partial H}{\partial u}(x,p,u)\leqslant-δ<0$ by analyzing the asymptotic behavior of the dynamical system $(C(\mathbb{S} ,\mathbb{R}),\{T_t\}_{t\geqslant 0})$, where $\{T_t\}_{t\geqslant 0}$ was introduced in \cite{WWY1}. Moreover, in view of the convergence of $T_{t_n}φ$, we get the existence of nontrivial periodic points of $T_t$, where $φ$ are initial data satisfying certain properties. This is a long-time behavior result for the solution to the above equation with initial data $φ$. At last, as an application, we describe to readers a bifurcation phenomenon for \[ \partial_t w(x,t) + H( x,\partial_x w(x,t),λw(x,t) )=0,\quad (x,t)\in \mathbb{S} \times [0,+\infty), \] when the sign of the parameter $λ$ varies. The structure of the unit circle $\mathbb{S}$ plays an essential role here. The most important novelty is the discovery of the nontrivial recurrence of $(C(\mathbb{S} ,\mathbb{R}),\{T_t\}_{t\geqslant 0})$.

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preprint2020arXiv

cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelity for postanalysis. Because supercomputers and HPC applications are becoming heterogeneous using accelerator-based architectures, in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existing state-of-the-art GPU-based lossy compressors suffer from either low compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version, cuSZ, for one of the best error-bounded lossy compressors-SZ. To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data. Our contributions are fourfold. (1) We propose a dual-quantization scheme to entirely remove the data dependency in the prediction step of SZ such that this step can be performed very efficiently on GPUs. (2) We develop an efficient customized Huffman coding for the SZ compressor on GPUs. (3) We implement cuSZ using CUDA and optimize its performance by improving the utilization of GPU memory bandwidth. (4) We evaluate our cuSZ on five real-world HPC application datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and GPUs. Experiments show that our cuSZ improves SZ's compression throughput by up to 370.1x and 13.1x, respectively, over the production version running on single and multiple CPU cores, respectively, while getting the same quality of reconstructed data. It also improves the compression ratio by up to 3.48x on the tested data compared with another state-of-the-art GPU supported lossy compressor.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Dependency Aware Filter Pruning

Convolutional neural networks (CNNs) are typically over-parameterized, bringing considerable computational overhead and memory footprint in inference. Pruning a proportion of unimportant filters is an efficient way to mitigate the inference cost. For this purpose, identifying unimportant convolutional filters is the key to effective filter pruning. Previous work prunes filters according to either their weight norms or the corresponding batch-norm scaling factors, while neglecting the sequential dependency between adjacent layers. In this paper, we further develop the norm-based importance estimation by taking the dependency between the adjacent layers into consideration. Besides, we propose a novel mechanism to dynamically control the sparsity-inducing regularization so as to achieve the desired sparsity. In this way, we can identify unimportant filters and search for the optimal network architecture within certain resource budgets in a more principled manner. Comprehensive experimental results demonstrate the proposed method performs favorably against the existing strong baseline on the CIFAR, SVHN, and ImageNet datasets. The training sources will be publicly available after the review process.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks

Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage. Of critical importance is ensuring the stability of the CNN inference process against soft errors. Traditional fault tolerance methods are not suitable for CNN inference because error-correcting code is unable to protect computational components, instruction duplication techniques incur high overhead, and existing algorithm-based fault tolerance (ABFT) techniques cannot protect all convolution implementations. In this paper, we focus on how to protect the CNN inference process against soft errors as efficiently as possible, with the following three contributions. (1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly.Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations. (2) We design a novel workflow integrating all the proposed schemes to obtain a high detection/correction ability with limited total runtime overhead. (3) We perform our evaluation using ImageNet with well-known CNN models including AlexNet, VGG-19, ResNet-18, and YOLOv2. Experimental results demonstrate that our implementation can handle soft errors with very limited runtime overhead (4%~8% in both error-free and error-injected situations).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Progress of Quantum Molecular Dynamics model and its applications in Heavy Ion Collisions

In this review article, we first briefly introduce the transport theory and quantum molecular dynamics model applied in the study of the heavy ion collisions from low to intermediate energies. The developments of improved quantum molecular dynamics model (ImQMD) and ultra-relativistic quantum molecular dynamics model (UrQMD), are reviewed. The reaction mechanism and phenomena related to the fusion, multinucleon transfer, fragmentation, collective flow and particle production are reviewed and discussed within the framework of the two models. The constraints on the isospin asymmetric nuclear equation of state and in-medium nucleon-nucleon cross sections by comparing the heavy ion collision data with transport models calculations in last decades are also discussed, and the uncertainties of these constraints are analyzed as well. Finally, we discuss the future direction of the development of the transport models for improving the understanding of the reaction mechanism, the descriptions of various observables, the constraint on the nuclear equation of state, as well as for the constraint on in-medium nucleon-nucleon cross sections.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search

Motivation: Predicting the secondary structure of an RNA sequence is useful in many applications. Existing algorithms (based on dynamic programming) suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Results: We present a novel alternative $O(n^3)$-time dynamic programming algorithm for RNA folding that is amenable to heuristics that make it run in $O(n)$ time and $O(n)$ space, while producing a high-quality approximation to the optimal solution. Inspired by incremental parsing for context-free grammars in computational linguistics, our alternative dynamic programming algorithm scans the sequence in a left-to-right (5'-to-3') direction rather than in a bottom-up fashion, which allows us to employ the effective beam pruning heuristic. Our work, though inexact, is the first RNA folding algorithm to achieve linear runtime (and linear space) without imposing constraints on the output structure. Surprisingly, our approximate search results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart), both of which are well known to be challenging for the current models. Availability: Our source code is available at https://github.com/LinearFold/LinearFold, and our webserver is at http://linearfold.org (sequence limit: 100,000nt).

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Vanishing contact structure problem and convergence of the viscosity solutions

This paper is devoted to study the vanishing contact structure problem which is a generalization of the vanishing discount problem. Let $H^λ(x,p,u)$ be a family of Hamiltonians of contact type with parameter $λ>0$ and converges to $G(x,p)$. For the contact type Hamilton-Jacobi equation with respect to $H^λ$, we prove that, under mild assumptions, the associated viscosity solution $u^λ$ converges to a specific viscosity solution $u^0$ of the vanished contact equation. As applications, we give some convergence results for the nonlinear vanishing discount problem.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Automatic City Region Analysis for Urban Routing

There are different functional regions in cities such as tourist attractions, shopping centers, workplaces and residential places. The human mobility patterns for different functional regions are different, e.g., people usually go to work during daytime on weekdays, and visit shopping centers after work. In this paper, we analyse urban human mobility patterns and infer the functions of the regions in three cities. The analysis is based on three large taxi GPS datasets in Rome, San Francisco and Beijing containing 21 million, 11 million and 17 million GPS points respectively. We categorized the city regions into four kinds of places, workplaces, entertainment places, residential places and other places. First, we provide a new quad-tree region division method based on the taxi visits. Second, we use the association rule to infer the functional regions in these three cities according to temporal human mobility patterns. Third, we show that these identified functional regions can help us deliver data in network applications, such as urban Delay Tolerant Networks (DTNs), more efficiently. The new functional-regions-based DTNs algorithm achieves up to 183% improvement in terms of delivery ratio.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Network Analysis of Urban Traffic with Big Bus Data

Urban traffic analysis is crucial for traffic forecasting systems, urban planning and, more recently, various mobile and network applications. In this paper, we analyse urban traffic with network and statistical methods. Our analysis is based on one big bus dataset containing 45 million bus arrival samples in Helsinki. We mainly address following questions: 1. How can we identify the areas that cause most of the traffic in the city? 2. Why there is a urban traffic? Is bus traffic a key cause of the urban traffic? 3. How can we improve the urban traffic systems? To answer these questions, first, the betweenness is used to identify the most import areas that cause most traffics. Second, we find that bus traffic is not an important cause of urban traffic using statistical methods. We differentiate the urban traffic and the bus traffic in a city. We use bus delay as an identification of the urban traffic, and the number of bus as an identification of the bus traffic. Third, we give our solutions on how to improve urban traffic by the traffic simulation on road networks. We show that adding more buses during the peak time and providing better bus schedule plan in the hot areas like railway station, metro station, shopping malls etc. will reduce the urban traffic.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts. While object skeleton extraction in natural images is a very challenging problem, as it requires the extractor to be able to capture both local and global image context to determine the intrinsic scale of each skeleton pixel. Existing methods rely on per-pixel based multi-scale feature computation, which results in difficult modeling and high time consumption. In this paper, we present a fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the sequential stages in the network and the skeleton scales they can capture, we introduce a scale-associated side output to each stage. We impose supervision to different stages by guiding the scale-associated side outputs toward groundtruth skeletons of different scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to localize skeleton pixels with multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors.

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preprint2016arXiv

The production of unknown neutron-rich isotopes in $^{238}$U+$^{238}$U collisions at near-barrier energy

The production cross sections for primary and residual fragments with charge number from $Z$=70 to 120 produced in the collision of $^{238}$U+$^{238}$U at 7.0 MeV/nucleon are calculated by the improved quantum molecular dynamics (ImQMD) model incorporated with the statistical evaporation model (HIVAP code). The calculation results predict that about sixty unknown neutron-rich isotopes from element Ra ($Z$=88) to Db ($Z$=105) can be produced with the production cross sections above the lower bound of $10^{-8}$ mb in this reaction. And almost all of unknown neutron-rich isotopes are emitted at the laboratory angles $θ_{lab}\leq$ 60$^\circ$. Two cases, i.e. the production of the unknown uranium isotopes with $A\geq$ 244 and that of rutherfordium with $A\geq$ 269 are investigated for understanding the production mechanism of unknown neutron-rich isotopes. It is found that for the former case the collision time between two uranium nuclei is shorter and the primary fragments producing the residues have smaller excitation energies of $\leq$ 30 MeV and the outgoing angles of those residues cover a range of 30$^\circ$-60$^\circ$. For the later case, the longer collision time is needed for a large number of nucleons being transferred and thus it results in the higher excitation energies and smaller outgoing angles of primary fragments, and eventually results in a very small production cross section for the residues of Rf with $A\geq$ 269 which have a small interval of outgoing angles of $θ_{lab}$=40$^\circ$-50$^\circ$.

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preprint2015arXiv

Correlations between the fragmentation modes and light charged particles emission in heavy ion collisions

The correlations between the shape of rapidity distribution of the yield of light charged particles and the fragmentation modes in semi-peripheral collisions for $^{70}$Zn+$^{70}$Zn, $^{64}$Zn+$^{64}$Zn and $^{64}$Ni+$^{64}$Ni at the beam energy of 35MeV/nucleon are investigated based on ImQMD05 code. Our studies show there is an interplay between the binary, ternary and multi-fragmentation break-up modes. The binary and ternary break-up modes more prefer to emit light charged particles at middle rapidity and give larger values of $R_{yield}^{mid}$ compared with the multi-fragmentation break-up mode does. The reduced rapidity distribution for the normalized yields of p, d, t, $^3$He, $^4$He and $^6$He and the corresponding values of $R_{yield}^{mid}$ can be used to estimate the probability of multi-fragmentation break-up modes. By comparing to experimental data, our results illustrate that $\ge$40\% of the collisions events belong to the multi-fragmentation break-up mode for the reactions we studied.

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preprint2015arXiv

Explaining the Power-law Distribution of Human Mobility Through Transportation Modality Decomposition

Human mobility has been empirically observed to exhibit Levy flight characteristics and behaviour with power-law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we analyze urban human mobility and we propose to explain the Levy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bicycle, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation to the emergence of Levy Walk patterns that characterize human mobility patterns.

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preprint2015arXiv

Fusion and quasi-fission dynamics in nearly-symmetric reactions

Some nearly-symmetric fusion reactions are systematically investigated with the improved quantum molecular dynamics (ImQMD) model. By introducing two-body inelastic scattering in the Fermi constraint procedure, the stability of an individual nucleus and the description of fusion cross sections at energies near the Coulomb barrier can be further improved. Simultaneously, the quasi-fission process in $^{154}$Sm+$^{160}$Gd is also investigated with the microscopic dynamics model for the first time. We find that at energies above the Bass barrier, the fusion probability is smaller than $10^{-5}$ for this reaction, and the nuclear contact-time is generally smaller than $1500$ fm/c. From the central collisions of Sm+Gd, the neutron-rich fragments such as $^{164,165}$Gd, $^{192}$W can be produced in the ImQMD simulations, which implies that the quasi-fission reaction could be an alternative way to synthesize new neutron-rich heavy nuclei.

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preprint2015arXiv

Macroscopic and direct light propulsion of bulk graphene material

It has been a great challenge to achieve the direct light manipulation of matter on a bulk scale. In this work, the direct light propulsion of matter was observed on a macroscopic scale for the first time using a bulk graphene based material. The unique structure and properties of graphene and the morphology of the bulk graphene material make it capable of not only absorbing light at various wavelengths but also emitting energetic electrons efficiently enough to drive the bulk material following Newtonian mechanics. Thus, the unique photonic and electronic properties of individual graphene sheets are manifested in the response of the bulk state. These results offer an exciting opportunity to bring about bulk scale light manipulation with the potential to realize long-sought proposals in areas such as the solar sail and space transportation driven directly by sunlight.

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preprint2014arXiv

Systematic study of 16O-induced fusions with the improved quantum molecular dynamics model

The heavy-ion fusion reactions with 16O bombarding on 62Ni, 65Cu, 74Ge, 148Nd, 180Hf, 186W, 208Pb, 238U are systematically investigated with the improved quantum molecular dynamics (ImQMD) model. The fusion cross sections at energies near and above the Coulomb barriers can be reasonably well reproduced by using this semi-classical microscopic transport model with the parameter sets SkP* and IQ3a. The dynamical nucleus-nucleus potentials and the influence of Fermi constraint on the fusion process are also studied simultaneously. In addition to the mean field, the Fermi constraint also plays a key role for the reliable description of fusion process and for improving the stability of fragments in heavy-ion collisions.

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preprint2014arXiv

Type-Driven Incremental Semantic Parsing with Polymorphism

Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation. We introduce three new techniques to tackle these problems. First, we design the first linear-time incremental shift-reduce-style semantic parsing algorithm which is more efficient than conventional cubic-time bottom-up semantic parsers. Second, our parser, being type-driven instead of syntax-driven, uses type-checking to decide the direction of reduction, which eliminates the need for a syntactic grammar such as CCG. Third, to fully exploit the power of type-driven semantic parsing beyond simple types (such as entities and truth values), we borrow from programming language theory the concepts of subtype polymorphism and parametric polymorphism to enrich the type system in order to better guide the parsing. Our system learns very accurate parses in GeoQuery, Jobs and Atis domains.

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preprint2013arXiv

n+1 Dimensional Gravity duals to quantum criticalities with spontaneous symmetry breaking

We reexamine the charged AdS domain wall solution to the Einstein-Abelian-Higgs model proposed by Gubser et al as holographic superconductors at quantum critical points and comment on their statement about the uniqueness of gravity solutions. We generalize their explorations from 3+1 dimensions to arbitrary $n+1$Ds and find that the $n+1\geqslant5$D charged AdS domain walls are unstable against electric perturbations.

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preprint2010arXiv

A UI Design Case Study and a Prototype of a Travel Search Engine

We review a case study of a UI design project for a complete travel search engine system prototype for regular and corporate users. We discuss various usage scenarios, guidelines, and so for, and put them into a web-based prototype with screenshots and the like. We combined into our prototype the best features found at the time (2002) on most travel-like sites and added more to them as a part of our research. We conducted feasibility studies, review common design guidelines and Nelson's heuristics while constructing this work. The prototype is itself open-source, but has no backend functionality, as the focus is the user-centered design of such a system. While the prototype is mostly static, some dynamic activity is present through the use of PHP.

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preprint2009arXiv

Mass parameters for relative and neck collective motions in heavy ion fusion reactions

Mass parameters for the relative and neck motions in fusion reactions of symmetric systems $^{90}$Zr+$^{90}$Zr, $^{110}$Pd+$^{110}$Pd, and $^{138}$Ba+$^{138}$Ba are studied by means of a microscopic transport model. The shape of the nuclear system is determined by an equi-density surface obtained from the density distribution of the system. The relative and neck motions are then studied and the mass parameters for these two motions are deduced. The mass parameter for the relative motion is around the reduced mass when the reaction partners are at the separated configuration and increases with decrease of the distance between two reaction partners after the touching configuration. The mass parameter for the neck motion first decreases slightly up to the touching configuration and then increases with the neck width, and its magnitude is from less than tenth to several times more than the total mass of the system. The mass parameters obtained from the microscopic transport model are larger than the ones obtained from the hydrodynamic model and smaller than those obtained from the linear response function theory. The mass parameters for both motions depend on the reaction systems, but the one for the relative motion depends on the incompressibility of the EoS more obviously than that for neck motion.

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preprint2009arXiv

The Study of Mass Distribution of products in 7.0 AMeV U238+U238 Collisions

Within the Improved Quantum Molecular Dynamics (ImQMD) Model incorporating the statistical decay Model, the reactions of U238+U238 at the energy of 7.0 AMeV have been studied. The charge, mass and excitation energy distributions of primary fragments are investigated within the ImQMD model and de-excitation processes of those primary fragments are described by the statistical decay model. The mass distribution of the final products in U238+U238 collisions is obtained and compared with the recent experimental data.

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