arXiv subjects

Jie Zhang

Publications and source records attributed to Jie Zhang.

At least 1,081 records · Page 60Linked to original sources

arXiv2017

TraceTracker: Hardware/Software Co-Evaluation for Large-Scale I/O Workload Reconstruction

Block traces are widely used for system studies, model verifications, and design analyses in both industry and academia. While such traces include detailed block access patterns, existing trace-driven research unfortunately often fails to find true-north due to a lack of runtime contexts such as user idle periods and system delays, which are fundamentally linked to the characteristics of target storage hardware. In this work, we propose TraceTracker, a novel hardware/software co-evaluation method that allows users to reuse a broad range of the existing block traces by keeping most their execution contexts and user scenarios while adjusting them with new system information. Specifically, our TraceTracker's software evaluation model can infer CPU burst times and user idle periods from old storage traces, whereas its hardware evaluation method remasters the storage traces by interoperating the inferred time information, and updates all inter-arrival times by making them aware of the target storage system. We apply the proposed co-evaluation model to 577 traces, which were collected by servers from different institutions and locations a decade ago, and revive the traces on a high-performance flash-based storage array. The evaluation results reveal that the accuracy of the execution contexts reconstructed by TraceTracker is on average 99% and 96% with regard to the frequency of idle operations and the total idle periods, respectively.

cs.DC↗

arXiv2017

SimpleSSD: Modeling Solid State Drives for Holistic System Simulation

Existing solid state drive (SSD) simulators unfortunately lack hardware and/or software architecture models. Consequently, they are far from capturing the critical features of contemporary SSD devices. More importantly, while the performance of modern systems that adopt SSDs can vary based on their numerous internal design parameters and storage-level configurations, a full system simulation with traditional SSD models often requires unreasonably long runtimes and excessive computational resources. In this work, we propose SimpleSSD, a highfidelity simulator that models all detailed characteristics of hardware and software, while simplifying the nondescript features of storage internals. In contrast to existing SSD simulators, SimpleSSD can easily be integrated into publicly-available full system simulators. In addition, it can accommodate a complete storage stack and evaluate the performance of SSDs along with diverse memory technologies and microarchitectures. Thus, it facilitates simulations that explore the full design space at different levels of system abstraction.

cs.AR↗

arXiv2017

Multitarget search on complex networks: A logarithmic growth of global mean random cover time

We investigate multitarget search on complex networks and derive an exact expression for the mean random cover time that quantifies the expected time a walker needs to visit multiple targets. Based on this, we recover and extend some interesting results of multitarget search on networks. Specifically, we observe the logarithmic increase of the global mean random cover time with the target number for a broad range of random search processes, including generic random walks, biased random walks, and maximal entropy random walks. We show that the logarithmic growth pattern is a universal feature of multi-target search on networks by using the annealed network approach and the Sherman-Morrison formula. Moreover, we find that for biased random walks, the global mean random cover time can be minimized, and that the corresponding optimal parameter also minimizes the global mean first passage time, pointing towards its robustness. Our findings further confirm that the logarithmic growth pattern is a universal law governing multitarget search in confined media.

physics.soc-ph↗

arXiv2017

Interacting Attention-gated Recurrent Networks for Recommendation

Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.

cs.IR↗

arXiv2017

Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images

Algorithmic image-based diagnosis and prognosis of neurodegenerative diseases on longitudinal data has drawn great interest from computer vision researchers. The current state-of-the-art models for many image classification tasks are based on the Convolutional Neural Networks (CNN). However, a key challenge in applying CNN to biological problems is that the available labeled training samples are very limited. Another issue for CNN to be applied in computer aided diagnosis applications is that to achieve better diagnosis and prognosis accuracy, one usually has to deal with the longitudinal dataset, i.e., the dataset of images scanned at different time points. Here we argue that an enhanced CNN model with transfer learning for the joint analysis of tasks from multiple time points or regions of interests may have a potential to improve the accuracy of computer aided diagnosis. To reach this goal, we innovate a CNN based deep learning multi-task dictionary learning framework to address the above challenges. Firstly, we pre-train CNN on the ImageNet dataset and transfer the knowledge from the pre-trained model to the medical imaging progression representation, generating the features for different tasks. Then, we propose a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), for learning different tasks by using shared and individual dictionaries and generating the sparse features required to predict the future cognitive clinical scores. We apply our new model in a publicly available neuroimaging cohort to predict clinical measures with two different feature sets and compare them with seven other state-of-the-art methods. The experimental results show our proposed method achieved superior results.

cs.CV↗

arXiv2017

Intensity Video Guided 4D Fusion for Improved Highly Dynamic 3D Reconstruction

The availability of high-speed 3D video sensors has greatly facilitated 3D shape acquisition of dynamic and deformable objects, but high frame rate 3D reconstruction is always degraded by spatial noise and temporal fluctuations. This paper presents a simple yet powerful intensity video guided multi-frame 4D fusion pipeline. Temporal tracking of intensity image points (of moving and deforming objects) allows registration of the corresponding 3D data points, whose 3D noise and fluctuations are then reduced by spatio-temporal multi-frame 4D fusion. We conducted simulated noise tests and real experiments on four 3D objects using a 1000 fps 3D video sensor. The results demonstrate that the proposed algorithm is effective at reducing 3D noise and is robust against intensity noise. It outperforms existing algorithms with good scalability on both stationary and dynamic objects.

cs.CV↗

arXiv2017

Smoothed and Average-case Approximation Ratios of Mechanisms: Beyond the Worst-case Analysis

The approximation ratio has become one of the dominant measures in mechanism design problems. In light of analysis of algorithms, we define the \emph{smoothed approximation ratio} to compare the performance of the optimal mechanism and a truthful mechanism when the inputs are subject to random perturbations of the worst-case inputs, and define the \emph{average-case approximation ratio} to compare the performance of these two mechanisms when the inputs follow a distribution. For the one-sided matching problem, \citet{FFZ:14} show that, amongst all truthful mechanisms, \emph{random priority} achieves the tight approximation ratio bound of $Θ(\sqrt{n})$. We prove that, despite of this worst-case bound, random priority has a \emph{constant smoothed approximation ratio}. This is, to our limited knowledge, the first work that asymptotically differentiates the smoothed approximation ratio from the worst-case approximation ratio for mechanism design problems. For the average-case, we show that our approximation ratio can be improved to $1+e$. These results partially explain why random priority has been successfully used in practice, although in the worst case the optimal social welfare is $Θ(\sqrt{n})$ times of what random priority achieves. These results also pave the way for further studies of smoothed and average-case analysis for approximate mechanism design problems, beyond the worst-case analysis.

cs.GT↗

arXiv2017

Direct observation of ultrafast thermal and non-thermal lattice deformation of polycrystalline Aluminum film

The dynamics of thermal and non-thermal lattice deformation of nanometer thick polycrystalline aluminum film has been studied by means of femtosecond (fs) time-resolved electron diffraction. We utilized two different pump wavelengths: 800 nm, the fundamental of Ti: sapphire laser and 1250 nm generated by a home-made optical parametric amplifier(OPA). Our data show that, although coherent phonons were generated under both conditions, the diffraction intensity decayed with the characteristic time of 0.9+/-0.3 ps and 1.7+/-0.3 ps under 800 nm and 1250 nm excitation, respectively. Because the 800 nm laser excitation corresponds to the strong interband transition of aluminum due to the 1.55 eV parallel band structure, our experimental data indicate the presence of non-thermal lattice deformation under 800 nm excitation, which occurs on a time-scale that is shorter than the thermal processes dominated by electron-phonon coupling under 1250 nm excitation.

cond-mat.mtrl-sci↗

arXiv2017

Origin of Non-cubic Scaling Law in Disordered Granular Packing

Recent diffraction experiments on metallic glasses have unveiled an unexpected non-cubic scaling law between density and average interatomic distance, which lead to the speculations on the presence of fractal glass order. Using X-ray tomography we identify here a similar non-cubic scaling law in disordered granular packing of spherical particles. We find that the scaling law is directly related to the contact neighbors within first nearest neighbor shell, and therefore is closely connected to the phenomenon of jamming. The seemingly universal scaling exponent around 2.5 arises due to the isostatic condition with contact number around 6, and we argue that the exponent should not be universal.

cond-mat.soft↗

arXiv2017

Microphone Subset Selection for MVDR Beamformer Based Noise Reduction

In large-scale wireless acoustic sensor networks (WASNs), many of the sensors will only have a marginal contribution to a certain estimation task. Involving all sensors increases the energy budget unnecessarily and decreases the lifetime of the WASN. Using microphone subset selection, also termed as sensor selection, the most informative sensors can be chosen from a set of candidate sensors to achieve a prescribed inference performance. In this paper, we consider microphone subset selection for minimum variance distortionless response (MVDR) beamformer based noise reduction. The best subset of sensors is determined by minimizing the transmission cost while constraining the output noise power (or signal-to-noise ratio). Assuming the statistical information on correlation matrices of the sensor measurements is available, the sensor selection problem for this model-driven scheme is first solved by utilizing convex optimization techniques. In addition, to avoid estimating the statistics related to all the candidate sensors beforehand, we also propose a data-driven approach to select the best subset using a greedy strategy. The performance of the greedy algorithm converges to that of the model-driven method, while it displays advantages in dynamic scenarios as well as on computational complexity. Compared to a sparse MVDR or radius-based beamformer, experiments show that the proposed methods can guarantee the desired performance with significantly less transmission costs.

cs.SD↗

arXiv2017

Shear-induced rigidity of frictional particles: Analysis of emergent order in stress space

Solids are distinguished from fluids by their ability to resist shear. In traditional solids, the resistance to shear is associated with the emergence of broken translational symmetry as exhibited by a non-uniform density pattern, which results from either minimizing the energy cost or maximizing the entropy or both. In this work, we focus on a class of systems, where this paradigm is challenged. We show that shear-driven jamming in dry granular materials is a collective process controlled solely by the constraints of mechanical equilibrium. We argue that these constraints lead to a broken translational symmetry in a dual space that encodes the statistics of contact forces and the topology of the contact network. The shear-jamming transition is marked by the appearance of this broken symmetry. We extend our earlier work, by comparing and contrasting real space measures of rheology with those obtained from the dual space. We investigate the structure and behavior of the dual space as the system evolves through the rigidity transition in two different shear protocols. We analyze the robustness of the shear-jamming scenario with respect to protocol and packing fraction, and demonstrate that it is possible to define a protocol-independent order parameter in this dual space, which signals the onset of rigidity.

cond-mat.soft↗

arXiv2017

Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression

Genome-wide association studies (GWAS) have achieved great success in the genetic study of Alzheimer's disease (AD). Collaborative imaging genetics studies across different research institutions show the effectiveness of detecting genetic risk factors. However, the high dimensionality of GWAS data poses significant challenges in detecting risk SNPs for AD. Selecting relevant features is crucial in predicting the response variable. In this study, we propose a novel Distributed Feature Selection Framework (DFSF) to conduct the large-scale imaging genetics studies across multiple institutions. To speed up the learning process, we propose a family of distributed group Lasso screening rules to identify irrelevant features and remove them from the optimization. Then we select the relevant group features by performing the group Lasso feature selection process in a sequence of parameters. Finally, we employ the stability selection to rank the top risk SNPs that might help detect the early stage of AD. To the best of our knowledge, this is the first distributed feature selection model integrated with group Lasso feature selection as well as detecting the risk genetic factors across multiple research institutions system. Empirical studies are conducted on 809 subjects with 5.9 million SNPs which are distributed across several individual institutions, demonstrating the efficiency and effectiveness of the proposed method.

cs.LG↗

arXiv2017

A Flexible Privacy-preserving Framework for Singular Value Decomposition under Internet of Things Environment

The singular value decomposition (SVD) is a widely used matrix factorization tool which underlies plenty of useful applications, e.g. recommendation system, abnormal detection and data compression. Under the environment of emerging Internet of Things (IoT), there would be an increasing demand for data analysis to better human's lives and create new economic growth points. Moreover, due to the large scope of IoT, most of the data analysis work should be done in the network edge, i.e. handled by fog computing. However, the devices which provide fog computing may not be trustable while the data privacy is often the significant concern of the IoT application users. Thus, when performing SVD for data analysis purpose, the privacy of user data should be preserved. Based on the above reasons, in this paper, we propose a privacy-preserving fog computing framework for SVD computation. The security and performance analysis shows the practicability of the proposed framework. Furthermore, since different applications may utilize the result of SVD operation in different ways, three applications with different objectives are introduced to show how the framework could flexibly achieve the purposes of different applications, which indicates the flexibility of the design.

cs.DC↗

arXiv2017

The optical absorption spectra of V centers in deformed MgO under pressure

The electronic structure and optical absorption spectrum of V^{0} and V^{-} center in MgO are investigated using first-principles calculations based on density functional theory. It is demonstrated that the configuration with distortion is energetically favorable in PBE0 functional. The six O atoms around Mg vacancy are non-equivalent due to the distortion. The defect states localized in the band gap are obtained by applying hybrid functional calculations, which is very important for optical properties. At ambient condition, the absorption peak at ~2.5 and ~2.6 eV is assigned to V^{0} and V^{-} center, respectively. With increasing pressure, the absorption peak shows blue shift. The trend is consistent with the experimental observation.

cond-mat.mtrl-sci↗

arXiv2017

The Origin of Solar Filament Plasma Inferred from in situ Observations of Elemental Abundances

Solar filaments/prominences are one of the most common features in the corona, which may lead to energetic coronal mass ejections (CMEs) and flares when they erupt. Filaments are about one hundred times cooler and denser than the coronal material, and physical understanding of their material origin remains controversial. Two types of scenarios have been proposed: one argues that the filament plasma is brought into the corona from photosphere or chromosphere through a siphon or evaporation/injection process, while the other suggests that the material condenses from the surrounding coronal plasma due to thermal instability. The elemental abundance analysis is a reasonable clue to constrain the models, as the siphon or evaporation/injection model would predict that the filament material abundances are close to the photospheric or chromospheric ones, while the condensation model should have coronal abundances. In this letter, we analyze the elemental abundances of a magnetic cloud that contains the ejected filament material. The corresponding filament eruption occurred on 1998 April 29, accompanying an M6.8 class soft X-ray flare located at the heliographic coordinates S18E20 (NOAA 08210) and a fast halo CME with the linear velocity of 1374 km s$^{-1}$ near the Sun. We find that the abundance ratios of elements with low and high First Ionization Potential such as Fe/O, Mg/O, and Si/O are 0.150, 0.050, and 0.070, respectively, approaching their corresponding photospheric values 0.065, 0.081, and 0.066, which does not support the coronal origin of the filament plasma.

astro-ph.SR↗

arXiv2017

Effects of biaxial strain on the electronic structures and band topologies of group-V elemental monolayers

Using first-principles calculations, we systematically investigate the electronic structures and band topologies of four kinds of group-V elemental (P, As, Sb and Bi) monolayers with buckled honeycomb structure. It is found that all these monolayers can change from semiconducting to semimetallic under compressive strain. If a tensile strain is however applied, the P, As and Sb monolayers undergo phase transition from topologically trivial to non-trivial regime, whereas the topological insulating nature of Bi monolayer remains unchanged. With tunability of the band gaps and band topologies, it can be expected that these elemental monolayers could be promising candidates for future optoelectronic and spintronic applications.

cond-mat.mes-hall↗

arXiv2016

Multi-Photon Transitions in Coupled Plasmon-Cyclotron Resonance Measured by Millimeter-Wave Reflection

We construct a low-temperature microwave waveguide interferometer for measuring high-frequency properties of two-dimensional electron gases (2DEGs). Coupled plasmon-cyclotron resonance (PCR) spectra are used to extract effective mass, bulk plasmon frequency, and carrier relaxation times. In contrast to traditional transmission spectroscopy, this method does not require sample preparation and is nondestructive. PCR signals can be resolved with a microwave power source as low as 10 nW. We observe PCR in the multi-photon transition regime, which has been proposed to be relevant to the microwave-induced resistance oscillations.

cond-mat.mes-hall↗

arXiv2016

Origin of Strong Magnetic Fields of Magnetars

Since there is 3P2 neutron superfluid in neutron star interior, it can be treated as a system of magnetic dipoles. Under the presence of background magnetic field, the magnetic dipoles tend to align in the same direction. When the temperature is lower than 10**7K, the strong magnetic fields of the magnetars may originate from the induced magnetic moment of the 3P2 neutron Cooper pairs in the anisotropic neutron superfluid. And this gives a convenient explanation of the strong magnetic field of magnetars.

astro-ph.HE↗