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Fan Zhang

Publications and source records attributed to Fan Zhang.

At least 829 records · Page 46Linked to original sources
arXiv2019

Timing and characterization of shaped pulses with MHz ADCs in a detector system: a comparative study and deep learning approach

Timing systems based on Analog-to-Digital Converters are widely used in the design of previous high energy physics detectors. In this paper, we propose a new method based on deep learning to extract the time information from a finite set of ADC samples. Firstly, a quantitative analysis of the traditional curve fitting method regarding three kinds of variations (long-term drift, short-term change and random noise) is presented with simulation illustrations. Next, a comparative study between curve fitting and the neural networks is made to demonstrate the potential of deep learning in this problem. Simulations show that the dedicated network architecture can greatly suppress the noise RMS and improve timing resolution in non-ideal conditions. Finally, experiments are performed with the ALICE PHOS FEE card. The performance of our method is more than 20% better than curve fitting in the experimental condition.

physics.data-an↗
arXiv2019

Data-driven Optimal Cost Selection for Distributionally Robust Optimization

Recently, (Blanchet, Kang, and Murhy 2016, and Blanchet, and Kang 2017) showed that several machine learning algorithms, such as square-root Lasso, Support Vector Machines, and regularized logistic regression, among many others, can be represented exactly as distributionally robust optimization (DRO) problems. The distributional uncertainty is defined as a neighborhood centered at the empirical distribution. We propose a methodology which learns such neighborhood in a natural data-driven way. We show rigorously that our framework encompasses adaptive regularization as a particular case. Moreover, we demonstrate empirically that our proposed methodology is able to improve upon a wide range of popular machine learning estimators.

stat.ML↗
arXiv2019

Noise-Tolerant Paradigm for Training Face Recognition CNNs

Benefit from large-scale training datasets, deep Convolutional Neural Networks(CNNs) have achieved impressive results in face recognition(FR). However, tremendous scale of datasets inevitably lead to noisy data, which obviously reduce the performance of the trained CNN models. Kicking out wrong labels from large-scale FR datasets is still very expensive, although some cleaning approaches are proposed. According to the analysis of the whole process of training CNN models supervised by angular margin based loss(AM-Loss) functions, we find that the $θ$ distribution of training samples implicitly reflects their probability of being clean. Thus, we propose a novel training paradigm that employs the idea of weighting samples based on the above probability. Without any prior knowledge of noise, we can train high performance CNN models with large-scale FR datasets. Experiments demonstrate the effectiveness of our training paradigm. The codes are available at https://github.com/huangyangyu/NoiseFace.

cs.CV↗
arXiv2019

Flavor Quantum Dots and Artificial Quark Model in Transition Metal Dichalcogenides

We show that the triply degenerate Q valleys in few-layer transition metal dichalcogenides provide a unique platform for exploring the rare flavor SU(3) symmetry in quantum dot geometry. The single and double dots are reminiscent of the quark model and eightfold way, and their many-body triplets and octets may be regarded as artificial quarks and hadrons. For the artificial quark transistor, each level hosts one central and two side Coulomb peaks of irrational height ratios, and flavor Kondo effects occur at 1/3 and 2/3 fillings with fractional conductance quantization in the unitary limit.

cond-mat.mes-hall↗
arXiv2019

Correlated Insulating and Superconducting States in Twisted Bilayer Graphene Below the Magic Angle

The emergence of flat bands and correlated behaviors in 'magic angle' twisted bilayer graphene (tBLG) has sparked tremendous interest, though many aspects of the system are under intense debate. Here we report observation of both superconductivity and the Mott-like insulating state in a tBLG device with a twist angle of approximately 0.93, which is smaller than the magic angle by 15%. At an electron concentration of +/-5 electrons per moire unit cell, we observe a narrow resistance peak with an activation energy gap of approximately 0.1 meV, indicating the existence of an additional correlated insulating state. This is consistent with theory predicting the presence of a high-energy band with an energetically flat dispersion. At a doping of +/-12 electrons per moire unit cell we observe a resistance peak due to the presence of Dirac points in the spectrum. Our results reveal that the magic range of tBLG is in fact larger than what is previously expected, and provide a wealth of new information to help decipher the strongly correlated phenomena observed in tBLG.

cond-mat.mes-hall↗
arXiv2019

Dolha - an Efficient and Exact Data Structure for Streaming Graphs

A streaming graph is a graph formed by a sequence of incoming edges with time stamps. Unlike static graphs, the streaming graph is highly dynamic and time related. In the real world, the high volume and velocity streaming graphs such as internet traffic data, social network communication data and financial transfer data are bringing challenges to the classic graph data structures. We present a new data structure: double orthogonal list in hash table (Dolha) which is a high speed and high memory efficiency graph structure applicable to streaming graph. Dolha has constant time cost for single edge and near linear space cost that we can contain billions of edges information in memory size and process an incoming edge in nanoseconds. Dolha also has linear time cost for neighborhood queries, which allow it to support most algorithms in graphs without extra cost. We also present a persistent structure based on Dolha that has the ability to handle the sliding window update and time related queries.

cs.DS↗
arXiv2019

Quantifying Legibility of Indoor Spaces Using Deep Convolutional Neural Networks: Case Studies in Train Stations

Legibility is the extent to which a space can be easily recognized. Evaluating legibility is particularly desirable in indoor spaces, since it has a large impact on human behavior and the efficiency of space utilization. However, indoor space legibility has only been studied through survey and trivial simulations and lacks reliable quantitative measurement. We utilized a Deep Convolutional Neural Network (DCNN), which is structurally similar to a human perception system, to model legibility in indoor spaces. To implement the modeling of legibility for any indoor spaces, we designed an end-to-end processing pipeline from indoor data retrieving to model training to spatial legibility analysis. Although the model performed very well (98% top-1 accuracy) overall, there are still discrepancies in accuracy among different spaces, reflecting legibility differences. To prove the validity of the pipeline, we deployed a survey on Amazon Mechanical Turk, collecting 4,015 samples. The human samples showed a similar behavior pattern and mechanism as the DCNN models. Further, we used model results to visually explain legibility in different architectural programs, building age, building style, visual clusterings of spaces and visual explanations for building age and architectural functions.

cs.CY↗
arXiv2019

Understanding Rowhammer Attacks through the Lens of a Unified Reference Framework

Rowhammer is a hardware-based bug that allows the attacker to modify the data in the memory without accessing it, just repeatedly and frequently accessing (or hammering) physically adjacent memory rows. So that it can break the memory isolation between processes, which is seen as the cornerstone of modern system security, exposing the sensitive data to unauthorized and imperceptible corruption. A number of previous works have leveraged the rowhammer bug to achieve various critical attacks. In this work, we propose a unified reference framework for analyzing the rowhammer attacks, indicating three necessary factors in a practical rowhammer attack: the attack origin, the intended implication and the methodology. Each factor includes multiple primitives, the attacker can select primitives from three factors to constitute an effective attack. In particular, the methodology further summarizes all existing attack techniques, that are used to achieve its three primitives: Location Preparation (LP), Rapid Hammering (RH), and Exploit Verification (EV). Based on the reference framework, we analyze all previous rowhammer attacks and corresponding countermeasures. Our analysis shows that how primitives in different factors are combined and used in previous attacks, and thus points out new possibility of rowhammer attacks, enabling proactive prevention before it causes harm. Under the framework, we propose a novel expressive rowhammer attack that is capable of accumulating injected memory changes and achieving rich attack semantics. We conclude by outlining future research directions.

cs.CR↗
arXiv2019

Quantum Parity Hall effect in ABA Graphene

The celebrated phenomenon of quantum Hall effect has recently been generalized from transport of conserved charges to that of other approximately conserved state variables, including spin and valley, which are characterized by spin- or valley-polarized boundary states with different chiralities. Here, we report a new class of quantum Hall effect in ABA-stacked graphene trilayers (TLG), the quantum parity Hall (QPH) effect, in which boundary channels are distinguished by even or odd parity under the systems mirror reflection symmetry. At the charge neutrality point and a small perpendicular magnetic field $B_{\perp}$, the longitudinal conductance $σ_{xx}$ is first quantized to $4e^2/h$, establishing the presence of four edge channels. As $B_{\perp}$ increases, $σ_{xx}$ first decreases to $2e^2/h$, indicating spin-polarized counter-propagating edge states, and then to approximately $0$. These behaviors arise from level crossings between even and odd parity bulk Landau levels, driven by exchange interactions with the underlying Fermi sea, which favor an ordinary insulator ground state in the strong $B_{\perp}$ limit, and a spin-polarized state at intermediate fields. The transitions between spin-polarized and unpolarized states can be tuned by varying Zeeman energy. Our findings demonstrate a topological phase that is protected by a gate-controllable symmetry and sensitive to Coulomb interactions.

cond-mat.mes-hall↗
arXiv2019

Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks

Big medical data poses great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists and engineers sit together to discuss several fundamental issues. First, what are the unique characteristics of big medical data different from those of the other domains? Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks? Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data? Fifth, what are the performance gaps of state-of-the-practice and state-of-the-art systems handling big medical data currently or in future? Finally but not least, are we, life scientists, clinicians, computer scientists and engineers, ready for working together? We believe answering the above issues will help define and shape the landscape of big medical data.

cs.CY↗
arXiv2018

Fractional Josephson Effect: A Missing Step Is A Key Step

Physicists are searching for superconducting materials that can host Majoranas. New evidence for these elusive particles is provided by missing Shapiro steps in a Josephson effect mediated by an accidental Dirac semimetal.

cond-mat.supr-con↗
arXiv2018

VeMo: Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration

Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic congestion and respond to the emergency. However, almost all existing works (e.g., based on cellphones, onboard devices, or traffic cameras) suffer from high costs, low penetration rates, or only aggregate results. To address these drawbacks, we utilize Electric Toll Collection systems (ETC) as a large-scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetration rate. Our novelty is how we address uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with crowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. More importantly, we evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data as ground truth. We compared VeMo with state-of-the-art benchmark mobility models, and the experimental results show that VeMo outperforms them by average 10% in terms of accuracy.

cs.NI↗
arXiv2018

100G Data Center Interconnections with Silicon Dual-Drive Mach-Zehnder Modulator and Direct Detection

In this paper, we experimentally demonstrate that a silicon dual-drive Mach-Zehnder modulator (DD-MZM) has great potential for next-generation data center interconnections (DCIs). For intra-data center interconnections, 120 Gb/s Nyquist 4-ary pulse amplitude modulation (PAM-4) signal is successfully generated with a silicon DD-MZM operating at C-band and transmitted over 2 km standard single-mode fiber (SSMF) with a bit error rate (BER) of 5.55x10-4. For inter-data center interconnections, single sideband (SSB) modulation is chosen to avoid power fading caused by fiber chromatic dispersion and square-law detection. We report the generation and transmission of 112 Gb/s Nyquist SSB PAM-4 signal by using the same silicon DD-MZM and Kramers-Kronig (KK) direct detection. A two-tap digital post filter and maximum likelihood sequence detection (MLSD) are applied to compensate for the limited system bandwidth. After 80 km SSMF transmission, the BER is 2.46x10-3 that is below the 7% HD-FEC threshold of 3.8x10-3. To the best of our knowledge, our work reports the highest single-lane bitrate of 80 km SSB transmission based on a silicon DD-MZM. Our study also shows the feasibility of silicon photonic modulator for DCI applications in the future.

eess.SP↗
arXiv2018

An extending strategy based on TENO framework for hyperbolic conservation laws

Recently, the targeted ENO (TENO) schemes give a novel framework to keep optimal high-order spatial reconstruction wherever discontinuity is deemed to be vanished, including at smooth critical points, and to avoid oscillations by completely removing stencils crossing discontinuities. Moreover, the smoothness measurement of TENO schemes is in fact acting as shock-detectors, which are capable for distinguishing discontinuities and smooth critical points. Following the idea of a recent improvement, i.e. TENO-NA, the shock-detection and stencil-selection are completely separated in this work. Higher-order polynomials using neighbouring points of the standard five-point TENO scheme are applied for achieving higher-order accuracy without significantly increasing computation cost, by exploring the neighbouring smoothness measurements. In this work, seventh-order spatial accuracy is achieved, and the computational complexity is similar to that of the five-point TENO scheme. Especially, the presented method introduces new flexibility in constructing high-order numerical methods. Numerical results are given to show the shock-capturing and wave-resolving capabilities.

physics.comp-ph↗
arXiv2018

Majorana Doublets, Flat Bands, and Dirac Nodes in s-Wave Superfluids

Topological superfluids protected by mirror and time-reversal symmetries are exotic states of matter possessing Majorana Kramers pairs (MKPs), yet their realizations have long been hindered by the requirement of unconventional pairing. We propose to realize such a topological superfluid by utilizing $s$-wave pairing and emergent mirror and time-reversal symmetries in two coupled 1D ultracold atomic Fermi gases with spin-orbit coupling. By stacking such systems into 2D, we discover topological and Dirac-nodal superfluids hosting distinct MKP flat bands. We show that the emergent symmetries make the MKPs and their flat bands stable against pairing fluctuations that otherwise annihilate paired Majoranas. Exploiting new experimental developments, our scheme provides a unique platform for exploring MKPs and their applications in quantum computation.

cond-mat.quant-gas↗
arXiv2018

Towards optimal high-order compact schemes for simulating compressible flows

Weighted compact nonlinear schemes (WCNS) [Deng and Zhang, JCP 165(2000): 22-44] were developed to improve the performance of the compact high-order nonlinear schemes (CNS) by utilizing the weighting technique originally designed for WENO schemes, and excellent shock capturing capability and high resolution are achieved. Various work has been given for further improving the performance of WCNSs since then. In this work, the ENO-like stencil selection procedure of Targeted ENO schemes [Fu et al. JCP 305(2016):333-359] is introduced for interpolating midpoint variables, targeting compact nonlinear schemes which fully abandon the oscillatory stencils crossing discontinuities, and directly apply optimal linear weights when the flow field is smooth, such that the optimal numerical resolution is fully recovered in smooth flow field. Several canonical numerical cases of scalar equations and the Euler equations of gas dynamics are given to examine the performance of the presented method.

physics.comp-ph↗
arXiv2018

Production of new neutron-rich heavy nuclei with $Z=56-80$ in the multinucleon transfer reactions of $^{136}$Xe+$^{198}$Pt

The multinucleon transfer reactions in collisions of $^{136}$Xe+$^{198}$Pt at incident energies $E_{\textrm{lab}}=$5.25, 6.20, 7.98, 10.0, and 15.0 MeV/nucleon are investigated by using the improved quantum molecular dynamics model. It is found that 6.20 MeV/nucleon is the optimal incident energy for producing the neutron-rich heavy nuclei. About 80 unknown neutron-rich nuclei might be produced in this reaction with cross sections from 10$^{-6}$ to 10$^{-2}$ mb. The angular distributions of the neutron-rich isotopes are predicted.

nucl-th↗