arXiv subjects

Tao Liu

Publications and source records attributed to Tao Liu.

At least 343 records · Page 19Linked to original sources

arXiv2020

Distributed Optimal Generation and Load-Side Control for Frequency Regulation in Power Systems

In order to deal with issues caused by the increasing penetration of renewable resources in power systems, this paper proposes a novel distributed frequency control algorithm for each generating unit and controllable load in a transmission network to replace the conventional automatic generation control (AGC). The targets of the proposed control algorithm are twofold. First, it is to restore the nominal frequency and scheduled net inter-area power exchanges after an active power mismatch between generation and demand. Second, it is to optimally coordinate the active powers of all controllable units in a distributed manner. The designed controller only relies on local information, computation, and peer-to-peer communication between cyber-connected buses, and it is also robust against uncertain system parameters. Asymptotic stability of the closed-loop system under the designed algorithm is analysed by using a nonlinear structure-preserving model including the first-order turbine-governor dynamics. Finally, case studies validate the effectiveness of the proposed method.

eess.SY↗

arXiv2019

Voice-Face Cross-modal Matching and Retrieval: A Benchmark

Cross-modal associations between voice and face from a person can be learnt algorithmically, which can benefit a lot of applications. The problem can be defined as voice-face matching and retrieval tasks. Much research attention has been paid on these tasks recently. However, this research is still in the early stage. Test schemes based on random tuple mining tend to have low test confidence. Generalization ability of models can not be evaluated by small scale datasets. Performance metrics on various tasks are scarce. A benchmark for this problem needs to be established. In this paper, first, a framework based on comprehensive studies is proposed for voice-face matching and retrieval. It achieves state-of-the-art performance with various performance metrics on different tasks and with high test confidence on large scale datasets, which can be taken as a baseline for the follow-up research. In this framework, a voice anchored L2-Norm constrained metric space is proposed, and cross-modal embeddings are learned with CNN-based networks and triplet loss in the metric space. The embedding learning process can be more effective and efficient with this strategy. Different network structures of the framework and the cross language transfer abilities of the model are also analyzed. Second, a voice-face dataset (with 1.15M face data and 0.29M audio data) from Chinese speakers is constructed, and a convenient and quality controllable dataset collection tool is developed. The dataset and source code of the paper will be published together with this paper.

cs.CV↗

arXiv2019

Chiral spin-wave velocities induced by all-garnet interfacial Dzyaloshinskii-Moriya interaction in ultrathin yttrium iron garnet films

Spin waves can probe the Dzyaloshinskii-Moriya interaction (DMI) which gives rise to topological spin textures, such as skyrmions. However, the DMI has not yet been reported in yttrium iron garnet (YIG) with arguably the lowest damping for spin waves. In this work, we experimentally evidence the interfacial DMI in a 7~nm-thick YIG film by measuring the nonreciprocal spin wave propagation in terms of frequency, amplitude and most importantly group velocities using all electrical spin-wave spectroscopy. The velocities of propagating spin waves show chirality among three vectors, i.e. the film normal direction, applied field and spin-wave wavevector. By measuring the asymmetric group velocities, we extract a DMI constant of 16~$μ$J/m$^{2}$ which we independently confirm by Brillouin light scattering. Thickness-dependent measurements reveal that the DMI originates from the oxide interface between the YIG and garnet substrate. The interfacial DMI discovered in the ultrathin YIG films is of key importance for functional chiral magnonics as ultra-low spin-wave damping can be achieved.

cond-mat.mtrl-sci↗

arXiv2019

Nonlocality test of energy-time entanglement via nonlocal dispersion cancellation with nonlocal detection

Energy-time entangled biphoton source plays a great role in quantum communication, quantum metrology and quantum cryptography due to its strong temporal correlation and capability of nonlocal dispersion cancellation. As a quantum effect, nonlocal dispersion cancellation is further proposed as an alternative way for nonlocality test of continuous variable entanglement via the violation of Bell-like inequality proposed by Wasak et al. [Phys. Rev. A, 82, 052120 (2010)]. However, to date there is no experimental report either on the inequality violation or on a nonlocal detection with single-photon detectors at long-distance transmission channel, which is key for a true nonlocality test. In this paper, we report an experimental realization of a violation of the inequality after 62km optical fiber transmission at telecom wavelength with a nonlocal detection based on event timers and cross-correlation algorithm, which indicates a successful nonlocal test of energy-time entanglement. This work provides a new feasibility for the strict test of the nonlocality for continuous variables in both long-distance communication fiber channel and free space.

quant-ph↗

arXiv2019

UER: An Open-Source Toolkit for Pre-training Models

Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks. While there does not exist a single pre-training model that works best in all cases, it is of necessity to develop a framework that is able to deploy various pre-training models efficiently. For this purpose, we propose an assemble-on-demand pre-training toolkit, namely Universal Encoder Representations (UER). UER is loosely coupled, and encapsulated with rich modules. By assembling modules on demand, users can either reproduce a state-of-the-art pre-training model or develop a pre-training model that remains unexplored. With UER, we have built a model zoo, which contains pre-trained models based on different corpora, encoders, and targets (objectives). With proper pre-trained models, we could achieve new state-of-the-art results on a range of downstream datasets.

cs.CL↗

arXiv2019

EmoSense: Computational Intelligence Driven Emotion Sensing via Wireless Channel Data

Emotion is well-recognized as a distinguished symbol of human beings, and it plays a crucial role in our daily lives. Existing vision-based or sensor-based solutions are either obstructive to use or rely on specialized hardware, hindering their applicability. This paper introduces EmoSense, a first-of-its-kind wireless emotion sensing system driven by computational intelligence. The basic methodology is to explore the physical expression of emotions from wireless channel response via data mining. The design and implementation of EmoSense {face} two major challenges: extracting physical expression from wireless channel data and recovering emotion from the corresponding physical expression. For the former, we present a Fresnel zone based theoretical model depicting the fingerprint of the physical expression on channel response. For the latter, we design an efficient computational intelligence driven mechanism to recognize emotion from the corresponding fingerprints. We prototyped EmoSense on the commodity WiFi infrastructure and compared it with main-stream sensor-based and vision-based approaches in the real-world scenario. The numerical study over $3360$ cases confirms that EmoSense achieves a comparable performance to the vision-based and sensor-based rivals under different scenarios. EmoSense only leverages the low-cost and prevalent WiFi infrastructures and thus constitutes a tempting solution for emotion sensing.

cs.HC↗

arXiv2019

Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation

The state-of-art DNN structures involve intensive computation and high memory storage. To mitigate the challenges, the memristor crossbar array has emerged as an intrinsically suitable matrix computation and low-power acceleration framework for DNN applications. However, the high accuracy solution for extreme model compression on memristor crossbar array architecture is still waiting for unraveling. In this paper, we propose a memristor-based DNN framework which combines both structured weight pruning and quantization by incorporating alternating direction method of multipliers (ADMM) algorithm for better pruning and quantization performance. We also discover the non-optimality of the ADMM solution in weight pruning and the unused data path in a structured pruned model. Motivated by these discoveries, we design a software-hardware co-optimization framework which contains the first proposed Network Purification and Unused Path Removal algorithms targeting on post-processing a structured pruned model after ADMM steps. By taking memristor hardware constraints into our whole framework, we achieve extreme high compression ratio on the state-of-art neural network structures with minimum accuracy loss. For quantizing structured pruned model, our framework achieves nearly no accuracy loss after quantizing weights to 8-bit memristor weight representation. We share our models at anonymous link https://bit.ly/2VnMUy0.

eess.SP↗

arXiv2019

Broadband Optical Detection using the Spin Seebeck Effect

The generation, control, and detection of spin currents in solid-state devices are critical for Joule-heating minimization, spin-based computation, and electrical energy generation from thermal gradients. Although incorporation of spin functionality into technologically important architectures is still in its infancy, advantages over all-electric devices are increasingly becoming clear. Here, we utilize the spin Seebeck effect (SSE) in Pt/Y3Fe5O12 devices to detect light from 390 to 2200 nm. We find the device responsivity is remarkably flat across this technologically important wavelength range, closely following the Pt absorption coefficient. As expected from a SSE-generation mechanism, we observe that the photovoltage and Pt heating dynamics are in strong agreement. To precisely determine the optically created thermal gradient produced from a point-like heat source, we introduce a field-modulation method for measuring the SSE. Our results show broadband optical detection can be performed with devices based solely on spin current generation and detection.

cond-mat.mtrl-sci↗

arXiv2019

Topological band theory for non-Hermitian systems from the Dirac equation

We identify and investigate two classes of non-Hermitian systems, i.e., one resulting from Lorentz-symmetry violation (LSV) and the other from a complex mass (CM) with Lorentz invariance, from the perspective of quantum field theory. The mechanisms to break, and approaches to restore, the bulk-boundary correspondence in these two types of non-Hermitian systems are clarified. The non-Hermitian system with LSV shows a non-Hermitian skin effect, and its topological phase can be characterized by mapping it to the Hermitian system via a non-compact $U(1)$ gauge transformation. In contrast, there exists no non-Hermitian skin effect for the non-Hermitian system with CM. Moreover, the conventional bulk-boundary correspondence holds in this (CM) system. We also consider a general non-Hermitian system in the presence of both LSV and CM, and we generalize its bulk-boundary correspondence.

cond-mat.mes-hall↗

arXiv2019

Fiber-Optic quantum two-way time transfer with frequency entangled pulses

High-precision time transfer is of fundamental interest in physics and metrology. Quantum time transfer technologies that use frequency-entangled pulses and their coincidence detection have been proposed, offering potential enhancements in precision and better guarantees of security. In this paper, we describe a fiber-optic two-way quantum time transfer experiment. Using quantum nonlocal dispersion cancellation, time transfer over a 20-km fiber link achieves a time deviation of 922 fs over 5 s and 45 fs over 40960 s. The time transfer accuracy as a function of fiber lengths from 15 m to 20 km is also investigated, and an uncertainty of 2.46 ps in standard deviation is observed. In comparison with its classical counterparts, the fiber-optic two-way quantum time transfer setup shows appreciable improvement, and further enhancements could be obtained by using new event timers with sub-picosecond precision and single-photon detectors with lower timing jitter for optimized coincidence detection. Combined with its security advantages, the femtosecond-scale two-way quantum time transfer is expected to have numerous applications in high-precision middle-haul synchronization systems.

quant-ph↗

arXiv2019

On Extension of Effective Resistance with Application to Graph Laplacian Definiteness and Power Network Stability

This paper extends the definitions of effective resistance and effective conductance to characterize the overall relation (positive coupling or antagonism) between any two disjoint sets of nodes in a signed graph. It generalizes the traditional definitions that only apply to a pair of nodes. The monotonicity and convexity properties are preserved by the extended definitions. The extended definitions provide new insights into graph Laplacian definiteness and power network stability. It is proved that the Laplacian matrix of a signed graph is positive semi-definite with only one zero eigenvalue if and only if the effective conductances between some specific pairs of node sets are positive. Also the number of Laplacian negative eigenvalues is upper bounded by the number of negative weighted edges. In addition, new conditions for the small-disturbance angle stability, hyperbolicity and type of power system equilibria are established, which intuitively interpret angle instability as the electrical antagonism between certain two sets of nodes in the defined active power flow graph. Moreover, a novel optimal power flow (OPF) model with effective conductance constraints is formulated, which significantly enhances power system transient stability. By the properties of extended effective conductance, the proposed OPF model admits a convex relaxation representation that achieves global optimality.

math.OC↗

arXiv2019

Nonfactorizable QCD Effects in Higgs Boson Production via Vector Boson Fusion

We discuss nonfactorizable QCD corrections to Higgs boson production in vector boson fusion at the Large Hadron Collider. We point out that these corrections can be computed in the eikonal approximation retaining all the terms that are not suppressed by the ratio of the transverse momenta of the tagging jets to the total center-of-mass energy. Our analysis shows that in certain kinematic distributions the nonfactorizable corrections can be as large as a percent making them quite comparable to their factorizable counter-parts.

hep-ph↗

arXiv2019

Fermi Level Dependent Spin Pumping from a Magnetic Insulator into a Topological Insulator

Topological spintronics aims to exploit the spin-momentum locking in the helical surface states of topological insulators for spin-orbit torque devices. We address a fundamental question that still remains unresolved in this context: does the topological surface state alone produce the largest values of spin-charge conversion efficiency or can the strongly spin-orbit coupled bulk states also contribute significantly? By studying the Fermi level dependence of spin pumping in topological insulator/ferrimagnetic insulator bilayers, we show that the spin Hall conductivity is constant when the Fermi level is tuned across the bulk band gap, consistent with a full bulk band calculation. The results suggest a new perspective, wherein "bulk-surface correspondence" allows spin-charge conversion to be simultaneously viewed either as coming from the full bulk band, or from spin-momentum locking of the surface state.

cond-mat.mtrl-sci↗

arXiv2019

Thread Batching for High-performance Energy-efficient GPU Memory Design

Massive multi-threading in GPU imposes tremendous pressure on memory subsystems. Due to rapid growth in thread-level parallelism of GPU and slowly improved peak memory bandwidth, the memory becomes a bottleneck of GPU's performance and energy efficiency. In this work, we propose an integrated architectural scheme to optimize the memory accesses and therefore boost the performance and energy efficiency of GPU. Firstly, we propose a thread batch enabled memory partitioning (TEMP) to improve GPU memory access parallelism. In particular, TEMP groups multiple thread blocks that share the same set of pages into a thread batch and applies a page coloring mechanism to bound each stream multiprocessor (SM) to the dedicated memory banks. After that, TEMP dispatches the thread batch to an SM to ensure high-parallel memory-access streaming from the different thread blocks. Secondly, a thread batch-aware scheduling (TBAS) scheme is introduced to improve the GPU memory access locality and to reduce the contention on memory controllers and interconnection networks. Experimental results show that the integration of TEMP and TBAS can achieve up to 10.3% performance improvement and 11.3% DRAM energy reduction across diverse GPU applications. We also evaluate the performance interference of the mixed CPU+GPU workloads when they are run on a heterogeneous system that employs our proposed schemes. Our results show that a simple solution can effectively ensure the efficient execution of both GPU and CPU applications.

cs.AR↗

arXiv2019

Classification using Ensemble Learning under Weighted Misclassification Loss

Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy (ART) requires periodic assessment of treatment failure, defined as having a viral load (VL) value above a certain threshold. In some resource limited settings, VL tests may be limited by cost or technology, and diagnoses are based on other clinical markers. Depending on scenario, higher premium may be placed on avoiding false-positives which brings greater cost and reduced treatment options. Here, the optimal rule is determined by minimizing a weighted misclassification loss/risk. We propose a method for finding and cross-validating optimal binary classification rules under weighted misclassification loss. We focus on rules comprising a prediction score and an associated threshold, where the score is derived using an ensemble learner. Simulations and examples show that our method, which derives the score and threshold jointly, more accurately estimates overall risk and has better operating characteristics compared with methods that derive the score first and the cutoff conditionally on the score especially for finite samples.

stat.ML↗

arXiv2019

Anatomy of the $tthh$ Physics at HL-LHC

The $tthh$ production at colliders contain rich information on the nature of Higgs boson. In this article, we systematically studied its physics at High-Luminosity Large Hadron Collider (HL-LHC), using exclusive channels with multiple ($\geq 5$) $b$-jets and one lepton ($5b1\ell$), multiple ($\geq 5$) $b$-jets and opposite-sign di-lepton ($5b2\ell$), same-sign di-lepton (SS2$\ell$), multiple leptons (multi-$\ell$), and di-tau resonance ($ττ$). The scenarios analyzed include: (1) the $tthh$ production in Standard Model; (2) the $tthh$ production mediated by anomalous cubic Higgs self-coupling and $tthh$ contact interaction; (3) heavy Higgs ($H$) production with $tt H \to tthh$; and (4) pair production of fermionic top partners ($T$) with $T T \to tthh$. To address the complication of event topologies and the mess of combinatorial backgrounds, a tool of Boosted-Decision-Tree was applied in the analyses. The $5b1\ell$ and SS2$\ell$ analyses define the two most promising channels, resulting in slightly different sensitivities. For non-resonant $tthh$ production, a combination of these exclusive analyses allows for its measurment in the SM with a statistical significance $\sim 0.9σ$ (with $S/B > 1 \%$), and may assist partially breaking the sensitivity degeneracy w.r.t. the cubic Higgs self-coupling, a difficulty usually thought to exist in gluon fusion di-Higgs analysis at HL-LHC. These sensitivities were also projected to future hadron colliders at 27 TeV and 100 TeV. For resonant $tthh$ productions, the heavy Higgs boson in type II Two-Higgs-Doublet-Model could be efficiently searched for between the mass thresholds $2 m_h < m_H < 2 m_t$ and even beyond that, for relatively small $\tanβ$, while the fermionic top partners in composite Higgs models could be probed for up to $\sim 1.5$ TeV and $\sim 1.7$ TeV, for Br$(T\to th)=25\%$ and $50\%$, respectively.

hep-ph↗

arXiv2019

Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

Image compression-based approaches for defending against the adversarial-example attacks, which threaten the safety use of deep neural networks (DNN), have been investigated recently. However, prior works mainly rely on directly tuning parameters like compression rate, to blindly reduce image features, thereby lacking guarantee on both defense efficiency (i.e. accuracy of polluted images) and classification accuracy of benign images, after applying defense methods. To overcome these limitations, we propose a JPEG-based defensive compression framework, namely "feature distillation", to effectively rectify adversarial examples without impacting classification accuracy on benign data. Our framework significantly escalates the defense efficiency with marginal accuracy reduction using a two-step method: First, we maximize malicious features filtering of adversarial input perturbations by developing defensive quantization in frequency domain of JPEG compression or decompression, guided by a semi-analytical method; Second, we suppress the distortions of benign features to restore classification accuracy through a DNN-oriented quantization refine process. Our experimental results show that proposed "feature distillation" can significantly surpass the latest input-transformation based mitigations such as Quilting and TV Minimization in three aspects, including defense efficiency (improve classification accuracy from $\sim20\%$ to $\sim90\%$ on adversarial examples), accuracy of benign images after defense ($\le1\%$ accuracy degradation), and processing time per image ($\sim259\times$ Speedup). Moreover, our solution can also provide the best defense efficiency ($\sim60\%$ accuracy) against the recent adaptive attack with least accuracy reduction ($\sim1\%$) on benign images when compared with other input-transformation based defense methods.

cs.CV↗

arXiv2019

Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds

Cloud based medical image analysis has become popular recently due to the high computation complexities of various deep neural network (DNN) based frameworks and the increasingly large volume of medical images that need to be processed. It has been demonstrated that for medical images the transmission from local to clouds is much more expensive than the computation in the clouds itself. Towards this, 3D image compression techniques have been widely applied to reduce the data traffic. However, most of the existing image compression techniques are developed around human vision, i.e., they are designed to minimize distortions that can be perceived by human eyes. In this paper we will use deep learning based medical image segmentation as a vehicle and demonstrate that interestingly, machine and human view the compression quality differently. Medical images compressed with good quality w.r.t. human vision may result in inferior segmentation accuracy. We then design a machine vision oriented 3D image compression framework tailored for segmentation using DNNs. Our method automatically extracts and retains image features that are most important to the segmentation. Comprehensive experiments on widely adopted segmentation frameworks with HVSMR 2016 challenge dataset show that our method can achieve significantly higher segmentation accuracy at the same compression rate, or much better compression rate under the same segmentation accuracy, when compared with the existing JPEG 2000 method. To the best of the authors' knowledge, this is the first machine vision guided medical image compression framework for segmentation in the clouds.

cs.CV↗