arXiv subjects

Tao Liu

Publications and source records attributed to Tao Liu.

At least 379 records · Page 21Linked to original sources

arXiv2018

Analogical Reasoning on Chinese Morphological and Semantic Relations

Analogical reasoning is effective in capturing linguistic regularities. This paper proposes an analogical reasoning task on Chinese. After delving into Chinese lexical knowledge, we sketch 68 implicit morphological relations and 28 explicit semantic relations. A big and balanced dataset CA8 is then built for this task, including 17813 questions. Furthermore, we systematically explore the influences of vector representations, context features, and corpora on analogical reasoning. With the experiments, CA8 is proved to be a reliable benchmark for evaluating Chinese word embeddings.

cs.CL↗

arXiv2018

Cycling Li-O2 Batteries via LiOH Formation and Decomposition

The rechargeable aprotic Li-air (O2) battery is a promising potential technology for next generation energy storage, but its practical realization still faces many challenges. In contrast to the standard Li-O2 cells, which cycle via the formation of Li2O2, we use a reduced graphene oxide electrode, the additive LiI, and the solvent dimethoxyethane to reversibly form/remove crystalline LiOH with particle sizes > 15 um during discharge/charge. This leads to high specific capacities, excellent energy efficiency (93.2%) with a voltage gap of only 0.2 V, and impressive rechargeability. The cells tolerate high concentrations of water, water being the dominant proton source for the LiOH; together with LiI it has a decisive impact on the chemical nature of the discharge product and battery performance.

physics.chem-ph↗

arXiv2018

Mechanistic Insights into the Challenges of Cycling a Non-aqueous Na-O2 Battery

Superoxide-based non-aqueous metal oxygen batteries have received considerable research attention, as they exhibit high energy densities and round-trip efficiencies. The cycling performance, however, is still poor. Here we study the cycling characteristic of a Na-O2 battery using solid-state nuclear magnetic resonance, Raman spectroscopy and scanning electron microscopy. We found that the poor cycling performance is primarily caused by the considerable side reactions stemming from the chemical aggressiveness of NaO2 both as a solid phase and dissolved species in the electrolyte. The side reaction products cover electrode surfaces and hinder electron transfer across the electrode-electrolyte interface, being a major reason for cell failure. In addition, the available electrode surface and porosity change considerably during cell discharging and charging, affecting the diffusion of soluble species (superoxide and water) and resulting in inhomogeneous reactions across the electrode. This study provides insights into the challenges associated with achieving long-lived superoxide based metal-O2 batteries.

physics.chem-ph↗

arXiv2018

Surface Sensitive NMR Detection of the SEI Layer on Reduced Graphene Oxide

The solid electrolyte interphase (SEI) is detrimental for rechargeable batteries performance and lifetime. Understanding its formation requires analytical techniques that provide molecular level insight. Here dynamic nuclear polarization (DNP) is utilized for the first time for enhancing the sensitivity of solid state NMR (ssNMR) spectroscopy to the SEI. The approach is demonstrated on reduced-graphene oxide (rGO) cycled in Li-ion cells in natural abundance and 13C-enriched electrolyte solvents. Our results indicate that DNP enhances the signal of outer SEI layers, enabling detection of natural abundance 13C spectra from this component of the SEI at reasonable timeframes. Furthermore, 13C- enriched electrolytes measurements at 100K provide ample sensitivity without DNP due to the vast amount of SEI filling the rGO pores, thereby allowing differentiating the inner and outer SEI layers composition. Developing this approach further will benefit the study of many electrode materials, equipping ssNMR with the needed sensitivity to efficiently probe the SEI.

physics.chem-ph↗

arXiv2018

The Effect of Water on Quinone Redox Mediators in Non- aqueous Li-O2 Batteries

The parasitic reactions associated with reduced oxygen species and the difficulty in achieving the high theoretical capacity have been major issues plaguing development of practical non-aqueous Li-O2 batteries. We hereby address the above issues by exploring the synergistic effect of 2,5-di-tert-butyl-1,4- benzoquinone and H2O on the oxygen chemistry in a non-aqueous Li-O2 battery. Water stabilizes the quinone monoanion and dianion, shifting the reduction potentials of the quinone and monoanion to more positive values (vs. Li+). When water and the quinone are used together in a (largely) non-aqueous Li-O2 battery, the cell discharge operates via a two-electron oxygen reduction reaction to form Li2O2, the battery discharge voltage, rate, capacity all being considerably increased and fewer side reactions being detected; Li2O2 crystals can grow up to 30 um, more than an order of magnitude larger than cases with the quinone alone or without any additives, suggesting that water is essential to promoting a solution dominated process with the quinone on discharging. The catalytic reduction of O2 by the quinone monoanion is predominantly responsible for the attractive features mentioned above. Water stabilizes the quinone monoanion via hydrogen bond formation and by coordination of the Li+ ions, and it also helps increase the solvation, concentration, life time and diffusion length of reduced oxygen species that dictate the discharge voltage, rate and capacity of the battery. When a redox mediator is also used to aid the charging process, a high-power, high energy- density, rechargeable Li-O2 battery is obtained.

physics.chem-ph↗

arXiv2018

Identifying the Structural Basis for the Increased Stability of the Solid Electrolyte Interphase Formed on Silicon with the Additive Fluoroethylene Carbonate

To elucidate the role of fluoroethylene carbonate (FEC) as an additive in the standard carbonate-based electrolyte for Li-ion batteries, the solid electrolyte interphase (SEI) formed during electrochemical cycling on silicon anodes was analyzed with a combination of solution and solid-state NMR techniques, including dynamic nuclear polarization. To facilitate characterization via 1D and 2D NMR, we synthesized 13C-enriched FEC, ultimately allowing a detailed structural assignment of the organic SEI. We find that the soluble PEO-like line- ar oligomeric electrolyte breakdown products that are observed after cycling in the standard ethylene carbonate (EC)-based electrolyte are suppressed in the presence of 10 vol % FEC additive. FEC is first defluorinated to form soluble vinylene carbonate and vinoxyl species, which react to form both soluble and insoluble branched ethylene-oxide based polymers. No evidence for branched polymers are observed in the absence of FEC.

physics.chem-ph↗

arXiv2018

Understanding LiOH Chemistry in a Ruthenium Catalyzed Li-O2 Battery

Non-aqueous Li-O2 batteries are promising for next generation energy storage. New battery chemistries based on LiOH, rather than Li2O2, have recently been reported in systems with added water, one using a soluble additive LiI and the other using solid Ru catalysts. Here, we focus on the mechanism of Ru-catalyzed LiOH chemistry. Using nuclear magnetic resonance, operando electrochemical pressure measurements and mass spectrometry, we show that on discharging LiOH forms via a 4 e- oxygen reduction reaction, the H in LiOH coming solely from added H2O and the O from both O2 and H2O. On charging, quantitative LiOH oxidation occurs at 3.1 V, with O being trapped in a form of dimethyl sulfone in the electrolyte. Compared to Li2O2, LiOH formation over Ru incurs hardly any side reactions, a critical advantage for developing a long-lived battery. An optimized metal catalyst-electrolyte couple needs to be sought that aids LiOH oxidation and is able stable towards attack by hydroxyl radicals.

physics.chem-ph↗

arXiv2018

Rigidity of spacelike translating solitons in pseudo-Euclidean space

In this paper, we investigate the parametric version and non-parametric version of rigidity theorem of spacelike translating solitons in pseudo-Euclidean space $\mathbb{R}^{m+n}_{n}$. Firstly, we classify $m$-dimensional complete spacelike translating solitons in $\mathbb{R}^{m+n}_{n}$ by affine technique and classical gradient estimates, and prove the only complete spacelike translating solitons in $\mathbb{R}^{m+n}_{n}$ are the spacelike $m$-planes. This result provides another proof of a nonexistence theorem for complete spacelike translating solitons in \cite{C-Q}, and a simple proof of rigidity theorem in \cite{X-H}. Secondly, we generalize the rigidity theorem of entire spacelike Lagrangian translating solitons in \cite{X-Z} to spacelike translating solitons with general codimensions. As a directly application of theorem, we obtain two interesting corollaries in terms of Gauss image.

math.DG↗

arXiv2018

Probing Quartic Higgs Self-Interaction

The Higgs self-interactions play a crucial role for exploring the underlying mechanism of electroweak symmetry breaking and the nature of the phase transition involved. In this article, we propose to probe the quartic Higgs self-interaction at lepton and hadron colliders, via the di-Higgs productions. We analyze the contributions of the quartic Higgs coupling, including the renormalization of the cubic Higgs coupling and the modification of the $VVhh$ form factor, to the vector-boson-fusion and the vector-boson associated di-Higgs productions at one-loop level. Such an effect is independent of the choice of gauge-fixing, if the quartic Higgs coupling is decoupled from the other ones in the contexts considered. Notably, a combination of these two di-Higgs productions is important for optimizing the collider sensitivities to probe the quartic Higgs coupling. With this guideline, we explore the ILC and CLIC sensitivities, and find that the ILC has a potential to measure the quartic Higgs coupling, normalized by its SM value, with a precision of $\sim \pm 25$ (500 GeV, 4 ab$^{-1}$ + 1 TeV, 2.5 ab$^{-1}$) and $\sim \pm20$ (500 GeV, 4 ab$^{-1}$ + 1 TeV, 8 ab$^{-1}$), at $1σ$ C.L., after marginalizing the cubic Higgs coupling in the $χ^2$ analysis. The dependence on the renormalization scheme of the cubic Higgs coupling is discussed.

hep-ph↗

arXiv2018

Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks

DNN is presenting human-level performance for many complex intelligent tasks in real-world applications. However, it also introduces ever-increasing security concerns. For example, the emerging adversarial attacks indicate that even very small and often imperceptible adversarial input perturbations can easily mislead the cognitive function of deep learning systems (DLS). Existing DNN adversarial studies are narrowly performed on the ideal software-level DNN models with a focus on single uncertainty factor, i.e. input perturbations, however, the impact of DNN model reshaping on adversarial attacks, which is introduced by various hardware-favorable techniques such as hash-based weight compression during modern DNN hardware implementation, has never been discussed. In this work, we for the first time investigate the multi-factor adversarial attack problem in practical model optimized deep learning systems by jointly considering the DNN model-reshaping (e.g. HashNet based deep compression) and the input perturbations. We first augment adversarial example generating method dedicated to the compressed DNN models by incorporating the software-based approaches and mathematical modeled DNN reshaping. We then conduct a comprehensive robustness and vulnerability analysis of deep compressed DNN models under derived adversarial attacks. A defense technique named "gradient inhibition" is further developed to ease the generating of adversarial examples thus to effectively mitigate adversarial attacks towards both software and hardware-oriented DNNs. Simulation results show that "gradient inhibition" can decrease the average success rate of adversarial attacks from 87.99% to 4.77% (from 86.74% to 4.64%) on MNIST (CIFAR-10) benchmark with marginal accuracy degradation across various DNNs.

cs.LG↗

arXiv2018

PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning

One of the most exciting advancements in AI over the last decade is the wide adoption of ANNs, such as DNN and CNN, in many real-world applications. However, the underlying massive amounts of computation and storage requirement greatly challenge their applicability in resource-limited platforms like the drone, mobile phone, and IoT devices etc. The third generation of neural network model--Spiking Neural Network (SNN), inspired by the working mechanism and efficiency of human brain, has emerged as a promising solution for achieving more impressive computing and power efficiency within light-weighted devices (e.g. single chip). However, the relevant research activities have been narrowly carried out on conventional rate-based spiking system designs for fulfilling the practical cognitive tasks, underestimating SNN's energy efficiency, throughput, and system flexibility. Although the time-based SNN can be more attractive conceptually, its potentials are not unleashed in realistic applications due to lack of efficient coding and practical learning schemes. In this work, a Precise-Time-Dependent Single Spike Neuromorphic Architecture, namely "PT-Spike", is developed to bridge this gap. Three constituent hardware-favorable techniques: precise single-spike temporal encoding, efficient supervised temporal learning, and fast asymmetric decoding are proposed accordingly to boost the energy efficiency and data processing capability of the time-based SNN at a more compact neural network model size when executing real cognitive tasks. Simulation results show that "PT-Spike" demonstrates significant improvements in network size, processing efficiency and power consumption with marginal classification accuracy degradation when compared with the rate-based SNN and ANN under the similar network configuration.

cs.NE↗

arXiv2018

MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation

Modern deep learning enabled artificial neural networks, such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN), have achieved a series of breaking records on a broad spectrum of recognition applications. However, the enormous computation and storage requirements associated with such deep and complex neural network models greatly challenge their implementations on resource-limited platforms. Time-based spiking neural network has recently emerged as a promising solution in Neuromorphic Computing System designs for achieving remarkable computing and power efficiency within a single chip. However, the relevant research activities have been narrowly concentrated on the biological plausibility and theoretical learning approaches, causing inefficient neural processing and impracticable multilayer extension thus significantly limitations on speed and accuracy when handling the realistic cognitive tasks. In this work, a practical multilayer time-based spiking neuromorphic architecture, namely "MT-Spike", is developed to fill this gap. With the proposed practical time-coding scheme, average delay response model, temporal error backpropagation algorithm, and heuristic loss function, "MT-Spike" achieves more efficient neural processing through flexible neural model size reduction while offering very competitive classification accuracy for realistic recognition tasks. Simulation results well validated that the algorithmic power of deep multi-layer learning can be seamlessly merged with the efficiency of time-based spiking neuromorphic architecture, demonstrating great potentials of "MT-Spike" in resource and power constrained embedded platforms.

cs.NE↗

arXiv2018

DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework

As one of most fascinating machine learning techniques, deep neural network (DNN) has demonstrated excellent performance in various intelligent tasks such as image classification. DNN achieves such performance, to a large extent, by performing expensive training over huge volumes of training data. To reduce the data storage and transfer overhead in smart resource-limited Internet-of-Thing (IoT) systems, effective data compression is a "must-have" feature before transferring real-time produced dataset for training or classification. While there have been many well-known image compression approaches (such as JPEG), we for the first time find that a human-visual based image compression approach such as JPEG compression is not an optimized solution for DNN systems, especially with high compression ratios. To this end, we develop an image compression framework tailored for DNN applications, named "DeepN-JPEG", to embrace the nature of deep cascaded information process mechanism of DNN architecture. Extensive experiments, based on "ImageNet" dataset with various state-of-the-art DNNs, show that "DeepN-JPEG" can achieve ~3.5x higher compression rate over the popular JPEG solution while maintaining the same accuracy level for image recognition, demonstrating its great potential of storage and power efficiency in DNN-based smart IoT system design.

cs.CV↗

arXiv2018

Quantum improved measurement of time transfer

Accurate time transfer has become a crucial issue for future space experiments which require increasing resolution over large distances. In 2008, a scheme combining homodyne detection and mode-locked femtosecond lasers was proposed that leads to a potential timing precision reaching the yoctosecond range; with a multimode quantum frequency comb as the input field, the sub-shot noise measurement for the time transfer can further improve the timing precision. Based on this scheme and applying the multimode squeezing frequency comb that was measured to have a phase quadrature quantum noise reduction of 1.5 dB at the analyzing frequency 2MHz, the measurable timing fluctuation was reduced from a shot-noise limited value of 8.9E-23s to 7.5E-23s. To our knowledge, this is the first experimental demonstration of the time transfer measurement that achieves a precision beyond the standard quantum limit (SQL).

quant-ph↗

arXiv2018

Feedback Capacity of Stationary Gaussian Channels Further Examined

It is well known that the problem of computing the feedback capacity of a stationary Gaussian channel can be recast as an infinite-dimensional optimization problem; moreover, necessary and sufficient conditions for the optimality of a solution to this optimization problem have been characterized, and based on this characterization, an explicit formula for the feedback capacity has been given for the case that the noise is a first-order autoregressive moving-average Gaussian process. In this paper, we further examine the above-mentioned infinite-dimensional optimization problem. We prove that unless the Gaussian noise is white, its optimal solution is unique, and we propose an algorithm to recursively compute the unique optimal solution, which is guaranteed to converge in theory and features an efficient implementation for a suboptimal solution in practice. Furthermore, for the case that the noise is a k-th order autoregressive moving-average Gaussian process, we give a relatively more explicit formula for the feedback capacity; more specifically, the feedback capacity is expressed as a simple function evaluated at a solution to a system of polynomial equations, which is amenable to numerical computation for the cases k=1, 2 and possibly beyond.

cs.IT↗

arXiv2018

Distributed Event-Triggered Control for Asymptotic Synchronization of Dynamical Networks

This paper studies synchronization of dynamical networks with event-based communication. Firstly, two estimators are introduced into each node, one to estimate its own state, and the other to estimate the average state of its neighbours. Then, with these two estimators, a distributed event-triggering rule (ETR) with a dwell time is designed such that the network achieves synchronization asymptotically with no Zeno behaviours. The designed ETR only depends on the information that each node can obtain, and thus can be implemented in a decentralized way.

eess.SY↗

arXiv2017

Exotic Decays of the 125 GeV Higgs Boson

We perform an extensive survey of non-standard Higgs decays that are consistent with the 125 GeV Higgs-like resonance. Our aim is to motivate a large set of new experimental analyses on the existing and forthcoming data from the Large Hadron Collider (LHC). The explicit search for exotic Higgs decays presents a largely untapped discovery opportunity for the LHC collaborations, as such decays may be easily missed by other searches. We emphasize that the Higgs is uniquely sensitive to the potential existence of new weakly coupled particles and provide a unified discussion of a large class of both simplified and complete models that give rise to characteristic patterns of exotic Higgs decays. We assess the status of exotic Higgs decays after LHC Run 1. In many cases we are able to set new nontrivial constraints by reinterpreting existing experimental analyses. We point out that improvements are possible with dedicated analyses and perform some preliminary collider studies. We prioritize the analyses according to their theoretical motivation and their experimental feasibility. This document is accompanied by a website that will be continuously updated with further information: http://exotichiggs.physics.sunysb.edu.

hep-ph↗

arXiv2017

High-Energy Limit of QCD beyond Sudakov Approximation

We study the high-energy limit of the scattering amplitudes suppressed by the leading power of the quark mass in perturbative quantum chromodynamics. We prove the factorization and perform all-order resummation of the double-logarithmic radiative corrections which determine the asymptotic behavior of the amplitudes. In contrast to the Sudakov logarithms, the mass-suppressed double-logarithmic corrections are induced by soft quark exchange. The structure of the corrections and the asymptotic behavior of the amplitudes in this case crucially depend on the color flow in a given process and are determined by the eikonal color charge nonconservation. We present explicit results for the Higgs boson production in gluon fusion mediated by a light-quark loop and for the leading power-suppressed contributions to the quark form factors, which reveal "magical" universality. Nontrivial relations between the asymptotic behavior of different amplitudes and the amplitudes in different gauge theories are found.

hep-ph↗