arXiv subjects

Yao Cheng

Publications and source records attributed to Yao Cheng.

At least 73 records · Page 4Linked to original sources

arXiv2020

Keyed Non-Parametric Hypothesis Tests

The recent popularity of machine learning calls for a deeper understanding of AI security. Amongst the numerous AI threats published so far, poisoning attacks currently attract considerable attention. In a poisoning attack the opponent partially tampers the dataset used for learning to mislead the classifier during the testing phase. This paper proposes a new protection strategy against poisoning attacks. The technique relies on a new primitive called keyed non-parametric hypothesis tests allowing to evaluate under adversarial conditions the training input's conformance with a previously learned distribution $\mathfrak{D}$. To do so we use a secret key $κ$ unknown to the opponent. Keyed non-parametric hypothesis tests differs from classical tests in that the secrecy of $κ$ prevents the opponent from misleading the keyed test into concluding that a (significantly) tampered dataset belongs to $\mathfrak{D}$.

cs.CR↗

arXiv2020

Load-velocity-temperature relationship in frictional response of microscopic contacts

Frictional properties of interfaces with dynamic chemical bonds have been the subject of intensive experimental investigation and modeling, as it provides important insights into the molecular origin of the empirical rate and state laws, which have been highly successful in describing friction from nano to geophysical scales. Using previously developed theoretical approaches requires time-consuming simulations that are impractical for many realistic tribological systems. To solve this problem and set a framework for understanding microscopic mechanisms of friction at interfaces including multiple microscopic contacts, we developed an analytical approach for description of friction mediated by dynamical formation and rupture of microscopic interfacial contacts, which allows to calculate frictional properties on the time and length scales that are relevant to tribological experimental conditions. The model accounts for the presence of various types of contacts at the frictional interface and predicts novel dependencies of friction on sliding velocity, temperature, and normal load, which are amenable to experimental observations. Our model predicts the velocity-temperature scaling, which relies on the interplay between the effects of shear and temperature on the rupture of interfacial contacts. The proposed scaling can be used to extrapolate the simulation results to a range of very low sliding velocities used in nanoscale friction experiments, which is still unreachable by simulations. For interfaces including two types of interfacial contacts with distinct properties, our model predicts novel double-peaked dependencies of friction on temperature and velocity. Our work provides a promising avenue for the interpretation of the experimental data on friction at interfaces including microscopic contacts and opens new pathways for the rational control of the frictional response.

cond-mat.mes-hall↗

arXiv2019

PowerNet: Neural Power Demand Forecasting in Smart Grid

Power demand forecasting is a critical task for achieving efficiency and reliability in power grid operation. Accurate forecasting allows grid operators to better maintain the balance of supply and demand as well as to optimize operational cost for generation and transmission. This article proposes a novel neural network architecture PowerNet, which can incorporate multiple heterogeneous features, such as historical energy consumption data, weather data, and calendar information, for the power demand forecasting task. Compared to two recent works based on Gradient Boosting Tree (GBT) and Support Vector Regression (SVR), PowerNet demonstrates a decrease of 33.3% and 14.3% in forecasting error, respectively. We further provide empirical results the two operational considerations that are crucial when using PowerNet in practice, i.e., how far in the future the model can forecast with a decent accuracy and how often we should re-train the forecasting model to retain its modeling capability. Finally, we briefly discuss a multilayer anomaly detection approach based on PowerNet.

cs.OH↗

arXiv2018

Gamma factors for Asai representations of ${\rm GL}_2$

Let $E$ be a quadratic semisimple extension of a local field $F$ of characteristic zero. We determine explicit relation between gamma factors for Asai representations of $R_{E/F}{\rm GL}_{2/E}$ defined by the Weil-Deligne representations and local zeta integrals. When $E = F\times F$, the results were due to Henniart and Jacquet. We completed the theory in this article based on explicit calculation.

math.NT↗

arXiv2018

On Deligne's conjecture for certain automorphic $L$-functions for ${\rm GL}(3)\times {\rm GL}(2)$

We prove Deligne's conjecture for central critical values of certain automorphic $L$-functions for ${\rm GL}(3)\times {\rm GL}(2)$. The proof is base on rationality results for central critical values of triple product $L$-functions, which follow from establishing explicit Ichino's formulae for trilinear period integrals for Hilbert cusp forms on totally real etale cubic algebras over $\mathbb{Q}$.

math.NT↗

arXiv2018

SCLib: A Practical and Lightweight Defense against Component Hijacking in Android Applications

Cross-app collaboration via inter-component communication is a fundamental mechanism on Android. Although it brings the benefits such as functionality reuse and data sharing, a threat called component hijacking is also introduced. By hijacking a vulnerable component in victim apps, an attack app can escalate its privilege for operations originally prohibited. Many prior studies have been performed to understand and mitigate this issue, but no defense is being deployed in the wild, largely due to the deployment difficulties and performance concerns. In this paper we present SCLib, a secure component library that performs in-app mandatory access control on behalf of app components. It does not require firmware modification or app repackaging as in previous works. The library-based nature also makes SCLib more accessible to app developers, and enables them produce secure components in the first place over fragmented Android devices. As a proof of concept, we design six mandatory policies and overcome unique implementation challenges to mitigate attacks originated from both system weaknesses and common developer mistakes. Our evaluation using ten high-profile open source apps shows that SCLib can protect their 35 risky components with negligible code footprint (less than 0.3% stub code) and nearly no slowdown to normal intra-app communications. The worst-case performance overhead to stop attacks is about 5%.

cs.CR↗

arXiv2017

First-Order Perturbation Analysis of the SECSI Framework for the Approximate CP Decomposition of 3-D Noise-Corrupted Low-Rank Tensors

The Semi-Algebraic framework for the approximate Canonical Polyadic (CP) decomposition via SImultaneaous matrix diagonalization (SECSI) is an efficient tool for the computation of the CP decomposition. The SECSI framework reformulates the CP decomposition into a set of joint eigenvalue decomposition (JEVD) problems. Solving all JEVDs, we obtain multiple estimates of the factor matrices and the best estimate is chosen in a subsequent step by using an exhaustive search or some heuristic strategy that reduces the computational complexity. Moreover, the SECSI framework retains the option of choosing the number of JEVDs to be solved, thus providing an adjustable complexity-accuracy trade-off. In this work, we provide an analytical performance analysis of the SECSI framework for the computation of the approximate CP decomposition of a noise corrupted low-rank tensor, where we derive closed-form expressions of the relative mean square error for each of the estimated factor matrices. These expressions are obtained using a first-order perturbation analysis and are formulated in terms of the second-order moments of the noise, such that apart from a zero mean, no assumptions on the noise statistics are required. Simulation results exhibit an excellent match between the obtained closed-form expressions and the empirical results. Moreover, we propose a new Performance Analysis based Selection (PAS) scheme to choose the final factor matrix estimate. The results show that the proposed PAS scheme outperforms the existing heuristics, especially in the high SNR regime.

cs.IT↗

arXiv2017

JSForce: A Forced Execution Engine for Malicious JavaScript Detection

The drastic increase of JavaScript exploitation attacks has led to a strong interest in developing techniques to enable malicious JavaScript analysis. Existing analysis tech- niques fall into two general categories: static analysis and dynamic analysis. Static analysis tends to produce inaccurate results (both false positive and false negative) and is vulnerable to a wide series of obfuscation techniques. Thus, dynamic analysis is constantly gaining popularity for exposing the typical features of malicious JavaScript. However, existing dynamic analysis techniques possess limitations such as limited code coverage and incomplete environment setup, leaving a broad attack surface for evading the detection. To overcome these limitations, we present the design and implementation of a novel JavaScript forced execution engine named JSForce which drives an arbitrary JavaScript snippet to execute along different paths without any input or environment setup. We evaluate JSForce using 220,587 HTML and 23,509 PDF real- world samples. Experimental results show that by adopting our forced execution engine, the malicious JavaScript detection rate can be substantially boosted by 206.29% using same detection policy without any noticeable false positive increase. We also make JSForce publicly available as an online service and will release the source code to the security community upon the acceptance for publication.

cs.CR↗

arXiv2013

Dynamics of Self-retracting motion of graphite micro-flakes

A sheared microscopic graphite mesa retracts spontaneously to minimize interfacial energy. Using an optical knife-edge technique, we report comparative measurements of the speeds of such self-retracting motion (SRM) in air and low vacuum (about 1 Pa). We found that the retracting speed Vm of SRM is almost the same in the two environments at both room temperature and high temperature (above 150 celsius degree). We also extract the acceleration of the SRM, and found some typical behaviors. In the high speed range (above 5 m/s), the acceleration decreases linearly with the logarithm of the SRM speed. We attribute this phenomenon to the thermally activated sliding process in SRM. In the low speed region (between 0.1 m/s and 1 m/s), the acceleration increases linearly with the logarithm of the SRM speed.

cond-mat.mtrl-sci↗

arXiv2012

Magnetoelectric effect induced by the delocalized 93mNb state

We report a temperature- and density-dependent decay of the 93mNb nuclear excitation and give a minimal interpretation on the underlying physics. This anomaly indicated nuclear resonant absorption as well as delocalization of the long-lived Moessbauer state in the crystal. A nonlinear magnetoelectric response, on low-frequency drive current, showed up in the bulk metal of a high-purity niobium crystal and then disappeared with vanishing benchmarks of delocalized nuclear excitation. Several nonlinear resonant peaks, on the order of several hundreds of Hz, grew up with the applied magnetic field. The central frequencies of these peaks decreased with temperature.

physics.gen-ph↗

arXiv2011

Interlayer binding energy of graphite -- A direct experimental determination

Despite interlayer binding energy is one of the most important material properties for graphite, there is still lacking report on its direct experimental determination. In this paper, we present a novel experimental method to directly measure the interlayer binding energy of highly oriented pyrolytic graphite (HOPG). The obtained values of the binding energy are 0.27($\pm $0.02)J/m$^{2}$, which can serve as a benchmark for other theoretical and experimental works.

cond-mat.mes-hall↗

arXiv2011

Heavy Long-lived Mossbauer State of Niobium

A heavy niobium state showing 1/3 residual resistance is discovered below the superconducting transition temperature. This niobium sample contains high-density long-lived Mossbauer excitation.

physics.gen-ph↗

arXiv2010

Graphite Nanoeraser

We present here a method for cleaning intermediate-size (5~50nm) contamination from highly oriented pyrolytic graphite. Electron beam deposition causes a continuous increase of carbonaceous material on graphene and graphite surfaces, which is difficult to remove by conventional techniques. Direct mechanical wiping using a graphite nanoeraser is observed to drastically reduce the amount of contamination. After the mechanical removal of contamination, the graphite surfaces were able to self-retract after shearing, indicating that van der Waals contact bonding is restored. Since contact bonding provides an indication of a level of cleanliness normally only attainable in a high-quality clean-room, we discuss potential applications in preparation of ultraclean surfaces.

cond-mat.mtrl-sci↗

arXiv2009

Nuclear spin-density wave theory

Recently [arXiv:0906.5417], we reported a quantum phase transition of 103mRh excited by bremsstrahlung pumping. The long-lived Moessbauer excitation is delocalized as a neutral quasiparticle carrying a spin current. This letter gives a general theory for a nuclear spin-density wave propagating on crystals consisting of identical nuclei with a multipolar transition.

physics.gen-ph↗

arXiv2009

Anomalous emissions of 103mRh biphoton transitions

In this report, the anomalous emissions, centered on the one half transition energy 39.76/2 keV, are observed from the long-lived Mossbauer state of 103mRh excited by bremsstrahlung exposure. Strong coupling with identical nuclei in Rh crystals opens cascade channels for biphoton transitions.

physics.gen-ph↗

arXiv2009

Quantum phase transition of the 103mRh spin-density wave

We induce a quantum phase transition of the 103mRh excitation at the critical density of 10^{12} cm^{-3} by bremsstrahlung pumping at 300 K. A massive 103mRh spin-density wave carrying a spin current moves on the identical 103Rh matrix like a quantum fluid. The collapse-and-revival spectral evolution indicates that the collective nuclear excitation undergoes dynamic Bose-Einstein condensation. Applying an external magnetic field, we observe the Onsager-Feynman quantization, which gives further evidence of the superfluidic phase.

quant-ph↗

arXiv2009

Observations on spectral deformations of 103mRh excited by bremsstrahlung

Spectral deformation of K alpha, K beta and gamma emissions from the nuclear state 103mRh excited by bremsstrahlung are investigated. Nonlinear increase for excitation number density of 103mRh with radiation exposure is observed. The spectral profiles are broadened, attributable to a triplet splitting. Interesting time-evolution behaviors of the spectral deformations are obtained.

physics.atom-ph↗

arXiv2009

Cooling effect in emissions of 103mRh excited by bremsstrahlung

Nonlinear characteristic emissions of K alpha, K beta and gamma with a significant triplet splitting at room temperature are observed from the long-lived nuclear state of 103mRh excited by bremsstrahlung irradiation. A pronounced phase-transition-like narrowing of the emission profiles occurs immediately after the sample is cooled down to 77 K. The room temperature profiles reappear again abruptly and almost reversibly as the temperature drifts freely back to approximately the ice point after the filling of liquid nitrogen is stopped. These emission properties at 300 K and at low temperature may indicate that the 103mRh nuclei are in collective states.

nucl-ex↗