arXiv subjects

Yang Cao

Publications and source records attributed to Yang Cao.

At least 325 records · Page 18Linked to original sources

arXiv2020

Roadmap and direction towards high performance MoS2 hydrogen evolution catalysts

MoS2 are facile, stable, non-toxic, affordable materials with reasonable price, and have exhibit its superior potential in catalysis, sensing, opt electrochemical, environmental relate application1, etc. Modification of MoS2 materials has been widely applied for adjusting and formulate the relative performance in certain application field. Making vacancy, doping with other atoms, expending of the layer space, heterojunction engineering, phase engineering etc. have been applied to facilitate the catalytic hydrogen evolution performance. We make a detailed summary on MoS2 characterization methods, synthesis, HER performance, mechanisms, to make a holistic understanding on the interplay between the structure, chemistry, and HER performance, which will give directions to elevate other electrochemical reaction by MoS2.

cond-mat.mtrl-sci↗

arXiv2020

Computational framework for polymer synthesis to study dielectric properties using polarizable reactive molecular dynamics

The increased energy and power density required in modern electronics poses a challenge for designing new dielectric polymer materials with high energy density while maintaining low loss at high applied electric fields. Recently, an advanced computational screening method coupled with hierarchical modelling has accelerated the identification of promising high energy density materials. It is well known that the dielectric response of polymeric materials is largely influenced by their phases and local heterogeneous structures as well as operational temperature. Such inputs are crucial to accelerate the design and discovery of potential polymer candidates. However, an efficient computational framework to probe temperature dependence of the dielectric properties of polymers, while incorporating effects controlled by their morphology is still lacking. In this paper, we propose a scalable computational framework based on reactive molecular dynamics with a valence-state aware polarizable charge model, which is capable of handling practically relevant polymer morphologies and simultaneously provide near-quantum accuracy in estimating dielectric properties of various polymer systems. We demonstrate the predictive power of our framework on high energy density polymer systems recently identified through rational experimental-theoretical co-design. Our scalable and automated framework may be used for high-throughput theoretical screenings of combinatorial large design space to identify next-generation high energy density polymer materials.

physics.chem-ph↗

arXiv2020

Enhanced photocatalytic activation of methane C-H bond by Incorporate Cerium into ETS-10

Ce-ETS-10 was prepared and their ability on photocatalysis coupling of methane has been studied in detail. Ce modification enhanced the photocatalysis performance of ETS-10 on methane conversion reaction, and the methane conversion can be carried out under irradiation of visible light. To study the various rare earth modification to ETS-10, various rare earth elements were introduced to ETS-10 molecular sieve, and the relative photocatalytic methane conversion performance were studied. Various characterizations were applied to examine rare earth modification behavior. The SPS results show that the Cerium modification induce visible light photovoltage, which means the strong electron-hole separation which enhance the photocatalytic performance. The charge-hole separation is non-traditional n-n heterojunction for the charge-hole separation is highly oriented and efficient at atomic scale. This mechanism shows significant enhancement for methane conversion reaction.

cond-mat.mtrl-sci↗

arXiv2020

Secure and Efficient Trajectory-Based Contact Tracing using Trusted Hardware

The COVID-19 pandemic has prompted technological measures to control the spread of the disease. Private contact tracing (PCT) is one of the promising techniques for the purpose. However, the recently proposed Bluetooth-based PCT has several limitations in terms of functionality and flexibility. The existing systems are only able to detect direct contact (i.e., human-human contact), but cannot detect indirect contact (i.e., human-object, such as the disease transmission through surface). Moreover, the rule of risky contact cannot be flexibly changed with the environmental situation and the nature of the virus. In this paper, we propose a secure and efficient trajectory-based PCT system using trusted hardware. We formalize trajectory-based PCT as a generalization of the well-studied Private Set Intersection (PSI), which is mostly based on cryptographic primitives and thus insufficient. We solve the problem by leveraging trusted hardware such as Intel SGX and designing a novel algorithm to achieve a secure, efficient and flexible PCT system. Our experiments on real-world data show that the proposed system can achieve high performance and scalability. Specifically, our system (one single machine with Intel SGX) can process thousands of queries on 100 million records of trajectory data in a few seconds.

cs.CR↗

arXiv2020

Geo-Graph-Indistinguishability: Location Privacy on Road Networks Based on Differential Privacy

In recent years, concerns about location privacy are increasing with the spread of location-based services (LBSs). Many methods to protect location privacy have been proposed in the past decades. Especially, perturbation methods based on Geo-Indistinguishability (Geo-I), which randomly perturb a true location to a pseudolocation, are getting attention due to its strong privacy guarantee inherited from differential privacy. However, Geo-I is based on the Euclidean plane even though many LBSs are based on road networks (e.g. ride-sharing services). This causes unnecessary noise and thus an insufficient tradeoff between utility and privacy for LBSs on road networks. To address this issue, we propose a new privacy notion, Geo-Graph-Indistinguishability (GG-I), for locations on a road network to achieve a better tradeoff. We propose Graph-Exponential Mechanism (GEM), which satisfies GG-I. Moreover, we formalize the optimization problem to find the optimal GEM in terms of the tradeoff. However, the computational complexity of a naive method to find the optimal solution is prohibitive, so we propose a greedy algorithm to find an approximate solution in an acceptable amount of time. Finally, our experiments show that our proposed mechanism outperforms a Geo-I's mechanism with respect to the tradeoff.

cs.CR↗

arXiv2020

Nighttime Dehazing with a Synthetic Benchmark

Increasing the visibility of nighttime hazy images is challenging because of uneven illumination from active artificial light sources and haze absorbing/scattering. The absence of large-scale benchmark datasets hampers progress in this area. To address this issue, we propose a novel synthetic method called 3R to simulate nighttime hazy images from daytime clear images, which first reconstructs the scene geometry, then simulates the light rays and object reflectance, and finally renders the haze effects. Based on it, we generate realistic nighttime hazy images by sampling real-world light colors from a prior empirical distribution. Experiments on the synthetic benchmark show that the degrading factors jointly reduce the image quality. To address this issue, we propose an optimal-scale maximum reflectance prior to disentangle the color correction from haze removal and address them sequentially. Besides, we also devise a simple but effective learning-based baseline which has an encoder-decoder structure based on the MobileNet-v2 backbone. Experiment results demonstrate their superiority over state-of-the-art methods in terms of both image quality and runtime. Both the dataset and source code will be available at https://github.com/chaimi2013/3R.

cs.CV↗

arXiv2020

Finding Acceptable Parameter Regions of Stochastic Hill functions for Multisite Phosphorylation Mechanism

Multisite phosphorylation plays an important role in regulating switchlike protein activity and has been used widely in mathematical models. With the development of new experimental techniques and more molecular data, molecular phosphorylation processes emerge in many systems with increasing complexity and sizes. These developments call for simple yet valid stochastic models to describe various multisite phosphorylation processes, especially in large and complex biochemical networks. To reduce model complexity, this work aims to simplify the multisite phosphorylation mechanism by a stochastic Hill function model. Further, this work optimizes regions of parameter space to match simulation results from the stochastic Hill function with the distributive multisite phosphorylation process. While traditional parameter optimization methods have been focusing on finding the best parameter vector, in most circumstances modelers would like to find a set of parameter vectors that generate similar system dynamics and results. This paper proposes a general $α$-$β$-$γ$ rule to return an acceptable parameter region of the stochastic Hill function based on a quasi-Newton stochastic optimization (QNSTOP) algorithm. Different objective functions are investigated characterizing different features of the simulation-based empirical data, among which the approximate maximum log-likelihood method is recommended for general applications. Numerical results demonstrate that with an appropriate parameter vector value, the stochastic Hill function model depicts the multisite phosphorylation process well except the initial (transient) period.

q-bio.MN↗

arXiv2020

Arithmetic purity of strong approximation for semi-simple simply connected groups

In this article we establish the arithmetic purity of strong approximation for certain semi-simple simply connected $k$-simple linear algebraic groups and their homogeneous spaces over a number field $k$. For instance, for any such group $G$ and for any open subset $U$ of $G$ with codim$(G\setminus U, G)\geq 2$, we prove that (i) if $G$ is $k$-isotropic, then $U$ satisfies strong approximation off any one (hence any finitely many) place; (ii) if $G$ is the spin group of a non-degenerate quadratic form which is non-compact over archimedean places, then $U$ satisfies strong approximation off all archimedean places. As a consequence, we prove that the same property holds for affine quadratic hypersurfaces. Our approach combines a fibration method with subgroup actions developed for induction on the codimension of $G\setminus U$, and an affine combinatorial sieve which allows to produce integral points with almost prime polynomial values.

math.NT↗

arXiv2020

Non-Hermitian Bulk-Boundary Correspondence in Periodically Driven System

Bulk-boundary correspondence, connecting the bulk topology and the edge states, is an essential principle of the topological phases. However, the bulk-boundary correspondence is broken down in general non-Hermitian systems. In this paper, we construct one-dimensional non-Hermitian Su-Schrieffer-Heeger model with periodic driving that exhibits non-Hermitian skin effect: all the eigenstates are localized at the boundary of the systems, whether the bulk states or the zero and the $π$ modes. To capture the topological properties, the non-Bloch winding numbers are defined by the non-Bloch periodized evolution operators based on the generalized Brillouin zone. Furthermore, the non-Hermitian bulk-boundary correspondence is established: the non-Bloch winding numbers ($W_{0,π}$) characterize the edge states with quasienergies $ε=0, π$. In our non-Hermitian system, a novel phenomenon can emerge that the robust edge states can appear even when the Floquet bands are topological trivial with zero non-Bloch band invariant, which is defined in terms of the non-Bloch effective Hamiltonian. We also show that the relation between the non-Bloch winding numbers ($W_{0,π}$) and the non-Bloch band invariant ($\mathcal{W}$): $\mathcal{W}= W_{0}- W_π$.

cond-mat.mes-hall↗

arXiv2020

PGLP: Customizable and Rigorous Location Privacy through Policy Graph

Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off between privacy and utility in many real-world applications. To address this issue, we propose a new location privacy notion called PGLP, i.e., \textit{Policy Graph based Location Privacy}, providing a rich interface to release private locations with customizable and rigorous privacy guarantee. First, we design the privacy metrics of PGLP by extending differential privacy. Specifically, we formalize a user's location privacy requirements using a \textit{location policy graph}, which is expressive and customizable. Second, we investigate how to satisfy an arbitrarily given location policy graph under adversarial knowledge. We find that a location policy graph may not always be viable and may suffer \textit{location exposure} when the attacker knows the user's mobility pattern. We propose efficient methods to detect location exposure and repair the policy graph with optimal utility. Third, we design a private location trace release framework that pipelines the detection of location exposure, policy graph repair, and private trajectory release with customizable and rigorous location privacy. Finally, we conduct experiments on real-world datasets to verify the effectiveness of the privacy-utility trade-off and the efficiency of the proposed algorithms.

cs.CR↗

arXiv2020

PANDA: Policy-aware Location Privacy for Epidemic Surveillance

In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from coronavirus crises attempt to access citizen's location data to eliminate the outbreak. However, it raises privacy concerns and may open the doors to more invasive forms of surveillance in the name of public health. It also brings a challenge for privacy protection techniques: how can we leverage people's mobile data to help combat the pandemic without scarifying our location privacy. We demonstrate that we can have the best of the two worlds by implementing policy-based location privacy for epidemic surveillance. Specifically, we formalize the privacy policy using graphs in light of differential privacy, called policy graph. Our system has three primary functions for epidemic surveillance: location monitoring, epidemic analysis, and contact tracing. We provide an interactive tool allowing the attendees to explore and examine the usability of our system: (1) the utility of location monitor and disease transmission model estimation, (2) the procedure of contact tracing in our systems, and (3) the privacy-utility trade-offs w.r.t. different policy graphs. The attendees can find that it is possible to have the full functionality of epidemic surveillance while preserving location privacy.

cs.DB↗

arXiv2020

Evidence of Flat Bands and Correlated States in Buckled Graphene Superlattices

Two-dimensional atomic crystals can radically change their properties in response to external influences such as substrate orientation or strain, resulting in essentially new materials in terms of the electronic structure. A striking example is the creation of flat-bands in bilayer-graphene for certain 'magic' twist-angles between the orientations of the two layers. The quenched kinetic-energy in these flat-bands promotes electron-electron interactions and facilitates the emergence of strongly-correlated phases such as superconductivity and correlated-insulators. However, the exquisite fine-tuning required for finding the magic-angle where flat-bands appear in twisted-bilayer graphene, poses challenges to fabrication and scalability. Here we present an alternative route to creating flat-bands that does not involve fine tuning. Using scanning tunneling microscopy and spectroscopy, together with numerical simulations, we demonstrate that graphene monolayers placed on an atomically-flat substrate can be forced to undergo a buckling-transition, resulting in a periodically modulated pseudo-magnetic field, which in turn creates a post-graphene material with flat electronic bands. Bringing the Fermi-level into these flat-bands by electrostatic doping, we observe a pseudogap-like depletion in the density-of-states, which signals the emergence of a correlated-state. The described approach of 2D crystal buckling offers a strategy for creating other superlattice systems and, in particular, for exploring interaction phenomena characteristic of flat-bands.

cond-mat.mes-hall↗

arXiv2020

Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services

Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users' location privacy by releasing a perturbed location to third parties such as location-based service providers. However, when a user's perturbed locations are released continuously, existing LPPMs may not protect the sensitive information about the user's spatiotemporal activities, such as "visited hospital in the last week" or "regularly commuting between Address 1 and Address 2" (it is easy to infer that Addresses 1 and 2 may be home and office), which we call it \textit{spatiotemporal event}. In this paper, we first formally define {spatiotemporal event} as Boolean expressions between location and time predicates, and then we define $ ε$-\textit{spatiotemporal event privacy} by extending the notion of differential privacy. Second, to understand how much spatiotemporal event privacy that existing LPPMs can provide, we design computationally efficient algorithms to quantify the privacy leakage of state-of-the-art LPPMs when an adversary has prior knowledge of the user's initial probability over possible locations. It turns out that the existing LPPMs cannot adequately protect spatiotemporal event privacy. Third, we propose a framework, PriSTE, to transform an existing LPPM into one protecting spatiotemporal event privacy against adversaries with \textit{any} prior knowledge. Our experiments on real-life and synthetic data verified that the proposed method is effective and efficient.

cs.DB↗

arXiv2020

Dispersion Limited versus Power Limited Terahertz Transmission Links Using Solid Core Subwavelength Dielectric Fibers

In this work, we present an in-depth experimental and numerical study of the short-range THz communications links that use subwavelength dielectric fibers for information transmission and define main challenges and tradeoffs in the link implementation. Particularly, we use air or foam-cladded polypropylene-core subwavelength dielectric THz fibers of various diameters (0.57-1.75 mm) to study link performance as a function of the link length of up to ~10 m, and data bitrates of up to 6 Gbps at the carrier frequency of 128 GHz (2.34 mm wavelength). We find that depending on the fiber diameter, the quality of the transmitted signal is mostly limited either by the modal propagation loss or by the fiber velocity dispersion (GVD). An error-free transmission over 10 meters is achieved for the bit rate of 4 Gbps using the fiber of smaller 0.57 mm diameter. Furthermore, since the fields of subwavelength fibers are weakly confined and extend deep into the air cladding, we study the modal field extent outside of the fiber core, as well as fiber bending loss. Finally, the power budget of the rod-in-air subwavelength THz fiber-based links is compared to that of free space communication links and we demonstrate that fiber links offer an excellent solution for various short-range applications.

physics.app-ph↗

arXiv2020

Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release

With the development of smart devices, such as the Amazon Echo and Apple's HomePod, speech data have become a new dimension of big data. However, privacy and security concerns may hinder the collection and sharing of real-world speech data, which contain the speaker's identifiable information, i.e., voiceprint, which is considered a type of biometric identifier. Current studies on voiceprint privacy protection do not provide either a meaningful privacy-utility trade-off or a formal and rigorous definition of privacy. In this study, we design a novel and rigorous privacy metric for voiceprint privacy, which is referred to as voice-indistinguishability, by extending differential privacy. We also propose mechanisms and frameworks for privacy-preserving speech data release satisfying voice-indistinguishability. Experiments on public datasets verify the effectiveness and efficiency of the proposed methods.

cs.CR↗

arXiv2020

One-Shot Texture Retrieval with Global Context Metric

In this paper, we tackle one-shot texture retrieval: given an example of a new reference texture, detect and segment all the pixels of the same texture category within an arbitrary image. To address this problem, we present an OS-TR network to encode both reference and query image, leading to achieve texture segmentation towards the reference category. Unlike the existing texture encoding methods that integrate CNN with orderless pooling, we propose a directionality-aware module to capture the texture variations at each direction, resulting in spatially invariant representation. To segment new categories given only few examples, we incorporate a self-gating mechanism into relation network to exploit global context information for adjusting per-channel modulation weights of local relation features. Extensive experiments on benchmark texture datasets and real scenarios demonstrate the above-par segmentation performance and robust generalization across domains of our proposed method.

cs.CV↗

arXiv2020

FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection

As massive data are produced from small gadgets, federated learning on mobile devices has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) has been widely used in federated learning for various machine learning models. To prevent privacy leakages from gradients that are calculated on users' sensitive data, local differential privacy (LDP) has been considered as a privacy guarantee in federated SGD recently. However, the existing solutions have a dimension dependency problem: the injected noise is substantially proportional to the dimension $d$. In this work, we propose a two-stage framework FedSel for federated SGD under LDP to relieve this problem. Our key idea is that not all dimensions are equally important so that we privately select Top-k dimensions according to their contributions in each iteration of federated SGD. Specifically, we propose three private dimension selection mechanisms and adapt the gradient accumulation technique to stabilize the learning process with noisy updates. We also theoretically analyze privacy, accuracy and time complexity of FedSel, which outperforms the state-of-the-art solutions. Experiments on real-world and synthetic datasets verify the effectiveness and efficiency of our framework.

cs.LG↗

arXiv2020

Sous-groupe de Brauer invariant et obstruction de descente itérée

For a quasi-projective smooth geometrically integral variety over a number field $k$, we prove that the iterated descent obstruction is equivalent to the descent obstruction. This generalizes a result of Skorobogatov, and this answers an open question of Poonen. The key idea is the notion of invariant Brauer subgroup and the notion of invariant étale Brauer-Manin obstruction for a $k$-variety equipped with an action of a connected linear algebraic group.

math.AG↗