arXiv subjects

Yang Cao

Publications and source records attributed to Yang Cao.

At least 343 records · Page 19Linked to original sources

arXiv2020

Effects of high-$j$ orbitals, pairing and deformed neutron shells on upbendings of ground-state bands in neutron-rich even-even isotopes $^{170-184}$Hf

The ground-state bands (GSBs) in the even-even hafnium isotopes $^{170-184}$Hf are investigated by using the cranked shell model (CSM) with pairing correlations treated by the particle-number conserving (PNC) method. The experimental kinematic moments of inertia are reproduced very well by theoretical calculations. The second upbending of the GSB at high frequency $\hbarω\approx0.5$ MeV observed (predicted) in $^{172}$Hf ($^{170,174-178}$Hf) attributes to the sudden alignments of the proton high-$j$ orbitals $\pi1i_{13/2}$ $(1/2^{+}[660])$, $\pi1h_{9/2}$ $(1/2^{-}[541])$ and orbital $\pi1h_{11/2}$ $(7/2^{-}[523])$. The first upbendings of GSBs at low frequency $\hbarω=0.2-0.3$ MeV in $^{170-178}$Hf, which locate below the deformed neutron shell $N=108$, attribute to the alignment of the neutron orbital $\nu1i_{13/2}$. For the heavier even-even isotopes $^{180-184}$Hf, compared to the lighter isotopes, the first band-crossing is delayed to the high frequency due to the existence of the deformed shells $N=108,116$. The upbendings of GSBs in $^{180-184}$Hf are predicted to occur at $\hbarω\approx0.5$MeV, which come from the sharp raise of the simultaneous alignments of both proton $\pi1i_{13/2}$, $\pi1h_{9/2}$ and neutron $\nu2g_{9/2}$ orbitals. The pairing correlation plays a very important role in the rotational properties of GSBs in even-even isotopes $^{180-184}$Hf. Its effects on upbendings and band-crossing frequencies are investigated.

nucl-th↗

arXiv2020

Providing Input-Discriminative Protection for Local Differential Privacy

Local Differential Privacy (LDP) provides provable privacy protection for data collection without the assumption of the trusted data server. In the real-world scenario, different data have different privacy requirements due to the distinct sensitivity levels. However, LDP provides the same protection for all data. In this paper, we tackle the challenge of providing input-discriminative protection to reflect the distinct privacy requirements of different inputs. We first present the Input-Discriminative LDP (ID-LDP) privacy notion and focus on a specific version termed MinID-LDP, which is shown to be a fine-grained version of LDP. Then, we focus on the application of frequency estimation and develop the IDUE mechanism based on Unary Encoding for single-item input and the extended mechanism IDUE-PS (with Padding-and-Sampling protocol) for item-set input. The results on both synthetic and real-world datasets validate the correctness of our theoretical analysis and show that the proposed mechanisms satisfying MinID-LDP have better utility than the state-of-the-art mechanisms satisfying LDP due to the input-discriminative protection.

cs.CR↗

arXiv2019

Deep Time-Frequency Representation and Progressive Decision Fusion for ECG Classification

Early recognition of abnormal rhythms in ECG signals is crucial for monitoring and diagnosing patients' cardiac conditions, increasing the success rate of the treatment. Classifying abnormal rhythms into exact categories is very challenging due to the broad taxonomy of rhythms, noises and lack of large-scale real-world annotated data. Different from previous methods that utilize hand-crafted features or learn features from the original signal domain, we propose a novel ECG classification method by learning deep time-frequency representation and progressive decision fusion at different temporal scales in an end-to-end manner. First, the ECG wave signal is transformed into the time-frequency domain by using the Short-Time Fourier Transform. Next, several scale-specific deep convolutional neural networks are trained on ECG samples of a specific length. Finally, a progressive online decision fusion method is proposed to fuse decisions from the scale-specific models into a more accurate and stable one. Extensive experiments on both synthetic and real-world ECG datasets demonstrate the effectiveness and efficiency of the proposed method.

cs.LG↗

arXiv2019

Super-resolution Orthogonal Deterministic Imaging Technique for Terahertz Subwavelength Microscopy

Terahertz subwavelength imaging aims at developing THz microscopes able to resolve deeply subwavelength features. To improve the spatial resolution beyond the diffraction limit, a current trend is to use various subwavelength probes to convert the near-field to the far-field. These techniques, while offering significant gains in spatial resolution, inherently lack the ability to rapidly obtain THz images due to the necessity of slow pixel-by-pixel raster scan and often suffer from low light throughput. In parallel, in the visible spectral range, several super-resolution imaging techniques were developed that enhance the image resolution by recording and statistically correlating multiple images of an object backlit with light from stochastically blinking fluorophores. Inspired by this methodology, we develop a Super-resolution Orthogonal Deterministic Imaging (SODI) technique and apply it in the THz range. Since there are no natural THz fluorophores, we use optimally designed mask sets brought in proximity with the object as artificial fluorophores. Because we directly control the form of the masks, rather than relying on statistical averages, we aim at employing the smallest possible number of frames. After developing the theoretical basis of SODI, we demonstrate the second-order resolution improvement experimentally using phase masks and binary amplitude masks with only 8 frames. We then numerically show how to extend the SODI technique to higher orders to further improve the resolution. As our formulation is based on the equation of linear imaging and it uses spatial modulation of either the phase or the amplitude of the THz wave, our methodology can be readily adapted for the use with existing phase-sensitive single pixel imaging systems or any amplitude sensitive THz imaging arrays.

physics.optics↗

arXiv2019

PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility

Data collection under local differential privacy (LDP) has been mostly studied for homogeneous data. Real-world applications often involve a mixture of different data types such as key-value pairs, where the frequency of keys and mean of values under each key must be estimated simultaneously. For key-value data collection with LDP, it is challenging to achieve a good utility-privacy tradeoff since the data contains two dimensions and a user may possess multiple key-value pairs. There is also an inherent correlation between key and values which if not harnessed, will lead to poor utility. In this paper, we propose a locally differentially private key-value data collection framework that utilizes correlated perturbations to enhance utility. We instantiate our framework by two protocols PCKV-UE (based on Unary Encoding) and PCKV-GRR (based on Generalized Randomized Response), where we design an advanced Padding-and-Sampling mechanism and an improved mean estimator which is non-interactive. Due to our correlated key and value perturbation mechanisms, the composed privacy budget is shown to be less than that of independent perturbation of key and value, which enables us to further optimize the perturbation parameters via budget allocation. Experimental results on both synthetic and real-world datasets show that our proposed protocols achieve better utility for both frequency and mean estimations under the same LDP guarantees than state-of-the-art mechanisms.

cs.CR↗

arXiv2019

Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement

Contrast enhancement and noise removal are coupled problems for low-light image enhancement. The existing Retinex based methods do not take the coupling relation into consideration, resulting in under or over-smoothing of the enhanced images. To address this issue, this paper presents a novel progressive Retinex framework, in which illumination and noise of low-light image are perceived in a mutually reinforced manner, leading to noise reduction low-light enhancement results. Specifically, two fully pointwise convolutional neural networks are devised to model the statistical regularities of ambient light and image noise respectively, and to leverage them as constraints to facilitate the mutual learning process. The proposed method not only suppresses the interference caused by the ambiguity between tiny textures and image noises, but also greatly improves the computational efficiency. Moreover, to solve the problem of insufficient training data, we propose an image synthesis strategy based on camera imaging model, which generates color images corrupted by illumination-dependent noises. Experimental results on both synthetic and real low-light images demonstrate the superiority of our proposed approaches against the State-Of-The-Art (SOTA) low-light enhancement methods.

eess.IV↗

arXiv2019

Trading Location Data with Bounded Personalized Privacy Loss

As personal data have been the new oil of the digital era, there is a growing trend perceiving personal data as a commodity. Although some people are willing to trade their personal data for money, they might still expect limited privacy loss, and the maximum tolerable privacy loss varies with each individual. In this paper, we propose a framework that enables individuals to trade their personal data with bounded personalized privacy loss, which raises technical challenges in the aspects of budget allocation and arbitrage-freeness. To deal with those challenges,we propose two arbitrage-free trading mechanisms with different advantages.

cs.CR↗

arXiv2019

State-Aware Rate Adaptation for UAVs by Incorporating On-Board Sensors

Nowadays unmanned aerial vehicles (UAVs) are being widely applied to a wealth of civil and military applications. Robust and high-throughput wireless communication is the crux of these UAV applications. Yet, air-to-ground links suffer from time-varying channels induced by the agile mobility and dynamic environments. Rate adaptation algorithms are generally used to choose the optimal data rate based on the current channel conditions. State-of-the-art approaches leverage physical layer information for rate adaptation, and they work well under certain conditions. However, the above protocols still have limitation under constantly changing flight states and environments for air-to-ground links. To solve this problem, we propose StateRate, a state-optimized rate adaptation algorithm that fully exploits the characteristics of UAV systems using a hybrid deep learning model. The key observation is that the rate adaptation strategy needs to be adjusted according to motion-dependent channel models, which can be reflected by flight states. In this work, the rate adaptation protocol is enhanced with the help of the on-board sensors in UAVs. To make full use of the sensor data, we introduce a learning-based prediction module by leveraging the internal state to dynamically store temporal features under variable flight states. We also present an online learning algorithm by employing the pre-trained model that adapts the rate adaptation algorithm to different environments. We implement our algorithm on a commercial UAV platform and evaluate it in various environments. The results demonstrate that our system outperforms the best-known rate adaptation algorithm up to 53% in terms of throughput when the velocity is 2-6~m/s.

cs.NI↗

arXiv2019

Sensor-Augmented Neural Adaptive Bitrate Video Streaming on UAVs

Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized a broad class of civil and military applications. However, the designs of wireless technologies that enable real-time streaming of high-definition video between UAVs and ground clients present a conundrum. Most existing adaptive bitrate (ABR) algorithms are not optimized for the air-to-ground links, which usually fluctuate dramatically due to the dynamic flight states of the UAV. In this paper, we present SA-ABR, a new sensor-augmented system that generates ABR video streaming algorithms with the assistance of various kinds of inherent sensor data that are used to pilot UAVs. By incorporating the inherent sensor data with network observations, SA-ABR trains a deep reinforcement learning (DRL) model to extract salient features from the flight state information and automatically learn an ABR algorithm to adapt to the varying UAV channel capacity through the training process. SA-ABR does not rely on any assumptions or models about UAV's flight states or the environment, but instead, it makes decisions by exploiting temporal properties of past throughput through the long short-term memory (LSTM) to adapt itself to a wide range of highly dynamic environments. We have implemented SA-ABR in a commercial UAV and evaluated it in the wild. We compare SA-ABR with a variety of existing state-of-the-art ABR algorithms, and the results show that our system outperforms the best known existing ABR algorithm by 21.4% in terms of the average quality of experience (QoE) reward.

eess.SP↗

arXiv2019

EGNet:Edge Guidance Network for Salient Object Detection

Fully convolutional neural networks (FCNs) have shown their advantages in the salient object detection task. However, most existing FCNs-based methods still suffer from coarse object boundaries. In this paper, to solve this problem, we focus on the complementarity between salient edge information and salient object information. Accordingly, we present an edge guidance network (EGNet) for salient object detection with three steps to simultaneously model these two kinds of complementary information in a single network. In the first step, we extract the salient object features by a progressive fusion way. In the second step, we integrate the local edge information and global location information to obtain the salient edge features. Finally, to sufficiently leverage these complementary features, we couple the same salient edge features with salient object features at various resolutions. Benefiting from the rich edge information and location information in salient edge features, the fused features can help locate salient objects, especially their boundaries more accurately. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on six widely used datasets without any pre-processing and post-processing. The source code is available at http: //mmcheng.net/egnet/.

cs.CV↗

arXiv2019

Dopant-free molecular hole transport material that mediates a 20% power conversion efficiency in a perovskite solar cell

Organic molecular hole-transport materials (HTMs) are appealing for the scalable manufacture of perovskite solar cells (PSCs) because they are easier to reproducibly prepare in high purity than polymeric and inorganic HTMs. There is also a need to construct PSCs without dopants and additives to avoid formidable engineering and stability issues. We report here a power conversion efficiency (PCE) of 20.6% with a molecular HTM in an inverted (p-i-n) PSC without any dopants or interlayers. This new benchmark was made possible by the discovery that annealing a spiro-based dopant-free HTM (denoted DFH) containing redox-active triphenyl amine (TPA) units undergoes preferential molecular organization normal to the substrate. This structural order, governed by the strong intermolecular interactions of the DFH dioxane groups, affords high intrinsic hole mobility (1x10-3 cm2 V-1 s-1). Annealing films of DFH also enables the growth of large perovskite grains (up to 2 um) that minimize charge recombination in the PSC. DFH can also be isolated at a fraction of the cost of any other organic HTM.

physics.chem-ph↗

arXiv2019

Effcient logging and querying for Blockchain-based cross-site genomic dataset access audit

Background: Genomic data have been collected by different institutions and companies and need to be shared for broader use. In a cross-site genomic data sharing system, a secure and transparent access control audit module plays an essential role in ensuring the accountability. The 2018 iDASH competition first track provides us with an opportunity to design efficient logging and querying system for cross-site genomic dataset access audit. We designed a blockchain-based log system which can provide a light-weight and widely compatible module for existing blockchain platforms. The submitted solution won the third place of the competition. In this paper, we report the technical details in our system. Methods: We present two methods: baseline method and enhanced method. We started with the baseline method and then adjusted our implementation based on the competition evaluation criteria and characteristics of the log system. To overcome obstacles of indexing on the immutable Blockchain system, we designed a hierarchical timestamp structure which supports efficient range queries on the timestamp field. Results: We implemented our methods in Python3, tested the scalability, and compared the performance using the test data supplied by competition organizer. We successfully boosted the log retrieval speed for complex AND queries that contain multiple predicates. For the range query, we boosted the speed for at least one order of magnitude. The storage usage is reduced by 25%. Conclusion: We demonstrate that Blockchain can be used to build a time and space efficient log and query genomic dataset audit trail. Therefore, it provides a promising solution for sharing genomic data with accountability requirement across multiple sites.

cs.DB↗

arXiv2019

TPM: A GPS-based Trajectory Pattern Mining System

With the development of big data and artificial intelligence, the technology of urban computing becomes more mature and widely used. In urban computing, using GPS-based trajectory data to discover urban dense areas, extract similar urban trajectories, predict urban traffic, and solve traffic congestion problems are all important issues. This paper presents a GPS-based trajectory pattern mining system called TPM. Firstly, the TPM can mine urban dense areas via clustering the spatial-temporal data, and automatically generate trajectories after the timing trajectory identification. Mainly, we propose a method for trajectory similarity matching, and similar trajectories can be extracted via the trajectory similarity matching in this system. The TPM can be applied to the trajectory system equipped with the GPS device, such as the vehicle trajectory, the bicycle trajectory, the electronic bracelet trajectory, etc., to provide services for traffic navigation and journey recommendation. Meantime, the system can provide support in the decision for urban resource allocation, urban functional region identification, traffic congestion and so on.

cs.OH↗

arXiv2019

Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives to continuously release private data for protecting privacy at each time point (i.e., event-level privacy), which assume that the data at different time points are independent, or that adversaries do not have knowledge of correlation between data. However, continuously generated data tend to be temporally correlated, and such correlations can be acquired by adversaries. In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations. First, we analyze the privacy leakage of a DP mechanism under temporal correlation that can be modeled using Markov Chain. Our analysis reveals that, the event-level privacy loss of a DP mechanism may \textit{increase over time}. We call the unexpected privacy loss \textit{temporal privacy leakage} (TPL). Although TPL may increase over time, we find that its supremum may exist in some cases. Second, we design efficient algorithms for calculating TPL. Third, we propose data releasing mechanisms that convert any existing DP mechanism into one against TPL. Experiments confirm that our approach is efficient and effective.

cs.DB↗

arXiv2019

3D-printed Terahertz Photonic Bandgap Waveguide-based Fluidic Sensor

A Bragg waveguide-based resonant fluidic sensor operating in THz band is studied. A fused deposition modeling 3D printing technique is employed to fabricate the sensor where the liquid analyte is flowing in the microfluidic channel integrated into the waveguide cladding. The analyte refractive index-dependent resonant defect state supported by the fluidic channel is probed by tracking the resulting absorption dip and phase change of the core-guided mode on waveguide transmission spectra. The proposed fluidic sensor can open new opportunities in applied chemical and biological sensing as it offers a non-contact measurement technique for monitoring refractive index changes in flowing liquids.

physics.optics↗

arXiv2019

When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy

In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and leakage of sensitive information, so it is necessary to protect the privacy of location information. For this purpose, people should know their location privacy preferences, that is, whether or not he/she can release location information at each place and time. However, it is not easy for each user to make such decisions and it is troublesome to set the privacy preference at each time. Therefore, we propose a method to recommend location privacy preferences for decision making. Comparing to existing method, our method can improve the accuracy of recommendation by using matrix factorization and preserve privacy strictly by local differential privacy, whereas the existing method does not achieve formal privacy guarantee. In addition, we found the best granularity of a location privacy preference, that is, how to express the information in location privacy protection. To evaluate and verify the utility of our method, we have integrated two existing datasets to create a rich information in term of user number. From the results of the evaluation using this dataset, we confirmed that our method can predict location privacy preferences accurately and that it provides a suitable method to define the location privacy preference.

cs.DB↗

arXiv2019

Blockchain-based Bidirectional Updates on Fine-grained Medical Data

Electronic medical data sharing between stakeholders, such as patients, doctors, and researchers, can promote more effective medical treatment collaboratively. These sensitive and private data should only be accessed by authorized users. Given a total medical data, users may care about parts of them and other unrelated information might interfere with the user interested data search and increase the risk of exposure. Besides accessing these data, users may want to update them and propagate to other sharing peers so that all peers keep identical data after each update. To satisfy these requirements, in this paper we propose a medical data sharing architecture that addresses the permission control using smart contracts on the blockchain and splits data into fined grained pieces shared with different peers then synchronize full data and these pieces with bidirectional transformations. Medical data reside on each userś local database and permission related data are stored on smart contracts. Only all peers have gained the newest shared data after updates can they start to do next operations on it, which are enforced by smart contracts. Blockchain based immutable shared ledge enables users to trace data updates history. This paper can provide a new perspective to view full medical data as different slices to be shared with various peers but consistency after updates between them are still promised, which can protect the privacy and improve data search efficiency.

cs.SE↗

arXiv2019

Flat Bands in Buckled Graphene Superlattices

Interactions between stacked two-dimensional (2D) atomic crystals can radically change their properties, leading to essentially new materials in terms of the electronic structure. Here we show that monolayers placed on an atomically flat substrate can be forced to undergo a buckling transition, which results in periodically strained superlattices. By using scanning tunneling microscopy and spectroscopy and support from numerical simulations, we show that such lateral superlattices in graphene lead to a periodically modulated pseudo-magnetic field, which in turn creates a post-graphene material with flat electronic bands. The described approach of controllable buckling of 2D crystals offers a venue for creating other superlattice systems and, in particular, for exploring interaction phenomena characteristic of flat bands.

cond-mat.mes-hall↗