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Jie Li

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preprint2026arXiv

Tibetan-TTS:Low-Resource Tibetan Speech Synthesis with Large Model Adaptation

Tibetan text-to-speech (TTS) has long been challenged by scarce speech resources, significant dialectal variation, and the complex mapping between written text and spoken pronunciation. To address these issues, this work presents, to the best of our knowledge, the first large-model-based Tibetan TTS system in the industry, built upon a large speech synthesis model developed by Xingchen AGI Lab. The proposed system integrates data quality enhancement, Tibetan-oriented text representation and tokenizer adaptation, and cross-lingual adaptive training for low-resource Tibetan speech synthesis. Experimental results show that the system can generate stable, natural, and intelligible Tibetan speech under low-resource conditions. In subjective evaluation, the MOS scores of the syllable-level and BPE-based systems reach 4.28 and 4.35, while their pronunciation accuracies reach 97.6% and 96.6%, respectively, outperforming an external commercial Tibetan TTS interface. These results demonstrate that combining a large-model backbone with Tibetan-oriented text representation adaptation and cross-lingual adaptive training enables highly usable low-resource Tibetan speech synthesis, and also provides a technical foundation for future unified multi-dialect Tibetan speech synthesis.

preprint2025arXiv

Observation of the $γ$-ray Emission from W43 with LHAASO

In this paper, we report the detection of the very-high-energy (VHE, $ 100{\rm\ GeV} < E < 100{\rm\ TeV} $) and ultra-high-energy (UHE, $E > 100\rm\ TeV$) $γ$-ray emissions from the direction of the young star-forming region W43, observed by the Large High Altitude Air Shower Observation (LHAASO). The extended $γ$-ray source was detected with a significance of ${\sim}16\,σ$ by KM2A and ${\sim}17\,σ$ by WCDA, respectively. The angular extension of this $γ$-ray source is about 0.5 degrees, corresponding to a physical size of about 50 pc. We discuss the origin of the $γ$-ray emission and possible cosmic ray acceleration in the W43 region using multi-wavelength data. Our findings suggest that W43 is likely another young star cluster capable of accelerating cosmic rays (CRs) to at least several hundred TeV.

preprint2025arXiv

Study of Ultra-High-Energy Gamma-Ray Source 1LHAASO J0056+6346u and Its Possible Origins

We report a dedicated study of the newly discovered extended UHE $γ$-ray source 1LHAASO J0056+6346u. Analyzing 979 days of LHAASO-WCDA data and 1389 days of LHAASO-KM2A data, we observed a significant excess of $γ$-ray events with both WCDA and KM2A. Assuming a point power-law source with a fixed spectral index, the significance maps reveal excesses of ${\sim}12.65\,σ$, ${\sim}22.18\,σ$, and ${\sim}10.24\,σ$ in the energy ranges of 1--25 TeV, 25--100 TeV, and $> 100$ TeV, respectively. We use a 3D likelihood algorithm to derive the morphological and spectral parameters, and the source is detected with significances of $12.65\,σ$ by WCDA and $25.27\,σ$ by KM2A. The best-fit positions derived from WCDA and KM2A data are (R.A. = $13.96^\circ\pm0.09^\circ$, Decl. = $63.92^\circ\pm0.05^\circ$) and (R.A. = $14.00^\circ\pm0.05^\circ$, Decl. = $63.79^\circ\pm0.02^\circ$), respectively. The angular size ($r_{39}$) of 1LHAASO J0056+6346u is $0.34^\circ\pm0.04^\circ$ at 1--25 TeV and $0.24^\circ\pm0.02^\circ$ at $> 25$ TeV. The differential flux of this UHE $γ$-ray source can be described by an exponential cutoff power-law function: $(2.67\pm0.25) \times 10^{-15} (E/20\,\text{TeV})^{-1.97\pm0.10} e^{-E/(55.1\pm7.2)\,\text{TeV}} \,\text{TeV}^{-1}\,\text{cm}^{-2}\,\text{s}^{-1}$. To explore potential sources of $γ$-ray emission, we investigated the gas distribution around 1LHAASO J0056+6346u. 1LHAASO J0056+6346u is likely to be a TeV PWN powered by an unknown pulsar, which would naturally explain both its spatial and spectral properties. Another explanation is that this UHE $γ$-ray source might be associated with gas content illuminated by a nearby CR accelerator, possibly the SNR candidate G124.0+1.4.

preprint2025arXiv

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Black holes (BH), one of the most intriguing objects in the universe, can manifest themselves through electromagnetic radiation initiated by the accretion flow. Some stellar-mass BHs drive relativistic jets when accreting matter from their companion stars, forming microquasars. Non-thermal emission from the radio to tera-electronvolt (TeV) gamma-ray band has been observed from microquasars, indicating the acceleration of relativistic particles. Here we report detection of four microquasars (SS 433, V4641 Sgr, GRS 1915+105, MAXI J1820+070) of spectrum extending to the ultrahigh-energy (UHE; photon energy $E>100$ TeV) band and one microquasar (Cygnus X-1) of spectrum approaching 100 TeV, using the Large High Altitude Air Shower Observatory (LHAASO). Notably, the total emission associated with SS 433 cannot be interpreted with a single leptonic component. In the UHE band, its emission is in spatial coincidence with a giant atomic cloud, which is consistent with a hadronic origin. An elongated source is discovered from V4641 Sgr with the spectrum continuing up to 800 TeV. The detection of UHE gamma rays demonstrates that accreting BHs and their environments can operate as extremely efficient accelerators of particles out of 1 peta-electronvolt (PeV), suggesting microquasars to be important contributors to Galactic cosmic rays especially around the `knee' region.

preprint2024arXiv

Progress and Prospects in 3D Generative AI: A Technical Overview including 3D human

While AI-generated text and 2D images continue to expand its territory, 3D generation has gradually emerged as a trend that cannot be ignored. Since the year 2023 an abundant amount of research papers has emerged in the domain of 3D generation. This growth encompasses not just the creation of 3D objects, but also the rapid development of 3D character and motion generation. Several key factors contribute to this progress. The enhanced fidelity in stable diffusion, coupled with control methods that ensure multi-view consistency, and realistic human models like SMPL-X, contribute synergistically to the production of 3D models with remarkable consistency and near-realistic appearances. The advancements in neural network-based 3D storing and rendering models, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have accelerated the efficiency and realism of neural rendered models. Furthermore, the multimodality capabilities of large language models have enabled language inputs to transcend into human motion outputs. This paper aims to provide a comprehensive overview and summary of the relevant papers published mostly during the latter half year of 2023. It will begin by discussing the AI generated object models in 3D, followed by the generated 3D human models, and finally, the generated 3D human motions, culminating in a conclusive summary and a vision for the future.

preprint2023arXiv

An Indoor Environment Sensing and Localization System via mmWave Phased Array

An indoor layout sensing and localization system in 60GHz millimeter wave (mmWave) band, named mmReality, is elaborated in this paper. The mmReality system consists of one transmitter and one mobile receiver, each with a phased array and a single radio frequency (RF) chain. To reconstruct the room layout, the pilot signal is delivered from the transmitter to the receiver via different pairs of transmission and receiving beams, so that the signals at all antenna elements can be resolved. Then, the spatial smoothing and two-dimensional multiple signal classification (MUSIC) algorithm is applied to detect the angle-of-arrival (AoAs) and angle-of-departure (AoDs) of the rays from the transmitter to the receiver. Moreover, the technique of multi-carrier ranging is adopted to measure the distance of each propagation path. Synthesizing the above geometrical parameters, the location of receiver relative to the transmitter can be pinpointed, both line-of-sight (LoS) and non-line-of-sight (NLoS) paths can also be determined. Therefore, the room layout can be reconstructed by moving the receiver and repeating the above measurement in different locations of the room. At the end, we show that the reconstructed room layout can be utilized to locate a mobile device according to its AoA spectrum, even with single access point.

preprint2023arXiv

Multi-scale multi-modal micro-expression recognition algorithm based on transformer

A micro-expression is a spontaneous unconscious facial muscle movement that can reveal the true emotions people attempt to hide. Although manual methods have made good progress and deep learning is gaining prominence. Due to the short duration of micro-expression and different scales of expressed in facial regions, existing algorithms cannot extract multi-modal multi-scale facial region features while taking into account contextual information to learn underlying features. Therefore, in order to solve the above problems, a multi-modal multi-scale algorithm based on transformer network is proposed in this paper, aiming to fully learn local multi-grained features of micro-expressions through two modal features of micro-expressions - motion features and texture features. To obtain local area features of the face at different scales, we learned patch features at different scales for both modalities, and then fused multi-layer multi-headed attention weights to obtain effective features by weighting the patch features, and combined cross-modal contrastive learning for model optimization. We conducted comprehensive experiments on three spontaneous datasets, and the results show the accuracy of the proposed algorithm in single measurement SMIC database is up to 78.73% and the F1 value on CASMEII of the combined database is up to 0.9071, which is at the leading level.

preprint2023arXiv

Reconstruction of compressed spectral imaging based on global structure and spectral correlation

In this paper, a convolutional sparse coding method based on global structure characteristics and spectral correlation is proposed for the reconstruction of compressive spectral images. The spectral data is regarded as the convolution sum of the convolution kernel and the corresponding coefficients, using the convolution kernel operates the global image information, preserving the structure information of the spectral image in the spatial dimension. To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the L_(2,1)norm to improve spectral accuracy. And, to solve the problem that convolutional sparse coding is insensitive to low frequency, the global total-variation (TV) constraint is added to estimate the low-frequency components. It not only ensures the effective estimation of the low-frequency but also transforms the convolutional sparse coding into a de-noising process, which makes the reconstructing process simpler. Simulations show that compared with the current mainstream optimization methods, the proposed method can improve the reconstruction quality by up to 4 dB in PSNR and 10% in SSIM, and has a great improvement in the details of the reconstructed image.

preprint2023arXiv

Workload Failure Prediction for Data Centers

Failed workloads that consumed significant computational resources in time and space affect the efficiency of data centers significantly and thus limit the amount of scientific work that can be achieved. While the computational power has increased significantly over the years, detection and prediction of workload failures have lagged far behind and will become increasingly critical as the system scale and complexity further increase. In this study, we analyze workload traces collected from a production cluster and train machine learning models on a large amount of data sets to predict workload failures. Our prediction models consist of a queue-time model that estimates the probability of workload failures before execution and a runtime model that predicts failures at runtime. Evaluation results show that the queue-time model and runtime model can predict workload failures with a maximum precision score of 90.61% and 97.75%, respectively. By integrating the runtime model with the job scheduler, it helps reduce CPU time, and memory usage by up to 16.7% and 14.53%, respectively.

preprint2022arXiv

A Dynamic 3D Spontaneous Micro-expression Database: Establishment and Evaluation

Micro-expressions are spontaneous, unconscious facial movements that show people's true inner emotions and have great potential in related fields of psychological testing. Since the face is a 3D deformation object, the occurrence of an expression can arouse spatial deformation of the face, but limited by the available databases are 2D videos, lacking the description of 3D spatial information of micro-expressions. Therefore, we proposed a new micro-expression database containing 2D video sequences and 3D point clouds sequences. The database includes 373 micro-expressions sequences, and these samples were classified using the objective method based on facial action coding system, as well as the non-objective method that combines video contents and participants' self-reports. We extracted 2D and 3D features using the local binary patterns on three orthogonal planes (LBP-TOP) and curvature algorithms, respectively, and evaluated the classification accuracies of these two features and their fusion results with leave-one-subject-out (LOSO) and 10-fold cross-validation. Further, we performed various neural network algorithms for database classification, the results show that classification accuracies are improved by fusing 3D features than using only 2D features. The database offers original and cropped micro-expression samples, which will facilitate the exploration and research on 3D Spatio-temporal features of micro-expressions.

preprint2022arXiv

A QoE Model in Point Cloud Video Streaming

Point cloud video has been widely used by augmented reality (AR) and virtual reality (VR) applications as it allows users to have an immersive experience of six degrees of freedom (6DoFs). Yet there is still a lack of research on quality of experience (QoE) model of point cloud video streaming, which cannot provide optimization metric for streaming systems. Besides, position and color information contained in each pixel of point cloud video, and viewport distance effect caused by 6DoFs viewing procedure make the traditional objective quality evaluation metric cannot be directly used in point cloud video streaming system. In this paper we first analyze the subjective and objective factors related to QoE model. Then an experimental system to simulate point cloud video streaming is setup and detailed subjective quality evaluation experiments are carried out. Based on collected mean opinion score (MOS) data, we propose a QoE model for point cloud video streaming. We also verify the model by actual subjective scoring, and the results show that the proposed QoE model can accurately reflect users' visual perception. We also make the experimental database public to promote the QoE research of point cloud video streaming.

preprint2022arXiv

A Smart Contract based Crowdfunding Mechanism for Hierarchical Federated Learning

Hierarchical Federated Learning (HFL) is introduced as a promising technique that allows model owners to fully exploit computational resources and bandwidth resources to train the global model. However, due to the high training cost, a single model owner may not be able to deploy HFL. To address this issue, we develop a smart contract based trust crowdfunding mechanism for HFL, which enables multiple model owners to obtain a crowdfunding model with high social utility for multiple crowdfunding participants. To ensure the authenticity of the crowdfunding mechanism, we implemented the Vickey-Clark-Croves (VCG) mechanism to encourage all crowdfunding participants and clients to provide realistic bids and offers. At the same time, in order to ensure guaranteed trustworthiness of crowdfunding and automatic distribution of funds, we develop and implement a smart contract to record the crowdfunding process and training results in the blockchain. We prove that the proposed scheme satisfies the budget balance and participant constraint. Finally, we implement a prototype of this smart contract on an Ethereoum private chain and evaluate the proposed VCG mechanism. The experimental results demonstrate that the proposed scheme can effectively improve social utility while ensuring the authenticity and trustworthiness of the crowdfunding process.

preprint2022arXiv

Anyonic braiding via quench dynamics in fractional quantum Hall liquids

In a Laughlin fractional quantum Hall state, one- and two-quasihole states can be obtained by diagonalizing the many-body Hamiltonian with a trapping potential or, for larger systems, from the linear combination of the edge Jack polynomials. The quasihole states live entirely in the subspace of the lowest-energy branch in the energy spectrum with a fixed number of orbits, or a hard-wall confinement. The reduction in the Hilbert space dimension facilitates the study of time evolution of the quasihole states after, say, the removal of the trapping potential. We explore the quench dynamics under a harmonic external potential, which rotates the quasiholes in the droplet, and discuss the effect of long-range interaction and more realistic confinement. Accurate evaluation of the mutual statistics phase of anyons for a wide range of anyon separation can be achieved from the Berry-phase calculation.

preprint2022arXiv

Application of Color Block Code in Image Scaling

Aiming at the high cost of embedding annotation watermark in a narrow small area and the information distortion caused by the change of annotation watermark image resolution, this paper proposes a color block code technology, which uses location information and color code to form recognizable graphics, which can not only simplify the annotation graphics, but also ensure the recognition efficiency. First, the constituent elements of color block code are designed, and then the coding and decoding method of color block code is proposed. Experiments show that color block code has high anti-scaling and anti-interference, and can be widely used in the labeling of small object surface and low resolution image.

preprint2022arXiv

Autonomous Smart Grid Fault Detection

Smart grid plays a crucial role for the smart society and the upcoming carbon neutral society. Achieving autonomous smart grid fault detection is critical for smart grid system state awareness, maintenance and operation. This paper focuses on fault monitoring in smart grid and discusses the inherent technical challenges and solutions. In particular, we first present the basic principles of smart grid fault detection. Then, we explain the new requirements for autonomous smart grid fault detection, the technical challenges and their possible solutions. A case study is introduced, as a preliminary study for autonomous smart grid fault detection. In addition, we highlight relevant directions for future research.

preprint2022arXiv

BGP-Multipath Routing in the Internet

BGP-Multipath (BGP-M) is a multipath routing technique for load balancing. Distinct from other techniques deployed at a router inside an Autonomous System (AS), BGP-M is deployed at a border router that has installed multiple inter-domain border links to a neighbour AS. It uses the equal-cost multi-path (ECMP) function of a border router to share traffic to a destination prefix on different border links. Despite recent research interests in multipath routing, there is little study on BGP-M. Here we provide the first measurement and a comprehensive analysis of BGP-M routing in the Internet. We extracted information on BGP-M from query data collected from Looking Glass (LG) servers. We revealed that BGP-M has already been extensively deployed and used in the Internet. A particular example is Hurricane Electric (AS6939), a Tier-1 network operator, which has implemented >1,000 cases of BGP-M at 69 of its border routers to prefixes in 611 of its neighbour ASes, including many hyper-giant ASes and large content providers, on both IPv4 and IPv6 Internet. We examined the distribution and operation of BGP-M. We also ran traceroute using RIPE Atlas to infer the routing paths, the schemes of traffic allocation, and the delay on border links. This study provided the state-of-the-art knowledge on BGP-M with novel insights into the unique features and the distinct advantages of BGP-M as an effective and readily available technique for load balancing.

preprint2022arXiv

Coherent mechanical noise cancellation and cooperativity competition in optomechanical arrays

Studying the interplay between multiple coupled mechanical resonators is a promising new direction in the field of optomechanics. Understanding the dynamics of the interaction can lead to rich new effects, such as enhanced coupling and multi-body physics. In particular, multi-resonator optomechanical systems allow for distinct dynamical effects due to the optical cavity coherently coupling mechanical resonators. Here, we study the mechanical response of two SiN membranes and a single optical mode, and find that the cavity induces a time delay between the local and cavity-transduced thermal noises experienced by the resonators. This results in an optomechanical phase lag that causes destructive interference, cancelling the mechanical thermal noise by up to 20 dB in a controllable fashion, matching our theoretical expectation. Based on the effective coupling between membranes, we further propose, derive and measure a collective effect, cooperativity competition on mechanical dissipation, whereby the linewidth of one resonator depends on the coupling efficiency (cooperativity) of the other resonator.

preprint2022arXiv

Digital-twin-enhanced metal tube bending forming real-time prediction method based on Multi-source-input MTL

As one of the most widely used metal tube bending methods, the rotary draw bending (RDB) process enables reliable and high-precision metal tube bending forming (MTBF). The forming accuracy is seriously affected by the springback and other potential forming defects, of which the mechanism analysis is difficult to deal with. At the same time, the existing methods are mainly conducted in offline space, ignoring the real-time information in the physical world, which is unreliable and inefficient. To address this issue, a digital-twin-enhanced (DT-enhanced) metal tube bending forming real-time prediction method based on multi-source-input multi-task learning (MTL) is proposed. The new method can achieve comprehensive MTBF real-time prediction. By sharing the common feature of the multi-close domain and adopting group regularization strategy on feature sharing and accepting layers, the accuracy and efficiency of the multi-source-input MTL can be guaranteed. Enhanced by DT, the physical real-time deformation data is aligned in the image dimension by an improved Grammy Angle Field (GAF) conversion, realizing the reflection of the actual processing. Different from the traditional offline prediction methods, the new method integrates the virtual and physical data to achieve a more efficient and accurate real-time prediction result. and the DT mapping connection between virtual and physical systems can be achieved. To exclude the effects of equipment errors, the effectiveness of the proposed method is verified on the physical experiment-verified FE simulation scenarios. At the same time, the common pre-training networks are compared with the proposed method. The results show that the proposed DT-enhanced prediction method is more accurate and efficient.

preprint2022arXiv

Double crossed biproducts and related structures

Let $H$ be a bialgebra. Let $σ: H\otimes H\to A$ be a linear map, where $A$ is a left $H$-comodule coalgebra, and an algebra with a left $H$-weak action $\triangleright$. Let $τ: H\otimes H\to B$ be a linear map, where $B$ is a right $H$-comodule coalgebra, and an algebra with a right $H$-weak action $\triangleleft$. In this paper, we improve the necessary conditions for the two-sided crossed product algebra $A\#^σ H~{^τ\#} B$ and the two-sided smash coproduct coalgebra $A\times H\times B$ to form a bialgebra (called double crossed biproduct) such that the condition $b_{[1]}\triangleright a_0\otimes b_{[0]}\triangleleft a_{-1}=a\otimes b$ in Majid's double biproduct (or double-bosonization) is one of the necessary conditions. On the other hand, we provide a more general two-sided crossed product algebra structure via Brzezński's crossed product and give some applications.

preprint2022arXiv

Efficient Recovery of a Shared Secret via Cooperation: Applications to SDMM and PIR

This work considers the problem of privately outsourcing the computation of a matrix product over a finite field $\mathbb{F}_q$ to $N$ helper servers. These servers are considered to be honest but curious, i.e., they behave according to the protocol but will try to deduce information about the user's data. Furthermore, any set of up to $X$ servers is allowed to share their data. Previous works considered this collusion a hindrance and the download cost of the schemes increases with growing $X$. We propose to utilize such linkage between servers to the user's advantage by allowing servers to cooperate in the computational task. This leads to a significant gain in the download cost for the proposed schemes. The gain naturally comes at the cost of increased communication load between the servers. Hence, the proposed cooperative schemes can be understood as outsourcing both computational cost and communication cost. Both information--theoretically secure and computationally secure schemes are considered, showing that allowing information leakage that is computationally hard to utilize will lead to further gains. The proposed server cooperation is then exemplified for specific secure distributed matrix multiplication (SDMM) schemes and linear private information retrieval (PIR). Similar ideas naturally apply to many other use cases as well, but not necessarily always with lowered costs.

preprint2022arXiv

Entangling mechanical vibrations of two massive ferrimagnets by fully exploiting the nonlinearity of magnetostriction

Quantum entanglement in the motion of macroscopic objects is of significance to both fundamental studies and quantum technologies. Here we show how to entangle the mechanical vibration modes of two massive ferrimagnets that are placed in the same microwave cavity. Each ferrimagnet supports a magnon mode and a low-frequency vibration mode coupled by the magnetostrictive force. The two magnon modes are, respectively, coupled to the microwave cavity by the magnetic dipole interaction. We first generate a stationary nonlocal entangled state between the vibration mode of the ferrimagnet-1 and the magnon mode of the ferrimagnet-2. This is realized by continuously driving the ferrimagnet-1 with a strong red-detuned microwave field and the entanglement is achieved by exploiting the magnomechanical parametric down-conversion and the cavity-magnon state-swap interaction. We then switch off the pump on the ferrimagnet-1 and, simultaneously, turn on a red-detuned pulsed drive on the ferrimagnet-2. The latter drive is used to activate the magnomechanical beamsplitter interaction, which swaps the magnonic and mechanical states of the ferrimagnet-2. Consequently, the previously generated phonon-magnon entanglement is transferred to the mechanical modes of two ferrimagnets. The work provides a scheme to prepare entangled states of mechanical motion of two massive objects, which may find applications in various studies exploiting macroscopic entangled states.

preprint2022arXiv

FedIPR: Ownership Verification for Federated Deep Neural Network Models

Federated learning models are collaboratively developed upon valuable training data owned by multiple parties. During the development and deployment of federated models, they are exposed to risks including illegal copying, re-distribution, misuse and/or free-riding. To address these risks, the ownership verification of federated learning models is a prerequisite that protects federated learning model intellectual property rights (IPR) i.e., FedIPR. We propose a novel federated deep neural network (FedDNN) ownership verification scheme that allows private watermarks to be embedded and verified to claim legitimate IPR of FedDNN models. In the proposed scheme, each client independently verifies the existence of the model watermarks and claims respective ownership of the federated model without disclosing neither private training data nor private watermark information. The effectiveness of embedded watermarks is theoretically justified by the rigorous analysis of conditions under which watermarks can be privately embedded and detected by multiple clients. Moreover, extensive experimental results on computer vision and natural language processing tasks demonstrate that varying bit-length watermarks can be embedded and reliably detected without compromising original model performances. Our watermarking scheme is also resilient to various federated training settings and robust against removal attacks.

preprint2022arXiv

Ferroelectrics and Photovoltaics in Endohedral Fullerenes-based van der Waals Heterostructures

Using the density functional theory calculations, we studied the ferroelectric, electrical and optical properties of series of bias-controllable endohedral fullerenes (TM@C28, TM= Ti-Ni). The important finding is that Cr@C28 has a large electric dipole moment and an appropriate bandgap (~1.2eV) as building blocks for ferroelectric-photovoltaic materials. By sandwiching Cr@C28 molecules between two van der Waals layers, e.g., graphene or transition metal dichalcogenides, the heterostructures may have exotic ferroelectric and photovoltaic properties with high tunability and performance.

preprint2022arXiv

Fully Attentional Network for Semantic Segmentation

Recent non-local self-attention methods have proven to be effective in capturing long-range dependencies for semantic segmentation. These methods usually form a similarity map of RC*C (by compressing spatial dimensions) or RHW*HW (by compressing channels) to describe the feature relations along either channel or spatial dimensions, where C is the number of channels, H and W are the spatial dimensions of the input feature map. However, such practices tend to condense feature dependencies along the other dimensions,hence causing attention missing, which might lead to inferior results for small/thin categories or inconsistent segmentation inside large objects. To address this problem, we propose anew approach, namely Fully Attentional Network (FLANet),to encode both spatial and channel attentions in a single similarity map while maintaining high computational efficiency. Specifically, for each channel map, our FLANet can harvest feature responses from all other channel maps, and the associated spatial positions as well, through a novel fully attentional module. Our new method has achieved state-of-the-art performance on three challenging semantic segmentation datasets,i.e., 83.6%, 46.99%, and 88.5% on the Cityscapes test set,the ADE20K validation set, and the PASCAL VOC test set,respectively.

preprint2022arXiv

Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction

Multi-scale biomedical knowledge networks are expanding with emerging experimental technologies that generates multi-scale biomedical big data. Link prediction is increasingly used especially in bipartite biomedical networks to identify hidden biological interactions and relationshipts between key entities such as compounds, targets, gene and diseases. We propose a Graph Neural Networks (GNN) method, namely Graph Pair based Link Prediction model (GPLP), for predicting biomedical network links simply based on their topological interaction information. In GPLP, 1-hop subgraphs extracted from known network interaction matrix is learnt to predict missing links. To evaluate our method, three heterogeneous biomedical networks were used, i.e. Drug-Target Interaction network (DTI), Compound-Protein Interaction network (CPI) from NIH Tox21, and Compound-Virus Inhibition network (CVI). Our proposed GPLP method significantly outperforms over the state-of-the-art baselines. In addition, different network incompleteness is analysed with our devised protocol, and we also design an effective approach to improve the model robustness towards incomplete networks. Our method demonstrates the potential applications in other biomedical networks.

preprint2022arXiv

Learning Correlations between Internal Coordinates to improve 3D Cartesian Coordinates for Proteins

We consider a generic representation problem of internal coordinates (bond lengths, valence angles, and dihedral angles) and their transformation to 3-dimensional Cartesian coordinates of a biomolecule. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. This general problem has been solved with machine learning for proteins, but with appropriately formulated data is extensible to any type of chain biomolecule including RNA, DNA, and lipids. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. We developed a machine learning algorithm, Int2Cart, to predict bond lengths and bond angles from backbone torsion angles and residue types of a protein, and allows reconstruction of protein structures better than using fixed bond lengths and bond angles, or a static library method that relies on backbone torsion angles and residue types on a single residue. The Int2Cart algorithm has been implemented as an individual python package at https://github.com/THGLab/int2cart.

preprint2022arXiv

Mechanical Bistability in Kerr-modified Cavity Magnomechanics

Bistable mechanical vibration is observed in a cavity magnomechanical system, which consists of a microwave cavity mode, a magnon mode, and a mechanical vibration mode of a ferrimagnetic yttrium-iron-garnet (YIG) sphere. The bistability manifests itself in both the mechanical frequency and linewidth under a strong microwave drive field, which simultaneously activates three different kinds of nonlinearities, namely, magnetostriction, magnon self-Kerr, and magnon-phonon cross-Kerr nonlinearities. The magnon-phonon cross-Kerr nonlinearity is first predicted and measured in magnomechanics. The system enters a regime where Kerr-type nonlinearities strongly modify the conventional cavity magnomechanics that possesses only a radiation-pressure-like magnomechanical coupling. Three different kinds of nonlinearities are identified and distinguished in the experiment. Our work demonstrates a new mechanism for achieving mechanical bistability by combining magnetostriction and Kerr-type nonlinearities, and indicates that such Kerr-modified cavity magnomechanics provides a unique platform for studying many distinct nonlinearities in a single experiment.

preprint2022arXiv

Object Permanence Emerges in a Random Walk along Memory

This paper proposes a self-supervised objective for learning representations that localize objects under occlusion - a property known as object permanence. A central question is the choice of learning signal in cases of total occlusion. Rather than directly supervising the locations of invisible objects, we propose a self-supervised objective that requires neither human annotation, nor assumptions about object dynamics. We show that object permanence can emerge by optimizing for temporal coherence of memory: we fit a Markov walk along a space-time graph of memories, where the states in each time step are non-Markovian features from a sequence encoder. This leads to a memory representation that stores occluded objects and predicts their motion, to better localize them. The resulting model outperforms existing approaches on several datasets of increasing complexity and realism, despite requiring minimal supervision, and hence being broadly applicable.

preprint2022arXiv

On Collective Robustness of Bagging Against Data Poisoning

Bootstrap aggregating (bagging) is an effective ensemble protocol, which is believed can enhance robustness by its majority voting mechanism. Recent works further prove the sample-wise robustness certificates for certain forms of bagging (e.g. partition aggregation). Beyond these particular forms, in this paper, \emph{we propose the first collective certification for general bagging to compute the tight robustness against the global poisoning attack}. Specifically, we compute the maximum number of simultaneously changed predictions via solving a binary integer linear programming (BILP) problem. Then we analyze the robustness of vanilla bagging and give the upper bound of the tolerable poison budget. Based on this analysis, \emph{we propose hash bagging} to improve the robustness of vanilla bagging almost for free. This is achieved by modifying the random subsampling in vanilla bagging to a hash-based deterministic subsampling, as a way of controlling the influence scope for each poisoning sample universally. Our extensive experiments show the notable advantage in terms of applicability and robustness.

preprint2022arXiv

On the impact of VR assessment on the Quality of Experience of Highly Realistic Digital Humans

Fuelled by the increase in popularity of virtual and augmented reality applications, point clouds have emerged as a popular 3D format for acquisition and rendering of digital humans, thanks to their versatility and real-time capabilities. Due to technological constraints and real-time rendering limitations, however, the visual quality of dynamic point cloud contents is seldom evaluated using virtual and augmented reality devices, instead relying on prerecorded videos displayed on conventional 2D screens. In this study, we evaluate how the visual quality of point clouds representing digital humans is affected by compression distortions. In particular, we compare three different viewing conditions based on the degrees of freedom that are granted to the viewer: passive viewing (2DTV), head rotation (3DoF), and rotation and translation (6DoF), to understand how interacting in the virtual space affects the perception of quality. We provide both quantitative and qualitative results of our evaluation involving 78 participants, and we make the data publicly available. To the best of our knowledge, this is the first study evaluating the quality of dynamic point clouds in virtual reality, and comparing it to traditional viewing settings. Results highlight the dependency of visual quality on the content under test, and limitations in the way current data sets are used to evaluate compression solutions. Moreover, influencing factors in quality evaluation in VR, and shortcomings in how point cloud encoding solutions handle visually-lossless compression, are discussed.

preprint2022arXiv

On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach

Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level embeddings by maximizing the Mutual Information (MI) between different augmented views of the same graph (positive pairs). However, the GCL is limited by dimensional collapse, i.e., embedding vectors only occupy a low-dimensional subspace. In this paper, we show that the smoothing effect of the graph pooling and the implicit regularization of the graph convolution are two causes of the dimensional collapse in GCL. To mitigate the above issue, we propose a non-maximum removal graph contrastive learning approach (nmrGCL), which removes "prominent'' dimensions (i.e., contribute most in similarity measurement) for positive pair in the pre-text task. Comprehensive experiments on various benchmark datasets are conducted to demonstrate the effectiveness of nmrGCL, and the results show that our model outperforms the state-of-the-art methods. Source code will be made publicly available.

preprint2022arXiv

Optical sensing of magnons via the magnetoelastic displacement

We show how to measure a steady-state magnon population in a magnetostatic mode of a ferromagnet or ferrimagnet, such as yttrium iron garnet. We adopt an optomechanical approach and utilize the magnetoelasticity of the ferromagnet. The magnetostrictive force dispersively couples magnons to the deformation displacement of the ferromagnet, which is proportional to the magnon population. By further coupling the mechanical displacement to an optical cavity that is resonantly driven by a weak laser, the magnetostrictively induced displacement can be sensed by measuring the phase quadrature of the optical field. The phase shows an excellent linear dependence on the magnon population for a not very large population, and can thus be used as a `magnometer' to measure the magnon population. We further study the effect of thermal noises, and find a high signal-to-noise ratio even at room temperature. At cryogenic temperatures, the resolution of magnon excitation numbers is essentially limited by the vacuum fluctuations of the phase, which can be significantly improved by using a squeezed light.

preprint2022arXiv

Passive Motion Detection via mmWave Communication System

In this paper, an integrated passive sensing and communication system working in 60 GHz band is elaborated, and the sensing performance is investigated in an application of hand gesture recognition. Specifically, in this integrated system, there are two radio frequency (RF) chains at the receiver and one at the transmitter. Each RF chain is connected with one phased array for analog beamforming. To facilitate simultaneous sensing and communication, the transmitter delivers one stream of information-bearing signals via two beam lobes, one is aligned with the main signal propagation path and the other is directed to the sensing target. Signals from the two lobes are received by the two RF chains at the receiver, respectively. By cross ambiguity coherent processing, the time-Doppler spectrograms of hand gestures can be obtained. Relying on the passive sensing system, a dataset of received signals, where three types of hand gestures are sensed, is collected by using Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) paths as the reference channel respectively. Then a neural network is trained by the dataset for motion detection. It is shown that the classification accuracy rate is high as long as sufficient sensing time is assured. Finally, an empirical model characterizing the relation between the classification accuracy and sensing duration is derived analytically.

preprint2022arXiv

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Objectives: To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model. Results: The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density ($p$ = 0.015), circumference ($p$ = 0.009), circularity ($p$ = 0.010), and orientation ($p$ = 0.012). Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC. The codes and dataset are available at https://github.com/bupt-ai-cz/BALNMP

preprint2022arXiv

Private and Secure Distributed Matrix Multiplication Schemes for Replicated or MDS-Coded Servers

In this paper, we study the problem of \emph{private and secure distributed matrix multiplication (PSDMM)}, where a user having a private matrix $A$ and $N$ non-colluding servers sharing a library of $L$ ($L>1$) matrices $B^{(0)}, B^{(1)},\ldots,B^{(L-1)}$, for which the user wishes to compute $AB^{(θ)}$ for some $θ\in [0, L)$ without revealing any information of the matrix $A$ to the servers, and keeping the index $θ$ private to the servers. Previous work is limited to the case that the shared library (\textit{i.e.,} the matrices $B^{(0)}, B^{(1)},\ldots,B^{(L-1)}$) is stored across the servers in a replicated form and schemes are very scarce in the literature, there is still much room for improvement. In this paper, we propose two PSDMM schemes, where one is limited to the case that the shared library is stored across the servers in a replicated form but has a better performance than state-of-the-art schemes in that it can achieve a smaller recovery threshold and download cost. The other one focuses on the case that the shared library is stored across the servers in an MDS-coded form, which requires less storage in the servers. The second PSDMM code does not subsume the first one even if the underlying MDS code is degraded to a repetition code as they are totally two different schemes.

preprint2022arXiv

Seeking Subjectivity in Visual Emotion Distribution Learning

Visual Emotion Analysis (VEA), which aims to predict people's emotions towards different visual stimuli, has become an attractive research topic recently. Rather than a single label classification task, it is more rational to regard VEA as a Label Distribution Learning (LDL) problem by voting from different individuals. Existing methods often predict visual emotion distribution in a unified network, neglecting the inherent subjectivity in its crowd voting process. In psychology, the \textit{Object-Appraisal-Emotion} model has demonstrated that each individual's emotion is affected by his/her subjective appraisal, which is further formed by the affective memory. Inspired by this, we propose a novel \textit{Subjectivity Appraise-and-Match Network (SAMNet)} to investigate the subjectivity in visual emotion distribution. To depict the diversity in crowd voting process, we first propose the \textit{Subjectivity Appraising} with multiple branches, where each branch simulates the emotion evocation process of a specific individual. Specifically, we construct the affective memory with an attention-based mechanism to preserve each individual's unique emotional experience. A subjectivity loss is further proposed to guarantee the divergence between different individuals. Moreover, we propose the \textit{Subjectivity Matching} with a matching loss, aiming at assigning unordered emotion labels to ordered individual predictions in a one-to-one correspondence with the Hungarian algorithm. Extensive experiments and comparisons are conducted on public visual emotion distribution datasets, and the results demonstrate that the proposed SAMNet consistently outperforms the state-of-the-art methods. Ablation study verifies the effectiveness of our method and visualization proves its interpretability.

preprint2022arXiv

Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback

Field of view (FoV) prediction is critical in 360-degree video multicast, which is a key component of the emerging Virtual Reality (VR) and Augmented Reality (AR) applications. Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks, nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance. This paper proposes a spherical convolution-empowered FoV prediction method, which is a multi-source prediction framework combining salient features extracted from 360-degree video with limited FoV feedback information. A spherical convolution neural network (CNN) is used instead of a traditional two-dimensional CNN to eliminate the problem of weight sharing failure caused by video projection distortion. Specifically, salient spatial-temporal features are extracted through a spherical convolution-based saliency detection model, after which the limited feedback FoV information is represented as a time-series model based on a spherical convolution-empowered gated recurrent unit network. Finally, the extracted salient video features are combined to predict future user FoVs. The experimental results show that the performance of the proposed method is better than other prediction methods.

preprint2022arXiv

Spin transfer from dark matter to gas during halo formation

In the protogalactic density field, diffuse gas and collision-less cold dark matter (DM) are often assumed sufficiently mixed that both components experience identical tidal torques. However, haloes in cosmological simulations consistently end up with a higher specific angular momentum (sAM) in gas, even in simulations without radiative cooling and galaxy formation physics. We refine this result by analysing the spin distributions of gas and DM in $\sim$50,000 well-resolved haloes in a non-radiative cosmological simulation from the SURFS suite. The sAM of the halo gas on average ends up $\sim$40\% above that of the DM. This can be pinned down to an excess AM in the inner halo ($<$50\% virial radius), paralleled by a more coherent rotation pattern in the gas. We uncover the leading driver for this AM difference through a series of control simulations of a collapsing ellipsoidal top-hat, where gas and DM are initially well mixed. These runs reveal that the pressurised inner gas shells collapse more slowly, causing the DM ellipsoid to spin ahead of the gas ellipsoid. The arising torque generally transfers AM from the DM to the gas. The amount of AM transferred via this mode depends on the initial spin, the initial axes ratios and the collapse factor. These quantities can be combined in a single dimensionless parameter, which robustly predicts the AM transfer of the ellipsoidal collapse. This simplistic model can quantitatively explain the average AM excess of the gas found in the more complex non-radiative cosmological simulation.

preprint2022arXiv

SpOT: Spatiotemporal Modeling for 3D Object Tracking

3D multi-object tracking aims to uniquely and consistently identify all mobile entities through time. Despite the rich spatiotemporal information available in this setting, current 3D tracking methods primarily rely on abstracted information and limited history, e.g. single-frame object bounding boxes. In this work, we develop a holistic representation of traffic scenes that leverages both spatial and temporal information of the actors in the scene. Specifically, we reformulate tracking as a spatiotemporal problem by representing tracked objects as sequences of time-stamped points and bounding boxes over a long temporal history. At each timestamp, we improve the location and motion estimates of our tracked objects through learned refinement over the full sequence of object history. By considering time and space jointly, our representation naturally encodes fundamental physical priors such as object permanence and consistency across time. Our spatiotemporal tracking framework achieves state-of-the-art performance on the Waymo and nuScenes benchmarks.

preprint2022arXiv

Steady entangled-state generation via cross-Kerr effect in a ferrimagnetic crystal

For solid-state spin systems, the collective spin motion in a single crystal embodies multiple magnetostatic modes. Recently, it was found that the cross-Kerr interaction between the higher-order magnetostatic mode and the Kittel mode introduces a new operable degree of freedom. In this work, we propose a scheme to entangle two magnon modes via the cross-Kerr nonlinearity when the bias field is inhomogeneous and the system is driven. Quantum entanglement persists at the steady state, as demonstrated by numerical results using experimentally feasible parameters. Furthermore, we also demonstrate that entangled states can survive better in the system where self-Kerr and cross-Kerr nonlinearities coexist. Our work provides insights and guidance for designing experiments to observe entanglement between different degrees of freedom within a single ferrimagnetic crystal. Additionally, it may stimulate potential applications in quantum information processing using spintronic devices.

preprint2022arXiv

The quasi-Keplerian motion in the regular Bardeen spacetime

The second post-Newtonian solution for the quasi-Keplerian motion of a test particle in the gravitational field of regular Bardeen black hole is derived. The solution is formulated in terms of the test particle's orbital energy and angular momentum, as well as the mass and magnetic charge of the Bardeen black hole. The leading effects of the magnetic charge on the test particle's orbit and motion including perihelion precession are displayed explicitly. In particular, it is shown that to the second post-Newtonian order the magnetic charge does not affect the test particle's orbital period.

preprint2022arXiv

TransFA: Transformer-based Representation for Face Attribute Evaluation

Face attribute evaluation plays an important role in video surveillance and face analysis. Although methods based on convolution neural networks have made great progress, they inevitably only deal with one local neighborhood with convolutions at a time. Besides, existing methods mostly regard face attribute evaluation as the individual multi-label classification task, ignoring the inherent relationship between semantic attributes and face identity information. In this paper, we propose a novel \textbf{trans}former-based representation for \textbf{f}ace \textbf{a}ttribute evaluation method (\textbf{TransFA}), which could effectively enhance the attribute discriminative representation learning in the context of attention mechanism. The multiple branches transformer is employed to explore the inter-correlation between different attributes in similar semantic regions for attribute feature learning. Specially, the hierarchical identity-constraint attribute loss is designed to train the end-to-end architecture, which could further integrate face identity discriminative information to boost performance. Experimental results on multiple face attribute benchmarks demonstrate that the proposed TransFA achieves superior performances compared with state-of-the-art methods.

preprint2021arXiv

A Density-Aware PointRCNN for 3D Object Detection in Point Clouds

We present an improved version of PointRCNN for 3D object detection, in which a multi-branch backbone network is adopted to handle the non-uniform density of point clouds. An uncertainty-based sampling policy is proposed to deal with the distribution differences of different point clouds. The new model can achieve about 0.8 AP higher performance than the baseline PointRCNN on KITTI val set. In addition, a simplified model using a single scale grouping for each set-abstraction layer can achieve competitive performance with less computational cost.

preprint2021arXiv

Approximate Optimal Filter for Linear Gaussian Time-invariant Systems

State estimation is critical to control systems, especially when the states cannot be directly measured. This paper presents an approximate optimal filter, which enables to use policy iteration technique to obtain the steady-state gain in linear Gaussian time-invariant systems. This design transforms the optimal filtering problem with minimum mean square error into an optimal control problem, called Approximate Optimal Filtering (AOF) problem. The equivalence holds given certain conditions about initial state distributions and policy formats, in which the system state is the estimation error, control input is the filter gain, and control objective function is the accumulated estimation error. We present a policy iteration algorithm to solve the AOF problem in steady-state. A classic vehicle state estimation problem finally evaluates the approximate filter. The results show that the policy converges to the steady-state Kalman gain, and its accuracy is within 2 %.

preprint2021arXiv

DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds

For current object detectors, the scale of the receptive field of feature extraction operators usually increases layer by layer. Those operators are called scale-oriented operators in this paper, such as the convolution layer in CNN, and the set abstraction layer in PointNet++. The scale-oriented operators are appropriate for 2D images with multi-scale objects, but not natural for 3D point clouds with multi-density but scale-invariant objects. In this paper, we put forward a novel density-oriented PointNet (DPointNet) for 3D object detection in point clouds, in which the density of points increases layer by layer. In experiments for object detection, the DPointNet is applied to PointRCNN, and the results show that the model with the new operator can achieve better performance and higher speed than the baseline PointRCNN, which verify the effectiveness of the proposed DPointNet.

preprint2021arXiv

Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations

Graph neural networks are emerging as continuation of deep learning success w.r.t. graph data. Tens of different graph neural network variants have been proposed, most following a neighborhood aggregation scheme, where the node features are updated via aggregating features of its neighboring nodes from layer to layer. Though related research surges, the power of GNNs are still not on-par-with their counterpart CNNs in computer vision and RNNs in natural language processing. We rethink this problem from the perspective of information propagation, and propose to enhance information propagation among GNN layers by combining heterogeneous aggregations. We argue that as richer information are propagated from shallow to deep layers, the discriminative capability of features formulated by GNN can benefit from it. As our first attempt in this direction, a new generic GNN layer formulation and upon this a new GNN variant referred as HAG-Net is proposed. We empirically validate the effectiveness of HAG-Net on a number of graph classification benchmarks, and elaborate all the design options and criterions along with.

preprint2021arXiv

Entangling the vibrational modes of two massive ferromagnetic spheres using cavity magnomechanics

We present a scheme to entangle the vibrational phonon modes of two massive ferromagnetic spheres in a dual-cavity magnomechanical system. In each cavity, a microwave cavity mode couples to a magnon mode (spin wave) via the magnetic dipole interaction, and the latter further couples to a deformation phonon mode of the ferromagnetic sphere via a nonlinear magnetostrictive interaction. We show that by directly driving the magnon mode with a red-detuned microwave field to activate the magnomechanical anti-Stokes process a cavity-magnon-phonon state-swap interaction can be realized. Therefore, if the two cavities are further driven by a two-mode squeezed vacuum field, the quantum correlation of the driving fields is successively transferred to the two magnon modes and subsequently to the two phonon modes, i.e., the two ferromagnetic spheres become remotely entangled. Our work demonstrates that cavity magnomechanical systems allow to prepare quantum entangled states at a more massive scale than currently possible with other schemes.

preprint2021arXiv

Geometry-Based Stochastic Line-of-Sight Probability Model for A2G Channels under Urban Scenarios

Line-of-sight (LoS) path is essential for the reliability of air-to-ground (A2G) communications, but the existence of LoS path is difficult to predict due to random obstacles on the ground. Based on the statistical geographic information and Fresnel clearance zone, a general stochastic LoS probability model for three-dimensional (3D) A2G channels under urban scenarios is developed. By considering the factors, i.e., building height distribution, building width, building space, carrier frequency, and transceiver's heights, the proposed model is suitable for different frequencies and altitudes. Moreover, in order to get a closed-form expression and reduce the computational complexity, an approximate parametric model is also built with the machine-learning (ML) method to estimate model parameters. The simulation results show that the proposed model has good consistency with existing models at the low altitude. When the altitude increases, it has better performance by comparing with that of the ray-tracing Monte-Carlo simulation data. The analytical results of proposed model are helpful for the channel modeling and performance analysis such as cell coverage, outage probability, and bit error rate in A2G communications.

preprint2021arXiv

Nonhomogeneous associative Yang-Baxter equations

We introduce the notion of associative (BiHom-)Yang-Baxter pair of weight $(λ,γ)$ which can provide the solution to the double curved Rota-Baxter (BiHom-)system. Equivalent characterizations of (quasitriangular) covariant BiHom-bialgebra are given. We also prove that associative BiHom-Yang-Baxter equation of weight $-1$ can be obtained by the unitary quasitriangular covariant BiHom-bialgebra. At last, we present two approaches to construct (BiHom-)pre-Lie modules from Rota-Baxter (BiHom-)paired modules.

preprint2021arXiv

Proxemics and Social Interactions in an Instrumented Virtual Reality Workshop

Virtual environments (VEs) can create collaborative and social spaces, which are increasingly important in the face of remote work and travel reduction. Recent advances, such as more open and widely available platforms, create new possibilities to observe and analyse interaction in VEs. Using a custom instrumented build of Mozilla Hubs to measure position and orientation, we conducted an academic workshop to facilitate a range of typical workshop activities. We analysed social interactions during a keynote, small group breakouts, and informal networking/hallway conversations. Our mixed-methods approach combined environment logging, observations, and semi-structured interviews. The results demonstrate how small and large spaces influenced group formation, shared attention, and personal space, where smaller rooms facilitated more cohesive groups while larger rooms made small group formation challenging but personal space more flexible. Beyond our findings, we show how the combination of data and insights can fuel collaborative spaces' design and deliver more effective virtual workshops.

preprint2021arXiv

Real-time tracking of COVID-19 and coronavirus research updates through text mining

The novel coronavirus (SARS-CoV-2) which causes COVID-19 is an ongoing pandemic. There are ongoing studies with up to hundreds of publications uploaded to databases daily. We are exploring the use-case of artificial intelligence and natural language processing in order to efficiently sort through these publications. We demonstrate that clinical trial information, preclinical studies, and a general topic model can be used as text mining data intelligence tools for scientists all over the world to use as a resource for their own research. To evaluate our method, several metrics are used to measure the information extraction and clustering results. In addition, we demonstrate that our workflow not only have a use-case for COVID-19, but for other disease areas as well. Overall, our system aims to allow scientists to more efficiently research coronavirus. Our automatically updating modules are available on our information portal at https://ghddi-ailab.github.io/Targeting2019-nCoV/ for public viewing.

preprint2021arXiv

Slow Spin Relaxation in Single Endohedral Fullerene Molecules

Well-protected magnetization, tunable quantum states and long coherence time are desired for the use of magnetic molecules in spintronics and quantum information technologies. In this work, endohedral fullerene molecules M@C28 with different transition metal cores were explored through systematic first-principles calculations and spin dynamics analyses. Many of them have stable magnetization, giant magnetic anisotropy energy and bias-tunable structure. In particular, some of them may have coherence time up to several milliseconds for their quantum spin states at high temperature (~10K) after full consideration of spin-vibration couplings. These results suggest that these M@C28 provide a rich pool for the development of single-molecule magnets, magnetic molecular junctions, and molecular qubits.

preprint2021arXiv

Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution

Being extremely dependent on iterative estimation of the degradation prior or optimization of the model from scratch, the existing blind super-resolution (SR) methods are generally time-consuming and less effective, as the estimation of degradation proceeds from a blind initialization and lacks interpretable degradation priors. To address it, this paper proposes a transitional learning method for blind SR using an end-to-end network without any additional iterations in inference, and explores an effective representation for unknown degradation. To begin with, we analyze and demonstrate the transitionality of degradations as interpretable prior information to indirectly infer the unknown degradation model, including the widely used additive and convolutive degradations. We then propose a novel Transitional Learning method for blind Super-Resolution (TLSR), by adaptively inferring a transitional transformation function to solve the unknown degradations without any iterative operations in inference. Specifically, the end-to-end TLSR network consists of a degree of transitionality (DoT) estimation network, a homogeneous feature extraction network, and a transitional learning module. Quantitative and qualitative evaluations on blind SR tasks demonstrate that the proposed TLSR achieves superior performances and costs fewer complexities against the state-of-the-art blind SR methods. The code is available at github.com/YuanfeiHuang/TLSR.

preprint2020arXiv

3D Gated Recurrent Fusion for Semantic Scene Completion

This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion. RGB images contain texture details of the object(s) which are vital for semantic scene understanding. Meanwhile, depth images capture geometric clues of high relevance for shape completion. Using both RGB and depth images can further boost the accuracy of SSC over employing one modality in isolation. We propose a 3D gated recurrent fusion network (GRFNet), which learns to adaptively select and fuse the relevant information from depth and RGB by making use of the gate and memory modules. Based on the single-stage fusion, we further propose a multi-stage fusion strategy, which could model the correlations among different stages within the network. Extensive experiments on two benchmark datasets demonstrate the superior performance and the effectiveness of the proposed GRFNet for data fusion in SSC. Code will be made available.

preprint2020arXiv

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.

preprint2020arXiv

A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery

With the development of remote sensing technology, the acquisition of remote sensing images is easier and easier, which provides sufficient data resources for the task of detecting remote sensing objects. However, how to detect objects quickly and accurately from many complex optical remote sensing images is a challenging hot issue. In this paper, we propose an efficient anchor free object detector, CenterFPANet. To pursue speed, we use a lightweight backbone and introduce the asymmetric revolution block. To improve the accuracy, we designed the FPA module, which links the feature maps of different levels, and introduces the attention mechanism to dynamically adjust the weights of each level of feature maps, which solves the problem of detection difficulty caused by large size range of remote sensing objects. This strategy can improve the accuracy of remote sensing image object detection without reducing the detection speed. On the DOTA dataset, CenterFPANet mAP is 64.00%, and FPS is 22.2, which is close to the accuracy of the anchor-based methods currently used and much faster than them. Compared with Faster RCNN, mAP is 6.76% lower but 60.87% faster. All in all, CenterFPANet achieves a balance between speed and accuracy in large-scale optical remote sensing object detection.

preprint2020arXiv

Age of Information for Multicast Transmission with Fixed and Random Deadlines in IoT Systems

In this paper, we consider the multicast transmission of a real-time Internet of Things (IoT) system, where an access point (AP) transmits time-stamped status updates to multiple IoT devices. Different from the existing studies that only considered multicast transmission without deadlines, we enforce a deadline for the service time of each multicast status update, taking into account both the fixed and randomly distributed deadlines. In particular, a status update is dropped when either its deadline expires or it is successfully received by a certain number of IoT devices. Considering deadlines is important for many emerging IoT applications, where the outdated status updates are of no use to IoT devices. We evaluate the timeliness of the status update delivery by applying a recently proposed metric, named the age of information (AoI), which is defined as the time elapsed since the generation of the most recently received status update. After deriving the distributions of the service time for all possible reception outcomes at IoT devices, we manage to obtain the closed-form expressions of both the average AoI and the average peak AoI. Simulations validate the performance analysis, which reveals that the multicast transmission with deadlines achieves a lower average AoI than that without deadlines and there exists an optimal value of the deadline that can minimize the average (peak) AoI. Results also show that the fixed and random deadlines have respective advantages in different deadline regimes.

preprint2020arXiv

Angular momentum-related probe of cold gas deficiencies

Recent studies of neutral atomic hydrogen (HI) in nearby galaxies found that all field disk galaxies are HI saturated, in that they carry roughly as much HI as permitted before this gas becomes gravitationally unstable. By taking this HI saturation for granted, the atomic gas fraction $f_{\rm atm}$ of galactic disks can be predicted as a function of the stability parameter $q=jσ/(GM)$, where $M$ and $j$ are the baryonic mass and specific angular momentum of the disk and $σ$ is the HI velocity dispersion Obreschkow et al. 2016. The log-ratio $Δf_q$ between this predictor and the observed atomic fraction can be seen as a physically motivated `HI deficiency'. While field disk galaxies have $Δf_q \approx0$, objects subject to environmental removal of HI are expected to have $Δf_q>0$. Within this framework, we revisit the HI deficiencies of satellite galaxies in the Virgo cluster and in clusters of the EAGLE simulation. We find that observed and simulated cluster galaxies are HI deficient and that $Δf_q$ slightly increases when getting closer to the cluster centres. The $Δf_q$ values are similar to traditional HI deficiency estimators, but $Δf_q$ is more directly comparable between observations and simulations than morphology-based deficiency estimators. By tracking the simulated HI deficient cluster galaxies back in time, we confirm that $Δf_q\approx0$ until the galaxies first enter a halo with $M_{\rm halo}>10^{13} {\rm M_{\odot}}$, at which moment they quickly lose HI by environmental effects. Finally, we use the simulation to investigate the links between $Δf_q$ and quenching of star formation.

preprint2020arXiv

Anisotropic Convolutional Networks for 3D Semantic Scene Completion

As a voxel-wise labeling task, semantic scene completion (SSC) tries to simultaneously infer the occupancy and semantic labels for a scene from a single depth and/or RGB image. The key challenge for SSC is how to effectively take advantage of the 3D context to model various objects or stuffs with severe variations in shapes, layouts and visibility. To handle such variations, we propose a novel module called anisotropic convolution, which properties with flexibility and power impossible for the competing methods such as standard 3D convolution and some of its variations. In contrast to the standard 3D convolution that is limited to a fixed 3D receptive field, our module is capable of modeling the dimensional anisotropy voxel-wisely. The basic idea is to enable anisotropic 3D receptive field by decomposing a 3D convolution into three consecutive 1D convolutions, and the kernel size for each such 1D convolution is adaptively determined on the fly. By stacking multiple such anisotropic convolution modules, the voxel-wise modeling capability can be further enhanced while maintaining a controllable amount of model parameters. Extensive experiments on two SSC benchmarks, NYU-Depth-v2 and NYUCAD, show the superior performance of the proposed method. Our code is available at https://waterljwant.github.io/SSC/

preprint2020arXiv

Balanced Symmetric Cross Entropy for Large Scale Imbalanced and Noisy Data

Deep convolution neural network has attracted many attentions in large-scale visual classification task, and achieves significant performance improvement compared to traditional visual analysis methods. In this paper, we explore many kinds of deep convolution neural network architectures for large-scale product recognition task, which is heavily class-imbalanced and noisy labeled data, making it more challenged. Extensive experiments show that PNASNet achieves best performance among a variety of convolutional architectures. Together with ensemble technology and negative learning loss for noisy labeled data, we further improve the model performance on online test data. Finally, our proposed method achieves 0.1515 mean top-1 error on online test data.

preprint2020arXiv

Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction

Medical image segmentation is usually regarded as one of the most important intermediate steps in clinical situations and medical imaging research. Thus, accurately assessing the segmentation quality of the automatically generated predictions is essential for guaranteeing the reliability of the results of the computer-assisted diagnosis (CAD). Many researchers apply neural networks to train segmentation quality regression models to estimate the segmentation quality of a new data cohort without labeled ground truth. Recently, a novel idea is proposed that transforming the segmentation quality assessment (SQA) problem intothe pixel-wise error map prediction task in the form of segmentation. However, the simple application of vanilla segmentation structures in medical image fails to detect some small and thin error regions of the auto-generated masks with complex anatomical structures. In this paper, we propose collaborative boundaryaware context encoding networks called AEP-Net for error prediction task. Specifically, we propose a collaborative feature transformation branch for better feature fusion between images and masks, and precise localization of error regions. Further, we propose a context encoding module to utilize the global predictor from the error map to enhance the feature representation and regularize the networks. We perform experiments on IBSR v2.0 dataset and ACDC dataset. The AEP-Net achieves an average DSC of 0.8358, 0.8164 for error prediction task,and shows a high Pearson correlation coefficient of 0.9873 between the actual segmentation accuracy and the predicted accuracy inferred from the predicted error map on IBSR v2.0 dataset, which verifies the efficacy of our AEP-Net.

preprint2020arXiv

Data Dissemination Using Interest Tree in Socially Aware Networking

Socially aware networking (SAN) exploits social characteristics of mobile users to streamline data dissemination protocols in opportunistic environments. Existing protocols in this area utilized various social features such as user interests, social similarity, and community structure to improve the performance of data dissemination. However, the interrelationship between user interests and its impact on the efficiency of data dissemination has not been explored sufficiently. In this paper, we analyze various kinds of relationships between user interests and model them using a layer-based structure in order to form social communities in SAN paradigm. We propose Int-Tree, an Interest-Tree based scheme which uses the relationship between user interests to improve the performance of data dissemination. The core of Int-Tree is the interest-tree, a tree-based community structure that combines two social features, i.e. density of a community and social tie, to support data dissemination. The simulation results show that Int-Tree achieves higher delivery ratio, lower overhead, in comparison to two benchmark protocols, PROPHET and Epidemic routing. In addition, Int-Tree can perform with 1.36 hop counts in average, and tolerable latency in terms of buffer size, time to live (TTL) and simulation duration. Finally, Int-Tree keeps stable performance with various parameters.

preprint2020arXiv

Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics

Feynman's path integral approach is to sum over all possible spatio-temporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in classical view. However, the complete characterization of quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose deep-learning-performed strong-field Feynman's formulation with pre-classification scheme which can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build up a bridge between deep learning and strong-field physics through the Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science, and shed a new light on the quantum-classical correspondence.

preprint2020arXiv

Depth Based Semantic Scene Completion with Position Importance Aware Loss

Semantic Scene Completion (SSC) refers to the task of inferring the 3D semantic segmentation of a scene while simultaneously completing the 3D shapes. We propose PALNet, a novel hybrid network for SSC based on single depth. PALNet utilizes a two-stream network to extract both 2D and 3D features from multi-stages using fine-grained depth information to efficiently captures the context, as well as the geometric cues of the scene. Current methods for SSC treat all parts of the scene equally causing unnecessary attention to the interior of objects. To address this problem, we propose Position Aware Loss(PA-Loss) which is position importance aware while training the network. Specifically, PA-Loss considers Local Geometric Anisotropy to determine the importance of different positions within the scene. It is beneficial for recovering key details like the boundaries of objects and the corners of the scene. Comprehensive experiments on two benchmark datasets demonstrate the effectiveness of the proposed method and its superior performance. Models and Video demo can be found at: https://github.com/UniLauX/PALNet.

preprint2020arXiv

Einstein-Podolsky-Rosen entanglement and asymmetric steering between distant macroscopic mechanical and magnonic systems

We propose a deterministic scheme for establishing hybrid Einstein-Podolsky-Rosen (EPR) entanglement channel between a macroscopic mechanical oscillator and a magnon mode in a distant yttrium-iron-garnet (YIG) sphere across about ten gigahertz of frequency difference. The system consists of a driven electromechanical cavity which is unidirectionally coupled to a distant electromagnonical cavity inside which a YIG sphere is placed. We find that far beyond the sideband-resolved regime in the electromechanical subsystem, stationary phonon-magnon EPR entanglement can be achieved. This is realized by utilizing the output field of the electromechanical cavity being an intermediary which distributes the electromechanical entanglement to the magnons, thus establishing a remote phonon-magnon entanglement. The EPR entanglement is strong enough such that phonon-magnon quantum steering can be attainable in an asymmetric manner. This long-distance macroscopic hybrid EPR entanglement and steering enable potential applications not only in fundamental tests of quantum mechanics at the macro scale, but also in quantum networking and one-sided device-independent quantum cryptography based on magnonics and electromechanics.

preprint2020arXiv

Extending Label Smoothing Regularization with Self-Knowledge Distillation

Inspired by the strong correlation between the Label Smoothing Regularization(LSR) and Knowledge distillation(KD), we propose an algorithm LsrKD for training boost by extending the LSR method to the KD regime and applying a softer temperature. Then we improve the LsrKD by a Teacher Correction(TC) method, which manually sets a constant larger proportion for the right class in the uniform distribution teacher. To further improve the performance of LsrKD, we develop a self-distillation method named Memory-replay Knowledge Distillation (MrKD) that provides a knowledgeable teacher to replace the uniform distribution one in LsrKD. The MrKD method penalizes the KD loss between the current model's output distributions and its copies' on the training trajectory. By preventing the model learning so far from its historical output distribution space, MrKD can stabilize the learning and find a more robust minimum. Our experiments show that LsrKD can improve LSR performance consistently at no cost, especially on several deep neural networks where LSR is ineffectual. Also, MrKD can significantly improve single model training. The experiment results confirm that the TC can help LsrKD and MrKD to boost training, especially on the networks they are failed. Overall, LsrKD, MrKD, and their TC variants are comparable to or outperform the LSR method, suggesting the broad applicability of these KD methods.

preprint2020arXiv

Facial Attribute Capsules for Noise Face Super Resolution

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.

preprint2020arXiv

Giant magnetic anisotropy energy and long coherence time of uranium substitution on defected Al2O3(0001)

Nanomagnets with giant magnetic anisotropy energy and long coherence time are desired for various technological innovations such as quantum information procession and storage. Based on the first-principles calculations and model analyses, we demonstrate that a single uranium atom substituting Al on the Al2O3(0001) surface may have high structural stability and large magnetic anisotropy energy up to 48 meV per uranium atom. As the magnetization resides in the localized f-shell and is not much involved in chemical bonding with neighbors, long coherence time up to ~1.6 mS can be achieved for the quantum spin states. These results suggest a new strategy for the search of ultrasmall magnetic units for diverse applications in the quantum information era.

preprint2020arXiv

Grid-Forming Converters control based on DC voltage feedback

The renewable energy is connected to the power grid through power electronic converters, which are lack of make the inertia of synchronous generator/machine (SM) be lost. The increasing penetration of renewable energy in power system weakens the frequency and voltage stability. The Grid-Forming Converters (GFCs) simulate the function of synchronous motor through control method in order to improve the stability of power grid by providing inertia and stability regulation mechanism. This kind of converter control methods include virtual synchronous machine, schedulable virtual oscillator control and so on. These control method mainly use AC side state feedback and do not monitor the DC side state. This paper analyzes the control strategy of GFC considering power grid stability, including Frequency Droop Control, Virtual Synchronous Machine Control and dispatchable Virtual Oscillator Control. The DC side voltage collapse problem is found when a large load disturbance occurs. The control methods of GFC considering DC side voltage feedback are proposed, which can ensure the synchronization characteristics of grid connection and solve the problem of DC side voltage collapse. The proposed method is verified by IEEE-9 bus system, which shows the effectiveness of the proposed method.

preprint2020arXiv

Inertia and feedback parameters adaptive control of virtual synchronous generator

The virtual synchronous generator technology analogs the characteristics of the synchronous generator via the controller design. It improved the stability of the grid systems which include the new energy. At the same time, according to the adjustable characteristics of the virtual synchronous generator parameters, the parameter adaptive adjustment is used to improve the dynamic performance of the system. However, the traditional virtual synchronous generator adaptive control technology still has two drawbacks: on the one hand, the large-scale adjustment of the damping droop coefficient and the virtual moment of inertia requires the system having a high energy storage margin; On the other hand, there is a power overshoot phenomenon in the transient regulation process, which is disadvantageous to the power equipment. First, this paper provides a convenient adjustment method for improving the transient stability of the system, the system damping is adjusted by introducing the output speed feedback. Second, according to the transient power-angle characteristics of the system, a parameter adaptive control strategy is proposed, which shortens the transient adjustment time and ensures that the deviation of the system frequency in the transient adjustment process is within the allowable range, and improves the transient performance of the grid frequency adjustment, at the same time, the power overshoot is suppressed. Finally, the experimental results show that the proposed control strategy is superior to the existing adaptive control strategy.

preprint2020arXiv

Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution

Benefiting from the strong capabilities of deep CNNs for feature representation and nonlinear mapping, deep-learning-based methods have achieved excellent performance in single image super-resolution. However, most existing SR methods depend on the high capacity of networks which is initially designed for visual recognition, and rarely consider the initial intention of super-resolution for detail fidelity. Aiming at pursuing this intention, there are two challenging issues to be solved: (1) learning appropriate operators which is adaptive to the diverse characteristics of smoothes and details; (2) improving the ability of model to preserve the low-frequency smoothes and reconstruct the high-frequency details. To solve them, we propose a purposeful and interpretable detail-fidelity attention network to progressively process these smoothes and details in divide-and-conquer manner, which is a novel and specific prospect of image super-resolution for the purpose on improving the detail fidelity, instead of blindly designing or employing the deep CNNs architectures for merely feature representation in local receptive fields. Particularly, we propose a Hessian filtering for interpretable feature representation which is high-profile for detail inference, a dilated encoder-decoder and a distribution alignment cell to improve the inferred Hessian features in morphological manner and statistical manner respectively. Extensive experiments demonstrate that the proposed methods achieve superior performances over the state-of-the-art methods quantitatively and qualitatively. Code is available at https://github.com/YuanfeiHuang/DeFiAN.

preprint2020arXiv

Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming

Point cloud video is the most popular representation of hologram, which is the medium to precedent natural content in VR/AR/MR and is expected to be the next generation video. Point cloud video system provides users immersive viewing experience with six degrees of freedom and has wide applications in many fields such as online education, entertainment. To further enhance these applications, point cloud video streaming is in critical demand. The inherent challenges lie in the large size by the necessity of recording the three-dimensional coordinates besides color information, and the associated high computation complexity of encoding. To this end, this paper proposes a communication and computation resource allocation scheme for QoE-driven point cloud video streaming. In particular, we maximize system resource utilization by selecting different quantities, transmission forms and quality level tiles to maximize the quality of experience. Extensive simulations are conducted and the simulation results show the superior performance over the existing schemes

preprint2020arXiv

Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight

Forecasting electricity demand plays a fundamental role in the operation and planning procedures of power systems and the publications about electricity demand forecasting increasing year by year. In this paper, we use Scientometric analysis to analyze the current state and the emerging trends in the field of electricity demand forecasting form 831 publications of web of science core collection during 20 years: 1999-2018. Employing statistical description, cooperative network analysis, keyword co-occurrence analysis, co-citation analysis, cluster analysis, and emerging trend analysis techniques, this article gives the most critical countries, institutions, journals, authors and publications in this field, cooperative networks relationships, research hotspots and emerging trends. The results of this article can provide meaningful guidance and some insights for researchers to find out crucial research, emerging trends and new developments in this area.

preprint2020arXiv

Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms

Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we focus on the interplay of machine learning algorithm with the chemically motivated descriptors and the size and type of data sets needed for molecular property prediction. Using Nuclear Magnetic Resonance chemical shift prediction as an example, we demonstrate that success is predicated on the choice of feature extracted or real-space representations of chemical structures, whether the molecular property data is abundant and/or experimentally or computationally derived, and how these together will influence the correct choice of popular machine learning algorithms drawn from deep learning, random forests, or kernel methods.

preprint2020arXiv

Magnetostrictively induced stationary entanglement between two microwave fields

We present a scheme to entangle two microwave fields by using the nonlinear magnetostrictive interaction in a ferrimagnet. The magnetostrictive interaction enables the coupling between a magnon mode (spin wave) and a mechanical mode in the ferrimagnet, and the magnon mode simultaneously couples to two microwave cavity fields via the magnetic dipole interaction. The magnon-phonon coupling is enhanced by directly driving the ferrimagnet with a strong red-detuned microwave field, and the driving photons are scattered onto two sidebands induced by the mechanical motion. We show that two cavity fields can be prepared in a stationary entangled state if they are respectively resonant with two mechanical sidebands. The present scheme illustrates a new mechanism for creating entangled states of optical fields, and enables potential applications in quantum information science and quantum tasks that require entangled microwave fields.

preprint2020arXiv

Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios. This paper presents a deep neural network approach namely Multi-Margin based Decorrelation Learning (MMDL) to extract decorrelation representations in a hyperspherical space for cross-domain face images. The proposed framework can be divided into two components: heterogeneous representation network and decorrelation representation learning. First, we employ a large scale of accessible visual face images to train heterogeneous representation network. The decorrelation layer projects the output of the first component into decorrelation latent subspace and obtains decorrelation representation. In addition, we design a multi-margin loss (MML), which consists of quadruplet margin loss (QML) and heterogeneous angular margin loss (HAML), to constrain the proposed framework. Experimental results on two challenging heterogeneous face databases show that our approach achieves superior performance on both verification and recognition tasks, comparing with state-of-the-art methods.

preprint2020arXiv

On the Age of Information for Multicast Transmission with Hard Deadlines in IoT Systems

We consider the multicast transmission of a real-time Internet of Things (IoT) system, where a server transmits time-stamped status updates to multiple IoT devices. We apply a recently proposed metric, named age of information (AoI), to capture the timeliness of the information delivery. The AoI is defined as the time elapsed since the generation of the most recently received status update. Different from the existing studies that considered either multicast transmission without hard deadlines or unicast transmission with hard deadlines, we enforce a hard deadline for the service time of multicast transmission. This is important for many emerging multicast IoT applications, where the outdated status updates are useless for IoT devices. Specifically, the transmission of a status update is terminated when either the hard deadline expires or a sufficient number of IoT devices successfully receive the status update. We first calculate the distributions of the service time for all possible reception outcomes at IoT devices, and then derive a closed-form expression of the average AoI. Simulations validate the performance analysis, which reveals that: 1) the multicast transmission with hard deadlines achieves a lower average AoI than that without hard deadlines; and 2) there exists an optimal value of the hard deadline that minimizes the average AoI.

preprint2020arXiv

Phone2Cloud: Exploiting Computation Offloading for Energy Saving on Smartphones in Mobile Cloud Computing

With prosperity of applications on smartphones, energy saving for smartphones has drawn increasing attention. In this paper we devise Phone2Cloud, a computation offloading-based system for energy saving on smartphones in the context of mobile cloud computing. Phone2Cloud offloads computation of an application running on smartphones to the cloud. The objective is to improve energy efficiency of smartphones and at the same time, enhance the application's performance through reducing its execution time. In this way, the user's experience can be improved. We implement the prototype of Phone2Cloud on Android and Hadoop environment. Two sets of experiments, including application experiments and scenario experiments, are conducted to evaluate the system. The experimental results show that Phone2Cloud can effectively save energy for smartphones and reduce the application's execution time.

preprint2020arXiv

PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving

In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning. However, this perception task is difficult, particularly for generic obstacles/objects, due to appearance and occlusion changes. To tackle this problem, we propose an end-to-end deep learning framework for LIDAR-based flow estimation in bird's eye view (BeV). Our method takes consecutive point cloud pairs as input and produces a 2-D BeV flow grid describing the dynamic state of each cell. The experimental results show that the proposed method not only estimates 2-D BeV flow accurately but also improves tracking performance of both dynamic and static objects.

preprint2020arXiv

Probabilistic 3D Multi-Object Tracking for Autonomous Driving

3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module. In this paper, we present our on-line tracking method, which made the first place in the NuScenes Tracking Challenge, held at the AI Driving Olympics Workshop at NeurIPS 2019. Our method estimates the object states by adopting a Kalman Filter. We initialize the state covariance as well as the process and observation noise covariance with statistics from the training set. We also use the stochastic information from the Kalman Filter in the data association step by measuring the Mahalanobis distance between the predicted object states and current object detections. Our experimental results on the NuScenes validation and test set show that our method outperforms the AB3DMOT baseline method by a large margin in the Average Multi-Object Tracking Accuracy (AMOTA) metric.

preprint2020arXiv

Progressive Perception-Oriented Network for Single Image Super-Resolution

Recently, it has been demonstrated that deep neural networks can significantly improve the performance of single image super-resolution (SISR). Numerous studies have concentrated on raising the quantitative quality of super-resolved (SR) images. However, these methods that target PSNR maximization usually produce blurred images at large upscaling factor. The introduction of generative adversarial networks (GANs) can mitigate this issue and show impressive results with synthetic high-frequency textures. Nevertheless, these GAN-based approaches always have a tendency to add fake textures and even artifacts to make the SR image of visually higher-resolution. In this paper, we propose a novel perceptual image super-resolution method that progressively generates visually high-quality results by constructing a stage-wise network. Specifically, the first phase concentrates on minimizing pixel-wise error, and the second stage utilizes the features extracted by the previous stage to pursue results with better structural retention. The final stage employs fine structure features distilled by the second phase to produce more realistic results. In this way, we can maintain the pixel, and structural level information in the perceptual image as much as possible. It is useful to note that the proposed method can build three types of images in a feed-forward process. Also, we explore a new generator that adopts multi-scale hierarchical features fusion. Extensive experiments on benchmark datasets show that our approach is superior to the state-of-the-art methods. Code is available at https://github.com/Zheng222/PPON.

preprint2020arXiv

Projection & Probability-Driven Black-Box Attack

Generating adversarial examples in a black-box setting retains a significant challenge with vast practical application prospects. In particular, existing black-box attacks suffer from the need for excessive queries, as it is non-trivial to find an appropriate direction to optimize in the high-dimensional space. In this paper, we propose Projection & Probability-driven Black-box Attack (PPBA) to tackle this problem by reducing the solution space and providing better optimization. For reducing the solution space, we first model the adversarial perturbation optimization problem as a process of recovering frequency-sparse perturbations with compressed sensing, under the setting that random noise in the low-frequency space is more likely to be adversarial. We then propose a simple method to construct a low-frequency constrained sensing matrix, which works as a plug-and-play projection matrix to reduce the dimensionality. Such a sensing matrix is shown to be flexible enough to be integrated into existing methods like NES and Bandits$_{TD}$. For better optimization, we perform a random walk with a probability-driven strategy, which utilizes all queries over the whole progress to make full use of the sensing matrix for a less query budget. Extensive experiments show that our method requires at most 24% fewer queries with a higher attack success rate compared with state-of-the-art approaches. Finally, the attack method is evaluated on the real-world online service, i.e., Google Cloud Vision API, which further demonstrates our practical potentials.

preprint2020arXiv

Proposal for optomechanical quantum teleportation

We present a novel discrete-variable quantum teleportation scheme using pulsed optomechanics. In our proposal, we demonstrate how an unknown optical input state can be transferred onto the joint state of a pair of mechanical oscillators, without physically interacting with one another. We further analyze how experimental imperfections will affect the fidelity of the teleportation and highlight how our scheme can be realized in current state-of-the-art optomechanical systems.

preprint2020arXiv

QoE-driven Coupled Uplink and Downlink Rate Adaptation for 360-degree Video Live Streaming

360-degree video provides an immersive 360-degree viewing experience and has been widely used in many areas. The 360-degree video live streaming systems involve capturing, compression, uplink (camera to video server) and downlink (video server to user) transmissions. However, few studies have jointly investigated such complex systems, especially the rate adaptation for the coupled uplink and downlink in the 360-degree video streaming under limited bandwidth constraints. In this letter, we propose a quality of experience (QoE)-driven 360-degree video live streaming system, in which a video server performs rate adaptation based on the uplink and downlink bandwidths and information concerning each user's real-time field-of-view (FOV). We formulate it as a nonlinear integer programming problem and propose an algorithm, which combines the Karush-Kuhn-Tucker (KKT) condition and branch and bound method, to solve it. The numerical results show that the proposed optimization model can improve users' QoE significantly in comparison with other baseline schemes.

preprint2020arXiv

Real-Time Panoptic Segmentation from Dense Detections

Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution. Current state-of-the-art approaches cannot run in real-time, and simplifying these architectures to improve efficiency severely degrades their accuracy. In this paper, we propose a new single-shot panoptic segmentation network that leverages dense detections and a global self-attention mechanism to operate in real-time with performance approaching the state of the art. We introduce a novel parameter-free mask construction method that substantially reduces computational complexity by efficiently reusing information from the object detection and semantic segmentation sub-tasks. The resulting network has a simple data flow that does not require feature map re-sampling or clustering post-processing, enabling significant hardware acceleration. Our experiments on the Cityscapes and COCO benchmarks show that our network works at 30 FPS on 1024x2048 resolution, trading a 3% relative performance degradation from the current state of the art for up to 440% faster inference.

preprint2020arXiv

Reinforcement Solver for H-infinity Filter with Bounded Noise

H-infinity filter has been widely applied in engineering field, but copping with bounded noise is still an open problem and difficult to solve. This paper considers the H-infinity filtering problem for linear system with bounded process and measurement noise. The problem is first formulated as a zero-sum game where the dynamic of estimation error is non-affine with respect to filter gain and measurement noise. A nonquadratic Hamilton-Jacobi-Isaacs (HJI) equation is then derived by employing a nonquadratic cost to characterize bounded noise, which is extremely difficult to solve due to its non-affine and nonlinear properties. Next, a reinforcement learning algorithm based on gradient descent method which can handle nonlinearity is proposed to update the gain of reinforcement filter, where measurement noise is fixed to tackle non-affine property and increase the convexity of Hamiltonian. Two examples demonstrate the convergence and effectiveness of the proposed algorithm.

preprint2020arXiv

Semantically-Guided Representation Learning for Self-Supervised Monocular Depth

Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories.

preprint2020arXiv

Speaker-aware speech-transformer

Recently, end-to-end (E2E) models become a competitive alternative to the conventional hybrid automatic speech recognition (ASR) systems. However, they still suffer from speaker mismatch in training and testing condition. In this paper, we use Speech-Transformer (ST) as the study platform to investigate speaker aware training of E2E models. We propose a model called Speaker-Aware Speech-Transformer (SAST), which is a standard ST equipped with a speaker attention module (SAM). The SAM has a static speaker knowledge block (SKB) that is made of i-vectors. At each time step, the encoder output attends to the i-vectors in the block, and generates a weighted combined speaker embedding vector, which helps the model to normalize the speaker variations. The SAST model trained in this way becomes independent of specific training speakers and thus generalizes better to unseen testing speakers. We investigate different factors of SAM. Experimental results on the AISHELL-1 task show that SAST achieves a relative 6.5% CER reduction (CERR) over the speaker-independent (SI) baseline. Moreover, we demonstrate that SAST still works quite well even if the i-vectors in SKB all come from a different data source other than the acoustic training set.

preprint2020arXiv

Stationary quantum entanglement between a massive mechanical membrane and a low frequency LC circuit

We study electro-mechanical entanglement in a system where a massive membrane is capacitively coupled to a {\it low frequency} LC resonator. In opto- and electro-mechanics, the entanglement between a megahertz (MHz) mechanical resonator and a gigahertz (GHz) microwave LC resonator has been widely and well explored, and recently experimentally demonstrated. Typically, coupling is realized through a radiation pressure-like interaction, and entanglement is generated by adopting an appropriate microwave drive. Through this approach it is however not evident how to create entanglement in the case where both the mechanical and LC oscillators are of low frequency, e.g., around 1 MHz. Here we provide an effective approach to entangling two low-frequency resonators by further coupling the membrane to an optical cavity. The cavity is strongly driven by a red-detuned laser, sequentially cooling the mechanical and electrical modes, which results in stationary electro-mechanical entanglement at experimentally achievable temperatures. The entanglement directly originates from the electro-mechanical coupling itself and due to its quantum nature will allow testing quantum theories at a more macroscopic scale than currently possible.

preprint2020arXiv

Temperature-dependent properties of liquid-vapour coexistence system with many-body dissipative particle dynamics with energy conservation

The dynamic properties of fluid, including density, surface tension, diffusivity and viscosity, are temperature-dependent and can significantly influence the flow dynamics of mesoscopic non-isothermal systems. To capture the correct temperature-dependence of a fluid, a many-body dissipative particle dynamics model with energy conservation (mDPDe) is developed by combining the temperature-dependent coefficient of the conservative force and weighting terms of the dissipative and random forces. The momentum and thermal diffusivity, viscosity, and surface tension of liquid water at various temperatures ranging from 273 K to 373 K are used as examples for verifying the proposed model. Simulations of a periodic Poiseuille flow driven by body forces and heat sources are carried out to validate the diffusivity of the present model. Also, a steady case of heat conduction for reproducing the Fourier law is used to validate the thermal boundary conditions. By using this mDPDe simulations, the thermocapillary motion of liquid water nanodroplets on hydrophobic substrates with thermal gradients is investigated. The migration of the droplet is observed on flat substrates with gradient temperature. The velocity of the migration becomes larger for higher temperature difference, which is in agreement with the present theoretical analysis and DVDWT simulations. The results illustrate that the modified model can be used to study Marangoni effect on a nanodroplet and other heat and mass transfer problems with free interface.

preprint2020arXiv

Ternary Policy Iteration Algorithm for Nonlinear Robust Control

The uncertainties in plant dynamics remain a challenge for nonlinear control problems. This paper develops a ternary policy iteration (TPI) algorithm for solving nonlinear robust control problems with bounded uncertainties. The controller and uncertainty of the system are considered as game players, and the robust control problem is formulated as a two-player zero-sum differential game. In order to solve the differential game, the corresponding Hamilton-Jacobi-Isaacs (HJI) equation is then derived. Three loss functions and three update phases are designed to match the identity equation, minimization and maximization of the HJI equation, respectively. These loss functions are defined by the expectation of the approximate Hamiltonian in a generated state set to prevent operating all the states in the entire state set concurrently. The parameters of value function and policies are directly updated by diminishing the designed loss functions using the gradient descent method. Moreover, zero-initialization can be applied to the parameters of the control policy. The effectiveness of the proposed TPI algorithm is demonstrated through two simulation studies. The simulation results show that the TPI algorithm can converge to the optimal solution for the linear plant, and has high resistance to disturbances for the nonlinear plant.

preprint2020arXiv

The Occurrence of Rocky Habitable Zone Planets Around Solar-Like Stars from Kepler Data

We present occurrence rates for rocky planets in the habitable zones (HZ) of main-sequence dwarf stars based on the Kepler DR25 planet candidate catalog and Gaia-based stellar properties. We provide the first analysis in terms of star-dependent instellation flux, which allows us to track HZ planets. We define $η_\oplus$ as the HZ occurrence of planets with radius between 0.5 and 1.5 $R_\oplus$ orbiting stars with effective temperatures between 4800 K and 6300 K. We find that $η_\oplus$ for the conservative HZ is between $0.37^{+0.48}_{-0.21}$ (errors reflect 68\% credible intervals) and $0.60^{+0.90}_{-0.36}$ planets per star, while the optimistic HZ occurrence is between $0.58^{+0.73}_{-0.33}$ and $0.88^{+1.28}_{-0.51}$ planets per star. These bounds reflect two extreme assumptions about the extrapolation of completeness beyond orbital periods where DR25 completeness data are available. The large uncertainties are due to the small number of detected small HZ planets. We find similar occurrence rates using both a Poisson likelihood Bayesian analysis and Approximate Bayesian Computation. Our results are corrected for catalog completeness and reliability. Both completeness and the planet occurrence rate are dependent on stellar effective temperature. We also present occurrence rates for various stellar populations and planet size ranges. We estimate with $95\%$ confidence that, on average, the nearest HZ planet around G and K dwarfs is about 6 pc away, and there are about 4 HZ rocky planets around G and K dwarfs within 10 pc of the Sun.

preprint2020arXiv

Towards Practical Private Information Retrieval from MDS Array Codes

Private information retrieval (PIR) is the problem of privately retrieving one out of $M$ original files from $N$ severs, i.e., each individual server learns nothing about the file that the user is requesting. Usually, the $M$ files are replicated or encoded by a maximum distance separable (MDS) code and then stored across the $N$ servers. Compared to mere replication, MDS coded servers can significantly reduce the storage overhead. Particularly, PIR from minimum storage regenerating (MSR) coded servers can simultaneously reduce the repair bandwidth when repairing failed servers. Existing PIR schemes from MSR coded servers either require large sub-packetization levels or are not capacity-achieving. In this paper, a PIR protocol from MDS array codes is proposed, subsuming PIR from MSR coded servers as a special case. Particularly, the case of non-colluding, honest-but-curious servers is considered. The retrieval rate of the new PIR protocol achieves the capacity of PIR from MDS/MSR coded servers. By choosing different MDS array codes, the new PIR protocol can have some advantages when compared with existing protocols, e.g., 1) small sub-packetization, 2) (near-) optimal repair bandwidth, 3) implementable over the binary field $\mathbf{F}_2$.

preprint2020arXiv

Video Face Super-Resolution with Motion-Adaptive Feedback Cell

Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN). Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR) frames is utilized to generate a single high resolution (HR) frame, and running a slide window to select LR frames over the entire video would obtain a series of HR frames. However, duo to the complex temporal dependency between frames, with the number of LR input frames increase, the performance of the reconstructed HR frames become worse. The reason is in that these methods lack the ability to model complex temporal dependencies and hard to give an accurate motion estimation and compensation for VSR process. Which makes the performance degrade drastically when the motion in frames is complex. In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but effective block, which can efficiently capture the motion compensation and feed it back to the network in an adaptive way. Our approach efficiently utilizes the information of the inter-frame motion, the dependence of the network on motion estimation and compensation method can be avoid. In addition, benefiting from the excellent nature of MAFC, the network can achieve better performance in the case of extremely complex motion scenarios. Extensive evaluations and comparisons validate the strengths of our approach, and the experimental results demonstrated that the proposed framework is outperform the state-of-the-art methods.

preprint2020arXiv

Weakly Supervised Temporal Action Localization with Segment-Level Labels

Temporal action localization presents a trade-off between test performance and annotation-time cost. Fully supervised methods achieve good performance with time-consuming boundary annotations. Weakly supervised methods with cheaper video-level category label annotations result in worse performance. In this paper, we introduce a new segment-level supervision setting: segments are labeled when annotators observe actions happening here. We incorporate this segment-level supervision along with a novel localization module in the training. Specifically, we devise a partial segment loss regarded as a loss sampling to learn integral action parts from labeled segments. Since the labeled segments are only parts of actions, the model tends to overfit along with the training process. To tackle this problem, we first obtain a similarity matrix from discriminative features guided by a sphere loss. Then, a propagation loss is devised based on the matrix to act as a regularization term, allowing implicit unlabeled segments propagation during training. Experiments validate that our method can outperform the video-level supervision methods with almost same the annotation time.

preprint2019arXiv

Boosting Real-Time Driving Scene Parsing with Shared Semantics

Real-time scene parsing is a fundamental feature for autonomous driving vehicles with multiple cameras. In this letter we demonstrate that sharing semantics between cameras with different perspectives and overlapped views can boost the parsing performance when compared with traditional methods, which individually process the frames from each camera. Our framework is based on a deep neural network for semantic segmentation but with two kinds of additional modules for sharing and fusing semantics. On the one hand, a semantics sharing module is designed to establish the pixel-wise mapping between the input images. Features as well as semantics are shared by the map to reduce duplicated workload which leads to more efficient computation. On the other hand, feature fusion modules are designed to combine different modal of semantic features, which leverage the information from both inputs for better accuracy. To evaluate the effectiveness of the proposed framework, we have applied our network to a dual-camera vision system for driving scene parsing. Experimental results show that our network outperforms the baseline method on the parsing accuracy with comparable computations.

preprint2019arXiv

Entangling two magnon modes via magnetostrictive interaction

We present a scheme to entangle two magnon modes in a cavity magnomechanical system. The two magnon modes are embodied by collective motions of a large number of spins in two macroscopic ferrimagnets, and couple to a single microwave cavity mode via magnetic dipole interaction. We show that by activating the nonlinear magnetostrictive interaction in one ferrimagnet, realized by driving the magnon mode with a strong red-detuned microwave field, the two magnon modes can be prepared in an entangled state. The entanglement is achieved by exploiting the nonlinear magnon-phonon coupling and the linear magnon-cavity coupling, and is in the steady state and robust against temperature. The entangled magnon modes in two massive ferrimagnets represent genuinely macroscopic quantum states, and may find applications in the study of macroscopic quantum mechanics and quantum information processing based on magnonics.

preprint2019arXiv

Evidence for nematic superconductivity of topological surface states in PbTaSe2

Spontaneous symmetry breaking has been a paradigm to describe the phase transitions in condensed matter physics. In addition to the continuous electromagnetic gauge symmetry, an unconventional superconductor can break discrete symmetries simultaneously, such as time reversal and lattice rotational symmetry. In this work we report a characteristic in-plane 2-fold behaviour of the resistive upper critical field and point-contact spectra on the superconducting semimetal PbTaSe2 with topological nodal-rings, despite its hexagonal lattice symmetry (or D_3h in bulk while C_3v on surface, to be precise). However, we do not observe any lattice rotational symmetry breaking signal from field-angle-dependent specific heat. It is worth noting that such surface-only electronic nematicity is in sharp contrast to the observation in the topological superconductor candidate, CuxBi2Se3, where the nematicity occurs in various bulk measurements. In combination with theory, superconducting nematicity is likely to emerge from the topological surface states of PbTaSe2, rather than the proximity effect. The issue of time reversal symmetry breaking is also addressed. Thus, our results on PbTaSe2 shed new light on possible routes to realize nematic superconductivity with nontrivial topology.

preprint2019arXiv

Identifying significant edges via neighborhood information

Heterogeneous nature of real networks implies that different edges play different roles in network structure and functions, and thus to identify significant edges is of high value in both theoretical studies and practical applications. We propose the so-called second-order neighborhood (SN) index to quantify an edge's significance in a network. We compare SN index with many other benchmark methods based on 15 real networks via edge percolation. Results show that the proposed SN index outperforms other well-known methods.

preprint2019arXiv

Macroscopic entanglement of two magnon modes via quantum correlated microwave fields

We present a scheme to entangle two magnon modes in two macroscopic yttrium-iron-garnet spheres. The two spheres are placed inside two microwave cavities, which are driven by a two-mode squeezed microwave field. By using the linear state-swap interaction between the cavity and the magnon mode in each cavity, the quantum correlation of the two driving fields is with high efficiency transferred to the two magnon modes. Considerable entanglement could be achieved under experimentally achievable conditions $g \gg κ_a \gg κ_m$, where $g$ is the cavity-magnon coupling rate and $κ_a$, $κ_m$ are the decay rates of the cavity and magnon modes, respectively. The entanglement is in the steady state and robust against temperature, surviving up to hundreds of milliKelvin with experimentally accessible two-mode squeezed source.

preprint2019arXiv

Noise robustness of synchronization of two nanomechanical resonators coupled to the same cavity field

We study synchronization of a room temperature optomechanical system formed by two resonators coupled via radiation pressure to the same driven optical cavity mode. By using stochastic Langevin equations and effective slowly-varying amplitude equations, we explore the long-time dynamics of the system. We see that thermal noise can induce significant non-Gaussian dynamical properties, including the coexistence of multi-stable synchronized limit cycles and phase diffusion. Synchronization in this optomechanical system is very robust with respect to thermal noise: in fact, even though each oscillator phase progressively diffuses over the whole limit cycle, their phase difference is locked, and such a phase correlation remains strong in the presence of thermal noise.

preprint2019arXiv

Possible Realization and Protection of Valley-Polarized Quantum Hall Effect in Mn/WS2

By using the first-principles calculations and model analyses, we found that the combination of defected tungsten disulfide monolayer and sparse manganese adsorption may give a KK` valley spin splitting up to 210 meV. This system also has a tunable magnetic anisotropy energy, a clean band gap, and an appropriate band alignment, with the Fermi level sitting right above the top of valence bands at the K-valleys. Therefore, it can be used for the realization of the valley-polarized anomalous Hall effect and for the exploration of other valley related physics without using optical methods. A protective environment can be formed by covering it with a hexagonal BN layer, without much disturbance to the benign properties of Mn/WS2.

preprint2016arXiv

Analytical Investigations on Carrier Phase Recovery in Dispersion-Unmanaged n-PSK Coherent Optical Communication Systems

Using coherent optical detection and digital signal processing, laser phase noise and equalization enhanced phase noise can be effectively mitigated using the feed-forward and feed-back carrier phase recovery approaches. In this paper, theoretical analyses of feed-back and feed-forward carrier phase recovery methods have been carried out in the long-haul high-speed n-level phase shift keying (n-PSK) optical fiber communication systems, involving a one-tap normalized least-mean-square (LMS) algorithm, a block-wise average algorithm, and a Viterbi-Viterbi algorithm. The analytical expressions for evaluating the estimated carrier phase and for predicting the bit-error-rate (BER) performance (such as the BER floors) have been presented and discussed in the n-PSK coherent optical transmission systems by considering both the laser phase noise and the equalization enhanced phase noise. The results indicate that the Viterbi-Viterbi carrier phase recovery algorithm outperforms the one-tap normalized LMS and the block-wise average algorithms for small phase noise variance (or effective phase noise variance), while the one-tap normalized LMS algorithm shows a better performance than the other two algorithms for large phase noise variance (or effective phase noise variance). In addition, the one-tap normalized LMS algorithm is more sensitive to the level of modulation formats.

preprint2016arXiv

Detection of Potential Transit Signals in 17 Quarters of Kepler Data: Results of the Final Kepler Mission Transiting Planet Search (DR25)

We present results of the final Kepler Data Processing Pipeline search for transiting planet signals in the full 17-quarter primary mission data set. The search includes a total of 198,709 stellar targets, of which 112,046 were observed in all 17 quarters and 86,663 in fewer than 17 quarters. We report on 17,230 targets for which at least one transit signature is identified that meets the specified detection criteria: periodicity, minimum of three observed transit events, detection statistic (i.e., signal-to-noise ratio) in excess of the search threshold, and passing grade on three statistical transit consistency tests. Light curves for which a transit signal is identified are iteratively searched for additional signatures after a limb-darkened transiting planet model is fitted to the data and transit events are removed. The search for additional planets adds 16,802 transit signals for a total of 34,032; this far exceeds the number of transit signatures identified in prior pipeline runs. There was a strategic emphasis on completeness over reliability for the final Kepler transit search. A comparison of the transit signals against a set of 3402 well-established, high-quality Kepler Objects of Interest yields a recovery rate of 99.8%. The high recovery rate must be weighed against a large number of false-alarm detections. We examine characteristics of the planet population implied by the transiting planet model fits with an emphasis on detections that would represent small planets orbiting in the habitable zone of their host stars.

preprint2016arXiv

Devaney chaos plus shadowing implies distributional chaos

We explore connections among the regional proximal relation, the asymptotic relation and the distal relation for a topological dynamical system with the shadowing property, and show that if a Devaney chaotic system has the shadowing property then it is distributionally chaotic.

preprint2016arXiv

Digital Adaptive Carrier Phase Estimation in Multi-Level Phase Shift Keying Coherent Optical Communication Systems

The analysis of adaptive carrier phase estimation is investigated in long-haul high speed n-level phase shift keying (n-PSK) optical fiber communication systems based on the one-tap normalized least-mean-square (LMS) algorithm. The close-form expressions for the estimated carrier phase and the bit-error-rate floor have been derived in the n-PSK coherent optical transmission systems. The results show that the one-tap normalized LMS algorithm performs pretty well in the carrier phase estimation, but will be less effective with the increment of modulation levels, in the compensation of both intrinsic laser phase noise and equalization enhanced phase noise.

preprint2016arXiv

Discriminating the effects of collapse models from environmental diffusion with levitated nanospheres

Collapse models postulate the existence of intrinsic noise which modifies quantum mechanics and is responsible for the emergence of macroscopic classicality. Assessing the validity of these models is extremely challenging because it is nontrivial to discriminate unambiguously their presence in experiments where other hardly controllable sources of noise compete to the overall decoherence. Here we provide a simple procedure able to probe the hypothetical presence of the collapse noise with a levitated nanosphere in a Fabry-Perot cavity. We show that the stationary state of the system is particularly sensitive, under specific experimental conditions, to the interplay between the trapping frequency, the cavity size, and the momentum diffusion induced by the collapse models, allowing to detect them even in the presence of standard environmental noises.

preprint2016arXiv

Frequency-Domain Chromatic Dispersion Equalization Using Overlap-Add Methods in Coherent Optical System

The frequency domain equalizers (FDEs) employing two types of overlap-add zero-padding (OLA-ZP) methods are applied to compensate the chromatic dispersion in a 112-Gbit/s non-return-to-zero polarization division multiplexed quadrature phase shift keying (NRZ-PDM-QPSK) coherent optical transmission system. Simulation results demonstrate that the OLA-ZP methods can achieve the same acceptable performance as the overlap-save method. The required minimum overlap (or zero-padding) in the FDE is derived, and the optimum fast Fourier transform length to minimize the computational complexity is also analyzed.

preprint2016arXiv

Graphical Representation for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification. HFR plays an important role in both biometrics research and industry. In spite of promising progresses achieved in recent years, HFR is still a challenging problem due to the difficulty to represent two heterogeneous images in a homogeneous manner. Existing HFR methods either represent an image ignoring the spatial information, or rely on a transformation procedure which complicates the recognition task. Considering these problems, we propose a novel graphical representation based HFR method (G-HFR) in this paper. Markov networks are employed to represent heterogeneous image patches separately, which takes the spatial compatibility between neighboring image patches into consideration. A coupled representation similarity metric (CRSM) is designed to measure the similarity between obtained graphical representations. Extensive experiments conducted on multiple HFR scenarios (viewed sketch, forensic sketch, near infrared image, and thermal infrared image) show that the proposed method outperforms state-of-the-art methods.

preprint2016arXiv

Monolayer MXenes: promising half-metals and spin gapless semiconductors

Half-metals and spin gapless semiconductors are promising candidates for spintronic applications due to the complete (100%) spin polarization of electrons around the Fermi level. Based on recent experimental and theoretical findings of graphene-like monolayer transition metal carbides and nitrides (also known as MXenes), we demonstrate from first-principles calculations that monolayer Ti2C and Ti2N exhibit nearly half-metallic ferromagnetism with the magnetic moments of 1.91 and 1.00 UB per formula unit, respectively, while monolayer V2C is a metal with instable antiferromagnetism, and monolayer V2N is a nonmagnetic metal. Interestingly, under a biaxial strain, there is a phase transition from nearly half-metal to truly half-metal, spin gapless semiconductor, and metal for monolayer Ti2C. Monolayer Ti2N is still a nearly half-metal under a proper biaxial strain. Large magnetic moments can be induced by the biaxial tensile and compressive strains for monolayer V2C and V2N, respectively. We also show that the structures of these four monolayer MXenes are stable according to the calculated formation energy and phonon spectrum. Our investigations suggest that, unlike monolayer graphene, monolayer MXenes Ti2C and Ti2N without vacancy, doping or external electric field exhibit intrinsic magnetism, especially the half-metallic ferromagnetism and spin gapless semiconductivity, which will stimulate further studies on possible spintronic applications for new two-dimensional materials of MXenes.

preprint2016arXiv

Phase Noise Influence in Coherent Optical OFDM Systems with RF Pilot Tone: Digital IFFT Multiplexing and FFT Demodulation

We present a comparative study of the influence of dispersion induced phase noise for CO-OFDM systems using Tx channel multiplexing and Rx matched filter (analogue hardware based); and FFT multiplexing/IFFT demultiplexing techniques (software based). An RF carrier pilot tone is used to mitigate the phase noise influence. From the analysis, it appears that the phase noise influence for the two OFDM implementations is very similar. The software based system provides a method for a rigorous evaluation of the phase noise variance caused by Common Phase Error (CPE) and Inter-Carrier Interference (ICI) and this, in turns, leads to a BER specification. Numerical results focus on a CO-OFDM system with 1GS/s QPSK channel modulation. Worst case BER results are evaluated and compared to the BER of a QPSK system with the same capacity as the OFDM implementation. Results are evaluated as a function of transmission distance, and for the QPSK system the influence of equalization enhanced phase noise (EEPN) is included. For both types of systems, the phase noise variance increases significantly with increasing transmission distance. An important and novel observation is that the two types of systems have very closely the same BER as a function of transmission distance for the same capacity. For the high capacity QPSK implementation, the increase in BER is due to EEPN, whereas for the OFDM approach it is due to the dispersion caused walk-off of the RF pilot tone relative to the OFDM signal channels. For a total capacity of 400 Gb/s, the transmission distance to have the BER < 10-4 is less than 277 km.

preprint2016arXiv

Phase Noise Influence in Optical OFDM Systems employing RF Pilot Tone for Phase Noise Cancellation

For coherent and direct-detection Orthogonal Frequency Division Multiplexed (OFDM) systems employing radio frequency (RF) pilot tone phase noise cancellation the influence of laser phase noise is evaluated. Novel analytical results for the common phase error and for the (modulation dependent) inter carrier interference are evaluated based upon Gaussian statistics for the laser phase noise. In the evaluation it is accounted for that the laser phase noise is filtered in the correlation signal detection. Numerical results are presented for OFDM systems with 4 and 16 PSK modulation, 200 OFDM bins and baud rate of 1 GS/s. It is found that about 225 km transmission is feasible for the coherent 4PSK-OFDM system over normal (G.652) fiber.

preprint2016arXiv

Planetary Candidates Observed by Kepler. VII. The First Fully Uniform Catalog Based on The Entire 48 Month Dataset (Q1-Q17 DR24)

We present the seventh Kepler planet candidate catalog, which is the first to be based on the entire, uniformly processed, 48 month Kepler dataset. This is the first fully automated catalog, employing robotic vetting procedures to uniformly evaluate every periodic signal detected by the Q1-Q17 Data Release 24 (DR24) Kepler pipeline. While we prioritize uniform vetting over the absolute correctness of individual objects, we find that our robotic vetting is overall comparable to, and in most cases is superior to, the human vetting procedures employed by past catalogs. This catalog is the first to utilize artificial transit injection to evaluate the performance of our vetting procedures and quantify potential biases, which are essential for accurate computation of planetary occurrence rates. With respect to the cumulative Kepler Object of Interest (KOI) catalog, we designate 1,478 new KOIs, of which 402 are dispositioned as planet candidates (PCs). Also, 237 KOIs dispositioned as false positives (FPs) in previous Kepler catalogs have their disposition changed to PC and 118 PCs have their disposition changed to FP. This brings the total number of known KOIs to 8,826 and PCs to 4,696. We compare the Q1-Q17 DR24 KOI catalog to previous KOI catalogs, as well as ancillary Kepler catalogs, finding good agreement between them. We highlight new PCs that are both potentially rocky and potentially in the habitable zone of their host stars, many of which orbit solar-type stars. This work represents significant progress in accurately determining the fraction of Earth-size planets in the habitable zone of Sun-like stars. The full catalog is publicly available at the NASA Exoplanet Archive.

preprint2016arXiv

Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition

Face images captured in heterogeneous environments, e.g., sketches generated by the artists or composite-generation software, photos taken by common cameras and infrared images captured by corresponding infrared imaging devices, usually subject to large texture (i.e., style) differences. This results in heavily degraded performance of conventional face recognition methods in comparison with the performance on images captured in homogeneous environments. In this paper, we propose a novel sparse graphical representation based discriminant analysis (SGR-DA) approach to address aforementioned face recognition in heterogeneous scenarios. An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors. To handle the complex facial structure and further improve the discriminability, a spatial partition-based discriminant analysis framework is presented to refine the adaptive sparse vectors for face matching. We conducted experiments on six commonly used heterogeneous face datasets and experimental results illustrate that our proposed SGR-DA approach achieves superior performance in comparison with state-of-the-art methods.

preprint2016arXiv

Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics

Face sketch synthesis has wide applications ranging from digital entertainments to law enforcements. Objective image quality assessment scores and face recognition accuracy are two mainly used tools to evaluate the synthesis performance. In this paper, we proposed a synthesized face sketch recognition framework based on full-reference image quality assessment metrics. Synthesized sketches generated from four state-of-the-art methods are utilized to test the performance of the proposed recognition framework. For the image quality assessment metrics, we employed the classical structured similarity index metric and other three prevalent metrics: visual information fidelity, feature similarity index metric and gradient magnitude similarity deviation. Extensive experiments compared with baseline methods illustrate the effectiveness of the proposed synthesized face sketch recognition framework. Data and implementation code in this paper are available online at www.ihitworld.com/WNN/IQA_Sketch.zip.

preprint2015arXiv

A Framework of Constructions of Minimal Storage Regenerating Codes with the Optimal Access/Update Property

In this paper, we present a generic framework for constructing systematic minimum storage regenerating codes with two parity nodes based on the invariant subspace technique. Codes constructed in our framework not only contain some best known codes as special cases, but also include some new codes with key properties such as the optimal access property and the optimal update property. In particular, for a given storage capacity of an individual node, one of the new codes has the largest number of systematic nodes and two of the new codes have the largest number of systematic nodes with the optimal update property.

preprint2015arXiv

Automatic Classification of Kepler Planetary Transit Candidates

In the first three years of operation the Kepler mission found 3,697 planet candidates from a set of 18,406 transit-like features detected on over 200,000 distinct stars. Vetting candidate signals manually by inspecting light curves and other diagnostic information is a labor intensive effort. Additionally, this classification methodology does not yield any information about the quality of planet candidates; all candidates are as credible as any other candidate. The torrent of exoplanet discoveries will continue after Kepler as there will be a number of exoplanet surveys that have an even broader search area. This paper presents the application of machine-learning techniques to the classification of exoplanet transit-like signals present in the \Kepler light curve data. Transit-like detections are transformed into a uniform set of real-numbered attributes, the most important of which are described in this paper. Each of the known transit-like detections is assigned a class of planet candidate; astrophysical false positive; or systematic, instrumental noise. We use a random forest algorithm to learn the mapping from attributes to classes on this training set. The random forest algorithm has been used previously to classify variable stars; this is the first time it has been used for exoplanet classification. We are able to achieve an overall error rate of 5.85% and an error rate for classifying exoplanets candidates of 2.81%.

preprint2015arXiv

Calculation of the coupled betatron motion introduced by cooler's solenoids of CSRm

Several solenoids are usually installed in electron cooler device to guide the motion of the electron beam in the cooler. However, the solenoids also have influence to the ion beam in the cooler storage ring. The transverse motion of the ion beam in storage ring will become coupled, if the solenoids installed in the electron cooler are not compensated perfectly. In this paper, the coupled transverse motion due to the uncompensated cooler's solenoids of CSRm (The main storage ring in the IMP, Lan Zhou, China) is studied, and the coupled beam envelopes are calculated by a new method.

preprint2015arXiv

Computational dynamics of acoustically-driven microsphere systems

We propose a computational framework for the self-consistent dynamics of a microsphere system driven by a pulsed acoustic field in an ideal fluid. Our framework combines a molecular dynamics integrator describing the dynamics of the microsphere system with a time-dependent integral equation solver for the acoustic field that makes use of fields represented as surface expansions in spherical harmonic basis functions. The presented approach allows us to describe the inter-particle interaction induced by the field as well as the dynamics of trapping in counter-propagating acoustic pulses. The integral equation formulation leads to equations of motion for the microspheres describing the effect of non-dissipative drag forces. We show (1) that the field-induced interactions between the microspheres give rise to effective dipolar interactions, with effective dipoles defined by their velocities, and (2) that the dominant effect of an ultrasound pulse through a cloud of microspheres gives rise mainly to a translation of the system, though we also observe both expansion and contraction of the cloud determined by the initial system geometry.

preprint2015arXiv

Detection of Potential Transit Signals in 17 Quarters of Kepler Mission Data

We present the results of a search for potential transit signals in the full 17-quarter data set collected during Kepler's primary mission that ended on May 11, 2013, due to the on-board failure of a second reaction wheel needed to maintain high precision, fixed, pointing. The search includes a total of 198,646 targets, of which 112,001 were observed in every quarter and 86,645 were observed in a subset of the 17 quarters. We find a total of 12,669 targets that contain at least one signal that meets our detection criteria: periodicity of the signal, a minimum of three transit events, an acceptable signal-to-noise ratio, and four consistency tests that suppress false positives. Each target containing at least one transit-like pulse sequence is searched repeatedly for other signals that meet the detection criteria, indicating a multiple planet system. This multiple planet search adds an additional 7,698 transit-like signatures for a total of 20,367. Comparison of this set of detected signals with a set of known and vetted transiting planet signatures in the Kepler field of view shows that the recovery rate of the search is 90.3%. We review ensemble properties of the detected signals and present various metrics useful in validating these potential planetary signals. We highlight previously undetected planetary candidates, including several small potential planets in the habitable zone of their host stars.

preprint2015arXiv

Discovery and Validation of Kepler-452b: A 1.6-Re Super Earth Exoplanet in the Habitable Zone of a G2 Star

We report on the discovery and validation of Kepler-452b, a transiting planet identified by a search through the 4 years of data collected by NASA's Kepler Mission. This possibly rocky 1.63$^{+0.23}_{-0.20}$ R$_\oplus$ planet orbits its G2 host star every 384.843$^{+0.007}_{0.012}$ days, the longest orbital period for a small (R$_p$ < 2 R$_\oplus$) transiting exoplanet to date. The likelihood that this planet has a rocky composition lies between 49% and 62%. The star has an effective temperature of 5757$\pm$85 K and a log g of 4.32$\pm$0.09. At a mean orbital separation of 1.046$^{+0.019}_{-0.015}$ AU, this small planet is well within the optimistic habitable zone of its star (recent Venus/early Mars), experiencing only 10% more flux than Earth receives from the Sun today, and slightly outside the conservative habitable zone (runaway greenhouse/maximum greenhouse). The star is slightly larger and older than the Sun, with a present radius of 1.11$^{+0.15}_{-0.09}$ R$_\odot$ and an estimated age of 6 Gyr. Thus, Kepler-452b has likely always been in the habitable zone and should remain there for another 3 Gyr.

preprint2015arXiv

Levels of generalized expansiveness

We study a class of generalized expansive dynamical systems for which at most countable orbits can be accompanied by an arbitrary given orbit. Examples of different levels of generalized expansiveness are constructed. When the dynamical system is countable, a characterization of n-expansiveness is given for any natural number n, and as a consequence examples of dynamical systems with van der Waerden depth equal to any given countable ordinal are demonstrated, which solves open questions existing in the literature.

preprint2015arXiv

Optimal Exact Repair Strategy for the Parity Nodes of the $(k+2,k)$ Zigzag Code

In this paper, we reinterprets the $(k+2,k)$ Zigzag code in coding matrix and then propose an optimal exact repair strategy for its parity nodes, whose repair disk I/O approaches a lower bound derived in this paper.

preprint2015arXiv

Planetary Candidates Observed by Kepler V: Planet Sample from Q1-Q12 (36 Months)

The Kepler mission discovered 2842 exoplanet candidates with 2 years of data. We provide updates to the Kepler planet candidate sample based upon 3 years (Q1-Q12) of data. Through a series of tests to exclude false-positives, primarily caused by eclipsing binary stars and instrumental systematics, 855 additional planetary candidates have been discovered, bringing the total number known to 3697. We provide revised transit parameters and accompanying posterior distributions based on a Markov Chain Monte Carlo algorithm for the cumulative catalogue of Kepler Objects of Interest. There are now 130 candidates in the cumulative catalogue that receive less than twice the flux the Earth receives and more than 1100 have a radius less than 1.5 Rearth. There are now a dozen candidates meeting both criteria, roughly doubling the number of candidate Earth analogs. A majority of planetary candidates have a high probability of being bonafide planets, however, there are populations of likely false-positives. We discuss and suggest additional cuts that can be easily applied to the catalogue to produce a set of planetary candidates with good fidelity. The full catalogue is publicly available at the NASA Exoplanet Archive.

preprint2015arXiv

Planetary Candidates Observed by Kepler VI: Planet Sample from Q1-Q16 (47 Months)

\We present the sixth catalog of Kepler candidate planets based on nearly 4 years of high precision photometry. This catalog builds on the legacy of previous catalogs released by the Kepler project and includes 1493 new Kepler Objects of Interest (KOIs) of which 554 are planet candidates, and 131 of these candidates have best fit radii <1.5 R_earth. This brings the total number of KOIs and planet candidates to 7305 and 4173 respectively. We suspect that many of these new candidates at the low signal-to-noise limit may be false alarms created by instrumental noise, and discuss our efforts to identify such objects. We re-evaluate all previously published KOIs with orbital periods of >50 days to provide a consistently vetted sample that can be used to improve planet occurrence rate calculations. We discuss the performance of our planet detection algorithms, and the consistency of our vetting products. The full catalog is publicly available at the NASA Exoplanet Archive.

preprint2015arXiv

Steady-state nested entanglement structures in harmonic chains with single-site squeezing manipulation

We show that a squeezed bath, that acts on the central element of a harmonic chain, can drive the whole system to a steady state featuring a series of nested entangled pairs of oscillators that, ideally, covers the whole chain regardless of its size. We study how to realize this effect in various physical implementations, including optomechanical and superconducting devices, using currently available technologies. In these cases no squeezed fields are actually needed, and the squeezed bath is, instead, simulated by quantum reservoir engineering with bichromatic drives.

preprint2015arXiv

Subdivision based Isogeometric Analysis technique for Electric Field Integral Equations for Simply Connected Structures

The analysis of electromagnetic scattering has long been performed on a discrete representation of the geometry. This representation is typically continuous but {\em not} differentiable. The need to define physical quantities on this geometric representation has led to development of sets of basis functions that need to satisfy constraints at the boundaries of the elements/tesselations (viz., continuity of normal or tangential components across element boundaries). For electromagnetics, these result in either curl/div-conforming basis sets. The geometric representation used for analysis is in stark contrast with that used for design, wherein the surface representation is higher order differentiable. Using this representation for {\em both} geometry and physics on geometry has several advantages, and is eludicated in Hughes et al., Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement, Computer Methods in Applied Mechanics and Engineering 194 (39-41) (2005). Until now, a bulk of the literature on isogeometric methods have been limited to solid mechanics, with some effort to create NURBS based basis functions for electromagnetic analysis. In this paper, we present the first complete isogeometry solution methodology for the electric field integral equation as applied to simply connected structures. This paper systematically proceeds through surface representation using subdivision, definition of vector basis functions on this surface, to fidelity in the solution of integral equations. We also present techniques to stabilize the solution at low frequencies, and impose a Calderón preconditioner. Several results presented serve to validate the proposed approach as well as demonstrate some of its capabilities.

preprint2015arXiv

Terrestrial Planet Occurrence Rates for the Kepler GK Dwarf Sample

We measure planet occurrence rates using the planet candidates discovered by the Q1-Q16 Kepler pipeline search. This study examines planet occurrence rates for the Kepler GK dwarf target sample for planet radii, 0.75<Rp<2.5 Rearth, and orbital periods, 50<Porb<300 days, with an emphasis on a thorough exploration and identification of the most important sources of systematic uncertainties. Integrating over this parameter space, we measure an occurrence rate of F=0.77 planets per star, with an allowed range of 0.3<F<1.9. The allowed range takes into account both statistical and systematic uncertainties, and values of F beyond the allowed range are significantly in disagreement with our analysis. We generally find higher planet occurrence rates and a steeper increase in planet occurrence rates towards small planets than previous studies of the Kepler GK dwarf sample. Through extrapolation, we find that the one year orbital period terrestrial planet occurrence rate, zeta_1=0.1, with an allowed range of 0.01<zeta_1<2, where zeta_1 is defined as the number of planets per star within 20% of the Rp and Porb of Earth. For G dwarf hosts, the zeta_1 parameter space is a subset of the larger eta_earth parameter space, thus zeta_1 places a lower limit on eta_earth for G dwarf hosts. From our analysis, we identify the leading sources of systematics impacting Kepler occurrence rate determinations as: reliability of the planet candidate sample, planet radii, pipeline completeness, and stellar parameters.

preprint2015arXiv

The Side-Effects of the Space Charge Field Introduced by Hollow Electron Beam in the Electron Cooler of CSRm

Electron cooler is used to improve the quality of the beam in synchrotron, however it also introduces nonlinear electromagnetic field, which cause tuneshift, tunespread and may drive resonances leading to beam loss. In this paper the tuneshift and the tunespread caused by nonlinear electromagnetic field of the hollow electron beam was investigated, and the resonance driving terms of the nonlinear electromagnetic field was analysed. The differences were presented comparing with the solid electron beam. The calculations were performed for $^{238}U^{32+}$ ions of energy 1.272MeV stored in CSRm, using the parameters given in table1. The conclusion is that in this situation nonlinear field caused by the hollow electron beam do not lead to serious resonances.

preprint2015arXiv

Tracing the Ingredients for a Habitable Earth from Interstellar Space through Planet Formation

We use the C/N ratio as a monitor of the delivery of key ingredients of life to nascent terrestrial worlds. Total elemental C and N contents, and their ratio, are examined for the interstellar medium, comets, chondritic meteorites and terrestrial planets; we include an updated estimate for the Bulk Silicate Earth (C/N = 49.0 +/- 9.3). Using a kinetic model of disk chemistry, and the sublimation/condensation temperatures of primitive molecules, we suggest that organic ices and macro-molecular (refractory or carbonaceous dust) organic material are the likely initial C and N carriers. Chemical reactions in the disk can produce nebular C/N ratios of ~1-12, comparable to those of comets and the low end estimated for planetesimals. An increase of the C/N ratio is traced between volatile-rich pristine bodies and larger volatile-depleted objects subjected to thermal/accretional metamorphism. The C/N ratios of the dominant materials accreted to terrestrial planets should therefore be higher than those seen in carbonaceous chondrites or comets. During planetary formation, we explore scenarios leading to further volatile loss and associated C/N variations owing to core formation and atmospheric escape. Key processes include relative enrichment of nitrogen in the atmosphere and preferential sequestration of carbon by the core. The high C/N BSE ratio therefore is best satisfied by accretion of thermally processed objects followed by large-scale atmospheric loss. These two effects must be more profound if volatile sequestration in the core is effective. The stochastic nature of these processes hints that the surface/atmospheric abundances of biosphere-essential materials will likely be variable.

preprint2015arXiv

When all closed subsets are recurrent?

In the paper we study relations of rigidity, equicontinuity and pointwise recurrence between a t.d.s. $(X,T)$ and the t.d.s. $(K(X),T_K)$ induced on the hyperspace $K(X)$ of all compact subsets of $X$, and provide some characterizations. Among other examples, we construct a minimal, non-equicontinuous, distal and uniformly rigid t.d.s. and a t.d.s. which has dense small periodic sets but does not have dense distal points, solving that way open questions existing in the literature.

preprint2014arXiv

Architecture of Kepler's Multi-transiting Systems: II. New investigations with twice as many candidates

We report on the orbital architectures of Kepler systems having multiple planet candidates identified in the analysis of data from the first six quarters of Kepler data and reported by Batalha et al. (2013). These data show 899 transiting planet candidates in 365 multiple-planet systems and provide a powerful means to study the statistical properties of planetary systems. Using a generic mass-radius relationship, we find that only two pairs of planets in these candidate systems (out of 761 pairs total) appear to be on Hill-unstable orbits, indicating ~96% of the candidate planetary systems are correctly interpreted as true systems. We find that planet pairs show little statistical preference to be near mean-motion resonances. We identify an asymmetry in the distribution of period ratios near first-order resonances (e.g., 2:1, 3:2), with an excess of planet pairs lying wide of resonance and relatively few lying narrow of resonance. Finally, based upon the transit duration ratios of adjacent planets in each system, we find that the interior planet tends to have a smaller transit impact parameter than the exterior planet does. This finding suggests that the mode of the mutual inclinations of planetary orbital planes is in the range 1.0-2.2 degrees, for the packed systems of small planets probed by these observations.

preprint2014arXiv

Detection of Potential Transit Signals in Sixteen Quarters of Kepler Mission Data

We present the results of a search for potential transit signals in four years of photometry data acquired by the Kepler Mission. The targets of the search include 111,800 stars which were observed for the entire interval and 85,522 stars which were observed for a subset of the interval. We found that 9,743 targets contained at least one signal consistent with the signature of a transiting or eclipsing object, where the criteria for detection are periodicity of the detected transits, adequate signal-to-noise ratio, and acceptance by a number of tests which reject false positive detections. When targets that had produced a signal were searched repeatedly, an additional 6,542 signals were detected on 3,223 target stars, for a total of 16,285 potential detections. Comparison of the set of detected signals with a set of known and vetted transit events in the Kepler field of view shows that the recovery rate for these signals is 96.9%. The ensemble properties of the detected signals are reviewed.

preprint2014arXiv

Fabrication of superconducting nanowires based on ultra-thin Nb films by means of nanoimprint lithography

Nanoimprint lithography (NIL) is an attractive nonconventional lithographic technique in the fabrication of superconducting nanowires for superconducting nanowire single-photon detectors (SNSPDs) with large effective detection areas or multi-element devices consisting of hundreds of SNSPDs, due to its low cost and high throughput. In this work, NIL was used to pattern superconducting nanowires with meander-type structures based on ultra-thin (~4 nm) Nb films deposited by DC-magnetron sputtering at room temperature. A combination of thermal-NIL and UV-NIL was exploited to transfer the meander pattern from the imprint hard mold to Nb films. The hard mold based on Si wafer was defined by e-beam lithography (EBL), which was almost nonexpendable due to the application of IPS as a soft mold to transfer the patterns to the imprint resist in the NIL process. The specimens fabricated by NIL keep good superconducting properties which are comparable to that by conventional EBL process.

preprint2014arXiv

More nonlocality with less entanglement in a tripartite atom-optomechanical system

We study quantum effects in hybrid atomic optomechanics in a system comprising a cloud of atoms and a mobile mirror mediated by a single-mode cavity. Tripartite nonlocality is observed in the atom-light-mirror system, as demonstrated by the violation of the Mermin-Klyshko (MK) inequality. It has been shown [C. Genes, et al., PRA 77, 050307 (R) (2008)] that tripartite entanglement is optimized when the cavity is resonant with the anti-Stokes sideband of the driving laser and the atomic frequency matches the Stokes one. However, we show that this is not the case for the nonlocality. The MK function achieves {\it minima} when the atoms are resonant with both the Stokes and anti-Stokes sidebands, and unexpectedly, we find violation of the MK inequality only in a parameter region where entanglement is far from being maximum. A negative relation exists between nonlocality and entanglement with consideration of the possibility of bipartite nonlocality in the violation of the MK inequality. We also study the non-classicality of the mirror by post-selected measurements, e.g. Geiger-like detection, on the cavity and/or the atoms. We show that with feasible parameters Geiger-like detection on the atoms can effectively induce mechanical non-classicality.

preprint2014arXiv

Recurrence properties and disjointness on the induced spaces

A topological dynamical system induces two natural systems, one is on the hyperspace and the other one is on the probability space. The connection among some dynamical properties on the original space and on the induced spaces are investigated. Particularly, a minimal weakly mixing system which induces a $P$-system on the probability space is constructed and some disjointness result is obtained.

preprint2014arXiv

The Generalized Method of Moments for Electromagnetic Boundary Integral Equations

The Generalized Method of Moments (GMM) is a partition of unity based technique for solving electromagnetic and acoustic boundary integral equations. Past work on the GMM for electromagnetics was confined to geometries modeled by piecewise flat tessellations and suffered from spurious internal line charges. In the present article, we redesign the GMM scheme and demonstrate its ability to model scattering from PEC scatterers composed of mixtures of smooth and non-smooth geometrical features. Furthermore, we demonstrate that because the partition of unity provides function and effective geometrical continuity between patches, the GMM permits mixtures of local geometry descriptions and approximation function spaces with significantly more freedom than traditional moment methods.

preprint2014arXiv

Time-dependent Debye-Mie Series Solutions for Electromagnetic Scattering

Frequency domain Mie solutions to scattering from spheres have been used for a long time. However, deriving their transient analogue is a challenge as it involves an inverse Fourier transform of the spherical Hankel functions (and their derivatives) that are convolved with inverse Fourier transforms of spherical Bessel functions (and their derivatives). Series expansion of these convolutions are highly oscillatory (therefore, poorly convergent) and unstable. Indeed, the literature on numerical computation of this convolution is very sparse. In this paper, we present a novel quasi-analytical approach to computing transient Mie scattering that is both stable and rapidly convergent. The approach espoused here is to use vector tesseral harmonics as basis function for the currents in time domain integral equations together with a novel addition theorem for the Green's functions that renders these expansions stable. This procedure results in an orthogonal, spatially-meshfree and singularity-free system, giving us a set of one dimensional Volterra Integral equations. Time-dependent multipole coefficients for each mode are obtained via a time marching procedure. Finally, several numerical examples are presented to show the accuracy and stability the proposed method.

preprint2013arXiv

A Search for Exozodiacal Clouds with Kepler

Planets embedded within dust disks may drive the formation of large scale clumpy dust structures by trapping dust into resonant orbits. Detection and subsequent modeling of the dust structures would help constrain the mass and orbit of the planet and the disk architecture, give clues to the history of the planetary system, and provide a statistical estimate of disk asymmetry for future exoEarth-imaging missions. Here we present the first search for these resonant structures in the inner regions of planetary systems by analyzing the light curves of hot Jupiter planetary candidates identified by the Kepler mission. We detect only one candidate disk structure associated with KOI 838.01 at the 3-sigma confidence level, but subsequent radial velocity measurements reveal that KOI 838.01 is a grazing eclipsing binary and the candidate disk structure is a false positive. Using our null result, we place an upper limit on the frequency of dense exozodi structures created by hot Jupiters. We find that at the 90% confidence level, less than 21% of Kepler hot Jupiters create resonant dust clumps that lead and trail the planet by ~90 degrees with optical depths >~5*10^-6, which corresponds to the resonant structure expected for a lone hot Jupiter perturbing a dynamically cold dust disk 50 times as dense as the zodiacal cloud.

preprint2013arXiv

A sub-Mercury-sized exoplanet

Since the discovery of the first exoplanet we have known that other planetary systems can look quite unlike our own. However, until recently we have only been able to probe the upper range of the planet size distribution. The high precision of the Kepler space telescope has allowed us to detect planets that are the size of Earth and somewhat smaller, but no previous planets have been found that are smaller than those we see in our own Solar System. Here we report the discovery of a planet significantly smaller than Mercury. This tiny planet is the innermost of three planets that orbit the Sun-like host star, which we have designated Kepler-37. Owing to its extremely small size, similar to that of Earth's Moon, and highly irradiated surface, Kepler-37b is probably a rocky planet with no atmosphere or water, similar to Mercury.

preprint2013arXiv

A super-Earth-sized planet orbiting in or near the habitable zone around Sun-like star

We present the discovery of a super-earth-sized planet in or near the habitable zone of a sun-like star. The host is Kepler-69, a 13.7 mag G4V-type star. We detect two periodic sets of transit signals in the three-year flux time series of Kepler-69, obtained with the Kepler spacecraft. Using the very high precision Kepler photometry, and follow-up observations, our confidence that these signals represent planetary transits is >99.1%. The inner planet, Kepler-69b, has a radius of 2.24+/-0.4 Rearth and orbits the host star every 13.7 days. The outer planet, Kepler-69c, is a super-Earth-size object with a radius of 1.7+/-0.3 Rearth and an orbital period of 242.5 days. Assuming an Earth-like Bond albedo, Kepler-69c has an equilibrium temperature of 299 +/- 19 K, which places the planet close to the habitable zone around the host star. This is the smallest planet found by Kepler to be orbiting in or near habitable zone of a Sun-like star and represents an important step on the path to finding the first true Earth analog.

preprint2013arXiv

Detection of Potential Transit Signals in the First Twelve Quarters of Kepler Mission Data

We present the results of a search for potential transit signals in the first three years of photometry data acquired by the Kepler Mission. The targets of the search include 112,321 targets which were observed over the full interval and an additional 79,992 targets which were observed for a subset of the full interval. From this set of targets we find a total of 11,087 targets which contain at least one signal which meets the Kepler detection criteria: those criteria are periodicity of the signal, an acceptable signal-to-noise ratio, and three tests which reject false positives. Each target containing at least one detected signal is then searched repeatedly for additional signals, which represent multi-planet systems of transiting planets. When targets with multiple detections are considered, a total of 18,406 potential transiting planet signals are found in the Kepler Mission dataset. The detected signals are dominated by events with relatively low signal-to-noise ratios and by events with relatively short periods. The distribution of estimated transit depths appears to peak in the range between 20 and 30 parts per million, with a few detections down to fewer than 10 parts per million. The detections exhibit signal-to-noise ratios from 7.1 sigma, which is the lower cut-off for detections, to over 10,000 sigma, and periods ranging from 0.5 days, which is the shortest period searched, to 525 days, which is the upper limit of achievable periods given the length of the data set and the requirement that all detections include at least 3 transits. The detected signals are compared to a set of known transit events in the Kepler field of view, many of which were identified by alternative methods; the comparison shows that the current search recovery rate for targets with known transit events is 98.3%.

preprint2013arXiv

Enhancing non-classicality in mechanical systems

We study the effects of post-selection measurements on both the non-classicality of the state of a mechanical oscillator and the entanglement between two mechanical systems that are part of a distributed optomechanical network. We address the cases of both Gaussian and non-Gaussian measurements, identifying the values of the parameters for which simple photon-counting and Geiger-like measurements are effective in distilling a strongly non-classical mechanical state and enhancing the purely mechanical entanglement between two elements of the network.

preprint2013arXiv

New Repair strategy of Hadamard Minimum Storage Regenerating Code for Distributed Storage System

The newly presented $(k+2,k)$ Hadamard minimum storage regenerating (MSR) code is the first class of high rate storage code with optimal repair property for all single node failures. In this paper, we propose a new simple repair strategy, which can considerably reduces the computation load of the node repair in contrast to the original one.

preprint2013arXiv

Planetary Candidates Observed by Kepler IV: Planet Sample From Q1-Q8 (22 Months)

We provide updates to the Kepler planet candidate sample based upon nearly two years of high-precision photometry (i.e., Q1-Q8). From an initial list of nearly 13,400 Threshold Crossing Events (TCEs), 480 new host stars are identified from their flux time series as consistent with hosting transiting planets. Potential transit signals are subjected to further analysis using the pixel-level data, which allows background eclipsing binaries to be identified through small image position shifts during transit. We also re-evaluate Kepler Objects of Interest (KOI) 1-1609, which were identified early in the mission, using substantially more data to test for background false positives and to find additional multiple systems. Combining the new and previous KOI samples, we provide updated parameters for 2,738 Kepler planet candidates distributed across 2,017 host stars. From the combined Kepler planet candidates, 472 are new from the Q1-Q8 data examined in this study. The new Kepler planet candidates represent ~40% of the sample with Rp~1 Rearth and represent ~40% of the low equilibrium temperature (Teq<300 K) sample. We review the known biases in the current sample of Kepler planet candidates relevant to evaluating planet population statistics with the current Kepler planet candidate sample.

preprint2013arXiv

Socially-Aware Networking: A Survey

The widespread proliferation of handheld devices enables mobile carriers to be connected at anytime and anywhere. Meanwhile, the mobility patterns of mobile devices strongly depend on the users' movements, which are closely related to their social relationships and behaviors. Consequently, today's mobile networks are becoming increasingly human centric. This leads to the emergence of a new field which we call socially-aware networking (SAN). One of the major features of SAN is that social awareness becomes indispensable information for the design of networking solutions. This emerging paradigm is applicable to various types of networks (e.g. opportunistic networks, mobile social networks, delay tolerant networks, ad hoc networks, etc) where the users have social relationships and interactions. By exploiting social properties of nodes, SAN can provide better networking support to innovative applications and services. In addition, it facilitates the convergence of human society and cyber physical systems. In this paper, for the first time, to the best of our knowledge, we present a survey of this emerging field. Basic concepts of SAN are introduced. We intend to generalize the widely-used social properties in this regard. The state-of-the-art research on SAN is reviewed with focus on three aspects: routing and forwarding, incentive mechanisms and data dissemination. Some important open issues with respect to mobile social sensing and learning, privacy, node selfishness and scalability are discussed.

preprint2013arXiv

Source Delay in Mobile Ad Hoc Networks

Source delay, the time a packet experiences in its source node, serves as a fundamental quantity for delay performance analysis in networks. However, the source delay performance in highly dynamic mobile ad hoc networks (MANETs) is still largely unknown by now. This paper studies the source delay in MANETs based on a general packet dispatching scheme with dispatch limit $f$ (PD-$f$ for short), where a same packet will be dispatched out up to $f$ times by its source node such that packet dispatching process can be flexibly controlled through a proper setting of $f$. We first apply the Quasi-Birth-and-Death (QBD) theory to develop a theoretical framework to capture the complex packet dispatching process in PD-$f$ MANETs. With the help of the theoretical framework, we then derive the cumulative distribution function as well as mean and variance of the source delay in such networks. Finally, extensive simulation and theoretical results are provided to validate our source delay analysis and illustrate how source delay in MANETs are related to network parameters.

preprint2012arXiv

Detection of Potential Transit Signals in the First Three Quarters of Kepler Mission Data

We present the results of a search for potential transit signals in the first three quarters of photometry data acquired by the Kepler Mission. The targets of the search include 151,722 stars which were observed over the full interval and an additional 19,132 stars which were observed for only 1 or 2 quarters. From this set of targets we find a total of 5,392 detections which meet the Kepler detection criteria: those criteria are periodicity of the signal, an acceptable signal-to-noise ratio, and a composition test which rejects spurious detections which contain non-physical combinations of events. The detected signals are dominated by events with relatively low signal-to-noise ratio and by events with relatively short periods. The distribution of estimated transit depths appears to peak in the range between 40 and 100 parts per million, with a few detections down to fewer than 10 parts per million. The detected signals are compared to a set of known transit events in the Kepler field of view which were derived by a different method using a longer data interval; the comparison shows that the current search correctly identified 88.1% of the known events. A tabulation of the detected transit signals, examples which illustrate the analysis and detection process, a discussion of future plans and open, potentially fruitful, areas of further research are included.

preprint2012arXiv

Direct entropy determination and application to artificial spin ice

From thermodynamic origins, the concept of entropy has expanded to a range of statistical measures of uncertainty, which may still be thermodynamically significant. However, laboratory measurements of entropy continue to rely on direct measurements of heat. New technologies that can map out myriads of microscopic degrees of freedom suggest direct determination of configurational entropy by counting in systems where it is thermodynamically inaccessible, such as granular and colloidal materials, proteins and lithographically fabricated nanometre-scale arrays. Here, we demonstrate a conditional-probability technique to calculate entropy densities of translation-invariant states on lattices using limited configuration data on small clusters, and apply it to arrays of interacting nanometre-scale magnetic islands (artificial spin ice). Models for statistically disordered systems can be assessed by applying the method to relative entropy densities. For artificial spin ice, this analysis shows that nearest-neighbour correlations drive longer-range ones.

preprint2012arXiv

Driven optomechanical systems for mechanical entanglement distribution

We consider the distribution of entanglement from a multi-mode optical driving source to a network of remote and independent optomechanical systems. By focusing on the tripartite case, we analyse the effects that the features of the optical input states have on the degree and sharing-structure of the distributed, fully mechanical, entanglement. This study, which is conducted looking at the mechanical steady-state, highlights the structure of the entanglement distributed among the nodes and determines the relative efficiency between bipartite and tripartite entanglement transfer. We discuss a few open points, some of which directed towards the bypassing of such limitations.

preprint2012arXiv

Magnetization states and switching in narrow-gapped ferromagnetic nanorings

We study permalloy nanorings that are lithographically fabricated with narrow gaps that break the rotational symmetry of the ring while retaining the vortex ground state, using both micromagnetic simulations and magnetic force microscopy (MFM). The vortex chirality in these structures can be readily set with an in-plane magnetic field and easily probed by MFM due to the field associated with the gap, suggesting such rings for possible applications in storage technologies. We find that the gapped ring edge characteristics (i.e., edge profile and gap shape) are critical in determining the magnetization switching field, thus elucidating an essential parameter in the controls of devices that might incorporate such structures.

preprint2012arXiv

Non-Markovian effects on non-locality of a qubit-oscillator system

Non-Markovian evolutions are responsible for a wide variety of physically interesting effects. Here, we study non-locality of the non-classical state of a system consisting of a qubit and an oscillator exposed to the effects of non-Markovian evolutions. We find that the different facets of non-Markovianity affect non-locality in different and non-obvious ways: ranging from pronounced insensitivity of the Bell function to quite a spectacular evidence of information kick-back.

preprint2012arXiv

Optimization of Analytic Window Functions

Analytic functions represent the state-of-the-art way of performing complex data analysis within a single SQL statement. In particular, an important class of analytic functions that has been frequently used in commercial systems to support OLAP and decision support applications is the class of window functions. A window function returns for each input tuple a value derived from applying a function over a window of neighboring tuples. However, existing window function evaluation approaches are based on a naive sorting scheme. In this paper, we study the problem of optimizing the evaluation of window functions. We propose several efficient techniques, and identify optimization opportunities that allow us to optimize the evaluation of a set of window functions. We have integrated our scheme into PostgreSQL. Our comprehensive experimental study on the TPC-DS datasets as well as synthetic datasets and queries demonstrate significant speedup over existing approaches.

preprint2012arXiv

Perpendicular Magnetization and Generic Realization of the Ising Model in Artificial Spin Ice

We have studied frustrated kagome arrays and unfrustrated honeycomb arrays of magnetostatically-interacting single-domain ferromagnetic islands with magnetization normal to the plane. The measured pairwise spin correlations of both lattices can be reproduced by models based solely on nearest-neighbor correlations. The kagome array has qualitatively different magnetostatics but identical lattice topology to previously-studied 'artificial spin ice' systems composed of in-plane moments. The two systems show striking similarities in the development of moment pair correlations, demonstrating a universality in artificial spin ice behavior independent of specific realization in a particular material system.

preprint2012arXiv

Planetary Candidates Observed by Kepler, III: Analysis of the First 16 Months of Data

New transiting planet candidates are identified in sixteen months (May 2009 - September 2010) of data from the Kepler spacecraft. Nearly five thousand periodic transit-like signals are vetted against astrophysical and instrumental false positives yielding 1,091 viable new planet candidates, bringing the total count up to over 2,300. Improved vetting metrics are employed, contributing to higher catalog reliability. Most notable is the noise-weighted robust averaging of multi-quarter photo-center offsets derived from difference image analysis which identifies likely background eclipsing binaries. Twenty-two months of photometry are used for the purpose of characterizing each of the new candidates. Ephemerides (transit epoch, T_0, and orbital period, P) are tabulated as well as the products of light curve modeling: reduced radius (Rp/R*), reduced semi-major axis (d/R*), and impact parameter (b). The largest fractional increases are seen for the smallest planet candidates (197% for candidates smaller than 2Re compared to 52% for candidates larger than 2Re) and those at longer orbital periods (123% for candidates outside of 50-day orbits versus 85% for candidates inside of 50-day orbits). The gains are larger than expected from increasing the observing window from thirteen months (Quarter 1-- Quarter 5) to sixteen months (Quarter 1 -- Quarter 6). This demonstrates the benefit of continued development of pipeline analysis software. The fraction of all host stars with multiple candidates has grown from 17% to 20%, and the paucity of short-period giant planets in multiple systems is still evident. The progression toward smaller planets at longer orbital periods with each new catalog release suggests that Earth-size planets in the Habitable Zone are forthcoming if, indeed, such planets are abundant.

preprint2012arXiv

The Derivation, Properties and Value of Kepler's Combined Differential Photometric Precision

The Kepler Mission is searching for Earth-size planets orbiting solar-like stars by simultaneously observing >160,000 stars to detect sequences of transit events in the photometric light curves. The Combined Differential Photometric Precision (CDPP) is the metric that defines the ease with which these weak terrestrial transit signatures can be detected. An understanding of CDPP is invaluable for evaluating the completeness of the Kepler survey and inferring the underlying planet population. This paper describes how the Kepler CDPP is calculated, and introduces tables of rms CDPP on a per-target basis for 3-, 6-, and 12-hour transit durations, which are now available for all Kepler observations. Quarter 3 is the first typical set of observations at the nominal length and completeness for a quarter, from 2009 September 18 to 2009 December 16, and we examine the properties of the rms CDPP distribution for this data set. Finally, we describe how to employ CDPP to calculate target completeness, an important use case.

preprint2012arXiv

The Neptune-Sized Circumbinary Planet Kepler-38b

We discuss the discovery and characterization of the circumbinary planet Kepler-38b. The stellar binary is single-lined, with a period of 18.8 days, and consists of a moderately evolved main-sequence star (M_A = 0.949 +/- 0.059 solar masses and R_A = 1.757 +/- 0.034 solar radii) paired with a low-mass star (M_B = 0.249 +/- 0.010 solar masses and R_B = 0.2724 +/- 0.0053 solar radii) in a mildly eccentric (e=0.103) orbit. A total of eight transits due to a circumbinary planet crossing the primary star were identified in the Kepler light curve (using Kepler Quarters 1 through 11), from which a planetary period of 105.595 +/- 0.053 days can be established. A photometric dynamical model fit to the radial velocity curve and Kepler light curve yields a planetary radius of 4.35 +/- 0.11 Earth radii, or 1.12 +/- 0.03 Neptune radii. Since the planet is not sufficiently massive to observably alter the orbit of the binary from Keplerian motion, we can only place an upper limit on the mass of the planet of 122 Earth masses (7.11 Neptune masses or 0.384 Jupiter masses) at 95% confidence. This upper limit should decrease as more Kepler data become available.

preprint2012arXiv

The Transiting Circumbinary Planets Kepler-34 and Kepler-35

Most Sun-like stars in the Galaxy reside in gravitationally-bound pairs of stars called "binary stars". While long anticipated, the existence of a "circumbinary planet" orbiting such a pair of normal stars was not definitively established until the discovery of Kepler-16. Incontrovertible evidence was provided by the miniature eclipses ("transits") of the stars by the planet. However, questions remain about the prevalence of circumbinary planets and their range of orbital and physical properties. Here we present two additional transiting circumbinary planets, Kepler-34 and Kepler-35. Each is a low-density gas giant planet on an orbit closely aligned with that of its parent stars. Kepler-34 orbits two Sun-like stars every 289 days, while Kepler-35 orbits a pair of smaller stars (89% and 81% of the Sun's mass) every 131 days. Due to the orbital motion of the stars, the planets experience large multi-periodic variations in incident stellar radiation. The observed rate of circumbinary planets implies > ~1% of close binary stars have giant planets in nearly coplanar orbits, yielding a Galactic population of at least several million.

preprint2012arXiv

Transferring entanglement to the steady-state of flying qubits

The transfer of entanglement from optical fields to qubits provides a viable approach to entangling remote qubits in a quantum network. In cavity quantum electrodynamics, the scheme relies on the interaction between a photonic resource and two stationary intracavity atomic qubits. However, it might be hard in practice to trap two atoms simultaneously and synchronize their coupling to the cavities. To address this point, we propose and study entanglement transfer from cavities driven by an entangled external field to controlled flying qubits. We consider two exemplary non-Gaussian driving fields: NOON and entangled coherent states. We show that in the limit of long coherence time of the cavity fields, when the dynamics is approximately unitary, entanglement is transferred from the driving field to two atomic qubits that cross the cavities. On the other hand, a dissipation-dominated dynamics leads to very weakly quantum-correlated atomic systems, as witnessed by vanishing quantum discord.

preprint2012arXiv

Transit Timing Observations from Kepler: II. Confirmation of Two Multiplanet Systems via a Non-parametric Correlation Analysis

We present a new method for confirming transiting planets based on the combination of transit timingn variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies are in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the transit timing variations of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.

preprint2012arXiv

Transit Timing Observations from Kepler: III. Confirmation of 4 Multiple Planet Systems by a Fourier-Domain Study of Anti-correlated Transit Timing Variations

We present a method to confirm the planetary nature of objects in systems with multiple transiting exoplanet candidates. This method involves a Fourier-Domain analysis of the deviations in the transit times from a constant period that result from dynamical interactions within the system. The combination of observed anti-correlations in the transit times and mass constraints from dynamical stability allow us to claim the discovery of four planetary systems Kepler-25, Kepler-26, Kepler-27, and Kepler-28, containing eight planets and one additional planet candidate.

preprint2012arXiv

Transit Timing Observations from Kepler: IV. Confirmation of 4 Multiple Planet Systems by Simple Physical Models

Eighty planetary systems of two or more planets are known to orbit stars other than the Sun. For most, the data can be sufficiently explained by non-interacting Keplerian orbits, so the dynamical interactions of these systems have not been observed. Here we present 4 sets of lightcurves from the Kepler spacecraft, which each show multiple planets transiting the same star. Departure of the timing of these transits from strict periodicity indicates the planets are perturbing each other: the observed timing variations match the forcing frequency of the other planet. This confirms that these objects are in the same system. Next we limit their masses to the planetary regime by requiring the system remain stable for astronomical timescales. Finally, we report dynamical fits to the transit times, yielding possible values for the planets' masses and eccentricities. As the timespan of timing data increases, dynamical fits may allow detailed constraints on the systems' architectures, even in cases for which high-precision Doppler follow-up is impractical.

preprint2011arXiv

Characteristics of planetary candidates observed by Kepler, II: Analysis of the first four months of data

On 1 February 2011 the Kepler Mission released data for 156,453 stars observed from the beginning of the science observations on 2 May through 16 September 2009. There are 1235 planetary candidates with transit like signatures detected in this period. These are associated with 997 host stars. Distributions of the characteristics of the planetary candidates are separated into five class-sizes; 68 candidates of approximately Earth-size (radius < 1.25 Earth radii), 288 super-Earth size (1.25 Earth radii < radius < 2 Earth radii), 662 Neptune-size (2 Earth radii < radius < 6 Earth radii), 165 Jupiter-size (6 Earth radii < radius < 15 Earth radii), and 19 up to twice the size of Jupiter (15 Earth radii < radius < 22 Earth radii). In the temperature range appropriate for the habitable zone, 54 candidates are found with sizes ranging from Earth-size to larger than that of Jupiter. Five are less than twice the size of the Earth. Over 74% of the planetary candidates are smaller than Neptune. The observed number versus size distribution of planetary candidates increases to a peak at two to three times Earth-size and then declines inversely proportional to area of the candidate. Our current best estimates of the intrinsic frequencies of planetary candidates, after correcting for geometric and sensitivity biases, are 6% for Earth-size candidates, 7% for super-Earth size candidates, 17% for Neptune-size candidates, and 4% for Jupiter-size candidates. Multi-candidate, transiting systems are frequent; 17% of the host stars have multi-candidate systems, and 33.9% of all the candidates are part of multi-candidate systems.

preprint2011arXiv

Ignoring your neighbors: Moment correlations dominated by indirect or distant interactions in an ordered nanomagnet array

We have studied the moment correlations within triangular lattice arrays of single-domain co-aligned nanoscale ferromagnetic islands. Independent variation of lattice spacing along and perpendicular to the island axis tunes the magnetostatic interactions between islands through a broad range of relative strengths. For certain lattice parameters, the sign of the correlations between near-neighbor island moments is opposite to that favored by the pair-wise interaction. This finding, supported by analysis of the total correlation in terms of direct and convoluted indirect contributions across multiple pairwise interactions, indicates that indirect interactions and/or those mediated by further neighbors can be tuned to be dominant, with implications for the wide range of systems composed of interacting nanomagnets.

preprint2011arXiv

Kepler-22b: A 2.4 Earth-radius Planet in the Habitable Zone of a Sun-like Star

A search of the time-series photometry from NASA's Kepler spacecraft reveals a transiting planet candidate orbiting the 11th magnitude G5 dwarf KIC 10593626 with a period of 290 days. The characteristics of the host star are well constrained by high-resolution spectroscopy combined with an asteroseismic analysis of the Kepler photometry, leading to an estimated mass and radius of 0.970 +/- 0.060 MSun and 0.979 +/- 0.020 RSun. The depth of 492 +/- 10ppm for the three observed transits yields a radius of 2.38 +/- 0.13 REarth for the planet. The system passes a battery of tests for false positives, including reconnaissance spectroscopy, high-resolution imaging, and centroid motion. A full BLENDER analysis provides further validation of the planet interpretation by showing that contamination of the target by an eclipsing system would rarely mimic the observed shape of the transits. The final validation of the planet is provided by 16 radial velocities obtained with HIRES on Keck 1 over a one year span. Although the velocities do not lead to a reliable orbit and mass determination, they are able to constrain the mass to a 3σ upper limit of 124 MEarth, safely in the regime of planetary masses, thus earning the designation Kepler-22b. The radiative equilibrium temperature is 262K for a planet in Kepler-22b's orbit. Although there is no evidence that Kepler-22b is a rocky planet, it is the first confirmed planet with a measured radius to orbit in the Habitable Zone of any star other than the Sun.

preprint2011arXiv

KOI-126: A Triply-Eclipsing Hierarchical Triple with Two Low-Mass Stars

The Kepler spacecraft has been monitoring the light from 150,000 stars in its primary quest to detect transiting exoplanets. Here we report on the detection of an eclipsing stellar hierarchical triple, identified in the Kepler photometry. KOI-126 (A,(B, C)), is composed of a low-mass binary (masses M_B = 0.2413+/-0.0030 M_Sun, M_C = 0.2127+/-0.0026 M_Sun; radii R_B = 0.2543+/-0.0014 R_Sun, R_C = 0.2318+/-0.0013 R_Sun; orbital period P_1 = 1.76713+/-0.00019 days) on an eccentric orbit about a third star (mass M_A = 1.347+/-0.032 M_Sun; radius R_A = 2.0254+/-0.0098 R_Sun; period of orbit around the low-mass binary P_2 = 33.9214+/-0.0013 days; eccentricity of that orbit e_2 = 0.3043+/-0.0024). The low-mass pair probe the poorly sampled fully-convective stellar domain offering a crucial benchmark for theoretical stellar models.

preprint2011arXiv

Magneto-optical Kerr Effect Studies of Square Artificial Spin Ice

We report a magneto-optical Kerr effect study of the collective magnetic response of artificial square spin ice, a lithographically-defined array of single-domain ferromagnetic islands. We find that the anisotropic inter-island interactions lead to a non-monotonic angular dependence of the array coercive field. Comparisons with micromagnetic simulations indicate that the two perpendicular sublattices exhibit distinct responses to island edge roughness, which clearly influence the magnetization reversal process. Furthermore, such comparisons demonstrate that disorder associated with roughness in the island edges plays a hitherto unrecognized but essential role in the collective behavior of these systems.

preprint2011arXiv

SafeZone: A Hierarchical Inter-Domain Authenticated Source Address Validation Solution

Next generation Internet is highly concerned about the issue of reliability. Principally, the foundation of reliability is authentication of the source IP address. With the signature-and-verification based defense mechanisms available today, unfortunately, there is a lack of hierarchical architecture, which makes the structure of the trust alliance excessively flat and single. Moreover, with the increasing scale of the trust alliance, costs of validation grow so quickly that they do not adapt to incremental deployment. Via comparison with traditional solutions, this article proposes a hierarchical, inter-domain authenticated source address validation solution named SafeZone. SafeZone employs two intelligent designs, lightweight tag replacement and a hierarchical partitioning scheme, each of which helps to ensure that SafeZone can construct trustworthy and hierarchical trust alliances without the negative influences and complex operations on de facto networks. Extensive experiments also indicate that SafeZone can effectively obtain the design goals of a hierarchical architecture, along with lightweight, loose coupling and "multi-fence support" and as well as an incremental deployment scheme.

preprint2011arXiv

The Distribution of Transit Durations for Kepler Planet Candidates and Implications for their Orbital Eccentricities

Doppler planet searches have discovered that giant planets follow orbits with a wide range of orbital eccentricities, revolutionizing theories of planet formation. The discovery of hundreds of exoplanet candidates by NASA's Kepler mission enables astronomers to characterize the eccentricity distribution of small exoplanets. Measuring the eccentricity of individual planets is only practical in favorable cases that are amenable to complementary techniques (e.g., radial velocities, transit timing variations, occultation photometry). Yet even in the absence of individual eccentricities, it is possible to study the distribution of eccentricities based on the distribution of transit durations (relative to the maximum transit duration for a circular orbit). We analyze the transit duration distribution of Kepler planet candidates. We find that for host stars with T_eff > 5100 K we cannot invert this to infer the eccentricity distribution at this time due to uncertainties and possible systematics in the host star densities. With this limitation in mind, we compare the observed transit duration distribution with models to rule out extreme distributions. If we assume a Rayleigh eccentricity distribution for Kepler planet candidates, then we find best-fits with a mean eccentricity of 0.1-0.25 for host stars with T_eff < 5100 K. We compare the transit duration distribution for different subsets of Kepler planet candidates and discuss tentative trends with planetary radius and multiplicity. High-precision spectroscopic follow-up observations for a large sample of host stars will be required to confirm which trends are real and which are the results of systematic errors in stellar radii. Finally, we identify planet candidates that must be eccentric or have a significantly underestimated stellar radius.

preprint2011arXiv

The Kepler-19 System: A Transiting 2.2 R_Earth Planet and a Second Planet Detected via Transit Timing Variations

We present the discovery of the Kepler-19 planetary system, which we first identified from a 9.3-day periodic transit signal in the Kepler photometry. From high-resolution spectroscopy of the star, we find a stellar effective temperature Teff=5541 \pm 60 K, a metallicity [Fe/H]=-0.13 \pm 0.06, and a surface gravity log(g)=4.59 \pm 0.10. We combine the estimate of Teff and [Fe/H] with an estimate of the stellar density derived from the photometric light curve to deduce a stellar mass of M_star = 0.936 \pm 0.040 M_Sun and a stellar radius of R_star = 0.850 \pm 0.018 R_Sun. We rule out the possibility that the transits result from an astrophysical false positive by first identifying the subset of stellar blends that reproduce the precise shape of the light curve. We conclude that the planetary scenario is more than three orders of magnitude more likely than a blend. The blend scenario is independently disfavored by the achromaticity of the transit: we measure a transit depth with Spitzer at 4.5 μm of 547+113-110 ppm, consistent with the depth measured in the Kepler optical bandpass of 567\pm6 ppm. We determine a physical radius of the planet Kepler-19b of R_p = 2.209 \pm 0.048 R_Earth. From radial-velocity observations of the star, we find an upper limit on the planet mass of 20.3 M_Earth, corresponding to a maximum density of 10.4 g cm^-3. We report a significant sinusoidal deviation of the transit times from a predicted linear ephemeris, which we conclude is due to an additional perturbing body in the system. We cannot uniquely determine the orbital parameters of the perturber, as various dynamical mechanisms match the amplitude, period, and shape of the transit timing signal and satisfy the host star's radial velocity limits. However, the perturber in these mechanisms has period <160 days and mass <6 M_Jup, confirming its planetary nature as Kepler-19c. [Abridged]

preprint2011arXiv

Transit Timing Observations from Kepler: I. Statistical Analysis of the First Four Months

The architectures of multiple planet systems can provide valuable constraints on models of planet formation, including orbital migration, and excitation of orbital eccentricities and inclinations. NASA's Kepler mission has identified 1235 transiting planet candidates (Borcuki et al 2011). The method of transit timing variations (TTVs) has already confirmed 7 planets in two planetary systems (Holman et al. 2010; Lissauer et al. 2011a). We perform a transit timing analysis of the Kepler planet candidates. We find that at least ~12% of planet candidates currently suitable for TTV analysis show evidence suggestive of TTVs, representing at least ~65 TTV candidates. In all cases, the time span of observations must increase for TTVs to provide strong constraints on planet masses and/or orbits, as expected based on n-body integrations of multiple transiting planet candidate systems (assuming circular and coplanar orbits). We find that the fraction of planet candidates showing TTVs in this data set does not vary significantly with the number of transiting planet candidates per star, suggesting significant mutual inclinations and that many stars with a single transiting planet should host additional non-transiting planets. We anticipate that Kepler could confirm (or reject) at least ~12 systems with multiple transiting planet candidates via TTVs. Thus, TTVs will provide a powerful tool for confirming transiting planets and characterizing the orbital dynamics of low-mass planets. If Kepler observations were extended to at least six years, then TTVs would provide much more precise constraints on the dynamics of systems with multiple transiting planets and would become sensitive to planets with orbital periods extending into the habitable zone of solar-type stars.

preprint2010arXiv

Discovery and Rossiter-McLaughlin Effect of Exoplanet Kepler-8b

We report the discovery and the Rossiter-McLaughlin effect of Kepler-8b, a transiting planet identified by the NASA Kepler Mission. Kepler photometry and Keck-HIRES radial velocities yield the radius and mass of the planet around this F8IV subgiant host star. The planet has a radius RP = 1.419 RJ and a mass, MP = 0.60 MJ, yielding a density of 0.26 g cm^-3, among the lowest density planets known. The orbital period is P = 3.523 days and orbital semima jor axis is 0.0483+0.0006/-0.0012 AU. The star has a large rotational v sin i of 10.5 +/- 0.7 km s^-1 and is relatively faint (V = 13.89 mag), both properties deleterious to precise Doppler measurements. The velocities are indeed noisy, with scatter of 30 m s^-1, but exhibit a period and phase consistent with the planet implied by the photometry. We securely detect the Rossiter-McLaughlin effect, confirming the planet's existence and establishing its orbit as prograde. We measure an inclination between the projected planetary orbital axis and the projected stellar rotation axis of lambda = -26.9 +/- 4.6 deg, indicating a moderate inclination of the planetary orbit. Rossiter-McLaughlin measurements of a large sample of transiting planets from Kepler will provide a statistically robust measure of the true distribution of spin-orbit orientations for hot jupiters in general.

preprint2010arXiv

Effective Temperature in an Interacting, Externally Driven, Vertex System: Theory and Experiment on Artificial Spin Ice

Frustrated arrays of interacting single-domain nanomagnets provide important model systems for statistical mechanics, because they map closely onto well-studied vertex models and are amenable to direct imaging and custom engineering. Although these systems are manifestly athermal, we demonstrate that the statistical properties of both hexagonal and square lattices can be described by an effective temperature based on the magnetostatic energy of the arrays. This temperature has predictive power for the moment configurations and is intimately related to how the moments are driven by an oscillating external field.

preprint2010arXiv

Initial Characteristics of Kepler Long Cadence Data For Detecting Transiting Planets

The Kepler Mission seeks to detect Earth-size planets transiting solar-like stars in its ~115 deg^2 field of view over the course of its 3.5 year primary mission by monitoring the brightness of each of ~156,000 Long Cadence stellar targets with a time resolution of 29.4 minutes. We discuss the photometric precision achieved on timescales relevant to transit detection for data obtained in the 33.5-day long Quarter 1 (Q1) observations that ended 2009 June 15. The lower envelope of the photometric precision obtained at various timescales is consistent with expected random noise sources, indicating that Kepler has the capability to fulfill its mission. The Kepler light curves exhibit high precision over a large dynamic range, which will surely permit their use for a large variety of investigations in addition to finding and characterizing planets. We discuss the temporal characteristics of both the raw flux time series and the systematic error-corrected flux time series produced by the Kepler Science Pipeline, and give examples illustrating Kepler's large dynamic range and the variety of light curves obtained from the Q1 observations.

preprint2010arXiv

Instrument Performance in Kepler's First Months

The Kepler Mission relies on precise differential photometry to detect the 80 parts per million (ppm) signal from an Earth-Sun equivalent transit. Such precision requires superb instrument stability on time scales up to ~2 days and systematic error removal to better than 20 ppm. To this end, the spacecraft and photometer underwent 67 days of commissioning, which included several data sets taken to characterize the photometer performance. Because Kepler has no shutter, we took a series of dark images prior to the dust cover ejection, from which we measured the bias levels, dark current, and read noise. These basic detector properties are essentially unchanged from ground-based tests, indicating that the photometer is working as expected. Several image artifacts have proven more complex than when observed during ground testing, as a result of their interactions with starlight and the greater thermal stability in flight, which causes the temperature-dependent artifact variations to be on the timescales of transits. Because of Kepler's unprecedented sensitivity and stability, we have also seen several unexpected systematics that affect photometric precision. We are using the first 43 days of science data to characterize these effects and to develop detection and mitigation methods that will be implemented in the calibration pipeline. Based on early testing, we expect to attain Kepler's planned photometric precision over 80%-90% of the field of view.

preprint2010arXiv

Kepler Mission Design, Realized Photometric Performance, and Early Science

The Kepler Mission, launched on Mar 6, 2009 was designed with the explicit capability to detect Earth-size planets in the habitable zone of solar-like stars using the transit photometry method. Results from just forty-three days of data along with ground-based follow-up observations have identified five new transiting planets with measurements of their masses, radii, and orbital periods. Many aspects of stellar astrophysics also benefit from the unique, precise, extended and nearly continuous data set for a large number and variety of stars. Early results for classical variables and eclipsing stars show great promise. To fully understand the methodology, processes and eventually the results from the mission, we present the underlying rationale that ultimately led to the flight and ground system designs used to achieve the exquisite photometric performance. As an example of the initial photometric results, we present variability measurements that can be used to distinguish dwarf stars from red giants.

preprint2010arXiv

On the Model Transform in Stochastic Network Calculus

Stochastic network calculus requires special care in the search of proper stochastic traffic arrival models and stochastic service models. Tradeoff must be considered between the feasibility for the analysis of performance bounds, the usefulness of performance bounds, and the ease of their numerical calculation. In theory, transform between different traffic arrival models and transform between different service models are possible. Nevertheless, the impact of the model transform on performance bounds has not been thoroughly investigated. This paper is to investigate the effect of the model transform and to provide practical guidance in the model selection in stochastic network calculus.

preprint2010arXiv

Overview of the Kepler Science Processing Pipeline

The Kepler Mission Science Operations Center (SOC) performs several critical functions including managing the ~156,000 target stars, associated target tables, science data compression tables and parameters, as well as processing the raw photometric data downlinked from the spacecraft each month. The raw data are first calibrated at the pixel level to correct for bias, smear induced by a shutterless readout, and other detector and electronic effects. A background sky flux is estimated from ~4500 pixels on each of the 84 CCD readout channels, and simple aperture photometry is performed on an optimal aperture for each star. Ancillary engineering data and diagnostic information extracted from the science data are used to remove systematic errors in the flux time series that are correlated with these data prior to searching for signatures of transiting planets with a wavelet-based, adaptive matched filter. Stars with signatures exceeding 7.1 sigma are subjected to a suite of statistical tests including an examination of each star's centroid motion to reject false positives caused by background eclipsing binaries. Physical parameters for each planetary candidate are fitted to the transit signature, and signatures of additional transiting planets are sought in the residual light curve. The pipeline is operational, finding planetary signatures and providing robust eliminations of false positives.

Jie Li

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178 published item(s)

Tibetan-TTS:Low-Resource Tibetan Speech Synthesis with Large Model Adaptation

Observation of the $γ$-ray Emission from W43 with LHAASO

Study of Ultra-High-Energy Gamma-Ray Source 1LHAASO J0056+6346u and Its Possible Origins

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Progress and Prospects in 3D Generative AI: A Technical Overview including 3D human

An Indoor Environment Sensing and Localization System via mmWave Phased Array

Multi-scale multi-modal micro-expression recognition algorithm based on transformer

Reconstruction of compressed spectral imaging based on global structure and spectral correlation

Workload Failure Prediction for Data Centers

A Dynamic 3D Spontaneous Micro-expression Database: Establishment and Evaluation

A QoE Model in Point Cloud Video Streaming

A Smart Contract based Crowdfunding Mechanism for Hierarchical Federated Learning

Anyonic braiding via quench dynamics in fractional quantum Hall liquids

Application of Color Block Code in Image Scaling

Autonomous Smart Grid Fault Detection

BGP-Multipath Routing in the Internet

Coherent mechanical noise cancellation and cooperativity competition in optomechanical arrays

Digital-twin-enhanced metal tube bending forming real-time prediction method based on Multi-source-input MTL

Double crossed biproducts and related structures

Efficient Recovery of a Shared Secret via Cooperation: Applications to SDMM and PIR

Entangling mechanical vibrations of two massive ferrimagnets by fully exploiting the nonlinearity of magnetostriction

FedIPR: Ownership Verification for Federated Deep Neural Network Models

Ferroelectrics and Photovoltaics in Endohedral Fullerenes-based van der Waals Heterostructures

Fully Attentional Network for Semantic Segmentation

Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction

Learning Correlations between Internal Coordinates to improve 3D Cartesian Coordinates for Proteins

Mechanical Bistability in Kerr-modified Cavity Magnomechanics

Object Permanence Emerges in a Random Walk along Memory

On Collective Robustness of Bagging Against Data Poisoning

On the impact of VR assessment on the Quality of Experience of Highly Realistic Digital Humans

On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach

Optical sensing of magnons via the magnetoelastic displacement

Passive Motion Detection via mmWave Communication System

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Private and Secure Distributed Matrix Multiplication Schemes for Replicated or MDS-Coded Servers

Seeking Subjectivity in Visual Emotion Distribution Learning

Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback

Spin transfer from dark matter to gas during halo formation

SpOT: Spatiotemporal Modeling for 3D Object Tracking

Steady entangled-state generation via cross-Kerr effect in a ferrimagnetic crystal

The quasi-Keplerian motion in the regular Bardeen spacetime

TransFA: Transformer-based Representation for Face Attribute Evaluation

A Density-Aware PointRCNN for 3D Object Detection in Point Clouds

Approximate Optimal Filter for Linear Gaussian Time-invariant Systems

DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds

Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations

Entangling the vibrational modes of two massive ferromagnetic spheres using cavity magnomechanics

Geometry-Based Stochastic Line-of-Sight Probability Model for A2G Channels under Urban Scenarios

Nonhomogeneous associative Yang-Baxter equations

Proxemics and Social Interactions in an Instrumented Virtual Reality Workshop

Real-time tracking of COVID-19 and coronavirus research updates through text mining

Slow Spin Relaxation in Single Endohedral Fullerene Molecules

Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution

3D Gated Recurrent Fusion for Semantic Scene Completion

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery

Age of Information for Multicast Transmission with Fixed and Random Deadlines in IoT Systems

Angular momentum-related probe of cold gas deficiencies

Anisotropic Convolutional Networks for 3D Semantic Scene Completion

Balanced Symmetric Cross Entropy for Large Scale Imbalanced and Noisy Data

Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction

Data Dissemination Using Interest Tree in Socially Aware Networking

Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics

Depth Based Semantic Scene Completion with Position Importance Aware Loss

Einstein-Podolsky-Rosen entanglement and asymmetric steering between distant macroscopic mechanical and magnonic systems

Extending Label Smoothing Regularization with Self-Knowledge Distillation

Facial Attribute Capsules for Noise Face Super Resolution

Giant magnetic anisotropy energy and long coherence time of uranium substitution on defected Al2O3(0001)

Grid-Forming Converters control based on DC voltage feedback

Inertia and feedback parameters adaptive control of virtual synchronous generator

Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution

Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming

Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight

Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms