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Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and occlusion scenes. We address this challenge by proposing a new matting dataset based on the COCO dataset, namely COCO-Matting. Specifically, the construction of our COCO-Matting includes accessory fusion and mask-to-matte, which selects real-world complex images from COCO and converts semantic segmentation masks to matting labels. The built COCO-Matting comprises an extensive collection of 38,251 human instance-level alpha mattes in complex natural scenarios. Furthermore, existing SAM-based matting methods extract intermediate features and masks from a frozen SAM and only train a lightweight matting decoder by end-to-end matting losses, which do not fully exploit the potential of the pre-trained SAM. Thus, we propose SEMat which revamps the network architecture and training objectives. For network architecture, the proposed feature-aligned transformer learns to extract fine-grained edge and transparency features. The proposed matte-aligned decoder aims to segment matting-specific objects and convert coarse masks into high-precision mattes. For training objectives, the proposed regularization and trimap loss aim to retain the prior from the pre-trained model and push the matting logits extracted from the mask decoder to contain trimap-based semantic information. Extensive experiments across seven diverse datasets demonstrate the superior performance of our method, proving its efficacy in interactive natural image matting. We open-source our code, models, and dataset at https://github.com/XiaRho/SEMat.

In recent years, the issue of radon emanation and diffusion has become a critical concern for rare decay experiments, such as JUNO and PandaX-4T. This paper introduces a detector design featuring a symmetric radon detector cavity for the quantitative assessment of membrane materials' radon blocking capabilities. The performance of this design is evaluated through the application of Fick's Law and the diffusion equation considering material solubility. Our detector has completed measurements of radon diffusion coefficients for four types of membrane materials currently used in experiments, which also confirms the rationality of this detector design. The findings are instrumental in guiding the selection and evaluation of optimal materials for radon shielding to reduce radon background, contributing to boost sensitivities of rare event research., Comment: 7 pages, 10 figures and 2 tables

While neural machine translation (NMT) models achieve success in our daily lives, they show vulnerability to adversarial attacks. Despite being harmful, these attacks also offer benefits for interpreting and enhancing NMT models, thus drawing increased research attention. However, existing studies on adversarial attacks are insufficient in both attacking ability and human imperceptibility due to their sole focus on the scope of language. This paper proposes a novel vision-fused attack (VFA) framework to acquire powerful adversarial text, i.e., more aggressive and stealthy. Regarding the attacking ability, we design the vision-merged solution space enhancement strategy to enlarge the limited semantic solution space, which enables us to search for adversarial candidates with higher attacking ability. For human imperceptibility, we propose the perception-retained adversarial text selection strategy to align the human text-reading mechanism. Thus, the finally selected adversarial text could be more deceptive. Extensive experiments on various models, including large language models (LLMs) like LLaMA and GPT-3.5, strongly support that VFA outperforms the comparisons by large margins (up to 81%/14% improvements on ASR/SSIM)., Comment: IJCAI 2024

Although the Unsupervised Domain Adaptation (UDA) method has improved the effect of remote sensing image classification tasks, most of them are still limited by access to the source domain (SD) data. Designs such as Source-free Domain Adaptation (SFDA) solve the challenge of a lack of SD data, however, they still rely on a large amount of target domain data and thus cannot achieve fast adaptations, which seriously hinders their further application in broader scenarios. The real-world applications of cross-domain remote sensing image classification require a balance of speed and accuracy at the same time. Therefore, we propose a novel and comprehensive test time adaptation (TTA) method -- Low Saturation Confidence Distribution Test Time Adaptation (LSCD-TTA), which is the first attempt to solve such scenarios through the idea of TTA. LSCD-TTA specifically considers the distribution characteristics of remote sensing images, including three main parts that concentrate on different optimization directions: First, low saturation distribution (LSD) considers the dominance of low-confidence samples during the later TTA stage. Second, weak-category cross-entropy (WCCE) increases the weight of categories that are more difficult to classify with less prior knowledge. Finally, diverse categories confidence (DIV) comprehensively considers the category diversity to alleviate the deviation of the sample distribution. By weighting the abovementioned three modules, the model can widely, quickly and accurately adapt to the target domain without much prior target distributions, repeated data access, and manual annotation. We evaluate LSCD-TTA on three remote-sensing image datasets. The experimental results show that LSCD-TTA achieves a significant gain of 4.96%-10.51% with Resnet-50 and 5.33%-12.49% with Resnet-101 in average accuracy compared to other state-of-the-art DA and TTA methods.

The optical appearance of the numerically black hole solutions within the higher derivative gravity illuminated by an accretion disk context is discussed. We obtain solutions for non-Schwarzschild black holes with r0 = 1, r0 = 2, and r0 = 3. Further analysis of spacetime trajectories reveals properties similar to Schwarzschild black holes, while the r0 = 2 black hole exhibits significant differences. The results reveal the presence of a repulsive potential barrier for the black hole, allowing only particles with energies exceeding a certain threshold to approach it, providing a unique gravitational scenario for non-Schwarzschild black holes. Additionally, the optical images are derived through numerical simulations by discussing the trajectories of photons intheblackholespacetime.The distribution of radiation flux and the effects of gravitational redshift and Doppler shift on the observed radiation flux are considered. Interestingly, previous analyses of the optical appearance of black holes were conducted within the framework of analytic solutions, whereas the analysis of numerical black hole solutions first appears in our analysis.

Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchronous event-driven manner, offering an energy-efficient paradigm for the next generation of machine intelligence. However, the current focus within the SNN community prioritizes accuracy optimization through the development of large-scale models, limiting their viability in resource-constrained and low-power edge devices. To address this challenge, we introduce a lightweight and hardware-friendly Quantized SNN (Q-SNN) that applies quantization to both synaptic weights and membrane potentials. By significantly compressing these two key elements, the proposed Q-SNNs substantially reduce both memory usage and computational complexity. Moreover, to prevent the performance degradation caused by this compression, we present a new Weight-Spike Dual Regulation (WS-DR) method inspired by information entropy theory. Experimental evaluations on various datasets, including static and neuromorphic, demonstrate that our Q-SNNs outperform existing methods in terms of both model size and accuracy. These state-of-the-art results in efficiency and efficacy suggest that the proposed method can significantly improve edge intelligent computing., Comment: 8 pages, 5 figures

Our study investigates the astronomical implications of Rastall gravity, particularly its behavior amidst a radiation field compared to Reissner-Nordstrom (RN) black holes. Our research delineates a crucial correlation between the dynamics of the accretion disk and the parameters Q and N_{\rm r}, which aptly reflect the influence of spacetime metrics on the disk's appearance. Elevated electric charge Q prompts contraction in the disk's orbit due to enhanced gravitational effects, while higher N_{\rm r} values lead to outward expansion, influenced by the radiation field's attributes. Interestingly, the charged black holes surrounded by radiation fields display distinct visual disparities from RN black holes. Brightness decreases and expansion occurs within the accretion disk's innermost stable circular orbit with rising N_{\rm r} values. Our study also reveals the process by which the accretion disk transitions from a conventional disk-like structure to a hat-like form at different observation angles, with the redshift effect gradually intensifying. Moreover, the results of the Rastall gravity radiation field we consider are consistent with the constraints of the host galaxy's gravitational lensing on the Rastall gravity parameters, enhancing the consistency between theoretical predictions and actual observations.

Recently, implicit neural representations (INR) have made significant strides in various vision-related domains, providing a novel solution for Multispectral and Hyperspectral Image Fusion (MHIF) tasks. However, INR is prone to losing high-frequency information and is confined to the lack of global perceptual capabilities. To address these issues, this paper introduces a Fourier-enhanced Implicit Neural Fusion Network (FeINFN) specifically designed for MHIF task, targeting the following phenomena: The Fourier amplitudes of the HR-HSI latent code and LR-HSI are remarkably similar; however, their phases exhibit different patterns. In FeINFN, we innovatively propose a spatial and frequency implicit fusion function (Spa-Fre IFF), helping INR capture high-frequency information and expanding the receptive field. Besides, a new decoder employing a complex Gabor wavelet activation function, called Spatial-Frequency Interactive Decoder (SFID), is invented to enhance the interaction of INR features. Especially, we further theoretically prove that the Gabor wavelet activation possesses a time-frequency tightness property that favors learning the optimal bandwidths in the decoder. Experiments on two benchmark MHIF datasets verify the state-of-the-art (SOTA) performance of the proposed method, both visually and quantitatively. Also, ablation studies demonstrate the mentioned contributions. The code will be available on Anonymous GitHub (https://anonymous.4open.science/r/FeINFN-15C9/) after possible acceptance.

The self-polarization of relativistic electrons or positrons moving in a magnetic field at a storage ring occurs through the emission of spin-flip synchrotron radiation, known as the Sokolov-Ternov effect. The resulting transverse polarizations of the colliding electrons and positrons, away from the depolarization resonances, allow for precise investigation of the spin entangled hyperon-antihyperon pairs via virtual photon or charmonium decay. The feasibility study reveals a promising increase in the statistical sensitivity of the \CP violation signal after considering the transverse polarizations of the lepton beams., Comment: 10 pages, 7 figures, typos corrected, figures 3 and 7 updated, published version

In this paper, a new simple and fast orthogonal block Kaczmarz (POBK) algorithm based on preprocessing techniques is proposed to solve large-scale sparse linear systems Ax = f. Firstly, the sparse Reverse Cuthill-McKee Algorithm (RCM) is used to preprocess the linear system, and then a new partitioning strategy is proposed to divide the mutually orthogonal blocks into one category, which greatly reduces computational complexity. In addition, we provide a theoretical proof of the convergence of the POBK algorithm and analyze its faster convergence. Finally, numerical simulations demonstrate that our method outperforms the latest Kaczmarz methods such as GRBK, RBK(k), GREBK(k), and aRBK in terms of CPU time., Comment: 25 pages, 12 figures, 3 tables

Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on heterophilic graphs, where the linked nodes have dissimilar features and different class labels, and the semantically related nodes might be multi-hop away. To address this limitation, this paper presents GraphRARE, a general framework built upon node relative entropy and deep reinforcement learning, to strengthen the expressive capability of GNNs. An innovative node relative entropy, which considers node features and structural similarity, is used to measure mutual information between node pairs. In addition, to avoid the sub-optimal solutions caused by mixing useful information and noises of remote nodes, a deep reinforcement learning-based algorithm is developed to optimize the graph topology. This algorithm selects informative nodes and discards noisy nodes based on the defined node relative entropy. Extensive experiments are conducted on seven real-world datasets. The experimental results demonstrate the superiority of GraphRARE in node classification and its capability to optimize the original graph topology., Comment: 14 pages, 7 figures

We investigate the exclusive $J/\psi$ production at the future Electron-ion collider in China by utilizing the eSTARlight event generator. We model the cross-section and kinematics by fitting to the world data of $J/\psi$ photoproduction. Projected statistical uncertainties on $J/\psi$ production are based on the design of a central detector, which consists of a tracker and vertex subsystem. The precision of the pseudo-data allows us to probe the near-threshold mechanism, e.g. the re-scattering effect. The significance of the forward amplitudes is discussed as well. The design and optimization of the detector enhance the potential for exploring the near-threshold region and the realm of high four-momentum transfer squared, which is of particular interest on several physics topics.., Comment: 11 pages, 11 figures

With the rapid development of GPU (Graphics Processing Unit) technologies and neural networks, we can explore more appropriate data structures and algorithms. Recent progress shows that neural networks can partly replace traditional data structures. In this paper, we proposed a novel DNN (Deep Neural Network)-based learned locality-sensitive hashing, called LLSH, to efficiently and flexibly map high-dimensional data to low-dimensional space. LLSH replaces the traditional LSH (Locality-sensitive Hashing) function families with parallel multi-layer neural networks, which reduces the time and memory consumption and guarantees query accuracy simultaneously. The proposed LLSH demonstrate the feasibility of replacing the hash index with learning-based neural networks and open a new door for developers to design and configure data organization more accurately to improve information-searching performance. Extensive experiments on different types of datasets show the superiority of the proposed method in query accuracy, time consumption, and memory usage.

Backward-compatible training circumvents the need for expensive updates to the old gallery database when deploying an advanced new model in the retrieval system. Previous methods achieved backward compatibility by aligning prototypes of the new model with the old one, yet they often overlooked the distribution of old features, limiting their effectiveness when the low quality of the old model results in a weakly feature discriminability. Instance-based methods like L2 regression take into account the distribution of old features but impose strong constraints on the performance of the new model itself. In this paper, we propose MixBCT, a simple yet highly effective backward-compatible training method that serves as a unified framework for old models of varying qualities. We construct a single loss function applied to mixed old and new features to facilitate backward-compatible training, which adaptively adjusts the constraint domain for new features based on the distribution of old features. We conducted extensive experiments on the large-scale face recognition datasets MS1Mv3 and IJB-C to verify the effectiveness of our method. The experimental results clearly demonstrate its superiority over previous methods. Code is available at https://github.com/yuleung/MixBCT .

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided., Comment: 8 pages, 5 figures, conference: AAAI

Achieving kJ/mol accuracy in the cohesive energy of molecular crystals, as necessary for crystal structure prediction and the resolution of polymorphism, is an ongoing challenge in computational materials science. Here, we evaluate the performance of second-order M{\o}ller-Plesset perturbation theory (MP2), including its spin-component scaled models, by calculating the cohesive energies of the 23 molecular crystals contained in the X23 dataset. Our calculations are performed with periodic boundary conditions and Brillouin zone sampling, and we converge results to the thermodynamic limit and the complete basis set limit to an accuracy of about 1 kJ/mol (0.25 kcal/mol), which is rarely achieved in previous MP2 calculations of molecular crystals. Comparing to experimental cohesive energies, we find that MP2 has a mean absolute error of 12.9 kJ/mol, which is comparable to that of DFT using the PBE functional and TS dispersion correction. Separate scaling of the opposite-spin and same-spin components of the correlation energy, with parameters previously determined for molecular interactions, reduces the mean absolute error to 9.5 kJ/mol, and reoptimizing the spin-component scaling parameters for the X23 set further reduces the mean absolute error to 7.5 kJ/mol.

Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition. Numerous algorithms, ranging from regularization-based optimization and deep learning, are being investigated to improve reconstruction quality, but they are still limited by the ill-posed and information-deficient nature of the standard SCI paradigm. To overcome these drawbacks, we propose a new key frames assisted hybrid encoding paradigm for compressive video sensing, termed KH-CVS, that alternatively captures short-exposure key frames without coding and long-exposure encoded compressive frames to jointly reconstruct photorealistic video. With the use of optical flow and spatial warping, a deep convolutional neural network framework is constructed to integrate the benefits of these two types of frames. Extensive experiments on both simulations and real data from the prototype we developed verify the superiority of the proposed method.

Multipole moments are the first order responses of the energy to spatial derivatives of the electric field strength. The quality of density functional theory (DFT) prediction of molecular multipole moments thus characterizes errors in modeling the electron density itself, as well as the performance in describing molecules interacting with external electric fields. However, only the lowest non-zero moment is translationally invariant, making the higher order moments origin-dependent. Therefore, instead of using the $3 \times 3$ quadrupole moment matrix, we utilize the translationally invariant $3 \times 3$ matrix of second cumulants (or spatial variances) of the electron density as the quantity of interest (denoted by $\mathcal{K}$). The principal components of ${\mathcal{K}}$ are the square of the spatial extent of the electron density along each axis. A benchmark dataset of the prinicpal components of ${\mathcal{K}}$ for 100 small molecules at the coupled cluster singles and doubles with perturbative triples (CCSD(T)) at the complete basis set (CBS) limit is developed, resulting in 213 independent ${\mathcal{K}}$ components. The performance of 47 popular and recent density functionals is assessed against this Var213 dataset. Several functionals, especially double hybrids, and also SCAN and SCAN0 yield reliable second cumulants, although some modern, empirically parameterized functionals yield more disappointing performance. The H and Be atoms in particular are challenging for nearly all methods, indicating that future functional development could benefit from inclusion of their density information in training or testing protocols.

We present the second prototype of a time-to-digital (TDC) ASIC for the upgrade of the ATLAS Monitored Drift Tube (MDT) detector for High-Luminosity LHC operations. Compared to the first prototype, triple modular redundancy has been implemented for the configuration and flow control logic. The total power consumption is increased by less than 10 mW while achieving the same time resolution and channel uniformity. A mini-DAQ system has been built to verify the front-end electronics chain with the new prototype together with other ASICs and boards in triggered mode. Cosmic ray tests with a small-diameter MDT chamber indicate that the configuration and data transmission of the readout electronics perform well. It is expected that this prototype design will be used in the final production., Comment: 11 pages, 7 figures

TianQin is a planned space-based gravitational wave (GW) observatory consisting of three earth orbiting satellites with an orbital radius of about $10^5~{\rm km}$. The satellites will form a equilateral triangle constellation the plane of which is nearly perpendicular to the ecliptic plane. TianQin aims to detect GWs between $10^{-4}~{\rm Hz}$ and $1~{\rm Hz}$ that can be generated by a wide variety of important astrophysical and cosmological sources, including the inspiral of Galactic ultra-compact binaries, the inspiral of stellar-mass black hole binaries, extreme mass ratio inspirals, the merger of massive black hole binaries, and possibly the energetic processes in the very early universe or exotic sources such as cosmic strings. In order to start science operations around 2035, a roadmap called the 0123 plan is being used to bring the key technologies of TianQin to maturity, supported by the construction of a series of research facilities on the ground. Two major projects of the 0123 plan are being carried out. In this process, the team has created a new generation $17~{\rm cm}$ single-body hollow corner-cube retro-reflector which has been launched with the QueQiao satellite on 21 May 2018; a new laser ranging station equipped with a $1.2~{\rm m}$ telescope has been constructed and the station has successfully ranged to all the five retro-reflectors on the Moon; and the TianQin-1 experimental satellite has been launched on 20 December 2019 and the first round result shows that the satellite has exceeded all of its mission requirements., Comment: 16 pages, 1 figure, invited article to a special section in PTEP

The TianQin-1 satellite (TQ-1), which is the first technology demonstration satellite for the TianQin project, was launched on 20 December 2019. The first round of experiment had been carried out from 21 December 2019 until 1 April 2020. The residual acceleration of the satellite is found to be about $1\times10^{-10}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$ and about $5\times10^{-11}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.05~{\rm Hz}\,$, measured by an inertial sensor with a sensitivity of $5\times10^{-12}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$. The micro-Newton thrusters has demonstrated a thrust resolution of $0.1~\mu{\rm N}$ and a thrust noise of $0.3~\mu{\rm N}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}$. The residual noise of the satellite with drag-free control is $3\times10^{-9}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$. The noise level of the optical readout system is about $30~{\rm pm}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$. The temperature stability at temperature monitoring position is controlled to be about $\pm3~{\rm mK}$ per orbit, and the mismatch between the center-of-mass of the satellite and that of the test mass is measured with a precision of better than $0.1~{\rm mm}$., Comment: 17 pages, 11 figures

Dimension reduction and visualization of high-dimensional data have become very important research topics because of the rapid growth of large databases in data science. In this paper, we propose using a generalized sigmoid function to model the distance similarity in both high- and low-dimensional spaces. In particular, the parameter b is introduced to the generalized sigmoid function in low-dimensional space, so that we can adjust the heaviness of the function tail by changing the value of b. Using both simulated and real-world data sets, we show that our proposed method can generate visualization results comparable to those of uniform manifold approximation and projection (UMAP), which is a newly developed manifold learning technique with fast running speed, better global structure, and scalability to massive data sets. In addition, according to the purpose of the study and the data structure, we can decrease or increase the value of b to either reveal the finer cluster structure of the data or maintain the neighborhood continuity of the embedding for better visualization. Finally, we use domain knowledge to demonstrate that the finer subclusters revealed with small values of b are meaningful.

Electro-production of several pentaquark states is investigated in this paper. eSTARlight package is adapted to study the electro-production of $J/\psi$ and $\Upsilon (1S)$ via pentaquark $P_c$ and $P_b$ resonance channels in $e p \to e eJ/\psi p$ and $e p \to e\Upsilon(1S) p$ scattering processes at proposed electron-ion colliders (EICs). The results in this paper are compared to the non-resonance $t$-channels, which is described in pomeron exchange model in our studies. Some pseudo-rapidity distributions rapidity distributions of $J/\psi$ and $\Upsilon(1S)$ are presented for proposed EICs including EicC and EIC-US. It is found that EicC is a good platform to identify $P_b$ states in the future.

We study the electroproduction of the LHCb pentaquark states with the assumption that they are resonant states. The main concern here is to investigate the final state distribution in the phase space in order to extract the feeble pentaquark signal from the large non-resonant background. Our results show that the ratio of the signal to background would increase significantly with proper kinematic cut, which would be very helpful for future experimental analysis., Comment: 16 pages, 5 figures, some minor modifications, accepted for publication in Chinese Physics C

Due to the discovery of the hidden-charm pentaquark $P_c$ states by the LHCb collaboration, the interests on the candidates of hidden-bottom pentaquark $P_b$ states are increasing. They are anticipated to exist as the analogues of the $P_c$ states in the bottom sector and predicted by many models. We give an exploration of searching for a typical $P_b$ in the $\gamma p \to \Upsilon p$ reaction, which shows a promising potential to observe it at an electron-ion collider. The possibility of searching for $P_b$ in open-bottom channels are also briefly discussed. Meanwhile, the $t$-channel non-resonant contribution, which in fact covers several interesting topics at low energies, is systematically investigated., Comment: 18 pages,, publication version, references updated

Given a locally compact group $G$ and $1\leq p<\infty$, a sufficient condition ensuring the \emph{disjoint hypercyclicity} of finitely many weighted translations on $L^p(G)$ was investigated in this paper., Comment: 10

Speech-related applications deliver inferior performance in complex noise environments. Therefore, this study primarily addresses this problem by introducing speech-enhancement (SE) systems based on deep neural networks (DNNs) applied to a distributed microphone architecture, and then investigates the effectiveness of three different DNN-model structures. The first system constructs a DNN model for each microphone to enhance the recorded noisy speech signal, and the second system combines all the noisy recordings into a large feature structure that is then enhanced through a DNN model. As for the third system, a channel-dependent DNN is first used to enhance the corresponding noisy input, and all the channel-wise enhanced outputs are fed into a DNN fusion model to construct a nearly clean signal. All the three DNN SE systems are operated in the acoustic frequency domain of speech signals in a diffuse-noise field environment. Evaluation experiments were conducted on the Taiwan Mandarin Hearing in Noise Test (TMHINT) database, and the results indicate that all the three DNN-based SE systems provide the original noise-corrupted signals with improved speech quality and intelligibility, whereas the third system delivers the highest signal-to-noise ratio (SNR) improvement and optimal speech intelligibility., Comment: deep neural network, multi-channel speech enhancement, distributed microphone architecture, diffuse noise environment

This paper proposed a hierarchical visible autoencoder in the adaptive phantom limbs generation according to the kinetic behavior of functional body-parts, which are measured by heterogeneous kinetic sensors. The proposed visible hierarchical autoencoder consists of interpretable and multi-correlated autoencoder pipelines, which is directly derived from the hierarchical network described in forest data-structure. According to specified kinetic script (e.g., dancing, running, etc.) and users' physical conditions, hierarchical network is extracted from human musculoskeletal network, which is fabricated by multiple body components (e.g., muscle, bone, and joints, etc.) that are bio-mechanically, functionally, or nervously correlated with each other and exhibit mostly non-divergent kinetic behaviors. Multi-layer perceptron (MLP) regressor models, as well as several variations of autoencoder models, are investigated for the sequential generation of missing or dysfunctional limbs. The resulting kinematic behavior of phantom limbs will be constructed using virtual reality and augmented reality (VR/AR), actuators, and potentially controller for a prosthesis (an artificial device that replaces a missing body part). The addressed work aims to develop practical innovative exercise methods that (1) engage individuals at all ages, including those with a chronic health condition(s) and/or disability, in regular physical activities, (2) accelerate the rehabilitation of patients, and (3) release users' phantom limb pain. The physiological and psychological impact of the addressed work will critically be assessed in future work., Comment: arXiv admin note: text overlap with arXiv:1612.06336 by other authors

Time and charge measurements over a large dynamic range from 1 Photo Electron (P.E.) to 4000 P.E. are required for the Water Cherenkov Detector Array (WCDA), which is one of the key components in the Large High Altitude Air Shower Observatory (LHAASO). To simplify the circuit structure of the readout electronics, a front end ASIC was designed. Based on the charge-to-time conversion method, the output pulse width of the ASIC corresponds to the input signal charge information while time information of the input signal is picked off through a discriminator, and thus the time and charge information can be digitized simultaneously using this ASIC and a following Time-to-Digital Converter (TDC). To address the challenge of mismatch among the channels observed in the previous prototype version, this work presents approaches for analyzing the problem and optimizing the circuits. A new version of the ASIC was designed and fabricated in the GLOBALFOUNDRIES 0.35 um CMOS technology, which integrates 6 channels (corresponding to the readout of the 3 PMTs) in each chip. The test results indicate that the mismatch between the channels is significantly reduced to less than 20% using the proposed approach. The time measurement resolution better than 300 ps is achieved, and the charge measurement resolution is better than 10% at 1 P.E., and 1% at 4000 P.E., which meets the application requirements.

The Large High Altitude Air Shower Observatory is in the R&D phase, in which the Water Cherenkov Detector Array is an important part. The signals of Photo-Multiplier Tubes would vary from single photo electron to 4000 photo electrons, and both high precision charge and time measurement is required. To simplify the signal processing chain, the charge-to-time conversion method is employed. A prototype of the front-end readout ASIC is designed and fabricated in Chartered 0.35 {\mu}m CMOS technology, which integrates time disctrimination and converts the input charge information to pulse widths. With Time-to-Digital Converters, both time and charge can be digitized at the same time. We have conducted initial tests on this chip, and the results indicate that a time resolution better than 0.5 ns is achieved over the full dynamic range (1~ 4000 photo electrons, corresponding to 0.75 ~ 3000 pC with the threshold of 0.188 pC); the charge resolution is better than 1% with large input amplitudes (500 ~ 4000 photo electrons), and remains better than 15% with a 1 photo electron input amplitude, which is beyond the application requirement.

Recent demonstrations of electrical detection and manipulation of antiferromagnets (AFMs) have opened new opportunities towards robust and ultrafast spintronics devices. However, it is difficult to establish the connection between the spin-transport behavior and the microscopic AFM domain states due to the lack of the real-time AFM domain imaging technique under the electric field. Here we report a significant Voigt rotation up to 60 mdeg in thin NiO(001) films at room temperature. Such large Voigt rotation allows us to directly observe AFM domains in thin-film NiO by utilizing a wide-field optical microscope. Further complementary XMLD-PEEM measurement confirms that the Voigt contrast originates from the NiO AFM order. We examine the domain pattern evolution at a wide range of temperature and with the application of external magnetic field. Comparing to large-scale-facility techniques such as the X-ray photoemission electron microscopy, the use with a wide-field, tabletop optical imaging method enables straightforward access to domain configurations of single-layer AFMs., Comment: 17 pages , 5 figures

The Zhaoshan long-baseline Atom Interferometer Gravitation Antenna (ZAIGA) is a new type of underground laser-linked interferometer facility, and is currently under construction. It is in the 200-meter-on-average underground of a mountain named Zhaoshan which is about 80 km southeast to Wuhan. ZAIGA will be equipped with long-baseline atom interferometers, high-precision atom clocks, and large-scale gyros. ZAIGA facility will take an equilateral triangle configuration with two 1-km-apart atom interferometers in each arm, a 300-meter vertical tunnel with atom fountain and atom clocks mounted, and a tracking-and-ranging 1-km-arm-length prototype with lattice optical clocks linked by locked lasers. The ZAIGA facility will be used for experimental research on gravitation and related problems including gravitational wave detection, high-precision test of the equivalence principle of micro-particles, clock based gravitational red-shift measurement, rotation measurement and gravito-magnetic effect., Comment: 20 pages, 15 figures

We present the prototype of a time-to-digital (TDC) ASIC for the upgrade of the ATLAS Monitored Drift Tube (MDT) detector for high-luminosity LHC operation. This ASIC is based on a previously submitted demonstrator ASIC designed for timing performance evaluation, and includes all features necessary for the various operation modes, as well as the migration to the TSMC 130 nm CMOS technology. We present the TDC design with the emphasis on added features and performance optimization. Tests of the timing performance demonstrate that this ASIC meets the design specifications. The TDC has a bin size of about 780 ps, and a timing bin variations within 40 ps for all 24 channels with leading and trailing edge digitization, while the power consumption has been limited to 250 mW, corresponding to a consumption of about 5.2 mW per edge measurement., Comment: 9 pages, 7 figures

In this paper, we study the weighted composition operator on the Fock space $\mf$ of slice regular functions. First, we characterize the boundedness and compactness of the weighted composition operator. Subsequently, we describe all the isometric composition operators. Finally, we introduce a kind of (right)-anti-complex-linear weighted composition operator on $\mf$ and obtain some concrete forms such that this (right)-anti-linear weighted composition operator is a (right)-conjugation. Specially, we present equivalent conditions ensuring weighted composition operators which are conjugate $\mathcal{C}_{a,b,c}-$commuting or complex $\mathcal{C}_{a,b,c}-$ symmetric on $\mf$, which generalized the classical results on $\mathcal{F}^2(\mathbb{C}).$ At last part of the paper, we exhibit the closed expression for the kernel function of $\mf.$, Comment: 39 pages

We develop a methodology to optimise the measurement of Baryon Acoustic Oscillation (BAO) from a given galaxy sample. In our previous work, we demonstrated that one can measure BAO from tracers in under-dense regions (voids). In this study, we combine the over-dense and under-dense tracers (galaxies & voids) to obtain better constraints on the BAO scale. To this end, we modify the de-wiggled BAO model with an additional parameter to describe both the BAO peak and the underlying exclusion pattern of void 2PCFs. We show that after applying BAO reconstruction to galaxies, the BAO peak scale of both galaxies and voids are unbiased using the modified model. Furthermore, we use a new 2PCF estimator for a multi-tracer analysis with galaxies and voids. In simulations, the joint sample improves by about 10% the constraint for the post-reconstruction BAO peak position compared to the result from galaxies alone, which is equivalent to an enlargement of the survey volume by 20%. Applying this method to the BOSS DR12 data, we have an 18% improvement for the low-z sample (0.2

We present an investigation of a coronal cavity observed above the western limb in the coronal red line Fe X 6374 {\AA} using a telescope of Peking University and in the green line Fe XIV 5303 {\AA} using a telescope of Yunnan Observatories, Chinese Academy of Sciences during the total solar eclipse on 2017 August 21. A series of magnetic field models are constructed based on the magnetograms taken by the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO) one week before the eclipse. The model field lines are then compared with coronal structures seen in images taken by the Atmospheric Imaging Assembly on board SDO and in our coronal red line images. The best-fit model consists of a flux rope with a twist angle of 3.1$\pi$, which is consistent with the most probable value of the total twist angle of interplanetary flux ropes observed at 1 AU. Linear polarization of the Fe XIII 10747 {\AA} line calculated from this model shows a "lagomorphic" signature that is also observed by the Coronal Multichannel Polarimeter of the High Altitude Observatory. We also find a ring-shaped structure in the line-of-sight velocity of Fe XIII 10747 {\AA}, which implies hot plasma flows along a helical magnetic field structure, in the cavity. These results suggest that the magnetic structure of the cavity is a highly twisted flux rope, which may erupt eventually. The temperature structure of the cavity has also been investigated using the intensity ratio of Fe XIII 10747 {\AA} and Fe X 6374 {\AA}., Comment: 7 figures, accepted by ApJ

The Baryon Acoustic Oscillations (BAO) refer to the ripples of material density in the Universe. As the most direct density tracers in the universe, galaxies have been commonly used in studies of BAO peak detection. The spatial number density of galaxies, to a certain extent, reflects the distribution of the material density of our Universe. Using galaxies as matter tracers, we can construct more overlapping empty spheres (DT voids) than the matter tracers, via Delaunay Triangulation technique. We show that their radii excellently reflect the galaxy number density round them, and they can serve as reliable different density region tracers. Using the data from an unprecedented large-scale $N$-body simulation "TianNu", we conduct some fundamental statistical studies and clustering analysis of the DT voids. We discuss in detail the representative features of two-point correlation functions of different DT void populations. We show that the peak, the position of which corresponds to the average radius of data samples, is the most representative feature of the two-point correlation function of the DT voids. In addition, we also construct another voids, the disjoint voids, and investigate their some statistical properties and clustering properties. And we find that the occupied space of all disjoint voids accounts for about $45\%$ of the volume of the simulation box, regardless of the number density of mock galaxies. We also investigate the BAO detections based on different tracers, i.e. mock galaxies, low-density region tracers, and high-density region tracers respectively. Our results show that BAO intensities detected by low/high-density region tracers are enhanced significantly compared to the BAO detection by mock galaxies, for the mock galaxy catalogue with the number density of $7.52\times10^{-5}$ $h^3$ Mpc$^{-3}$., Comment: 15 pages, 12 figures, submitted to ApJS

Using cosmological $N$-body simulation which coevolves cold dark matter (CDM) and neutrino particles, we discover the local effect of massive neutrinos on the spatial distribution of CDM halos, reflected on properties of the Delaunay Triangulation (DT) voids. Smaller voids are generally in regions with higher neutrino abundance and so their surrounding halos are impacted by a stronger neutrino free streaming. This makes the voids larger (surrounding halos being washed outward the void center). On the contrary, larger voids are generally in regions with lower neutrino abundance and so their surrounding halos are less impacted by neutrino free streaming, making the voids smaller (surrounding halos being squeezed toward the void center). This characteristic change of the spatial distribution of the halos suppresses the 2-point correlation function of halos on scales $\sim$ 1 Mpc$/h$ and significantly skews the number function of the DT voids, which serve as measurable neutrino effects in current or future galaxy surveys.

The upgrade of the ATLAS muon spectrometer for high-luminosity LHC requires new trigger and readout electronics for the various elements of the detector. We present the design of a time-to-digital converter (TDC) ASIC prototype for the ATLAS Monitored Drift Tube (MDT) detector. The chip was fabricated in a GlobalFoundries 130 nm CMOS technology. Studies indicate that its timing and power consumption characteristics meet the design specifications, with a timing bin variation of 40 ps for all 48 channels with a power consumption of about 6.5 mW per channel., Comment: 9 pages, 12 figures

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V., Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected)

With the Jaynes-Cummings model, we have studied the atom and light field quantum entanglement of multiphoton transition, and researched the effect of initial state superposition coefficient $C_{1}$, the transition photon number $N$, the quantum discord $\delta$ and the nonlinear coefficient $\chi$ on the quantum entanglement degrees. We have given the quantum entanglement degrees curves with time evolution, and obtained some results, which should have been used in quantum computing and quantum information., Comment: arXiv admin note: text overlap with arXiv:1404.0821, arXiv:1205.0979 by other authors

A proper total weighting of a graph $G$ is a mapping $\phi$ which assigns to each vertex and each edge of $G$ a real number as its weight so that for any edge $uv$ of $G$, $\sum_{e \in E(v)}\phi(e)+\phi(v) \ne \sum_{e \in E(u)}\phi(e)+\phi(u)$. A $(k,k')$-list assignment of $G$ is a mapping $L$ which assigns to each vertex $v$ a set $L(v)$ of $k$ permissible weights and to each edge $e$ a set $L(e)$ of $k'$ permissible weights. An $L$-total weighting is a total weighting $\phi$ with $\phi(z) \in L(z)$ for each $z \in V(G) \cup E(G)$. A graph $G$ is called $(k,k')$-choosable if for every $(k,k')$-list assignment $L$ of $G$, there exists a proper $L$-total weighting. As a strenghtening of the well-known 1-2-3 conjecture, it was conjectured in [ Wong and Zhu, Total weight choosability of graphs, J. Graph Theory 66 (2011), 198-212] that every graph without isolated edge is $(1,3)$-choosable. It is easy to verified this conjecture for trees, however, to prove it for wheels seemed to be quite non-trivial. In this paper, we develop some tools and techniques which enable us to prove this conjecture for generalized Halin graphs.

We propose a fully cooperative coinfection model in which singly infected individuals are more likely to acquire a second disease than those who are susceptible, and doubly infected individuals are also assumed to be more contagious than those infected with one disease. The dynamics of such fully cooperative coinfection model between two interacting infectious diseases is investigated through well-mixed and network-based approaches. We show that the former approach exhibits three types of hysteresis, namely, $C$, $S_l$ and $S_r$ types, where the last two types have not been identified before. The first (resp., the second and the third) type exhibits (resp., exhibit) discontinuous outbreak transition from the disease free (resp., low prevalence) state to the high prevalence state when a transmission rate crosses a threshold from the below. Moreover, the third (resp., the first and the second) type possesses (resp., possess) discontinuous eradication transition from the high prevalence state to the low prevalence (resp., disease free) state when the transmission rate reaches a threshold from the above. Complete characterization of these three types of hysteresis in term of parameters measuring the uniformity of the model is also provided. Finally, we assess numerically this epidemic dynamics in random networks., Comment: 10 pages, 12 figures

Let $S(\mathbb{D})$ be the collection of all holomorphic self-maps on $\mathbb{D}$ of the complex plane $\mathbb{C}$, and $C_{\varphi}$ the composition operator induced by $\varphi\in S(\mathbb{D})$. We obtain that there are no hypercyclic composition operators on the little Bloch space $\mathcal{B}_0$ and the Besov space $B_p$., Comment: 10 pages

This paper studies the generalizations of the Stirling numbers of both kinds and the Lah numbers in association with the normal order problem in the Weyl algebra $W=\langle x,D|Dx-xD=1\rangle$. Any word $\omega\in W$ with $m$ $x$'s and $n$ $D$'s can be expressed in the normally ordered form $\omega=x^{m-n}\sum_{k\ge 0} {{\omega}\brace {k}} x^{k}D^{k}$, where ${{\omega}\brace {k}}$ is known as the Stirling number of the second kind for the word $\omega$. This study considers the expansions of restricted words $\omega$ in $W$ over the sequences $\{(xD)^{k}\}_{k\ge 0}$ and $\{xD^{k}x^{k-1}\}_{k\ge 0}$. Interestingly, the coefficients in individual expansions turn out to be generalizations of the Stirling numbers of the first kind and the Lah numbers. The coefficients will be determined through enumerations of some combinatorial structures linked to the words $\omega$, involving decreasing forest decompositions of quasi-threshold graphs and non-attacking rook placements on Ferrers boards. Extended to $q$-analogues, weighted refinements of the combinatorial interpretations are also investigated for words in the $q$-deformed Weyl algebra., Comment: 24 pages, 9 figures

We found several new equivalent characterizations for the boundedness and compactness of the differences of weighted differentiation composition operators from Bloch-type space to weighted-type space. Especially, we estimated its essential norm in terms of the $n$-th power of the induced analytic self-maps on the unit disk., Comment: 20 pages

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) began observations in July 2014. It pursues three core programs: APOGEE-2, MaNGA, and eBOSS. In addition, eBOSS contains two major subprograms: TDSS and SPIDERS. This paper describes the first data release from SDSS-IV, Data Release 13 (DR13), which contains new data, reanalysis of existing data sets and, like all SDSS data releases, is inclusive of previously released data. DR13 makes publicly available 1390 spatially resolved integral field unit observations of nearby galaxies from MaNGA, the first data released from this survey. It includes new observations from eBOSS, completing SEQUELS. In addition to targeting galaxies and quasars, SEQUELS also targeted variability-selected objects from TDSS and X-ray selected objects from SPIDERS. DR13 includes new reductions of the SDSS-III BOSS data, improving the spectrophotometric calibration and redshift classification. DR13 releases new reductions of the APOGEE-1 data from SDSS-III, with abundances of elements not previously included and improved stellar parameters for dwarf stars and cooler stars. For the SDSS imaging data, DR13 provides new, more robust and precise photometric calibrations. Several value-added catalogs are being released in tandem with DR13, in particular target catalogs relevant for eBOSS, TDSS, and SPIDERS, and an updated red-clump catalog for APOGEE. This paper describes the location and format of the data now publicly available, as well as providing references to the important technical papers that describe the targeting, observing, and data reduction. The SDSS website, http://www.sdss.org, provides links to the data, tutorials and examples of data access, and extensive documentation of the reduction and analysis procedures. DR13 is the first of a scheduled set that will contain new data and analyses from the planned ~6-year operations of SDSS-IV., Comment: Full information on DR13 available at http://www.sdss.org. Comments welcome to spokesperson@sdss.org. To be published in ApJS

Cosmic voids found in galaxy surveys are defined based on the galaxy distribution in redshift space. We show that the large scale distribution of voids in redshift space traces the fluctuations in the dark matter density field \delta(k) (in Fourier space with \mu being the line of sight projected k-vector): \delta_v^s(k) = (1 + \beta_v \mu^2) b^s_v \delta(k), with a beta factor that will be in general different than the one describing the distribution of galaxies. Only in case voids could be assumed to be quasi-local transformations of the linear (Gaussian) galaxy redshift space field, one gets equal beta factors \beta_v=\beta_g=f/b_g with f being the growth rate, and b_g, b^s_v being the galaxy and void bias on large scales defined in redshift space. Indeed, in our mock void catalogs we measure void beta factors being in good agreement with the galaxy one. Further work needs to be done to confirm the level of accuracy of the beta factor equality between voids and galaxies, but in general the void beta factor needs to be considered as a free parameter for RSD studies., Comment: 10 pages, 9 figures; matches the version accepted by PRD

Quantum steering enables one party to communicate with another remote party even if the sender is untrusted. Such characteristics of quantum systems not only provide direct applications to quantum information science, but are also conceptually important for distinguishing between quantum and classical resources. While concrete illustrations of steering have been shown in several experiments, quantum steering has not been certified for higher dimensional systems. Here, we introduce a simple method to experimentally certify two different kinds of quantum steering: Einstein-Podolsky-Rosen (EPR) steering and single-system (SS) steering (i.e., temporal steering), for dimensionality (d) up to d=16. The former reveals the steerability among bipartite systems, whereas the latter manifests itself in single quantum objects. We use multidimensional steering witnesses to verify EPR steering of polarization-entangled pairs and SS steering of single photons. The ratios between the measured witnesses and the maximum values achieved by classical mimicries are observed to increase with $d$ for both EPR and SS steering. The designed scenario offers a new method to study further the genuine multipartite steering of large dimensionality and potential uses in quantum information processing.

Nitride films are promising for advanced optoelectronic and electronic device applications. However, some challenges continue to impede development of high aluminum-containing devices. The two major difficulties are growth of high crystalline quality films with aluminum-rich compositions, and efficiently doping such films p-type. These problems have severely limited use of aluminum-rich nitride films grown by molecular beam epitaxy. A way around these problems is through use of a liquid-metal-enabled approach to molecular beam epitaxy. Although the presence of a liquid metal layer at the growth front is reminiscent of conventional liquid phase epitaxy, this approach is different in its details. Conventional liquid epitaxy is a near-thermodynamic equilibrium process which liquid-metal assisted molecular beam epitaxy is not. Growth of aluminum-rich nitrides is primarily driven by the kinetics of the molecular vapor fluxes, and the surface diffusion of adatoms through a liquid metal layer before incorporation. This paper reports on growth of high crystalline quality and highly doped aluminum-containing nitride films. Measured optical and electrical characterization data show that the approach is viable for growth of atomically smooth aluminum-containing nitride heterostructures. Extremely high p-type doping of up to $6 \times 10$$^{17}$ cm$^{-3}$ and n-type doping of up to $1 \times 10$$^{20}$ cm$^{-3}$ in Al$_{0.7}$Ga$_{0.3}$N films was achieved. Use of these metal-rich conditions is expected to have a significant impact on high efficiency and high power optoelectronic and electronic devices that require both high crystalline quality and highly doped (Al,Ga)N films.