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Feature selection is a widely used data pre‐processing method. However, the research on feature selection stability is very rare. Although there are some related studies that mainly focus on filters rather than wrappers, especially wrappers based on evolutionary algorithms. In this paper, a stable feature selection method called stable brain storm optimisation (SBSO) is proposed. It puts forward a new population initialisation strategy, which treats stable feature selection results as guide information for initialisation and utilises such chaos to initialise population. SBSO also sets up an information archive to store the historical optimal individuals dynamically. A large number of experiments show that SBSO gives excellent classification performance when compared with other methods, and superior feature selection stability when compared with other wrappers.

The electromagnetic situation, which can promote the abilities of understanding and decision‐making for the battlefield, has attracted significant interest recently in information‐based warfare. This paper investigates the deep learning‐based electromagnetic situation analysis and judgment in a complicated battlefield environment. To comprehensively simulate the two‐sided battling process, a turn‐based confrontation strategy is proposed, and an electromagnetic situation analysis and judgment model are then designed based on the AlphaGo Zero algorithm to achieve efficient situation analysis and decision‐making. In addition, an electromagnetic situation‐based attack‐defense platform is developed to realize and evaluate this designed model. Simulation results demonstrate that this designed model achieves significant performance in electromagnetic situation analysis and judgment compared with the Monte Carlo Tree Search based baseline.

High-frequency vibration of motion platform leads to paired echo for synthetic aperture radar (SAR) imaging, especially in terahertz band due to its shorter wave length. Different from most existing parameters estimation methods only considering single component high-frequency vibration, in this paper a novel method considering multi-components vibration model is proposed based on fractional Fourier transform (FrFT) with combination of quasi-maximum likelihood (QML) and random sample consensus (RANSAC). Based on the model establishment of high-frequency vibration error in the echo, its instantaneous chirp rate (ICR) is firstly estimated by FrFT in sliding sub-aperture, followed which the vibration parameters are coarsely obtained through spectrum analysis and least square (LS) regression. To further refine the parameters estimates, QML is developed for compensating the deviation both caused by the frequency spectrum leakage and the error propagation effects by one-dimensional search over the vibration frequency. Meanwhile, RANSAC is adopted for avoiding the outlier of the ICR estimates in LS regression, especially at low signal-to-noise ratio (SNR). Thus, the refinement strategy based on the combination of QML and RANSAC is developed, whose utilization improves the estimation accuracy of vibration parameters. Finally, the paired echo in terahertz SAR (THz-SAR) imaging is effectively suppressed by the proposed method, and the high-quality THz-SAR imaging results are achieved. Both simulations of single component and multi-components high-frequency vibration are used to verify the validity of the proposed method. The simulation results show that the proposed method has higher estimation accuracy even at low SNR.

The range migration algorithm (RMA) based on Fourier transformation is widely applied in millimeter-wave (MMW) close-range imaging because of its few operations and small approximation. However, its interpolation stage is not effective due to the involved intensive logic controls, which limits the speed performance in a graphics processing unit (GPU) platform. Therefore, in this paper, we present an acceleration optimization method based on the hybrid GPU and central processing unit (CPU) parallel computation for implementing the RMA. The proposed method exploits the strong logic-control capability of the CPU to assist the GPU in processing the logic controls of the interpolation stage. The common positions of wavenumber-domain components to be interpolated are calculated by the CPU and stored in the constant memory for broadcast at any time. This avoids the repetitive computation consumed in a GPU-only scheme. Then the GPU is responsible for the remaining matrix-related steps and outputs the needed wavenumber-domain values. The imaging experiments verify the acceleration efficiency of the proposed method and demonstrate that the speedup ratio of our proposed method is more than 15 times of that by the CPU-only method, and more than 2 times of that by the GPU-only method.

We report the first dark matter search results using the commissioning data from PandaX-4T. Using a time projection chamber with 3.7-tonne of liquid xenon target and an exposure of 0.63 tonne$\cdot$year, 1058 candidate events are identified within an approximate nuclear recoil energy window between 5 and 100 keV. No significant excess over background is observed. Our data set a stringent limit to the dark matter-nucleon spin-independent interactions, with a lowest excluded cross section (90% C.L.) of $3.8\times10^{-47} $cm$^2$ at a dark matter mass of 30 GeV/$c^2$., Comment: proof draft as PRL

High‐dimensional data exists widely in the real world, such as gene, magnetic resonance imaging (MRI), text, web data and so on. Feature selection is an effective and powerful method that is often adopted to reduce dimensions of high‐dimensional data for promoting learning algorithm's ability to obtain useful information from them. However, feature selection stability lacks attention for a long time, which plays an important role in getting compelling results. This study proposes a stable feature selection approach called stable ant‐antlion optimiser (SALO) that is a modified hybrid evolutionary method combining ant colony optimisation and antlion optimiser. Several relative stable filter methods are employed to rank features as guide information, fisher discriminant rate is used to evaluate features as heuristic information, and F1 indicator and the similarity between feature subset and stable ranked feature list are taken as optimisation objects. Comprehensive experiments show the fantasy stability and excellent classification performance of our proposed sophisticated method.

Track reconstruction is necessary for time projection chamber (TPC), because TPCs usually face the measurement error that impedes gaining precise spacial and angular resolution. Kalman filter is a well-performed and applicable algorithm to denoise and reconstruct the event track. In this paper, we develop a six-dimensional Kalman filter to reconstruct the particle track in high-energy physics experiments, while the most common form of Kalman filter used in many research fields is four-dimensional. The modelisation is based on a gaseous TPC, and the whole reconstruction process is first tested by a toy Monte Carlo simulation. The results show the Kalman filter can effectively reduce the noise and improve the detector resolution. Then, the performance of the Kalman filter is also verified with the data produced by the Geant4 toolkit.

We present PandaX-II constraints on candidate WIMP-nucleon effective interactions involving the nucleon or WIMP spin, including, in addition to standard axial spin-dependent (SD) scattering, various couplings among vector and axial currents, magnetic and electric dipole moments, and tensor interactions. The data set corresponding to a total exposure of 54-ton-days is reanalyzed to determine constraints as a function of the WIMP mass and isospin coupling. We obtain WIMP-nucleon cross section bounds of $\rm 1.6 \times 10^{-41} cm^2$ and $\rm 9.0 \times 10^{-42} cm^2$ ($90\%$ c.l.) for neutron-only SD and tensor coupling, respectively, for a mass $M_\mathrm{WIMP} \sim {\rm 40~GeV}/c^2$. The SD limits are the best currently available for $M_\mathrm{WIMP} > {\rm 40~GeV}/c^2$. We show that PandaX-II has reached a sensitivity sufficient to probe a variety of other candidate spin-dependent interactions at the weak scale., 6 pages, 4 figures

Truth discovery methods aim to identify which piece of information is trustworthy from multi-sourced data. Most existing truth discovery methods, however, are designed for structured data and fail to meet the strong need to extract trustworthy information from raw text data. More specifically, existing methods ignore the semantic information of text answers, i.e., answers may contain multiple factors, the word usages may be diverse, and the answers may be partially correct. In addition, ubiquitous long-tail phenomenon exists in the tasks, i.e., most users provide only a few answers and only a few users provide plenty of answers, which causes the user reliability estimation for small users to be unreasonable. To tackle these challenges, we propose a Graph Convolutional Network (GCN) based truth discovery model to automatically discover trustworthy information from text data. Firstly, Smooth Inverse Frequency (SIF) is utilized to learn real-valued vector representations for answers. Then, we construct undirected graph with these vectors to capture the structural information of answers. Finally, the GCN is utilized to store and update the reliability of these answers, and sums up all the feature vectors of all neighboring answers to improve the accuracy and efficiency of truth discovery. Different from traditional methods, we use vectors to store the reliability of answers which have higher representation capability compared with real numbers, and network is utilized to capture complex relationships among answers rather than simplified functions. The experiment results on real datasets show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art truth discovery methods.

We report constraints on light dark matter through its interactions with shell electrons in the PandaX-II liquid xenon detector with a total 46.9 tonne$\cdot$day exposure. To effectively search for these very low energy electron recoils, ionization-only signals are selected from the data. 1821 candidates are identified within ionization signal range between 50 to 75 photoelectrons, corresponding to a mean electronic recoil energy from 0.08 to 0.15 keV. The 90% C.L. exclusion limit on the scattering cross section between the dark matter and electron is calculated based on Poisson statistics. Under the assumption of point interaction, we provide the world's most stringent limit within the dark matter mass range from 15 to 30 $\rm MeV/c^2$, with the corresponding cross section from $2.5\times10^{-37}$ to $3.1\times10^{-38}$ cm$^2$., 6 pages, 5 figures, 2 tables

In this paper, we propose a novel meta-heuristic algorithm, called Baby Search Algorithm (BSA), for solving complex optimization problems. BSA mimics the behavior of babies who have strong curiosity about external things and can actively look for things of interest. Generally speaking, babies' search behaviors are divided into two types, one is to find another interesting thing, which is similar to exploration in the optimization problem; the other is to approach the most interesting thing at present, which is similar to exploitation. In order to balance exploration and exploitation, we utilize two probability thresholds to control it. In addition, we further combine Levy flight and mutation operations in BSA, which can make the babies' search get closer to the nature of searching behavior. We validate the proposed algorithm against test functions and then compare its performance with those of twelve state-of-the-art algorithms. Finally, we discuss the implication of the results and suggestion for further research.

We report a systematic determination of the responses of PandaX-II, a dual phase xenon time projection chamber detector, to low energy recoils. The electron recoil (ER) and nuclear recoil (NR) responses are calibrated, respectively, with injected tritiated methane or $^{220}$Rn source, and with $^{241}$Am-Be neutron source, within an energy range from $1-25$ keV (ER) and $4-80$ keV (NR), under the two drift fields of 400 and 317 V/cm. An empirical model is used to fit the light yield and charge yield for both types of recoils. The best fit models can well describe the calibration data. The systematic uncertainties of the fitted models are obtained via statistical comparison against the data., Comment: 12 pages, 7 figures

The precision of local oscillator (LO) signal in in-phase and quadrature (IQ) demodulation strongly affects the imaging performance of millimeter wave (mmWave) radars. Therefore, to eliminate the requirement for high-precision LO, a simple yet effective digital IQ demodulation method has been proposed with the aid of a specified sampling scheme in order to eliminate the demand for LO. Based on the bandpass sampling theorem, the characteristic of the intermediate frequency signal of mmWave imaging indicates that the LO is unrequired if the sampling rate is twice of the frequency of the carrier of the intermediate signal. In this way, the in-phase signal would be directly and accurately obtained by performing the Binary-Phase-Shift-Keying (BPSK) modulation on the samples, based on which the IQ demodulation would be completed by using the Hilbert transform. The proposed method does not employ LO and thus simplifies the demodulation process and is suitable for implementation in a Field-Programmable Gate Array (FPGA) with fewer hardware resources. To verify the method, a three-dimensional mmWave radar imaging is carried out at the 30-34 GHz bandwidth, where the sampling and digital IQ demodulation are realized by an ADC (AD9250) and FPGA (XC7K325T), respectively. The results show a simplified transceiver with lower requirements and the prospect of the proposed method being applied in radar imaging and other related fields.

The PandaX-III experiment uses high pressure gaseous time projection chamber to search for the neutrinoless double beta decay of 136Xe. A modular slow control system (SCS) has been designed to monitor all the critical parameters of the experiment. It ensures the proper operation of the experiment and provides necessary information for data corrections. The main subsystems of the experiment will be constantly monitored by the data collection module of the SCS, which collects data from sensors and sends them to the centralized database. When an alarm message is generated by the anomaly detection module, it will be sent to an alert website and related on-call experts immediately. A demonstrator of the SCS has been built for the PandaX-III prototype detector. The long-term test of it provided valuable experience for the final design of the SCS for PandaX-III.

Symmetry fractionalization, generating a large amount of symmetry-protected topological phases, provides scenarios for continuous phase transitions different from spontaneous symmetry breaking. However, it is hard to detect these symmetry-protected topological phase transitions experimentally. Motivated by the recent development of highly tunable ultracold polar molecules, we show that the setup in a zigzag optical lattice of this system provides a perfect platform to realize symmetry-protected topological phase transitions. By using infinite time-evolving block decimation, we obtain the phase diagram in a large parameter regions and find another scheme to realize the long-sought vector chiral phase, which is robust from quantum fluctuations. We discuss the existence of the chiral phase by an effective field analysis., 7 pages, 5 figures

Compared with microwave synthetic aperture radar (SAR), terahertz SAR (THz-SAR) is easier to achieve ultrahigh-resolution image due to its higher frequency and shorter wavelength. However, higher carrier frequency makes THz-SAR image quality very sensitive to high-frequency vibration error of motion platform. Therefore, this paper proposes a novel high-frequency vibration error estimation and compensation algorithm for THz-SAR imaging based on local fractional Fourier transform (LFrFT). Firstly, the high-frequency vibration error of the motion platform is modeled as a simple harmonic motion and THz-SAR echo signal received in each range pixel can be considered as a sinusoidal frequency modulation (SFM) signal. A novel algorithm for the parameter estimation of the SFM signal based on LFrFT is proposed. The instantaneous chirp rate of the SFM signal is estimated by determining the matched order of LFrFT in a sliding small-time window and the vibration acceleration is obtained. Hence, the vibration frequency can be estimated by the spectrum analysis of estimated vibration acceleration. With the estimated vibration acceleration and vibration frequency, the SFM signal is reconstructed. Then, the corresponding THz-SAR imaging algorithm is proposed to estimate and compensate the phase error caused by the high-frequency vibration error of the motion platform and realize high-frequency vibration error estimation and compensation for THz-SAR imaging. Finally, the effectiveness of the novel algorithm proposed in this paper is demonstrated by simulation results.

Learning from multi-modal data is very often in current data mining and knowledge management applications. However, the information imbalance between modalities brings challenges for many multi-modal learning tasks, such as cross-modal retrieval, image captioning, and image synthesis. Understanding the cross-modal information gap is an important foundation for designing models and choosing the evaluating criteria of those applications. Especially for text and image data, existing researches have proposed the abstractness to measure the information imbalance. They evaluate the abstractness disparity by training a classifier using the manually annotated multi-modal sample pairs. However, these methods ignore the impact of the intra-modal relationship on the inter-modal abstractness; besides, the annotating process is very labor-intensive, and the quality cannot be guaranteed. In order to evaluate the text-image relationship more comprehensively and reduce the cost of evaluating, we propose the relative abstractness index (RAI) to measure the abstractness between multi-modal items, which measures the abstractness of a sample according to its certainty of differentiating the items of another modality. Besides, we proposed a cycled generating model to compute the RAI values between images and text. In contrast to existing works, the proposed index can better describe the image-text information disparity, and its computing process needs no annotated training samples.

Collaborative computing among vehicles on the road has been regarded as a promising application for connected and intelligent vehicles as new computing and communication technologies emerge. Instead of using cloud computing services, a vehicle can complete computing-intensive tasks by making use of neighboring vehicles' idle computing resources and meanwhile, pay for this service to the assisting vehicles to increase their incentive to participate in such collaborative computing tasks. In collaborative computing among vehicles, how to balance the profits of both sides need to be investigated. What's more, the reliability of transactions is also a concerning problem. In this paper, we first introduce a collaborative computing scenario and propose a two-layer blockchain architecture to enhance the reliability of transactions. Then, we propose a coalition formation game-based collaborative computing algorithm to efficiently achieve effective computing coalition formation in a distributed manner. Simulation results show that the proposed algorithm can significantly improve the vehicles' utilities as well as the task performance.

We report the dark matter search results obtained using the full 132 ton$\cdot$day exposure of the PandaX-II experiment, including all data from March 2016 to August 2018. No significant excess of events is identified above the expected background. Upper limits are set on the spin-independent dark matter-nucleon interactions. The lowest 90% confidence level exclusion on the spin-independent cross section is $2.2\times 10^{-46}$ cm$^2$ at a WIMP mass of 30 GeV/$c^2$., Comment: 23 pages, 19 figures, 6 tables, version published in Chinese Physics C

Collaborative filtering recommendation with implicit feedbacks (e.g., clicks, views, and plays) is regarded as one of the most challenging issues in both academia and industry. From implicit feedbacks, we can only get a small fraction of observed data (positive examples), and the massive unobserved data are the mixture of negative examples and unlabeled positive examples. However, most of the existing efforts either treat unobserved data equally by assigning a uniform weight or uniformly weight observed data while ignoring the hidden information (i.e., visit frequency) in implicit feedbacks. This assumption may not hold in real-life scenarios since they cannot distinguish the contributions of the whole data and it easily leads to prediction bias. Besides, those approaches still suffer from low-efficiency issue. To this end, we propose an all-weighted matrix factorization and fast optimization strategy for effective and efficient recommendation. We first design a frequency-aware weighting scheme for observed data and a user-oriented weighting scheme for unobserved data nonuniformly. Then, the weighting schemes of both observed and unobserved data are combined in a unified way to form an all-weighted matrix factorization model. Afterwards, we present a surrogate objective function and develop a fast optimization strategy to enhance the efficiency. Extensive experimental results on real-world datasets demonstrate that our method outperforms the competitive baselines on several evaluation metrics.

High-frequency vibration error of a moving radar platform easily introduces a non-negligible phase of periodic modulation in radar echoes and greatly degrades terahertz synthetic aperture radar (THz-SAR) image quality. For solving the problem of THz-SAR image-quality degradation, the paper proposes a multi-component high-frequency vibration error estimation and compensation approach based on the short-time Fourier transform (STFT). To improve the robustness of the method against noise effects, STFT is used to extract the instantaneous frequency (IF) of a high-frequency vibration error signal, and the vibration parameters are coarsely obtained by the least square (LS) method. To reduce the influence of the STFT window widths, a method based on the maximum likelihood function (MLF) is developed for determining the optimal window width by a one-dimensional search of the window widths. In the case of high noise, many IF estimation values seriously deviate from the true ones. To avoid the singular values of IF estimation in the LS regression, the random sample consensus (RANSAC) is introduced to improve estimation accuracy. Then, performing the STFT with the optimal window width, the accurate vibration parameters are estimated by LS regression, where the singular values of IF estimation are excluded. Finally, the vibration error is reconstructed to compensate for the non-negligible phase of the platform-induced periodic modulation. The simulation results prove that the error compensation method can meet THz-SAR imaging requirements, even at a low signal-to-noise ratio (SNR).

In dark matter direct detection experiments, neutron is a serious source of background, which can mimic the dark matter-nucleus scattering signals. In this paper, we present an improved evaluation of the neutron background in the PandaX-II dark matter experiment by a novel approach. Instead of fully relying on the Monte Carlo simulation, the overall neutron background is determined from the neutron-induced high energy signals in the data. In addition, the probability of producing a dark-matter-like background per neutron is evaluated with a complete Monte Carlo generator, where the correlated emission of neutron(s) and γ(s) in the (α, n) reactions and spontaneous fissions is taken into consideration. With this method, the neutron backgrounds in the Run 9 (26-ton-day) and Run 10 (28-ton-day) data sets of PandaX-II are estimated to be (0.66±0.24) and (0.47±0.25) events, respectively.

We report the Neutrino-less Double Beta Decay (NLDBD) search results from PandaX-II dual-phase liquid xenon time projection chamber. The total live time used in this analysis is 403.1 days from June 2016 to August 2018. With NLDBD-optimized event selection criteria, we obtain a fiducial mass of 219 kg of natural xenon. The accumulated xenon exposure is 242 kg$\cdot$yr, or equivalently 22.2 kg$\cdot$yr of $^{136}$Xe exposure. At the region around $^{136}$Xe decay Q-value of 2458 keV, the energy resolution of PandaX-II is 4.2%. We find no evidence of NLDBD in PandaX-II and establish a lower limit for decay half-life of 2.4 $ \times 10^{23} $ yr at the 90% confidence level, which corresponds to an effective Majorana neutrino mass $m_{\beta \beta} < (1.3 - 3.5)$ eV. This is the first NLDBD result reported from a dual-phase xenon experiment., Comment: Submitted to Chinese Physics C

Dissimilarity measures play a crucial role in clustering and, are directly related to the performance of clustering algorithms. However, effectively measuring the dissimilarity is not easy, especially for categorical data. The main difficulty of the dissimilarity measurement for categorical data is that its representation lacks a clear space structure. Therefore, the space structure-based representation has been proposed to provide the categorical data with a clear linear representation space. This representation improves the clustering performance obviously but only applies to small data sets because its dimensionality increases rapidly with the size of the data set. In this paper, we investigate the possibility of reducing the dimensionality of the space structure-based representation while maintaining the same representation ability. A lightweight representation scheme is proposed by taking a set of representative objects as the reference system (called the reference set) to position other objects in the Euclidean space. Moreover, a preclustering-based strategy is designed to select an appropriate reference set quickly. Finally, the representation scheme together with the $k$ -means algorithm provides an efficient method to cluster the categorical data. The theoretical and the experimental analysis shows that the proposed method outperforms state-of-the-art methods in terms of both accuracy and efficiency.

Cross-modal entity resolution aims to find semantically similar items from objects of different modalities(e.g. image and text). The core way to solve the problem is to construct a shared space where multi-modal examples can be represented uniformly. In this paper, we propose a novel Co-Attentional Generative Adversarial Network(CAGAN) method for solving cross-modal entity resolution, which seeks an effective space based on co-attention mechanism and adversarial learning. The generative adversarial network that we design contains two parts, Generator and Discriminator, the generator aims to generate a shared space through intra-modal loss and inter-modal loss, while discriminator is a classifier which tries to discriminate the modalities based on the generated representation. In order to eliminate the imbalance of information between modalities, generate more consistent representation and accelerate the convergence speed of the network, co-attention mechanism is introduced into the network. Experimental results performed on two cross-modal datasets demonstrated the outstanding performance of the proposed method for cross-modal entity resolution.

We report a search for new physics signals using the low energy electron recoil events in the complete data set from PandaX-II, in light of the recent event excess reported by XENON1T. The data correspond to a total exposure of 100.7 ton⋅day with liquid xenon. With robust estimates of the dominant background spectra, we perform sensitive searches on solar axions and neutrinos with enhanced magnetic moment. It is found that the axion-electron coupling g Ae < 4.6 × 10–12 for an axion mass less than 0.1 keV/c 2 and the neutrino magnetic moment μν < 4.9 × 10–11 μ B at 90 % confidence level. The observed excess from XENON1T is within our experimental constraints.

Attacks detection is an important issue in collaborative spectrum sensing (CSS) system of cognitive radio networks. Many approaches are proposed to cope with the malicious behaviour of attackers. In existing works, spectrum sensing data (SSD) received by the fusion centre is generally assumed to be integrated. However, in practical scenarios, the received SSD may be incomplete due to the imperfect reporting channel or specific CSS schemes. The performance of existing attacks detection approaches may degrade especially when the probability of missing data is large. To alleviate this challenge, the authors focus on pre-processing of incomplete SSD and propose a practical imputation algorithm, which is derived from the maximum a posteriori probability criterion, to fill in the missing values. Simulation results indicate that the proposed algorithm restores the characteristics of the SSD, and mitigate the impacts of missing value on existing attacks detection algorithm effectively.

The PandaX project consists of a series of xenon-based experiments that are used to search for dark matter (DM) particles and to study the fundamental properties of neutrinos. The next DM experiment PandaX-4T will be using 4 ton liquid xenon in the sensitive volume, which is nearly a factor of seven larger than that of the previous experiment PandaX-II. Due to the increasing target mass, the sensitivity of searching for both DM and neutrinoless double-beta decay ($0\nu\beta\beta$) signals in the same detector will be significantly improved. However, the typical energy of interest for $0\nu\beta\beta$ signals is at the MeV scale, which is much higher than that of most popular DM signals. In the baseline readout scheme of the photomultiplier tubes (PMTs), the dynamic range is very limited. Signals from the majority of PMTs in the top array of the detector are heavily saturated at MeV energies. This deteriorates the $0\nu\beta\beta$ search sensitivity. In this paper we report a new design of the readout base board of the PMTs for future PandaX DM experiments and present its improved performance on the dynamic range.

Dissimilarity measurement plays a key role in clustering analysis. Due to the lack of order relation between categorical values, the clustering of categorical data is harder than that of numerical data. To improve the clustering quality of categorical data, SBC (space structure based clustering) algorithm proposed a novel representation scheme for the space structures of them. The representation scheme improved the discriminability of categorical data, while caused problems either: low-efficiency and high-dimensionality. In this work, we prove that it is possible to represent categorical data with the space structure more efficiently while maintaining the same clustering performance. To achieve that, a fraction of representative objects is selected as the reference set, with which a low-dimensional space structure matrix would be built. Since the reference set directly affect the dissimilarity measure, a cluster-based method is proposed to get better reference set. The theoretical and experimental proofs show that, compared with SBC method, the proposed methods are more efficient and extendable maintaining the approximately same clustering performance.

With Cloud computing widely used in various applications in recent years, high availability(HA), has become a key characteristic for the quality of these cloud computing platforms. And there are already many fault-tolerant mechanisms that can keep services available when fault happens. However, whether the service performance is degraded under the occurrence of failures is not concerned. Such feature, is now called "resilience", as the extension of high availability, and has become a hot topic in cloud computing. Based on former works of high availability testing on Network Function Virtualization(NFV) Platforms, this paper proposes a resilience test case design method. The method focuses on the service performance of NFV Platforms in face of high work-load and fault-load, and evaluates resilience of Virtual Network Function(VNF) services. Then a case study is conducted on an open-source NFV application, Clearwater. By comparing the test results to HA testing results, we learned more lessons on what failures impact the platform, which proved the feasibility and effectiveness of the proposed method.

Availability was considered a critical factor of Network Function Virtualization(NFV) platforms. And many existing testing tools with fault injection features for NFV, only aim at availability, while now more and more attention have been paid to the performance degradation when failure happens. Moreover, performance data on hardware/software resources, like CPU consumption, memory usage, are required to collect in performance and fault-tolerant testing. To fulfill the requirements above, this paper proposes a service-based testing framework for NFV platform performance evaluation, under workloads and fault loads. By taking advantage of the Microservice Architecture, the proposed framework will integrate some existing open-source testing frameworks, including performance testing tools, fault injection tools, and monitoring tools, and build a more comprehensive testing scenario. A case study is conducted on Clearwater, a widely used open-source NFV application, to validate the efficiency and usability of the proposed framework.

The PandaX-4T experiment, a four-ton scale dark matter direct detection experiment, is being planned at the China Jinping Underground Laboratory. In this paper we present a simulation study of the expected background in this experiment. In a 2.8-ton fiducial mass and the signal region between 1 to 10 keV electron equivalent energy, the total electron recoil background is found to be 4.9x10^{-5} /(kg day keV). The nuclear recoil background in the same region is 2.8x10^{-7}/(kg day keV). With an exposure of 5.6 ton-years, the sensitivity of PandaX-4T could reach a minimum spin-independent dark matter-nucleon cross section of 6x10^{-48} cm^{2} at a dark matter mass of 40 GeV/c^{2}., 11 pages, 13 figures

We search for nuclear recoil signals of dark matter models with a light mediator in PandaX-II, a direct detection experiment in China Jinping underground Laboratory. Using data collected in 2016 and 2017 runs, corresponding to a total exposure of 54 ton day, we set upper limits on the zero-momentum dark matter-nucleon cross section. These limits have a strong dependence on the mediator mass when it is comparable to or below the typical momentum transfer. We apply our results to constrain self-interacting dark matter models with a light mediator mixing with standard model particles, and set strong limits on the model parameter space for the dark matter mass ranging from $5~{\rm GeV}$ to $10~{\rm TeV}$., Version accepted by PRL, new figures (Fig.2 ) added

An (e, 2e+ion) Electron momentum spectrometer (EMS) upgraded from the third-generation (e, 2e) spectrometer is constructed in the University of Science and Technology of China (USTC). In the EMS, the energy-momentum density of electrons is derived through the charge measurement using the signals of the position sensitive detector (PSD). The information of ion fragments is derived through the time measurement and that information will be used for more research on collision dynamics. A PXI-based data acquisition (DAQ) system was designed. The system mainly consists of a 6-channel charge digital module (CDM) and a 9-channel time digital module (TDM). Shaping amplifiers (SAs) are also designed for charge measurement with CDM. According to the design specification, the CDM with SAs needs to measure charge from 100 to 900 fC with an accuracy of better than 1%. The TDM needs to measure the flight time from 0 to 15 us with a resolution of better than 100 ps. The TDM and CDM can be combined to run synchronously using a coincidence unit which also rejects the background events based on the tracking information of the electrons and the ions. Test results indicate that, within the dynamic range, the accuracy of the CDM is better than 0.4%. The RMS time resolution of better than 30 ps is achieved by using the method of time-to-digital converter (TDC) implemented in a field-programmable gate arrays (FPGA). The DAQ system has been assembled and works well with the EMS detectors.

Treated wastewater reuse has an important significance to solve the water shortage in the West Bank-Palestine. Based on fuzzy comprehensive evaluation method, the water quality of Al-Bireh WWTP is evaluated by referring to Chinese water quality evaluation criteria. It can help the Palestinians understand the current situation of sewage treatment and establish nationally appropriate water quality evaluation system.

A portable, low power, digital spectroscopy for CdZnTe (CZT) detector is designed and described in this paper. Its readout electronics is implemented with waveform sampling, Flash-based FPGA (Field Programmable Gate Array) and USB (universal serial bus) technique. The CZT detector, with an effective thickness of 3mm and a diameter of 5mm, is mounted on the same PCB board with high voltage supply module and low noise preamplifier. Current signal from CZT detector is integrated by a charge sensitive amplifier and then shaped by a CR-RC 3 shaper. The shaped signal is sampled by a 12-bit ADC and then processed by an IGLOO Flash-based low power FPGA in a digital module. A USB interface is adopted both for low voltage and high voltage power supply, and communicating with a host computer, which results in a portable solution with maximum power consumption of about 450mW. In the host computer, a graphic user interface (GUI) software is developed based on Labwindows/CVI, for real-timely displaying the energy spectrum or waveform of shaping signal. Test results showed that the dynamic range of the spectroscopy can reach 240fC (equivalent to about 7 MeV energy deposition in CZT detector) and the intrinsic noise (in RMS) for the electronics is less than 0.3fC (equivalent to about 8.7 keV).