Miracle-dz/UniFAR-FTUs huggingface dataset:
REVIEW AND PROSPECTS OF RF SOLID STATE POWER AMPLIFIERS FOR PARTICLE ACCELERATORS

Thanks to the growth of high power semiconductor technology, solid state power amplifier (SSPA) systems with several hundred kW RF power are now available for various accelerator fields. Following the successful development at 352 MHz that took place at SOLEIL in the 2000s, the technology was transferred to industry and SSPAs at different frequencies, power levels, and pulse lengths have been widely adopted. In this paper we report about the SOLEIL experience with SSPAs and review the used or planned SSPAs in other accelerator facilities.
SMU Data Science Review SMU Data

In this paper, we present a new model to predict the probability that a personal computer will become infected with malware. The dataset is selected from a Kaggle competition supported by Microsoft. The data includes computer conﬁguration, owner information, installed software, and conﬁguration information. In our research, several classiﬁcation models are utilized to assign a probability of a machine being infected with malware. The LightGBM classiﬁer is the optimum machine learning model by performing faster with higher eﬃciency and lower memory usage in this research. The LightGBM algorithm obtained a cross-validation ROC-AUC score of 74%. Leading factors and feature importance are also identiﬁed by LightGBM technique. Our research revealed that variables related to location, ﬁrmware version, operating system, and anti-virus software are the most important variables that have the highest weight in predicting malware detection.
J un 2 01 1 Koopmans ’ theorem in statistical Hartree-Fock theory

In this short paper, the validity of Koopmans’ theorem in the Hartree-Fock theory at non-zero temperature (Hartree-Fock statistical theory) is investigated. It is shown that Koopmans’ theorem does not apply in the grand-canonical ensemble, due to a missing contribution to the energy proportional to the interaction between two electrons belonging to the same orbital. Hartree-Fock statistical theory has also been applied in the canonical ensemble [Blenski et al., Phys. Rev. E 55, R4889 (1997)] for the purpose of photo-absorption calculations. In that case, the Hartree-Fock self-consistent-field equations are derived in the super-configuration approximation. It is shown that Koopmans’ theorem does not hold in the canonical ensemble, but that a restricted version of the theorem can be obtained, by assuming that a particular quantity multiplying the interaction matrix element in the expression of the energy does not change during the removal of an electron.
Towards Open Data in Digital Education Platforms

Despite the traction gained by the open data movement and the rise of big data and learning analytics in education, there is limited support for researchers in education to generate, access, and share experimental data using openly-available digital education platforms. To explore how this gap could be addressed and elicit requirements, we conducted a survey with 40 researchers in the field of technology-enhanced learning, examining their experience and needs handling research data. Drawing on the results of our survey, we devised a set of features that educational platforms should provide to address the identified requirements, enabling researchers in education to run studies within typical learning environments, adhere to legal and ethical frameworks concerning privacy, and share their data confidently with a wider audience. We then categorized these features into five stages that represent the user flow, namely (1) Bootstrapping Research Studies, (2) Ensuring Consent, (3) Gathering Data, (4) Managing Data Sets, and (5) Supporting Open Research and Collaboration. Our aim is to guide forthcoming research and developments to relieve researchers of the burdens of conducting data-sensitive experiments, support the adoption of best practices, and pave the way for open data policies in digital education.
Neural Dynamics Underlying the Evaluation Process of Ambiguous Options During Reward-Related Decision-Making

Ambiguous decision-making involves different processes. However, few studies have focused on the evaluation process. In this study, event-related potentials (ERPs) and event-related spectrum perturbation (ERSP) techniques were used to explore the neural dynamics underlying the evaluation process of ambiguous options through an ambiguous choice task. Some important results emerged. We found a preference for lotteries with low ambiguity regardless of reward amount, suggesting that subjects were averse to ambiguity in our paradigm. Our electroencephalography (EEG) results clarified the neural dynamics underlying the evaluation process. In the time domain, lotteries with both a larger reward and lower ambiguity elicited a larger P3. In the time-frequency domain, larger amplitudes of delta activity at 200–400 ms and 500–600 ms post-stimulus were elicited by lotteries with low ambiguity. Moreover, lotteries with a larger reward elicited larger amplitudes of delta activity at 400–600 ms post-stimulus. Our ERPs and ERSP results suggested that individuals in our paradigm evaluated ambiguity and reward separately, and then integrated their evaluation to form subjective values of different lotteries.
Multiresolution Modeling for Real-Time Facial Animation

This paper proposes the generation scheme of multiresolution mesh for real-time facial animation. Since previous works consider only the geometry of facial model, during generating a multiresolution facial model they may lose the feature points of facial animation. But the proposed scheme can automatically preserve those features, so that the quality of facial animation with a lower-level model is as good as that of the original. In this paper we prove that the multiresolution facial model generated by our scheme is effective in facial animation by applying an expression-retargeting technique to it.
Human NK Cell Development: One Road or Many?

CD3−CD56+ NK cells develop from CD34+ hematopoietic progenitors (HPCs) in vivo, and this process can be recapitulated in vitro. The prevailing model is that human NK cell development occurs along a continuum whereby common lymphocyte progenitors (CLPs) gradually downregulate CD34 and upregulate CD56. Acquisition of CD94 marks commitment to the CD56bright stage, and CD56bright NK cells subsequently differentiate into CD56dim NK cells that upregulate CD16 and killer immunoglobulin-like receptors (KIR). Support for this linear model comes from analyses of cell populations in secondary lymphoid tissues and in vitro studies of NK cell development from HPCs. However, several lines of evidence challenge this linear model and suggest a more branched model whereby different precursor populations may independently develop into distinct subsets of mature NK cells. A more definitive understanding of human NK cell development is needed to inform in vitro differentiation strategies designed to generate NK cells for immunotherapy. In this review, we summarize current evidence supporting the linear and branched models of human NK cell development and the challenges associated with reaching definitive conclusions.
Shared Dynamic Data Audit Supporting Anonymous User Revocation in Cloud Storage

Collusion between revoked users and cloud service providers can pose a threat to the security of cloud storage data. If the original legitimate users cannot be revoked securely, it will lead to the leakage of shared data, thus affecting the security of cloud storage. In this paper, we combine vector commitment and anonymous revocation of group signature to propose an integrity audit scheme for cloud storage data that can support data modification. The anonymity of the group signature ensures that users’ privacy information will not be snooped by the server. The proposed scheme supports the dynamic operation of stored data by legitimate group users besides data owners. When the user behaves improperly, the membership can be revoked by the group manager. After the user-modified data is stored in the cloud, whether the cloud server correctly stores the data can be audited by a trusted third party. Security analysis and experimental results demonstrate that our scheme is secure and efficient.
Capillary condensation in an active bath

We study capillary condensation in a bath of active Brownian particles (ABPs) and the forces acting on the capillary close to the motility-induced phase separation (MIPS). The capillary is modelled as two parallel rods, which are fixed in space. We consider a bath of ABPs having a self-propulsion speed much larger than the critical speed necessary for MIPS to occur. We gradually increase the packing fraction of ABPs, starting from a dilute phase of ABPs and going towards the binodal of MIPS. In stark contrast to conventional capillary condensation, we do not observe any hysteresis in the capillary packing fraction and attribute this to strong temporal fluctuations in . Depending on the packing fraction of ABPs and capillary width, we find that the effective force between the capillary rods can be either attractive or repulsive. In fact, with increasing width it shows damped oscillations as long as capillary condensation occurs. We analyze them in detail by studying the distribution of particle distances from the inner and outer wall of the capillary, respectively. In addition, we examine the capillary in the active bath close to the critical point. We do not observe signs of the presence of long-range Casimir interactions.
High Dynamic Range Image Watermarking Based on Tucker Decomposition

High dynamic range (HDR) imaging technique has received much attentions in the recent years for its abundant details and wide dynamic range of luminance. In order to display HDR images on low dynamic range display devices, tone mapping (TM) process is required. However, the TM can be deemed as an inevitable attack in the field of HDR image copyright protection. In this paper, a highly robust HDR image watermarking algorithm based on Tucker decomposition is proposed. Firstly, Tucker decomposition is carried out on HDR color image to obtain the first feature map of the core tensor, which contains most of the energies in the HDR host image. Then, a Auto-Regressive prediction method is used to establish a local correlation model of the first feature map, so that watermark can be embedded in the first feature map according to the prediction result and its true value. Furthermore, a low-complexity luminance masking approach is designed based on modified specular free map to exclude the areas not suitable for watermark embedding, and an embedding intensity selection strategy is used to balance the imperceptibility and robustness of the watermark. The experimental results show that the proposed algorithm is robust against existing TM attacks while keeping better image imperceptibility and embedding capacity.
Gd-EOB-DTPA-enhanced MRI versus Ultrasound Elastography in evaluation and staging of liver fibrosis

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Incremental Prediction Model of Disk Failures Based on the Density Metric of Edge Samples

Disks are the main equipment for data storage in data centers. The prediction of disk failure is of great significance for the reliability and security of data. On account of the few abnormal samples in the disk datasets, it is difficult to satisfy the requirement of supervised and semi-supervised algorithms for the number of abnormal data while the unsupervised algorithms have poor performance on recall rate when solving the problems of local anomalies and wrapped anomalies. This paper presents an incremental learning disk failure prediction model using the density metric of edge samples. An isolation region is built by searching the nearest neighbor of each sample. We calculate the nearest training point of the test point which is not a global anomaly and the nearest training point of the obtained nearest training point by Euclidean distance. The global metric of abnormal degree of the test sample comes from the ratio of the radius of the region where the two nearest training points are located. Then, the local metric of abnormal degree of the test sample comes from the ratio between the nearest distance from the test point to the edge of the training point region and the radius of the region. Abnormal scores of test points can be obtained by combining two measurements. We identify the SMART attributes that are significantly related to disk failures and promote their weights in the next time the attributes are inputted. The experiments are carried on the synthetic and public datasets which contain local anomalies and wrapped anomalies. The proposed method outperforms the typical unsupervised algorithms such as iNNE, iForest and LOF, and the achieved recall rates increase at most 7%. Furthermore, the contrast tests on the public disk datasets also verify the proposed method has better performance on recall rate.
Equitable Colorings of Borel Graphs

Hajnal and Szemeredi proved that if $G$ is a finite graph with maximum degree $\Delta$, then for every integer $k \geqslant \Delta+1$, $G$ has a proper coloring with $k$ colors in which every two color classes differ in size at most by $1$; such colorings are called equitable. We obtain an analog of this result for infinite graphs in the Borel setting. Specifically, we show that if $G$ is an aperiodic Borel graph of finite maximum degree $\Delta$, then for each $k \geqslant \Delta + 1$, $G$ has a Borel proper $k$-coloring in which every two color classes are related by an element of the Borel full semigroup of $G$. In particular, such colorings are equitable with respect to every $G$-invariant probability measure. We also establish a measurable version of a result of Kostochka and Nakprasit on equitable $\Delta$-colorings of graphs with small average degree. Namely, we prove that if $\Delta \geqslant 3$, $G$ does not contain a clique on $\Delta + 1$ vertices, and $\mu$ is an atomless $G$-invariant probability measure such that the average degree of $G$ with respect to $\mu$ is at most $\Delta/5$, then $G$ has a $\mu$-equitable $\Delta$-coloring. As steps towards the proof of this result, we establish measurable and list coloring extensions of a strengthening of Brooks's theorem due to Kostochka and Nakprasit.
NEW MINERAL NAMES*

An apparently new member of the eudialyte group, conditionally designated eudialyte 3248 has been found in intimate intergrowths with alluaivite at the Lovozero massif, Kola Peninsula, Russia. It has a low iron content (0.5 oxide wt%), and high contents of Mn and S. The empirical formula of the mineral is Na94102.4K1. 51.92Sr3. 23.8Ba0. 37Ce2.52.9Y0. 931.20Al0. 120.18 Ca25.927.9Zr16. 818.1Hf0. 090.15Fe0. 961.26Mn8. 211.2Ti1. 081.35Nb1. 291.44 Si152.9154.8S1. 01.5Cl3. 84.9F0. 37 (Z = 1). Unit-cell parameters are a = 14.179(1), c = 60.67(1) Å, space group R3m, Dcalc = 2.90 g/cm3. The crystal structure was reÞ ned using 1184 observed [F > 3σ(F), MoKα] single-crystal reß ections to Raniso = 0.049, using the alluaivite structure as a starting basis. Doubling of the c cell edge permitted modelling of the structure as a modulation of two layer fragments along z, giving the structural formula (Z = 1): Na45.1Zr18[Si144O396(O,OH)36]{(Ca11.7Na2.88Ce2.22Y1.2)· (Na16.2Sr1.8)[(Mn7.38Fe0.54)(Na5.53Ce0.05)][Mn2.64(Nb1.3Hf0.15) (S1.4Ti1.3Nb0.3)]}·{(Ca16.2Sr1.8)(Na15.03K1.98Ce0.63Ba0.36)}[Na7.2 Na3.6][Si2.16Si1. 5Mn1.08]}(OH)13.86(O,OH)7.92(H2O)4.58Cl3. 3, where braces represent the contents of the different modules. The ideal formula (Z = 3) is: Na29Ca12Zr6[Si48O132(O,OH)12]{[Na]4 [Si]2}{[Mn]3[Mn,Nb,Ti]2}(OH,H2O,Cl)10. Common features of the crystal structure are basically those of the eudialyte structure. Highlights of the structure and differences between the modules include: (1) Predominance of Ca at M1' and M1'' (where the prime and double-prime symbols denote modules one and two, respectively); (2) M2' is occupied by IVNa, whereas M2'' is dominantly occupied by VMn; (3) M3'' is split into three subsites along the triad axis, one dominated by IVMn and two by IVSi; (4) M4 is split into three subsites, one dominated by IVMn, one by VINb and the other by IV(S,Ti); (5) A1 is split into two subsites, one dominated by Na, the other Sr; (6) A2, A3, and A4 are unsplit and dominated by Na; (7) A5 is split into three Na-dominated subsites, two of which are eight-coordinated, the other Þ ve-co-
FCSS: Fog-Computing-based Content-Aware Filtering for Security Services in Information-Centric Social Networks

Social networks are very important social cyberspaces for people. Currently, information-centric networks (ICN) are the main trend of next-generation networks, which promote traditional social networks to information-centric social networks (IC-SN). Because of the complexity and openness of social networks, the filtering of security services for users is a key issue. However, existing schemes were proposed for traditional social networks and cannot satisfy the new requirements of IC-SN including extendibility, data mobility, use of non-IP addresses, and flexible deployment. To address this challenge, a fog-computing-based content-aware filtering method for security services, FCSS, is proposed in information centric social networks. In FCSS, the assessment and content- matching schemes and the fog-computing-based content-aware filtering scheme is proposed for security services in IC-SN. FCSS contributes to IC-SN as follows. First, fog computing is introduced into IC-SN to shifting intelligence and resources from remote servers to network edge, which provides low-latency for security service filtering and end to end communications. Second, content-label technology based efficient content-aware filtering scheme is adapted for edge of IN-SN to realize accurate filtering for security services. The simulations and evaluations show the advantages of FCSS in terms of hit ratio, filtering delay, and filtering accuracy.
Compositional Microservices for Immersive Social Visual Analytics

As humans, we have developed to process highly complex visual data from our surroundings. This is why data visualization and interaction is one of the quickest ways to facilitate investigation and communicate understanding. To perform visual analytics effectively at the \textit{big data} scale it is crucial that we develop an integrated processing and visualization ecosystem. However, to date, in Large High-Resolution Display (LHRD) environments the worlds of data processing and visualization remain largely disconnected. In this paper, we propose a common architectural approach to enable integrated data processing and distributed visualization via the composition of discrete microservices. Each of these microservices provides a very specific clearly-defined function, such as analyzing data, creating a visualization, sharding data or providing a synchronization source. By defining common transport, data and API formats we enable the composition of these microservices from processing raw data through to analytics, visualization and rendering. This compositionality, inspired by successful data-driven visualization frameworks provides a common platform for immersive social visual analytics.
ANDERATION: A New Anti-Community Detection Algorithm and its Application to Explore Incompatibility of Traditional Chinese Medicine

The problem of community detection has attracted great attentions from various fields and community structure is one of the most important characteristics in complex networks. However, there are few researches on the detection of anti-community structure, where the nodes share no or few connections inside their group they belong to but most of their connections outside. In Traditional Chinese Medicine (TCM), the incompatibility problem of herbs is a great challenge to clinical medication safety, which becomes a serious threat to public health. In this paper, a new ANti-community detection algorithm based on the DEgree and the RATio between the Inner degree and the Outer degree of a Node (ANDERATION), is proposed, in which these two factors are firstly introduced to detect anti-community structure by first creating the initial anti-community structure and then maximizing the objective function. Experimental results on 15 synthetic and 14 real-world networks demonstrate that the proposed algorithm can detect better anti-community structures with less running time than the existing algorithms. By applying ANDERATION to the herb network, we find that it is effective in exploring incompatible herb combinations in TCM.
Feature Selection Using an Improved Gravitational Search Algorithm

Feature selection is an important issue in the field of machine learning, which can reduce misleading computations and improve classification performance. Generally, feature selection can be considered as a binary optimization problem. Gravitational Search Algorithm (GSA) is a population-based heuristic algorithm inspired by Newton’s laws of gravity and motion. Although GSA shows good performance in solving optimization problems, it has a shortcoming of premature convergence. In this paper, the concept of global memory is introduced and the definition of exponential $Kbest$ is used in an improved version of GSA called IGSA. In this algorithm, the position of the optimal solution obtained so far is memorized, which can effectively prevent particles from gathering together and moving slowly. In this way, the exploitation ability of the algorithm gets improved, and a proper balance between exploration and exploitation gets established. Besides, the exponential $Kbest$ can significantly decrease the running time. In order to solve feature selection problem, a binary IGSA (BIGSA) is further introduced. The proposed algorithm is tested on a set of standard datasets and compared with other algorithms. The experimental results confirm the high efficiency of BIGSA for feature selection.