Miracle-dz/UniFAR-FTUs huggingface dataset:
Giant Magnetic Anisotropy of Single Cobalt Atoms and Nanoparticles

The isotropic magnetic moment of a free atom is shown to develop giant magnetic anisotropy energy due to symmetry reduction at an atomically ordered surface. Single cobalt atoms deposited onto platinum (111) are found to have a magnetic anisotropy energy of 9 millielectron volts per atom arising from the combination of unquenched orbital moments (1.1 Bohr magnetons) and strong spin-orbit coupling induced by the platinum substrate. By assembling cobalt nanoparticles containing up to 40 atoms, the magnetic anisotropy energy is further shown to be dependent on single-atom coordination changes. These results confirm theoretical predictions and are of fundamental value to understanding how magnetic anisotropy develops in finite-sized magnetic particles.
Design of active/passive hybrid compliance in the frequency domain-shaping dynamic compliance of humanoid shoulder mechanism

Design and control of mechanical compliance would be one of the most important technical foci in making humanoid robots really interactive with humans. For task execution and safety insurance the issue must be discussed and offers useful and realistic solutions. We propose a theoretical design principle of mechanical compliance. Passive compliance implies a mechanically embedded compliance in drive systems and is reliable but not tunable in nature, while active compliance is a controlled compliance and, therefore, widely tunable, but less reliable specially in the high frequency domain. The basic idea of the paper is to use active compliance in the lower frequency domain and to rely on passive compliance in the higher frequency. H/sub /spl infin// control theory based on systems identification allows a systematic method to design the hybrid compliance in frequency domain. The proposed design is applied to the shoulder mechanism of a humanoid torso robot. Its implementation and experiments are to be shown with successful results.
Prevention of bacterial endocarditis. Recommendations by the American Heart Association.

OBJECTIVE To update recommendations issued by the American Heart Association last published in 1990 for the prevention of bacterial endocarditis in individuals at risk for this disease. PARTICIPANTS An ad hoc writing group appointed by the American Heart Association for their expertise in endocarditis and treatment with liaison members representing the American Dental Association, the Infectious Diseases Society of America, the American Academy of Pediatrics, and the American Society for Gastrointestinal Endoscopy. EVIDENCE The recommendations in this article reflect analyses of relevant literature regarding procedure-related endocarditis, in vitro susceptibility data of pathogens causing endocarditis, results of prophylactic studies in animal models of endocarditis, and retrospective analyses of human endocarditis cases in terms of antibiotic prophylaxis usage patterns and apparent prophylaxis failures. MEDLINE database searches from 1936 through 1996 were done using the root words endocarditis, bacteremia, and antibiotic prophylaxis. Recommendations in this document fall into evidence level III of the US Preventive Services Task Force categories of evidence. CONSENSUS PROCESS The recommendations were formulated by the writing group after specific therapeutic regimens were discussed. The consensus statement was subsequently reviewed by outside experts not affiliated with the writing group and by the Science Advisory and Coordinating Committee of the American Heart Association. These guidelines are meant to aid practitioners but are not intended as the standard of care or as a substitute for clinical judgment. CONCLUSIONS Major changes in the updated recommendations include the following: (1) emphasis that most cases of endocarditis are not attributable to an invasive procedure; (2) cardiac conditions are stratified into high-, moderate-, and negligible-risk categories based on potential outcome if endocarditis develops; (3) procedures that may cause bacteremia and for which prophylaxis is recommended are more clearly specified; (4) an algorithm was developed to more clearly define when prophylaxis is recommended for patients with mitral valve prolapse; (5) for oral or dental procedures the initial amoxicillin dose is reduced to 2 g, a follow-up antibiotic dose is no longer recommended, erythromycin is no longer recommended for penicillin-allergic individuals, but clindamycin and other alternatives are offered; and (6) for gastrointestinal or genitourinary procedures, the prophylactic regimens have been simplified. These changes were instituted to more clearly define when prophylaxis is or is not recommended, improve practitioner and patient compliance, reduce cost and potential gastrointestinal adverse effects, and approach more uniform worldwide recommendations.
Decoying the cap- mRNA degradation system by a double-stranded RNA virus and poly(A)- mRNA surveillance by a yeast antiviral system

The major coat protein of the L-A double-stranded RNA virus of Saccharomyces cerevisiae covalently binds m7 GMP from 5' capped mRNAs in vitro. We show that this cap binding also occurs in vivo and that, while this activity is required for expression of viral information (killer toxin mRNA level and toxin production) in a wild-type strain, this requirement is suppressed by deletion of SKI1/XRN1/SEP1. We propose that the virus creates decapped cellular mRNAs to decoy the 5'-->3' exoribonuclease specific for cap- RNA encoded by XRN1. The SKI2 antiviral gene represses the copy numbers of the L-A and L-BC viruses and the 20S RNA replicon, apparently by specifically blocking translation of viral RNA. We show that SKI2, SKI3, and SKI8 inhibit translation of electroporated luciferase and beta-glucuronidase mRNAs in vivo, but only if they lack the 3' poly(A) structure. Thus, L-A decoys the SKI1/XRN1/SEP1 exonuclease directed at 5' uncapped ends, but translation of the L-A poly(A)- mRNA is repressed by Ski2,3,8p. The SKI2-SKI3-SKI8 system is more effective against cap+ poly(A)- mRNA, suggesting a (nonessential) role in blocking translation of fragmented cellular mRNAs.
2016 Ieee International Conference on Big Data (big Data) Efficient Triangle Listing for Billion-scale Graphs

—This paper addresses the classical triangle listing problem, which aims at enumerating all the tuples of three vertices connected with each other by edges. This problem has been intensively studied in internal and external memory, but it is still an urgent challenge in distributed environment where multiple machines across the network can be utilized to achieve good performance and scalability. As one of the de facto computing methodologies in distributed environment, MapReduce has been used in some of existing triangle listing algorithms. However, these algorithms usually need to shuffle a huge amount of intermediate data, which seriously hinders the scalability on large scale graphs. In this paper, we propose a new triangle listing algorithm in MapReduce, FTL, which utilizes a light weight data structure to substantially reduce the intermediate data transferred during the shuffle stage, and also is equipped with multiple-round techniques to ease the burden on memory and network bandwidth when dealing with graphs at billion scale. We prove that the size of the intermediate data can be well bounded near to the number of triangles in the graph. To further reduce the shuffle size in each round, we also devise a compact data structure to store the intermediate data, which can save space up to 2 3. The extensive experimental results show that our algorithms outperform existing competitors by several times on large real world graphs.
Free form metallization of solar cells using Laser Induced Forward Transfer

Front metallization is an expensive, fundamental step in the fabrication of solar cells. Laser additive direct writing techniques, such as Laser Induced Forward Transfer (LIFT), can be used for printing optimized metallization patterns or free form personalized designs with applications in building integrated photovoltaics. In this work, metallic fingers and busbars have been printed onto different rigid and flexible photovoltaic materials using commercial, high viscosity, micron-sized particles, silver-based pastes. Printed lines show very large aspect ratios, low electrical resistivity and good adherence to the substrate. Functional test cells have been metallized from unfinished CIGS flexible solar cells.
Electrophysiological and morphological heterogeneity of rat dorsal vagal neurones which project to specific areas of the gastrointestinal tract

1 The electrophysiological properties of rat dorsal motor nucleus of the vagus (DMV) neurones (n= 162) were examined using whole cell patch clamp recordings from brainstem slices. Recordings were made from DMV neurones whose projections to the gastrointestinal tract had been identified by previously applying fluorescent retrograde tracers to the gastric fundus, corpus or antrum/pylorus, or to the duodenum or caecum. 2 The neuronal groups were markedly heterogeneous with respect to several electrophysiological properties. For example, neurones which projected to the fundus had a higher input resistance (400 ± 25 MΩ), a smaller and shorter after‐hyperpolarization (16.7 ± 0.49 mV and 63.5 ± 3.9 ms) and a higher frequency of action potential firing (19.3 ± 1.4 action potentials s−1) following injection of depolarizing current (270 pA) when compared with caecum‐projecting neurones (302 ± 22 MΩ; 23.5 ± 0.87 mV and 81.1 ± 5.3 ms; 9.7 ± 1.1 action potentials s−1; P < 0.05 for each parameter). Differences between neuronal groups were also apparent with respect to the distribution of several voltage‐dependent potassium currents. Inward rectification was present only in caecum‐projecting neurones, for example. 3 Neurones (n= 82) were filled with the intracellular stain Neurobiotin allowing post‐fixation morphological reconstruction. Neurones projecting to the caecum had the largest cell volume (5238 ± 535 μm3), soma area (489 ± 46 μm2) and soma diameter (24.6 ± 1.24 μm) as well as the largest number of dendritic branch segments (23 ± 2). 4 In summary, these results suggest that DMV neurones are heterogeneous with respect to some electrophysiological as well as some morphological properties and can be divided into subgroups according to their gastrointestinal projections.
Curative Effect of Garlic on Alcoholic Liver Diseased Patients

Alcohol is currently recognized as the most prevalent known cause of abnormal human development. Our aim was to investigate the effect of raw garlic on patients suffering from alcoholic liver disease. 20 alcoholic patients and 20 healthy individuals were selected. Both patients and normal individuals were subjected to detailed clinical examination and laboratory investigations. Blood samples were collected and the liver disease was assessed by measuring the activities of liver marker enzymes such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma glutamyl transferase (GGT) and lactate dehydrogenase (LDH) which were elevated in alcoholic patients. Increased lipid peroxidation in alcoholic patients was accompanied by decreased activities of Superoxide dismutase (SOD), Catalase (CAT), Glutathione peroxidase (GPx). Oral supplementation of 2 small sized raw garlic cloves (1 clove = 1.2g) to alcoholic patients for 45 days, significantly lowered the activities of liver marker enzymes, decreased the levels of lipid peroxidation and enhanced the antioxidant status to near normal. Thus, the data of the present study suggest that raw garlic offers protection against oxidative stress and antioxidant activities in alcoholic liver disease patients.
Analysis of Methods for Extracting Matrix Proteins from Human Kidney Stones

Five techniques for the extraction of proteins from kidney stones were compared for the purpose of maximizing yield for mass spec analysis. The proteomics of kidney stones may provide insight into the underlying mechanisms of stone formation and why some people are more prone to developing kidney stones than others. Kidney stones contain proteins that may serve as markers for a wide variety of disease states. Mass spectrometry offers the ability to identify and quantify the complete proteome of a kidney stone. To date, there have been only a limited number of research projects aimed at studying the protein composition of kidney stones, possibly due to the difficulty in extracting these proteins while preserving the ability to subject the samples to mass spectrometry. We compared five methods for extracting kidney stone matrix proteins, three from previously published studies and two methods we designed based on previous studies. We were able to establish which methods produced the highest yield of protein for both calcium oxalate and uric acid stones.
Latent space policy search for robotics

Learning motor skills for robots is a hard task. In particular, a high number of degrees-of-freedom in the robot can pose serious challenges to existing reinforcement learning methods, since it leads to a high-dimensional search space. However, complex robots are often intrinsically redundant systems and, therefore, can be controlled using a latent manifold of much smaller dimensionality. In this paper, we present a novel policy search method that performs efficient reinforcement learning by uncovering the low-dimensional latent space of actuator redundancies. In contrast to previous attempts at combining reinforcement learning and dimensionality reduction, our approach does not perform dimensionality reduction as a preprocessing step but naturally combines it with policy search. Our evaluations show that the new approach outperforms existing algorithms for learning motor skills with high-dimensional robots.
Ion Irradiation Modification of Silicon Nanowires

Ion irradiation modification of silicon nanowires has been explored in-situ using the Microscope and Ion Accelerator for Materials Investigations (MIAMI) facility at the University of Huddersfield. Ion irradiation experiments were designed using the Stopping and Range of Ions in Matter (SRIM) Monte Carlo computer code. A multislice SRIM method was developed to estimate the damage and ion implantation in a nanowires geometry and a code was developed to incorporate SRIM into MatLab. In-situ Transmission Electron Microscopy (TEM) has been used to explore the ion-beam-induced bending of silicon nanowires under different irradiation conditions and the underlying mechanisms which drive nanowire bending have been identified. Furthermore, a tipping point for direction reversal of bending under different irradiation conditions has been identified. Recrystallisation of silicon nanowires was also investigated using thermal annealing. The effects of 7 keV Xe+ ions on silicon nanowires have been investigated under industrial processing conditions. Structural and morphological changes of silicon nanowires have been observed. These include nanowire bending, amorphisation, bubble formation and sputtering. The depth of damage has also been measured experimentally and was compared with the predictive damage using SRIM. In order to calculate the temperature along a nanowire during an in-situ TEM heating experiment, the relevant parameters have been found from the literature which will be used to set up a finite element model. Atomic Force Microscopy (AFM) was used to measure the surface roughness of silicon nanowires and molybdenum grids. Other parameters of interest include the Hamaker’s constant for the Van der Waals forces as well as the Young’s modulus, thermal conductivity and specific heat capacity of silicon nanowires and the ultra-thin oxide layer on their surface.
Towards Expanding Relevance Vector Machines to Large Scale Datasets

In this paper we develop and analyze methods for expanding automated learning of Relevance Vector Machines (RVM) to large scale text sets. RVM rely on Bayesian inference learning and while maintaining state-of-the-art performance, offer sparse and probabilistic solutions. However, efforts towards applying RVM to large scale sets have met with limited success in the past, due to computational constraints. We propose a diversified set of divide-and-conquer approaches where decomposition techniques promote the definition of smaller working sets that permit the use of all training examples. The rationale is that by exploring incremental, ensemble and boosting strategies, it is possible to improve classification performance, taking advantage of the large training set available. Results on Reuters-21578 and RCV1 are presented, showing performance gains and maintaining sparse solutions that can be deployed in distributed environments.
Cr@ck3n: A cyber alerts visualization object

With the increasing number of connected devices and given the complexity of computer networks, to identify cyber anomalies is more and more challenging. Either at home, in the work place or for military defense purposes a better cyber situation awareness is needed. However, the visualization methods are often made for specialists and the information difficult to interpret. In this paper we describe an object made for the visualization of abnormal network events in a user-friendly way using colors, sound and information scrolling. It is still under development but the first user feedback are encouraging.
Genome Wide Identification and Characterization of Apple bHLH Transcription Factors and Expression Analysis in Response to Drought and Salt Stress

The bHLH (basic helix-loop-helix) transcription factor family is the second largest in plants. It occurs in all three eukaryotic kingdoms, and plays important roles in regulating growth and development. However, family members have not previously been studied in apple. Here, we identified 188 MdbHLH proteins in apple “Golden Delicious” (Malus × domestica Borkh.), which could be classified into 18 groups. We also investigated the gene structures and 12 conserved motifs in these MdbHLHs. Coupled with expression analysis and protein interaction network prediction, we identified several genes that might be responsible for abiotic stress responses. This study provides insight and rich resources for subsequent investigations of such proteins in apple.
The Relationship between Self-Awareness of Attentional Status, Behavioral Performance and Oscillatory Brain Rhythms

High-level cognitive factors, including self-awareness, are believed to play an important role in human visual perception. The principal aim of this study was to determine whether oscillatory brain rhythms play a role in the neural processes involved in self-monitoring attentional status. To do so we measured cortical activity using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) while participants were asked to self-monitor their internal status, only initiating the presentation of a stimulus when they perceived their attentional focus to be maximal. We employed a hierarchical Bayesian method that uses fMRI results as soft-constrained spatial information to solve the MEG inverse problem, allowing us to estimate cortical currents in the order of millimeters and milliseconds. Our results show that, during self-monitoring of internal status, there was a sustained decrease in power within the 7-13 Hz (alpha) range in the rostral cingulate motor area (rCMA) on the human medial wall, beginning approximately 430 msec after the trial start (p < 0.05, FDR corrected). We also show that gamma-band power (41-47 Hz) within this area was positively correlated with task performance from 40–640 msec after the trial start (r = 0.71, p < 0.05). We conclude: (1) the rCMA is involved in processes governing self-monitoring of internal status; and (2) the qualitative differences between alpha and gamma activity are reflective of their different roles in self-monitoring internal states. We suggest that alpha suppression may reflect a strengthening of top-down interareal connections, while a positive correlation between gamma activity and task performance indicates that gamma may play an important role in guiding visuomotor behavior.
Opportunistic file transfer over a fading channel under energy and delay constraints

We consider transmission control (rate and power) strategies for transferring a fixed-size file (finite number of bits) over fading channels under constraints on both transmit energy and transmission delay. The goal is to maximize the probability of successfully transferring the entire file over a time-varying wireless channel modeled as a finite-state Markov process. We study two implementations regarding the delay constraints: an average delay constraint and a strict delay constraint. We also investigate the performance degradation caused by the imperfect (delayed or erroneous) channel knowledge. The resulting optimal policies are shown to be a function of the channel-state information (CSI), the residual battery energy, and the number of residual information bits in the transmit buffer. It is observed that the probability of successful file transfer increases significantly when the CSI is exploited opportunistically. When the perfect instantaneous CSI is available at the transmitter, the faster channel variations increase the success probability under delay constraints. In addition, when considering the power expenditure in the pilot for channel estimation, the optimal policy shows that the transmitter should use the pilot only if there is sufficient energy left for packet transfer; otherwise, a channel-independent policy should be used.
Sensor fused indoor positioning using dual band WiFi signal measurements

In this paper, signal strengths from known WiFi access points are used together with a particle filter to perform indoor navigation. It is shown that more information is obtained by using signals of both 2.4 and 5.0 GHz, compared to using only one frequency. Thus, using both frequencies provides a more accurate positioning. The second contribution is an algorithm where WiFi measurements are combined with pedestrian dead reckoning (PDR), which is based on step counting using an accelerometer and hypotheses of the heading using a gyroscope. This was found to provide further accuracy compared to more conventional methods.
A Portal for Access to Complex Distributed Information about Energy

The Digital Government Research Center (DGRC) has completed phase one of the Energy Data Collection (EDC) project. In this paper, we present the results of building and evaluating system components, along with plans for phase two of the project. Phase one focused on data about petroleum products' prices and volumes, provided by the Energy Information Administration, the Bureau of Labor Statistics, and the Census Bureau, and the California Energy Commission, in the form of over 50,000 data tables. This research centers on providing dynamically planned access to multiple non-homogeneous databases and other data collections, using a query planner and a largescale (90,000-node) concept ontology and a domain model, both of which are accessed via various interfaces, including cascaded menus, a natural language question analyzer, and an ontology browser. Other data access research focuses on the caching and very fast display of massive amounts of data. In order to more rapidly construct the domain models, systems were developed for automatically identifying terminology glossary files from websites, extracting and formalizing the glossary definitions, clustering them appropriately, and automatically embedding them into the existing ontology and domain model. Work on evaluation focuses on measuring the effectiveness of the use of this system by a variety of users at various levels of expertise.