Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Efficient Cluster Computing via Accurate Workload Characterization

This paper presents an eco-friendly daemon that reduces power and energy consumption while better maintaining high performance via an accurate workload characterization that infers “processor stall cycles due to off-chip activities. ” The eco-friendly daemon is an interval-based, run-time algorithm that uses the workload characterization to dynamically adjust a processor’s frequency and voltage to reduce power and energy consumption with little impact on application performance. Using the NAS Parallel Benchmarks as our workload, we then evaluate our eco-friendly daemon on a cluster computer. The results indicate that our workload characterization allows the power-aware daemon to more tightly control performance (5% loss instead of 11%) while delivering substantial energy savings (11% instead of 8%).
The tidal stripping of satellites

We present an improved analytic calculation for the tidal radius of satellites and test our results against N-body simulations. The tidal radius in general depends upon four factors: the potential of the host galaxy, the potential of the satellite, the orbit of the satellite and the orbit of the star within the satellite. We demonstrate that this last point is critical and suggest using three tidal radii to cover the range of orbits of stars within the satellite. In this way we show explicitly that prograde star orbits will be more easily stripped than radial orbits; while radial orbits are more easily stripped than retrograde ones. This result has previously been established by several authors numerically, but can now be understood analytically. For point mass, power-law (which includes the isothermal sphere), and a restricted class of split power-law potentials our solution is fully analytic. For more general potentials, we provide an equation which may be rapidly solved numerically. Over short times (≲1–2 Gyr ∼1 satellite orbit), we find excellent agreement between our analytic and numerical models. Over longer times, star orbits within the satellite are transformed by the tidal field of the host galaxy. In a Hubble time, this causes a convergence of the three limiting tidal radii towards the prograde stripping radius. Beyond the prograde stripping radius, the velocity dispersion will be tangentially anisotropic.
Experimental study of a Motorcycle platform for dynamic model extraction

Designing security systems for a two wheels vehicles are quite hard to do. This is related to the fact that we must take in account more characteristics (stability, mass, compactness, roll, tilt, speed. etc.). Consequently, there is no study about electronics safety systems on motorcycles (not officially). This paper presents our approach to design data logger system to acquire motorcycle embedded sensors. One of the goals is to extract dynamic model methodology of the combined human/motorcycle system. We will focus on the accuracy of the data logger system. This work aims to design motorcycle danger indicator
CUDA-enabled Particle-Based 3D Fluid Haptic Simulation

This paper describes a method to accomplish hap tic three dimensional (3D) computer simulations of fluids by approximating natural hydrodynamic behavior using particles that, as a whole, simulate different kinds of liquid media depending on their parameters. The importance of this research lies in that it enables haptic simulations of fluids by using a particles approach. The benefit of a particles approach is that it could be extended for other kind of media such as solid and gas for a seamless platform for haptic interaction. The physics are emulated by means of a basic particle model that interacts with the other particles in the system implementing a simplified physics model that, while keeping low the processing needed, emulates real-world behavior like gravity, inertia, mass, and acceleration. Furthermore, several parameters are maintained configurable to vary the likeness of the liquid to different real world fluids. Previous work on this subject has obtained particle fluid simulations, but hasn't implemented haptic feedback for liquids, other works have implemented haptic simulation of gases, albeit by an Eulerian approach. Haptic liquid simulations were found to be possible, behaving approximately like a fluid medium, but the realism was limited by the number of particles because of the amount of processing needed. This research's unique contribution centers in the simplified particle model by which haptic applications can viably incorporate fluids by means of particles, which could be part of a unified framework of physics simulation.
Vanishing point detection based on slope trend for omnidirectional vision-based robot localization

This paper proposes one novel method to detect vanishing points in indoor environment. The original environmental data are from a standard omnidirectional camera equipped on one mobile robot. The orientation information in the panoramic image captured by the omnidirectional camera is computed by any standard edge detection, and further analyzed by the proposed slope trend approach. Related experimental studies in different scenarios are performed to demonstrate the feasibility of the approach.
Evaluation of pharmacokinetics of single-dose chloroquine in malnourished children with malaria- a comparative study with normally nourished children

Objectives: Studies on antimalarial kinetics in children or adults who are undernourished or malnourished are both limited and have yielded conflicting results. The present study was carried out with the objectives of evaluating the pharmacokinetics of single dose chloroquine and its metabolite desethylchloroquine in children who were undernourished and compare them with children who were normally nourished. Methods: Children of either gender between the ages of 5 and 12 years, smear positive for P. vivax malaria and classified either as well nourished or undernourished were included. Undernourishment was adjudged based on the Indian Academy of Pediatrics (IAP) classification of protein energy malnutrition [PEM] which in turn was based on Khadilkar's growth charts. All participants received 10 mg/kg on the first day followed by 10 mg/kg on Day 2 and 5 mg/kg on Day 3 along with supportive treatment. Blood samples for the levels of chloroquine [CQ] and desethylchloroquine [DECQ] were collected at 0, 0.5, 1, 2 4, 8, 12, 24, 48, 72 hours and 14 days after the first dose and levels assessed by High Performance Liquid Chromatography. Results: A total of 12 children who were normally nourished and 13 who were undernourished were studied. Wide inter-individual variability was seen in the levels of both drug and metabolite in both groups of patients. However, the differences in Cmax, AUC 0-inf, Clearance, half life and Vd between the two groups were not significantly different. Discussion: Our results indicate that dosage requirement is unlikely to be needed for chloroquine in undernourished children with uncomplicated P. vivax malaria.
An Analysis of Markov Password Against Brute Force Attack for Effective Web Applications

Computer is omnipresent. With massive growth in the field of computers, advancement in digital technology, development in software’s gives improvement to computer field on one side. Hacking the systems and cracking the login Passwords makes the field in endure on the other hand. Authentication to access an application in networks is mostly based on alphanumeric Password. A novel method of Alphanumeric Password for improving the security is “The Markov Password”. Markov Passwords are created using the model of the Markov chain. This technique can be used as authentication for web applications. Password Crackers use different techniques with available large number of tools to crack down Password easily. Common attacks on Password s are Brute force attack, Dictionary attack and Hybrid attack. In this paper, a report on a study of brute force attack on Markov Passwords has been done. Analysis on Markov Password against Brute force attack is carried out using two open source tools. For analysis 40 random Password generated by Markov Chain are considered. The results are incorporated by means of graph: Password vs. Seconds to crack that Password. Average time, Maximum and Minimum time to crack Markov Password are also
Data mining for proteins characteristic of clades

A synapomorphy is a phylogenetic character that provides evidence of shared descent. Ideally a synapomorphy is ubiquitous within the clade of related organisms and nonexistent outside the clade, implying that it arose after divergence from other extant species and before the last common ancestor of the clade. With the recent proliferation of genetic sequence data, molecular synapomorphies have assumed great importance, yet there is no convenient means to search for them over entire genomes. We have developed a new program called Conserv, which can rapidly assemble orthologous sequences and rank them by various metrics, such as degree of conservation or divergence from out-group orthologs. We have used Conserv to conduct a largescale search for molecular synapomorphies for bacterial clades. The search discovered sequences unique to clades, such as Actinobacteria, Firmicutes and γ-Proteobacteria, and shed light on several open questions, such as whether Symbiobacterium thermophilum belongs with Actinobacteria or Firmicutes. We conclude that Conserv can quickly marshall evidence relevant to evolutionary questions that would be much harder to assemble with other tools.
GREY-BOX SYSTEM IDENTIFICATION WITH VARIABLE- STRUCTURE NEURAL NETWORKS

Grey-box’ models take advantage of physical insight about the dynamical system in question. This reduces uncertainty and facilitates the remaining system identification process. Here a grey-box system identification (GBSI) method for nonlinear dynamical systems is presented, which first establishes a linear model (the ‘white box’) using a priori knowledge and conventional tools, and then adds a neural network (the ‘black box’) for learning the residual nonlinear dynamics. What distinguishes this from other GBSI methods is the Variable Structure Learning algorithm, which trains both the neural network connection weights and structure, i.e. number of hidden neurons. The method is tested with two different neural networks using a nonlinear reference function and a realistic aircraft model. INTRODUCTION System identification (SI) refers to the design of a sufficiently accurate model of dynamical systems. ‘Grey-box’ system identification (GBSI) takes advantage of physical insight about the dynamical system in question, for example the model structure and/or certain parameter values. This reduces uncertainty and facilitates identifying the residual, unknown dynamics. Here a nonlinear GBSI method is proposed, which first establishes a linear model (the ‘white box’) using a priori knowledge and conventional tools, and then synergetically adds an artificial neural network, or ANN (the ‘black box’), for learning the residual nonlinear and possibly unmodelled linear dynamics. What distinguishes this from other GBSI methods, for example [1][5][8][12], is the Variable Structure Learning algorithm, which not only trains the ANN connection weights but also the network structure, i.e. number of hidden neurons [9]. The method is tested with two different ANNs using a nonlinear reference function and a high-performance aircraft model. The GBSI process involves three steps: • Create a linear time-invariant model (structure and parameters) based on available a priori knowledge. • Identify the unknown model parameters using conventional linear SI methods and tools. • Train an ANN (weights and structure) to model the residuals dynamics.
Absent activation in medial prefrontal cortex and temporoparietal junction but not superior temporal sulcus during the perception of biological motion in schizophrenia: a functional MRI study

Background Patients with schizophrenia show disturbances in both visual perception and social cognition. Perception of biological motion (BM) is a higher-level visual process, and is known to be associated with social cognition. BM induces activation in the “social brain network”, including the superior temporal sulcus (STS). Although deficits in the detection of BM and atypical activation in the STS have been reported in patients with schizophrenia, it remains unclear whether other nodes of the “social brain network” are also atypical in patients with schizophrenia. Purpose We aimed to explore whether brain regions other than STS were involved during BM perception in patients with schizophrenia, using functional magnetic resonance imaging (fMRI). Methods and patients Seventeen patients with schizophrenia, and 17 age- and sex- matched healthy controls, underwent fMRI scanning during a one-back visual task, containing three experimental conditions: (1) BM, (2) scrambled motion (SM), and (3) static condition. We used one-sample t-tests to examine neural responses selective to BM versus SM within each group, and two-sample t-tests to directly compare neural patterns to BM versus SM in schizophrenics versus controls. Results We found significant activation in the STS region when BM was contrasted with SM in both groups, with no significant difference between groups. On the contrary, significant activation in the medial prefrontal cortex (MPFC) and bilateral temporoparietal junction (TPJ) was found only in the control group. When we directly compared the two groups, the healthy controls showed significant greater activation in left MPFC and TPJ to BM versus SM than patients with schizophrenia. Conclusion Our findings suggest that patients with schizophrenia show normal activation to biologically and socially relevant motion stimuli in the STS, but atypical activation in other regions of the social brain network, specifically MPFC and TPJ. Moreover, these results were not due to atypical processing of motion, suggesting that patients with schizophrenia lack in the recruitment of neural circuits needed for the visual perception of social cognition.
Classification of C 2 H 2 Zinc Finger Domains Using Support Vector Machines

Zinc finger proteins include nuclear receptors for steroid hormones and are mainly DNA-binding transcription factors. Thus those are supposed to be target proteins for drug discovery. C2H2 zinc finger gene family is one of the most popular and complex superfamilies. C2H2 zinc finger domains are composed of approximately 25 to 30 amino acid residues including the paired cysteines and histidines that form coordinate bonds with zinc ion. Although C2H2 domains are well-studied, it is difficult to detect the domains with high accuracy by means of homology search or hidden Markov models(HMMs) owing to a wide variety of the sequences. In this research, we have extended the Support Vector Machine(SVM) based method using the Fisher kernel [1] in order to achieve better accuracy than an HMM. The Fisher kernel extracts a fixed length vector of features known as a Fisher score vector (FSV) from a variable length sequence with an HMM. The method in [1] classifies G-protein coupled receptors (GPCRs) into GPCR subfamilies.
Time domain reflectometry measurement principles and applications

Time domain reflectometry (TDR) is a highly accurate and automatable method for determination of porous media water content and electrical conductivity. Water content is inferred from the dielectric permittivity of the medium, whereas electrical conductivity is inferred from TDR signal attenuation. Empirical and dielectric mixing models are used to relate water content to measured dielectric permittivity. Clay and organic matter bind substantial amounts of water, such that measured bulk dielectric constant is reduced and the relationship with total water content requires individual calibration. A variety of TDR probe configurations provide users with site‐ and media‐specific options. Advances in TDR technology and in other dielectric methods offer the promise not only for less expensive and more accurate tools for electrical determination of water and solute contents, but also a host of other properties such as specific surface area, and retention properties of porous media. Copyright © 2002 John Wiley & Sons, Ltd.
Joint Learning of Discriminative Prototypes and Large Margin Nearest Neighbor Classifiers

In this paper, we raise important issues concerning the evaluation complexity of existing Mahalanobis metric learning methods. The complexity scales linearly with the size of the dataset. This is especially cumbersome on large scale or for real-time applications with limited time budget. To alleviate this problem we propose to represent the dataset by a fixed number of discriminative prototypes. In particular, we introduce a new method that jointly chooses the positioning of prototypes and also optimizes the Mahalanobis distance metric with respect to these. We show that choosing the positioning of the prototypes and learning the metric in parallel leads to a drastically reduced evaluation effort while maintaining the discriminative essence of the original dataset. Moreover, for most problems our method performing k-nearest prototype (k-NP) classification on the condensed dataset leads to even better generalization compared to k-NN classification using all data. Results on a variety of challenging benchmarks demonstrate the power of our method. These include standard machine learning datasets as well as the challenging Public Figures Face Database. On the competitive machine learning benchmarks we are comparable to the state-of-the-art while being more efficient. On the face benchmark we clearly outperform the state-of-the-art in Mahalanobis metric learning with drastically reduced evaluation effort.
Influence of Aggregation and Route of Injection on the Biodistribution of Mouse Serum Albumin

Protein aggregates are a major risk factor for immunogenicity. Until now most studies on aggregate-driven immunogenicity have focused on linking physicochemical features of the aggregates to the formation of anti-drug antibodies. Lacking is however, basic knowledge on the effect of aggregation on the biodistribution and clearance of therapeutic proteins in vivo. The aim of current study was to get insight into the effect of aggregation on biodistribution in mice using different routes of administration. Fluorescently labeled stressed and unstressed mouse serum albumin was injected via different routes in mice and detected via in vivo fluorescence imaging up to 48 hrs post-injection. We found that biodistribution of stressed MSA significantly differed from its unstressed counterpart. Subcutaneous and intramuscular administration resulted in accumulation of protein at the site of injection, from which clearance of stressed MSA was considerably slower than clearance of unstressed MSA. Upon intravenous and intraperitoneal injection of stressed MSA, fluorescent “hotspots” were observed in the spleens, livers and lungs. Further and more detailed examination of biodistribution after intraperitoneal injection showed higher fluorescence in most of tested organs suggesting more efficient diffusion and/or lymphatic uptake from peritoneum of unstressed MSA than the stressed formulation.
Word Sketches for Turkish

Word sketches are one-page, automatic, corpus-based summaries of a word's grammatical and collocational behaviour. In this paper we present word sketches for Turkish. Until now, word sketches have been generated using a purpose-built finite-state grammars. Here, we use an existing dependency parser. We describe the process of collecting a 42 million word corpus, parsing it, and generating word sketches from it. We evaluate the word sketches in comparison with word sketches from a language independent sketch grammar on an external evaluation task called topic coherence, using Turkish WordNet to derive an evaluation set of coherent topics.
Sleep and alertness in children with attention-deficit/hyperactivity disorder: a systematic review of the literature.

STUDY OBJECTIVE To review evidence on sleep and alertness in children with attention-deficit/hyperactivity disorder (ADHD) controlling for potential confounding factors. METHODS A PubMed search. Studies using ADHD diagnostic criteria other than DSM-III-R or IV and studies not excluding or controlling for psychiatric comorbidity or medication status were not included in the review. Results from objective studies were combined using meta-analysis. RESULTS From the 46 studies located, 13 were retained. With regard to objective studies, the proportion of subjects who fell asleep during the Multiple Sleep Latency Test, the number of movements in sleep, and the apnea-hypopnea index were significantly higher in children with ADHD than in controls. We found no significant differences in other objective parameters (sleep-onset latency; number of stage changes; percentages of stage 1 sleep, stage 2 sleep, slow-wave sleep, or rapid eye movement sleep; rapid eye movement sleep latency; and sleep efficiency). Limited evidence from subjective studies suggests no significant differences in sleep-onset difficulties and bedtime resistance between children with ADHD and controls, after controlling for comorbidity and medication status. Data on sleep duration, night and morning awakenings, and parasomnias are still very limited. CONCLUSION Results from our systematic review suggest that children with ADHD have higher daytime sleepiness, more movements in sleep, and higher apnea-hypopnea indexes compared with controls. Given the limited number of studies controlling for confounding factors, further subjective and objective studies are needed to better understand alterations in sleep and alertness in children with ADHD.
Visual Selection: Usually Fast and Automatic; Seldom Slow and Volitional

Recently it was argued that in addition to top-down and bottom-up processes, lingering biases of selection history play a major role in visual selection (Awh, Belopolsky & Theeuwes, 2012). Since its publication there has been a growing controversy about the terms top-down, bottom-up and selection-history in relation to visual selection. In the current paper we define these terms, discuss some controversies about these terms and explain what kind of effects should be considered to be the result of lingering biases of selection history, i.e., priming, reward/fear, and statistical learning. We discuss the properties of top-down selection (slow, effortful, and controlled) versus the properties of lingering biases of selection history (fast, effortless, and automatic). We adhere the position that the experience with selecting a particular feature or the location of a feature, may boost and sharpen its representation within the priority selection map above and beyond its physical salience. It is as if the experience may render a feature or location subjectively more salient. Our message of the current review is that true top-down control of visual selection occurs far less often than what is typically assumed. Most of the time, selection is based on experience and history. It is fast, automatic and occurs without much, if any, effort.