Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of a discrete sliding mode technique to a HDD dual-stage track-seek and track-following servo system

This paper describes the application of a discrete-time sliding mode controller to a dual-stage actuator system in a hard disk drive. It is shown how the sliding mode control scheme can be incorporated into the observer based control system for the secondary actuator. Improved performance for the track-seeking and track-settling process is demonstrated. The dynamics of the discrete-time sliding-mode state-feedback controller are designed using a singular LQR-approach so that dead-beat behaviour for the sliding mode reaching dynamics is achieved in close proximity to the ideal sliding surface. The reaching dynamics are modified when the states are a significant distance away from the sliding surface to avoid any high magnitude control action. The control law also takes into account constraints on the actuator amplitudes. The design approach for this controller is novel in its own right.
Energy Efficient Compressed Sensing in Wireless Sensor Networks via Random Walk

In this paper, we explore the problem of data acquisition using compressive sensing (CS) in wireless sensor networks. Unique properties of wireless sensor networks require we minimize communication cost for efficient power usage. At first, a compressive distributed sensing (CDS) algorithm is proposed but is then modified to decrease communication costs. The final algorithm presented is compressive distributed sensing with random walk CDS(RW); an algorithm that combines the data gathering and projection generation process of CDS.CDS(RW) uses rateless encoding, graph algorithms, and belief propagation decoding to improve upon the communication cost associated with CDS. In the end, we show that the communication cost of CDS(RW) versus existing CS algorithms is far superior, while still having satisfactory decoding accuracy.
Inference for Lévy‐Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo

Abstract. We investigate simulation methodology for Bayesian inference in Lévy‐driven stochastic volatility (SV) models. Typically, Bayesian inference from such models is performed using Markov chain Monte Carlo (MCMC); this is often a challenging task. Sequential Monte Carlo (SMC) samplers are methods that can improve over MCMC; however, there are many user‐set parameters to specify. We develop a fully automated SMC algorithm, which substantially improves over the standard MCMC methods in the literature. To illustrate our methodology, we look at a model comprised of a Heston model with an independent, additive, variance gamma process in the returns equation. The driving gamma process can capture the stylized behaviour of many financial time series and a discretized version, fit in a Bayesian manner, has been found to be very useful for modelling equity data. We demonstrate that it is possible to draw exact inference, in the sense of no time‐discretization error, from the Bayesian SV model.
Dynamic and evolutionary updates of classificatory schemes in scientific journal structures

Can the inclusion of new journals in the Science Citation Index be used for the indication of structural change in the database, and how can this change be compared with reorganizations of relations among previously included journals? Change in the number of journals (n) is distinguished from change in the number of journal categories (m). Although the number of journals can be considered as a given at each moment in time, the number of journal categories is based on a reconstruction that is time-stamped ex post. The reflexive reconstruction is in need of an update when new information becomes available in a next year. Implications of this shift towards an evolutionary perspective are specified.
Aggregated Randomly Generated Math Curriculum–Based Measurements for Middle School Students: Reliability, Predictive Validity, and Cut Score Precision

Approximately, 283 sixth-grade students completed four randomly generated Math Curriculum–Based Measures (M-CBMs) from interventioncentral.org twice during a fall semester. These M-CBMs measured foundational skills in addition, subtraction, multiplication, and division. We found that using mean fluency or accuracy scores from four M-CBMs produced more reliable test–retest estimates (e.g., r = .86–.88) in comparison with using individual scores (e.g., r = .55–.73). We also found that aggregated (mean or median) and individual M-CBM scores were strongly correlated (e.g., r = .82–.85) with the Palmetto Achievement Challenge Test (PACT) math section, an annual state standardized test. However, only aggregated M-CBMs produced efficient cut scores useful in detecting students who would pass or fail the PACT.
On designing MAC protocols for wireless networks using directional antennas

We investigate the possibility of using directional antennas for medium access control in wireless ad hoc networks. Previous research in ad hoc networks typically assumes the use of omnidirectional antennas at all nodes. With omnidirectional antennas, while two nodes are communicating using a given channel, MAC protocols such as IEEE 802.11 require all other nodes in the vicinity to remain silent. With directional antennas, two pairs of nodes located in each other's vicinity may potentially communicate simultaneously, increasing spatial reuse of the wireless channel. Range extension due to higher gain of directional antennas can also be useful in discovering fewer hop routes. However, new problems arise when using directional beams that simple modifications to 802.11 may not be able to mitigate. This paper identifies these problems and evaluates the tradeoffs associated with them. We also design a directional MAC protocol (MMAC) that uses multihop RTSs to establish links between distant nodes and then transmits CTS, DATA, and ACK over a single hop. While MMAC does not address all the problems identified with directional communication, it is an attempt to exploit the primary benefits of beamforming in the presence of some of these problems. Results show that MMAC can perform better than IEEE 802.11, although we find that the performance is dependent on the topology and flow patterns in the system.
Trend Analysis of Surface Water Quality in Hunan Province

The trend of the surface water quality of Hunan province is analyzed by the seasonal Kendall test method, basing on the main pollutants during 2001-2005.The conclusions are as follows: The pollutions of BOD5 and NH3-N of Xiangjiang river, TP of Yuanjiang river, As and CYN of Lishui river, As,FN and TP of Dongting lake have an obvious upward trend; the pollutions of Cd, Pb, TP and petroleum of Zijiang river, petroleum of Dongting lake have an obvious downward trend.
Design and analysis of a many-core processor architecture for multimedia applications

We present a design of many-core processor architecture with superior cost-effectiveness to fulfill the rapid increasing demand of high-speed embedded multimedia applications. The prototype platform consists of sixteen processor cores and a 4-by-4 mesh-based duplex network interconnection with external memory. The hardware and software interface in a bare-metal environment, i.e., without an Operating System (OS), has been emphasized in our architecture. An on-chip communication library is developed for practical parallel applications. In addition, we propose two memory-based file handling approaches to manipulate files with the lack of file-system support by OS. Our file handling approach can effectively reduce the minimum requirement of local memory without page swapping for each core from 4 MB to 64 KB in a case study of JPEG encoding. Furthermore, the analysis of instruction and data caches is addressed for the trade-off between area and speed. The experimental result indicates that our many-core platform with its application development infrastructure is efficient in delivering cost-effective multimedia applications in a bare-metal environment.
Acute neuronal cell death Neuronal cell disruption Alterations in dendritic architecture Decreased synaptic density Mitochondrial deterioration Decreased in neurotrophic factors Impaired long-term cognitive , function

Objectives At the completion of this course, the reader should be able to: 1. Discuss the historical evolution of pediatric anesthesia relative to the physiology of pain interpretation of the very young. 2. Describe the role of normal and accelerated apoptosis in the developing brain. 3. Describe the limitations of prospective animal research and retrospective human research focusing on the anesthetic effects of subsequent neurocognition. 4. Describe an understanding of the proposed mechanism of action of inhalation anesthetics and sedatives in the role of apoptosis. 5. Identify how to address parental concerns regarding the anesthetic management of their child.
Passive Irradiated Circumstellar Disks with an Inner Hole

A model for irradiated dust disks around Herbig Ae stars is proposed. The model is based on the flaring disk model given by Chiang and Goldreich in 1997, but with the central regions of the disk removed. The inner rim of the disk is puffed up and is much hotter than the rest of the disk because it is directly exposed to the stellar flux. If located at the dust evaporation radius, its reemitted flux produces a conspicuous bump in the spectral energy distribution (SED) that peaks at 2-3 μm. We propose that this emission is the explanation for the near-infrared bump observed in the SEDs of Herbig Ae stars. We study for which stellar parameters this bump would be observable and find that it is the case for Herbig Ae stellar parameters but not for T Tauri stars, confirming what is found from the observations. We also study the effects of the shadow cast by the inner rim over the rest of the flaring disk. The shadowed region can be quite large, and under some circumstances the entire disk may lie in the shadow. This shadowed region will be much cooler than an unshadowed flaring disk, since its only heating sources are radial radiative diffusion and possible indirect sources of irradiation. Under certain special circumstances the shadowing effect can suppress, or even completely eliminate, the 10 μm emission feature from the spectrum, which might explain the anomalous SEDs of some isolated Herbig Ae stars in the recent sample of Meeus and colleagues. At much larger radii the disk emerges from the shadow and continues as a flaring disk toward the outer edge. The emission from the inner rim contributes significantly to the irradiation of this flaring disk. The complete semianalytical model, including structure of the inner edge, the shadowed region, and the flared outer part, is described in detail in this paper, and we show examples of the general behavior of the model for varying parameters.
QUANTUM MONTE CARLO SIMULATIONS OF FERMIONS: A MATHEMATICAL ANALYSIS OF THE FIXED-NODE APPROXIMATION

The Diffusion Monte Carlo (DMC) method is a powerful strategy to estimate the ground state energy E0 of an N-body Schrodinger Hamiltonian H = -½Δ + V with high accuracy. It consists of writing E0 as the long-time limit of an expectation value of a drift-diffusion process with a source term, and numerically simulating this process by means of a collection of random walkers. As for a number of stochastic methods, a DMC calculation makes use of an importance sampling function ψI which hopefully approximates some ground state ψ0 of H. In the fermionic case, it has been observed that the DMC method is biased, except in the special case when the nodal surfaces of ψI coincide with those of a ground state of H. The approximation due to the fact that, in practice, the nodal surfaces of ψI differ from those of the ground states of H, is referred to as the Fixed Node Approximation (FNA). Our purpose in this paper is to provide a mathematical analysis of the FNA. We prove that, under convenient hypotheses, a DMC calculation performed with the importance sampling function ψI, provides an estimation of the infimum of the energy 〈ψ, Hψ〉 on the set of the fermionic test functions ψ that exactly vanish on the nodal surfaces of ψI.
A Low-Cost Fault-Tolerant Approach for Hardware Implementation of Artificial Neural Networks

Artificial Neural Networks (ANNs) are widely used in computational and industrial applications. As technology is developed the scale of hardware is progressively becoming smaller and the number of faults is increasing. Therefore, fault-tolerant methods are necessary especially for ANNs used in critical applications. In this work, we propose a new method for fault-tolerant implementation of neural networks. In hidden and output layers, we add a spare neuron, and one of hidden and output neurons is tested by each input pattern. Our technique detects and corrects any single fault in the network. We achieve complete fault tolerance for single faults with at most 40% area overhead.
Genetic parameters for blood oxygen saturation, body weight and breast conformation in 4 meat-type chicken lines

1. The objective of the study was to explore the genetic architecture of blood oxygen saturation (SaO) (an indicator trait, negatively correlated with ascites susceptibility), body weight (Weight) and fleshing score (Flesh, a measure of breast conformation) for 4 meat-type chicken lines reared in commercial conditions. 2. Genetic components, including heritabilities and genetic correlations, were estimated by Restricted Maximum likelihood for these traits measured at 6 weeks of age. 3. Data were collected over eight generations of selection and pedigrees comprised in excess of 130 000 birds. 4. Univariate analyses were performed to allow model definition and to obtain starting values for trivariate analyses. The basic model included a random animal effect and, in further models explored, a maternal environmental effect or a genetic maternal effect or both were fitted. Models were compared using likelihood ratio tests. 5. Estimated heritabilities for SaO ranged from 0·1 to 0·2, and there was no evidence of genetic maternal effects for SaO. The environmental maternal component was significant for one of the populations only. Estimated heritabilities for both Weight and Flesh were between 0·2 and 0·4, and there was evidence of environmental and genetic maternal effects for these traits in all populations. 6. Genetic correlations between SaO and Weight and between SaO and Flesh were low and negative. This suggests that, in principle, genetic selection to simultaneously increase SaO, and therefore decrease ascites susceptibility, and Weight and Flesh could be performed using traditional (marker-free) selection methods. We discuss how a putative interaction between ascites and production traits could jeopardise the success of such methods.
Image interpolation based on inter-scale dependency in wavelet domain

Image interpolation in the wavelet domain can be considered as the estimation of wavelet coefficients in the highest frequency subband. In this paper, a novel image interpolation method based on inter-scale dependency in the wavelet domain is proposed. In our method, the Gaussian mixture model (GMM) is used to estimate the magnitude of the wavelet coefficient, and the parameters of the GMM are derived from subbands with no training. The sign of the estimated wavelet coefficient is also obtained by using the inter-scale dependency of wavelet subbands. In the simulation results, the proposed method shows an improved PSNR and subjective quality compared with conventional bicubic method and the statistical method (K. Kinebuchi et al., May, 2001) that exploits the hidden Markov tree (HMT) model with training.
A Large Scale URL Verification Pipeline Using Hadoop

Data quality is a key element for local search and advertising. Inaccurate, out-of-date or missing information causes an unpleasant search experience for users and affects competitiveness of service providers. This paper addresses the problem of evaluating link quality for business listings in local search and online advertising domain. We introduce a novel system where we apply data mining technologies on a Hadoop-based platform to provide an efficient and highly scalable solution for the problem. Due to various reasons, links associated with business listings do not always point to their business websites. Possible noises include parked domain, broken links, third-party advertisers, irrelevant websites etc. To detect above noises and improve link quality, we formulate this problem as a binary classification problem: whether a given URL is the business website of the associated listing. Experiments conducted on real-world data show that our system can verify millions of business listings against about 100 million web pages in a couple of hours with 93% classification accuracy.
Small group methods in medical teaching

A teaching skill essential for all medical educators is small group competence. Lack of it can never have been more damaging: medical teachers have now to promote active learning, for which small group methods are optimal. The only kind of medical student now needed is an activated one, equipped for independent lifelong learning and teamwork; current educational innovation, such as problem based learning (Walton & Matthews 1989) depends on small group teaching. Small group work is a method for generating free communication between the group leader and the members, and among all the participants themselves. The group leader can make positive use of the differences in knowledge and attitudes among participants, made evident as they interact with each other. Small group work enables participants to gain a great deal from their fellows, in a type of communication which cannot take place in a lecture hall (Westberg & Jason 1996). Group methods have the particular merit that ± when properly used ± all participants have the opportunity to take part, and each can see for themselves the impact of their views on the other members of the group. Whenever anybody speaks the response evoked is immediately visible, whether it be assent, disagreement or disinterest. A group is also, most importantly so, a change system: those taking part in the dialogue are in̄uenced as they attend to the participants who speak, the facts being expressed, and the attitudes conveyed, and reconsider their own positions. The group leader (tutor, instructor, moderator, chairman, facilitator) is the crucial agent, not present merely to listen to the views being stated, but responsible for helping the group to identify any errors, misperceptions or biases of its members. This is achieved by encouraging them to communicate their ideas and to correct each others' errors of grasp as these gradually emerge. Only when participants themselves fail to do so is there need for the leader to step in to provide corrective feedback. Minimal intervention characterizes a competent group leader, but that in no way implies passivity or mere compliance on his or her part. Quite the contrary. The group leader is an expert at active listening, attentive always to the self-esteem of participants, and competent to conduct a group meeting by quite speci®c methods which result in an enhancing intellectual experience for all the members.
Resilience Improves Neurocognition and Treatment Outcomes in Schizophrenia: A Hypothesis

There has been a revolutionary advance in the treatment and management of schizophrenia from a clinical aspect yet the social and functional outcomes remain poor. Cognitive function is impaired in schizophrenia and shows various domains of dysfunction like verbal memory, processing speed and working memory. It is also known to be a factor associated with poor outcome in schizophrenia. Resilience is a new concept psychobiological concept which is defined as individual’s ability to adapt swiftly to adverse life events and bounces back to normalcy. Resilience has genetic, neurobiological, neurochemical and psychological underpinnings. It is the ability to effectively deal with psychosocial stressors and appears to be one of the many factors associated with favourable outcomes in schizophrenia. Besides several neurobiological abnormalities associated with resilience, neucognitive functions are of particular interest. Persistent psychosocial stressors also lead to significant neurobiological changes which may be synergetic to poor outcome due to cognitive changes. Though there has been extensive research in the field of cognitive function in schizophrenia, the trajectory of its pathway of poor outcome remains undetermined. Resilience being a protective factor may be one of the psychobiological functions which modulate the effect of neurocognition on the outcome of schizophrenia. There has been some success with interventions aimed at improving cognitive function in schizophrenia whether pharmacological or non pharmacological. In this paper, we discuss a hypothesis that resilience may be a “linkage” between cognition and outcome. There is a need for interventions aimed at increasing resilience in patients with schizophrenia and we hypothesize giving evidence that this may in turn improve outcome and neurocognitive functioning in schizophrenia.
An End-to-End Probabilistic Network Calculus with Moment Generating Functions

Network calculus is a min-plus system theory for performance evaluation of queuing networks. Its elegance steins from intuitive convolution formulas for concatenation of deterministic servers. Recent research dispenses with the worst-case assumptions of network calculus to develop a probabilistic equivalent that benefits from statistical multiplexing. Significant achievements have been made, owing for example to the theory of effective bandwidths; however, the outstanding scalability set up by concatenation of deterministic servers has not been shown. This paper establishes a concise, probabilistic network calculus with moment generating functions. The presented work features closed-form, end-to-end, probabilistic performance bounds that achieve the objective of scaling linearly in the number of servers in series. The consistent application of moment generating functions put forth in this paper utilizes independence beyond the scope of current statistical multiplexing of flows. A relevant additional gain is demonstrated for tandem servers with independent cross-traffic
A Turbulent Origin for Flocculent Spiral Structure in Galaxies. II. Observations and Models of M33

Fourier transform power spectra of azimuthal scans of the optical structure of M33 are evaluated for B, V, and R passbands and fitted to fractal models of continuum emission with superposed star formation. Power spectra are also determined for Hα. The best models have intrinsic power spectra with one-dimensional slopes of around -0.7 ± 0.7, significantly shallower than the Kolmogorov spectrum (slope = -1.7) but steeper than pure noise (slope = 0). A fit to the power spectrum of the flocculent galaxy NGC 5055 gives a steeper slope of around -1.5 ± 0.2, which could be from turbulence. Both cases model the optical light as a superposition of continuous and pointlike stellar sources that follow an underlying fractal pattern. Foreground bright stars are clipped in the images, but they are so prominent in M33 that even their residual affects the power spectrum, making it shallower than what is intrinsic to the galaxy. A model consisting of random foreground stars added to the best model of NGC 5055 fits the observed power spectrum of M33 as well as the shallower intrinsic power spectrum that was made without foreground stars. Thus the optical structure in M33 could result from turbulence as well.