Miracle-dz/UniFAR-FTUs huggingface dataset:
Population Size and Migration of Anopheles gambiae in the Bancoumana Region of Mali and Their Significance for Efficient Vector Control

We present results of two intensive mark-release-recapture surveys conducted during the wet and dry seasons of 2008 in the villages of Fourda and Kenieroba, Mali. The former is a small fishing village by the Niger River with a moderate to high densities of Anopheles gambiae Giles s.s. (Diptera: Culicidae) throughout the year, while the latter is a large agricultural community 2 km inland that experiences strong seasonal fluctuation in An. gambiae densities. We estimate the population size of female An. gambiae in Fourda to be in less than 3,000 during the dry season. We found evidence of large population size and migration from Fourda in Kenieroba during the wet season, but very low numbers and no sign of migrants during the dry season. We suggest that malaria vector control measures aimed at adult mosquitoes might be made more efficient in this region and other seasonal riparian habitats by targeting disruption of mosquito populations by the river during the dry season. This would decrease the size of an already small population, and would be likely to delay the explosive growth in vector numbers in the larger inland villages as rainfall increases.
Activity of right premotor-parietal regions dependent upon imagined force level: an fMRI study

In this study, we utilized functional magnetic resonance imaging (fMRI) to measure blood oxygenation level-dependent (BOLD) signals. This allowed us to evaluate the relationship between brain activity and imagined force level. Subjects performed motor imagery of repetitive right hand grasping with three different levels of contractile force; 10%, 30%, and 60% of their maximum voluntary contraction (MVC). We observed a common activation among each condition in the following brain regions; the dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), supplementary motor area (SMA), premotor area (PM), insula, and inferior parietal lobule (IPL). In addition, the BOLD signal changes were significantly larger at 60% MVC than at 10% MVC in the right PM, the right IPL, and the primary somatosensory cortex (SI). These findings indicate that during motor imagery right fronto-parietal activity increases as the imagined contractile force level is intensified. The present finding that the right brain activity during motor imagery is clearly altered depending on the imagined force level suggests that it may be possible to decode intended force level during the motor imagery of patients or healthy subjects.
Level-Ground Walk Based on Passive Dynamic Walking for a Biped Robot with Torso

This study presents a design technique of an efficient biped walking robot on level ground with a simple mechanism based on passive-dynamic walking. A torso is used to generate active power replacing gravity used in passive walk. Swing-leg control is introduced to create a steady gait. Numerical simulations show that a biped robot with knees and a torso can walk efficiently on level ground. When we choose an appropriate parameter of the swing-leg control, the biped robot can walk stably over a wide range of speed. Furthermore, the walking performance of the robot increases with the increase of the radius of circular feet.
Learning Phonetic Features Using Connectionist Networks

A method for learning phonetic features from speech data using connectionist networks is described. A temporal flow model is introduced in which sampled speech data flows through a parallel network from input to output units. The network uses hidden units with recurrent links to capture spectral/temporal characteristics of phonetic features. A supervised learning algorithm is presented which performs gradient descent in weight space using a coarse approximation of the desired output as an evaluation function. A simple connectionist network with recurrent links was trained on a single instance of the word pair "no" and "go", and successful learned a discriminatory mechanism. The trained network also correctly discriminated 98% of 25 other tokens of each word by the same speaker. A single integrated spectral feature was formed without segmentation of the input, and without a direct comparison of the two items.
Biologically-inspired episodic memory model considering the context information

Episodic memory can store time sequential events and retrieve them anytime with specific cues. However, if the episodic memory only stores events comprised of actions and objects, execution of episodes may fail if current situation is different from the settings it learned in. As a solution, we propose Deep C-ART (Context-Adaptive Resonance Theory) which considers not only time sequential events but also their contexts. In addition to the learning process of Deep ART, Deep C-ART stores context information such as situation of objects, states of robots, place, and time of episodes. Since context changes over each event in an episode, Deep C-ART forms an episode with an event sequence and a context sequence. During retrieval and execution of episode, it compares the current situation with the learned one to verify that it is executable or in an anomaly situation. The effectiveness of Deep C-ART is demonstrated through computer simulations.
Depressive Symptoms and 24-Hour Ambulatory Blood Pressure in Africans: The SABPA Study

Disturbances in circadian rhythm might play a central role in the neurobiology of depression. We examined the association between depressive symptoms and 24-hour ambulatory BP in a sample of 405 (197 black and 208 Caucasian) urbanized African teachers aged 25 to 60 yrs (mean 44.6 ± 9.6 yrs). Depressive symptoms were assessed using the self-administered 9-item Patient Health Questionnaire (PHQ-9). After adjusting for age, sex, and ethnicity, participants with severe depressive symptoms (PHQ-9 ≥ 15) had higher odds of hypertension defined from ambulatory BP and/or use of antihypertensive medication (odds ratio = 2.19, 95% CI, 1.00–4.90) in comparison to participants with no symptoms. Compared to Caucasians with no depressive symptoms, those with severe symptoms had blunted nocturnal systolic BP drop of 4.7 mmHg (95% CI, −0.5 to 10.0, P = 0.07). In summary, depressive symptoms were associated with the circadian BP profile in black and Caucasian Africans.
QLWFQ: a queue length based weighted fair queueing algorithm in ATM networks

A work conserving O(1) per-VC queueing algorithm named QLWFQ (Queue Length based Weighted Fair Queueing) for high-speed ATM networks is proposed. The basic process in QLWFQ is the comparison of the current queue length with the weight when a cell arrival or departure occurs. QLWFQ has upward compatibility with fair queueing and FIFO, and can be considered to be a simplified version of weighted round robin. A delay bound and a fairness index are derived analytically for the QLWFQ algorithm. The simulation results for heavily and lightly loaded conditions are also presented. The analytical and simulation results show that QLWFQ achieves a better balance between traffic isolation and traffic sharing than timestamp based algorithms.
Creating shared realities through communication: exploring the agenda‐building role of the media and its sources in the E. coli contamination of a Canadian public drinking water supply

The media's role as an agenda-setter has been documented in the literature, however less attention has been given to the role of the media as an agenda-builder . The role of news agencies in constructing and shaping news stories serve to mediate risk messages to the public by virtue of what elements of a story they report, the types of sources they use in reporting the different 'sides', and how they package a story. The media are not the only ones who seek to set and build agendas. The sources, upon which the news media rely, can and do similarly engage in agenda-building activities. They do so by appropriating the very 'media-ted' process to use the mass media as a delivery vehicle for their own communication claims through their own packaged 'take' on the story. Drawing on a national print and televised media analysis of the Walkerton, Ontario, Canada drinking water contamination event, this study explores the agenda-building role of the media and of Walkerton residents and citizen group members (Concerned Walkerton Citizens) quoted in media stories. The implications of this for risk communication will be discussed.
Raven's Progressive Matrices as Applied to Iranian Children

This study reports the results of the application of Raven's Progressive Matrices (PM) to a representative sample of Iranian children attending schools in Teheran, the capital city of Iran. The study showed that boys did score slightly higher on the PM than did the girls, the differences being significant up to age 13. There was a consistent increase in PM scores at successive age levels, but the increments were not significant beyond age 15. Iranian children scored considerably below the British norms, although this decrement might have been due to the timed administration of PM in Iranian samples and to a variety of cultural factors. The reliability and validity of the PM were found to be satisfactory. Although the findings indicated the general suitability of the PM for use with Iranian children, there is a need for further normative and validity studies.
Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification

In this paper, we present a robust vision-based system for vehicle tracking and classification devised for traffic flow surveillance. The system performs in real time, achieving good results, even in challenging situations, such as with moving casted shadows on sunny days, headlight reflections on the road, rainy days, and traffic jams, using only a single standard camera. We propose a robust adaptive multicue segmentation strategy that detects foreground pixels corresponding to moving and stopped vehicles, even with noisy images due to compression. First, the approach adaptively thresholds a combination of luminance and chromaticity disparity maps between the learned background and the current frame. It then adds extra features derived from gradient differences to improve the segmentation of dark vehicles with casted shadows and removes headlight reflections on the road. The segmentation is further used by a two-step tracking approach, which combines the simplicity of a linear 2-D Kalman filter and the complexity of a 3-D volume estimation using Markov chain Monte Carlo (MCMC) methods. Experimental results show that our method can count and classify vehicles in real time with a high level of performance under different environmental situations comparable with those of inductive loop detectors.
AN EXPLORATION OF THE EXPERIENCE OF VICARIOUS TRAUMATIZATION AMONG CLINCIAL PSYCHOLOGY GRADUATE STUDENTS IN TRAINING

This study explored the experience of vicarious traumatization among clinical psychology graduate students while either in, or having just completed their first or second year of clinical training. Three research questions were of particular interest: (a) What is the lived experience of clinical psychology graduate students who have been vicariously traumatized as a result of working with trauma survivors? ; (b) Has the experience of working with trauma survivors affected clinical psychology graduate students’ cognitive schemas? ; and (c) How do clinical psychology graduate student trainees cope with vicarious trauma symptoms? The study was conducted utilizing heuristic research methods, specifically Moustakas’ six phases of heuristic inquiry. The findings were based on in-depth interviews with nine clinical psychology graduate students; seven females and two males; ranging in age from 25-31. All participants were enrolled at various psychology programs in the San Francisco Bay Area and identified as Caucasian, with one participant specifying Caucasian/Greek and another Caucasian/Jewish.
Synchronization as a Classroom Dynamic: A Practitioner's Perspective

While carefully plotted lesson plans are invaluable in a classroom, tuning into the flow of the moment is just as essential. My experience has shown me that the most effective teaching happens when everyone in the room is in synch with one another. Teaching requires intuiting what each student is experiencing individually and adjusting in order to bring the room to a united rhythm. Too often we define teaching in terms of curriculum, assessment, and practical measures that do not encompass this instinctive evolutionary skill. Teaching is a natural cognitive ability that requires human interaction and is optimal when a form of synchrony or flow exists in interactions. As an experienced classroom teacher, I describe how these principles infuse my practice, which I hope serves as a useful lens for the Mind, Brain, and Education (MBE) research community in its quest to uncover the underlying processes of teaching.
The evolutionary history and diagnostic utility of the CRISPR-Cas system within Salmonella enterica ssp. enterica

Evolutionary studies of clustered regularly interspaced short palindromic repeats (CRISPRs) and their associated (cas) genes can provide insights into host-pathogen co-evolutionary dynamics and the frequency at which different genomic events (e.g., horizontal vs. vertical transmission) occur. Within this study, we used whole genome sequence (WGS) data to determine the evolutionary history and genetic diversity of CRISPR loci and cas genes among a diverse set of 427 Salmonella enterica ssp. enterica isolates representing 64 different serovars. We also evaluated the performance of CRISPR loci for typing when compared to whole genome and multilocus sequence typing (MLST) approaches. We found that there was high diversity in array length within both CRISPR1 (median = 22; min = 3; max = 79) and CRISPR2 (median = 27; min = 2; max = 221). There was also much diversity within serovars (e.g., arrays differed by as many as 50 repeat-spacer units among Salmonella ser. Senftenberg isolates). Interestingly, we found that there are two general cas gene profiles that do not track phylogenetic relationships, which suggests that non-vertical transmission events have occurred frequently throughout the evolutionary history of the sampled isolates. There is also considerable variation among the ranges of pairwise distances estimated within each cas gene, which may be indicative of the strength of natural selection acting on those genes. We developed a novel clustering approach based on CRISPR spacer content, but found that typing based on CRISPRs was less accurate than the MLST-based alternative; typing based on WGS data was the most accurate. Notwithstanding cost and accessibility, we anticipate that draft genome sequencing, due to its greater discriminatory power, will eventually become routine for traceback investigations.
A Subtractive Relational Fuzzy C-Medoids Clustering Approach To Cluster Web User Sessions from Web Server Logs

In this paper, a subtractive relational fuzzy c-medoids clustering approach is discussed to identify web user session clusters from weblogs, based on their browsing behavior. In this approach, the internal arrangement of data along with the density of pairwise dissimilarity values is favored over arbitrary starting estimations of medoids as done in the conventional relational fuzzy c-medoids algorithm. It is assumed that straightforward binary session dissimilarity measure proposed and utilized as a part of prior detailed work is not especially logical, and instinctive to speak to session dissimilarities instigated from web client's habits, interest, and expectations. The idea of augmented sessions is utilized to infer page relevance based web client’s session dissimilarity matrix. The discussed approach is applied on an augmented session dissimilarity matrix obtained from an openly accessible NASA web server log data. The produced clusters are assessed by utilizing diverse fuzzy validity measures, and results are contrasted with conventional fuzzy c-medoids clustering. Experimental results demonstrated that quality of fuzzy clusters produced by utilizing proposed subtractive relational fuzzy c-medoids clustering is superior as compared with the conventional relational fuzzy c-medoids approach.
Attendance and Academic Performance of Students in Secondary Schools: A Correlational Approach

Abstract This study identified average percentage of attendance, academic performance and the correlation between attendance and academic performance of students in secondary schools. A checklist was used to collect 2860 students’ percentage scores in attendance and academic performance from 58 secondary schools, used in this study. Three research questions were raised and answered using percentage mean and linear regression equation. Two hypotheses were formulated and tested using Pearson r. The study revealed that; the mean score of students in attendance was 68% and academic performance 66%. It also revealed a fair positive correlation between attendance and academic performance. It showed the coefficient of determination r2 = 0.22, an indication that 22% of students academic performance was influenced by attendance in secondary schools in Delta State, Nigeria.
Physically Based Simulation of Fluid Movement

A physically based algorithm for fluid movement simulation is proposed. Compared with the traditional simulation technique, physically based simulation can represent realistic movement better. The Navier-Stokes equations are adopted as the model, and the operator splitting method is employed to split the model into external force term, advection term, diffusion term and projection term. Every step is stable, so the whole process is also stable. Thus, the large time step can be taken to ensure the simulation speed in the fluid movement simulation.
SPECTRAL CLUSTERING FROM A GEOMETRIC VIEWPOINT

Spectral methods have received attention as powerful theoretical and practical approaches to a number of machine learning problems. The methods are based on the solution of the eigenproblem of a similarity matrix formed from distance kernels. In this article we discuss three problems that are endemic in current implementations of spectral clustering: (1) the need to use another clustering method such as k-means as a final step, (2) the determination of the number of clusters, and (3) the failure of spectral clustering on multi-scale examples. These three problems are manifest even when the clusters are separated connected components. We advocate the use of the LU-factorization to solve (1), and treat clustering as a geometry problem to attack the second two problems. Specifically, the ideas of persistence and reconstruction of the Laplace-Beltrami operator are introduced as solutions to (2) and (3). We show that these suggested solutions are robust in a series of illustrative examples. 1. Introduction. Division of a set of points into clusters is a fundamental machine learning problem. Clustering underlies segmentation problems in network theory, image analysis, graph theory, and many other areas. In recent years, the clustering problem has attracted the attention of many researchers These methods apply a kernel function W ij = W (x i , x j) to all pairs of data points, forming a square " affinity " matrix. In the typical spectral clustering approach, the data is projected onto an eigenspace of the kernel matrix, and a more conventional clustering algorithm is applied to the data in the new coordinates. The reasoning behind spectral methods is that they are matrix versions of the maximization of graph cuts, which compare pairwise distances within and outside the assigned cluster. Comprehensive introductions to spectral clustering can be found in the tutorials of Chung [7] and Von Luxborg [27]. In this article, we argue that a more geometric treatment of spectral clustering can better illuminate the underlying workings of the method, which in turn motivates a more powerful algorithm for multiscale problems. Our contribution has three parts: (1) a new approach to the last step of spectral clustering, the unmixing of the eigenspace basis that results in indicator functions for cluster assignment; (2) the systematic use of persistence as a way to simultaneously choose an appropriate global scaling and the correct number of clusters; and (3) the use of a geometry-motivated local scaling to solve the …
Role of stoichiometry between mRNA, translation factor SeIB and selenocysteyl‐tRNA in selenoprotein synthesis

The specialized translation factor SeIB forms a quaternary complex in vitro with selenocysteyl‐tRNASec, the selenoprotein mRNA and guanine nucleotides. To gain information on whether this complex is required for selenocysteine insertion in vivo we have studied the effect of unbalanced ratios of the individual components of the complex on UGA readthrough. It was found that overproduction of SeIB in an otherwise wild‐type genetic background reduced UGA read‐through to less than 1 %. Concomitant overexpression of seIC (the gene for selenocysteine‐specific tRNASec) completely reversed the inhibition. Truncation of SeIB from the C‐terminal end abolished function as a translation factor but the truncated molecules, when overproduced, were still able to suppress UGA read‐through. The inhibition was also reversed by overproduction of tRNASec. The most plausible explanation is that overproduction of SeIB impairs the statistics of formation of the quaternary complex and that the C‐terminally truncated molecules are still able to bind selenocysteyl‐tRNASec and remove it from the pool. The mRNA‐binding capacity, therefore, is physically separated from the selenocysteyl‐tRNA‐binding domain.
A Preliminary Study of Tweet Summarization using Information Extraction

Although the ideal length of summaries differs greatly from topic to topic on Twitter, previous work has only generated summaries of a pre-fixed length. In this paper, we propose an event-graph based method using information extraction techniques that is able to create summaries of variable length for different topics. In particular, we extend the Pageranklike ranking algorithm from previous work to partition event graphs and thereby detect finegrained aspects of the event to be summarized. Our preliminary results show that summaries created by our method are more concise and news-worthy than SumBasic according to human judges. We also provide a brief survey of datasets and evaluation design used in previous work to highlight the need of developing a standard evaluation for automatic tweet summarization task.
Il ruolo della pratica e delle istruzioni nei training di memoria per anziani

Il ruolo della pratica e delle istruzioni nei training di memoria per anzianiThe present study analyzes the effectiveness of memory training in elderly people. The validity of the training is measured by evaluating the capacity to generalize the strategies to new material. Indeed, the transfer of the strategies on new tasks represents one of the main problems of memory interventions in aging. For this reason, two groups of elderly participants (younger vs. older elderly people) have been trained to apply two memory strategies (i.e., interactive images and sentence generation) to two memory tasks: paired associates and free recall of words. Moreover, instructions on how to apply the strategies to other material have been provided. Data show that both age groups took advantage from the training. The efficacy of the training is confirmed by the generalization effect that is equally present in all participants. Significantly, it seems that the capacity to learn how to use the strategies without practice is maintained in older adults as well.