Miracle-dz/UniFAR-FTUs huggingface dataset:
A Voronoi-Based Depth-Adjustment Scheme for Underwater Wireless Sensor Networks

Abstract Underwater wireless sensor network (UWSN) is a special kind of wireless sensor network which is composed of a large quantity number of wireless sensor nodes deployed in the water. While there are extensive studies on deploy-issue of terrestrial wireless sensor networks (WSN), UWSN has not been paid enough attention due to the challenges of UWSN, such as low available bandwidth, highly varying multipath, and large propagation delays. In this paper, we propose a depth-adjustment scheme to maximize the coverage in 3D space. After deploying nodes in the water surface, we use Voronoi diagram to compute redundant nodes whose disappearance will not decrease the coverage in 2D space, and then we determine the depth that redundant nodes should be moved towards. After all the redundant nodes have moved to the lower layer, the algorithm continues to schedule redundant nodes of the lower layer until 3D space coverage is fulfilled.
Effect of a Non-Tight Constructions of DC-Link and Damping Oscillation to Improve the Efficiency of Flyback Converter

This paper deals with improvement of the efficiency of fly-back converter that was used in traction drives application. There are lots of researches in this area and also some techniques to increase the performance of the converter. In this paper, the reference of the converter was modified in a non-tight construction of DC-Link and change the snubber circuit to reduce the damping oscillation. The result of performance the converter was simulated (theoretical) and measured. The result show that is changing the dc-link construction and snubber circuit improve the efficiency of the converter.
CONSENSUS Project: Identifying Publicly Acceptable Policy Implementations

Even though it is unrealistic to expect citizens to pinpoint the policy implementation that they prefer from the set of alternatives, it is still possible to infer such information through an exercise of ranking the importance of policy objectives according to their opinion. Assuming that the mapping between policy options and objective evaluations is a priori known (through models and simulations), this can be achieved either implicitly through appropriate analysis of social media content related to the policy objective in question or explicitly through the direct feedback provided in the frame of a game. This document focuses on the presentation of a policy model, which reduces the policy to a multi-objective optimization problem and mitigates the shortcoming of the lack of social objective functions (public opinion models) with a black-box, games-for-crowds approach.
Teaching and Learning Science as a Visual Experience

This chapter brings about concepts about implementing a framework for teaching and learning across the disciplines by introducing topics and activities pertaining to science, computing, and graphic arts as a unified cognitive and visual learning experience. First, theoretical framework is presented, to support designing integrative projects for cognitive learning. This part provides basic information about brain and mind, feelings and emotions, cognitive thinking, intelligence and cognitive styles, along with some basics about technologies used for studying cognitive activity. Then follows short introduction to ways to communicate knowledge, visual thinking, visual literacy, and knowledge visualization concepts and methods. Next, concerns about science education draw attention to a need of including into curriculum new developments in science and information about currently emerging disciplines. The goal is to enhance technological literacy of students, activate their interest, motivation, abstract thinking, and elicit a wish to achieve their aims.
System Level Hardening by Computing with Matrices

Continuous advances in transistor manufacturing have enabled technology scaling along the years, sustaining Moore's law. As transistors sizes rapidly shrink, and voltage scales, the amount of charge in a node also rapidly decreases. A particle hitting the core will probably cause a transient fault to spam over several clock cycles. In this scenario, embedded systems using state-of-the-art technologies will face the challenge of operating in an environment susceptible to multiple errors, but with restricted resources available to deploy fault-tolerance, as these techniques severely increase power consumption. One possible solution to this problem is the adoption of software based fault-tolerance at the system level, aiming at reduced energy levels to ensure reliability and low energy dissipation. In this paper, we claim the detection and correction of errors on generic data structures at system level by using matrices to encode any program and algorithm. With such encoding, it is possible to employ established techniques of detection and correction of errors occurring in matrices, running with inexpressive overhead of power and energy. We evaluated this proposal using two case studies significant for the embedded system domain. Using the proposed approach, we observed in some cases an overhead of only 5% in performance and 8% in program size.
Design of Two-Stage Miller-Compensated Amplifiers Based on an Optimized Settling Model

A new methodology for the design of two-stage Miller-compensated operational amplifiers in closed-loop applications is proposed in this paper. This methodology that is based upon a new fundamental settling-based analysis specifies the proper open-loop parameters of the opamp regarding to its desired closed-loop specifications. As the conventional model for the step settling response of two-stage opamps have always been overestimated, a simple yet accurate settling model is also suggested to optimize the methodology. The proposed methodology is especially helpful in those applications where the settling response is important. Simulation results are presented to show the effectiveness of the design methodology and to compare the proposed model with the conventional model
Assessing Organizations Readiness toward Business Intelligence Systems: A Proposed Hypothesized Model

Nowadays, the concept of Business Intelligence (BI) is being implemented within many organizations around the globe. Organizations invest billions of US dollars aiming to improve their timeliness and quality of the input to the decision process, where BI is the targeted keyword. However, organizations are often faced with many challenges in achieving the expected outcomes of BI systems within their firms. Here, one of the most cumbersome obstacles is the organizations' readiness toward BI systems. Therefore, it is highly justified to assess organization's readiness toward BI systems to avoid any loss and to enhance the overall revenue. This paper presents a brief history on BI systems and the challenges of their implementation within organizations. A review and analysis of models for assessing organizations' readiness toward BI systems is established to understand their weakness and strength points. Finally, this paper proposes a hypothesized framework for solving readiness assessment issues within organizations which will be evaluated in the coming studies.
Intersecting Heap Abstractions with Applications to Compile-time Memory Management

We consider the problem of computing the intersection (meet) of heap abstractions, namely the common value of a set of abstract memory stores. This problem proves to have many applications in shape analysis, such as interpreting program conditions, refining abstract configurations, reasoning about procedures, and proving temporal properties of heap-manipulating programs, either via greatest fixed point approximation over the trace semantics, or in a staged manner over the collecting semantics. However, computing the meet of heap abstractions is nontrivial; its definition as the least upper bound of all lower bounds does not lead to an effective algorithm. We describe a constructive formulation of meet that is based on finding certain relations between abstract heap objects. The enumeration of those relations is reduced to finding constrained matchings over bipartite graphs. A simple heuristic is applied in order to reduce computational overhead, and is supposed to behave well for common real-life scenarios. We describe a prototype implementation of the algorithm for proving temporal heap properties via staged analysis. It is applied to obtain information for compile-time garbage collection on several small but interesting Java programs.
Visualizing Graphs and Clusters as Maps

Information visualization is essential in making sense of large datasets. Often, high-dimensional data are visualized as a collection of points in 2D space through dimensionality reduction techniques. However, these traditional methods often don't capture the underlying structural information, clustering, and neighborhoods well. GMap is a practical algorithmic framework for visualizing relational data with geographic-like maps. This approach is effective in various domains.
Fairness in Distributive Justice by 3- and 5-Year-Olds Across Seven Cultures

This research investigates 3- and 5-year-olds' relative fairness in distributing small collections of even or odd numbers of more or less desirable candies, either with an adult experimenter or between two dolls. The authors compare more than 200 children from around the world, growing up in seven highly contrasted cultural and economic contexts, from rich and poor urban areas, to small-scale traditional and rural communities. Across cultures, young children tend to optimize their own gain, not showing many signs of self-sacrifice or generosity. Already by 3 years of age, self-optimizing in distributive justice is based on perspective taking and rudiments of mind reading. By 5 years, overall, children tend to show more fairness in sharing. What varies across cultures is the magnitude of young children's self-interest. More fairness (less self-interest) in distributive justice is evident by children growing up in small-scale urban and traditional societies thought to promote more collective values.
1α,25(OH)2 Vitamin D3 Modulates Avian T Lymphocyte Functions without Inducing CTL Unresponsiveness

1,25-Dihydroxyvitamin D3 (Vitamin D) is a naturally synthesized fat soluble vitamin shown to have immunomodulatory, anti-inflammatory and cancer prevention properties in human and murine models. Here, we studied the effects of Vitamin D on the functional abilities of avian T lymphocytes using chicken Interferon (IFN)-γ ELISPOT assay, BrdU proliferation assay, Annexin V apoptosis assay and PhosFlow for detecting phosphorylated signalling molecules. The results demonstrate that Vitamin D significantly inhibited the abilities of T lymphocytes to produce IFN-γ and proliferate in vitro (P≤0.05), but retained their ability to undergo degranulation, which is a maker for cytotoxicity of these cells. Similarly, Vitamin D did not inhibit Extracellular signal-Regulated Kinase (ERK) 1/2 phosphorylation, a key mediator in T cell signalling, in the stimulated T lymphocytes population, while reduced ERK1/2 phosphorylation levels in the unstimulated cells. Our data provide evidence that Vitamin D has immuno-modulatory properties on chicken T lymphocytes without inducing unresponsiveness and by limiting immuno-pathology can promote protective immunity against infectious diseases of poultry.
Applying Data Mining in Prediction and Classification of Urban Traffic

Data mining is a branch of computer science which recently has a great use for enterprises. Applying data mining methods, huge databases have been analyzed and processed. Data mining techniques are usually hired to mine knowledge and models from enormous data sets for prediction of new events. Furthermore these techniques are commonly used in fields which generate great amount of data that can not be processed by ordinary methods. During the last decade traffic management became a new field of science which produced unlimited data, and this amount of data needed new methods to be processed. It is clear that one of the most important fields in traffic management is Traffic prediction. As a result data mining methods were chosen to generate dependable patterns. In this paper we applied Classification methods to learn traffic behavior and prediction of new events.
Affine invariant diffusion smoothing strategy for vector-valued images

The Gaussian noise introduced into the diffusion tensor images (DTIs) can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Gaussian noise, many Euclidean invariant gradient (EIG) based anisotropic diffusion denoising methods have been presented. In this paper, the effects of the Gaussian noise on calculated tensors were analyzed and an affine invariant gradient (AIG) based nonlinear anisotropic smooting strategy was presented. The AIG based smoothing strategy is the development of the affine invariant nonlinear anisotropic diffusion (AINAD) restoration model, introduced by Guillermo Sapiro, and adopted to restore vector-valued data. To evaluate the efficiency of the AINAD model in accounting for the Gaussian noise introduced into the vector-valued data, the peak to peak signal-to-noise ratio (PSNR) and signal-to-mean squared error ratio (SMSE) metrics are used. The experiment results acquired from the synthetic and real data prove the good performance of the presented filter.
Partitioning K clones: hardness results and practical algorithms for the K-populations problem

Given a set of m molecules, derived from K homologous clones, we wish to partition these molecules into K populations, each giving rise to distinct ordered restriction maps, thus providing simple means for stuclying biological variations. With the emergence of single molecule methods, such as optical mapping, that wn create individual ordered restriction maps reliably and with high throughput, it becomes interesting to study the related algorithmic problemsIn particular, we provide a complete computational complezity analysis of the “Kpopulations”problem as well as some simple polynomial heuristics, while ezposing the relations among various error sources that the optical mapping approach may need to cope with. We believe that these results will be of interest to computational biologists in devising better algorithms, to biochemists in understanding the tradeoffs among the error sources andfinally, to biologists in creating reliable protocols for population study.
Unfulfilled Potential: High-Achieving Minority Students and the High School Achievement Gap in Math

Abstract: This study uses multilevel modeling to examine a subset of the highest performing 9th graders and explores the extent that achievement gaps in math widen for high performing African American and Latino students and their high performing White and Asian peers during high school. Using nationally representative data from the High School Longitudinal Study of 2009 (HSLS:09), this study aims to address three research questions: 1) To what degree does the achievement gap widen between different ethnic groups during high school; 2) To what extent can a widening achievement gap be explained by individual and school characteristics of students? ; and 3) To what extent are the effects of various factors on achievement moderated by race/ethnicity? The study finds that factors such as advanced course placement and socio-economic status (both individual and school SES) explain away the gap for high achieving Latino 11th graders and some of the gap for African American 11th graders while student background explains away the large advantage for Asian 11th graders. Notably, the effect of math efficacy on math achievement is shown to vary by race/ethnicity. This study has important implications for post-secondary opportunities for high-achieving African American and Latino students.
Interaction between Facilitation and Depression at a Large CNS Synapse Reveals Mechanisms of Short-Term Plasticity

The two fundamental forms of short-term plasticity, short-term depression and facilitation, coexist at most synapses, but little is known about their interaction. Here, we studied the interplay between short-term depression and facilitation at calyx of Held synapses. Stimulation at a “low” frequency of 10 or 20 Hz, which is in the range of the spontaneous activity of these auditory neurons in vivo, induced synaptic depression. Surprisingly, an instantaneous increase of the stimulation frequency to 100 or 200 Hz following the low-frequency train uncovered a robust facilitation of EPSCs relative to the predepressed amplitude level. This facilitation decayed rapidly (∼30 ms) and depended on presynaptic residual Ca2+, but it was not caused by Ca2+ current facilitation. To probe the release probability of the remaining readily releasable vesicles following the low-frequency train we made presynaptic Ca2+ uncaging experiments in the predepressed state of the synapse. We found that low-frequency stimulation depletes the fast-releasable vesicle pool (FRP) down to ∼40% of control and that the remaining FRP vesicles are released with ∼2-fold slower release kinetics, indicating a hitherto unknown intrinsic heterogeneity among FRP vesicles. Thus, vesicles with an intrinsically lower release probability predominate after low frequency stimulation and undergo facilitation during the onset of subsequent high-frequency trains. Facilitation in the predepressed state of the synapse might help to stabilize the amount of transmitter release at the onset of high-frequency firing at these auditory synapses.
Transposing Chroma Representations to a Common Key

Abstract—Chroma representations of musical excerpts arenowadays very popular and used for a wide variety of appli-cations. Within these, transposition to a common key or tona litycan represent an important aspect, usually having a dramaticimpact to ﬁnal system’s accuracy. We present and evaluatea new and straightforward way for transposing two chromarepresentations to a common key that outperforms previousmethods based on key estimation and that, without detrimentof accuracy, is computationally faster than trying all possibletranspositions. In addition, we also provide some insights intothe internal organization of this new tool, suggesting it wouldorganize transposition indices in a coherent manner.Index Terms—Music, Information retrieval, Acoustic signalanalysis, Multidimensional sequences, Symbol manipulation I. I NTRODUCTION T RANSPOSING musical excerpts to a common key ortonality is a necessary feature when comparing melodies,harmonies or any tonal representation of these musical ex-cerpts. This process is specially crucial in many music in-formation retrieval (MIR) tasks related to music similaritysuch as audio matching and alignment [1], [2], song structureanalysis [3] or cover song identiﬁcation [4], [5], where mel odicor harmonic representations of musical excerpts are used.Furthermore, this is a necessary feature for any music retrievalor recommendation engine comparing tonal information.Chroma features or pitch class proﬁles (PCP) have becomevery popular and widely used among these and many otherMIR-related tasks (e.g. key and chord estimation [6], [7])as they provide a description of the audio tonal contentthat, ideally [8], (a) represents the pitch class distribution ofboth monophonic and polyphonic signals, (b) considers thepresence of harmonic frequencies, (c) is robust to noise andnon-tonal sounds, (d) is independent of timbre and playedinstrument, (e) is independent of loudness and dynamics and(f) is independent of tuning, so that the reference frequencycan be slightly different from the standard A 440 Hz. Chromafeatures (ﬁgure 1) are derived from the energy found within agiven frequency range (typically from 50 to 5000 Hz) in short-time spectral representations (e.g. 100 msec) of audio signalsextracted on a frame-by-frame basis. This energy is usuallycollapsed into an octave-independent histogram representingthe presence (or relative intensity) of each of the 12 semitonesof an equal-tempered chromatic scale.
Effect of hormone replacement therapy on cardiovascular events in recently postmenopausal women: randomised trial

Objective To investigate the long term effect of hormone replacement therapy on cardiovascular outcomes in recently postmenopausal women. Design Open label, randomised controlled trial. Setting Denmark, 1990-93. Participants 1006 healthy women aged 45-58 who were recently postmenopausal or had perimenopausal symptoms in combination with recorded postmenopausal serum follicle stimulating hormone values. 502 women were randomly allocated to receive hormone replacement therapy and 504 to receive no treatment (control). Women who had undergone hysterectomy were included if they were aged 45-52 and had recorded values for postmenopausal serum follicle stimulating hormone. Interventions In the treatment group, women with an intact uterus were treated with triphasic estradiol and norethisterone acetate and women who had undergone hysterectomy received 2 mg estradiol a day. Intervention was stopped after about 11 years owing to adverse reports from other trials, but participants were followed for death, cardiovascular disease, and cancer for up to 16 years. Sensitivity analyses were carried out on women who took more than 80% of the prescribed treatment for five years. Main outcome measure The primary endpoint was a composite of death, admission to hospital for heart failure, and myocardial infarction. Results At inclusion the women on average were aged 50 and had been postmenopausal for seven months. After 10 years of intervention, 16 women in the treatment group experienced the primary composite endpoint compared with 33 in the control group (hazard ratio 0.48, 95% confidence interval 0.26 to 0.87; P=0.015) and 15 died compared with 26 (0.57, 0.30 to 1.08; P=0.084). The reduction in cardiovascular events was not associated with an increase in any cancer (36 in treated group v 39 in control group, 0.92, 0.58 to 1.45; P=0.71) or in breast cancer (10 in treated group v 17 in control group, 0.58, 0.27 to 1.27; P=0.17). The hazard ratio for deep vein thrombosis (2 in treated group v 1 in control group) was 2.01 (0.18 to 22.16) and for stroke (11 in treated group v 14 in control group) was 0.77 (0.35 to 1.70). After 16 years the reduction in the primary composite outcome was still present and not associated with an increase in any cancer. Conclusions After 10 years of randomised treatment, women receiving hormone replacement therapy early after menopause had a significantly reduced risk of mortality, heart failure, or myocardial infarction, without any apparent increase in risk of cancer, venous thromboembolism, or stroke. Trial registration ClinicalTrials.gov NCT00252408.