Miracle-dz/UniFAR-FTUs huggingface dataset:
Network pruning for extending satellite service life in LEO satellite constellations

We address the problem of network pruning for extending the service life of satellites in LEO constellations. Satellites in LEO constellations can spend over 30 % of their time under the earth’s umbra, time during which they are powered by batteries. While the batteries are recharged by solar energy, the depth of discharge they reach during eclipse significantly affects their lifetime—and by extension, the service life of the satellites themselves. For batteries of the type that power Iridium satellites, a 15 % increase to the depth of discharge can practically cut their service lives in half. In this paper, we present the design and evaluation of two forms of network pruning schemes that reduce the energy consumption of LEO satellite network. First, we propose a new lightweight traffic-agnostic metric for quantifiying the quality of a frugal topology, the Adequacy Index (ADI). After showing that the problem of minimizing the power consumption of a LEO network subject to a given ADI threshold is NP-hard, we propose heuristcs to solve it. Second, we propose traffic-aware metric for quantifiying the quality of a frugal topology, the maximum link utilization (MLU). Also, with the problem being NP-hard subject to a given MLU threshold, we propose heuristics to solve it. We evaluate both forms using realistic LEO topologies and traffic matrices. Results show that traffic-agnostic pruning and traffic-aware pruning can increase the satellite service life by as much as 85 and 80 %, respectively. This is accomplished by trading off very little in terms of average path length and congestion.
Reachback Firefly Synchronicity with Late Sensitivity Window in Wireless Sensor Networks

Event synchronicity is a popular requirement in wireless sensor network. Firefly synchronicity phenomenon provides some inspiration. In this paper, we introduce a late sensitivity window to the Reachback Firefly Algorithm. The simulation result shows that the introduction of late sensitivity window not only reduces the time to synchronization, but also the overload of the network. Furthermore, it releases the dependency on choosing of optimized parameters, such as the couple strength, to work in a specific environment. Thus, synchronization on a network can be easily achieved in various scenarios.
Capacidad predictiva de la autoefi cacia académica sobre las dimensiones del autoconcepto en una muestra de adolescentes chilenos Predictive Ability of Academic Self-effi cacy on Self-concept Dimensions in a Sample of Chilean Adolescents

Resumen: El objetivo de este estudio fue analizar la capacidad predictiva de la autoefi cacia académica sobre las dimensiones del autoconcepto en una muestra de 860 estudiantes chilenos. El análisis de regresión logística reveló que la autoefi cacia académica fue un predictor positivo y signifi cativo de las escalas académicas (Matemáticas, Verbal y Académica General), no académicas (Habilidades Físicas, Apariencia Física, Relaciones con el Sexo Opuesto, Relaciones con el Mismo Sexo, Relación con los Padres, SinceridadVeracidad), y de la escala de Autoestima, excepto de la escala de Estabilidad Emocional. Esta relación de predicción fue de mayor magnitud con las escalas académicas y autoestima.
A Polynomial-Time Algorithm for Computing Shortest Paths of Bounded Curvature AmidstModerate Obstacles

In this paper, we consider the problem of computing shortest paths of bounded curvature amidst obstacles in the plane. More precisely, given two prescribed initial and final configurations (specifying the location and the direction of travel) and a set of obstacles in the plane, we want to compute a shortest $C^1$ path joining those two configurations, avoiding the obstacles, and with the further constraint that, on each $C^2$ piece, the radius of curvature is at least 1. In this paper, we consider the case of moderate obstacles (as introduced by Agarwal et al.) and present a polynomial-time exact algorithm to solve this problem.
Exercise and Bone Mineral Density in Premenopausal Women: A Meta-Analysis of Randomized Controlled Trials

Objective. Examine the effects of exercise on femoral neck (FN) and lumbar spine (LS) bone mineral density (BMD) in premenopausal women. Methods. Meta-analysis of randomized controlled exercise trials ≥24 weeks in premenopausal women. Standardized effect sizes (g) were calculated for each result and pooled using random-effects models, Z score alpha values, 95% confidence intervals (CIs), and number needed to treat (NNT). Heterogeneity was examined using Q and I 2. Moderator and predictor analyses using mixed-effects ANOVA and simple metaregression were conducted. Statistical significance was set at P ≤ 0.05. Results. Statistically significant improvements were found for both FN (7g's, 466 participants, g = 0.342, 95% CI = 0.132, 0.553, P = 0.001, Q = 10.8, P = 0.22, I 2 = 25.7%, NNT = 5) and LS (6g's, 402 participants, g = 0.201, 95% CI = 0.009, 0.394, P = 0.04, Q = 3.3, P = 0.65, I 2 = 0%, NNT = 9) BMD. A trend for greater benefits in FN BMD was observed for studies published in countries other than the United States and for those who participated in home versus facility-based exercise. Statistically significant, or a trend for statistically significant, associations were observed for 7 different moderators and predictors, 6 for FN BMD and 1 for LS BMD. Conclusions. Exercise benefits FN and LS BMD in premenopausal women. The observed moderators and predictors deserve further investigation in well-designed randomized controlled trials.
The Tail Probability of Discounted Sums of Pareto-like Losses in Insurance

In an insurance context, the discounted sum of losses within a finite or infinite time period can be described as a randomly weighted sum of a sequence of independent random variables. These independent random variables represent the amounts of losses in successive development years, while the weights represent the stochastic discount factors. In this paper, we investigate the problem of approximating the tail probability of this weighted sum in the case when the losses have Pareto-like distributions and the discount factors are mutually dependent. We also give some simulation results.
Pak3 Promotes Cell Cycle Exit and Differentiation of β-Cells in the Embryonic Pancreas and Is Necessary to Maintain Glucose Homeostasis in Adult Mice

The transcription factor neurogenin3 (Ngn3) triggers islet cell differentiation in the developing pancreas. However, little is known about the molecular mechanisms coupling cell cycle exit and differentiation in Ngn3+ islet progenitors. We identified a novel effector of Ngn3 endocrinogenic function, the p21 protein–activated kinase Pak3, known to control neuronal differentiation and implicated in X-linked intellectual disability in humans. We show that Pak3 expression is initiated in Ngn3+ endocrine progenitor cells and next maintained in maturing hormone-expressing cells during pancreas development as well as in adult islet cells. In Pak3-deficient embryos, the proliferation of Ngn3+ progenitors and β-cells is transiently increased concomitantly with an upregulation of Ccnd1. β-Cell differentiation is impaired at E15.5 but resumes at later stages. Pak3-deficient mice do not develop overt diabetes but are glucose intolerant under high-fat diet (HFD). In the intestine, Pak3 is expressed in enteroendocrine cells but is not necessary for their differentiation. Our results indicate that Pak3 is a novel regulator of β-cell differentiation and function. Pak3 acts downstream of Ngn3 to promote cell cycle exit and differentiation in the embryo by a mechanism that might involve repression of Ccnd1. In the adult, Pak3 is required for the proper control of glucose homeostasis under challenging HFD.
A DECISION-THEORETIC INTELLIGENT AGENT MODEL YONGHUI

Several methods are employed in multi-agent learning and organization problem such as temporal difference (TD(λ)), genetic algorithms, and learning classifier systems. The main disadvantage of these methods is that they perform badly when the data is not fully observable. In this paper, we first introduce explain learning systems in artificial intelligence(AI) and Self-organization systems. Then, we study the structure of self-organization of the intelligent agents, Finally, we design the decision-theoretic intelligent agent system, feedback control and adaptive control.
Fiscal Policy and Exchange Rates

This paper examines the dynamics of the nominal exchange rate and fiscal deficits in a continuous time optimizing general equilibrium model with OLG. It is shown that alternative financing modes of budget deficits imply different patterns of adjustment along the transitional path towards the steady-state equilibrium. In particular, the respect of public solvency without money financing is not sufficient to avoid the depreciation of the exchange rate in the long-run after a fiscal expansion. In addition, money and tax financing generate opposite intergenerational reallocation of wealth.
The Web as a Semantic Source

In this extended abstract we describe several semantic sources that c an be found in the Web that are either explicit, e.g. Wikipedia, or implicit, e.g. derived from Web data. We also show how we are using them to i prove search and to generate new semantic resources, as our final goal is to produce a virtuous feedback circuit for semantic e hancement based in machine learning.
Which Came First? Assessing Transactional Relationships Between Children’s Violent Media Use and ADHD-Related Behaviors

This longitudinal study investigated transactional relationships between violent media use and attention-deficit/hyperactivity disorder (ADHD)–related behaviors among young children (ages 4-8 years). To investigate study hypotheses, we employed a random intercept cross-lagged panel model (RI-CLPM) using structural equation modeling with panel data from 890 children. Results provided evidence in support of a media selection process rather than media effects process, whereby an increase in a child’s ADHD-related behaviors predicted an increase in the child’s violent media use 1 year later. Results indicated that this longitudinal relationship was accounted for by within-child fluctuations over time rather than stable between-child differences. The findings highlight the importance of investigating transactional relationships as well as distinguishing between-person and within-person relationships.
Miniature broadband bandpass filters using double-layer coupled stripline resonators

A novel double-layer coupled stripline resonator structure is introduced to realize miniature broadband bandpass filters. Filters with relative bandwidth up to 60% and size less than lambda/8timeslambda/8timesh (lambda is wavelength at the midband frequency; h is the substrate height, which is much smaller than lambda/8) can be fulfilled using such resonators. Two possible filter configurations are proposed in this paper: combline and interdigital. The filter synthesis procedure follows the classical coupling matrix approach that generates very good initial responses. Optimization by the mode-matching method and fine tuning in Ansoft's High Frequency Structure Simulator are combined to improve the filter performance. Two filter design examples are given to validate the feasibility. Low temperature co-fired ceramic (LTCC) technology is employed to manufacture the filters. Experimental results of the two manufactured filters are presented. The effects of LTCC manufacturing procedure on the filter performance are also discussed
Using multi-scale distribution and movement effects along a montane highway to identify optimal crossing locations for a large-bodied mammal community

Roads are a major cause of habitat fragmentation that can negatively affect many mammal populations. Mitigation measures such as crossing structures are a proposed method to reduce the negative effects of roads on wildlife, but the best methods for determining where such structures should be implemented, and how their effects might differ between species in mammal communities is largely unknown. We investigated the effects of a major highway through south-eastern British Columbia, Canada on several mammal species to determine how the highway may act as a barrier to animal movement, and how species may differ in their crossing-area preferences. We collected track data of eight mammal species across two winters, along both the highway and pre-marked transects, and used a multi-scale modeling approach to determine the scale at which habitat characteristics best predicted preferred crossing sites for each species. We found evidence for a severe barrier effect on all investigated species. Freely-available remotely-sensed habitat landscape data were better than more costly, manually-digitized microhabitat maps in supporting models that identified preferred crossing sites; however, models using both types of data were better yet. Further, in 6 of 8 cases models which incorporated multiple spatial scales were better at predicting preferred crossing sites than models utilizing any single scale. While each species differed in terms of the landscape variables associated with preferred/avoided crossing sites, we used a multi-model inference approach to identify locations along the highway where crossing structures may benefit all of the species considered. By specifically incorporating both highway and off-highway data and predictions we were able to show that landscape context plays an important role for maximizing mitigation measurement efficiency. Our results further highlight the need for mitigation measures along major highways to improve connectivity between mammal populations, and illustrate how multi-scale data can be used to identify preferred crossing sites for different species within a mammal community.
Social Preference Ontologies for Enriching User and Item Data in Recommendation Systems

Some of the known issues of recommendation algorithms are a result of the so called "Cold Start Problem" that is caused by a lack of sufficient data of users, items or the content, which are essential for the calculation of context-sensitive predictions. Along with this comes the "Sparsity Problem" which also exposes the problem of recommendation systems which are being provided with too little information of user feedback such as likes and views. As a consequent collaborative and knowledge-based filtering algorithms are unable of precise prediction which is causing a decline of the customer satisfaction. If beyond that there also is a lack of metadata, the calculation of similarities through content-based filtering algorithms is likely to fail as well. This paper introduces preference ontologies and how they help to reduce these issues by analyzing external data, in terms of texts from social networks and other web sources. Thereby we introduce a self-designed semantic engine, performing sentiment analysis and semantic keyword extraction. These novel ontologies represent the mined information and thus, describe the users interest in automatic analyzed topics and map them to the meta data of items in recommendation engines.
A Path Select Algorithm with Error Control Schemes and Energy Efficient Wireless Sensor Networks

A wireless sensor network consists of a large numbe r of sensor nodes that are spread densely to observe the phenomenon. The whole network lifetime relies on th e lifetime of the each sensor node. If one node dies, it could lead to a separation of the sensor network. Also a multi hop structure and broadcast channel of wireless sensor necessitate error control scheme to achieve reliabl data transmission. Automatic repeat request (ARQ) and fo rward error correction (FEC) are the key error control st rategies in wire sensor network. In this paper we propose a pat h selection algorithm with error control schemes usin g energy efficient analysis.
Managing flash memory in personal communication devices

This paper describes a flash memory server (FMS) for personal communication devices and embedded home information systems, such as set-top boxes and Internet phones. Flash memory is small, lightweight, shock-resistant, nonvolatile, and requires little power. Writing to flash memory segments requires erasing the segment in advance. However, erase operations are slow and power-wasting and usually decrease system performance. The number of erase cycles is also limited. To reduce the number of erase operations needed and to evenly wear the flash memory, a new flash memory management scheme has been designed. A new cleaning policy is also proposed to reduce cleaning overhead. Performance evaluations show that erase operations can be reduced by 55%.
Face and texture analysis using local descriptors: A comparative analysis

In contrast to global image descriptors which compute features directly from the entire image, local descriptors representing the features in small local image patches have proved to be more effective in real world conditions. This paper considers three recent yet popular local descriptors, namely Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF), and provides extensive comparative analysis on two different research problems (gender and texture classification) using benchmark datasets. The three descriptors are analyzed in terms of both classification accuracy and computational costs. Furthermore, experiments on combining these descriptors are provided, pointing out useful insight into their complementarity.
Model Predictive Direct Torque Control—Part I: Concept, Algorithm, and Analysis

This paper focuses on direct torque control (DTC) for three-phase AC electric drives. A novel model predictive control scheme is proposed that keeps the motor torque, the stator flux, and (if present) the inverter's neutral point potential within given hysteresis bounds while minimizing the switching frequency of the inverter. Based on an internal model of the drive, the controller predicts several future switch transitions, extrapolates the output trajectories, and chooses the sequence of inverter switch positions (voltage vectors) that minimizes the switching frequency. The advantages of the proposed controller are twofold. First, as underlined by the experimental results in the second part of this paper, it yields a superior performance with respect to the industrial state of the art. Specifically, the switching frequency is reduced by up to 50% while the torque and flux are kept more accurately within their bounds. Moreover, the fast dynamic torque response is inherited from standard DTC. Second, the scheme is applicable to a large class of (three-phase) AC electric machines driven by inverters.
Toward Fast and Deterministic Clone Detection for Large Anonymous RFID Systems

Cloning attacks seriously impede the security of Radio-Frequency Identification (RFID) applications. In this paper, we tackle deterministic clone detection for anonymous RFID systems without tag identifiers (IDs) as a priori. Existing clone detection protocols either cannot apply to anonymous RFID systems due to necessitating the knowledge of tag IDs or achieve only probabilistic detection with a few clones tolerated. We propose two protocols, BASE and DeClone, toward fast and deterministic clone detection for large anonymous RFID systems. BASE leverages the observation that clone tags make tag cardinality exceed ID cardinality. DeClone is built on a recent finding that clone tags cause collisions that are hardly reconciled through re-arbitration. For DeClone to achieve detection certainty, we design breadth first tree traversal toward quickly verifying unreconciled collisions and hence the cloning attack. We validate their detection performance through analysis and simulation. The results show that BASE delivers faster detection for small systems while DeClone for large ones especially when clone ratio increases.
Becoming metal: narrative reflections on the early formation and embodiment of heavy metal identities

ABSTRACT Heavy metal music has long been researched as a risk factor for youth development. Over the last decade, however, there has been a significant shift towards studies that are more sympathetic to metal fans, but still we know very little about young people’s pathways to forming metal identities. What is the allure of metal as an identity choice? What can be gained from the early embodiment of metal identities? To explore these questions, this paper reports on findings from qualitative research with metal youth in Australia that captured rich, narrative reflections on ‘becoming’ metal. The results show that metal was vitally important when participants felt vulnerable to bullying and exclusion by popular peers at school. But crucially, the young ‘metalheads’ were able to disrupt power relations at school by embodying ‘chosen’ heavy metal identities as a strategic response for countering ‘unchosen’ marginal school-based identities. The politically transformative properties of subculture at the level of the individual are revealed through ways that the metal youth, as self-described outsiders, were able to act alone to challenge dominant school norms and enter into social relationships on their own terms, protecting themselves from social threats to their mental health and well-being in the process.
ROSEMARY MACKAY FUND ARTICLE: Ecological stoichiometry of trophic interactions in the benthos: understanding the role of C:N:P ratios in lentic and lotic habitats

This paper considers how the theory of ecological stoichiometry may be applied to issues of importance to benthic ecologists. Ecological stoichiometry considers both the causes of elemental (C:N:P) imbalances between trophic levels and their consequences on foodweb dynamics (e.g., predator–prey interactions) and ecosystem processes (e.g., nutrient cycling). Elemental imbalances are created between consumers and their food, in part, by the accumulation of C relative to other nutrients (N and P) in benthic organic matter as a result of the deposition of detritus and/or unbalanced growth in aquatic producers. High C:N and C:P ratios in food material can reduce growth and reproduction and alter related processes such as nutrient release in benthic consumers. By affecting consumer metabolism, elemental imbalances may affect population dynamics, trophic interactions, and gross transfer efficiencies in benthic systems. Future work is needed to quantify the frequency and magnitude of elemental imbalances, to determine why elemental ratios differ within and among trophic levels, and to examine how stoichiometric imbalances affect fundamental ecosystem processes (e.g., nutrient cycling and spiraling, consumer growth dynamics, and responses to environmental disturbance) in benthic systems.
Element Retrieval Using a Passage Retrieval Approach

Element and passage retrieval systems are able to extract and rank parts of documents and return them to the user rather than the whole document. Element retrieval is used to search XML documents and identify relevant XML elements, while passage retrieval is used to identify relevant passages. This paper reports a series of experiments on element retrieval, using a general passage retrieval algorithm. Firstly, an XML document is divided into overlapping or non-overlapping fixed size windows (passages), then the relevant passages which contain query terms are found. Given the position of a passage in the XML document, the smallest element which contains this passage is found. The experiments were conducted with the INEX 2005 ad hoc test collection and evaluation tool. Two passage extraction methods, three weight functions and various window sizes were tested. A comparison with element retrieval systems was also conducted. The experimental results show that a robust passage retrieval algorithm can yield an acceptable level of performance in XML element retrieval.