Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Importance Sampling for Bayesian Inference in Gaussian Process models

Abstract Gaussian process (GP) models are nowadays considered among the standard tools in modern control system engineering. They are routinely used for model-based control, time- series prediction, modelling and estimation in engineering applications. While the underlying theory is completely in line with the principles of Bayesian inference, in practice this property is lost due to approximation steps in the GP inference. In this paper we propose a novel inference algorithm for GP models, which relies on adaptive importance sampling strategy to numerically evaluate the intractable marginalization over the hyperparameters. This is required in the case of broad-peaked or multi-modal posterior distribution of the hyperparameters where the point approximations turn out to be insufficient. The benefits of the algorithm are that is retains the Bayesian nature of the inference, has sufficient convergence properties, relatively low computational load and does not require heavy prior knowledge due to its adaptive nature. All the key advantages are demonstrated in practice using numerical examples.
A Survey on Association Rule Mining for Finding Frequent Item Pattern

Data mining turns into a tremendous territory of examination in recent years. A few investigates have been made in the field of information mining. The Association Rule Mining (ARM) is likewise an incomprehensible territory of exploration furthermore an information mining method. In this paper a study is done on the distinctive routines for ARM. In this paper the Apriori calculation is characterized and focal points and hindrances of Apriori calculation are examined. FP-Growth calculation is additionally talked about and focal points and inconveniences of FP-Growth are likewise examined. In Apriori incessant itemsets are created and afterward pruning on these itemsets is connected. In FP-Growth a FP-Tree is produced. The detriment of FP-Growth is that FP-Tree may not fit in memory. In this paper we have review different paper in light of mining of positive and negative affiliation rules.
A hybrid algorithm for restructuring distributed Object-oriented software

Distributed Object-oriented software has been used in a large number of applications for solving complex problems in different scientific fields like: machine learning, data mining, pattern recognition, image analysis and bioinformatics. However, we need to collect objects into groups such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. This collection is called cluster analysis or clustering. Each cluster will be assigned to different computer to minimize communication among objects and speedup the execution of tasks. There have been several clustering techniques applied to objects. In this paper, we introduce three algorithms for clustering objects into present numbers of clusters to match the target hardware (i.e. software restructuring). These algorithms, through simulation results, achieve better performance than the existing algorithms as they generate more accurate clusters in less time.
A Legacy Systems use Case Recovery Method

During the development of a legacy system reverse engineering method we developed a technique to help with the recovery of the system’s use-cases. In fact, our reverse-engineering method starts with the redocumentation of the system’s use-case by observing its actual users. But these use-cases are never complete and accurate. In particular, the many alternative flows are often overlooked by the users. This paper presents our use-case recovery methodology as well as the techniques we implemented to identify all the flows of the legacy system’s use-case. Starting from an initial use-case based on the observation of the users, we gather the corresponding execution trace by running the system according to this use-case. The analysis of this execution trace coupled with a static analysis of the source code lets us find the possible alternative execution paths of the system. The execution conditions for these paths are analyzed to establish the link to the use-case level. This lets us synthesize alternative flows for the use-case. Next we run the system again following these alternative flows to uncover possible new alternative paths, until one converges to a stable use-case model.
A NOVEL, HIGH-SPEED IMAGE TRANSMITTER FOR WIRELESS CAPSULE ENDOSCOPY

Wireless capsule endoscopy (WCE) was developed as a painless diagnostic tool for endoscopic examination of the gastrointestinal (GI) tract, but, to date, the low operating power of the capsule and the high data rate of the RF telemetry system are still key concerns. Innovative, novel solutions must be developed to address these concerns before WCE can be used extensively in clinical applications. In this paper, we propose a novel RF transmitter for WCE applications that only requires 1.5V to transmit the required data as opposed to using a DC power supply. Our proposed, direct- conversion transmitter system consists of a current reuse oscillator, an envelope fllter, and an L-section matching network. The oscillator is powered by the transmitting data which keep the oscillator in turned on and ofi for the transmitting 1 and 0bit respectively and results in the on-ofi keying (OOK) of the modulated signal at the output of the oscillator. The rate of data transmission at the modulated signal is limited by the transient period of the oscillator start-up. When the start-up time of the oscillator is optimized, an OOK modulation rate of 100Mb/s can be attained. In order to eliminate the oscillator decay noise, we used an envelope fllter connected in series with the oscillator to fllter out the decay part of the oscillation. Finally, the output impedance of the envelope fllter is matched to the 50-› antenna with an L-section, low-pass, matching network to ensure maximum
A Cross-Program View of School Improvement Results.

This paper provides a cross-program view of the results of three successful school improvement programs that regional educational laboratories developed and implemented. The programs and their developers are: (1) A+chieving Excellence from the Mid-Continental Regional Educational Laboratory; (2) Onward to Excellence (OTE) from the Northwest Regional Educational Laboratory; and (3) the School Improvement Partnership Process (SIPP) from the Southwest Educational Development Laboratory. Ail three programs have been field tested and have demonstrated their efficacy for improving schools. Their intellectual roots lie in research on effw,Itive schools and school improvement processes. All three programs attend closely to involving school-based practitioners in decisions regarding improvements, maintain an information component so participants share knowledge, foster data-based decision making, provide personnel with a perspective that change takes time, and protect schools from unreasonable expectations. In each case, outside assistance is a feature of the program. Limitations include: (1) disaggregation of data beyond student achievement; (2) a limited research base; and (3) inadequate methods of assessment. These programs represent solid efforts to provide research-based guidance to teachers as they work to improve schools. There is a 21-item list of references. (SLD) *********************************************************************** * Reproductions supplied by EDRS are the best that can be made from the orlginal document. ****************************M****************9***********************
Mining Interesting Rare Items with Maximum Constraint Model Based on Tree Structure

Rare association rule mining provides useful information from large database. Traditional association mining techniques generate frequent rules based on frequent item sets with reference to user defined: minimum support threshold and minimum confidence threshold. It is known as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting Rules. Rare association rule contains Rare Items. Rare Association Rules represents unpredictable or unknown associations, so that it becomes more interesting than frequent association rule mining. The main goal of rare association rule mining is to discover relationships among set of items in a database that occurs uncommonly. We have proposed a Maximum Constraint based method for generating rare association rule with tree structure. Tentative results show that MCRP-Tree takes less time for rule generation compared to the existing algorithm as well as it finds more interesting rare items.
Comparing Synchronous Remote and Local Usability Studies for an Expert Interface

Synchronous remote usability studies can be a convenient and cost-effective alternative to conventional local usability studies. Although they are common in the field, there has been little research comparing synchronous remote usability studies with local studies. In our comparison of remote and local studies for an expert interface, the primary differences were in the participant’s and facilitator’s qualitative experience. The number of usability issues found, their type, and severity were very similar. While more research is needed, our experience suggests that evaluators of expert interfaces will have comparable success identifying usability issues with either remote or local studies.
Breast-Feeding and Risk for Childhood Obesity

OBJECTIVE— We sought to evaluate whether maternal diabetes or weight status attenuates a previously reported beneficial effect of breast-feeding on childhood obesity. RESEARCH DESIGN AND METHODS— Growing Up Today Study (GUTS) participants were offspring of women who participated in the Nurses’ Health Study II. In the present study, 15,253 girls and boys (aged 9–14 years in 1996) were included. Maternal diabetes and weight status and infant feeding were obtained by maternal self-report. We defined maternal overweight as BMI ≥25 kg/m2. Childhood obesity, from self-reported height and weight, was based on the Centers for Disease Control and Prevention definitions as normal, at risk for overweight, or overweight. Maternal status categories were nondiabetes/normal weight, nondiabetes/overweight, or diabetes. Logistic regression models used generalized estimating equations to account for nonindependence between siblings. RESULTS— For all subjects combined, breast-feeding was associated with reduced overweight (compared with normal weight) in childhood. Compared with exclusive use of formula, the odds ratio (OR) for exclusive breast-feeding was 0.66 (95% CI 0.53–0.82), adjusted for age, sex, and Tanner stage. Results did not differ according to maternal status (nondiabetes/normal weight OR 0.73 [95% CI 0.49–1.09]; nondiabetes/overweight 0.75 [0.57–0.99]; and diabetes 0.62 [0.24–1.60]). Further adjustment for potential confounders attenuated results, but results remained consistent across strata of maternal status (P value for interaction was 0.50). CONCLUSIONS— Breast-feeding was inversely associated with childhood obesity regardless of maternal diabetes status or weight status. These data provide support for all mothers to breast-feed their infants to reduce the risk for childhood overweight.
Autoconfiguration protocol for a multiple interfaces OLSR network

Network configuration is the assignment of network parameters necessary for a device to integrate the network, examples being: an IP address, netmask, the IP address of the gateway, etc . In the case of Mobile Ad hoc NETworks (MANETs), the connectivity of nodes is highly dynamic and a central administration or configuration by the user is very difficult. In this paper, we are interested in presenting an autoconfiguration solution for ad hoc networks running OLSR [1] (Optimized Link state Routing protocol). This solution is based on an efficient Duplicate Address Detection (DAD) algorithm which takes advantage of the genuine optimization of the OLSR routing protocol. The proposed autoconfiguration algorithm is proved to operate correctly in a multiple interfaces OLSR network. Keywords– MANET, Autoconfiguration, Routing protocol,
Distributed Real-Time Data ow: An Execution Paradigm for Image Processing and Anti-Submarine Warfare Applications

The central thesis of this project is that real-time scheduling theory can be combined with data ow methodologies to bound latency and memory utilization of distributed signal processing applications, such as those found in anti-submarine warfare and image processing. To this end, we propose a new real-time data ow paradigm that is based on the Navy's Processing Graph Method (PGM) [5], which is similar to the data ow methodology employed by Ptolemy [1, 4] of the University of California at Berkeley and the Rapid Prototyping of Application Speci c Signal Processors (RASSP) project, funded by the Advanced Research Projects Agency (ARPA). While our data ow methodology itself is not novel, our application of real-time scheduling theory to the model is.
Recognition as a Measure of Learning from Television Commercials

Recognition has not received extensive acceptance by practitioners as a response variable for learning of television commercials. The authors review some of the distinctions between recall and recognition, and some of the problems associated with each. In a laboratory experiment they developed and tested a measure of recognition useful for low involvement situations in which a recall level of learning may not be necessary. Data and implications about the usefulness and limitations of the measure are presented.
Distributional Lexical Semantics for Stop Lists

In this paper, we consider the use of techniques that lead naturally towards using distributional lexical semantics for the automatic construction of corpora-specific stop word lists. We propose and evaluate a method for calculating stop words based on collocation, frequency information and comparisons of distributions within and across samples. This method is tested against the Enron email corpus and the MuchMore Springer Bilingual Corpus of medical abstracts. We identify some of the data cleansing challenges related to the Enron corpus, and particularly how these necessarily relate to the profile of a corpus. We further consider how we can and should investigate behaviours of subsamples of such a corpus to ascertain whether the lexical semantic techniques employed might be used to identify and classify variations in contextual use of keywords that may help towards content separation in “unclean” collections: the challenge here is the separation of keywords in the same or very similar contexts, that may be conceived as a “pragmatic difference”. Such work may also be applicable to initiatives in which the focus is on constructing (clean) corpora from the web, deriving knowledge resources from wikis, and finding key information within other textual social media.
On Finite-Gain Stabilizability of Linear Systems Subject to Input Saturation

This paper deals with (global) finite-gain input/output stabilization of linear systems with saturated controls. For neutrally stable systems, it is shown that the linear feedback law suggested by the passivity approach indeed provides stability, with respect to every $L^p$-norm. Explicit bounds on closed-loop gains are obtained, and they are related to the norms for the respective systems without saturation. These results do not extend to the class of systems for which the state matrix has eigenvalues on the imaginary axis with nonsimple (size $>1$) Jordan blocks, contradicting what may be expected from the fact that such systems are globally asymptotically stabilizable in the state-space sense; this is shown in particular for the double integrator.
Alpha Reactivity to Complex Sounds Differs during REM Sleep and Wakefulness

We aimed at better understanding the brain mechanisms involved in the processing of alerting meaningful sounds during sleep, investigating alpha activity. During EEG acquisition, subjects were presented with a passive auditory oddball paradigm including rare complex sounds called Novels (the own first name - OWN, and an unfamiliar first name - OTHER) while they were watching a silent movie in the evening or sleeping at night. During the experimental night, the subjects’ quality of sleep was generally preserved. During wakefulness, the decrease in alpha power (8–12 Hz) induced by Novels was significantly larger for OWN than for OTHER at parietal electrodes, between 600 and 900 ms after stimulus onset. Conversely, during REM sleep, Novels induced an increase in alpha power (from 0 to 1200 ms at all electrodes), significantly larger for OWN than for OTHER at several parietal electrodes between 700 and 1200 ms after stimulus onset. These results show that complex sounds have a different effect on the alpha power during wakefulness (decrease) and during REM sleep (increase) and that OWN induce a specific effect in these two states. The increased alpha power induced by Novels during REM sleep may 1) correspond to a short and transient increase in arousal; in this case, our study provides an objective measure of the greater arousing power of OWN over OTHER, 2) indicate a cortical inhibition associated with sleep protection. These results suggest that alpha modulation could participate in the selection of stimuli to be further processed during sleep.
Neoliberalism and the "Obesity Epidemic"

NEOLIBERALISM AND THE “OBESITY EPIDEMIC” by Dianne Rubinstein Advisor: Professor Hester Eisenstein Neoliberalism has been described as a political and economic theory that proposes “human well-being can best be advanced by liberating. freedoms.within an institutional framework [of]. strong private property rights [and] free markets. [I]f markets do not exist (.such as in. health care) then they must be created, by state action if necessary. ” I would argue that the “Obesity Epidemic” is just such a created Neoliberal market, and in fact does not actually exist as an “epidemic. ” By changing the computation of BMI (Body Mass Index) in 1998, the federal government created the “Obesity Epidemic” by definitional fiat, despite myriad contradictory evidence.
Tunable Assembly of Heterogeneously Charged Colloids

The self-assembly of colloidal particles is a route to designed materials production that combines high flexibility, cost effectiveness, and the opportunity to create ordered structures at length scales ranging from nano- to micrometers. For many practical applications in electronics, photovoltaics, and biomimetic material synthesis, ordered mono- and bilayers are often needed. Here we present a novel and simple way to tune via external parameters the ordering of heterogeneously charged colloids into quasi two-dimensional structures. Depending on the charges of the underlying substrate and of the particles, a rich and versatile assembly scenario takes place, resulting from the complex interplay between directional attractive and repulsive particle–particle and particle–substrate interactions. Upon subtle variations of the relative charge of the system components, emerging via pH modification, reversible changes either from extended aggregates to a monomeric phase or from triangular to square domains are observed.
La desigualdad horizontal en el impuesto sobre la renta de las personas físicas

El objetivo de este trabajo ha sido evaluar la desigualdad horizontal del IRPF en Espana entre 1982 y 1990 a traves de una serie de indices que se han pro-puesto en la literatura. Hemos optado por incluir el calculo de un indice nuevo, propuesto por Salas (1995), que permite la evaluacion de la desigual-dad horizontal como cambio distributivo, y que puede introducirse en una Funcion de Bienestar Social. Se observa un cierto paralelismo en la medicion de la desigualdad horizontal, con independencia del indice utilizado. Al mismo tiempo, hemos observado que no hay que buscar las razones de la va-riacion de la desigualdad horizontal en la evolucion del sistema fiscal, y en particular en la evolucion del sistema de las deducciones. Mas bien afloran causas exogenas al propio sistema, relacionadas concretamente con la com-posicion de la renta de las familias.
Supervised Learning for Link Prediction in Weighted Networks

Link prediction is an important task in Social Network Analysis. This problem refers to predicting the emergence of future relationships between nodes in a social network. Our work focuses on a supervised machine learning approach for link prediction. Here, the target attribute is a class label indicating the existence or absence of a link between a node pair. The predictor attributes are metrics computed from the network structure, describing the given pair. The majority of works for supervised prediction only considers unweighted networks. In this light, our aim is to investigate the relevance of using weights to improve supervised link prediction. Link weights express the ‘strength’ of relationships and could bring useful information for prediction. However, the relevance of weights for unsupervised approaches of link prediction was not always verified (in some cases, the performance was even harmed). Our preliminary results on supervised prediction on a co-authorship network revealed satisfactory results when weights were considered, which encourage us for further analysis.
Information Efficient 3D Visual SLAM in Unstructured Domains

This paper presents a strategy for increasing the efficiency of simultaneous localisation and mapping (SLAM) in unknown and unstructured environments using a vision-based sensory package. Traditional feature-based SLAM, using either the extended Kalman filter (EKF) or its dual, the extended information filter (EIF), leads to heavy computational costs while the environment expands and the number of features increases. In this paper we propose an algorithm to reduce computational cost for real-time systems by giving robots the 'intelligence' to select, out of the steadily collected data, the maximally informative observations to be used in the estimation process. We show that, although the actual evaluation of information gain for each frame introduces an additional computational cost, the overall efficiency is significantly increased by keeping the matrix compact. The noticeable advantage of this strategy is that the continuously gathered data is not heuristically segmented prior to be input to the filter. Quite the opposite, the scheme lends itself to be statistically optimal.
Linear Combinations of DVFS-Enabled Processor Frequencies to Modify the Energy-Aware Scheduling Algorithms

The energy consumption issue in distributed computing systems has become quite critical due to environmental concerns. In response to this, many energy-aware scheduling algorithms have been developed primarily by using the dynamic voltage-frequency scaling (DVFS) capability incorporated in recent commodity processors. The majority of these algorithms involve two passes: schedule generation and slack reclamation. The latter is typically achieved by lowering processor frequency for tasks with slacks. In this paper, we revisit this energy reduction technique from a different perspective and propose a new slack reclamation algorithm which uses a linear combination of the maximum and minimum processor frequencies to decrease energy consumption. This algorithm has been evaluated based on results obtained from experiments with three different sets of task graphs: 1,500 randomly generated task graphs, and 300 task graphs of each of two real-world applications (Gauss-Jordan and LU decomposition). The results show that the amount of energy saved in the proposed algorithm is 13.5%, 25.5% and 0.11% for random, LU decomposition and Gauss-Jordan task graphs, respectively, these percentages for the reference DVFSbased algorithm are 12.4%, 24.6% and 0.1%, respectively.