Miracle-dz/UniFAR-FTUs huggingface dataset:
Books and Balls: Antecedents and Outcomes of College Identification.

Identification plays a central role in models of giving to an organization. This study presents and tests a general model of giving that highlights status based and affect based drivers of identification. The model was tested using a sample of 114 alumni from 74 different colleges participated in an online survey. Identification was found to positively affect giving intentions to athletics, academic areas and general university funds as well as alumni promotion of the university. Factors affecting college identification are perceived academic prestige, perceived athletic prestige, attendance of university sponsored cultural events, attendance of academic events, and student organization involvement.
Multiple-context Mobile Robot Control Tasks Using Deds, Qualitative Image Measures and Adaptation

This paper introduces a framework for the task of observing and controlling the movement of large objects by the use of pushing manipulation in the presence of obstacles. Accomplishing this task requires that sub-tasks behaviors be executed simultaneously. The control of the sub-tasks, based on Discrete Event Dynamic System Theory (DEDS), is accomplished by introducing a task supervisor. Its role is that of controlling and arbitrating the diierent behaviors to guarantee correct execution of the task. The issuing of various control patterns, or commands, to the agent pushing the object, is a consequence of feedback obtained from the environment through one or more observers. The individual behaviors are driven by qualitative measurements from the visual space rather than explicit full three dimensional reconstruction of the space around the pushing agents. Hence real-time dynamic control can be performed by the individual behaviors and constraints are imposed and modulated by the intervention of the control from the supervisor. Furthermore, in the approach presented here we make no assumption as to the mass distribution, shape, friction properties or material constituting the object. Rather we introduce an unsupervised vision{based method for learning manipulatory actions that allow the agent to push the object to the desired locus in image{space.
Design of a new mm-Wave low power LNA in 0.18 μm CMOS technology

A new low power 33 GHz low noise amplifier (LNA) is proposed in a 0. 18μm CMOS technology. It is composed of a single-stage cascode topology consisting of two Common-Gate (CG) amplifiers which provides gain requirements in high frequencies with very low power dissipation. The designed LNA achieves a power gain of 12 dB, IIP3 of −1dBm, and noise figure from 2.96–3.86 dB over the 3dB bandwidth from 3036GHz frequency range. This LNA consumes 7.6 mW from a 1.8-V power supply.
Investigating a fluctuating-accretion model for the spectral-timing properties of accreting black hole systems

The fluctuating-accretion model of Lyubarskii and its extension by Kotov, Churazov & Gilfanov seek to explain the spectral-timing properties of the X-ray variability of accreting black holes in terms of inward-propagating mass accretion fluctuations produced at a broad range of radii. The fluctuations modulate the X-ray emitting region as they move inwards and can produce temporal-frequency-dependent lags between energy bands, and energy-dependent power spectral densities (PSDs) as a result of the different emissivity profiles, which may be expected at different X-ray energies. Here, we use a simple numerical implementation to investigate in detail the X-ray spectral-timing properties of the model and their relation to several physically interesting parameters, namely the emissivity profile in different energy bands, the geometrical thickness and viscosity parameter of the accretion flow, the strength of damping on the fluctuations and the temporal coherence (measured by the 'quality factor', Q) of the fluctuations introduced at each radius. We find that a geometrically thick flow with large viscosity parameter is favoured, and we confirm that the predicted lags are quite robust to changes in the emissivity profile and physical parameters of the accretion flow, which may help to explain the similarity of the lag spectra in the low/hard and high/soft states of Cyg X-1. We also demonstrate the model regime where the light curves in different energy bands are highly spectrally coherent. We compare model predictions directly to X-ray data from the narrow line Seyfert 1 galaxy NGC 4051 and the black hole X-ray binary (BHXRB) Cyg X -1 in its high/soft state, and we show that this general scheme can reproduce simultaneously the time lags and energy-dependence of the PSD.
Insider Reputation and Selling Decisions: The Unwinding of Venture Capital Investments During Equity Ipos

Using data on selling by venture capitalists during the IPOs of their portfolio companies, we examine the relation between insider selling decisions and reputation. We hypothesize that, in deciding whether to sell, venture capitalists balance the costs of continued entrepreneurial involvement against the adverse reaction to insider selling, and that they facilitate unwinding of investment positions by developing reputations for not selling overpriced shares. Evidence on the timing of IPOs and selling decisions of venture capitalists confirms the importance of reputation as a determinant of the organization of the venture capital market and as a factor affecting insider selling decisions.
Reduced-error pruning with significance tests

When building classiication models, it is common practice to prune them to counter spurious eeects of the training data: this often improves performance and reduces model size. \Reduced-error pruning" is a fast pruning procedure for decision trees that is known to produce small and accurate trees. Apart from the data from which the tree is grown, it uses an independent \pruning" set, and pruning decisions are based on the model's error rate on this fresh data. Recently it has been observed that reduced-error pruning overrts the pruning data, producing unnecessarily large decision trees. This paper investigates whether standard statistical signiicance tests can be used to counter this phenomenon. The problem of overrtting to the pruning set highlights the need for signiicance testing. We investigate two classes of test, \parametric" and \non-parametric. " The standard chi-squared statistic can be used both in a parametric test and as the basis for a non-parametric permutation test. In both cases it is necessary to select the signiicance level at which pruning is applied. We show empirically that both versions of the chi-squared test perform equally well if their signiicance levels are adjusted appropriately. Using a collection of standard datasets, we show that signiicance testing improves on standard reduced error pruning if the signiicance level is tailored to the particular dataset at hand using cross-validation, yielding consistently smaller trees that perform at least as well and sometimes better.
Detection of Premature Ventricular Contraction for real-time applications

This paper proposes a real-time algorithm of Premature Ventricular Contraction (PVC) detection based on a low computational method. This algorithm considers three time-domain features which are RR-interval, QRS-width and QRS-pattern. Simple decision rules are used in the classifier process which is suitable for embedded applications. The algorithm was tested with 26 ECG records from MIT-BIH Arrhythmia Database (MIT-DB). After evaluation, the performance of the proposed method has 91.05% of sensitivity and 99.55% of specificity.
The Anatomy of a Nano-publication

As the amount of scholarly communication increases, it is increasingly difficult for specific core scientific statements to be found, connected and curated. Additionally, the redundancy of these statements in multiple fora makes it difficult to determine attribution, quality, and provenance. To tackle these challenges, the Concept Web Alliance has promoted the notion of nanopublications (core scientific statements with associated context). In this document, we present a model of nanopublications along with a Named Graph/RDF serialization of the model. Importantly, the serialization is defined completely using already existing community developed technologies. Finally, we discuss the importance of aggregating nanopublications and the role that the Concept Wiki plays in facilitating it.
General lower and best case upper bounds on energy optimal multicasting in wireless ad hoc and sensor networks

Given a source node and a set of k destination nodes, what is the minimum total energy required for sending a message from source to destinations when multicasting along optimal placed relaying nodes is applied? In this work I will answer this question under the assumption that non-cooperative relaying is applied and that communication over a distance d requires energy proportional to dα + β for some a > 1 and β > 0. I derive lower bound expressions for a MAC layer model which does not exploit the broadcast property of wireless communication and for a MAC layer model which exploits it. I show further, in case of optimal relay positions, how multicasts can be constructed whose energy consumption always stays below the derived lower bound plus an additive constant expression depending on α, β, k. The usefulness of the bound derived in this work is exemplified with three different example applications.
Efficacy and safety of canagliflozin in patients with type 2 diabetes

Background:Canagliflozin is a new SGLT2 inhibitor which has been approved as an adjunct to diet and exercise for the treatment of adults with type 2 diabetes (T2D) mellitus in more than 30 countries. To evaluate the efficacy and safety of canagliflozin in patients with T2D, we carried out a meta-analysis of phase III clinical trials to offer an additional evidence of the efficacy and safety of canagliflozin for evidence-based clinical practice, strictly restricting the treatment durations to 26 weeks (core period) and 52 weeks (extension period). Methods:Randomized controlled trials (RCTs) published in English were searched in PubMed, Embase, and the Cochrane Library database (before April 2016). The studies reporting the efficacy and safety of canagliflozin in patients with T2DM were considered. Two authors separately performed data extraction. The differences were discussed and resolved. Pooled weighted mean differences (WMDs) or relative risks and 95% confidence intervals (CIs) were computed by using either fixed- or random-effects models. Results:At the end of the selection process, 7 RCTs were collected and included in the present analysis. Placebo-subtracted WMDs (%) of glycosylated hemoglobin (HbA1c) were −0.63 (95% CI: −0.77, −0.49) and −0.80 (95% CI: −0.98, −0.62) for canagliflozin 100 and 300 mg, respectively, from baseline to week 26. At week 26, canagliflozin 100 and 300 mg significantly reduced the body weight from baseline when compared with that of placebo, with a WMD of −2.23 and −3.00 in percent changes (P < 0.001 for both). The fasting and postmeal glucose, blood pressure (BP), and triglycerides were also reduced. These reductions were sustained over 52 weeks but had no significant differences between the 100 and 300 mg doses. The overall safety of canagliflozin was good, with the exception of high incidence of genital mycotic infections and osmotic diuresis-related adverse events. Conclusion:Canagliflozin was found to reduce HbA1c, fasting and postmeal glucose, body weight, BP, and triglycerides, and it was generally well tolerated in patients with T2DM.
A Group(cid:2)Theoretical Approach to Disjoint Paths in Directed Graphs

This is in general a hard problem The problem is NP complete Knuth cf even if we restrict ourselves to planar undirected graphs Lynch assuming that k is variable On the other hand it is a deep result of Robertson and Seymour that for each xed k the problem can be solved in polynomial time in any undirected graph However for directed graphs the problem remains di cult even for k This was shown by Fortune Hopcroft and Wyllie
Asymptotic normality and efficiency of two Sobol index estimators

Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variability of a quantity of interest (output of the model). One of the statistical tools used to quantify the influence of each input variable on the output is the Sobol sensitivity index. We consider the statistical estimation of this index from a finite sample of model outputs: we present two estimators and state a central limit theorem for each. We show that one of these estimators has an optimal asymptotic variance. We also generalize our results to the case where the true output is not observable, and is replaced by a noisy version.
Solution to GW TEM-Circular Polarized TE11 Mode Converter Design for High Frequency Bands

The solution utilized to design a compact transverse electromagnetic mode (TEM)-CPT (circular polarized TE11) mode converter with high power handling capability and high efficiency, especially at high frequency bands is proposed in this paper. The analysis shows that increasing the quantity of the arms of the converter could make the converter work under overmoded state. The problem that the power capability of the converter working at high frequency bands is low will be addressed. To verify the feasibility of this solution, simulations and experiments on a 20 arms converter are carried out, and the results show that the converter would convert the TEM mode into CPT mode efficiently in a 2–3 free wavelengths longitudinal size and that the power capability is in gigawatt (GW) class. Moreover, this kind of converter can also be utilized in the design of GW TEM phase shifter, whether at low or high frequency bands, which plays a key role in the power combining technology.
A Study to Compare the Radiation Absorbed Dose of the C-arm Fluoroscopic Modes

Background Although many clinicians know about the reducing effects of the pulsed and low-dose modes for fluoroscopic radiation when performing interventional procedures, few studies have quantified the reduction of radiation-absorbed doses (RADs). The aim of this study is to compare how much the RADs from a fluoroscopy are reduced according to the C-arm fluoroscopic modes used. Methods We measured the RADs in the C-arm fluoroscopic modes including 'conventional mode', 'pulsed mode', 'low-dose mode', and 'pulsed + low-dose mode'. Clinical imaging conditions were simulated using a lead apron instead of a patient. According to each mode, one experimenter radiographed the lead apron, which was on the table, consecutively 5 times on the AP views. We regarded this as one set and a total of 10 sets were done according to each mode. Cumulative exposure time, RADs, peak X-ray energy, and current, which were viewed on the monitor, were recorded. Results Pulsed, low-dose, and pulsed + low-dose modes showed significantly decreased RADs by 32%, 57%, and 83% compared to the conventional mode. The mean cumulative exposure time was significantly lower in the pulsed and pulsed + low-dose modes than in the conventional mode. All modes had pretty much the same peak X-ray energy. The mean current was significantly lower in the low-dose and pulsed + low-dose modes than in the conventional mode. Conclusions The use of the pulsed and low-dose modes together significantly reduced the RADs compared to the conventional mode. Therefore, the proper use of the fluoroscopy and its C-arm modes will reduce the radiation exposure of patients and clinicians.
Cluster: An Effective Solution to the Problem of Heavy-Tailed Distribution in P2P Networks

Unstructured peer-to-peer (P2P) network is a prevalent model in current P2P networks. In such systems, due to the existence of free-riders, some users cannot finally complete their download tasks for the scarcity of certain resources. Many unstructured P2P models divide download files into chunks. This paper introduces a working model called cluster to solve the heavy-tailed problem in file downloads. It analyses the distribution of file chunks and increases the replication speed of the rarest chunks transmitted among the group. Our simulation experiments show the proposed model can transmit the rarest chunk efficiently and improve the overall download completion rate.
Event Detection via LDA for the MediaEval2012 SED Task

In this paper we present our methodology for the Social Event Detection Task of the MediaEval 2012 Benchmarking Initiative. We adopt topic discovery using Latent Dirichlet Allocation (LDA), city classification using TF-IDF analysis, and other statistical and natural language processing methods. After describing the approach we employed, we present the corresponding results, and discuss the problems we faced, as well as the conclusions we drew.
Detecting Cheaters in MOOCs Using Item Response Theory and Learning Analytics

The focus of this work is on developing a general method for identifying cheaters in MOOCs in a way that does not assume a particular method of cheating. For that, we develop a classification model that takes as input a set of features that operationalize performance and behavioral parameters that are known to be associated with cheating. These include students’ ability, the level of interaction with the course resources, solving time, and Item Response Theory (IRT) person fit parameters. We start with a list of six candidate features, and after a feature selection process, remain with four. We use these to build a probabilistic classifier (logistic regression) that yields an Area Under the Curve (AUC) of 0.826. Our data is based on an Introductory Physics MOOC. The features are computed using data-mining and standard IRT packages. We consider only the users who received a certificate in the course. Each of these users is considered as an example for the classifier. The positive examples are the set of users who were detected as“using multiple accounts to harvest solutions”by a different algorithm that was reported in a previous publication.
Prenatal Immune Activation Interacts with Genetic Nurr1 Deficiency in the Development of Attentional Impairments

Prenatal exposure to infection has been linked to increased risk of neurodevelopmental brain disorders, and recent evidence implicates altered dopaminergic development in this association. However, since the relative risk size of prenatal infection appears relatively small with respect to long-term neuropsychiatric outcomes, it is likely that this prenatal insult interacts with other factors in shaping the risk of postnatal brain dysfunctions. In the present study, we show that the neuropathological consequences of prenatal viral-like immune activation are exacerbated in offspring with genetic predisposition to dopaminergic abnormalities induced by mutations in Nurr1, a transcription factor highly essential for normal dopaminergic development. We combined a mouse model of heterozygous genetic deletion of Nurr1 with a model of prenatal immune challenge by the viral mimetic poly(I:C) (polyriboinosinic polyribocytidilic acid). In our gene–environment interaction model, we demonstrate that the combination of the genetic and environmental factors not only exerts additive effects on locomotor hyperactivity and sensorimotor gating deficits, but further produces synergistic effects in the development of impaired attentional shifting and sustained attention. We further demonstrate that the combination of the two factors is necessary to trigger maldevelopment of prefrontal cortical and ventral striatal dopamine systems. Our findings provide evidence for specific gene–environment interactions in the emergence of enduring attentional impairments and neuronal abnormalities pertinent to dopamine-associated brain disorders such as schizophrenia and attention deficit/hyperactivity disorder, and further emphasize a critical role of abnormal dopaminergic development in these etiopathological processes.
A Dynamic Logging Approach on Flash-Based DBMS

Flash memory, especially NAND flash memory, is being rapidly deployed as data storage for mobile devices such as mobile phones, digital cameras and PDApsilas. With its capacity increasing and price dropping, flash memory has been installed in many portable computers instead of magnetic disk for its light weight, small size, physical stability and low power consumption. We may expect that flash memory will finally take the place of magnetic disk in the next few years. Therefore, it is possible for us to consider running a full database on the flash computing platforms. However, disk-based database technology can not be used on flash memory directly because of different characteristics between flash memory and magnetic disk. Some researches are being done currently on flash-based database technology. In this paper, we propose a new approach called dynamic logging. This new approach makes use of the characteristics of flash memory effectively to acquire more benefits from using traditional database technology on flash memory.
A non-recursive method for solving the general discrete-time riccati equations related to the H∞ control problem

In this paper we propose a non-recursive method for solving the general discrete-time algebraic Riccati equation related to the H<sub>∞</sub>, control problem (H<sub>∞</sub>-DARE). We have achieved this by casting the problem of solving a given H<sub>∞</sub>-DARE to the problem of solving an auxiliary continuous-time algebraic Riccati equation associated with the H<sub>∞</sub> control problem (H<sub>∞</sub>-CARE) for which the well known non-recursive methods of solving are available. The advantages of our approach are: it reduces the computation involved in the recursive algorithms while giving much more accurate solutions, and it readily provides the properties of the general H<sub>∞</sub>-DARE.
MeSoOnTV: a media and social-driven ontology-based TV knowledge management system

Searching, browsing and analyzing web contents is today a challenging problem when compared to early Internet ages. This is due to the fact that web content is multimedial, social and dynamic. Moreover, concepts referred by videos, news, comments, posts, are implicitly linked by the fact that people on the Web talks about something, somewhere at some time and these connections may change as the perception of users on the Web changes over time. We define a model for the integration of the heterogeneous and dynamic data coming from different knowledge sources (broadcasters' archives, online newspapers, blogs, web encyclopedias, social media platforms, social networks, etc.). We use a knowledge graph to model all the heterogenous aspects of the information in an homogeneous way. Through a case study on social TV, we provide a non trivial cross-domain analysis scenario on real data gathered from YouTube and Twitter, and related to an Italian TV talk show on politics, broadcasted by RAI, the Italian public-service broadcasting organization.