Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified Wisconsin Card Sorting Test (M-WCST): Normative data for Spanish-speaking pediatric population.

OBJECTIVE To generate normative data for the Modified Wisconsin Card Sorting Test (M-WCST) in Spanish-speaking pediatric populations. METHOD The sample consisted of 4,373 healthy children from nine countries in Latin America (Chile, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, and Puerto Rico) and Spain. Each participant was administered the M-WCST as part of a larger neuropsychological battery. Number of categories, perseverative errors, and total error scores were normed using multiple linear regressions and standard deviations of residual values. Age, age2, sex, and mean level of parental education (MLPE) were included as predictors in the analyses. RESULTS The final multiple linear regression models indicated main effects for age on all scores, such that the number of categories correct increased and total number of perseverative errors and total number of errors decrease linearly as a function of age. Age2 had a significant effect in Chile, Cuba, Ecuador, and Spain for numbers of categories; a significant effect for number of perseverative errors in Chile, Cuba, Mexico, and Spain; and a significant effect for number of total errors in Chile, Cuba, Peru, and Spain. Models showed an effect for MLPE in Cuba (total errors), Ecuador (categories and total errors), Mexico (all scores), Paraguay (perseverative errors and total error), and Spain (categories and total errors). Sex affected number of total errors for Ecuador. CONCLUSIONS This is the largest Spanish-speaking pediatric normative study in the world, and it will allow neuropsychologists from these countries to have a more accurate way to interpret the M-WCST with pediatric populations.
Do female sex workers have lower uptake of HIV treatment services than non-sex workers? A cross-sectional study from east Zimbabwe

Objective Globally, HIV disproportionately affects female sex workers (FSWs) yet HIV treatment coverage is suboptimal. To improve uptake of HIV services by FSWs, it is important to identify potential inequalities in access and use of care and their determinants. Our aim is to investigate HIV treatment cascades for FSWs and non-sex workers (NSWs) in Manicaland province, Zimbabwe, and to examine the socio-demographic characteristics and intermediate determinants that might explain differences in service uptake. Methods Data from a household survey conducted in 2009–2011 and a parallel snowball sample survey of FSWs were matched using probability methods to reduce under-reporting of FSWs. HIV treatment cascades were constructed and compared for FSWs (n=174) and NSWs (n=2555). Determinants of service uptake were identified a priori in a theoretical framework and tested using logistic regression. Results HIV prevalence was higher in FSWs than in NSWs (52.6% vs 19.8%; age-adjusted OR (AOR) 4.0; 95% CI 2.9 to 5.5). In HIV-positive women, FSWs were more likely to have been diagnosed (58.2% vs 42.6%; AOR 1.62; 1.02–2.59) and HIV-diagnosed FSWs were more likely to initiate ART (84.9% vs 64.0%; AOR 2.33; 1.03–5.28). No difference was found for antiretroviral treatment (ART) adherence (91.1% vs 90.5%; P=0.9). FSWs’ greater uptake of HIV treatment services became non-significant after adjusting for intermediate factors including HIV knowledge and risk perception, travel time to services, physical and mental health, and recent pregnancy. Conclusion FSWs are more likely to take up testing and treatment services and were closer to achieving optimal outcomes along the cascade compared with NSWs. However, ART coverage was low in all women at the time of the survey. FSWs’ need for, knowledge of and proximity to HIV testing and treatment facilities appear to increase uptake.
How to (not) Share a Password: Privacy Preserving Protocols for Finding Heavy Hitters with Adversarial Behavior

Bad choices of passwords were and are a pervasive problem. Users choosing weak passwords do not only compromise themselves, but the whole ecosystem. E.g, common and default passwords in IoT devices were exploited by hackers to create botnets and mount severe attacks on large Internet services, such as the Mirai botnet DDoS attack. We present a method to help protect the Internet from such large scale attacks. Our method enables a server to identify popular passwords (heavy hitters), and publish a list of over-popular passwords that must be avoided. This filter ensures that no single password can be used to compromise a large percentage of the users. The list is dynamic and can be changed as new users are added or when current users change their passwords. We apply maliciously secure two-party computation and differential privacy to protect the users' password privacy. Our solution does not require extra hardware or cost, and is transparent to the user. Our private heavy hitters construction is secure even against a malicious coalition of devices which tries to manipulate the protocol to hide the popularity of some password that the attacker is exploiting. It also ensures differential privacy under continual observation of the blacklist as it changes over time. As a reality check we conducted three tests: computed the guarantees that the system provides wrt a few publicly available databases, ran full simulations on those databases, and implemented and analyzed a proof-of-concept on an IoT device. Our construction can also be used in other settings to privately learn heavy hitters in the presence of an active malicious adversary. E.g., learning the most popular sites accessed by the Tor network.
Factors of Rowing Ergometer Performance in High-Level Female Rowers

Abstract The present study investigated morphological and physiological factors of rowing ergometer performance over 2000 m (P2000, W) in 70 national and international level [27 lightweight (LW) and 43 heavyweight (HW)] female rowers. Maximal oxygen uptake (V̇O2max, L.min−1), maximal aerobic power (Pamax, W), power output corresponding to 4 mmol.L−1 blood lactate concentration expressed in absolute (PLa4, W) and relative to Pamax (PLa4%, %) values, peak power output (Ppeak, W), and rowing gross efficiency (RGE, %) were determined during an incremental rowing test. In the whole group, Ppeak was the best predictor of P2000 (r=0.89, p<0.001), as it was shown in men. PLa4 (r=0.87), V̇O2max (r=0.83), body mass (r=0.65), and height (r=0.64) were also significantly correlated with P2000 (p<0.001 for all). Ppeak was also the best predictor of P2000 when the two sub-groups LW and HW were considered separately. It was concluded that Ppeak is an overall index of physiological rowing capacity in groups of high-level LW and HW female rowers. The predictive value of Ppeak is similar to that of PLa4, but Ppeak presents the advantage of being obtained with a simple ergometer test without biological measurements.
A Novel Distributed Pseudo-TDMA Channel Access Protocol for Multi-Transmit-Receive Wireless Mesh Networks

A key approach to improve the capacity of wireless mesh networks (WMNs) is to equip routers with multiple transmissions or receptions (MTR) capability. Thus, the resulting MTR WMN has significantly higher network capacity because routers can activate multiple links simultaneously. This, however, requires an MTR link scheduler that maximizes network capacity or equivalently, one that is capable of deriving a short schedule. Henceforth, we propose period controlled pseudo time-division multiple access (PCP-TDMA), a link scheduler that allows nodes to cooperatively reduce an initial link schedule or superframe over time in a distributed manner. Routers are able to adapt the superframe size iteratively using only local information to accommodate any topological changes. This means PCP-TDMA is particularly suited for use in large-scale MTR WMNs. We have evaluated PCP-TDMA in various network topologies, and compared it against ALGO-2, a centralized algorithm that uses global topological information to derive a schedule and thus serves as a benchmark. We also compare PCP-TDMA against two distributed approaches: JazzyMAC and ROMA. The results show that PCP-TDMA achieves similar performance with the centralized algorithm in all scenarios, and outperforms the distributed approaches significantly. Specifically, in a fully connected network, the resulting superframe length of PCP-TDMA is less than $1/3$ and $1/2$ of JazzyMAC and ROMA, respectively.
EURECA: On-Chip Configuration Generation for Effective Dynamic Data Access

This paper describes Effective Utilities for Run-timE Configuration Adaptation (EURECA), a novel memory architecture for supporting effective dynamic data access in reconfigurable devices. EURECA exploits on-chip configuration generation to reconfigure active connections in such devices cycle by cycle. When integrated into a baseline architecture based on the Virtex-6 SX475T, the EURECA memory architecture introduces small area, delay and power overhead. Three benchmark applications are developed with the proposed architecture targeting social networking (Memcached), scientific computing (sparse matrix-vector multiplication), and in-memory database (large-scale sorting). Compared with conventional static designs, up to 14.9 times reduction in area, 2.2 times reduction in critical-path delay, and 32.1 times reduction in area-delay product are achieved.
Electron Microscopy Imaging of Flux Pinning Defects in YBCO Superconductors

High-temperature superconductors are of great interest because they can transport electrical current without loss. For real-world applications, the amount of current, known as the critical current Ic, that can be carried by superconducting wires is the key figure of merit. Large Ic values are necessary to off-set the higher cost of these wires. The factors that improve Ic (microstructure/performance relationship) in the state-of-the-art coated conductor wires based on YBa₂Cu₃O₇ (YBCO) are not fully understood. However, microstructural defects that immobilise (or pin) tubes of magnetic flux (known as vortices) inside the coated conductors are known to play a role in improving Ic. In this thesis, the vortex-defect interaction in YBCO superconductors was investigated with high-end transmission electron microscopy (TEM) techniques using two approaches. First, the effect of dysprosium (Dy) addition and oxygenation temperature on the microstructure and critical current were investigated in detail. Changing only the oxygenation temperature leads to many microstructural changes in pure YBCO coated conductors. It was found that Dy addition reduces the sensitivity of the YBCO to the oxygenation temperature, in particular it lowers the microstructural disorder while maintaining the formation of nanoparticles, which both contribute to the enhancement of Ic. In the second approach, two TEM based techniques (off-axis electron holography and Lorentz microscopy) were used to study the magnetic flux vortices. Vortex imaging was attempted with a TEM operated at 300 kV on both a YBCO crystal as well as a YBCO coated conductor. Many challenges were encountered including sample preparation, inhomogeneity, and geometry, in addition to the need to perform measurements at cryogenic temperatures. Although vortices were not able to be observed in the coated conductors, tentative observation of vortices in a YBCO crystal was made using Lorentz microscopy. Improvements for future electron holography experiments on YBCO at low voltage are suggested. This work represents a pioneering step towards directly imaging vortices in YBCO using more widely available microscopes with the aim of better understanding flux pinning to ultimately boost Ic in superconducting wires.
Unsupervised Semantic Deep Hashing

In recent years, deep hashing methods have been proved to be efficient since it employs convolutional neural network to learn features and hashing codes simultaneously. However, these methods are mostly supervised. In real-world application, it is a time-consuming and overloaded task for annotating a large number of images. In this paper, we propose a novel unsupervised deep hashing method for large-scale image retrieval. Our method, namely unsupervised semantic deep hashing (\textbf{USDH}), uses semantic information preserved in the CNN feature layer to guide the training of network. We enforce four criteria on hashing codes learning based on VGG-19 model: 1) preserving relevant information of feature space in hashing space; 2) minimizing quantization loss between binary-like codes and hashing codes; 3) improving the usage of each bit in hashing codes by using maximum information entropy, and 4) invariant to image rotation. Extensive experiments on CIFAR-10, NUSWIDE have demonstrated that \textbf{USDH} outperforms several state-of-the-art unsupervised hashing methods for image retrieval. We also conduct experiments on Oxford 17 datasets for fine-grained classification to verify its efficiency for other computer vision tasks.
Gas-Solid Flow and Shaft Injected Gas Penetration in an Oxygen Blast Furnace Analyzed Using a Three-Dimensional DEM-CFD Coupling Mathematical Model

According to IEA statistics,1) globally the iron and steel industry accounts for the highest share of CO2 emissions from the manufacturing sector, at about 27%. The importance of minimizing CO2 emissions in the steel industry has emerged as a critical concern in slowing the rate of global warming. Oxygen blast furnace (OBF) which was initially proposed by Wenzel2) in 1970 is a modified ironmaking process for this purpose. For the next few decades, scholars proposed some improved processes.3–5) Some large projects related to OBF industrial test have been carried out, such as ULCOS in Europe,6–9) experiment OBF in Japan10,11) and Russia.12) These tests proved the feasibility and advanced of OBF process. Academic research had placed the focus on the traditional blast furnace (TBF) simulation for many years. Various physical13–15) and mathematical models16,17) have been adopted to investigate the multiphase flow and complicated thermo-chemical behaviors in TBF. The mathematical model ideas were also improved from one-dimensional to multi-dimensional, single-phase to multi-phase and conGas-Solid Flow and Shaft Injected Gas Penetration in an Oxygen Blast Furnace Analyzed Using a Three-Dimensional DEM-CFD Coupling Mathematical Model
Functional Mixed Effects Model for Small Area Estimation

Functional data analysis has become an important area of research because of its ability of handling high‐dimensional and complex data structures. However, the development is limited in the context of linear mixed effect models and, in particular, for small area estimation. The linear mixed effect models are the backbone of small area estimation. In this article, we consider area‐level data and fit a varying coefficient linear mixed effect model where the varying coefficients are semiparametrically modelled via B‐splines. We propose a method of estimating the fixed effect parameters and consider prediction of random effects that can be implemented using a standard software. For measuring prediction uncertainties, we derive an analytical expression for the mean squared errors and propose a method of estimating the mean squared errors. The procedure is illustrated via a real data example, and operating characteristics of the method are judged using finite sample simulation studies.
Automated Learning Content Generation from Knowledge Bases in the STUDYBATTLES Environment

E-learning environments provide an orthogonal approach to transfer relevant knowledge. For example, sales representatives can improve their sales knowledge more independently from related courses offered. Major challenges for successfully establishing e-learning technologies in a company are to develop learning content in an efficient fashion, to recommend only relevant content to system users, and to motivate them to utilize the learning environment in a sustainable fashion. In this paper, we present the gamification-based e-learning environment STUDYBATTLES. We provide an overview of STUDYBATTLES functionalities including content creation, gamification techniques, learning performance analysis, and automated question generation. We show how STUDYBATTLES can be utilized for different learning purposes in academic and professional environments. In addition, we introduce an approach to automatically generate product and sales domain learning content from recommender knowledge bases to be exploited in STUDY.
On the electric inductive capacities of dry paper and of solid cellulose

Although dry paper is widely used as a supporting and insulating material in telephone cables, the published data with regard to its specific inductive capacity (or permittivity) appear to be very meagre. For this reason Mr. Gavey, C. B. , Engineer-in-Chief of the Post Office, asked us to investigate the matter, and sent for test a large number of samples of paper obtained from four different cable manufacturers. All the samples consisted of what is known as “chemical wood paper,” presumably free from lignified fibre. This type of paper, according to the Society of Arts Report on the Durability of Papers, is better in lasting quality than “mechanical wood paper” or paper made from straw, jute or esparto grass. Mr. Gavey, in addition, has kindly supplied the results of some tests on actual cables and further data for some of the samples of paper. This information is embodied in Part II of the present paper. Part I. –Tests on dry paper. One of the main difficulties in the testing of paper lies in the fact that it absorbs moisture so readily; and the presence of moisture has a large effect on the specific inductive capacity and an enormous effect on the insulation-resistance. The nature of these effects is well illustrated by the curve in fig. 6 (Part II, p. 204), which shows how the capacity increases and the resistance decreases as a well-dried cable is allowed to absorb moisture from the atmosphere.
Elucidating the Role of CD84 and AHR in Modulation of LPS-Induced Cytokines Production by Cruciferous Vegetable-Derived Compounds Indole-3-Carbinol and 3,3′-Diindolylmethane

Modulation of the immune system by cancer protective food bioactives has preventive and therapeutic importance in prostate cancer, but the mechanisms remain largely unclear. The current study tests the hypothesis that the diet-derived cancer protective compounds, indole-3-carbinol (I3C) and 3,3′-diindolylmethane (DIM), affect the tumor microenvironment by regulation of inflammatory responses in monocytes and macrophages. We also ask whether I3C and DIM act through the aryl hydrocarbon (AHR)-dependent pathway or the signaling lymphocyte activation molecule (SLAM) family protein CD84-mediated pathway. The effect of I3C and DIM was examined using the human THP-1 monocytic cell in its un-differentiated (monocyte) and differentiated (macrophage) state. We observed that I3C and DIM inhibited lipopolysaccharide (LPS) induction of IL-1β mRNA and protein in the monocyte form but not the macrophage form of THP-1. Interestingly, CD84 mRNA but not protein was inhibited by I3C and DIM. AHR siRNA knockdown experiments confirmed that the inhibitory effects of I3C and DIM on IL-1β as well as CD84 mRNA are regulated through AHR-mediated pathways. Additionally, the AHR ligand appeared to differentially regulate other LPS-induced cytokines expression. Hence, cross-talk between AHR and inflammation-mediated pathways, but not CD84-mediated pathways, in monocytes but not macrophages may contribute to the modulation of tumor environments by I3C and DIM in prostate cancer.
Exploring factors behind pregnant women’s quality of life in Iran: a qualitative study

Background and objective Pregnancy-related physiologic and psychosocial alterations can impact on the body and cause symptoms which may affect quality of life. Since qualitative studies can provide more in-depth understanding of quality of life and its determining factors, this study was conducted with the aim of exploring factors affecting pregnant women’s quality of life. Methods A qualitative descriptive study with conventional content analysis approach was made using the conventional content analysis approach on a purposeful sample of sixteen pregnant Iranian women in Hamadan, Iran from May 2015 to December 2015. Sampling was continued until data saturation. Data were collected through in-depth semi-structured personal interviews, and were analyzed using the conventional content analysis approach. Results Data analysis resulted in three main categories, namely, the effects of pregnancy on different aspects of health (including psychological disorders, impaired interactions, disturbances in doing daily activities, disturbed body image, alterations in sexual relationships, physical disorders, and alterations in dietary habits and treatment regimens), pregnancy-related concerns (regarding the gender of the fetus, financial problems, childbirth, health, and the future), and coping with pregnancy (through strategies such as spirituality, positive attitude toward pregnancy, distraction and imagination, and support). Conclusions This study showed that different factors can affect pregnant women’s quality of life. Nonetheless, prenatal care services are mainly focused on pregnancy-related physical problems, and other aspects of care are usually taken for granted. Consequently, healthcare professionals need to pay greater attention to pregnant women’s quality of life and its contributing factors.
Simultaneous Bayesian Clustering and Feature Selection Through Student’s ${t}$ Mixtures Model

In this paper, we proposed a generative model for feature selection under the unsupervised learning context. The model assumes that data are independently and identically sampled from a finite mixture of Student’s ${t}$ distributions, which can reduce the sensitiveness to outliers. Latent random variables that represent the features’ salience are included in the model for the indication of the relevance of features. As a result, the model is expected to simultaneously realize clustering, feature selection, and outlier detection. Inference is carried out by a tree-structured variational Bayes algorithm. Full Bayesian treatment is adopted in the model to realize automatic model selection. Controlled experimental studies showed that the developed model is capable of modeling the data set with outliers accurately. Furthermore, experiment results showed that the developed algorithm compares favorably against existing unsupervised probability model-based Bayesian feature selection algorithms on artificial and real data sets. Moreover, the application of the developed algorithm on real leukemia gene expression data indicated that it is able to identify the discriminating genes successfully.
A smooth distributed feedback for global rendezvous of unicycles

This paper presents a solution to the rendezvous control problem for a network of unicycles on the plane. A smooth, time-invariant control law is presented that drives the unicycles to a common position from arbitrary initial conditions. Each unicycle is equipped with an onboard camera and can measure its relative displacement to its neighbors in body frame. The feedback is a function only of these onboard measurements and no global positioning system is required, nor any information about the unicycles' orientations.
Scalable Entity Resolution Using Probabilistic Signatures on Parallel Databases

Accurate and efficient entity resolution is an open challenge of particular relevance to intelligence organisations that collect large datasets from disparate sources with differing levels of quality and standard. Starting from a first-principles formulation of entity resolution, this paper presents a novel entity resolution algorithm that introduces a data-driven blocking and record linkage technique based on the probabilistic identification of entity signatures in data. The scalability and accuracy of the proposed algorithm are evaluated using benchmark datasets and shown to achieve state-of-the-art results. The proposed algorithm can be implemented simply on modern parallel databases, which we have done in the financial intelligence domain with tens of Terabytes of noisy data.