Miracle-dz/UniFAR-FTUs huggingface dataset:
Probiotics for Constipation in Parkinson Disease

Objective To determine whether probiotics are effective for constipation, a common and often difficult-to-treat problem, in Parkinson disease (PD). Methods In this double-blind, randomized, placebo-controlled, single-center trial, 280 patients with PD were screened, and 72 eligible patients were block-randomized (1:1) to receive either multistrain probiotics capsules (n = 34) or identical-appearing placebo (n = 38), for 4 weeks. The primary endpoint was the change in the average number of spontaneous bowel movements (SBM) per week during the last 2 weeks of intervention compared with the 2-week preintervention phase, recorded by daily stool diary. Secondary outcome measures included changes in stool consistency, constipation severity score, and quality of life related to constipation. Satisfaction with intervention received was assessed. Change in levels of fecal calprotectin, a marker of intestinal inflammation, was an exploratory outcome. Results SBM increased by 1.0 ± 1.2 per week after treatment with probiotics and decreased by 0.3 ± 1.0 per week in the placebo group (mean difference 1.3, 95% confidence interval 0.8–1.8, p < 0.001). Significant improvements were also seen for secondary outcomes after correction for multiple comparisons, including stool consistency (p = 0.009) and quality of life related to constipation (p = 0.001). In the treatment group, 65.6% reported satisfaction with the intervention vs only 21.6% in the placebo group (p < 0.001). One patient (2.9%) in the treatment group withdrew due to a nonserious adverse event. Fecal calprotectin did not change significantly during the study. Conclusions Multistrain probiotics treatment was effective for constipation in PD. Further studies are needed to investigate the long-term efficacy and safety of probiotics in PD, as well as their mechanisms of action. ClinicalTrials.gov Identifier NCT03377322. Classification of Evidence This study provides Class I evidence that, for people with PD, multistrain probiotics significantly increased the average number of SBM per week.
New Evidence on Teacher Turnover in Early Childhood

This brief provides a systemwide look at early childhood teacher turnover using data from all publicly funded, center-based early childhood programs in Louisiana, including subsidized child care, Head Start, and pre-kindergarten. It provides new evidence on the prevalence of turnover and explores whether teachers who leave differ from those who stay on a widely used measure of teacher–child interaction quality. Results show that more than one third of teachers leave their program from one year to the next, and the vast majority of teachers who leave are not teaching in another program the following year. Turnover rates are higher among teachers working in child care and with younger children. Compared with teachers who stay, those who leave are rated lower on interaction quality.
Toward robust mammography-based models for breast cancer risk

An algorithm to predict breast cancer risk, Mirai, outperforms clinical risk models across test cohorts from the United States, Sweden, and Taiwan. Don’t risk it Mammograms are a common but imperfect way of assessing breast cancer risk. Current U.S. breast cancer screening guidelines all use a component of cancer risk assessment to inform clinical course. Yala et al. developed a machine learning model called “Mirai” to predict breast cancer risk based on traditional mammograms. The authors’ risk model performed better than Tyrer-Cuzick and previous deep learning models at identifying both 5-year breast cancer risk and high-risk patients across multiple international cohorts. Mirai also performed similarly across race and ethnicity categories, suggesting the potential for improvement in patient care across the board. Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines. To bring deep learning risk models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. We developed Mirai, a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and tested on held-out test sets from MGH, Karolinska University Hospital in Sweden, and Chang Gung Memorial Hospital (CGMH) in Taiwan, obtaining C-indices of 0.76 (95% confidence interval, 0.74 to 0.80), 0.81 (0.79 to 0.82), and 0.79 (0.79 to 0.83), respectively. Mirai obtained significantly higher 5-year ROC AUCs than the Tyrer-Cuzick model (P < 0.001) and prior deep learning models Hybrid DL (P < 0.001) and Image-Only DL (P < 0.001), trained on the same dataset. Mirai more accurately identified high-risk patients than prior methods across all datasets. On the MGH test set, 41.5% (34.4 to 48.5) of patients who would develop cancer within 5 years were identified as high risk, compared with 36.1% (29.1 to 42.9) by Hybrid DL (P = 0.02) and 22.9% (15.9 to 29.6) by the Tyrer-Cuzick model (P < 0.001).
Autoionization resonances and configuration-mixing in the emission spectra of Zn I and Cd I

From new emission spectra of Cd I and Zn I the nsnp 1P0-np2 3P2 features have been identified in each case (n = 4 for Zn and n = 5 for Cd). Absolute photometric study of the resonances has proved that they have the same half-widths as their shorter-wavelength companions nsnp 3P0-np2 3P2. The measured auto-ionization probability for the Cd I 5p2 3P2 level is 4.15 x 1013 s-1 and for the Zn I 4p2 3P2 level is 4.84 x 1012 s-1. From the absolute intensity measurements we have determined the configuration-mixing coefficients and, for Cd I, the capture cross-section for the 5sE(d) 1D2 continuum associated with the resonance at 310.7 nm.
Study on the Contamination of Chlorides in Ammonium Nitrate

A terrible mass explosion of about 400 tons of ammonium nitrate (AN) occurred in Toulouse AZF plant on 21 September 2001. According to the review of the accidental investigation, there may be resources of impurities to be introduced into the mass of ammonium nitrate. In this study, several chloride salts, including ammonium chloride, barium chloride, calcium chloride, sodium chloride and potassium chloride, were added into ammonium nitrate at concentrations of 0.05, 0.1, 0.5, 1 and 2%, respectively. A differential scanning calorimeter and a heat flux calorimeter C80 with higher sensitivity were used to measure the decomposition of ammonium nitrate and its mixtures with these impurities. All the results demonstrated that the addition of a small account of chloride gives rise to a significant decrease in the initial decomposition temperature of AN by nearly 100°C and the magnification of heat release from the reaction. The intensity of the decomposition of ammonium nitrate with and without impurities was also examined by a modified pressure vessel test (MCPVT), in which the rate of pressure rise of AN in the presence of chlorides in the MCPVT was multiple. It implied that, with the addition of impurities, the decomposition of AN is more intense and occurs at lower temperatures. However, in the BAM 50/60 steel tube test, no detonation was observed for either pure AN or contaminated AN under the test conditions. However a detonation is predicted under more severe conditions.
Player Motivation in Therapy Games for Parkinson's Disease: A Scoping Review: Understanding meaningful play, self-determination and flow

Good games are engaging for the right audience. They compel their players to come back and play, motivate their participation with meaningful moment-to-moment actions (meaningful play), help them develop a long-term engagement (self-determination), and make them feel in-the-moment (flow). In this context, the characteristics of a good game revolves around player motivation, and how well it is addressed from a micro level to a macro level of game design. Since therapy games rely on the idea that a game experience can make exercising more entertaining and increase adherence, a good exergame seems to be bounded with the same motivational conundrum. In this paper, we discuss the concept of player motivation in exergame design and explore whether and how this concept has been used in previously developed exergames for rehabilitation of Parkinson's Diseases. We hope that we can develop a better understanding about long-term appeal towards creating experiences for long-term use.
Gender Differences when Adopting Avatars for Educational Games

Avatars act as digital representations of players or non-playing characters in games and other online environments, and also play a key role key in educational games. This study looks at gender differences that may impact on human avatar interactions, exploring the impact of player gender differences and avatar gender differences that might impact on acceptance of game technology for education. We also consider gender preferences for avatars across a range of subject areas. We survey 332 participants and examine general acceptance of educational games using the UTAUT framework. We find both males (n=202) and females (n=131) rank performance expectancy, effort expectancy, social influence, facilitating conditions and hedonic motivation, along with their behavioural intention to use the technology favourably, although female rankings on all scales are significantly lower than male ratings. This suggests less confidence in females for adopting game technology for use in education. Participants also ranked a series of 5 male and 5 female avatars on how well they might be applied to various educational domains. In most cases, the male and female participants rank avatars consistently, although there are a few exceptions. Most evident is the way the female avatars and male avatars are ranked for use in educational games, indicating clear preferences by users for how avatar gender should be selected.
Phytate and total phosphorus content of field beans (Vicia faba L.)

Considerable variation was found in the phytate and total phosphorus contents of varieties, selections and genotypes of field beans (Vicia faba L.) The phytate phosphorus content of seeds taken from single plants of the variety Dacre indicated that large variations (1.93-4.03 g kg−1), not significantly correlated with protein content, existed within an established variety. Although some variation in the ratio of phytate to total phosphorus was observed (0.40-0.60), highly significant correlation coefficients were obtained for total and phytate phosphorus contents. The significance of the results obtained is discussed in relation to the potential for improving the nutritive value of field beans by breeding for lower phytate content.
System-Level Verification of Linear and Non-Linear Behaviors of RF Amplifiers using Metamorphic Relations

System-on-Chips (SoC) have imposed new yet stringent design specifications on the Radio Frequency (RF) subsystems. The Timed Data Flow (TDF) model of computation available in SystemC-AMS offers here a good trade-off between accuracy and simulation-speed at the system-level. However, one of the main challenges in system-level verification is the availability of reference models traditionally used to verify the correctness of the Design Under Verification (DUV). Recently, Metamorphic testing (MT) introduced a new verification perspective in the software domain to alleviate this problem. MT uncovers bugs just by using and relating test-cases.In this paper, we present a novel MT-based verification approach to verify the linear and non-linear behaviors of RF amplifiers at the system-level. The central element of our MT-approach is a set of Metamorphic Relations (MRs) which describes the relation of the inputs and outputs of consecutive DUV executions. For the class of Low Noise Amplifiers (LNAs) we identify 12 high-quality MRs. We demonstrate the effectiveness of our proposed MT-based verification approach in an extensive set of experiments on an industrial system-level LNA model without the need of a reference model.
Physical Synthesis for Advanced Neural Network Processors

The remarkable breakthroughs in deep learning have led to a dramatic thirst for computational resources to tackle interesting real-world problems. Various neural network processors have been proposed for the purpose, yet, far fewer discussions have been made on the physical synthesis for such specialized processors, especially in advanced technology nodes. In this paper, we review several physical synthesis techniques for advanced neural network processors. We especially argue that datapath design is an essential methodology in the above procedures due to the organized computational graph of neural networks. As a case study, we investigate a wafer-scale deep learning accelerator placement problem in detail.
Electrical Parameters Degradation of E-mode GaN Under Repeated Short-Circuit Impacts

Electrical parameters degradation of the 650V/30A E-mode GaN HEMT under repeated short circuit (RSC) impacts have been investigated. The repeated short-circuit impacts conditions are as followes: short circuit voltage is 100V, gate pulse voltage is 5V, pulse period is 0.5S, and the pulse duty is 6ppm. The number of short-circuit impacts is 100, 300, 500, 1k, 5k and 10k times respectively. After short-circuit impact, the threshold voltage Vth, the static on-state resistance Ron_s and the dynamic on-resistance Ron_d are measured. The test results show that all of these parameters have been increased with the increase of the RSC impacts number. The increase rates of Vth, Ron_s and Ron_d are 23.7%, 9.8% and 10.8% respectively. After 3 days room temperature annealing, both Ron_s and Ron_d almost recovered to their initial value, but the threshold voltage is still 4.1% larger than the original value.
A text as unique as fingerprint: AVASUS text analysis and authorship recognition

Authorship Attribution, study to deduce an author for a given text based on then writing characteristics is a problem with a long history and refers to the task of author recognition of a text in a group of candidate authors, based on relevant features extracted from the text (Stylometry). Stylometry occurs by identifying relevant attributes that define a space in which authors can be distinguished from each other. Writers use language in different ways to express their ideas, and this variation in language makes authorship recognition possible. In this paper the problem of authorship recognition is being exposed in order to make it a tool for use in the distance education platform of the Ministry of Health: AVASUS. Therefore, the stylometric characteristics are extracted from the collected data set and different classification algorithms are trained to predict the authors of texts longer than 30 characters. The results were relevant obtaining values of 92% accuracy in some classifiers and showed that extraction techniques of stylometric characteristics could be used to recognize the author of a text.
Knowledge Transfer for Entity Resolution with Siamese Neural Networks

The integration of multiple data sources is a common problem in a large variety of applications. Traditionally, handcrafted similarity measures are used to discover, merge, and integrate multiple representations of the same entity—duplicates—into a large homogeneous collection of data. Often, these similarity measures do not cope well with the heterogeneity of the underlying dataset. In addition, domain experts are needed to manually design and configure such measures, which is both time-consuming and requires extensive domain expertise. We propose a deep Siamese neural network, capable of learning a similarity measure that is tailored to the characteristics of a particular dataset. With the properties of deep learning methods, we are able to eliminate the manual feature engineering process and thus considerably reduce the effort required for model construction. In addition, we show that it is possible to transfer knowledge acquired during the deduplication of one dataset to another, and thus significantly reduce the amount of data required to train a similarity measure. We evaluated our method on multiple datasets and compare our approach to state-of-the-art deduplication methods. Our approach outperforms competitors by up to +26 percent F-measure, depending on task and dataset. In addition, we show that knowledge transfer is not only feasible, but in our experiments led to an improvement in F-measure of up to +4.7 percent.
DExIE - An IoT-Class Hardware Monitor for Real-Time Fine-Grained Control-Flow Integrity

The Dynamic Execution Integrity Engine (DExIE) is a lightweight hardware monitor that can be flexibly attached to many IoT-class processor pipelines. It is guaranteed to catch both inter- and intra-function illegal control flows in time to prevent any illegal instructions from touching memory. The performance impact of attaching DExIE to a core depends on the concrete pipeline structure. In some especially suitable cases, extending a processor with DExIE will have no performance penalty. DExIE is real-time capable, as it causes no or only up to 10.4 % additional and then predictable pipeline stalls. Depending on the monitored processor’s size and structure, DExIE is faster than software-based monitoring and often smaller than a separate guard processor. We present not just the hardware architecture, but also the automated programming flow, and discuss compact adaptable storage formats to hold fine-grained control flow information.
Efficient Accuracy Recovery in Approximate Neural Networks by Systematic Error Modelling

Approximate Computing is a promising paradigm for mitigating the computational demands of Deep Neural Networks (DNNs), by leveraging DNN performance and area, throughput or power. The DNN accuracy, affected by such approximations, can be then effectively improved through retraining. In this paper, we present a novel methodology for modelling the approximation error introduced by approximate hardware in DNNs, which accelerates retraining and achieves negligible accuracy loss. To this end, we implement the behavioral simulation of several approximate multipliers and model the error generated by such approximations on pre-trained DNNs for image classification on CIFAR10 and ImageNet. Finally, we optimize the DNN parameters by applying our error model during DNN retraining, to recover the accuracy lost due to approximations. Experimental results demonstrate the efficiency of our proposed method for accelerated retraining (11× faster for CIFAR10 and 8× faster for ImageNet) for full DNN approximation, which allows us to deploy approximate multipliers with energy savings of up to 36% for 8-bit precision DNNs with an accuracy loss lower than 1%.
Who am I? - Development and Analysis of an Interactive 3D Game for Psychometric Testing

Psychometric testing has become a popular scientific method to measure individuals’ mental capabilities and behavioural styles. Traditional psychometric tests are standardised and usually require the completion of paper forms. This means they are tedious to complete, can cause anxiety, and it is obvious to participants that a psychometric test is being conducted, potentially affecting how questions are answered. We develop and analyse an interactive 3D game which measures similar metrics to a psychometric test, but is unobtrusive and fun to complete. Specifically developed game elements are used to measure three psychometric properties related to motivation: extraversion, neuroticism and conscientiousness. A user study with 30 participants revealed that the game provided a valid measure for neuroticism when compared with traditional assessments, but was less successful for other traits. Participants were accepting a longer playtime compared to traditional tests, and most were unaware they completed a personality test.
Value-Aware Error Detection and Correction for SRAM Buffers in Low-Bitwidth, Floating-Point CNN Accelerators

Low-power CNN accelerators are a key technique to enable the future artificial intelligence world. Dynamic voltage scaling is an essential low-power strategy, but it is bottlenecked by on-chip SRAM. More specifically, SRAM can exhibit stuck-at (SA) faults at a rate as high as 0.1% when the supply voltage is lowered to, e.g., 0.5 V. Although this issue has been studied in CPU cache design, since their solutions are tailored for CPUs instead of CNN accelerators, they inevitably incur unnecessary design complexity and SRAM capacity overhead.To address the above issue, we conduct simulations and analyses to enable us to propose error detecting and correcting mechanisms that are tailored for our targeting low-bitwidth, floating-point (LBFP) CNN accelerators. We analyze the impacts of SA faults in different SRAM positions, and we also analyze the impacts of different SA types, i.e., stuck-at-one (SA1) and stuck-at-zero (SA0). The analysis results lead us to the error detecting and correcting mechanisms that prioritize fixing SA1 appearing at SRAM positions where the exponent bits of LBFP are stored. The evaluation results show that our proposed mechanisms can help to push the voltage scaling limit down to a voltage level with 0.1% SA faults (e.g., 0.5 V).
Permission Management and User Privacy Based On Android: Permission Management On Android

Nowadays, smartphones become widely popular and indispensable in our daily life. The largest proportion of the smartphone market is the Android operating system. Because of its modifiability and customization, it is favored by smartphone manufacturers and users. However, because of its flexibility, there are many user privacy risks when using Android applications. Although the country and users already pay attention to the protection of user privacy information, there is still no particularly perfect method to solve this problem for the Android operating system. The purpose of this project is to find a suitable method to protect the privacy information of Android application users. The project conducted a questionnaire survey on smartphone users to obtain their understanding of the protection of private information. At the same time, some existing methods are compared to find out their shortcomings. Based on the above comparison, this project chose the MIUI 12 operating system of Xiaomi. Then, the project made corresponding tests and analyses based on the privacy protection functions of the MIUI 12 operating system. When testing the privacy protection functions of the MIUI 12, in addition to testing from the perspective of users, the project also developed the TestPermission application to test from the perspective of developers. At the end of the project, we concluded that the new privacy protection functions provided by MIUI 12 were a suitable choice for protecting user privacy and controlling application permissions. It is simple and intuitive for general users. At the same time, for the limitations of MIUI 12, the project proposed to reset the OAID and refine the granularity of permissions to be further optimised in future work.
Volatile Components of the Essential Oil From Bauhinia ungulata L.

Abstract Air-dried leaves of Bauhinia ungulata L. were submitted to volatile oil isolation by hydrodistillation to produce an oil in 0.007% yield. The oil obtained was examined by gas chromatography (GC-FID) and gas chromatography/mass spectrometry (GC/MS). Thirteen compounds comprising 95.9% of the total peak area were identified. The main constituents identified were spathulenol (47.7%) and caryophyllene oxide (18.3%).