Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Evolution of Topics in Large-Scale Temporal Text Corpora

Large text temporal collections provide insights into social and cultural change over time. To quantify changes in topics in these corpora, embedding methods have been used as a diachronic tool. However, they have limited utility for modeling changes in topics due to the stochastic nature of training. We propose a new computational approach for tracking and detecting temporal evolution of topics in a large collection of texts. This approach for identifying dynamic topics and modeling their evolution combines the advantages of two methods: (1) word embeddings to learn contextual semantic representation of words from temporal snapshots of the data and (2) dynamic network analysis to identify dynamic topics by using dynamic semantic similarity networks developed using embedding models. Experimenting with two large temporal data sets from the legal and real estate domains, we show that this approach performs faster (due to parallelizing different snapshots), uncovers more coherent topics (compared to available dynamic topic modeling approaches), and effectively enables modeling evolution leveraging the network structure.
Occupational Therapy for Children and Youth Using Sensory Integration Theory and Methods in School-Based Practice.

The American Occupational Therapy Association (AOTA) recognizes that occupational therapists and occupational therapy assistants 1 working within public school settings may provide intervention to students in general and special education programs. When the processing and integrating of sensory information interferes with a child’s performance in school activities, occupational therapy practitioners 2 may use sensory-based interventions or a sensory integration (SI) approach (Ayres, 1972a) to support the child’s ability to participate in his or her educational program. Evidence to support SI and sensory processing interventions can be found in Watling, Koenig, Davies, and Schaaf (2011) and also in Dunn (2014). Occupational therapy practitioners working in schools use evidence-based sensory-based interventions or a SI approach when sensory-related issues are identified and affect a child’s ability to benefit from his or her education. The that in
The Clinical Application Methods of the Nu-tritional Support Team

Purpose: Most hospitals have been reply to the consultation if the needed departments consult with the Nutritional Support Team (NST) for patient evaluation and management. However, as a common occurrence, some people requiring treatment cannot receive care due to lack of cognition of nutritional support and lack of resources for assessment of nutritional status. Methods : We provide an indication for screening of patients and for active nutritional support. Indications include symptoms that reflect the conditions of increasing nutritional requirement and decreasing nutritional supply. Two surgical wards and surgical intensive care unit were included and nursing teams recommended patients to the NST by interviewing inpatients if they met the indications. Results: We included 60 patients referred to the NST before applying for screening, from January 2005 to January 2009 and 44 patients selected for only five months by the NST after applying for screening. The number of con-sultations showed a slow decrease, 27 in 2005, 23 in 2006 to 10 in 2007, before applying for screening, but in-creased significantly to 44 for only five months after applying for screening. While the number of departments applying to the NST was five departments in 2005, six departments in 2006, and two departments in 2007, it in-creased significantly to eight departments. After applying for screening, the most common cause was ‘ artificial nutrition longer than five days ’ in 48.1%; the second cause was ‘ poor nutrition status ’ in 13.5%, and the third was ‘ significant weight loss ’ or ‘ gastrointestinal function change ’ in 8%. Moderate risk patients showed improvement and shifted to minor risk patients and normal patients. The rate of improvement was approximately 47.7%. Conclusion: The screening tools should be simple, as well as easy to use. Therefore, using this tool, we must actively select patients with poor nutrition and apply the screening tools rapidly. (J Korean Soc Parenter Enter Nutr 2013;5(3):117-121)
Divide-by-2 injection-locked frequency divider using distributed spiral resonator: 2:1 distributed spiral resonator injection-locked frequency divider

This paper investigates the locking range property of a divide-by-2 RLC cross-coupled injection-locked frequency divider (ILFD). The ILFD uses a RLC resonator and it is originally targeting for having two overlapped locking ranges at 0 dB m injection power. At the power consumption 4.57 mW and at the incident power of 0 dBm, the locking range of this NFET ILFD is 7.94 GHz, from the injection frequency 2.2 GHz to 10.14 GHz. At low injection power, the ILFD can have two or three non-overlapped locking ranges, indicating that the on-chip spiral inductor shows the effect of distributed capacitance and the ILFD uses a distributed spiral resonator.
Transfer Learning for Improved Audio-Based Human Activity Recognition

Human activities are accompanied by characteristic sound events, the processing of which might provide valuable information for automated human activity recognition. This paper presents a novel approach addressing the case where one or more human activities are associated with limited audio data, resulting in a potentially highly imbalanced dataset. Data augmentation is based on transfer learning; more specifically, the proposed method: (a) identifies the classes which are statistically close to the ones associated with limited data; (b) learns a multiple input, multiple output transformation; and (c) transforms the data of the closest classes so that it can be used for modeling the ones associated with limited data. Furthermore, the proposed framework includes a feature set extracted out of signal representations of diverse domains, i.e., temporal, spectral, and wavelet. Extensive experiments demonstrate the relevance of the proposed data augmentation approach under a variety of generative recognition schemes.
Tightly integrated genomic and epigenomic data mining using tensor decomposition

Motivation: Complex diseases such as cancers often involve multiple types of genomic and/or epigenomic abnormalities. Rapid accumulation of multiple types of omics data demands methods for integrating the multidimensional data in order to elucidate complex relationships among different types of genomic and epigenomic abnormalities. Results: In the present study, we propose a tightly integrated approach based on tensor decomposition. Multiple types of data, including mRNA, methylation, copy number variations and somatic mutations, are merged into a high‐order tensor which is used to develop predictive models for overall survival. The weight tensors of the models are constrained using CANDECOMP/PARAFAC (CP) tensor decomposition and learned using support tensor machine regression (STR) and ridge tensor regression (RTR). The results demonstrate that the tensor decomposition based approaches can achieve better performance than the models based individual data type and the concatenation approach. Supplementary information: Supplementary data are available at Bioinformatics online.
Assessing Mozambicans’ willingness and determinants to use pre-exposure prophylactic HIV medication

In anticipation of the introduction of the pre-exposure prophylaxis drugs as an additional HIV prevention tool, we mapped the different positions of Mozambicans’ willingness to use it. Overall, 507 adults indicated willingness to use under different conditions varying as a function of perceived susceptibility to and severity of infection, side effects, administration protocol, and cost. Three qualitatively different positions were found: Unwillingness irrespective of conditions (4%), depend on circumstances (76%), and unconditional willingness (8%). A large majority of participants were willing to use pre-exposure prophylaxis drugs, provided that the administration is not too constraining, and the adverse side effects can be minimized.
FIB-induced damage in graphene electrodes for piezoelectric resonators

A first vibrating inertial quartz resonator with graphene electrodes patterned by Focused Ion Beam (FIB) is presented in this paper. The first electrical measurements have however shown that the quality factor is below the theoretical expectation. Several hypotheses are analyzed. The FIB etching process of graphene can lead to the deposition of amorphous carbon. Its impact on the mechanical behavior of the resonator is examined. The effect of ion damages at the quartz surface through implantation and amorphization as well as intrinsic losses emerging from friction at the interface between graphene and quartz are furthermore discussed.
Inversion of Rough Surface Parameters From SAR Images Using Simulation-Trained Convolutional Neural Networks

This letter investigates the inversion of rough surface parameters (the root mean square height and the correlation length) from microwave images by using deep convolutional neural networks (CNNs). Training data for the deep CNN are simulated numerically using computational electromagnetic method. As CNN is powerful in extracting image features, scattering field from rough surfaces is first converted to microwave images via interpolated fast Fourier transform and then fed into the CNN. In order to reduce overfitting, the regularization technique and dropout layer are used. The proposed CNN consists of five pairs of convolutional and maxpooling layers and two additional convolution layers for feature extraction and two fully connected layers for parameter regression. The experimental results demonstrated the feasibility using deep neural networks for the parameter inversion of rough surface from electromagnetic scattering fields. It suggests potential application of CNN for rough surface parameter inversion from microwave sensing data.
Load forecast based on least square SVR and HHT

Load forecast provides a decision-making basis for scheduling system of power system, synchronous control, operate plan and development program. To achieve high-accuracy of load forecast, many scholars are trying many methods. But the non-stationary, volatile and nonlinear properties of load data makes it much harder for precise load forecast. In this paper a load forecast method is proposed, which is based on least squares support vector regression (LSSVR), an evolutive version of standard support vector regression, and Hilbert-Huang Transform (HHT). This method firstly decomposed the load data to several sub-level data with clear tendency by HHT. Then these sub-level data are forecasted with LSSVR respectively. At last these results were recomposed as the forecast output of the original load data. The appropriate emulation of above forecast model were checked based on some sample data.
Classification of malignant-benign pulmonary nodules in lung CT images using an improved random forest (Use style: Paper title)

To help better radiologists with the early diagnosis of lung cancer on CT scans, we propose a novel classification scheme for malignant-benign classification of pulmonary nodules. We first segment pulmonary nodules by consulting the segmented results drawn by four radiologists. Then, we compute image features (gray feature, shape feature and texture feature) to represent the segmented nodules. Finally, malignant-benign classification of pulmonary nodules is performed using an improved-RF. Experiments were performed on a database of 603 pulmonary nodules, including 288 benign and 315 malignant nodules. And the provided method is compared with some classic classifiers and existing classification schemes of malignant-benign pulmonary nodules, respectively. Experimental results demonstrate that the proposed scheme outperforms the competing techniques.
Design and implementation of action recognition system based on RFID sensor

Human action recognition is widely used in smart home, virtual training, game, human computer interaction and robots. However, most of action recognition method is based on the visual action recognition technology which data are explosive growth and those data is hardly to process in big data environment. In this paper, we use Radio Frequency Identification (RFID) technology to build the model for left, right, front and back action of human. A player system based RFID sensor is designed and implemented. The test results show that our proposed system is effective and performs well in non-visual action recognition.
Limits to sustained energy intake. XXVIII. Beneficial effects of high dietary fat on lactation performance in mice

ABSTRACT Maximal animal performance may be limited by the ability of an animal to dissipate heat: the heat dissipation limitation (HDL) theory. Because the incidental heat produced during digestion [specific dynamic action (SDA)] varies among diets, the HDL theory predicts that lactating female mice consuming diets with lower SDA should have increased reproductive performance. Dietary fat has a lower SDA than dietary carbohydrate. Female mice were fed low (LF), medium (MF) or high (HF) fat diets (10, 45 and 60% energy from fat, respectively) from days 4–18 of lactation. HF- and MF-fed mice weaned significantly heavier litters than LF mice. This was because they not only consumed more energy [metabolisable energy intake (Emei); HF: 306.5±25.0, MF: 340.5±13.5 kJ day−1] at peak lactation, but also delivered more milk energy to their pups [milk energy output (Emilk); HF: 203.2±49.9, MF 229.3±42.2 kJ day−1] than the LF-fed mice (Emei=266.7±4.5, Emilk=164.60±30.59 kJ day−1). A mathematical model based on the predictions from the HDL theory showed that this effect was greater than predicted from differences in SDA between the diets. Fatty acid profiles of the diets, milk and pups showed significant correlations between the profiles. Besides reduced SDA, HF- and MF-fed mice were probably able to directly transfer absorbed dietary fat into milk, reducing the heat production of lactogenesis and enabling them to perform better than expected from the HDL model. In summary, HF and MF diets had beneficial effects on reproductive performance compared with the LF diet because they enabled mice to generate milk more efficiently with less incidental heat production. Summary: Mice fed high-fat diets have reduced specific dynamic action and show improved reproductive performance, in agreement with predictions from the heat dissipation limitations theory.
University of Birmingham Location-specific responses to nociceptive input support the purposeful nature of motor adaptation to pain

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A speculative parallel simulated annealing algorithm based on Apache Spark

Simulated annealing (SA) is an effective method for solving unconstrained optimization problems and has been widely used in machine learning and neural network. Nowadays, in order to optimize complex problems with big data, the SA algorithm has been implemented on big data platform and obtains a certain speedup. However, the efficiency for such implementation is still limited because the conventional SA algorithm still runs with low parallelism on new platforms and the computing resource cannot be fully utilized. For these problems, this paper raised a speculative parallel SA algorithm based on Apache Spark to expand the algorithm's parallelism and enhance its efficiency. In this paper, first, the inner dependencies, which stop conventional algorithm, run in parallel, are analyzed. Then, based on the analysis, the Software Thread‐Level Speculation technique is employed to help the conventional algorithm overcome the dependencies and make it run concurrently. Finally, a new parallel SA algorithm with speculation mechanism is proposed and implemented on Apache Spark. The experiments show that, for big data problems, the proposed algorithm could achieve an optimal parallelism when comparing the traditional algorithm without speculation on Apache Spark. Moreover, the execution efficiency of simulated annealing process can be markedly enhanced by the proposed algorithm.
Body Flexibility Effects on Foot Loading in Quadruped Bounding Based on a Simple Analytical Model

Body flexibility plays an important role in the bounding gait of quadruped animals. In a previous study, we investigated the effects of body flexibility on foot loading by comparing two different physical models, with and without a flexible body-joint. Testing both models with equal mechanical energy revealed two conflicting results: Body flexibility reduced the maximum ground reaction force, but a stiffer body exhibited a smaller maximum ground reaction force than a highly flexible body. Because of the complex nature of the model dynamics, the mechanisms underlying these results are unclear. In the current study, we used a simplified analytical model by assuming physical constraints on our previous model to clarify the dynamic roles of body flexibility, particularly regarding foot loading. Approximate periodic solutions were derived by linearization of the governing equations and provide an explanation for our conflicting results. Furthermore, we discussed the biological relevance of our findings using a comparison of our analytical results with observations of movement in cheetahs. Our findings will not only improve the understanding of motor control mechanisms for fast locomotion in quadruped animals, but also provide a guiding principle for the mechanical and control design of legged robots capable of fast locomotion.
Reserved Optimisation: Handling Incident Priorities in Emergency Response Systems

Emergency (medical, fire or criminal) Management Systems (EMSs) are crucial for ensuring public safety and security. Typically in many cities, less than 20% of the cases received by EMSs belong to the extremely serious category and require immediate help. Rest of the incidents typically are less serious and thereby allow more flexibility in response time. Therefore, for efficient management of EMS requests, several EMSs now categorise an incoming emergency request into a priority level based on well studied ``triaging" methods. Leading research on optimising emergency response has either focussed on data-driven models for settings with homogenous incidents or on generic heuristics (that are not data-driven) in multi-priority incident settings. In this paper, we provide data-driven models that employ tiered optimisation of allocation and dispatch simultaneously to ensure high priority incidents are served effectively. To that end, we make the following contributions in this paper: (1) For a given dataset of historical incidents, we first provide an optimisation model that maximises the percentage of highest priority incidents served within a threshold response time, while ensuring threshold response times for other priority incidents. Apart from optimising the allocation, this optimisation model also provides a detailed dispatch strategy that fits the given set of historical incidents well; (2) To better handle high variance (spatial and temporal) in arrival of high priority incidents, we reserve a set of ERVs for high priority incidents. Our second contribution is in modifying our optimisation model to reserve a subset of ERVs for high priority incidents while considering a minor modification to nearest available ERV dispatch strategy; and (3) Finally, using a real-world EMS data set, we experimentally demonstrate that our solution with a detailed dispatch strategy outperforms the existing benchmark approach. Moreover, due to the presence of few high priority incidents and significant spatio-temporal uncertainty associated with them, we show that a simple dispatch strategy with reserved ERVs outperforms the detailed dispatch strategy.
Journal of Reproduction and Development

The intimate association of cumulus cells with one another and with the oocyte is important for regulating oocyte meiotic arrest and resumption. The objective of this study was to determine the effects of heat stress on cumulus cell communication and functions that may be related to accelerated oocyte meiosis during early maturation. Bovine cumulusoocyte complexes underwent in vitro maturation for up to 6 h at thermoneutral control (38.5°C) or elevated (40.0, 41.0 or 42.0°C) temperatures. Gap junction communication between the cumulus cells and the oocyte was assessed using the fluorescent dye calcein after 4 h of in vitro maturation. Dye transfer was reduced in cumulus-oocyte complexes matured at 41.0°C or 42.0°C; transfer at 40.0°C was similar to control (P < 0.0001). Subsequent staining of oocytes with Hoechst revealed that oocytes matured at 41.0 or 42.0°C contained chromatin at more advanced stages of condensation. Maturation of cumulus-oocyte complexes at elevated temperatures reduced levels of active 5’ adenosine monophosphate activated kinase (P = 0.03). Heat stress exposure had no effect on active extracellular-regulated kinase 1/2 in oocytes (P = 0.67), associated cumulus cells (P = 0.60) or intact cumulus-oocyte complexes (P = 0.44). Heat-induced increases in progesterone production by cumulus-oocyte complexes were detected during the first 6 h of maturation (P = 0.001). Heat-induced alterations in gap junction communication and other cumulus-cell functions likely cooperate to accelerate bovine oocyte meiotic progression.
Identification of Maximal Running Intensities During Elite Hurling Match-Play.

Young, D, Malone, S, Beato, M, Mourot, L, and Coratella, G. Iidentification of maximal running intensities during elite hurling match-play. J Strength Cond Res XX(X): 000-000, 2018-The current study aimed to describe the duration-specific running intensities of elite hurling players during competition with respect to position using a rolling average method. Global positioning systems (10-Hz Viper; STATSport, Viper, Newry, Northern Ireland) were used to collect data from 36 elite hurling players across 2 seasons. Players were categorized according to playing positions (full-backs, half-backs, midfielders, half-forwards, and full-forwards). A total of 230 full match samples were obtained from 22 competitive games for analysis. The velocity-time curve was analyzed using a rolling average method, in which the maximum relative total distance (TD; m·min), high-speed running distance (HSR; m·min), and sprint distance (SD; m·min) intensities were calculated across 10 different rolling time durations (1-10 minutes) within each game. There were large to very large (effect sizes [ES] = 0.66-4.33) differences between 1 minute rolling averages and all other durations for TD, HSR, and SD. However, pairwise comparisons between 6 and 10 minutes for TD, HSR, and SD were smaller and more variable (ES = 0.07, trivial to ES = 0.85, moderate). Half-backs, midfielders, and half-forwards achieved a higher maximal relative TD and HSR in all duration-specific fields when compared with full-backs and full-forwards. No positional difference was observed in 1- and 2-minute durations for SD. Because the rolling average duration increased the maximum TD, HSR and SD running intensities decreased across all positions. These data provide knowledge of the peak running intensities of elite hurling competition and can be used to design training activities to sufficiently prepare players for these "worst-case scenarios. "