Miracle-dz/UniFAR-FTUs huggingface dataset:
Economic Determinants of the Nominal Treasury Yield Curve

We study the effect of different types of macroeconomic impulses on the nominal yield curve. We employ two distinct approaches to identifying economic shocks in VARs. Our first approach uses a structural VAR due to Gali (1992). Our second strategy identifies fundamental impulses from alternative empirical measures of economic shocks proposed in the literature. We find that most of the long-run variability of interest rates of all maturities is driven by macroeconomic impulses. Shocks to preferences for current consumption consistently induce large, persistent, and statistically significant shifts in the level of the yield curve. In contrast, technology shocks induce weaker and less robust patterns of interest rate responses, since they move real rates and expected inflation in opposite directions. Monetary policy shocks are the only macroeconomic shocks with a consistent and significant impact on the slope of the yield curve. We find no evidence that fiscal policy shocks induce any significant interest rate responses.
A Method of Feature Extraction for EEG Signals Recognition Using ROC Curve

The feature extraction of Electroencephalograph (EEG) signals plays an important role in mental task recognition of brain-computer interaction (BCI). In this study, a novel method of EEG signal feature extraction is proposed using techniques of fast Fourier transform (FFT) and receiver operating characteristic (ROC) curve. In the proposed method, the raw EEG data was transformed into power spectrum of FFT at first, and then to find frequencies decided by area under curve (AUC) of ROC between the value of spectrums of different classes of metal tasks. Experiment results using benchmark data of EEG signals showed the effectiveness of the proposed feature extraction method when support vector machine (SVM) was used as a classifier.
Study of genetic variation among soybean [Glycine Max (L.) Merrill] accessions in CID values

A set of 91 diverse soybean accessions were analysed for 10 morphological traits with special reference to water use efficiency using carbon isotope discrimination (CID) technique. The coefficient of variation for CID values was found to be 3.36%. There is negative correlation between Δ13C values (CID values) and WUE. The genotypes which registered lower values of Δ13C are more water efficient than the genotypes with higher Δ13C values. The range of Δ13C values observed in this experiment are slightly lower than that reported in sugar beet (Δ13C 17.66‰ to 22.96‰), (Rajabi et al., 2009). Genotype UPSL309 has highest CID value (22.91‰) indicating low WUE and genotype DS9813 has lowest CID value (19.95 ‰) with high WUE. In present study randomly classified genotypes as high WUE (CID less than 20.5 ‰), medium WUE (CID between 20.6 to 22.4 ‰) and low WUE (CID above 22.5 ‰). There were 7 genotypes which were found to show high WUE, and 13 exhibited low WUE whereas, rests of the 71 genotypes were medium in WUE.
Growth performance of an acidophilic microalga cultivated in treated wastewater

a Department of Fisheries Engineering, Santa Catarina State University, UDESC, Rua Cel. Fernandes Martins no270, Progresso, 88.780-000, Laguna SC, Brasil. e-mail: fabio.neves@udesc.br* (corresponding author) b Department of Aquaculture, Federal University of Santa Catarina, UFSC, Trindade, 88.040-900, Florianópolis, SC, Brasil c Department of Botany, Federal University of Santa Catarina, UFSC, Trindade, 88.040-900, Florianópolis, SC, Brasil d Department of Sanitary and Environmental Engineering, Federal University of Santa Catarina, UFSC, Rua Delfino Conti no261, Trindade, 88.040-970, Florianópolis, SC, Brasil Abstract
Automating Targeted Property-Based Testing

Targeted property-based testing is an enhanced form of property-based testing (PBT) where the input generation is guided by a search strategy instead of being random, thereby combining the strengths of QuickCheck-like and search-based testing techniques. To use it, however, the user currently needs to specify a search strategy and also supply all ingredients that the search strategy requires. This is often a laborious process and makes targeted PBT less attractive than its random counterpart. In this paper, we focus on simulated annealing, the default search strategy of our tool, and present a technique that automatically creates all the ingredients that targeted PBT requires starting from only a random generator. Our experiments, comparing the automatically generated ingredients to fine-tuned manually written ones, show that the performance that one obtains is sufficient and quite competitive in practice.
Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers

The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. The HEMS incorporates a demand response (DR) tool that shifts and curtails demand to improve home energy consumption. This system commonly creates optimal consumption schedules by considering several factors, such as energy costs, environmental concerns, load profiles, and consumer comfort. With the deployment of smart meters, performing load control using the HEMS with DR-enabled appliances has become possible. This paper provides a comprehensive review on previous and current research related to the HEMS by considering various DR programs, smart technologies, and load scheduling controllers. The application of artificial intelligence for load scheduling controllers, such as artificial neural network, fuzzy logic, and adaptive neural fuzzy inference system, is also reviewed. Heuristic optimization techniques, which are widely used for optimal scheduling of various electrical devices in a smart home, are also discussed.
Gene Expression Profiling Confirms the Dosage-Dependent Additive Neuroprotective Effects of Jasminoidin in a Mouse Model of Ischemia-Reperfusion Injury

Recent evidence demonstrates that a double dose of Jasminoidin (2·JA) is more effective than Jasminoidin (JA) in cerebral ischemia therapy, but its dosage-effect mechanisms are unclear. In this study, the software GeneGo MetaCore was used to perform pathway analysis of the differentially expressed genes obtained in microarrays of mice belonging to four groups (Sham, Vehicle, JA, and 2·JA), aiming to elucidate differences in JA and 2·JA's dose-dependent pharmacological mechanism from a system's perspective. The top 10 enriched pathways in the 2·JA condition were mainly involved in neuroprotection (70% of the pathways), apoptosis and survival (40%), and anti-inflammation (20%), while JA induced pathways were mainly involved in apoptosis and survival (60%), anti-inflammation (20%), and lipid metabolism (20%). Regarding shared pathways and processes, 3, 1, and 3 pathways overlapped between the Vehicle and JA, Vehicle and 2·JA, and JA and 2·JA conditions, respectively; for the top ten overlapped processes these numbers were 3, 0, and 4, respectively. The common pathways and processes in the 2·JA condition included differentially expressed genes significantly different from those in JA. Seven representative pathways were only activated by 2·JA, such as Gamma-Secretase regulation of neuronal cell development. Process network comparison indicated that significant nodes, such as alpha-MSH, ACTH, PKR1, and WNT, were involved in the pharmacological mechanism of 2·JA. Function distribution was different between JA and 2·JA groups, indicating a dosage additive mechanism in cerebral ischemia treatment. Such systemic approach based on whole-genome multiple pathways and networks may provide an effective and alternative approach to identify alterations underlining dosage-dependent therapeutic benefits of pharmacological compounds on complex disease processes.
Role of the light source position in freely falling hoverflies' stabilization performances

The stabilization of plummeting hoverflies was filmed and analysed in terms of their wingbeat initiation times as well as the crash and stabilization rates. The flies experienced near-weightlessness for a period of time that depended on their ability to counteract the free fall by triggering their wingbeats. In this paradigm, hoverflies' flight stabilization strategies were investigated here for the first time under two different positions of the light source (overhead and bottom lighting). The crash rates were higher in bottom lighting conditions than with top lighting. In addition, adding a texture to the walls reduced the crash rates only in the overhead lighting condition. The position of the lighting also significantly affected both the stabilization rates and the time taken by the flies to stabilize, which decreased and increased under bottom lighting conditions, respectively, whereas textured walls increased the stabilization rates under both lighting conditions. These results support the idea that flies may mainly base their flight control strategy on visual cues and particularly that the light distribution in the visual field may provide reliable, efficient cues for estimating their orientation with respect to an allocentric reference frame. In addition, the finding that the hoverflies' optic flow-based motion detection ability is affected by the position of the light source in their visual field suggests the occurrence of interactions between movement perception and this visual vertical perception process.
Working in small-scale, homelike dementia care: effects on staff burnout symptoms and job characteristics. A quasi-experimental, longitudinal study

Aim This paper reports on a quasi-experimental, longitudinal study on the effects of working in a new type of dementia care facility (i.e. small-scale living facilities) on staff burnout symptoms and job characteristics (job autonomy, social support, physical demands and workload). Methods It is hypothesised that nursing staff working in small-scale facilities experience fewer burnout symptoms, more autonomy and social support, and fewer symptoms of physical demands and workload compared with staff in regular wards. Two types of long-term institutional nursing care settings were included: 28 houses in small-scale living facilities and 21 regular psychogeriatric wards in nursing homes. At baseline and at follow-ups after 6 and 12 months nursing staff were assessed by means of self-report questionnaires. In total, 305 nursing staff members were included in the study, 114 working in small-scale living facilities (intervention group) and 191 in regular wards (control group). Results No overall effects on burnout symptoms were detected. Significantly fewer physical demands and lower workload were experienced by staff working in small-scale living facilities compared with staff in regular wards. They also experienced more job autonomy. No significant effect was found for overall social support in the total group. Conclusions This study suggests positive effects of the work environment on several work characteristics. Organisational climate differs in the two conditions, which might account for our results. This may influence nursing staff well-being and has important implications for nursing home managers and policy makers. Future studies should enhance our understanding of the influence of job characteristics on outcomes.
Microsatellite Based Genotyping of the Helianthus annuus L.

Genetic diversity among 35 genotypes of sunflower was determined using microsatellites (SSR markers) in the present study. Thirty five genotypes (3 CMS A lines and 32 R lines) were subjected to 34 SSR markers. Thirty four primers revealed 72 alleles with average 2.20 alleles per locus. PIC value for the primer pairs varied from 0.12 (ORS 591) to 0.71 (ORS 317) with average PIC value of 0.42. The phylogenetic tree divided genotypes into two main clusters viz. cluster 1 and cluster 2, which further are divided into four sub-clusters. Range of genetic distances based on SSR marker analysis varied from 0.32 (6A × P188R) 0.73 (11A × P174R). The distances measured based on SSR primers showed that the CMS 11A was most divergent genotype with a mean genetic dissimilarity coefficient of 0.59. Immense genetic diversity was observed in the present study, which can be used for further designing hybridization programme.
Birds of a feather: the geographic interconnection of Australian universities on Twitter

Purpose – Regardless of their virtual nature, research suggests that social media networks are still influenced by geography. The purpose of this paper is to investigate the connections between Australian universities on the Twitter social media system. Design/methodology/approach – This research employs network analysis and visualisation to characterise the connections between Australian universities on Twitter. Findings – A strong relationship to geography, both at the intra-state level and the inter-state level, was observed in the connections between Australian universities on Twitter. A relationship between number of followers and time since joining Twitter was also observed. Research limitations/implications – The research presented is limited to Australian universities only and represents a snapshot in time only. Practical implications – Australian universities have the opportunity to reach beyond the geographically restricted connections observed here, to actively seek new audiences, and to realis.
LRP5 regulates the expression of STK40, a new potential target in triple-negative breast cancers

Triple-negative breast cancers (TNBCs) account for a large proportion of breast cancer deaths, due to the high rate of recurrence from residual, resistant tumor cells. New treatments are needed, to bypass chemoresistance and improve survival. The WNT pathway, which is activated in TNBCs, has been identified as an attractive pathway for treatment targeting. We analyzed expression of the WNT coreceptors LRP5 and LRP6 in human breast cancer samples. As previously described, LRP6 was overexpressed in TNBCs. However, we also showed, for the first time, that LRP5 was overexpressed in TNBCs too. The knockdown of LRP5 or LRP6 decreased tumorigenesis in vitro and in vivo, identifying both receptors as potential treatment targets in TNBC. The apoptotic effect of LRP5 knockdown was more robust than that of LRP6 depletion. We analyzed and compared the transcriptomes of cells depleted of LRP5 or LRP6, to identify genes specifically deregulated by LRP5 potentially implicated in cell death. We identified serine/threonine kinase 40 (STK40) as one of two genes specifically downregulated soon after LRP5 depletion. STK40 was found to be overexpressed in TNBCs, relative to other breast cancer subtypes, and in various other tumor types. STK40 depletion decreased cell viability and colony formation, and induced the apoptosis of TNBC cells. In addition, STK40 knockdown impaired growth in an anchorage-independent manner in vitro and slowed tumor growth in vivo. These findings identify the largely uncharacterized putative protein kinase STK40 as a novel candidate treatment target for TNBC.
Using Morphological Knowledge in Open-Vocabulary Neural Language Models

Languages with productive morphology pose problems for language models that generate words from a fixed vocabulary. Although character-based models allow any possible word type to be generated, they are linguistically naïve: they must discover that words exist and are delimited by spaces—basic linguistic facts that are built in to the structure of word-based models. We introduce an open-vocabulary language model that incorporates more sophisticated linguistic knowledge by predicting words using a mixture of three generative processes: (1) by generating words as a sequence of characters, (2) by directly generating full word forms, and (3) by generating words as a sequence of morphemes that are combined using a hand-written morphological analyzer. Experiments on Finnish, Turkish, and Russian show that our model outperforms character sequence models and other strong baselines on intrinsic and extrinsic measures. Furthermore, we show that our model learns to exploit morphological knowledge encoded in the analyzer, and, as a byproduct, it can perform effective unsupervised morphological disambiguation.
Systematic Review and Meta-Analysis Effects of tea intake on blood pressure : a meta-analysis of randomised controlled trials

The effect of tea intake on blood pressure (BP) is controversial. We performed a meta-analysis of randomised controlled trials to determine the changes in systolic and diastolic BP due to the intake of black and green tea. A systematic search was conducted in MEDLINE, EMBASE and the Cochrane Controlled Trials Register up to May 2014. The weighted mean difference was calculated for net changes in systolic and diastolic BP using fixed-effects or random-effects models. Previously defined subgroup analyses were performed to explore the influence of study characteristics. A total of twenty-five eligible studies with 1476 subjects were selected. The acute intake of tea had no effects on systolic and diastolic BP. However, after long-term tea intake, the pooled mean systolic and diastolic BP were lower by 21·8 (95 % CI 22·4, 21·1) and 21·4 (95 % CI 22·2, 20·6) mmHg, respectively. When stratified by type of tea, green tea significantly reduced systolic BP by 2·1 (95 % CI 22·9, 21·2) mmHg and decreased diastolic BP by 1·7 (95 % CI 22·9, 20·5) mmHg, and black tea showed a reduction in systolic BP of 1·4 (95 % CI 22·4, 20·4) mmHg and a decrease in diastolic BP of 1·1 (95 % CI 21·9, 20·2) mmHg. The subgroup analyses showed that the BP-lowering effect was apparent in subjects who consumed tea more than 12 weeks (systolic BP 22·6 (95 % CI 23·5, 21·7) mmHg and diastolic BP 22·2 (95 % CI 23·0, 21·3) mmHg, both P,0·001). The present findings suggest that long-term ($12 weeks) ingestion of tea could result in a significant reduction in systolic and diastolic BP.
Neural Tensor Networks with Diagonal Slice Matrices

Although neural tensor networks (NTNs) have been successful in many NLP tasks, they require a large number of parameters to be estimated, which often leads to overfitting and a long training time. We address these issues by applying eigendecomposition to each slice matrix of a tensor to reduce its number of paramters. First, we evaluate our proposed NTN models on knowledge graph completion. Second, we extend the models to recursive NTNs (RNTNs) and evaluate them on logical reasoning tasks. These experiments show that our proposed models learn better and faster than the original (R)NTNs.
Betweenness Centrality Revisited on Four Processors

The betweenness centrality measure has been widely adopted in various graph analytics applications, such as community detection and brain network analysis. Due to the high intensity of BC computation and rapid data growth, there have been a number of studies on parallel BC computation, either on CPUs or GPUs. However, there has not been a comprehensive comparative study on the BC algorithm on different processors. In this paper, we revisit shared-memory parallel BC computation on four kinds of processors, including multi-core CPUs, many-core GPUs, and two generations of Intel MIC processors. We find that, with suitable parallelization strategies and data-oriented optimizations, commodity multi-core CPUs are the fastest, followed by the second generation MIC. These two processors are faster than the state-of-the-art GPU implementations across all kinds of graphs. In comparison, the GPU outperforms the first generation MIC only on small-diameter graphs and is the slowest on the other kinds of graphs.
A Neural Field Model of Word Recognition

We show how temporal and spatial information can be represented as stable patterns in a dynamical system. We describe a model in which category perception arises from the incremental recognition of temporal patterns from sequences of inputs and this is accomplished by decoding a pool of recurrently connected artificial neurons which is called a neural field. In an example application, we use these patterns to identify a set of words which share the word onset represented by the input sequence, consistent with the Marslen-Wilson COHORT model of word recognition. Similarly, we evaluate the extent to which information contained in the bottom-up sensory signal can be used to determine word boundaries. We suggest it is plausible that a neural field offers a naturalistic explanation of how perception arises in word processing.
LncRNAnet: long non‐coding RNA identification using deep learning

Motivation Long non‐coding RNAs (lncRNAs) are important regulatory elements in biological processes. LncRNAs share similar sequence characteristics with messenger RNAs, but they play completely different roles, thus providing novel insights for biological studies. The development of next‐generation sequencing has helped in the discovery of lncRNA transcripts. However, the experimental verification of numerous transcriptomes is time consuming and costly. To alleviate these issues, a computational approach is needed to distinguish lncRNAs from the transcriptomes. Results We present a deep learning‐based approach, lncRNAnet, to identify lncRNAs that incorporates recurrent neural networks for RNA sequence modeling and convolutional neural networks for detecting stop codons to obtain an open reading frame indicator. lncRNAnet performed clearly better than the other tools for sequences of short lengths, on which most lncRNAs are distributed. In addition, lncRNAnet successfully learned features and showed 7.83%, 5.76%, 5.30% and 3.78% improvements over the alternatives on a human test set in terms of specificity, accuracy, F1‐score and area under the curve, respectively. Availability and implementation Data and codes are available in http://data.snu. ac.kr/pub/lncRNAnet.
The Timing of Lexical Memory Retrievals in Language Production

This paper explores the time course of lexical memory retrieval by modeling fluent language production. The duration of retrievals is predicted using the ACT-R cognitive architecture. In a large-scale observational study of a spoken corpus, we find that language production at a time point preceding a word is sped up or slowed down depending on activation of that word. This computational analysis has consequences for the theoretical model of language production. The results point to interference between lexical and phonological stages as well as a quantifiable buffer for lexical information that opens up the possibility of non-sequential retrievals.
Meaner on Mobile: Incivility and Impoliteness in Communicating Contentious Politics on Sociotechnical Networks

This study explores the nature of how mobile social media may potentially be sharpening the tenor of communicating online. Specifically, randomized representative Twitter data were collected for several controversial issues, and then examined to determine the extent to which mobile or web-based content tends more toward greater incivility and impoliteness. Additional analyses further model how certain dialogic features, such as explicitly mentioning other users and retweeting others’ posts, positively relate to hostility in the discourse. Building on the basis of technological affordances and user negotiation in digitally mediated environments, this study contributes to a better understanding of how individuals express themselves on mobile devices as these are rapidly becoming normalized modes for communicating with one another online.