Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Yet Accurate Timing and Power Prediction of Artificial Neural Networks Deployed on Clock-Gated Multi-Core Platforms

When deploying Artificial Neural Networks (ANNs) onto multi-core embedded platforms, an intensive evaluation flow is necessary to find implementations that optimize resource usage, timing and power. ANNs require indeed significant amounts of computational and memory resources to execute, while embedded execution platforms offer limited resources with strict power budget. Concurrent accesses from processors to shared resources on multi-core platforms can lead to bottlenecks with impact on performance and power. Existing approaches show limitations to deliver fast yet accurate evaluation ahead of ANN deployment on the targeted hardware. In this paper, we present a modeling flow for timing and power prediction in early design stage of fully-connected ANNs on multi-core platforms. Our flow offers fast yet accurate predictions with consideration of shared communication resources and scalability in regards of the number of cores used. The flow is evaluated on real measurements for 42 mappings of 3 fully-connected ANNs executed on a clock-gated multi-core platform featuring two different communication modes: polling or interrupt-based. Our modeling flow predicts timing with accuracy and power with accuracy on the tested mappings for an average simulation time of 0.23 s for 100 iterations. We then illustrate the application of our approach for efficient design space exploration of ANN implementations.
A 16-Week Open-Label Study of the Effects of Treatment With Pimavanserin on Activities of Daily Living in Subjects With Parkinson’s Disease Psychosis

Abstract Introduction Accurate assessment of disability associated with Parkinson’s Disease Psychosis (PDP) is essential and has been poorly studied. Patients often have poor insight on impact of PDP on daily function. This phase 4 study is the first to evaluate the impact of pimavanserin on activities of daily living (ADL) in PDP patients. Methods Eligible PDP patients entered a 16-week single-arm, open-label study of oral pimavanserin (34 mg) taken once daily. Primary endpoint (modified Functional Status Questionnaire [mFSQ]) and secondary endpoints (MDS-UPDRS I & II; Schwab and England ADL; CGI-S, CGI-I, and PGI-I) were measured as change from baseline to Week 16 using mixed-effects model for repeated measures (MMRM) and least-squares means (LSM). Results 29 patients were treated with pimavanserin, of which 24 (82.8%) completed the study. Treated patients demonstrated significant improvements in LSM (SE) mFSQ score change from baseline to Week 12 (11.5 [2.44]) and Week 16 (14.0 [2.50]; both p<0.0001). Significant improvements (p<0.05) were also observed for all secondary outcomes at Week 16 (MDS-UPDRS Part I: -6.3 [0.97]; MDS-UPDRS Part II: -2.6 [0.98]; CGI-S: -1.5 [0.25]; CGI-I: 1.9 [0.17]; PGI-I: 2.0 [0.22], except for Schwab and England ADL. No new safety signals were observed. Conclusion Functional outcomes and psychosis measures improved in PDP patients treated with pimavanserin, with safety findings consistent with previous studies. Our findings highlight the positive effect of pimavanserin in improving ADLs in patients with PDP. Funding Acadia Pharmaceuticals Inc.
The Social Determinants of Loneliness During COVID-19: Personal, Community, and Societal Predictors and Implications for Treatment

Abstract Objective The COVID-19 pandemic dramatically altered social determinants of health including work, education, social connections, movement, and perceived control; and loneliness was commonly experienced. This longitudinal study examined how social determinants at the personal (micro), community (meso), and societal (macro) levels predicted loneliness during the pandemic. Methods Participants were 2056 Australian adults surveyed up to three times over 18 months in 2020 and 2021. Multi-level mixed-effect regressions were conducted predicting loneliness from social determinants at baseline and two follow-ups. Results Loneliness was associated with numerous micro determinants: male gender, lifetime diagnosis of a mental health disorder, experience of recent stressful event(s), low income, living alone or couples with children, living in housing with low natural light, noise, and major building defects. Lower resilience and perceived control over health and life were also associated with greater loneliness. At the meso level, reduced engagement with social groups, living in inner regional areas, and living in neighbourhoods with low levels of belongingness and collective resilience was associated with increased loneliness. At the macro level, increased loneliness was associated with State/Territory of residence. Conclusions Therapeutic initiatives must go beyond psychological intervention, and must recognise the social determinants of loneliness at the meso and macro levels.
Beyond replication: An exact replication study of Łodzikowski (2021)

Abstract Replication studies have become an emerging line of research in recent decades, including in computer-assisted language learning (CALL). Exact replication, which closely follows a study’s protocol, is rare as it is hard to recreate results without establishing a highly controlled environment. However, using data available online, we were able to conduct an exact replication of Łodzikowski’s (2021) study, which reported on the use of an allophonic transcription tool by 55 Polish learners of English. Allophonic features are used by native speakers to produce acoustic variants of the same phoneme. The original study offered learners an allophonic transcription tool, examined how they used it and considered its association with phonological awareness. This study extended the original research by addressing the limitations of its regression and transcription analyses. Our findings allowed us to offer several suggestions on (1) how an allophonic transcription tool can be better designed to help learners, (2) how CALL researchers can acquire more data for more useful research and (3) why more replication studies are needed in CALL.
Optimized power flow management based on Harris Hawks optimization for an islanded DC microgrid

Abstract This article presents an energy management system (EMS) in a DC microgrid (MG) operating in an islanded mode to control the power flow in the distribution network. The microgrid system considered in this research consists of distributed generation sources like a solar photovoltaic system, a fuel cell energy system, and an energy storage system controlled by an optimized energy management system. As the distributed energy sources used are primarily renewable, unpredictable weather conditions may cause irregular energy generation. These variations impact the power flow in the DC bus, making it challenging to maintain a supply and demand balance. Therefore, an intelligent energy management system using the Harris Hawks Optimization (HHO) is implemented to enhance the microgrid’s performance and efficiency. The HHO algorithm is based on the hunting nature of the Harris Hawks, and the EMS is developed to maintain the optimal power flow and to handle the constraints. The performance of the presented system is analyzed with the particle swarm optimization (PSO) based Proportional Integral (PI) controller in different operating scenarios to validate the effectiveness of the DC microgrid system.
Are Searches in OCR-generated Archives Trustworthy?

Abstract Digitised archives are revolutionary tools for research that, in a few seconds, generate results that earlier often took years to obtain. But do they provide all results for the terms searched for? The accuracy of searches was tested by performing sample searches of leading newspaper databases. The test revealed several weaknesses in the search process, including an average 18 percent error rate for single words in body text, and a far higher error rates for advertisements. Such high error rates encourage a critical look at the 20-year-old sector. Although these errors can be reduced by a re-digitation and with new improved OCR engines and new search algorithms, searches will nevertheless return manipulated results. In response, and to identify infringed bias and skewed representation, database owners need to provide thorough metadata to ensure source criticism.
Generative adversarial learning for optimizing ontology alignment

The edge computing in knowledge‐defined network (KDN) is a kind of distributed computing architecture, and the format of edge resources stored in different edge computing nodes are different, which yields the data heterogeneity problem and hampers the interaction between edge nodes. Ontology is considered as the solution of data heterogeneity on Semantic Web, and matching ontologies is a high‐efficiency method of addressing the data heterogeneity problem. Ontology meta‐matching investigates how to determine the optimal weights to aggregate multiple similarity measures to achieve high‐quality ontology alignment, which is a challenge about nonlinear mathematical problem in ontology matching domain. To face this challenge, unsupervised learning method such as generative adversarial network (GAN) becomes an effective methodology. GAN consists of two models of different targets that are opposed to each other in training to produce the final best result. To improve the GAN's efficiency, this work further proposes a GAN with simulated annealing algorithm (SA‐GAN), where the stagnation counter is introduced to accelerate GAN's the convergence speed. The experiment uses the famous benchmark in the ontology domain, and the comparisons with the advanced ontology matching systems shows that SA‐GAN is able to find high‐quality alignments to help build bridges between edge nodes on edge computing.
DNA Repair Function Scores for 2172 Variants in the BRCA1 Amino-Terminus

Single nucleotide variants are the most frequent type of sequence changes detected in the genome and these are frequently variants of uncertain significance (VUS). VUS are changes in DNA for which disease risk association is unknown. Thus, methods that classify the functional impact of a VUS can be used as evidence for variant interpretation. In the case of the breast and ovarian cancer specific tumor suppressor protein, BRCA1, pathogenic missense variants frequently score as loss of function in an assay for homology-directed repair (HDR) of DNA double-strand breaks. We previously published functional results using a multiplexed assay for 1056 amino acid substitutions residues 2-192 in the amino terminus of BRCA1. In this study, we have re-assessed the data from this multiplexed assay using an improved analysis pipeline. These new analysis methods yield functional scores for more variants in the first 192 amino acids of BRCA1, plus we report new results for BRCA1 amino acid residues 193-302. We now present the functional classification of 2172 BRCA1 variants in BRCA1 residues 2-302 using the multiplexed HDR assay. Comparison of the functional determinations of the missense variants with clinically known benign or pathogenic variants indicated 93% sensitivity and 100% specificity for this assay. The results from BRCA1 variants tested in this assay are a resource for clinical geneticists for evidence to evaluate VUS in BRCA1. AUTHOR SUMMARY Most missense substitutions in BRCA1 are variants of unknown significance (VUS), and individuals with a VUS in BRCA1 cannot know from genetic information alone whether this variant predisposes to breast or ovarian cancer. We apply a multiplexed functional assay for homology directed repair of DNA double strand breaks to assess variant impact on this important BRCA1 protein function. We analyzed 2172 variants in the amino-terminus of BRCA1 and demonstrate that variants that are known as pathogenic have a loss of function in the DNA repair assay. Conversely, variants that are known to be benign are functionally normal in the multiplexed assay. We suggest that these functional determinations of BRCA1 variants can be used to augment the information that clinical cancer geneticists provide to patients who have a VUS in BRCA1.
An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics

Multi-omic analyses contribute to understanding complex biological processes, but also to making reliable predictions about, for example, disease outcomes. Several linear joint dimensionality reduction methods exist, but recently neural networks are more commonly used to embed different-omics into the same non-linear manifold. We compared linear to non-linear joint embedding methods using bulk and single-cell data. For modality imputation, non-linear methods had a clear advantage. Comparisons in downstream supervised tasks lead to the following insights: First, concatenating the principal components of each modality is a competitive baseline for multi-modal prediction. If only one modality was available at test time, joint embeddings yielded significant performance improvements with respect to a unimodal predictor. Second, imputed omics profiles can be fed to classifiers trained on real data with limited performance drops. Overall, the product-of-experts architecture performed well in most tasks while a common encoder of concatenated modalities performed poorly.
RDF-Analytics: Interactive Analytics over RDF Knowledge Graphs

The formulation of structured queries in knowledge graphs is a challenging task that presupposes familiarity with the syntax of the query language and the contents of the knowledge graph. To alleviate this difficulty in this paper we introduce RDF-ANALYTICS , a novel system that enables plain users to formulate analytic queries over complex, i.e. not necessarily star-schema based, RDF knowledge graphs. To come up with an intuitive interface, we leverage the familiarity of users with Faceted Search (FS) systems, i.e. we extend FS with actions that enable users to formulate analytic queries, too. Distinctive characteristics of the approach is the ability to include arbitrarily long paths in the analytic query (accompanied with count information), interactive formulation of HAVING restrictions, the support of both Faceted Search (i.e. the locating of the desired resources in a faceted search manner) and analytic queries, and the ability to formulate nested analytic queries. Finally, we present the results of a preliminary task-based evaluation with users, which are very promising.
New Trends in Time Series Anomaly Detection

Anomaly detection is an important problem in data analytics with applications in many domains. In recent years, there has been an increasing interest in anomaly detection tasks applied to time series. In this tutorial, we take a holistic view on anomaly detection in time series, starting from the core definitions and tax-onomies related to time series and anomaly types, to an extensive description of the anomaly detection methods proposed by different communities in the literature. Then, we discuss shortcomings in traditional evaluation measures. Finally, we present new solutions to assess the quality of anomaly detection approaches and new benchmarks capturing diverse domains and applications.
Incremental Stream Query Merging

Stream Processing Engines (SPEs) execute long-running queries on unbounded data streams. They mainly focus on achieving high throughput and low-latency for a single query. This focus neglects the possible sharing opportunities of data and compute among multiple, long-running queries. Common approaches in batch-oriented systems mainly utilize simple and fast query merging algorithms based on syntactic similarities as the overhead of more extensive approaches would not amortize over the short query runtime. In contrast, streaming queries are continuous and long-running, such that extensive approaches, like taking the semantics of queries into account, may pay off. Furthermore, the long-running nature of streaming queries requires the merging of existing and newly arriving queries, unlike batch queries where merging is performed only among a batch of arriving queries. In this paper, we propose Incremental Stream Query Merging ( ISQM ), an end-to-end solution to identify and maintain sharing among thousands of stream queries. ISQM captures the semantic information of stream queries to enable merging even in the presence of syntactic differences. Our evaluation shows that ISQM exploits up to 65x more sharing opportunities than the naive baseline using hash-based signatures, scales linearly for thousands of queries, and saves a significant amount of resources compared to state-of-the-art approaches.
Daphnia as a model organism to probe biological responses to nanomaterials—from individual to population effects via adverse outcome pathways

The importance of the cladoceran Daphnia as a model organism for ecotoxicity testing has been well-established since the 1980s. Daphnia have been increasingly used in standardised testing of chemicals as they are well characterised and show sensitivity to pollutants, making them an essential indicator species for environmental stress. The mapping of the genomes of D. pulex in 2012 and D. magna in 2017 further consolidated their utility for ecotoxicity testing, including demonstrating the responsiveness of the Daphnia genome to environmental stressors. The short lifecycle and parthenogenetic reproduction make Daphnia useful for assessment of developmental toxicity and adaption to stress. The emergence of nanomaterials (NMs) and their safety assessment has introduced some challenges to the use of standard toxicity tests which were developed for soluble chemicals. NMs have enormous reactive surface areas resulting in dynamic interactions with dissolved organic carbon, proteins and other biomolecules in their surroundings leading to a myriad of physical, chemical, biological, and macromolecular transformations of the NMs and thus changes in their bioavailability to, and impacts on, daphnids. However, NM safety assessments are also driving innovations in our approaches to toxicity testing, for both chemicals and other emerging contaminants such as microplastics (MPs). These advances include establishing more realistic environmental exposures via medium composition tuning including pre-conditioning by the organisms to provide relevant biomolecules as background, development of microfluidics approaches to mimic environmental flow conditions typical in streams, utilisation of field daphnids cultured in the lab to assess adaption and impacts of pre-exposure to pollution gradients, and of course development of mechanistic insights to connect the first encounter with NMs or MPs to an adverse outcome, via the key events in an adverse outcome pathway. Insights into these developments are presented below to inspire further advances and utilisation of these important organisms as part of an overall environmental risk assessment of NMs and MPs impacts, including in mixture exposure scenarios.
Exploring environmental policy adoption enablers for indoor air quality management in higher educational institutions in South Africa

Purpose: This study seeks to investigate the extent to which the indoor air quality (IAQ) management of higher educational institutions (HEIs) in South Africa could be improved through the appropriate implementation of environmental policy adoption enablers. Multiple challenges have been documented to the improvement of IAQ standards in HEIs. However, an alternative to the management of IAQ is possible through environmental behavioral change. Research Methodology: A philosophical stance of post-positivism influences the adoption of a quantitative research approach for this study. Primary data on the views shared by various academic and administrative staff employed by HEIs across South Africa were collected using a closed-ended questionnaire survey. A literature review uncovered 16 influential environmental policy adoption enablers that could further the objective of the study. A four-phase data analytical approach was adopted to interpret the empirical data through screening and reliability assessment, together with descriptive and inferential statistical evaluations, to ascertain the influence held by the surveyed policy adoption enablers on improving the management of IAQ in HEIs. Results: The inferential statistical evaluation using exploratory factor analysis revealed three crucial environmental policy adoption metrical approaches (stakeholder dialogue, institutional commitment, and policy composition) to the management of IAQ in HEIs. Discussion: From a practical perspective, the administrative council of HEIs could consider the identified policy adoption enablers as a catalyst for pro-environmental behavior and the management of IAQ in all respective institutions. Theoretically, this study contributes to the body of knowledge by providing factors associated with environmental policy adoptions for IAQ management and laying the groundwork for future research in environmental behavior, this has been lacking in previous IAQ studies and current environmental management discourse. As an instrumental enabler, the identified policy adoption approaches could inform any existing or new institutional policy adoption initiative aimed at improving current individuals’ perception of workplace comfort, satisfaction, and performance directly associated with their indoor environmental conditions.
Heterologous expression of MirMAN enhances root development and salt tolerance in Arabidopsis

Introduction β-Mannanase is a plant cell wall remodeling enzyme involved in the breakdown of hemicellulose and plays an important role in growth by hydrolyzing the mannan-like polysaccharide, but its function in adaptation to salt stress has been less studied. Methods Based on cloned the mannanase (MAN) gene from Mirabilis jalapa L. , the study was carried out by heterologously expressing the gene in Arabidopsis thaliana, and then observing the plant phenotypes and measuring relevant physiological and biochemical indicators under 150 mM salt treatment. Results and discussion The results indicate that MirMAN is a protein with a glycohydrolase-specific structural domain located in the cell wall. We first found that MirMAN reduced the susceptibility of transgenic Arabidopsis thaliana to high salt stress and increased the survival rate of plants by 38%. This was corroborated by the following significant changes, including the reduction in reactive oxygen species (ROS) levels, increase in antioxidant enzyme activity, accumulation of soluble sugars and increase of the expression level of RD29 in transgenic plants. We also found thatthe heterologous expression of MirMAN promoted root growth mainly by elongating the primary roots and increasing the density of lateral roots. Meanwhile, the expression of ARF7, ARF19, LBD16 and LBD29 was up-regulated in the transgenic plants, and the concentration of IAA in the roots was increased. Those results indicate that MirMAN is involved in the initiation of lateral root primordia in transgenic plants through the IAA-ARF signalling pathway. In conclusion, MirMAN improves plant salt tolerance not only by regulating ROS homeostasis, but also by promoting the development of lateral roots. Reflecting the potential of the MirMAN to promote root plastic development in adaptation to salt stress adversity.