Miracle-dz/UniFAR-FTUs huggingface dataset:
ProM 6: The Process Mining Toolkit

Process mining has been around for a decade, and it has proven to be a very fertile and successful research field. Part of this success can be contributed to the ProM tool, which combines most of the existing process mining techniques as plug-ins in a single tool. ProM 6 removes many limitations that existed in the previous versions, in particular with respect to the tight integration between the tool and the GUI. ProM 6 has been developed from scratch and uses a completely redesigned architecture. The changes were driven by many real-life applications and new insights into the design of process analysis software. Furthermore, the introduction of XESame in this toolkit allows for the conversion of logs to the ProM native format without programming.
A fault-tolerant PMSG drive for wind turbine applications with minimal increase of the hardware requirements

Fault-tolerant permanent magnet synchronous generator (PMSG) drives for wind turbine applications play a major role in improving reliability and availability levels, since power converters are very prone to fail. In this paper, a fault-tolerant converter with the ability to handle power switch open-circuit faults is addressed. The main concern of proposed converter topology is the minimization of the hardware requirements, leading to a low increase of the system cost. Firstly, the employed fault diagnostic technique does not require additional measurements, nor high computational effort. Secondly, the circuit topology reconfiguration implies a minimum number of extra components as well as minimal oversizing of the standard ones. Accordingly, a four-switch three-phase converter with the dc bus midpoint connected to the transformer neutral point and a three-switch three-phase rectifier are adopted for post-fault operation of the grid- and PMSG-side converters, respectively. Vector control strategies are proposed for both converters under analysis, focusing the issues of capacitor voltages balancing and torque ripple minimization. The performance of the proposed fault-tolerant PMSG drive is analyzed by means of experimental results.
Comparison of Bi- and Tri-Linear PLS Models for Variable Selection in Metabolomic Time-Series Experiments

Metabolomic studies with a time-series design are widely used for discovery and validation of biomarkers. In such studies, changes of metabolic profiles over time under different conditions (e.g., control and intervention) are compared, and metabolites responding differently between the conditions are identified as putative biomarkers. To incorporate time-series information into the variable (biomarker) selection in partial least squares regression (PLS) models, we created PLS models with different combinations of bilinear/trilinear X and group/time response dummy Y. In total, five PLS models were evaluated on two real datasets, and also on simulated datasets with varying characteristics (number of subjects, number of variables, inter-individual variability, intra-individual variability and number of time points). Variables showing specific temporal patterns observed visually and determined statistically were labelled as discriminating variables. Bootstrapped-VIP scores were calculated for variable selection and the variable selection performance of five PLS models were assessed based on their capacity to correctly select the discriminating variables. The results showed that the bilinear PLS model with group × time response as dummy Y provided the highest recall (true positive rate) of 83–95% with high precision, independent of most characteristics of the datasets. Trilinear PLS models tend to select a small number of variables with high precision but relatively high false negative rate (lower power). They are also less affected by the noise compared to bilinear PLS models. In datasets with high inter-individual variability, bilinear PLS models tend to provide higher recall while trilinear models tend to provide higher precision. Overall, we recommend bilinear PLS with group x time response Y for variable selection applications in metabolomics intervention time series studies.
SETD1A Methyltransferase Is Physically and Functionally Linked to the DNA Damage Repair Protein RAD18*

Endogenous immunoprecipitation was used to investigate protein interactions of SETD1A complex subunits. An unexpected interaction with the DNA damage protein RAD18 was confirmed for SETD1A but not for other subunits. SETD1A and RAD18 evoked a DNA damage repair phenotype and influenced each other's mRNA and protein expression. Graphical Abstract Highlights Protein interaction screen of SETD1A/COMPASS complex subunits. Unexpected interaction with DNA damage protein RAD18 was confirmed for SETD1A, but not for other subunits. SETD1A and/or RAD18 influence each other's mRNA and protein expression levels, and disruption of either gene elicits a similar DNA damage sensitivity phenotype. SETD1A is a SET domain-containing methyltransferase involved in epigenetic regulation of transcription. It is the main catalytic component of a multiprotein complex that methylates lysine 4 of histone H3, a histone mark associated with gene activation. In humans, six related protein complexes with partly nonredundant cellular functions share several protein subunits but are distinguished by unique catalytic SET-domain proteins. We surveyed physical interactions of the SETD1A-complex using endogenous immunoprecipitation followed by label-free quantitative proteomics on three subunits: SETD1A, RBBP5, and ASH2L. Surprisingly, SETD1A, but not RBBP5 or ASH2L, was found to interact with the DNA damage repair protein RAD18. Reciprocal RAD18 immunoprecipitation experiments confirmed the interaction with SETD1A, whereas size exclusion and protein network analysis suggested an interaction independent of the main SETD1A complex. We found evidence of SETD1A and RAD18 influence on mutual gene expression levels. Further, knockdown of the genes individually showed a DNA damage repair phenotype, whereas simultaneous knockdown resulted in an epistatic effect. This adds to a growing body of work linking epigenetic enzymes to processes involved in genome stability.
Synaptic ‘Noise’: Experiments, Computational Consequences and Methods to Analyse Experimental Data

In the cerebral cortex of awake animals, neurons are subject to a tremendous fluctuating activity mostly of synaptic origin and termed “synaptic noise”. S ynaptic noise is the dominant source of membrane potential fluctuations in neurons and can have a s trong influence on their integrative properties. We review here the experimental measurements o f synaptic noise, and its modeling by conductance-based stochastic processes. We next review the consequences of synaptic noise on neuronal integrative properties, as predicted by comput ational models and investigated experimentally using dynamic-clamp. We also review analysis meth ods, such as spike-triggered average or conductance analysis, which are derived from the modelin g of synaptic noise by stochastic processes. These different approaches aim at understanding th e integrative properties of neocortical neurons in the intact brain.
Fast two-party secure computation with minimal assumptions

Almost all existing protocols for secure two-party computation require a specific hardness assumption, such as DDH, discrete logarithm, or a random oracle, even after assuming oracle access to the oblivious transfer functionality for their correctness and/or efficiency. We propose and implement a Yao-based protocol that is secure against malicious adversaries and enjoys the following benefits: it requires the minimal hardness assumption, i.e., OTs; it uses 10 rounds of communication plus OT rounds; it has the optimal overhead complexity (for an approach that uses the circuit-level cut-and-choose technique); and it is embarrassingly parallelizable in the sense that each circuit can be processed in a pipelined manner, and all circuits can be processed in parallel. To achieve these properties, we describe novel solutions for the three main obstacles for achieving security against malicious adversaries in a cut-and-choose garbled-circuit protocol. We propose an efficient proof to establish the generator's output authenticity; we suggest the use of an auxiliary circuit that computes a hash to ensure the generator's input consistency; and we advance the performance of Pinkas and Lindell's state-of-the-art approach for handling the selective failure attack. Not only does our protocol require weaker cryptographic assumptions, but our implementation of this protocol also demonstrates a several factor improvement over the best prior work which relies on specific number-theoretic assumptions. Thus, we show that performance does not require specific algebraic assumptions.
Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection

We present a matrix factorization model inspired by challenges we encountered while working on the Xbox movies recommendation system. The item catalog in a recommender system is typically equipped with meta-data features in the form of labels. However, only part of these features are informative or useful with regard to collaborative filtering. By incorporating a novel sparsity prior on feature parameters, the model automatically discerns and utilizes informative features while simultaneously pruning non-informative features. The model is designed for binary feedback, which is common in many real-world systems where numeric rating data is scarce or non-existent. However, the overall framework is applicable to any likelihood function. Model parameters are estimated with a Variational Bayes inference algorithm, which is robust to over-fitting and does not require cross-validation and fine tuning of regularization coefficients. The efficacy of our method is illustrated on a sample from the Xbox movies dataset as well as on the publicly available MovieLens dataset. In both cases, the proposed solution provides superior predictive accuracy, especially for long-tail items. We then demonstrate the feature selection capabilities and compare against the common case of simple Gaussian priors. Finally, we show that even without features, our model performs better than a baseline model trained with the popular stochastic gradient descent approach.
1 On agency and action

To date writers in the field of organizational knowledge appear to have been reluctant to engage directly with discourse theory, perhaps through a concern that introducing discourse means that knowledge and knowing will disappear from view. However, our aim in this paper is to suggest that engaging with notions of discourse can usefully enable us to shed light on aspects of organizational knowledge that have hitherto been hidden in the shadows. We illustrate this through considering two oft-debated issues within the field of organizational discourse agency and action. We do not suggest that this will produce quick or easy answers nor that discourse theory provides the ‘best’ way for thinking about organizational knowledge. Rather, in common with recent calls (e.g. Contu and Willmott, 2003, Hull, 1999, Alvesson, 2004), we believe that it will enable us to engage in debates that are important for the development of both theory and research strategies in the field. We offer this paper then, as an initial foray into these debates, with the hope that it will prompt others to join us in the discussion. Academic Track: The role of language and discourse in the creation and circulation of knowledge
Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.
Heat shock protein DNAJA1 stabilizes PIWI proteins to support regeneration and homeostasis of planarian Schmidtea mediterranea

PIWI proteins are key regulators of germline and somatic stem cells throughout different evolutionary lineages. However, how PIWI proteins themselves are regulated remains largely unknown. To identify candidate proteins that interact with PIWI proteins and regulate their stability, here we established a yeast two-hybrid (Y2H) assay in the planarian species Schmidtea mediterranea. We show that DNAJA1, a heat shock protein 40 family member, interacts with the PIWI protein SMEDWI-2, as validated by the Y2H screen and co-immunoprecipitation assays. We found that DNAJA1 is enriched in planarian adult stem cells, the nervous system, and intestinal tissues. DNAJA1-knockdown abolished planarian regeneration and homeostasis, compromised stem cell maintenance and PIWI-interacting RNA (piRNA) biogenesis, and deregulated SMEDWI-1/2 target genes. Mechanistically, we observed that DNAJA1 is required for the stability of SMEDWI-1 and SMEDWI-2 proteins. Furthermore, we noted that human DNAJA1 binds to Piwi-like RNA-mediated gene silencing 1 (PIWIL1) and is required for PIWIL1 stability in human gastric cancer cells. In summary, our results reveal not only an evolutionarily conserved functional link between PIWI and DNAJA1 that is essential for PIWI protein stability and piRNA biogenesis, but also an important role of DNAJA1 in the control of proteins involved in stem cell regulation.
Short-Time β-Alanine Supplementation on the Acute Strength Performance after High-Intensity Intermittent Exercise in Recreationally Trained Men

(1) Background: We investigated the effects of 28 days of beta-alanine (β-alanine) supplementation on the acute interference effect of high-intensity intermittent exercise (HIIE) on lower-body resistance exercise performance, body composition, and strength when combined with a resistance training program. (2) Methods: Twenty-two males were randomized into: β-alanine supplementation (6.4 g/day) or placebo (6.4 g/day maltodextrin) during 28 days. Total body water, intracellular and extracellular water, fat-free mass (FFM), and fat mass were assessed using bioelectrical impedance. Participants performed 5000-m HIIE (1:1 effort and rest ratio) followed by resistance exercise (four sets of 80% at 45° leg press until muscular failure) at baseline and after 28 days. The resistance training program consisted of three sets of 10 to 12 RM with 90 s of rest, four days per week. (3) Results: For the post-HIIE leg press volume, higher values were observed post-training than pre-training, but no group x time interaction was observed. There was a non-significant trend for an interaction in the FFM change (β-alanine = 2.8% versus placebo = 1.0%, p = 0.072). (4) Conclusion: Twenty-eight days of β-alanine supplementation did not prevent acute strength loss during resistance exercise after high-intensity interval exercise, nor increase strength or hypertrophic adaptations associated with resistance training.
Classification of Different Indian Songs Based on Fractal Analysis

In this paper some of the prevailing classifications of Indian songs are quantified by measuring their fractal dimension. Samples were collected from three categories: Classical, Semiclassical, and Light. After appropriate processing, the samples were converted into time series datasets and their fractal dimension was computed. Based on these results, an online method for classification is offered.
Power-Aware Hierarchical Epidemics in Peer-to-Peer Systems

Epidemic or gossip-based mechanisms are preferred in several distributed protocols for their ease of deployment, simplicity, robustness against failures, load-balancing and limited resource usage. In flat neighborhood epidemics, peers have similar responsibilities and all participate in gossiping via neighboring peers. We have proposed an energy cost model for a generic peer using flat neighborhood epidemics, and examined the effect of protocol parameters to characterize energy consumption. Although it has been shown that a peers power consumption amount is independent of population size, peers always need to be active to process incoming gossip messages. In this study, we consider power awareness features of flat and hierarchical epidemics in P2P systems, and propose a power-aware hierarchical epidemic approach with its energy cost model and analysis. In this adaptive approach, only a subset of peer population is active in gossiping by forming an overlay, so that the other peers can switch to idle state. It also allows data aggregation that can be utilized to reduce gossip message size. As a case study for epidemic protocol, we use our approach and simulation model for frequent item set discovery in unstructured P2P networks.
A Cost-Benefit Approach to Fault Tolerant Communication and Information Access

Fault tolerant information access and communication are becoming crucial for almost every computer application. Even traditional computer applications that did not require network connectivity just a few years ago, rely on the network for their smooth execution. This trend will only increase with the proliferation of non-traditional networked computing devices. At the same time, attacks on the network have become more sophisticated and harder to contain. The distributed nature of network control further complicates the problem. Traditional network protocols were developed to handle simple adversaries, such as network problems resulting from normal operational events. For example, link congestion can lead to buffer overflow, which then causes message omission. Another example is infrequent link downtime that may lead to short-term network partitions. While existing protocols can handle such problems, they will fail miserably under slightly more sophisticated attacks. We develop the theory and algorithms required to overcome strong network faults and attacks, while providing theoretically provable performance bounds. We build a system that incorporates these algorithms, and that exhibits good performance in practice. Since we are interested in information access and communication, our work focuses on two areas: network routing, and replication. Robust network routing allows passing and accessing information even when the network is under heavy attack. Replication is required to allow information access even when there is no network connectivity
ViRBase: a resource for virus–host ncRNA-associated interactions

Increasing evidence reveals that diverse non-coding RNAs (ncRNAs) play critically important roles in viral infection. Viruses can use diverse ncRNAs to manipulate both cellular and viral gene expression to establish a host environment conducive to the completion of the viral life cycle. Many host cellular ncRNAs can also directly or indirectly influence viral replication and even target virus genomes. ViRBase (http://www.rna-society.org/virbase) aims to provide the scientific community with a resource for efficient browsing and visualization of virus-host ncRNA-associated interactions and interaction networks in viral infection. The current version of ViRBase documents more than 12 000 viral and cellular ncRNA-associated virus–virus, virus–host, host–virus and host–host interactions involving more than 460 non-redundant ncRNAs and 4400 protein-coding genes from between more than 60 viruses and 20 hosts. Users can query, browse and manipulate these virus–host ncRNA-associated interactions. ViRBase will be of help in uncovering the generic organizing principles of cellular virus–host ncRNA-associated interaction networks in viral infection.
Forecasting irish inflation using ARIMA models

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models - the Box Jenkins approach and the objective penalty function methods. The emphasis is on forecast performance which suggests more focus on minimising out-of-sample forecast errors than on maximising in-sample ‘goodness of fit’. Thus, the approach followed is unashamedly one of ‘model mining’ with the aim of optimising forecast performance. Practical issues in ARIMA time series forecasting are illustrated with reference to the harmonised index of consumer prices (HICP) and some of its major sub-components.
Associations between sociodemographic characteristics and substance use disorder severity among methamphetamine-using men who have sex with men

Abstract Background: Men who have sex with men (MSM) have elevated rates of substance use disorders (SUDs) and differences across sociodemographic sub-groups of MSM are associated with a greater risk of deleterious outcomes. Although studies have shown that MSM report greater rates of polysubstance use relative to other adult populations, the associations between sociodemographic characteristics and both acute substance use and substance use severity among methamphetamine-using MSM are unknown. Objectives: The present study examines associations between sociodemographic characteristics and (a) recent substance use and (b) SUD severity. Method: From March 2014 to January 2016, 286 methamphetamine-using MSM were recruited to complete a baseline Audio Computer-Assisted Self-Interview (ACASI) assessment and the SCID MINI. Multivariable analyses employed generalized structural equation modeling given the non-continuous nature of the endogenous variables. Results: All measured sociodemographic characteristics except gay self-identification were significantly associated with recent substance use (all ps ≤ .05), and all characteristics except current homelessness were significantly associated with diagnostic SUD severity (all ps ≤ .05). However, nuanced risks were observed in participants’ use of specific substances regarding recent substance use and substance use severity. Conclusion: These results suggest that multiple factors contribute to the risks of SUD severity among methamphetamine-using MSM. As such, these results are useful in the tailoring of clinical and psychosocial intervention strategies that serve this and other high-risk populations.