Miracle-dz/UniFAR-FTUs huggingface dataset:
Taxonomy-as-a-Service: How To Structure Your Related Work

Structuring related work is a daunting task encompassing literature review, classification, comparison (primarily in the form of concepts), and gap analysis. Building taxonomies is a compelling way to structure concepts in the literature yielding reusable and extensible models. However, constructing taxonomies as a product of literature reviews could become, to our experiences, immensely complex and error-prone. Including new literature or addressing errors may cause substantial changes (ripple effects) in taxonomies coping with which requires adequate tools. To this end, we propose a \emph{Taxonomy-as-a-Service (TaaS)} platform. TaaS combines the systematic paper review process with taxonomy development, visualization, and analysis capabilities. We evaluate the effectiveness and efficiency of our platform by employing it in the development of a real-world taxonomy. Our results indicate that our TaaS can be used to effectively craft and maintain UML-conforming taxonomies and thereby structure related work. The screencast of our tool demonstration is available at \url{this https URL}.
Extending Broyden's method to interaction problems

The solution of problems involving the interaction of different systems is a domain of ongoing research, although often a good solver already exists for each system separately. In this paper we draw our ideas from one of the best known all-round quasi-Newton methods: Broyden's rank-one update, which we extend to algorithms using 2 approximate Jacobians. A comparison is made with the iterative substructuring method and Aitken's acceleration method. It is shown that a Broyden method using only a single approximate Jacobian performs best.
Modulated Bayesian Optimization using Latent Gaussian Process Models

We present an approach to Bayesian Optimization that allows for robust search strategies over a large class of challenging functions. Our method is motivated by the belief that the trends useful to exploit in search of the optimum typically are a subset of the characteristics of the true objective function. At the core of our approach is the use of a Latent Gaussian Process Regression model that allows us to modulate the input domain with an orthogonal latent space. Using this latent space we can encapsulate local information about each observed data point that can be used to guide the search problem. We show experimentally that our method can be used to significantly improve performance on challenging benchmarks.
h- and p-refined Multilevel Monte Carlo Methods for Uncertainty Quantification in Structural Engineering

Practical structural engineering problems are often characterized by significant uncertainties. Historically, one of the prevalent methods to account for this uncertainty has been the standard Monte Carlo (MC) method. Recently, improved sampling methods have been proposed, based on the idea of variance reduction by employing a hierarchy of mesh refinements. We combine an h- and p-refinement hierarchy with the Multilevel Monte Carlo (MLMC) and Multilevel Quasi-Monte Carlo (MLQMC) method. We investigate the applicability of these novel combination methods on three structural engineering problems, for which the uncertainty resides in the Young's modulus: the static response of a cantilever beam with elastic material behavior, its static response with elastoplastic behavior, and its dynamic response with elastic behavior. The uncertainty is either modeled by means of one random variable sampled from a univariate Gamma distribution or with multiple random variables sampled from a gamma random field. This random field results from a truncated Karhunen-Lo\`eve (KL) expansion. In this paper, we compare the computational costs of these Monte Carlo methods. We demonstrate that MQLMC and MLMC have a significant speedup with respect to MC, regardless of the mesh refinement hierarchy used. We empirically demonstrate that the MLQMC cost is optimally proportional to 1/epsilon under certain conditions, where epsilon is the tolerance on the root-mean-square error (RMSE). In addition, we show that, when the uncertainty is modeled as a random field, the multilevel methods combined with p-refinement have a significant lower computation cost than their counterparts based on h-refinement. We also illustrate the effect the uncertainty models have on the uncertainty bounds in the solutions.
Expected Sarsa(λ) with Control Variate for Variance Reduction

Off-policy learning is powerful for reinforcement learning. However, the high variance of off-policy evaluation is a critical challenge, which causes off-policy learning falls into an uncontrolled instability. In this paper, for reducing the variance, we introduce control variate technique to $\mathtt{Expected}$ $\mathtt{Sarsa}$($\lambda$) and propose a tabular $\mathtt{ES}$($\lambda$)-$\mathtt{CV}$ algorithm. We prove that if a proper estimator of value function reaches, the proposed $\mathtt{ES}$($\lambda$)-$\mathtt{CV}$ enjoys a lower variance than $\mathtt{Expected}$ $\mathtt{Sarsa}$($\lambda$). Furthermore, to extend $\mathtt{ES}$($\lambda$)-$\mathtt{CV}$ to be a convergent algorithm with linear function approximation, we propose the $\mathtt{GES}$($\lambda$) algorithm under the convex-concave saddle-point formulation. We prove that the convergence rate of $\mathtt{GES}$($\lambda$) achieves $\mathcal{O}(1/T)$, which matches or outperforms lots of state-of-art gradient-based algorithms, but we use a more relaxed condition. Numerical experiments show that the proposed algorithm performs better with lower variance than several state-of-art gradient-based TD learning algorithms: $\mathtt{GQ}$($\lambda$), $\mathtt{GTB}$($\lambda$) and $\mathtt{ABQ}$($\zeta$).
Flow Cytometry to Assess the Counts and Physiological State of Cronobacter sakazakii Cells after Heat Exposure

Cronobacter sakazakii is an opportunistic pathogen that is associated with outbreaks of neonatal necrotizing enterocolitis, septicaemia, and meningitis. Reconstituted powdered infant formulae is the most common vehicle of infection. The aim of the present study is to gain insight into the physiological states of C. sakazakii cells using flow cytometry to detect the compromised cells, which are viable but non-culturable using plate-based methods, and to evaluate the impact of milk heat treatments on those populations. Dead-cell suspensions as well as heat-treated and non-heat-treated cell suspensions were used. After 60 or 65 °C treatments, the number of compromised cells increased as a result of cells with compromised membranes shifting from the heat-treated suspension. These temperatures were not effective at killing all bacteria but were effective at compromising their membranes. Thus, mild heat treatments are not enough to guarantee the safety of powered infant formulae. Flow cytometry was capable of detecting C. sakazakii’s compromised cells that cannot be detected with classical plate count methods; thus, it could be used as a screening test to decrease the risk derived from the presence of pathogenic viable but non-culturable cells in this food that is intended for newborns’ nutrition.
The 320 EeV Fly's Eye event: a key messenger or a statistical oddity?

Almost three decades ago, the Fly's Eye experiment recorded the most energetic cosmic-ray ever observed. With an energy of 320 EeV, this event is well beyond the suppression region of the ultra-high energy cosmic rays (UHECR) spectrum. Modern and larger observatories, with an exposure up to 60 times larger, have never observed an event with even remotely comparable energy. Thus, if the energy of the Fly's Eye event was indeed well measured, as strongly suggested by the data, then it remains a great mystery or an unbelievable stroke of luck. At such high energies, the Universe is very opaque to electromagnetic interacting particles, whether photons, protons or heavy nuclei, and therefore its source must be relatively close. Using numerical simulations for the propagation of protons and nuclei, we reexamine the problem by testing different possibilities for the nature of the primary, the injection spectrum and the location of the source. Based on these calculations, we show that the most feasible scenario corresponds to a nearby (∼ 2–3 Mpc) bursting source of heavy nuclei in the northern sky, which injected a hard spectrum (γ ≤ 1.5) with an energy cut-off between 300 and 1000 EeV . Such scheme generates a significant probability for the observation of one event by Fly's Eye combined with a null result of Telescope Array at the same energy.
Computer-Supported Collaborative Learning in Software Engineering Education: A Systematic Mapping Study

Computer-supported collaborative learning (CSCL) has been a steady topic of research since the early 1990s, and the trend has continued to this date. The basic benefits of CSCL in the classroom have been established in many fields of education to improve especially student motivation and critical thinking. In this paper we present a systematic mapping study about the state of research of computer-supported collaborative learning in software engineering education. The mapping study examines published articles from 2003 to 2013 to find out how this field of science has progressed. Ongoing research topics in CSCL in software engineering education concern wider learning communities and the effectiveness of different collaborative approaches. We found that while the research establishes the benefits of CSCL in several different environments from local to global ones, these approaches are not always detailed and comparative enough to pinpoint which factors have enabled their success.
Essence Knowledge Distillation for Speech Recognition

It is well known that a speech recognition system that combines multiple acoustic models trained on the same data significantly outperforms a single-model system. Unfortunately, real time speech recognition using a whole ensemble of models is too computationally expensive. In this paper, we propose to distill the knowledge of essence in an ensemble of models (i.e. the teacher model) to a single model (i.e. the student model) that needs much less computation to deploy. Previously, all the soften outputs of the teacher model are used to optimize the student model. We argue that not all the outputs of the ensemble are necessary to be distilled. Some of the outputs may even contain noisy information that is useless or even harmful to the training of the student model. In addition, we propose to train the student model with a multitask learning approach by utilizing both the soften outputs of the teacher model and the correct hard labels. The proposed method achieves some surprising results on the Switchboard data set. When the student model is trained together with the correct labels and the essence knowledge from the teacher model, it not only significantly outperforms another single model with the same architecture that is trained only with the correct labels, but also consistently outperforms the teacher model that is used to generate the soft labels.
A New Look at Cell-Free Massive MIMO: Making It Practical With Dynamic Cooperation

This paper takes a new look at Cell-free Massive MIMO (multiple-input multiple-output) through the lens of the dynamic cooperation cluster framework from the Network MIMO literature. The purpose is to identify and address scalability issues that appear in prior work. We provide distributed algorithms for initial access, pilot assignment, cluster formation, precoding, and combining that are scalable in the sense of being implementable with arbitrarily many users. Interestingly, the suggested precoding and combining outperform conjugate beamforming and matched filtering, respectively, while also being fully distributed.
Star formation in CALIFA early-type galaxies: a matter of discs

The star formation main sequence (SFMS) is a tight relation between the galaxy star formation rate (SFR) and its total stellar mass (M⋆). Early-type galaxies (ETGs) are often considered as low-SFR outliers of this relation. We study, for the first time, the separated distribution in the SFR versus M⋆ of bulges and discs of 49 ETGs from the CALIFA survey. This is achieved using c2d, a new code to perform spectrophotometric decompositions of integral field spectroscopy data cubes. Our results reflect that: (i) star formation always occurs in the disc component and not in bulges; (ii) star-forming discs in our ETGs are compatible with the SFMS defined by star-forming galaxies at z ∼ 0; (iii) the star formation is not confined to the outskirts of discs, but it is present at all radii (even where the bulge dominates the light); (iv) for a given mass, bulges exhibit lower sSFR than discs at all radii; and (v) we do not find a deficit of molecular gas in bulges with respect to discs for a given mass in our ETGs. We speculate our results favour a morphological quenching scenario for ETGs.
Learning Soft-Attention Models for Tempo-invariant Audio-Sheet Music Retrieval

Connecting large libraries of digitized audio recordings to their corresponding sheet music images has long been a motivation for researchers to develop new cross-modal retrieval systems. In recent years, retrieval systems based on embedding space learning with deep neural networks got a step closer to fulfilling this vision. However, global and local tempo deviations in the music recordings still require careful tuning of the amount of temporal context given to the system. In this paper, we address this problem by introducing an additional soft-attention mechanism on the audio input. Quantitative and qualitative results on synthesized piano data indicate that this attention increases the robustness of the retrieval system by focusing on different parts of the input representation based on the tempo of the audio. Encouraged by these results, we argue for the potential of attention models as a very general tool for many MIR tasks.
High Dimensional Discrete Integration over the Hypergrid

Recently Ermon et al. (2013) pioneered a way to practically compute approximations to large scale counting or discrete integration problems by using random hashes. The hashes are used to reduce the counting problem into many separate discrete optimization problems. The optimization problems then can be solved by an NP-oracle such as commercial SAT solvers or integer linear programming (ILP) solvers. In particular, Ermon et al. showed that if the domain of integration is $\{0,1\}^n$ then it is possible to obtain a solution within a factor of $16$ of the optimal (a 16-approximation) by this technique. In many crucial counting tasks, such as computation of partition function of ferromagnetic Potts model, the domain of integration is naturally $\{0,1,\dots, q-1\}^n, q>2$, the hypergrid. The straightforward extension of Ermon et al.'s method allows a $q^2$-approximation for this problem. For large values of $q$, this is undesirable. In this paper, we show an improved technique to obtain an approximation factor of $4+O(1/q^2)$ to this problem. We are able to achieve this by using an idea of optimization over multiple bins of the hash functions, that can be easily implemented by inequality constraints, or even in unconstrained way. Also the burden on the NP-oracle is not increased by our method (an ILP solver can still be used). We provide experimental simulation results to support the theoretical guarantees of our algorithms.
Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest

ABSTRACT We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.
Genome-Wide Profiling of Polyadenylation Events in Maize Using High-Throughput Transcriptomic Sequences

Polyadenylation is an essential post-transcriptional modification of eukaryotic transcripts that plays critical role in transcript stability, localization, transport, and translational efficiency. About 70% genes in plants contain alternative polyadenylation (APA) sites. Despite availability of vast amount of sequencing data, to date, a comprehensive map of the polyadenylation events in maize is not available. Here, 9.48 billion RNA-Seq reads were analyzed to characterize 95,345 Poly(A) Clusters (PAC) in 23,705 (51%) maize genes. Of these, 76% were APA genes. However, most APA genes (55%) expressed a dominant PAC rather than favoring multiple PACs equally. The lincRNA genes with PACs were significantly longer in length than the genes without any PAC and about 48% genes had APA sites. Heterogeneity was observed in 52% of the PACs supporting the imprecise nature of the polyadenylation process. Genomic distribution revealed that the majority of the PACs (78%) were located in the genic regions. Unlike previous studies, large number of PACs were observed in the intergenic (n = 21,264), 5′-UTR (735), CDS (2,542), and the intronic regions (12,841). The CDS and introns with PACs were longer in length than without PACs, whereas intergenic PACs were more often associated with transcripts that lacked annotated 3′-UTRs. Nucleotide composition around PACs demonstrated AT-richness and the common upstream motif was AAUAAA, which is consistent with other plants. According to this study, only 2,830 genes still maintained the use of AAUAAA motif. This large-scale data provides useful insights about the gene expression regulation and could be utilized as evidence to validate the annotation of transcript ends.
PLA2G4A/cPLA2-mediated lysosomal membrane damage leads to inhibition of autophagy and neurodegeneration after brain trauma

ABSTRACT Lysosomal membrane permeabilization (LMP) is observed under many pathological conditions, leading to cellular dysfunction and death. However, the mechanisms by which lysosomal membranes become leaky in vivo are not clear. Our data demonstrate that LMP occurs in neurons following controlled cortical impact induced (CCI) traumatic brain injury (TBI) in mice, leading to impaired macroautophagy (autophagy) and neuronal cell death. Comparison of LC-MS/MS lysosomal membrane lipid profiles from TBI and sham animals suggested a role for PLA2G4A/cPLA2 (phospholipase A2, group IVA [cytosolic, calcium-dependent]) in TBI-induced LMP. Activation of PLA2G4A caused LMP and inhibition of autophagy flux in cell lines and primary neurons. In vivo pharmacological inhibition of PLA2G4A attenuated TBI-induced LMP, as well as subsequent impairment of autophagy and neuronal loss, and was associated with improved neurological outcomes. Inhibition of PLA2G4A in vitro limited amyloid-β-induced LMP and inhibition of autophagy. Together, our data indicate that PLA2G4A -mediated lysosomal membrane damage is involved in neuronal cell death following CCI-induced TBI and potentially in other neurodegenerative disorders. Abbreviations AACOCF3, arachidonyl trifluoromethyl ketone; ACTB/β-actin, actin, beta; AD, Alzheimer disease; ATG5, autophagy related 5; ATG7, autophagy related 7; ATG12, autophagy related 12; BECN1, beclin 1, autophagy related; C1P, ceramide-1-phosphate; CCI, controlled cortical impact; CTSD, cathepsin D; CTSL, cathepsin L; GFP, green fluorescent protein; IF, immunofluorescence; LAMP1, lysosomal-associated membrane protein 1; LAMP2, lysosomal-associated membrane protein 2; LC-MS/MS, liquid chromatography-tandem mass spectrometry; LMP, Lysosomal membrane permeabilization; LPC, lysophosphatidylcholine; LPE, lysophosphatidylethanolamine; MAP1LC3/LC3, microtuble-associated protein 1 light chain 3; NAGLU, alpha-N-acetylglucosaminidase (Sanfilippo disease IIIB); PC, diacyl glycerophosphatidylcholine; PE, diacyl glycerophosphatidylethanolamine; PE-O, plasmanyl glycerophosphatidylethanolamine; PE-P, plasmenyl glycerophosphatidylethanolamine; PLA2G4A/cPLA2, phospholipase A2, group IVA (cytosolic, calcium-dependent); RBFOX3, RNA binding protein, fox-1 homolog (C. elegans) 3; RFP, red fluorescent protein; ROS, reactive oxygen species; SQSTM1, sequestosome 1; TUBA1/α-tubulin, tubulin, alpha; TBI, traumatic brain injury; TFEB, transcription factor EB; ULK1, unc-51 like kinase 1.
An Analysis of Archive Update for Vector Evaluated Particle Swarm Optimization

Multi-objective optimization problem is commonly found in many real world problems. In computational intelligence, Particle Swarm Optimization (PSO) algorithm is a popular method in solving optimization problems. An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO) has been introduced to solve multi-objective optimization problems. VEPSO algorithm requires an archive, which is used to record the solutions found. However, the outcome may be differ depending on how the archive is used. Hence, in this study, the performance of VEPSO algorithm when updates the archive at different instance is investigated by measuring the convergence and diversity by using standard test functions. The results show that the VEPSO algorithm performs better when update the archive during the search process, in the iterations.
Object Tracking Based on Online Classification Boosted by Discriminative Features

We propose a robust object tracking algorithm under “tracking by detection” framework. A sparse random Gaussian-distributed measurement matrix is used to build an object appearance model in the compressed domain, and detection is done by our semi-supervised learning process. Our method trains a group of weaker classifiers corresponding to every feature, and linearly combines them using the weights computed by its discriminative power to build a strong classification function, which detects a target from background. Kalman filtering is also adopted to smoothen the tracking results. Experiments show the proposed classification improves performance in tracking without drift.
EngVallex - English Valency Lexicon

EngVallex is the English counterpart of the PDT-Vallex valency lexicon, using the same view of valency, valency frames and the description of a surface form of verbal arguments. EngVallex contains links also to PropBank and Verbnet, two existing English predicate-argument lexicons used, i.a. , for the PropBank project. The EngVallex lexicon is fully linked to the English side of the PCEDT parallel treebank, which is in fact the PTB re-annotated using the Prague Dependency Treebank style of annotation. The EngVallex is available in an XML format in our repository, and also in a searchable form with examples from the PCEDT.