Miracle-dz/UniFAR-FTUs huggingface dataset:
Trophic Transfer of Macroalgal Fatty Acids in Two Urchin Species: Digestion, Egestion, and Tissue Building

Sea urchins are ecosystem engineers of nearshore benthic communities because of their influence on the abundance and distribution of macroalgal species. Urchins are notoriously inefficient in assimilation of their macroalgal diets, so their fecal production can provide a nutritional subsidy to benthic consumers that cannot capture and handle large macroalgae. We studied the assimilation of macroalgal diets by urchins by analyzing the profiles of trophic biomarkers such as fatty acids (FAs). We tracked macroalgal diet assimilation in both Strongylocentrotus droebachiensis and S. purpuratus. Juvenile S. droebachiensis and adult S. purpuratus were maintained for 180 and 70 days, respectively, on one of three monoculture diets from three algal phyla: Nereocystis luetkeana, Pyropia sp. , or Ulva sp. We then analyzed FA profiles of the macroalgal tissue fed to urchins as well as urchin gonad, gut, digesta, and egesta (feces) to directly evaluate trophic modification and compare nutritional quality of urchin food sources, urchin tissues, and fecal subsidies. In the S. purpuratus assay, there were significantly more total lipids in the digesta and egesta than in the algae consumed. The FA profiles of urchin tissues differed among urchin species, all diets, and tissue types. Despite these differences, we observed similar patterns in the relationships between the urchin and macroalgal tissues for both species. Egesta produced by urchins fed each of the three diets were depleted with respect to the concentration of important long chain polyunsaturated fatty acids (LCPUFAs), but did not differ significantly from the source alga consumed. Both urchin species were shown to synthesize and selectively retain both the precursor and resulting LCPUFAs involved in the synthesis of the LCPUFAs 20:4⍵6 and 20:5⍵3. S. droebachiensis and S. purpuratus exhibited consistent patterns in the respective depletion and retention of precursor FAs and resulting LCPUFAs of Pyropia and Ulva tissues, suggesting species level control of macroalgal digestion or differential tissue processing by gut microbiota. For both S. droebachiensis and S. purpuratus, macroalgal diet was a surprisingly strong driver of urchin tissue fatty acids; this indicates the potential of fatty acids for future quantitative trophic estimates of urchin assimilation of algal phyla in natural settings.
Un circuito transhumántico entre la costa de Pisagua y el borde occidental de la Pampa del Tamarugal

Recientemente hemos detectado la existencia de un circuito transhumantico entre grupos preagricolas que utilizaban la costa con cierta estabilidad y se complementaban con estacionamientos temporales en ambientes de vertientes, valles y pampas al interior de la costa en el perfil costa‑altiplano de la provincia de Tarapaca (norte de Chile).
Natural Language Requirements Processing: From Research to Practice

Automated manipulation of natural language requirements, for classification, tracing, defect detection, information extraction, and other tasks, has been pursued by requirements engineering (RE) researchers for more than two decades. Recent technological advancements in natural language processing (NLP) have made it possible to apply this research more widely within industrial settings. This technical briefing targets researchers and practitioners, and aims to give an overview of what NLP can do today for RE problems, and what could do if specific research challenges, also emerging from practical experiences, are addressed. The talk will: survey current research on applications of NLP to RE problems; present representative industrially-ready techniques, with a focus on defect detection and information extraction problems; present enabling technologies in NLP that can play a role in RE research, including distributional semantics representations; discuss criteria for evaluation of NLP techniques in the RE context; outline the main challenges for a systematic application of the techniques in industry. The crosscutting topics that will permeate the talk are the need for domain adaptation, and the essential role of the human-in-the-loop.
The Role of Context in Processing Chinese Three-Character Verb–Object Metaphors: An Event-Related Potential Study

Whether and how context plays a role in metaphor processing remains a controversial issue. One major theory on metaphor comprehension, the graded salience hypothesis (GSH) model, emphasizes salience as the key factor determining the precedence of semantic access. Using event-related potential technique, the present study examined Chinese metaphors to investigate whether the salient meaning is always processed first regardless of context. The experiment employed a Prime-Target-Probe paradigm. Three-character Chinese verb–object metaphors were used as the Target proceeded by one of the three contexts (the Prime): (1) metaphorical context priming the Target’s metaphorical meaning, (2) literal context priming the Target’s literal meaning, and (3) irrelevant context as the control condition. The Target was then followed by the Probe, which was always related to the Target (except in the filler condition). Forty participants were asked to judge whether the Target and the following Probe were semantically related. The N400 elicited by the Target showed no contextual effect. The N400 amplitude elicited by the Probe was smaller in the metaphorical priming condition compared with the literal priming condition, while the N400 in the irrelevant control condition was between the other two conditions, demonstrating a clear context effect. In addition, an unexpected P240 component also showed the similar graded pattern. Our results mostly support the GSH model, indicating that the salient meaning invariantly gets activated first before the activation of the nonsalient meaning at the lexical access stage. However, context does play a role in a parallel way either facilitating or suppressing this interpretation in the latter meaning integration stage.
MCP: a multi-component learning machine for prediction of protein secondary structure

The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through translation of this sequence into protein and formation of a certain 3D structure. Proteins biological function is tightly connected to its specific 3D structure. Prediction of protein secondary structure is a crucial intermediate step towards elucidating its 3D structure and function. Traditional experimental methods for prediction of protein secondary structure are expensive and time-consuming. Therefore, in the past 45 years, various machine learning approaches have been put forth. Nevertheless, their average accuracy has hardly reached beyond 80%. The possible underlying reasons are abstruse sequence structure relation, noise, class imbalance and high dimensionality of encoding schemes which represent protein sequences. In this paper, we have developed an accurate multi-component prediction machine to overcome challenges of protein secondary structure prediction. The principal tenet behind the proposed approach is to directly process amino-acid sequences to reveal deeper and, simultaneously, more biologically meaningful sequence structure relation. Taking this approach, it is possible to prevent losing rich information hidden in sequence data, which is biologically believed to be sufficient for structure adoption. Additionally, it facilitates resolving the high dimensionality of the numerical representation for protein sequences. Moreover, the multi-component designation can better address the high complexity of the relation between sequence and structure. To pursue these objectives, we have employed various components in our framework. The learning components of our framework are SVM and fuzzy KNN along with edit kernel and a compound dissimilarity measure symbolized as d̃. Also a fuzzy aggregation pool and a † School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran § ST Electronics SUTD Cyber Security Laboratory, Singapore University of Technology and Design, Singapore # School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia 1 Leila.Khalatbari@gmail.com 2 kangavari@iust.ac. ir 3 saeid.hosseini@uq.net. au 4 h.yin1@uq.edu. au 5 ngaiman cheung@sutd.edu. sg ar X iv :1 80 6. 06 39 4v 1 [ cs . C E ] 1 7 Ju n 20 18 ii Saeid Hosseini et al. biological filtering module are utilized. Utilizing the popular RS126 dataset, our multi-component framework demonstrates a prediction accuracy of 87.29%. The proposed framework is a flexible solution whose effectiveness can be further improved via parameter optimization.
Antimicrobial and Efflux Inhibitor Activity of Usnic Acid Against Mycobacterium abscessus

Abstract New drugs are needed to treat infections with antimicrobial-resistant Mycobacterium abscessus; therefore, we evaluated usnic acid as an antimicrobial agent and efflux inhibitor (EI) against M. abscessus. Usnic acid showed antimicrobial activity, and synergistically, the EI verapamil increased this activity. In addition, when we evaluated the interaction of antimicrobials with usnic acid, the increase of their activity was observed. Finally, usnic acid showed an efflux inhibitory effect between the classical EIs verapamil and carbonyl cyanide m-chlorophenylhydrazine. In conclusion, usnic acid showed both antimicrobial and EI activity, indicating that this natural compound may be a promising scaffold for new drugs against this difficult-to-treat microorganism.
Enabling Union Page Cache to Boost File Access Performance of NVRAM-Based Storage Device

Due to the fast access performance, byte-addressability, and non-volatility of non-volatile random access memory (NVRAM), NVRAM has emerged as a popular candidate for the design of memory/storage systems on mobile computing systems. For example, the latest 3D xPoint memory could be a kind of NVRAM with much longer life expectancy than NAND flash and could ease the possible endurance issue. When NVRAM is considered as both main memory and storage in mobile computing systems, existing page cache mechanisms introduce too many unnecessary data movements between main memory and storage. To resolve this issue, we propose the concept of "union page cache," which jointly manages data of the page cache in both main memory and storage. To realize this concept, a partial page cache strategy is designed to consider both main memory and storage as its management space and to eliminate unnecessary data movements between main memory and storage without sacrificing the data consistency of file systems. Experimental results show that the proposed strategy can boost the file accessing performance upto 85.62% when using PCM as a case study.
On Enhancing Speech Emotion Recognition using Generative Adversarial Networks

Generative Adversarial Networks (GANs) have gained a lot of attention from machine learning community due to their ability to learn and mimic an input data distribution. GANs consist of a discriminator and a generator working in tandem playing a min-max game to learn a target underlying data distribution; when fed with data-points sampled from a simpler distribution (like uniform or Gaussian distribution). Once trained, they allow synthetic generation of examples sampled from the target distribution. We investigate the application of GANs to generate synthetic feature vectors used for speech emotion recognition. Specifically, we investigate two set ups: (i) a vanilla GAN that learns the distribution of a lower dimensional representation of the actual higher dimensional feature vector and, (ii) a conditional GAN that learns the distribution of the higher dimensional feature vectors conditioned on the labels or the emotional class to which it belongs. As a potential practical application of these synthetically generated samples, we measure any improvement in a classifier's performance when the synthetic data is used along with real data for training. We perform cross-validation analyses followed by a cross-corpus study.
Non-linear ICA Analysis of Resting-State fMRI in Mild Cognitive Impairment

Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample t-tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample t-tests were carried out. We found that the functional connectivity (FC) of RSNs in MCI patients was abnormal. Compared with HC, MCI patients showed the increased and decreased FC in default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), somato-motor network (SMN), visual network(VN), MCI patients displayed the specifically decreased FC in auditory network (AN), self-referential network (SRN). The FC of core network (CN) did not reveal significant group difference. The results indicate that the abnormal FC in RSNs is selective in MCI patients.
Genetic Algorithm-Based Beam Refinement for Initial Access in Millimeter Wave Mobile Networks

Initial access (IA) is identified as a key challenge for the upcoming 5G mobile communication system operating at high carrier frequencies, and several techniques are currently being proposed. In this paper, we extend our previously proposed efficient genetic algorithm- (GA-) based beam refinement scheme to include beamforming at both the transmitter and the receiver and compare the performance with alternative approaches in the millimeter wave multiuser multiple-input-multiple-output (MU-MIMO) networks. Taking the millimeter wave communications characteristics and various metrics into account, we investigate the effect of different parameters such as the number of transmit antennas/users/per-user receive antennas, beamforming resolutions, and hardware impairments on the system performance employing different beam refinement algorithms. As shown, our proposed GA-based approach performs well in delay-constrained networks with multiantenna users. Compared to the considered state-of-the-art schemes, our method reaches the highest service outage-constrained end-to-end throughput with considerably less implementation complexity. Moreover, taking the users’ mobility into account, our GA-based approach can remarkably reduce the beam refinement delay at low/moderate speeds when the spatial correlation is taken into account. Finally, we compare the cases of collaborative users and noncollaborative users and evaluate their difference in system performance.
Bordetella Pertussis Adenylate Cyclase Toxin Does Not Possess a Phospholipase A Activity; Serine 606 and Aspartate 1079 Residues Are Not Involved in Target Cell Delivery of the Adenylyl Cyclase Enzyme Domain

The adenylate cyclase toxin-hemolysin (CyaA, ACT, or AC-Hly) plays a crucial role in virulence and airway colonization capacity of the whooping cough agent Bordetella pertussis. The toxin penetrates target cell membranes and exhibits three distinct biological activities. A population of CyaA conformers forms small cation-selective pores that permeabilize the cell membrane for potassium efflux, which can provoke colloid-osmotic (oncotic) cell lysis. The other two activities are due to CyaA conformers that transiently form calcium influx conduits in the target cell membrane and translocate the adenylate cyclase (AC) enzyme into cytosol of cells. A fourth putative biological activity has recently been reported; an intrinsic phospholipase A (PLA) activity was claimed to be associated with the CyaA polypeptide and be involved in the mechanism of translocation of the AC enzyme polypeptide across cell membrane lipid bilayer. However, the conclusions drawn by the authors contradicted their own results and we show them to be erroneous. We demonstrate that highly purified CyaA is devoid of any detectable phospholipase A1 activity and that contrary to the published claims, the two putative conserved phospholipase A catalytic residues, namely the Ser606 and Asp1079 residues, are not involved in the process of membrane translocation of the AC domain of CyaA across target membranes.
Does Parent-Child Discussion of Peer Provocations Reduce Young Children's Hostile Attributional Bias?

Two studies investigated whether parent-child discussion of peer provocations reduces young children's hostile attributional bias. Study 1 (N = 109, age 4-7)-an observational study-showed that parent-child discussion of nonhostile attributions (when reading a picture book) predicted reductions in children's hostile attributional bias from pre- to postdiscussion. Study 2 (N = 160, age 4-6)-an experimental study-showed that stimulating parents to discuss either nonhostile attributions or normative beliefs (vs. a control condition) reduced children's hostile attributional bias in response to hypothetical vignettes, but not in response to a staged peer provocation. These findings suggest that by framing social situations, parents may help their children perceive less hostility in their social worlds.
Poster: Use of Trace Link Types in Issue Tracking Systems

Issue tracking systems (ITS) are widely used to describe and manage activities in software systems. Within the ITS, software developers create issues and establish typed links among these artifacts. A variety of different link types exists, e. g. that one issue clones another. The rational about choosing a specific link type is not always obvious. In this paper, we study link type selection and focus on the relationship of textual properties of connected issues to picked link type. We performed a study on seven open-source systems and quantified the usage of typed links. Further, we report preliminary results indicating, that depending on link type, a link mostly captures textual similarity of issues and thus may provide only limited additional information.
QG-net: a data-driven question generation model for educational content

The ever growing amount of educational content renders it increasingly difficult to manually generate sufficient practice or quiz questions to accompany it. This paper introduces QG-Net, a recurrent neural network-based model specifically designed for automatically generating quiz questions from educational content such as textbooks. QG-Net, when trained on a publicly available, general-purpose question/answer dataset and without further fine-tuning, is capable of generating high quality questions from textbooks, where the content is significantly different from the training data. Indeed, QG-Net outperforms state-of-the-art neural network-based and rules-based systems for question generation, both when evaluated using standard benchmark datasets and when using human evaluators. QG-Net also scales favorably to applications with large amounts of educational content, since its performance improves with the amount of training data.
LC-HRMS Metabolomics for Untargeted Diagnostic Screening in Clinical Laboratories: A Feasibility Study

Today’s high-resolution mass spectrometers (HRMS) allow bioanalysts to perform untargeted/global determinations that can reveal unexpected compounds or concentrations in a patient’s sample. This could be performed for preliminary diagnosis attempts when usual diagnostic processes and targeted determinations fail. We have evaluated an untargeted diagnostic screening (UDS) procedure. UDS is a metabolome analysis that compares one sample (e.g., a patient) with control samples (a healthy population). Using liquid chromatography (LC)-HRMS full-scan analysis of human serum extracts and unsupervised data treatment, we have compared individual samples that were spiked with one xenobiotic or a higher level of one endogenous compound with control samples. After the use of different filters that drastically reduced the number of metabolites detected, the spiked compound was eventually revealed in each test sample and ranked. The proposed UDS procedure appears feasible and reliable to reveal unexpected xenobiotics (toxicology) or higher concentrations of endogenous metabolites. HRMS-based untargeted approaches could be useful as preliminary diagnostic screening when canonical processes do not reveal disease etiology nor establish a clear diagnosis and could reduce misdiagnosis. On the other hand, the risk of overdiagnosis of this approach should be reduced with mandatory biomedical interpretation of the patient’s UDS results and with confirmatory targeted and quantitative determinations.
Enhancing Inter-Class Representation with a New Global Center Loss

The challenge of training convolutional neural networks (CNNs) is mapping a set of high dimensional low data to a lower dimensional feature point on the manifold. However, the dimensionality reduced feature from CNN always suffers from verifying a new unseen class, such as measuring a computably meaningful distance on the dimensionality distorted space. The mapped feature on the lower dimension is formed by interrelated with training dataset and measure of penalization of learning. These methods can boost performance by training a large dataset or applying extra penalizations, which help forming feature more discriminatively. The discriminatively learned feature has a balanced inter-class distance and a reduced intra-class variation. In this paper, we propose a new loss function so called global center loss to extract more meaningful distance on feature space. The method leads to a sufficient inter-class variation which helps forming feature more discriminatively. The insufficient enforcement of negative-log likelihood or local center loss can be complementarily enhanced by utilizing the proposed global center method which is a valuable joint supervisory signal.
Calibrating Process Variation at System Level with In-Situ Low-Precision Transfer Learning for Analog Neural Network Processors

Process Variation (PV) may cause accuracy loss of the analog neural network (ANN) processors, and make it hard to be scaled down, as well as feasibility degrading. This paper first analyses the impact of PV on the performance of ANN chips. Then proposes an in-situ transfer learning method at system level to reduce PV's influence with low-precision back-propagation. Simulation results show the proposed method could increase 50% tolerance of operating point drift and 70%∼00% tolerance of mismatch with less than 1% accuracy loss of benchmarks. It also reduces 66.7% memories and has about 50 × energy-efficiency improvement of multiplication in the learning stage, compared with the conventional full-precision (32bit float) training system.