Miracle-dz/UniFAR-FTUs huggingface dataset:
Confocal imaging of cytosolic Ca2+ and fuzzy clustering reveal the circuit topology details underlying synchronization in hippocampal neurons

Neuronal synchronization contributes to various cognitive functions and disruption in synchronicity may lead to various diseased conditions. However, measurement of synchronicity at a higher spatial resolution remains challenging. Specifically, investigation on understanding the role of network topology in tuning the network activity and synchronicity remains sparse. In this context, we propose imaging of intracellular Ca2+ in primary cultures of hippocampal neurons using Fluo-4 as the fluorescent indicator using the confocal microscope. In order to identify the synchronous response from a set of heterogeneous Ca2+ spiking, we present fuzzy clustering of the oscillatory responses. Further, the synchronicity was measured through evaluation of the correlation between Ca2+ spiking trends. Confocal imaging and analysis show that neuronal connectivity and topology play an essential role in tuning the synchronicity of the neuronal network.
Mouse embryo assay to evaluate polydimethylsiloxane (PDMS) embryo-toxicity*

In vitro embryo culture to support In Vitro Fertilization (IVF) procedures is a well-established but still critical technique. In the last decade first attempts to use microfluidic devices in IVF have shown positive results, enabling to control the culture conditions and to preserve the quality of the embryos during their development. In this study we completed an industry standard mouse embryo assay (MEA) to exclude potential toxic effects of PDMS.
A Malware Analysis and Artifact Capture Tool

Malware authors attempt to obfuscate and hide their code in its static and dynamic states. This paper provides a novel approach to aid analysis by intercepting and capturing malware artifacts and providing dynamic control of process flow. Capturing malware artifacts allows an analyst to more quickly and comprehensively understand malware behavior and obfuscation techniques and doing so interactively allows multiple code paths to be explored. The faster that malware can be analyzed the quicker the systems and data compromised by it can be determined and its infection stopped. This research proposes an instantiation of an interactive malware analysis and artifact capture tool.
Combined Nonlinear Moving Horizon Estimation and Model Predictive Control Applied to a Compressor for Active Surge Control

This paper presents a combined nonlinear model predictive control and moving horizon estimation strategy for active surge control in compression systems. The main advantage of using model predictive control is the ability to include constraints, particularly on the actuators. The manipulated variables represent the pressure drop across the close-coupled valve (CCV) and the torque applied to the compressor shaft. Both manipulated variables are limited in magnitude and rate of change for real compression systems. Only positive values are feasible for both of them, for example. The performance of the control and estimation strategy is demonstrated in numerical simulations with added measurement and process noises.
Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement

This paper describes the system of our team Phoenix for participating CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Given the annotated gold standard data in CoNLL-U format, we train the tokenizer, tagger and parser separately for each treebank based on an open source pipeline tool UDPipe. Our system reads the plain texts for input, performs the pre-processing steps (tokenization, lemmas, morphology) and finally outputs the syntactic dependencies. For the low-resource languages with no training data, we use cross-lingual techniques to build models with some close languages instead. In the official evaluation, our system achieves the macro-averaged scores of 65.61%, 52.26%, 55.71% for LAS, MLAS and BLEX respectively.
Behavior Change Interventions to Reduce Illegal Fishing

Illegal fishing is a serious problem that threatens the sustainability of fisheries around the world. Historically, policy makers and fishery managers have relied on the “deterrence model” to alleviate it. This approach focuses on the imposition of strict sanctions and relatively intensive monitoring and enforcement programs in an attempt to increase the costs of illegal behavior. However, while this sort of command-and-control approach can sometimes be successful in large-scale fisheries, small-scale fisheries rarely have the associated resources or necessary governance structures required, and thus strong and reliable monitoring and enforcement has proven to be an elusive goal. Beyond these top-down approaches, non-monetary factors, whether they are cultural, social or psychological, can also influence illegal fishing behaviors, either positively or negatively, and failing to address these factors can undermine the efficacy of an otherwise effective and well-designed fishery management system. Here we explore the use of strategies to reduce illegal fishing behaviors based on insights from behavioral science, in order to augment and secure the effectiveness of conventional deterrence strategies as well as to provide support for alternative means of deterring illegal fishing often required for small-scale fisheries. We draw on theory and studies from the behavioral science literature to show how successful behavior change interventions have been implemented in other sectors, and describe a process for designing interventions for changing specific illegal fishing behaviors. The process begins with an in-depth stakeholder characterization to capture existing norms, beliefs, and modes of thinking about illegal fishing as well as descriptions of specific illegal fishing behaviors. Potential interventions that may disrupt the beliefs, norms, and thought modes that give rise to these behaviors, along with those that encourage desirable behaviors, are developed by applying principles gleaned from the behavioral science literature. These “hypothetical interventions” are then tested using artefactual experiments, piloted with small groups of actual stakeholders and, finally, scaled such that illegal fishing is significantly reduced.
Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing

This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system predicts the part-of-speech tag and dependency tree jointly. For the basic tasks, including tokenization, lemmatization and morphology prediction, we employ the official baseline model (UDPipe). To train the low-resource languages, we adopt a sampling method based on other richresource languages. Our system achieves a macro-average of 68.31% LAS F1 score, with an improvement of 2.51% compared with the UDPipe.
Evaluation of Different Signal Processing Methods in Time and Frequency Domain for Brain-Computer Interface Applications

Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one’s movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such real-time BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used.
Sample Entropy of High Frequency Oscillations for Epileptogenic Zone Localization

For epileptic patients whose seizures are poorly controlled with medication, removing the brain region responsible for seizure onset is a treatment option. This requires the epileptogenic zone (EZ) to be accurately delineated. In this paper, a two-stage approach for EZ delineation is proposed. The algorithm starts by detecting events of high-frequency oscillations (HFOs) directly from the multi-channel intracranial electroencephalograms (iEEGs). The sample entropy is then computed for each of their channels that will be used for determining the channels correlated with the EZ. The performance of our proposed method is evaluated using the receiver operating characteristic curve analysis, and the results indicate that our proposed approach can provide an accurate estimation of the EZ.
A Novel Atrial Fibrillation Prediction Algorithm Applicable to Recordings from Portable Devices

Atrial Fibrillation (AFib) is by itself a strong risk factor for many life-threatening heart diseases. An estimated 2.7 to 6.1 million people in the United States have AFib. With the aging of the U.S. population, this number is expected to increase. In this preliminary study, a heart rate-duration criteria region is proposed to automatically label symptomatic AFib events using recordings from portable ECG monitors. A Markov Chain algorithm is implemented to classify prediction intervals that are 2 minutes before the symptomatic AFib events. The method yields an overall accuracy value of 82% with 0.91 AUC.
Angular Sampling, Test Signal, and Near-Field Aspects for Over-The-Air Total Radiated Power Assessment in Anechoic Chambers

5th-generation mobile network systems operating in millimeter-wave bands will employ base stations with integrated active antenna systems. Such systems are capable of beam-tracking using narrow beams obtained from antenna arrays. Regulatory limits for unwanted radio frequency and Over-The-Air methods for TRP of unwanted emissions are needed. The method investigated here uses power density measurements on a spherical surface in an anechoic chamber. Two major challenges with such a method are: need for a large number of angular points and search for a worst case antenna configuration per frequency for electrically large devices at high frequencies. These challenges are addressed by investigating sparse sampling and use of beam sweeping. Finally, it is investigated how and in which spatial regions near-field tangential electric field measurements can be used to assess TRP.
Estimators for Multivariate Information Measures in General Probability Spaces

Information theoretic quantities play an important role in various settings in machine learning, including causality testing, structure inference in graphical models, time-series problems, feature selection as well as in providing privacy guarantees. A key quantity of interest is the mutual information and generalizations thereof, including conditional mutual information, multivariate mutual information, total correlation and directed information. While the aforementioned information quantities are well defined in arbitrary probability spaces, existing estimators add or subtract entropies (we term them $\Sigma H$ methods). These methods work only in purely discrete space or purely continuous case since entropy (or differential entropy) is well defined only in that regime. In this paper, we define a general graph divergence measure ($\mathbb{GDM}$), as a measure of incompatibility between the observed distribution and a given graphical model structure. This generalizes the aforementioned information measures and we construct a novel estimator via a coupling trick that directly estimates these multivariate information measures using the Radon-Nikodym derivative. These estimators are proven to be consistent in a general setting which includes several cases where the existing estimators fail, thus providing the only known estimators for the following settings: (1) the data has some discrete and some continuous-valued components (2) some (or all) of the components themselves are discrete-continuous mixtures (3) the data is real-valued but does not have a joint density on the entire space, rather is supported on a low-dimensional manifold. We show that our proposed estimators significantly outperform known estimators on synthetic and real datasets.
Linguistically-Based Deep Unstructured Question Answering

In this paper, we propose a new linguistically-based approach to answering non-factoid open-domain questions from unstructured data. First, we elaborate on an architecture for textual encoding based on which we introduce a deep end-to-end neural model. This architecture benefits from a bilateral attention mechanism which helps the model to focus on a question and the answer sentence at the same time for phrasal answer extraction. Second, we feed the output of a constituency parser into the model directly and integrate linguistic constituents into the network to help it concentrate on chunks of an answer rather than on its single words for generating more natural output. By optimizing this architecture, we managed to obtain near-to-human-performance results and competitive to a state-of-the-art system on SQuAD and MS-MARCO datasets respectively.
Evaluating Associations Between Perfectionism, Emotion Regulation, and Eating Disorder Symptoms in a Mixed-Gender Sample

Abstract Prior research supports maladaptive perfectionism as a risk factor for eating disorders; however, not all individuals with elevated levels of perfectionism endorse eating pathology, suggesting additional variables may interact with perfectionism to account for this association. The current study examined the influence of difficulties in emotion regulation on the relation between perfectionism and eating disorders. Undergraduate students (N = 309, 50.7% male) from a large university completed measures of perfectionism, emotion dysregulation, and eating pathology. The results indicated that high levels of perfectionism only accounted for significant variance in eating disorder symptoms among individuals with limited access to adaptive strategies to regulate emotions, but not among those with greater access to adaptive strategies. Findings demonstrate that clinicians and researchers should consider the role of emotion regulation among individuals with elevated levels of perfectionism and eating pathology. Future research should prospectively evaluate these associations and examine mechanisms that may further elucidate these relations.
Preparation-Free Measurement of Event-Related Potential in Oddball Tasks From Hairy Parts Using Candle-Like Dry Microneedle Electrodes

This paper reports successful measurement of even-related potential (ERP) using candle-like dry microneedle electrodes, which can acquire high-quality electroencephalogram (EEG) from hairy parts without any pretreatment. In our previous work, we successfully measured spontaneous EEG activity and its application to assess the stress state of the subjects. ERPs originate from electrophysiological response to stimulus and are one of the most important indices to capture the cognitive and sensory activities. In this work, using the candle-like dry microelectrodes, we demonstrate successful measurement of ERPs elicited by oddball tasks. Two oddball tasks using pure tone stimuli and speech stimuli were assigned to the subjects, where EEG was acquired from the parietal region (Cz in international 10-20 system). Note that no pretreatment, such as removal of hairs and abrasion of the scalp, was applied. As a result, P300 and mismatch negativity (MMN) were successfully measured in the both oddball tasks from the averaged EEG after the stimuli. Based on these results and given the attractive natures of the candle-like dry microneedle electrodes; they do not need any skin treatment and conductive gels and they can measure EEG from the hairy parts, the developed electrodes will accelerate cognitive neuroscience research using ERPs.
Loss of cyclin-dependent kinase 1 impairs bone formation, but does not affect the bone-anabolic effects of parathyroid hormone

Bone mass is maintained by a balance between osteoblast-mediated bone formation and osteoclast-mediated bone resorption. Although recent genetic studies have uncovered various mechanisms that regulate osteoblast differentiation, the molecular basis of osteoblast proliferation remains unclear. Here, using an osteoblast-specific loss-of-function mouse model, we demonstrate that cyclin-dependent kinase 1 (Cdk1) regulates osteoblast proliferation and differentiation. Quantitative RT-PCR analyses revealed that Cdk1 is highly expressed in bone and is down-regulated upon osteoblast differentiation. We also noted that Cdk1 is dispensable for the bone-anabolic effects of parathyroid hormone (PTH). Cdk1 deletion in osteoblasts led to osteoporosis in adult mice due to low bone formation, but did not affect osteoclast formation in vivo. Cdk1 overexpression in osteoblasts promoted proliferation, and conversely, Cdk1 knockdown inhibited osteoblast proliferation and promoted differentiation. Of note, we provide direct evidence that PTH's bone-anabolic effects occur without enhancing osteoblast proliferation in vivo. Furthermore, we found that Cdk1 expression in osteoblasts is essential for bone fracture repair. These findings may help reduce the risk of nonunion after bone fracture and identify patients at higher risk for nonresponse to PTH treatment. Collectively, our results indicate that Cdk1 is essential for osteoblast proliferation and that it functions as a molecular switch that shifts osteoblast proliferation to maturation. We therefore conclude that Cdk1 plays an important role in bone formation.
Temperature Dependence of the Internal Quantum Efficiency of Cu(In,Ga)Se2-Based Solar Cells

We measured the temperature-dependent internal quantum efficiency (IQE) of Cu(In,Ga)Se2-based (CIGS) solar cells. The largest differences in IQE spectra measured between 100 and 300 K were observed in the wavelength range, corresponding to the light absorbed exclusively in the CIGS layer. Absorbers in the investigated cells were grown using a one-stage process. Since all elements are supplied at a constant rate, the obtained layers are free of the band gap grading, therefore collection in the bulk of the layer is not affected by a quasi-electrical field. This allows us to discuss temperature changes in collection solely in terms of recombination. We associate the change in IQE with recombination via defects present in the bulk of absorber. The two cases of donor and acceptor defects are discussed. Using SCAPS software and basic handbook formulas that describe the emission and capture rates of carriers, we estimate a range of basic parameters of the possible bulk defects in CIGS that are responsible for the temperature change of IQE spectra. Our results suggest that IQE may be controlled by shallow defects of ionization energy of 45 and 60 meV for the donor and acceptor cases, respectively. We calculate IQE spectra at different temperatures. The temperature change of simulated spectra reproduces the same tendency as experimental characteristics.
SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations

We describe the SEx BiST parser (Semantically EXtended Bi-LSTM parser) developed at Lattice for the CoNLL 2018 Shared Task (Multilingual Parsing from Raw Text to Universal Dependencies). The main characteristic of our work is the encoding of three different modes of contextual information for parsing: (i) Treebank feature representations, (ii) Multilingual word representations, (iii) ELMo representations obtained via unsupervised learning from external resources. Our parser performed well in the official end-to-end evaluation (73.02 LAS – 4th/26 teams, and 78.72 UAS – 2nd/26); remarkably, we achieved the best UAS scores on all the English corpora by applying the three suggested feature representations. Finally, we were also ranked 1st at the optional event extraction task, part of the 2018 Extrinsic Parser Evaluation campaign.
Learning Block Programming using Scratch among School Children in Malaysia and Australia: An Exploratory Study

Over the past decade, there is a growing interest in block-based programming that aims support early learning and teaching of programming for young children. Block-based programming is suitable for children aged between 8 to 12 years old who are too young to understand and learn the logic and scripting language in view of their low maturity level and lack of exposure to programming. However, the visual Integrated Development Environment (IDE) of block programming allows children aged between 8 to 12 years old to quickly grab the underlying logics, code the program and solve the given problem. This paper explores the learning of block programming using Scratch programming among schoolchildren between the ages of 9 to 11 years old. The school children are characterized into three categories who are (1) Australian kids based in Perth, (2) Malaysian kids based in Perth and (3) Malaysian kids based in Kuala Lumpur. These school children have been selected as they have been exposed to Scratch programming through a week long session of programming camp. The preliminary analysis indicates that the children have enjoyed learning Scratch programing. They feel the language is fun and easy to learn. They are very interested to learn more and would eventually like to master the programming language. They look forward to enrolling to the Scratch programing classes in schools if it is being offered and even as programs conducted in external classes. The findings gathered are still preliminary in nature, therefore, a much-detailed study needs to be carried out before these findings can be generalized and concluded. The learning of programming like Scratch has indirectly promoted the learning of Science, Technology, Engineering and Mathematics (STEM) in Malaysia and Australia.
Magnetic Au-Ag-γ-Fe2O3/rGO Nanocomposites as an Efficient Catalyst for the Reduction of 4-Nitrophenol

In this paper, a facile route has been developed to prepare magnetic trimetallic Au-Ag-γ-Fe2O3/rGO nanocomposites. The impact of the preparation method (the intensity of reductant) on the catalytic performance was investigated. The nanocomposites were characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The prepared nanocomposites show fine catalytic activity towards the reduction reaction of 4-nitrophenol (4-NP). The nanocomposites also have superparamagnetism at room temperature, which can be easily separated from the reaction systems by applying an external magnetic field.