Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of interprofessional learning among medical and pharmacy students using a virtual patient simulation

This paper presents the use of a virtual patient simulation to encourage interprofessional engagement in undergraduate medical and pharmacy students. Twenty students from each discipline were selected to participate in the study, and invited to complete the simulation on their own or in cross-disciplinary pairs. The Readiness for Interprofessional Learning Scale, which considers attitudes towards teamwork and collaboration, professional identity, and roles and responsibilities, was used to assess the students' attitudes towards interprofessional engagement pre- and post-simulation. These results are presented and discussed.
Dynamic Voltage Conditioner: A New Concept for Smart Low-Voltage Distribution Systems

Power Quality (PQ) improvement in distribution level is an increasing concern in modern electrical power systems. One of the main problems in low voltage (LV) networks is related to load voltage stabilization close to the nominal value. Usually this problem is solved by smart distribution transformers, hybrid transformers and solid-state transformers, but also dynamic voltage conditioners (DVC) can be an innovative and a cost effective solution. The paper introduces a new control method of a single-phase DVC system able to compensate these long duration voltage drifts. For these events, it is mandatory to avoid active power exchanges so, the controller is designed to work with nonactive power only. Operation limits for quadrature voltage injection control is formulated and reference voltage update procedure is proposed to guarantee its continuous operating. DVC performance for main voltage and load variation is examined. Proposed solution is validated with simulation study and experimental laboratory tests. Some simulation and experimental results are illustrated to show the prototype device's performance.
Capacity evaluation on the long-distance orbital angular momentum non-orthogonal transmission

Electromagnetic (EM) wave with Orbital Angular Momentum (OAM) can achieve high channel capacity via OAM mode multiplexing. However, due to the beam divergence angle, it is impossible to receive the whole phase front in long-distance. Consequently, partial aperture receiving gradually draws attention in the long-distance OAM transmission. In this paper, the channel capacity of such long-distance non-orthogonal transmission is analyzed. Specifically, the long-distance OAM non-orthogonal transmission system structure and the mathematical model of the channel capacity are established. Meanwhile, simulations are carried out to evaluate the channel capacities. Compared with the plane wave received by antenna array, the OAM non-orthogonal transmission can also result in a significant channel capacity gain. Similar to the Multiple-Input and Multiple-Output (MIMO) system, the capacity gain non-linearly decreases when the multiplexed OAM modes increase, which means a limited number of OAM modes is sufficient and appreciated in the practical applications.
Deepcode: Feedback Codes via Deep Learning

The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly outperforms state-of-the-art codes designed over several decades of research. The communication channel under consideration is the Gaussian noise channel with feedback, whose study was initiated by Shannon; feedback is known theoretically to improve reliability of communication, but no practical codes that do so have ever been successfully constructed. We break this logjam by integrating information theoretic insights harmoniously with recurrent-neural-network based encoders and decoders to create novel codes that outperform known codes by 3 orders of magnitude in reliability and achieve a 3dB gain in terms of SNR. We also demonstrate several desirable properties of the codes: (a) generalization to larger block lengths, (b) composability with known codes, and (c) adaptation to practical constraints. This result also has broader ramifications for coding theory: even when the channel has a clear mathematical model, deep learning methodologies, when combined with channel-specific information-theoretic insights, can potentially beat state-of-the-art codes constructed over decades of mathematical research.
Modulation of Spike-Timing Dependent Plasticity: Towards the Inclusion of a Third Factor in Computational Models

In spike-timing dependent plasticity (STDP) change in synaptic strength depends on the timing of pre- vs. postsynaptic spiking activity. Since STDP is in compliance with Hebb’s postulate, it is considered one of the major mechanisms of memory storage and recall. STDP comprises a system of two coincidence detectors with N-methyl-D-aspartate receptor (NMDAR) activation often posited as one of the main components. Numerous studies have unveiled a third component of this coincidence detection system, namely neuromodulation and glia activity shaping STDP. Even though dopaminergic control of STDP has most often been reported, acetylcholine, noradrenaline, nitric oxide (NO), brain-derived neurotrophic factor (BDNF) or gamma-aminobutyric acid (GABA) also has been shown to effectively modulate STDP. Furthermore, it has been demonstrated that astrocytes, via the release or uptake of glutamate, gate STDP expression. At the most fundamental level, the timing properties of STDP are expected to depend on the spatiotemporal dynamics of the underlying signaling pathways. However in most cases, due to technical limitations experiments grant only indirect access to these pathways. Computational models carefully constrained by experiments, allow for a better qualitative understanding of the molecular basis of STDP and its regulation by neuromodulators. Recently, computational models of calcium dynamics and signaling pathway molecules have started to explore STDP emergence in ex and in vivo-like conditions. These models are expected to reproduce better at least part of the complex modulation of STDP as an emergent property of the underlying molecular pathways. Elucidation of the mechanisms underlying STDP modulation and its consequences on network dynamics is of critical importance and will allow better understanding of the major mechanisms of memory storage and recall both in health and disease.
A reference cytochrome c oxidase subunit I database curated for hierarchical classification of arthropod metabarcoding data

Metabarcoding is a popular application which warrants continued methods optimization. To maximize barcoding inferences, hierarchy-based sequence classification methods are increasingly common. We present methods for the construction and curation of a database designed for hierarchical classification of a 157 bp barcoding region of the arthropod cytochrome c oxidase subunit I (COI) locus. We produced a comprehensive arthropod COI amplicon dataset including annotated arthropod COI sequences and COI sequences extracted from arthropod whole mitochondrion genomes, the latter of which provided the only source of representation for Zoraptera, Callipodida and Holothyrida. The database contains extracted sequences of the target amplicon from all major arthropod clades, including all insect orders, all arthropod classes and Onychophora, Tardigrada and Mollusca outgroups. During curation, we extracted the COI region of interest from approximately 81 percent of the input sequences, corresponding to 73 percent of the genus-level diversity found in the input data. Further, our analysis revealed a high degree of sequence redundancy within the NCBI nucleotide database, with a mean of approximately 11 sequence entries per species in the input data. The curated, low-redundancy database is included in the Metaxa2 sequence classification software (http://microbiology.se/software/metaxa2/). Using this database with the Metaxa2 classifier, we performed a cross-validation analysis to characterize the relationship between the Metaxa2 reliability score, an estimate of classification confidence, and classification error probability. We used this analysis to select a reliability score threshold which minimized error. We then estimated classification sensitivity, false discovery rate and overclassification, the propensity to classify sequences from taxa not represented in the reference database. Our work will help researchers design and evaluate classification databases and conduct metabarcoding on arthropods and alternate taxa.
Dispersed Ag2O/Ag on CNT-Graphene Composite: An Implication for Magnificent Photoreduction and Energy Storage Applications

A simple hydrothermal route assisted by a triblock copolymer was used to synthesize Ag2O/Ag nanoparticles on a robotic support consists of functionalized MWCNTs and graphene composite (Ag2O/Ag/CNT-graphene). The composites together with the individual analog of Ag/CNT and Ag/graphene were characterized by means of XRD, TEM-SAED, N2 sorptiometry, Raman, FTIR, UV-Vis, and photoluminescence spectroscopy. These nanomaterials were then tested for the catalytic reduction of 4-nitrophenol (4-NP) to the technologically beneficial 4-aminophenol (4-AP). The Ag2O@Ag@CNT-graphene composite calcined at 400°C has shown fascinating reduction performances for 4-NP either in the dark (k = 0.014 s−1) or under visible light illumination (k = 0.039 s−1) in the presence of 5 mM NaBH4 compared to Ag/CNT (0.0112 s−1) and Ag/graphene (0.010 s−1) catalysts. This was chiefly because Ag2O@Ag@CNT-graphene comprises the highest pore volume (0.49 cm3/g) and involves three types of pores in the margin from 1.8 to 4.0 nm in front of only one modal type of pores for the rest of the catalysts and thus maximizes the adsorptive capacity of the reactants (4-NP and NaBH4). Moreover, the former composite exhibits the highest concentration of the Ag2O component as established by numerous techniques in addition to the cyclic voltammetry, proposing it's facile reaction with 4-NP along with the simultaneous transfer of surface hydrogen and electrons from NaBH4 ions to produce 4-AP. The promotion of the p-n junction evaluated using the Mott-schottky equation on Ag2O@Ag@CNT-graphene assisted by charges separation and surface plasmon resonance bands of Ag and Ag2O are found to be advantageous for 4-NP reduction. The latter composite delivers a specific capacitance of 355 F g−1 at 1.0 A g−1 exceeding those of Ag/CNT (230 F g−1) and Ag/graphene (185 F g−1). The EIS study establishes the high electronic conductivity of the metallic Ag and Ag2O moieties, low internal resistance of CNT-graphene as well as the marked ionic transfer facilitated by the composite porous nature.
Television Is Still “Easy” and Print Is Still “Tough”? More Than 30 Years of Research on the Amount of Invested Mental Effort

We provide a literature overview of 30 years of research on the amount of invested mental effort (AIME, Salomon, 1984), illuminating relevant literature in this field. Since the introduction of AIME, this concept appears to have vanished. To obtain a clearer picture of where the theory of AIME has diffused, we conducted a literature search focusing on the period 1985–2015. We examined scientific articles (N = 244) that cite Salomon (1984) and content-analyzed their keywords. Based on these keywords, we identified seven content clusters: affect and motivation, application fields, cognition and learning, education and teaching, media technology, learning with media technology, and methods. We present selected works of each content cluster and describe in which research field the articles had been published. Results indicate that AIME was most commonly (but not exclusively) referred to in the area of educational psychology indicating its importance regarding learning and education, thereby investigating print and TV, as well as new media. From a methodological perspective, research applied various research methods (e.g., longitudinal studies, experimental designs, theoretical analysis) and samples (e.g., children, college students, low income families). From these findings, the importance of AIME for further research is discussed.
Multiple dispersal out of Anatolia: biogeography and evolution of oriental green lizards

The oriental green lizards of the Lacerta trilineata group are widely distributed in Greece, Anatolia, the eastern Mediterranean, the southern Caucasus, and the Zagros mountains in Iran. We studied their phylogeography using three mitochondrial markers with comprehensive sampling from most representatives of the group. Their phylogeny and divergence times (implementing fossil-based molecular clock calibrations) were inferred using Bayesian methods, and haplotype networks were reconstructed to assess how genetic diversity and current distributional patterns were shaped. According to our phylogenetic analyses, the group constitutes a well-supported monophylum containing several distinct evolutionary lineages with high haplotype diversity. Vicariance might explain the divergences within most lineages that have accumulated by range restriction and expansion of populations as a result of Quaternary climate oscillations and glacial refugia. However, niche divergence appears to be a major force promoting speciation, and large scale distributional patterns between lineages were shaped earlier by multiple, independent dispersals out of Anatolia during the Pliocene and early Pleistocene. The results of the present study also suggest that the group is in need of a taxonomical revision because the identified lineages and genetic diversity are not congruent with the currently recognized subspecies. © 2013 The Linnean Society of London, Biological Journal of the Linnean Society, 2013, 110, 398–408.
Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera

Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving. However, depth completion faces 3 main challenges: the irregularly spaced pattern in the sparse depth input, the difficulty in handling multiple sensor modalities (when color images are available), as well as the lack of dense, pixel-level ground truth depth labels for training. In this work, we address all these challenges. Specifically, we develop a deep regression model to learn a direct mapping from sparse depth (and color images) input to dense depth prediction. We also propose a self-supervised training framework that requires only sequences of color and sparse depth images, without the need for dense depth labels. Our experiments demonstrate that the self-supervised framework outperforms a number of existing solutions trained with semi-dense annotations. Furthermore, when trained with semi-dense annotations, our network attains state-of-the-art accuracy and is the winning approach on the KITTI depth completion benchmark1 at the time of submission.1http://www.cvlibs. net/datasets/kitti/eval_depth. php? benchmark=depth_completion
Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

Manually labeled corpora are expensive to create and often not available for low-resource languages or domains. Automatic labeling approaches are an alternative way to obtain labeled data in a quicker and cheaper way. However, these labels often contain more errors which can deteriorate a classifier’s performance when trained on this data. We propose a noise layer that is added to a neural network architecture. This allows modeling the noise and train on a combination of clean and noisy data. We show that in a low-resource NER task we can improve performance by up to 35% by using additional, noisy data and handling the noise.
Flashover performance and arc resistance of surface fluorinated epoxy insulators

This report demonstrates the effects of direct fluorination on the flashover performance and arc resistance of epoxy resin insulators. Two types of epoxy resin were investigated, which are anhydride-cured and amine-cured DGEBA epoxy resins. The former is the pure epoxy resin without any inorganic filler, and the latter is reinforced by the glass fiber. Similar effects of direct fluorination were found for the two types of epoxy resins. The dc flashover performance and arc resistance are definitely improved, although direct fluorination only leads to a marginal increase in ac flashover voltage in a fast rising voltage mode. The improvement in flashover performance is attributed to the increase in surface conduction. The enhancement in arc resistance is due to the formation of C-F bonds.
Improving Goal-Oriented Visual Dialog Agents via Advanced Recurrent Nets with Tempered Policy Gradient

Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic. However, training text-generating agents efficiently is still a considerable challenge. Commonly used policy-based dialogue agents often end up focusing on simple utterances and suboptimal policies. To mitigate this problem, we propose a class of novel temperature-based extensions for policy gradient methods, which are referred to as Tempered Policy Gradients (TPGs). These methods encourage exploration with different temperature control strategies. We derive three variations of the TPGs and show their superior performance on a recently published AI-testbed, i.e., the GuessWhat? game. On the testbed, we achieve significant improvements with two innovations. The first one is an extension of the state-of-the-art solutions with Seq2Seq and Memory Network structures that leads to an improvement of 9%. The second one is the application of our newly developed TPG methods, which improves the performance additionally by around 5% and, even more importantly, helps produce more convincing utterances. TPG can easily be applied to any goal-oriented dialogue systems.
CoASD: A tabletop game to support the collaborative work of users with autism spectrum disorder

This paper presents the design and evaluation of CoASD (Collaborative Game for people with Autism Spectrum Disorder). CoASD is a game designed to offer support for collaborative work for users with Autism Spectrum Disorder (ASD). Individuals with ASD have impairments in social communication and behavioral areas, presenting difficulty to perceive, interact, communicate and share with others. CoASD Game has been projected with specific characteristics to support the performance of the required collaborative tasks, as well as to encourage the collaborative work of ASD users with higher difficulties in the affected areas. The game has been assessed upon a group of seven boys with high degrees of impairment. The results have shown that CoASD Game favorably contributes to draw the attention of users to perceive and perform the tasks, motivating them to act and collaborate with their partners.
The Influence of Context on the Learning of Metrical Stress Systems Using Finite-State Machines

Languages vary in the way stress is assigned to syllables within words. This article investigates the learnability of stress systems in a wide range of languages. The stress systems can be described using finite-state automata with symbols indicating levels of stress (primary, secondary, or no stress). Finite-state automata have been the focus of research in the area of grammatical inference for some time now. It has been shown that finite-state machines are learnable from examples using state-merging. One such approach, which aims to learn k-testable languages, has been applied to stress systems with some success. The family of k-testable languages has been shown to be efficiently learnable (in polynomial time). Here, we extend this approach to k, l-local languages by taking not only left context, but also right context, into account. We consider empirical results testing the performance of our learner using various amounts of context (corresponding to varying definitions of phonological locality). Our results show that our approach of learning stress patterns using state-merging is more reliant on left context than on right context. Additionally, some stress systems fail to be learned by our learner using either the left-context k-testable or the left-and-right-context k, l-local learning system. A more complex merging strategy, and hence grammar representation, is required for these stress systems.
Prenatal sonographic findings and prognosis of craniosynostosis diagnosed during the fetal and neonatal periods

The aim of the study was to explore the sonographic findings of fetuses with craniosynostosis and investigate their prognosis. We conducted a 5‐year, multicenter retrospective study and collected data on patients with craniosynostosis diagnosed in the perinatal period. Of 41 cases, 30 cases (73%) were syndromic craniosynostosis, eight cases (20%) were non‐syndromic craniosynostosis and the other three cases (7%) were secondary craniosynostosis of chromosomal deletion syndromes. The prenatal ultrasound detection rate was 61%. Half of the cases of syndromic craniosynostosis detected during the perinatal period were Pfeiffer syndrome; there were also six cases of Apert syndrome, three cases of Crouzon syndrome and other rare form of syndromic craniosynostosis (Beare‐Stevenson syndrome, Saethre‐Chotzen syndrome, cranioectodermal dysplasia, and thanatophoric dysplasia). Abnormal shape of the skull was the most common finding leading to prenatal diagnosis of craniosynostosis. Abnormal head biometry, which was the second most frequent finding, was closely correlated with deformation of the cranial shape. Three cases presented with ventriculomegaly and exophthalmos but normal cranial shape and size. The overall survival rate of infants with syndromic craniosynostosis was 79%, while all of the infants with non‐syndromic craniosynostosis survived. In conclusion, prenatal diagnosis of craniosynostosis is difficult, especially when dysmorphic change of the fetal cranium is not evident. Abnormal head biometry and ventriculomegaly could potentially be additional markers of fetal craniosynostosis and consequently increase the prenatal detection rate.
How To Backdoor Federated Learning

Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other. For example, multiple smartphones can jointly train a next-word predictor for keyboards without revealing what individual users type. We demonstrate that any participant in federated learning can introduce hidden backdoor functionality into the joint global model, e.g., to ensure that an image classifier assigns an attacker-chosen label to images with certain features, or that a word predictor completes certain sentences with an attacker-chosen word. We design and evaluate a new model-poisoning methodology based on model replacement. An attacker selected in a single round of federated learning can cause the global model to immediately reach 100% accuracy on the backdoor task. We evaluate the attack under different assumptions for the standard federated-learning tasks and show that it greatly outperforms data poisoning. Our generic constrain-and-scale technique also evades anomaly detection-based defenses by incorporating the evasion into the attacker's loss function during training.