Miracle-dz/UniFAR-FTUs huggingface dataset:
Si/SiGe superlattice I/O finFETs in a vertically-stacked Gate-All-Around horizontal Nanowire Technology

This work presents Si/SiGe superlattice finFETs (FF) for 1.8V/2.5V I/O applications in vertically-stacked Gate-All-Around horizontal nanowire technology (hNW) technology. Superlattice FF have a higher ION than I/O hNW reference devices and can be more easily integrated into a GAA hNW technology than Si I/O FF. These novel I/O FET structures exhibit competitive analog performance and are superior as ESD protection devices.
A Miniaturized Dual-Orbital-Angular-Momentum (OAM)–Mode Helix Antenna

Traditional helix antenna is firstly modified to radiate orbital angular momentum (OAM) wave, which has a compact size and an outstanding gain. Firstly, general principle of generating OAM by helix antenna is introduced. Secondly, field pattern and electromagnetic field distribution on helix OAM antenna are analyzed. Then, based on the preceding analysis, a dual-OAM-mode helix antenna is designed, fabricated, and measured. Instead of using traditional helical wire, this prototype is realized by PCB on flexible dielectric laminate, which is rolled into a hollow cylinder. This makes the proposed antenna easy to be fabricated and fully miniaturized. Both simulation and measurement show that it can generate OAM with mode number l = 2 and 3 independently on 5 GHz. From 4.8 to 5.2 GHz, vortex phase of both OAM modes is clearly observed, VSWR remains below 1.6, and the simulated peak gain varies from 8.1 to 8.7 dB, which stands out among existing OAM antennas with similar size.
EEG Neural Correlates of Self-Paced Left- and Right-Hand Movement Intention during a Reaching Task

The study of neural correlates of self-paced movement intention helps develop a more natural and practical brain computer interface. In this paper, we studied EEG neural correlates of self-paced left- and right-hand movement intention during a reaching task. A slow decreased movement related cortical potential (MRCP) related to movement intention was found before both the left- and right-hand movements. The temporal start point and decrement of MRCP before the left- and right-hand reaching movements showed differently. Moreover, alpha band power increase/decrease was observed before the onset of both left- and right-hand movements. Alpha band powers increase before both movement conditions in the frontal-central area. In the central area, alpha band powers decrease consistently at electrodes C3, Cz and C4 before the right-hand reaching movement. While alpha band powers increase at electrodes C3 and Cz before the left-hand reaching movement.
Simulating the Outcome of Heart Allocation Policies Using Deep Neural Networks

We created a system to simulate the heart allocation process in a transplant queue, using a discrete event model and a neural network algorithm, which we named the Lund Deep Learning Transplant Algorithm (LuDeLTA). LuDeLTA is utilized to predict the survival of the patients both in the queue and after transplant. We tried four different allocation policies: wait time, clinical rules and allocating the patients using either LuDeLTA or The International Heart Transplant Survival Algorithm (IHTSA) model. Both IHTSA and LuDeLTA were used to evaluate the results. The predicted mean survival for allocating according to wait time was about 4,300 days, clinical rules 4,300 days and using neural networks 4,700 days.
The SLT-Interactions Parsing System at the CoNLL 2018 Shared Task

This paper describes our system (SLT-Interactions) for the CoNLL 2018 shared task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system performs three main tasks: word segmentation (only for few treebanks), POS tagging and parsing. While segmentation is learned separately, we use neural stacking for joint learning of POS tagging and parsing tasks. For all the tasks, we employ simple neural network architectures that rely on long short-term memory (LSTM) networks for learning task-dependent features. At the basis of our parser, we use an arc-standard algorithm with Swap action for general non-projective parsing. Additionally, we use neural stacking as a knowledge transfer mechanism for cross-domain parsing of low resource domains. Our system shows substantial gains against the UDPipe baseline, with an average improvement of 4.18% in LAS across all languages. Overall, we are placed at the 12th position on the official test sets.
Risk factors for substance use: perception of student leaders.

OBJECTIVE to analyze the perceptions of student leaders of the undergraduate course in Nursing about the motivations and/or risk factors for substance use in the university environment. METHOD qualitative, transversal, descriptive study developed in a university campus in the state of São Paulo. Thirty members of associations affiliated with the course in Nursing participated. A questionnaire and a semi-structured interview were used, and the information was subjected to content analysis. RESULTS the influence of the social environment was the central element in the results. The circumstances of the context, some conditions that favor the use of substances and the students' expectations regarding their use were perceived as risk factors, among which the influence of friends (96%), curiosity and search for fun (93%) stood out. FINAL CONSIDERATIONS alternatives in the university context, in conjunction with student leaders, aimed at reducing the exposure to physical and psychological suffering, constitute important resources to prevent substance abuse.
The Absolute Number of Oligodendrocytes in the Adult Mouse Brain

The central nervous system is a highly complex network composed of various cell types, each one with different subpopulations. Each cell type has distinct roles for the functional operation of circuits, and ultimately, for brain physiology in general. Since the absolute number of each cell type is considered a proxy of its functional complexity, one approach to better understand how the brain works is to unravel its absolute cellularity and the quantitative relations between cell populations; in other words, how one population of cells is quantitatively structured, in relation to another. Oligodendrocytes are one of these cell types – mainly, they provide electric insulation to axons, optimizing action potential conduction. Their function has recently been revisited and their role extended, one example being their capability of providing trophic support to long axons. To determine the absolute cellularity of oligodendroglia, we have developed a protocol of oligodendrocyte quantification using the isotropic fractionator with a pan-marker for this cell type. We report a detailed assessment of specificity and universality of the oligodendrocyte transcription factor 2 (Olig2), through systematic confocal analyses of the C57BL/6 mouse brain. In addition, we have determined the absolute number (17.4 million) and proportion (about 20%) of this cell type in the brain (and in different brain regions), and tested if this population, at the intraspecific level, scales with the number of neurons in an allometric-based approach. Considering these numbers, oligodendrocytes proved to be the most numerous of glial cells in the mouse brain.
Semi-unsupervised Learning of Human Activity using Deep Generative Models

We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning where, in the training data, some classes are sparsely labelled and others entirely unlabelled. Models able to learn from training data of this type are potentially of great use as many real-world datasets are like this. Here we demonstrate a new deep generative model for classification in this regime. Our model, a Gaussian mixture deep generative model, demonstrates superior semi-unsupervised classification performance on MNIST to model M2 from Kingma and Welling (2014). We apply the model to human accelerometer data, performing activity classification and structure discovery on windows of time series data.
Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus

News editorials are said to shape public opinion, which makes them a powerful tool and an important source of political argumentation. However, rarely do editorials change anyone’s stance on an issue completely, nor do they tend to argue explicitly (but rather follow a subtle rhetorical strategy). So, what does argumentation quality mean for editorials then? We develop the notion that an effective editorial challenges readers with opposing stance, and at the same time empowers the arguing skills of readers that share the editorial’s stance — or even challenges both sides. To study argumentation quality based on this notion, we introduce a new corpus with 1000 editorials from the New York Times, annotated for their perceived effect along with the annotators’ political orientations. Analyzing the corpus, we find that annotators with different orientation disagree on the effect significantly. While only 1% of all editorials changed anyone’s stance, more than 5% meet our notion. We conclude that our corpus serves as a suitable resource for studying the argumentation quality of news editorials.
A Modular Multiplier Implemented With Truncated Multiplication

In this brief, we propose a modular multiplication algorithm with four truncated multiplications to reduce the critical path. According to our algorithm, a high-speed 3-stage modular multiplier is constructed. Moreover, synthesized with TSMC 90 nm, our design can perform a 256-bit modular multiplication for every clock period of 3.58 ns with circuit scale of approximately 629K equivalent gates. Further, by utilizing the modular multiplier, an elliptic curves cryptography processor is constructed, and it can perform a scalar multiplication in 19.4 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, which is one of the fastest to date in published literatures.
Effect of IV alteplase on the ischemic brain lesion at 24–48 hours after ischemic stroke

Objective To determine whether alteplase alters the development of ischemic lesions on brain imaging after stroke. Methods The Third International Stroke Trial (IST-3) was a randomized controlled trial of IV alteplase for ischemic stroke. We assessed CT or brain MRI at baseline (pretreatment) and 24 to 48 hours posttreatment for acute lesion visibility, extent, and swelling, masked to all other data. We analyzed associations between treatment allocation, change in brain tissue appearances between baseline and follow-up imaging, and 6-month functional outcome in IST-3. We performed a meta-analysis of randomized trials of alteplase vs control with pre- and postrandomization imaging. Results Of 3,035 patients recruited in IST-3, 2,916 had baseline and follow-up brain imaging. Progression in either lesion extent or swelling independently predicted poorer 6-month outcome (adjusted odds ratio [OR] = 0.92, 95% confidence interval [CI] 0.88–0.96, p < 0.001; OR = 0.73, 95% CI 0.66–0.79, p < 0.001, respectively). Patients allocated alteplase were less likely than controls to develop increased lesion visibility at follow-up (OR = 0.77, 95% CI 0.67–0.89, p < 0.001), but there was no evidence that alteplase reduced progression of lesion extent or swelling. In meta-analysis of 6 trials including IST-3 (n = 4,757), allocation to alteplase was associated with a reduction in ischemic lesion extent on follow-up imaging (OR = 0.85, 95% CI 0.76–0.95, p = 0.004). Conclusion Alteplase was associated with reduced short-term progression in lesion visibility. In meta-analysis, alteplase reduced lesion extent. These findings may indicate that alteplase improves functional outcome by reducing tissue damage. Classification of evidence This study provides Class II evidence that IV alteplase impedes the progression of ischemic brain lesions on imaging after stroke.
Impact of Austria's 2009 trans fatty acids regulation on all-cause, cardiovascular and coronary heart disease mortality

Abstract Background Unhealthy diet, especially consumption of trans fatty acids (TFAs), is a known risk factor for cardiovascular disease (CVD), a leading cause of death in Austria. In 2009, Austria introduced a law regulating the content of TFAs in foods. The aim of this study was to assess the impact of the TFA regulation on CVD-related outcomes. Methods The study evaluated the TFA regulation as an intervention in a natural experiment. Two study periods were assessed: pre-intervention (1995–2009) and post-intervention (2010–14). The study compared the age-standardized death rates per 100 000 population for CVD outcomes with those of a ‘synthetic’ international comparator population, created from data of OECD countries where TFA regulation has not been implemented, but where the population is otherwise comparable. Results There was a continuous decrease in CVD-related mortality throughout the study period in both the synthetic international comparator population, as well as in the adult Austrian population, with no significant change in this trend observed as an effect of TFA regulation. Conclusions Whilst the results are counterintuitive, given the established link between TFA consumption and an increased risk of CVD, there are many possible explanations: high prevalence of tobacco smoking, changes in TFA content in foods due to international guidance as opposed to formal regulation and a beneficial impact of TFA regulation on sub-groups of the population that might not be detected with nationally aggregated data. However, reduction in TFAs should still be considered an important part of risk factor reduction for CVD and other non-communicable diseases.
Mitochondrial‐Mediated Oxidative Ca2+/Calmodulin‐Dependent Kinase II Activation Induces Early Afterdepolarizations in Guinea Pig Cardiomyocytes: An In Silico Study

Background Oxidative stress–mediated Ca2+/calmodulin‐dependent protein kinase II (CaMKII) phosphorylation of cardiac ion channels has emerged as a critical contributor to arrhythmogenesis in cardiac pathology. However, the link between mitochondrial‐derived reactive oxygen species (mdROS) and increased CaMKII activity in the context of cardiac arrhythmias has not been fully elucidated and is difficult to establish experimentally. Methods and Results We hypothesize that pathological mdROS can cause erratic action potentials through the oxidation‐dependent CaMKII activation pathway. We further propose that CaMKII‐dependent phosphorylation of sarcolemmal slow Na+ channels alone is sufficient to elicit early afterdepolarizations. To test the hypotheses, we expanded our well‐established guinea pig cardiomyocyte excitation‐contraction coupling, mitochondrial energetics, and ROS‐induced‐ROS‐release model by incorporating oxidative CaMKII activation and CaMKII‐dependent Na+ channel phosphorylation in silico. Simulations show that mdROS mediated‐CaMKII activation elicits early afterdepolarizations by augmenting the late Na+ currents, which can be suppressed by blocking L‐type Ca2+ channels or Na+/Ca2+ exchangers. Interestingly, we found that oxidative CaMKII activation–induced early afterdepolarizations are sustained even after mdROS has returned to its physiological levels. Moreover, mitochondrial‐targeting antioxidant treatment can suppress the early afterdepolarizations, but only if given in an appropriate time window. Incorporating concurrent mdROS‐induced ryanodine receptors activation further exacerbates the proarrhythmogenic effect of oxidative CaMKII activation. Conclusions We conclude that oxidative CaMKII activation–dependent Na channel phosphorylation is a critical pathway in mitochondria‐mediated cardiac arrhythmogenesis.
Reading Customer Reviews to Answer Product-related Questions

The e-commerce websites are ready to build the community question answering (CQA) service, as it can facilitate questioners (potential buyers) to obtain satisfying answers from experienced customers and furthermore stimulate consumption. Given that more than 50% product-related questions only anticipate a binary response (i.e., “Yes” or “No”), the research on product-related question answering (PQA), which aims to automatically provide instant and correct replies to ques-tioners, emerges rapidly. The mainstream approaches on PQA generally employ customer reviews as the evidence to help predict answers to the questions which are product-speciﬁc and concerned more about subjective personal experiences. However, the supportive features either extracted by heuristic rules or acquired from unsupervised manners are not able to perform well on PQA. In this paper, we contribute an end-to-end neural architecture directly fed by the raw text of product-related questions and customer reviews to predict the answers. Concretely, it teaches machines to generate and to synthesize multiple question-aware review representations in a reading comprehension fashion to make the ﬁnal decision. We also extract a real-world dataset crawled from 9 categories in Amazon.com for PQA to assess the performance of our neural reading architecture (NRA) and other mainstream approaches such as COR-L [12], MOQA [12], and AAP [21]. Experimental results show that our NRA sets up a new state-of-the-art performance on this dataset, signiﬁcantly outper-forming existing algorithms.
Middle-Out Decoding

Despite being virtually ubiquitous, sequence-to-sequence models are challenged by their lack of diversity and inability to be externally controlled. In this paper, we speculate that a fundamental shortcoming of sequence generation models is that the decoding is done strictly from left-to-right, meaning that outputs values generated earlier have a profound effect on those generated later. To address this issue, we propose a novel middle-out decoder architecture that begins from an initial middle-word and simultaneously expands the sequence in both directions. To facilitate information flow and maintain consistent decoding, we introduce a dual self-attention mechanism that allows us to model complex dependencies between the outputs. We illustrate the performance of our model on the task of video captioning, as well as a synthetic sequence de-noising task. Our middle-out decoder achieves significant improvements on de-noising and competitive performance in the task of video captioning, while quantifiably improving the caption diversity. Furthermore, we perform a qualitative analysis that demonstrates our ability to effectively control the generation process of our decoder.
Auditory-visual convergence at the superior colliculus in rat using functional MRI

The superior colliculus (SC) of the midbrain has been a model structure for multisensory processing. Many neurons in the intermediate and deep SC layers respond to two or more of auditory, visual, and somatosensory stimuli as assessed by electrophysiology. In contrast, noninvasive and large field of view functional magnetic resonance imaging (fMRI) studies have focused on multisensory processing in the cortex. In this study, we applied blood oxygenation leveldependent (BOLD) fMRI on Sprague-Dawley rats receiving monaural (auditory) and binocular (visual) stimuli to study subcortical multisensory processing. Activation was observed in the left superior olivary complex, lateral lemniscus, and inferior colliculus and both hemispheres of the superior colliculus during auditory stimulation. The SC response was bilateral even though the stimulus was monaural. During visual stimulation, activation was observed in both hemispheres of the SC and lateral geniculate nucleus. In both hemispheres of the SC, the number of voxels in the activation area $( \mathrm {p}<10 -8$) and BOLD signal changes $( \mathrm {p}<0.01)$ were significantly greater during visual than auditory stimulation. These results provide functional imaging evidence that the SC is a site of auditoryvisual convergence due to its involvement in both auditory and visual processing. The auditory and visual fMRI activations likely reflect the firing of unisensory and multisensory neurons in the SC. The present study lays the groundwork for noninvasive functional imaging studies of multisensory convergence and integration in the SC.
Temporal Pattern of Ripple Events in Temporal Lobe Epilepsy: Towards a Pattern-based Localization of the Seizure Onset Zone

Ripples (80–250 Hz) are brief high-frequency oscillations that are often detected in intracranial EEG (iEEG) and are currently investigated as a potential biomarker to facilitate the Iocalization of the seizure onset zone (SOZ) in patients with drug-resistant epilepsy. While the rate and shape of these oscillations have been positively correlated with the SOZ, the temporal pattern of these oscillations in the epileptic brain still requires investigation. In this study, we investigate the temporal pattern of ripple events in five patients with temporal lobe epilepsy (TLE), which is one of the most common forms of epilepsy. The rate of ripple events is positively correlated with the SOZ in TLE but its diagnostic utility in localizing the SOZ remains unclear, which suggests that additional ripple-related features should be investigated. By combining point process modeling and cluster analysis, we show that a recurrent, non-stationary bursting pattern characterizes the SOZ channels consistently across patients, while the non-SOZ channels have poor between-channel similarity and no consistent pattern over time nor across patients. Furthermore, the degree of separation between SOZ and non-SOZ model parameter vectors is significantly higher (ANOVA test, ${P}$-value $P\lt 0.01$) than the degree of separation between the ripple rates, which suggests that the temporal pattern more than the rate may contribute to the pre- surgical Iocalization of the SOZ.
Nonlinear Model Predictive Control of Ironless Linear Motors

As the demands for accuracy and throughput in industrial positioning systems are increasing, classical control of ironless linear motors (ILMs) is facing its limit. Classical control scheme of an ILM typically consists of a simple sinusoidal commutation algorithm and a PID feedback controller. Classical commutation cannot compensate for parasitic effects, while classical PID feedback controller cannot guarantee constraints satisfaction. This problem can be addressed by replacing classical commutation with optimal commutation and PID controller with linear model predictive controller (LMPC). However, this LMPC and optimal commutation scheme requires solving two separate optimization problems, which is not optimal and can lead to infeasibility. In this paper we present a nonlinear model predictive control (NMPC) scheme for ILMs. The scheme requires solving only a single optimization problem. It can guarantee constraints satisfaction and is capable of compensating for parasitic forces. Simulation results are presented for demonstration.