Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wireless System for EEG Acquisition and Processing in an Earbud Form Factor with 600 Hours Battery Lifetime

In recent years, in-ear electroencephalography (EEG) was demonstrated to record signals of similar quality compared to standard scalp-based EEG, and clinical applications of objective hearing threshold estimations have been reported. Existing devices, however, still lack important features. In fact, most of the available solutions are based on wet electrodes, require to be connected to external acquisition platforms, or do not offer on-board processing capabilities. Here we overcome all these limitations, presenting an ear-EEG system based on dry electrodes that includes all the acquisition, processing, and connectivity electronics directly in the ear bud. The earpiece is equipped with an ultra-low power analog front-end for analog-to-digital conversion, a low-power MEMS microphone, a low-power inertial measurement unit, and an ARM Cortex-M4 based microcontroller enabling on-board processing and Bluetooth Low Energy connectivity. The system can stream raw EEG data or perform data processing directly in-ear. We test the device by analysing its capability to detect brain response to external auditory stimuli, achieving 4 and 1.3 mW power consumption for data streaming or on board processing, respectively. The latter allows for 600 hours operation on a PR44 zinc-air battery. To the best of our knowledge, this is the first wireless and fully self-contained ear-EEG system performing on-board processing, all embedded in a single earbud. Clinical relevance— The proposed ear-EEG system can be employed for diagnostic tasks such as objective hearing threshold estimations, outside of clinical settings, thereby enabling it as a point-of-care solution. The long battery lifetime is also suitable for a continuous monitoring scenario.
Automated Quantification of Inflamed Lung Regions in Chest CT by UNet++ and SegCaps: A Comparative Analysis in COVID-19 Cases

During the current COVID-19 pandemic, a high volume of lung imaging has been generated in the aid of the treating clinician. Importantly, lung inflammation severity, associated with the disease outcome, needs to be precisely quantified. Producing consistent and accurate reporting in high-demand scenarios can be a challenge that can compromise patient care with significant inter- or intra-observer variability in quantifying lung inflammation in a chest CT scan. In this backdrop, automated segmentation has recently been attempted using UNet++, a convolutional neural network (CNN), and results comparable to manual methods have been reported. In this paper, we hypothesize that the desired task can be performed with comparable efficiency using capsule networks with fewer parameters that make use of an advanced vector representation of information and dynamic routing. In this paper, we validate this hypothesis using SegCaps, a capsule network, by direct comparison, individual comparison with CT severity score, and comparing the relative effect on a ML(machine learning)-based prognosis model developed elsewhere. We further provide a scenario, where a combination of UNet++ and SegCaps achieves improved performance compared to individual models.
Design of a Printed Dipole Antenna by Using the Substrate-Integrated Double Line Technology

In the present paper, a novel printed dipole antenna is proposed with gain enhancement by using the newly emerging substrate-integrated double line (SIDL) technology for potential 5G millimeter-wave (mmWave) applications. As a three-wire transmission line, the SIDL incorporates two parallel inner strips and one grounded outer conductor. The inner strip pair protrudes and is bent bilaterally to form the dipole’s arms. The outer conductor is stretched out and spilt into four strips in a fourfold symmetry along the longitudinal axis to boost the gain. A bilateral slotline technology is used to couple the energy from the feed to the SIDL structure, providing a broadband transition. The shaped SIDL and the transition contribute to the proposed dipole element and then are expanded to a 1×2 array, both of which are designed and fabricated by using the standard PCB process. The measurement discloses that the element has an actual -10-dB impedance bandwidth from 33.5~47.4 GHz (34.4%@40.4 GHz) with a practical peak gain of 7.00 dBi, which for the array are 32.0~45.1 GHz (34.0%@38.6 GHz) and 8.88 dBi, respectively. Good agreement between the simulated and experimental results demonstrates the feasibility of SIDL’s utilization in designing printed dipole antenna for high-gain and wideband mmWave applications.
A U - Net Deep Learning Model for Infant Heart Rate Estimation from Ballistography*

Ballistography(BSG) is a non-intrusive and low- cost alternative to electrocardiography (ECG) for heart rate (HR) monitoring in infants. Due to the inter-patient variance and susceptibility to noise, heartbeat detection in the BSG waveform remains a challenge. The aim of this study was to estimate HR from a bed-based pressure mat BSG signal using a deep learning approach. We trained a U-Net deep neural network through supervised learning by deriving ground truth as the location of the heartbeats from simultaneously recorded ECG signals after peak matching. For improved generalization, we modified an existing U - Net to include an IC-layer. A predictive performance of 80% was achieved using the U-Net without the IC-layer. The inclusion of the IC-layer, while improving the generalization ability of the model to detect heartbeats, did not improve the HR estimation performance.
Frequency-Reconfigurable Circularly Polarized Omnidirectional Antenna

A concept of frequency-reconfigurable circularly polarized (CP) antenna with omnidirectional radiation characteristics is introduced. The antenna is based on a center-fed circular patch with shorting vias, surrounded by three arc-shaped arms. Omnidirectional CP is attained through its two orthogonal radiators with a quadrature phase difference. Three varactor-loaded meandered slots are etched onto the top patch to miniaturize the antenna and empower it with reconfigurability. The resonance frequency of the vertically and horizontally polarized modes can be simultaneously tuned by varying the bias voltage applied to the varactors, and thus frequency-reconfigurable CP is achieved. The realization demonstrates a wide CP tuning range of 28.5% from 3.55 to 4.73 GHz. Stable omnidirectional patterns in the azimuth plane are achieved across the tuning range. All these results suggest that the antenna is suitable for communication systems that require an omnidirectional CP antenna with a frequency-tunable feature.
A comparative Study Between Electrical and Morphological Features for Short-Circuit Faults Detection and Discrimination in Power Grid Lines

This paper outlines the adopted methodology to construct an intelligent system, which is able to detect and to discriminate between short circuit faults in high voltage power lines (220 kV, 50 Hz) with a length of 300 km. Based on the current study, two approaches for feature extraction are presented and compared. Firstly, the voltage and current signals are decomposed into 20 ms segments, and two distinct sets of descriptors are then calculated; The first one, consists on a set of 102 morphological, and the second one, consists on a set of 12 electrical parameters. Finally, two direct probabilistic multiclass support vector machines (M-SVM) are trained separately to discriminate between 10 short-circuit faults plus a normal case, each of them receives as inputs one of the previously calculated sets.The study shows that the obtained results are very satisfactory, however, the M-SVM presents higher accuracy when it’s trained by morphological parameters; with a classification rates of 96.74% and 91.23% for the first and second method respectively
Comparison of different emotion stimulation modalities: an EEG signal analysis

Emotions processing is a complex mechanism that involves different physiological systems. In particular, the Central Nervous System (CNS) is considered to play a key role in this mechanism and one of the main modalities to study the CNS activity is the Electroencephalographic signal (EEG). To elicit emotions, different kinds of stimuli can be used e.g.: audio, visual or a combination of the two. Literature studies focus more on the correct classification of the different types of emotions or which kind of stimulation gives the best performance in terms of classification accuracy. However, it is still unclear how the different stimuli elicit the emotions and which are the results in terms of brain activity. In this paper, we analysed and compared EEG signals given by eliciting emotions using audio and visual stimuli or a combination of the latter two. Data were collected during experiments conducted in our laboratories using IAPS and IADS dataset. Our study confirmed literature physiological studies about emotions highlighting higher brain activity in the frontal and central regions and in the $\delta$ and $\theta$ bands for each kind of stimulus. However, audio stimulation was found to have higher responses when compared to the other two modalities of stimulation in almost all the comparisons performed. Higher values of the $\delta/\beta$ ratios, an index related to negative emotions, have been achieved when using only sounds as stimuli. Moreover, the same type of stimuli, resulted in higher $\delta-\beta$ coupling, suggesting a better attention control. We concluded that stimulating subjects without letting them know (seeing) what is actually happening may give a higher perception of emotions, even if this mechanism remains highly subjective. Clinical Relevance— This paper suggests that audio stimuli may give higher perception of the elicited emotion resulting in higher brain activity in the physiological areas and more focused subjects. Thus using only audio in emotion related studies may give more reliable and consistent results
Long-Term Traffic Speed Prediction Based on Geometric Algebra ConvLSTM and Graph Attention

Traffic speed prediction is an incredibly important subject of Intelligent transportation system (ITS). Efficient speed prediction methods greatly contribute to reducing traffic congestion. Most existing models focus on short term while the long-term speed prediction for a whole day is not completely developed. In this paper, a Geometric Algebra Convolutional LSTM and Graph Attention (GAConvLSTM-GAT) model is proposed to raise a potential for achieving long-term speed prediction. The proposed model is composed of a Geometric Algebra ConvLSTM (GAConvLSTM) module to extract the spatial-temporal feature, and a Graph Attention (GAT) module to make speed predictions based on the features. The experiments are evaluated by two elevated highway traffic datasets. The presented results illustrate that our GAConvLSTM model outperforms multiple baseline neural network methods.
Dali: Scaling Lazy Compilation & JIT Fusion

The growing computational needs of many large scale machine learning applications have motivated the need for better JIT Fusion and graph manipulation tools that are able to improve utilization or stretch the on-chip memory capabilities through algorithmic tricks such as pipelining and recomputation. While there is strong evidence that these techniques can help keep up with the computational demands, they are typically hand written and require domain expertise in the internals of machine learning frameworks, GPU programming, and computational graph manipulation. Recent work, such as AutoTVM, FlexFlow, or Dali have shown that many of these optimizations can be automatically discovered and applied by the framework without human labor or expertise. Unfortunately, many of the proposed algorithms cannot handle fusion across entire deep networks or require hours of auto-tuning, and as we remark in this paper, choosing what operations to fuse is equally challenging. We propose to improve Dali’s compiler through the addition of a more robust fusion boundary detector and variable elimination during code generation. We demonstrate the impact of these changes by showing how JIT Fusion and graph optimizations are able to accelerate deep Transformers and RNNs. We compare our approach to TensorFlow, TensorFlow with XLA, and PyTorch, and observe a 1 . 23 − 2 . 13 × speedup over TensorFlow, and 1 . 08 − 1 . 92 × speedup over PyTorch.
A meta-analysis on efficacy and safety of rituximab for neuromyelitis optica spectrum disorders

Background: To assess the efficacy and safety of rituximab (RTX) in the treatment of neuromyelitis optica spectrum diseases (NMOSDs), and give a guideline on clinical medication Methods: The databases of Pubmed, Embase, Cochrane Library, CNKI, and Wan fang were systematically searched by computer, and the search period was from the establishment of the databases until January 2022. To collect the trials of RTX in the treatment of NMOSDs, two researchers completed literature screening, quality assessment, and data extraction independently. Statistical analysis was performed using Review Manager 5.3 and Stata 15.1 software. Results: There were 37 studies in the meta-analysis, including 5 randomized controlled trials (RCTs) and 32 observational studies. Meta-analysis results revealed that NMOSDs patients treated with RTX significantly reduced the annualized relapse rate (ARR) (weighted mean difference [WMD] = 1.45, 95% confidence interval [CI]: 1.24–1.66, P < .01) and the Expanded disability status scale (EDSS) scores (WMD = 1.34, 95%CI: 1.25–1.44, P < .01). RTX is more effective than azathioprine (AZA) in the treatment of NMOSDs (ARR: WMD = −0.54, 95% CI: −0.75 to −0.33; EDSS: WMD = −0.65, 95% CI: −0.83 to −0.48; P < .0001). There was no difference in ARR and EDSS scores between anti-aquapor in-4-antibody seropositive NMOSD and seronegative NMOSD patients treated with RTX (ARR: WMD = −0.01, 95% CI: −0.25 to 0.24, P = .96 > 0.05; EDSS: WMD = 0, 95% CI: −0.30 to 0.31, P = .99 > 0.05). In this study, 681 patients were recorded safety data of RTX therapy, 23% (156 patients) had adverse events, and 0.7% (5 patients) of NMOSDs discontinued due to severe adverse reactions. Conclusions: NMOSDs patients treated with RTX can significantly reduce the relapse frequency and EDSS scores, and also improve neurological dysfunction, besides the efficacy is better than azathioprine. RTX has a high incidence of adverse reactions, which are mild and with certain self limited, it should be cautious in clinical medication
Open-World Taxonomy and Knowledge Graph Co-Learning jiaying.lu@emory.eduEmoryUniversityAtlanta,Georgia

Taxonomies and knowledge graphs (KGs), which represent real-world entities’ abstract concepts and properties/behaviors/facts, constitute the essential information in knowledge bases (KBs). How-ever, most existing KBs are constructed under the closed-world assumption, which often corresponds to a � xed schema and requires ad-hoc canonicalization to include new knowledge. To empower KBs towards easy accommodation of emerging entities and relations, we propose to create open-world T ��� KG based on existing automatically constructed taxonomies and open KGs, where taxonomies serve to provide a loosely-de � ned schema and mitigate the reliance on ad-hoc canonicalization. To further improve the com-pleteness of T ��� KG , we collect several new benchmark datasets towards the development of H ��� GCN , an innovative hierarchy-aware graph-friendly model for T ��� KG completion. H ��� GCN learns to leverage the mutual enhancement between taxonomies and KGs, following the human reasoning process to generalize and conceptualize over taxonomic and non-taxonomic relations. Through extensive experiments, we demonstrate H ��� GCN to outperform various state-of-the-art KB completion methods on both taxonomy concept prediction and KG relation prediction tasks based on both standard metrics and human evaluations. The benchmark datasets and the implementation of H ��� GCN are available at https://anonymous.4open. science/r/Hake-GCN/.
FiO2∗PEEP) for Classification and Prognostication of Patients with Acute Respiratory Distress Syndrome

Department of Respiratory erapy, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India Department of Anaesthesiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India Department of Critical Care Medicine, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India Department of Infectious Diseases, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India Department of Oral Pathology & Microbiology, Manipal College of Dental Sciences, Mangalore 575001, Karnataka, India
ABOVE & BELOW: Investigating Ceiling and Floor for Augmented Reality Content Placement

Augmented Reality (AR) interfaces support users by providing access to digital content within real-world environments. However, displaying content at the users’ eye level might result in the occlusion of the real world. Therefore, it requires finding AR content placement areas that free the users’ field of vision. In this work, we systematically investigate two content placement areas beyond the users’ eye level: the ceiling and floor. To understand how potential users perceive virtual content on the ceiling and floor and how the content should be placed on these areas, we conducted two user studies. While the first exploratory study showed the general usefulness of either area, the second quantitative study allowed us to define optimal placement parameters regarding visibility and comfort. With insights from our studies, we provide design recommendations for future AR applications that support 2D content presentation on the ceiling and the floor.
Tupían Language Ressources: Data, Tools, Analyses

TuLaR (Tupian Language Resources) is a project for collecting, documenting, analyzing, and developing computational and pedagogical material for low-resource Brazilian indigenous languages. It provides valuable data for language research regarding typological, syntactic, morphological, and phonological aspects. Here we present TuLaR’s databases, with special consideration to TuDeT (Tupian Dependency Treebanks), an annotated corpus under development for nine languages of the Tupian family, built upon the Universal Dependencies framework. The annotation within such a framework serves a twofold goal: enriching the linguistic documentation of the Tupian languages due to the rapid and consistent annotation, and providing computational resources for those languages, thanks to the suitability of our framework for developing NLP tools. We likewise present a related lexical database, some tools developed by the project, and examine future goals for our initiative.
Principle B, VP Ellipsis, and Interpretation in Child Grammar

Among the universal principles are those known as the principles of the binding theory. These principles constrain the range of interpretations that can be assigned to sentences containing reflexives and reciprocals, pronouns, and referring expressions. The principle that is relevant for pronouns, Principle B, has provided a fertile ground for the study of linguistic development. Although it has long been known that children make certain kinds of errors that appear to contradict this principle, further experimental and theoretical investigation reveals that the child does know the grammatical principle, but implements the pragmatic knowledge incorrectly. In fact, discoveries concerning children's knowledge of Principle B are among the most well-known in the study of language acquisition because of the dissociation between syntactic and pragmatic knowledge (binding versus reference). In this book the authors deepen and extend the results of years of developmental investigation of Principle B by studying the interaction of Principle B with verb phrase ellipsis and properties of the interpretation of empty pronouns in ellipsis -- properties of "strict" and "sloppy" interpretation. This is the first experimental study of these topics in the developmental literature. The striking results show that detailed predictions from the "pragmatic deficiency" theory seem to be correct. Many novel experimental results concern the question of how children interpret pronouns, including elided pronouns, and how they understand VP ellipsis. The authors present the necessary theoretical background on Principle B, review and critique previous accounts of childrens errors, and present a novel account of why children misinterpret pronouns. The book will thus be of interest not only to readers interested in the development of the binding theory, but to those interested in the development of interpretation and reference by children.
Variational Bayesian analysis of nonhomogeneous hidden Markov models with long and ultralong sequences

Nonhomogeneous hidden Markov models (NHMMs) are useful in modeling sequential and autocorrelated data. Bayesian approaches, particularly Markov chain Monte Carlo (MCMC) methods, are principal statistical inference tools for NHMMs. However, MCMC sampling is computationally demanding, especially for long observation sequences. We develop a variational Bayes (VB) method for NHMMs, which utilizes a structured variational family of Gaussian distributions with factorized covariance matrices to approximate target posteriors, combining a forward-backward algorithm and stochastic gradient ascent in estimation. To improve efﬁciency and handle ultra-long sequences, we further propose a subsequence VB (SVB) method that works on subsamples. The SVB method exploits the memory decay property of NHMMs and uses buffers to control for bias caused by breaking sequential dependence from subsampling. We highlight that the local nonho-mogeneity of NHMMs substantially affects the required buffer lengths and propose the use of local Lyapunov exponents that characterize local memory decay rates of NHMMs and adaptively determine buffer lengths. Our methods are validated in simulation studies and in modeling ultra-long sequences of customers’ telecom records to uncover the relationship between their mobile Internet usage behaviors and conventional telecommunication behaviors.
Structural and Optical Properties of Synthesized Manganese doped ZnS Quantum Dots

ZnS:MnPP nanoparticles were prepared by a simple microwave irradiation method under mild condition. The starting materials for the synthesis of ZnS:MnP P quantum dots were zinc acetate as zinc source, thioacetamide as a sulfur source, manganese chloride as manganese source (R & M Chemical) and ethylene glycol as a solvent. All chemicals were analytical grade products and used without further purification. The quantum dots of ZnS:Mn P P with cubic structure were characterized by X-ray powder diffraction (XRD), the morphology of the film is seen by scanning electron microscopy (SEM) also by field effect scanning electron microscopy (FESEM). The composition of the samples is analysed by EDS. UV-Visible absorption spectroscopy analysis shows that the absorption peak of the as-prepared ZnS sample (310 nm) displays a blue-shift comparing to the bulk ZnS (345 nm).