Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Savings of Multi-Channel Neurostimulators with Non-Rectangular Current-Mode Stimuli Using Multiple Supply Rails

In neuromodulation applications, conventional current mode stimulation is often preferred over its voltage mode equivalent due to its good control of the injected charge. However, it comes at the cost of less energy-efficient output stages. To increase energy efficiency, recent studies have explored non-rectangular stimuli. The current work highlights the importance of an adaptive supply for an output stage with programmable non-rectangular stimuli and accordingly proposes a system-level architecture for multi-channel stimulators. In the proposed architecture, a multi-output DC/DC Converter (DDC) allows each channel to choose among the available supply levels (i.e., DDC outputs) independently and based on its instant voltage/current requirement. A system-level analysis is carried out in Matlab to calculate the possible energy savings of this solution, compared to the conventional approach with a fixed supply. The energy savings have been simulated for a variety of supply levels and waveform amplitudes, suggesting energy savings of up to 83% when employing 6 DDC outputs and the lowest current amplitude explored ($250\ \mu \mathrm{A}$), and as high as 26% for a full-scale amplitude (4 mA).
Outlier Eigenvalues for Deformed I.I.D. Random Matrices

We consider a square random matrix of size N of the form A + Y where A is deterministic and Y has i.i. d. entries with variance 1/N. Under mild assumptions, as N grows the empirical distribution of the eigenvalues of A + Y converges weakly to a limit probability measure β on the complex plane. This work is devoted to the study of the outlier eigenvalues, i.e., eigenvalues in the complement of the support of β. Even in the simplest cases, a variety of interesting phenomena can occur. As in earlier works, we give a sufficient condition to guarantee that outliers are stable and provide examples where their fluctuations vary with the particular distribution of the entries of Y or the Jordan decomposition of A. We also exhibit concrete examples where the outlier eigenvalues converge in distribution to the zeros of a Gaussian analytic function. © 2016 Wiley Periodicals, Inc.
A Novel Micro-channel Package Based on Stress Cushioning of Ceramic Interlayer

In this paper, the thermal failure of the new micro-channel package is studied, and the ceramic substrate is used as the stress buffer layer between the silicon micro-channel and the metal structure to realize the highly reliable encapsulation of micro-channel heat dissipation structure. Integration of micro-channel cooling technology can solve the problem of high power chip heat dissipation, on the other hand it is also compatible with 3D integrated systems encapsulation, but due to the silicon with external metal structure between the lager thermal mismatch, lead to many engineering application problems, including welding thermal stress between micro-channel and metal structure, reliability of micro-channel to external metal interface. This article explores the high thermal conductivity ceramic base slabs stress cushion structure and micro flow channel, and optimize the structure of metal materials, sizes , parameters, such as the structure size is obtained by finite element simulation analysis on its packaging stress, assembling and verify the feasibility of the technology scheme, which can realize 50 × 50 mm of silicon micro flow channel and metal structure large structure interconnection. The relevant research results can guide the application of similar packaging and reduce the reliability risk.
Improving Fast MRI Reconstructions with Pretext Learning in Low-Data Regime

Supervised deep learning methods have shown great promise for making magnetic resonance (MR) imaging scans faster. However, these supervised deep learning models need large volumes of labelled data to learn valuable representations and produce high-fidelity MR image reconstructions. The data used to train these models are often fully-sampled raw MR data, retrospectively under-sampled to simulate different MR acquisition acceleration factors. Obtaining high-quality, fully sampled raw MR data is costly and time-consuming. In this paper, we exploit the self supervision based learning by introducing a pretext method to boost feature learning using the more commonly available under-sampled MR data. Our experiments using different deep-learning-based reconstruction models in a low data regime demonstrate that self-supervision ensures stable training and improves MR image reconstruction.
A Broadband Low-Profile Monopulse Comparator for Dual-Circularly Polarized Feeder

This article proposes a scheme to design a broadband low-profile monopulse comparator for the dual-circularly polarized feeder. For obtaining a compact and easily integrated structure, a monopulse comparator with a ring-shaped symmetrical planner topology is proposed by using the 3 dB coupler and the phase shifter based on the groove gap waveguide (GGW). Then, in order to achieve the equal-amplitude distribution in a broad bandwidth, the asymmetrical coupling structure and the novel dispersion-matched transition are proposed to improve the directivity of a 3 dB coupler. Moreover, a ridge waveguide with periodic grooves featuring stable dispersion change is adopted to design the phase shifter, which contributes to obtain an accurate phase control in the broad frequency band. To validate experimentally the broadband performance, a dual-circularly polarized four-horn feeder with two identical monopulse comparators is designed and fabricated. The results show that the symmetrical radiation patterns with 32 dB null-depth can be obtained from 27 to 31 GHz. The monopulse comparator characterizes broad bandwidth, low loss, and a compact size, which provides a monopulse comparator with accurate amplitude/phase control for the research of the monopulse antenna.
Social Network Influence: Making the Case for Semantic Analysis

Influence on the social network platform has become an interesting research area. A typical social network influencer is known by the amount of reactions that he/she attracts based on the posts made. The amount of reactions received is usually a reflection of the overall visibility of the influencer on the social network arena. This visibility has attracted commercial benefits to such influencers through word of mouth marketing, political endorsements as well as ambassadorial appointments. However, existing methods in literature that are used to scientifically quantify the amount of social influence that can be attributed to an influencer, tend to lump the amount of social influence in a single basket labelled either as positive or negative influence. The effect is a binary classification of influencers as either negative or positive. In this article, we make the case that this is not necessarily accurate. The reason being that in a collection of comments that a post from an influencer attracts, it it seldom that all the comments would be absolutely positive or negative. There is a fuzzy space defined by comments that do not necessarily belong to either of these categories. In this work, we have used data harvested from Facebook comments and grouped into four different categories - those in full support of the opinion expressed, those in full opposition of the opinion, those that somewhat support and those that somewhat oppose the opinion expressed. Results have shown that the fuzzy space created by the comments in between the full support and full opposition negates the assumption that an influencer can be a positive or negative influencer just because the number of positive or negative comments are the majority.
Applying Big Transfer-based classifiers to the DEAP dataset

Affective brain-computer interfaces are a fast-growing area of research. Accurate estimation of emotional states from physiological signals is of great interest to the fields of psychology and human-computer interaction. The DEAP dataset is one of the most popular datasets for emotional classification. In this study we generated heat maps from spectral data within the neurological signals found in the DEAP dataset. To account for the class imbalance within this dataset, we then discarded images belonging to the larger class. We used these images to fine-tune several Big Transfer neural networks for binary classification of arousal, valence, and dominance affective states. Our best classifier was able to achieve greater than 98% accuracy and 990% balanced accuracy in all three classification tasks. We also investigated the effects of this balancing method on our classifiers.
Lightweight and Efficient Convolutional Neural Network for Traffic Signs Classification

Recently, Intelligent Transportation Systems (ITS) has obtained a large interest in scientific research, due to the intense increase in the number of vehicles in the traffic scene. In fact, ITS is able to solve many problems using computer vision, such as traffic signs recognition. Lately, Convolutional Neural Network (CNN) approaches have been applied in traffic signs classification due to the robust feature extraction with size and rotational invariance. However, the majority of the work realized in this task focuses on accuracy rather than the number of required parameters, which makes applications of traffic signs classification inappropriate for real-time uses. To solve this issue, we propose a lighter and efficient CNN model called Lightweight Traffic Signs Network (LTSNet), which requires fewer parameters while having good accuracy. The experiments are performed on the public benchmark dataset of traffic signs GTSRB to prove the effectiveness of our proposed network in terms of accuracy and parameter requirements.
Multi-Task Local-Global Graph Network for Flight Delay Prediction

Airline on-time performance has always been a key factor in evaluating the punctuality of the civil aviation industry and has a profound impact on airlines, airports, and passengers. As a result, there have been increasing demands for the systematic analysis of flight delays and the development of accurate and efficient tools for flight delay prediction. In this paper, a deep learning framework based on graph convolutional networks and multi-task learning is proposed for flight delay prediction. We first use graph convolutional networks to capture the local and global spatial dependencies among the airports. A multi-decoder sequence-to-sequence model is developed to extract the temporal correlation from the data. We further apply a hierarchical graph fusion approach to combine features at different levels of the network to exploit their cross-modality correlations. The model is trained using a dynamic multi-task learning strategy to predict flight arrival and departure delays at the same time to boost the model's generalization and performance. The proposed model is evaluated on a large-scale public flight record dataset against several state-of-the-art methods. The experimental results demonstrate that our model can outperform all baseline methods in predicting short to medium-term flight delays.
Characterization of a Second-Best Rationalizable Choice Function

The idea of rationalizability of a choice function assumes considerable importance in the theory of consumer behaviour as well as in the theory of social choice. Rationalizability of a choice function ensures the existence of a binary relation such that, for every set in the domain of that choice function its choice set is the set of its best elements according to that same binary relation. In social choice theory the quest for rationalizability conditions for choice functions began with Uzawa (1956). Arrow (1959) characterized a choice function (with full domain) that is rationalizable by an ordering. Plott (1973) did the same for a reflexive, connected and quasi-transitive rationalization. Blair et al. (1976) found necessary and sufficient conditions for rationalization of a choice function with full domain by a reflexive, connected and acyclic binary relation.
Spatio-temporal Tensor Multi-Task Learning for Predicting Alzheimer's Disease in a Longitudinal study

The utilisation of machine learning techniques to predict Alzheimer's Disease (AD) progression will substantially assist researchers and clinicians in establishing effective AD prevention and treatment strategies. In this research, we present a novel Multi-Task Learning (MTL) model for modelling AD progression based on tensor formation from spatio-temporal similarity measures of brain biomarkers. In this model, each patient sample's prediction in the tensor is assigned to a task, with each task sharing a set of latent factors acquired via tensor decomposition. To further improve the performance of the model, we present a novel regularisation term which utilises the convex combination of disease progression to modify longitudinal stability and ensure that two regression models have a minimal variation at successive time points. The model can be utilised to effectively predict AD progression with magnetic resonance imaging (MRI) data and cognitive scores of AD patients at various stages. We conducted extensive experiments to evaluate the performance for the proposed model and algorithm utilising data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared to single-task and state-of-the-art multi-task regression techniques, our proposed method has greater accuracy and stability for predicting AD progress in terms of root mean square error, with an average reduction of 2.60 compared to single-task regression methods and 1.17 compared to multi-task regression methods in the Mini-Mental State Examination (MMSE) questionnaire; with an average reduction of 5.08 compared to single-task regression methods and 2.71 compared to multi-task regression methods in the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog).
Control system for an innovative electrostatic enameling process of water tanks' interiors

In this paper, a PLC-oriented control system dedicated to the real-life technological line of the enameling process is presented. Following the recently built industrial device employing the electrostatic method, the complex control layer is proposed and its advantages are deeply discussed. In addition, the entire safety mechanism is suggested in order to maintain a set of correct operations. The next step of the advanced studies will be focused on the different practical tests finally leading to the process and product innovation observed in the Elektromet Company.
Entailed feedback : Evidence from a ranking experiment

In this paper we investigate relationships between entailment relations among communicative functions and dominance judgements in an annotation task in which participants are instructed to rank utterance functions in terms of their importance. It is hypothesised that on average entailed functions should be assigned lower ranks than entailing functions. Preliminary results of an experiment are reported for positive auto-feedback functions which are argued to be entailed by backward-looking functions such as Confirm.
Max-Min Beamforming for Multipath Exploitation

This letter considers the robust spatial-filter design problem for multipath exploitation. The imprecise multipath return results in the output Signal-to-Interference-plus-Noise-Ratio (SINR) performance deterioration of traditional beamformers. We focus on enhancing the worst-case output SINR over the uncertain Direction-Of-Arrival (DOA) sector. To achieve robustness against covariance matrix errors caused by the desired signals contamination, we reconstruct the Interference-Noise Covariance Matrix (INCM) from Sample Covariance Matrix (SCM) based on the proposed residual noise components removal idea. Then, we cast the robust spatial-filter design problem as a max-min optimization problem, aiming at improving the worst-case output SINR. The filter vector is finally obtained via semi-definite relaxation followed by randomization synthesis. Simulation results demonstrate the effectiveness of the proposed algorithm.
Improving penalized semi supervised nonnegative matrix factorization result’s confidence using deep residual learning approach in spectrum analysis

Signa1 denoising is one of the most crucial tasks to progress in today’s main issues including edge Tokamak plasmas’ atomic spectra and astrophysical data analyses. In the study of radiative spectra, noise subtraction is the key to significantly enhance methods developed to identify spectra’s prominent constituent elements. One of these methods is the penalized semi supervised nonnegative matrix factorization (PSNMF): it extracts the spectra’s prominent elements successfully, but its results’ confidence are sensitive to noise and can ultimately hurt the method’s performance. On the other hand, artificial intelligence (AI) shows very good ability to disentangle complex inputs and allows image unblurring, pattern recognition and so on. The work presented here aims to use a specific 1D convolutional neural network in the residual learning approach to identify and subtract the noise from noisy spectra. This framework improves the confidence of the PSNMF output without hurting its already good recognition performance. For the most noisy data considered, we can infer the good element set with a confidence close to the noiseless case.
A Reinforcement Learning Application for Optimal Fluid and Vasopressor Interventions in Septic ICU Patients

Sepsis is one of the leading causes of death in ICU and its timely recognition and management are of primary importance. Resuscitation from hypotension in patients with sepsis is one of the first challenges that require fluid and/or vasopressor administrations. Unfortunately, clinical guidelines provide only indications of the strategy that should be adopted in this critical population but personalized strategies are still missing. In this study, we propose a comparative analysis of reinforcement learning applications on ICU data collected in the electronic health records and publicly available within the MIMIC-III database. The ultimate goal of the study is to estimate the optimal fluid and vasopressor administrations. Results show that, after the use of principal component analysis for reducing feature space dimensionality, model performances increased, thus suggesting that additional preprocessing strategies might be used for both reducing the computational cost and refining model performances. Clinical relevance In a context where clinical guidelines are not able to provide the best treatment strategies at a patient level, reinforcement learning applications trained on retrospectively collected data may be used for developing models able to suggest to clinicians the optimal dosage of fluids and/or vasopressors in order to improve 90-day patients' survival.
An Adaptive Deadtime Model for Better Quantification in PET

It is essential to have a proper deadtime correction method prior to scatter correction and the reconstruction process. Imperfections in the deadtime model might potentially affect the scatter correction and hence provide inaccurate quantification in the reconstructed images. Even though deadtime is ideally a function of singles rates, correction methods for count losses in PET systems might become more complicated mainly due to processing detector signals especially for the piled-up and scattered piled-up events, changes in scatter distribution for detector pairs at different energy levels and multiplexing the detector readout to minimize the number of electronics channels both in singles and coincidence event processors. PET scanners with long axial long field-of-view (LAFOV) introduce an additional complexity due to increased sensitivity in detection of scatter.In this investigation, a new deadtime correction model based on singles rates is discussed with an additional adaptive coefficient. The new model has the flexibility of using two energy windows to accommodate the change in scatter and piled-up scatter events due to various patient/phantom sizes and activity distribution as well. Proposed deadtime model was tested on three phantoms with various shapes and sizes. Two different scanner models were used to test the performance of the new deadtime model. Quantitative accuracy within 2% was achieved up to 150kcps average energy qualified (EQ) block rate for Siemens Biograph Vision Quadra PET/CT scanner with long axial field of views. Quantitative accuracy within 5% was achieved up to 400kcps average energy qualified block rate for Siemens Biograph Vision 600 PET/CT clinical scanner.
A PFC Based Onboard Battery Charger using Isolated Full-Bridge DC-DC Converter for Electric Vehicle Application

A low voltage two-stage onboard battery charger of 1.5 kW rating for an electric vehicle application is presented in this paper. The first stage is the AC-DC conversion rectifier with a power factor correction circuit. The interleaving of the PFC boost converter helps in reducing the inductor size and output current ripple. The power factor is improved with reduced distortion in the input current waveform. The second stage is the battery charging circuit using isolated full-bridge DC-DC converter for a 48V/100Ah battery. The detailed design of the system and control method of both the converters are presented. The overall efficiency of the converter is found around 93% with improvement in input power quality. The simulation is carried out using MATLAB Simulink toolbox and the experiment results are also discussed.
A D-Band Corporate-Feed Gap-Cavity Slot Array Antenna Using Virtual PEC Method

A D-band corporate-feed gap-cavity slot array is proposed with wide bandwidth and high aperture efficiency. The cavity with higher-order modes is implemented by using the gap waveguide technology, namely a gap-cavity. To ensure that the gap-cavity is operating with the desired higher-order mode over a wide bandwidth, the corporate-feed architecture is applied for uniform excitation and with alternating-phase between adjacent slots. The gap-cavity is split into multiple elements by using zero-thickness virtual perfect electric conductor (PEC) walls. With this unique approach, the design, fabrication and assembly complexities are reduced. For experimental verification, a gap-cavity with an 8×8 slot array is designed in the 120 GHz band and an E-plane waveguide feeding network is proposed to excite the TE880-mode. Two metal fences are introduced on either side of the radiating slot and an inductive metal pin is introduced below the radiating slot to improve the radiation performance and broaden the bandwidth. The measured impedance matching bandwidth is around 110–144.8 GHz (27.3%). The measured peak gain is 26.22 dBi at 137.64 GHz and the measured peak aperture efficiency is 86.2% at 115.325 GHz. The 3-dB gain bandwidth is the same as the impedance matching bandwidth.