Miracle-dz/UniFAR-FTUs huggingface dataset:
A quantum homomorphic encryption scheme for polynomial-sized circuits

Quantum homomorphic encryption (QHE) is an encryption method that allows quantum computation to be performed on one party's private data with the program provided by another party, without revealing much information about the data nor about the program to the opposite party. It usually allows only two rounds of communication. It is known that information-theoretically-secure QHE for arbitrary circuits would require exponential resources, and efficient computationally-secure QHE schemes for polynomial-sized quantum circuits have been constructed. In this paper we propose an information-theoretically-secure QHE scheme suitable for quantum circuits of size polynomial in the number of data qubits. The scheme keeps the data perfectly secure, and the circuit quite secure with the help of a polynomial amount of entanglement and classical communication.
Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions

Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in multiple sclerosis offers itself to be studied through the lens of network analyses. Recent research into multiple sclerosis has taken such a network approach by making use of functional connectivity. In this review, we briefly introduce measures of functional connectivity and how to compute them. We then identify several common observations resulting from this approach: (a) high likelihood of altered connectivity in deep-gray matter regions, (b) decrease of brain modularity, (c) hemispheric asymmetries in connectivity alterations, and (d) correspondence of behavioral symptoms with task-related and task-unrelated networks. We propose incorporating such connectivity analyses into longitudinal studies in order to improve our understanding of the underlying mechanisms affected by multiple sclerosis, which can consequently offer a promising route to individualizing imaging-related biomarkers for multiple sclerosis.
Convolutional Neural Networks and x-vector Embedding for DCASE2018 Acoustic Scene Classification Challenge

In this paper, the Brno University of Technology (BUT) team submissions for Task 1 (Acoustic Scene Classification, ASC) of the DCASE-2018 challenge are described. Also, the analysis of different methods on the leaderboard set is provided. The proposed approach is a fusion of two different Convolutional Neural Network (CNN) topologies. The first one is the common two-dimensional CNNs which is mainly used in image classification. The second one is a one-dimensional CNN for extracting fixed-length audio segment embeddings, so called x-vectors, which has also been used in speech processing, especially for speaker recognition. In addition to the different topologies, two types of features were tested: log mel-spectrogram and CQT features. Finally, the outputs of different systems are fused using a simple output averaging in the best performing system. Our submissions ranked third among 24 teams in the ASC sub-task A (task1a).
Digital Learning Resources for Prosthodontic Education: The Perspectives of a Long‐Term Dental Educator Regarding 4 Key Factors

Technological advances have led to the introduction of 3D education programs specifically designed for dentistry, leading to the author's use of these programs in the education of dental students. Based on this usage, this paper proposes there are 4 key factors that can enhance student education (spatial ability, interactivity, critical thinking, and clinical correlations with integration of multiple dental disciplines). These key factors can be incorporated into student learning through the use of 3D education programs in class. Lessons learned from using these programs include the importance of regular use in class as well as testing students both visually and textually on the content present in such programs. In this way, students will use the program and thereby enhance their 3D visualization skills while learning the required didactic information. Simply providing students with access to such programs without regular use in class and without testing the students on the content leads to these programs' lack of use. As a result, the students miss an opportunity to enhance their ability to visualize structures three dimensionally and manipulate them in their minds, a process known as spatial ability that is linked to success in the sciences.
Intelligent Patient Flow Management System at a Primary Healthcare Center - The Effect on Service Use and Costs

An intelligent patient flow management system (IPFM) was piloted at a large primary healthcare center in Finland in August 2017. The goals of the system are to help patients avoid unnecessary calls and visits to their health center and to enhance the use of professional resources through more streamlined patient pathways and the re-allocation of professionals from assessment tasks to actual patient care. These goals should be reflected in the decreased service costs through optimized contact forms. Using multiple regression analysis, we studied the associations between IPFM and patients' service utilization (17,943 patients; 73,038 service contacts) during the first five months of the pilot in 2017. The results indicated that the use of IPFM by the patient was associated with a decrease of EUR 31 in the total service costs of the patient in the study period. This decrease is 14% of patient's average total service cost.
Headgear for Mobile Neurotechnology: looking into Alternatives for EEG and NIRS probes

Brain-computer interfaces are now entering real-life environments. Particular hybrid systems using more than one input signal, e.g. electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), offer a broad spectrum of applications in basic research and clinical neuroscience. Here, we provide an overview of recent EEG-electrode and fNIRSoptode approaches that aim to improve usability. We include our new multi-function clip-on design that allows the use of conventional gel-based ring electrodes with water. For EEG electrode approaches (conventional gel, solid gel, new custom water-based) we compared impedances and frequency response over multi-hour recordings. While the water-based solutions showed comparable performance in terms of signal quality, applicability and comfort, solid-gel electrodes on hairy skin required additional contact pressure. Overall, however, all tested EEG electrode types were well compatible with concurrent fNIRS recordings using a novel hybrid fNIRS/EEG headgear, paving the way for cognitive workload experiments under real-life conditions.
Segmentation, hierarchy, mathematical morphology filtering, and application to image analysis

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Segmentation, hierarchy, mathematical morphology filtering, and application to image analysis Jean Cousty
Methylenedioxymethamphetamine (MDMA) in Psychiatry: Pros, Cons, and Suggestions

Background For a number of mental health disorders, including posttraumatic stress disorders (PTSD), there are not many available treatment options. Recently, there has been renewed interest in the potential of methylenedioxymethamphetamine (MDMA) to restore function for patients with these disorders. The primary hypothesis is that MDMA, via prosocial effects, increases the ability of patients to address the underlying psychopathology of the disorder. However, the use of MDMA poses potential problems of neurotoxicity, in addition to its own potential for misuse. Methods In this article, the proposed potential of MDMA as an adjunct to psychotherapy for PTSD is evaluated. The rationale for the use of MDMA and the positive results of studies that have administered MDMA in the treatment of PTSD are provided (pros). A description of potential adverse effects of treatment is also presented (cons). An overview of MDMA pharmacology and pharmacokinetics and a description of potential adverse effects of treatments are also presented. Methylenedioxymethamphetamine-produced oxytocin release and decreased expression of fear conditioning as well as one of the MDMA enantiomers (the n R− entaniomer) are suggested as potential mechanisms for the beneficial effects of MDMA in PTSD (suggestions). Results There is some evidence that MDMA facilitates recovery of PTSD. However, the significant adverse effects of MDMA raise concern for its adoption as a pharmacotherapy. Alternative potential treatments with less adverse effects and that are based on the ubiquitous pharmacology of MDMA are presented. Conclusions We suggest that additional research investigating the basis for the putative beneficial effects of MDMA might reveal an effective treatment with fewer adverse effects. Suggestions of alternative treatments based on the behavioral pharmacology and toxicology of MDMA and its enantiomers are presented.
Revitalizing Copybacks in Modern SSDs: Why and How

For modern flash-based SSDs, the performance overhead of internal data migrations is dominated by the data transfer time, not by the flash program time as in old SSDs. In order to mitigate the performance impact of data migrations, we propose rCopyback, a restricted version of copyback. Rcopyback works like the original copyback except that only n consecutive copybacks are allowed. By limiting the number of successive copybacks, it guarantees that no data reliability problem occurs when data is internally migrated using rCopyback. In order to take a full advantage of rCopyback, we developed a rCopyback-aware FTL, rcFTL, which intelligently decides whether rCopyback should be used or not by exploiting varying host workloads. Our evaluation results show that rcFTL can improve the overall I/O throughput by 54% on average over an existing FTL which does not use copybacks.
A Fast Polynomial-time Primal-Dual Projection Algorithm for Linear Programming

Traditionally, there are several polynomial algorithms for linear programming including the ellipsoid method, the interior point method and other variants. Recently, Chubanov [Chubanov, 2015] proposed a projection and rescaling algorithm, which has become a potentially \emph{practical} class of polynomial algorithms for linear feasibility problems and also for the general linear programming. However, the Chubanov-type algorithms usually perform much better on the infeasible instances than on the feasible instances in practice. To explain this phenomenon, we derive a new theoretical complexity bound for the infeasible instances based on the condition number, which shows that algorithms can indeed run much faster on infeasible instances in certain situations. In order to speed up the feasible instances, we propose a \emph{Polynomial-time Primal-Dual Projection} algorithm (called PPDP) by explicitly developing the dual algorithm. The numerical results validate that our PPDP algorithm achieves a quite balanced performance between feasible and infeasible instances, and its performance is remarkably better than previous algorithms.
Unsupervised noise removal technique based on constrained NMF

This paper presents a novel approach to mitigate background interference noise in a noisy audio environment based on unsupervised speech enhancement without the prior knowledge of speech as well as noise signal. In this scheme, a noisy speech signal is represented by a non-negative matrix factorisation (NMF) with sparseness constraints. Using NMF with constraints, noise density spectrum is estimated to design the spectral subtraction method. In the earlier method of NMF, correlation between the noise and the audio signals was not considered. The proposed method explores the correlation between the noise and speech signals. A sparse constraint NMF-based spectral subtraction method is designed to eliminate the noise. The performance is evaluated by considering various noise environments in terms of quality and intelligibility measures. Simulation results highlight the improvement in the enhanced speech with the suggested approach.
Pollen-Food Allergy Syndrome in Korean Pollinosis Patients: A Nationwide Survey

Purpose Pollen-food allergy syndrome (PFAS) is an immunoglobulin E (IgE)-mediated allergy in pollinosis patients caused by raw fruits and vegetables and is the most common food allergy in adults. However, there has been no nationwide study on PFAS in Korea. In this study, we investigated the prevalence and clinical characteristics of PFAS in Korea. Methods Twenty-two investigators participated in this study, in which patients with allergic rhinoconjunctivitis and/or bronchial asthma with pollen allergy were enrolled. The questionnaires included demographic characteristics, a list of fruits and vegetables, and clinical manifestations of food allergy. Pollen allergy was diagnosed by skin prick test and/or measurement of the serum level of specific IgE. Results A total of 648 pollinosis patients were enrolled. The prevalence of PFAS was 41.7% (n = 270). PFAS patients exhibited cutaneous (43.0%), respiratory (20.0%), cardiovascular (3.7%) or neurologic symptoms (4.8%) in addition to oropharyngeal symptoms. Anaphylaxis was noted in 8.9% of the PFAS patients. Seventy types of foods were linked to PFAS; e.g., peach (48.5%), apple (46.7%), kiwi (30.4%), peanut (17.4%), plum (16.3%), chestnut (14.8%), pineapple (13.7%), walnut (14.1%), Korean melon (12.6%), tomato (11.9%), melon (11.5%) and apricot (10.7%). Korean foods such as taro/taro stem (8.9%), ginseong (8.2%), perilla leaf (4.4%), bellflower root (4.4%), crown daisy (3.0%), deodeok (3.3%), kudzu root (3.0%) and lotus root (2.6%) were also linked to PFAS. Conclusions This was the first nationwide study of PFAS in Korea. The prevalence of PFAS was 41.7%, and 8.9% of the PFAS patients had anaphylaxis. These results will provide clinically useful information to physicians.
Applying passive brain-Computer-Interfaces in Autonomous driving: a Case of Taking over control

: This paper communicates the research plan for a dissertation in the field of Passive Brain-Computer Interfaces. The main aim is the detection of a driver’s mental state in real time and its use in autonomous driving. One example of this is embedded in the context of driver taking over control of the automated vehicle. A offline experiment in laboratory as well as both an online and offline experiment in a driving simulator will be conducted. This paper proposes an experiment planning as well as materials and methods to be used. The conduction of experiments and data analysis are pending. The outcome of these studies is expected to contribute to the design of the driver-vehicle-interaction in autonomous driving by identifying driver’s mental states during mode transition.
Loss of Nuclear and Membrane Estrogen Receptor-α Differentially Impairs Insulin Secretion and Action in Male and Female Mice

Estrogens favor glucose homeostasis primarily through the estrogen receptor-α (ERα), but the respective importance of nuclear ERα (NOER) and membrane ERα (MOER) pools to glucose homeostasis are unknown. We studied glucose homeostasis, insulin secretion, and insulin sensitivity in male and female mice expressing either the NOER or the MOER. Male and female MOER mice exhibited fasting and fed hyperglycemia and glucose intolerance. Female MOER mice displayed impaired central insulin signaling associated with hyperinsulinemia and insulin resistance due to unrestrained hepatic gluconeogenesis, without alterations in glucose-stimulated insulin secretion (GSIS). In contrast, male MOER mice did not exhibit detectable insulin resistance, but showed impaired GSIS associated with reduced brain glucose sensing. Female NOER mice exhibited milder hepatic insulin resistance and glucose intolerance. In conclusion, nuclear ERα signaling is predominant in maintaining glucose homeostasis in mice of both sexes. Lack of nuclear ERα alters the central control of insulin sensitivity in females and predominantly impairs the central regulation of insulin secretion in males.
Evaluation of BCI researchers' Opinions regarding the Future of BCIs: Results of BCI Roadmap Questionnaire 2014

: The field of Brain-Computer Interface (BCI) research has seen a steep expansion during the last years, and interesting progress has been made in all different aspects of the BCI pipeline. Despite that, BCIs are not yet widely used by either the diseased or healthy target populations. In the current study, we asked BCI researchers worldwide to fill out a questionnaire about how they see the future of BCI research, what hurdles need to be taken for BCIs to become available and widely used applications, and the research that is needed to accomplish this. The data reveal that researchers foresee that real BCI applications will appear on the market in the coming years, but that important improvements are needed in especially the hardware, performance and user friendliness of BCIs.
Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training

Robot-assisted therapy affords effective advantages to the rehabilitation training of patients with motion impairment problems. To meet the challenge of integrating the active participation of a patient in robotic training, this study presents an admittance-based patient-active control scheme for real-time intention-driven control of a powered upper limb exoskeleton. A comprehensive overview is proposed to introduce the major mechanical structure and the real-time control system of the developed therapeutic robot, which provides seven actuated degrees of freedom and achieves the natural ranges of human arm movement. Moreover, the dynamic characteristics of the human-exoskeleton system are studied via a Lagrangian method. The patient-active control strategy consisting of an admittance module and a virtual environment module is developed to regulate the robot configurations and interaction forces during rehabilitation training. An audiovisual game-like interface is integrated into the therapeutic system to encourage the voluntary efforts of the patient and recover the neural plasticity of the brain. Further experimental investigation, involving a position tracking experiment, a free arm training experiment, and a virtual airplane-game operation experiment, is conducted with three healthy subjects and eight hemiplegic patients with different motor abilities. Experimental results validate the feasibility of the proposed scheme in providing patient-active rehabilitation training.
Omalizumab for Severe Asthma: Beyond Allergic Asthma

Different subsets of asthma patients may be recognized according to the exposure trigger and the frequency and severity of clinical signs and symptoms. Regarding the exposure trigger, generally asthma can be classified as allergic (or atopic) and nonallergic (or nonatopic). Allergic and nonallergic asthma are distinguished by the presence or absence of clinical allergic reaction and in vitro IgE response to specific aeroallergens. The mechanisms of allergic asthma have been extensively studied with major advances in the last two decades. Nonallergic asthma is characterized by its apparent independence from allergen exposure and sensitization and a higher degree of severity, but little is known regarding the underlying mechanisms. Clinically, allergic and nonallergic asthma are virtually indistinguishable in exacerbations, although exacerbation following allergen exposure is typical of allergic asthma. Although they both show several distinct clinical phenotypes and different biomarkers, there are no ideal biomarkers to stratify asthma phenotypes and guide therapy in clinical practice. Nevertheless, some biomarkers may be helpful to select subsets of atopic patients which might benefit from biologic agents, such as omalizumab. Patients with severe asthma, uncontrolled besides optimal treatment, notwithstanding nonatopic, may also benefit from omalizumab therapy, although currently there are no randomized double-blind placebo controlled clinical trials to support this suggestion. However, omalizumab discontinuation according to each patient's response to therapy and pharmacoeconomical analysis are questions that remain to be answered.
Dynamic vortical flow profile quantitatively characterizes mitral valvular-left ventricular hemodynamic coupling: In vivo analysis by dynamic 3D enstrophy mapping from 4D Flow MRI

Vortical blood flow in the human left ventricular (LV) inflow initiates from the mitral valve (MV) and evolves within the LV during diastolic E-filling. Hence, vortical flow links MV and LV hemodynamics. This study sought to elucidate and quantitatively characterize the in vivo 3D dynamics of LV vortical flow over E-filling and relation to MV-LV hemodynamic coupling using 4D Flow MRI flow field. 34 healthy volunteers and 5 example patients underwent 4D Flow MRI. Vortical blood flow evolution was mapped in the LV over E-wave using enstrophy density. A new dimensionless profile PMV-LV was derived as a function of both MV vortex formation time (VFT) and LV volumetric enstrophy density. Results reveal that 3D vortical flow evolution in the healthy LV follows a bi-phasic behavior with a vortical growth phase followed by a vortical decay phase. In healthy LVs studied, the PMV-LV profile showed that the vortical growth and decay phases are characterized by a vortical growth time T_growth= 1.23+/-0.25, growth rate {\alpha}=0.80+/-0.17, decay time T_decay= 0.96+/-0.39 and decay rate \b{eta}=-1.02+/-0.49. Distinctly altered parameters were found in the pilot patients studied. The derived PMV-LV profile quantitatively characterizes MV-LV hemodynamic coupling by vortical flow dynamics. Results herein unravel new insights into cardiac physiology and could enable a novel standardized methodology to study MV-LV hemodynamic coupling and association to cardiac function in health and disease.
Deep Learning Applications for Biomedical Data and Natural Language Processing

The human brain can be seen as an ensemble of interconnected neurons, more or less specialized to solve different cognitive and motor tasks. In computer science, the term deep learning is often applied to signify sets of interconnected nodes, where deep means that they have several computational layers. Development of deep learning is essentially a quest to mimic how the human brain, at least partially, operates.In this thesis, I will use machine learning techniques to tackle two different domain of problems. The first is a problem in natural language processing. We improved classification of relations within images, using text associated with the pictures. The second domain is regarding heart transplant. We created models for pre- and post-transplant survival and simulated a whole transplantation queue, to be able to asses the impact of different allocation policies. We used deep learning models to solve these problems.As introduction to these problems, I will present the basic concepts of machine learning, how to represent data, how to evaluate prediction results, and how to create different models to predict values from data. Following that, I will also introduce the field of heart transplant and some information about simulation. (Less)