Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk factors and treatment of refractory anaphylaxis - a review of case reports

ABSTRACT Introduction: Patients experiencing anaphylaxis who do not recover after treatment with intramuscular adrenaline are regarded as suffering from refractory anaphylaxis. The incidence of refractory anaphylaxis is estimated to range between 3–5% of anaphylaxis cases. The risk factors for refractory anaphylaxis are unknown. Areas covered: In the present analysis, we aimed to evaluate the management and risk factors of refractory anaphylaxis to highlight possible clinical implications for updating current management algorithms. Expert commentary: According to international guidelines, adrenaline given through the intramuscular (i.m.) route is a rapid and safe treatment but may be insufficient. Therefore, defined standardized treatment protocols for such cases of refractory anaphylaxis are needed to optimize the treatment. Point-of-care diagnostics may enable doctors to identify patients experiencing severe, refractory anaphylaxis early in order to initiate intensified critical care treatment.
Segmentation via Incremental Transductive Learning

In this paper, we propose a novel unsupervised clustering method for feature space analysis. We combine mean shift with a transductive learning method, semisupervised discriminant analysis (SDA), in an incremental learning scheme. We use mean shift clustering to generate the class label, and use SDA to do subspace selection. Both these steps are performed alternately. Our clustering result could maintain good spatial consistency for all data in feature space. On image segmentation, we directly apply our clustering method to the L*a*b* color feature space generated from superpixels, and set each pixel with the clustering label of its superpixel. We test our image segmentation method on Berkeley image data set.
The Senses of Agency and Ownership: A Review

Usually, we do not question that we possess a body and act upon the world. This pre-reflective awareness of being a bodily and agentive self can, however, be disrupted by different clinical conditions. Whereas sense of ownership (SoO) describes the feeling of mineness toward one’s own body parts, feelings or thoughts, sense of agency (SoA) refers to the experience of initiating and controlling an action. Although SoA and SoO naturally coincide, both experiences can also be made in isolation. By using many different experimental paradigms, both experiences have been extensively studied over the last years. This review introduces both concepts, with a special focus also onto their interplay. First, current experimental paradigms, results and neurocognitive theories about both concepts will be presented and then their clinical and therapeutic relevance is discussed.
From manga to stage: An adaptation study of 2.5-D musical live spectacle naruto –akatsuki no shirabe–

This study aims to describe the content presentation process of 2.5-D musicals (theatre performances from stories of popular Japanese culture such as anime, manga, and game) of NARUTO –Akatsuki no Shirabe– (hereafter referred to as NARUTO –AnS–) from manga to on-stage performance and to understand the motivation behind its adaptation. This study is conducted through text analysis method in order to map the presentation difference from manga to on-stage theatre, and through literature study. From this study it is found that there are several adjustments in NARUTO –AnS– in order to fulfil requirements in its new medium, though in some areas still remain loyal to its adapted work. Its loyalty towards the adapted work is to fulfil requirements of the new medium; to meet its creative an economic motivations. This study reveals that in accordance to Balodis’ theory, loyalty towards the adapted work is based on two motivations, creative and economical motivation.
Semi-supervised Domain Adaptation via Minimax Entropy

Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision. However, we show that these techniques perform poorly when even a few labeled examples are available in the target domain. To address this semi-supervised domain adaptation (SSDA) setting, we propose a novel Minimax Entropy (MME) approach that adversarially optimizes an adaptive few-shot model. Our base model consists of a feature encoding network, followed by a classification layer that computes the features’ similarity to estimated prototypes (representatives of each class). Adaptation is achieved by alternately maximizing the conditional entropy of unlabeled target data with respect to the classifier and minimizing it with respect to the feature encoder. We empirically demonstrate the superiority of our method over many baselines, including conventional feature alignment and few-shot methods, setting a new state of the art for SSDA.
What Do You Mean , My Statistical Results Are Incorrect ? The Impact of Multicollinearity and Measurement Error in Tests of Statistical Significance Completed Research

When two or more predictor variables are highly correlated and contain measurement error (M+ME), regression and Partial Least Squares (PLS) beta coefficients and t statistics can be inaccurate (Goodhue, Lewis and Thompson, 2017). Corrections to the misleading values can be made by using the application created by Goodhue et al., or by correcting correlations for attenuation before running a regression. We examined research articles in three Management Information Systems journals over multiple years and discovered that, of the regression and PLS papers that reported correlations, about one-half (48%) were operating in the danger zone. In the 10 papers that provided sufficient information to use the Goodhue et al. (2017) application we found that, on average, the t statistics were biased by about 0.70. This suggests that when using regression or PLS, researchers should check for the M+ME bias and correct for it when found.
Using sparse learning for feature selection with locality structure preserving based on positive data

Feature selection aims to select the most relevant subset of features to make classifiers more accurate, fast, and easy to understand. Regularized sparse feature selection methods using L1-norm regularization term in their optimization problem, have received much attention in recent years. In this paper, besides the L1-norm regularization term a locality structure preserving regularization term based on positive data points is proposed. Positive data is defined as the unique data points involved in the must-link constraints, which are the most informative data. The experimental results on a number of datasets, show the efficacy of the proposed method for classification tasks compared with similar feature selection methods.
Peer Effects in Parental Leave Decisions

This paper analyzes to what extent parental leave decisions of mothers with young children depend on the decisions made by their coworkers. The identification of peer effects, which are defined as indirect effects of the behavior of a social reference group on individual outcomes, bears various challenges due to correlated characteristics within social groups and endogenous group membership. We overcome these challenges by exploiting quasi-random variation in the costs of parental leave during a narrow window around a cutoff date, induced by a parental leave benefit reform in Germany. The reform encourages mothers to remain at home during the first year following childbirth. Administrative linked employer-employee panel data enable us to assign a peer group to all individuals who work in the same establishment and occupational group. While there is a growing literature on peer effects, few studies look at peer effects in the context of parental leave decisions. We argue, however, that mothers with young children are particularly susceptible to peer behavior at the workplace due to preferences for conformity with peer group behavior as well as the career-related uncertainty that mothers face. Our results suggest that maternal decisions regarding the length of parental leave are significantly influenced by coworker decisions, in particular in situations with high uncertainty.
Efficient FPGA and ASIC Realization of a DA-based Reconfigurable FIR Digital Filter

In this paper, we present the design optimization ofoneand two-dimensional fully-pipelined computing structuresfor area-delay-powerefficient implementation of finite impulseresponse (FIR) filter by systolic decomposition of distributedarithmetic (DA)based inner-product computation. The systolicdecomposition scheme is found to offer a flexible choice ofthe address length of the look-up-tables (LUT) for DA-basedcomputation to decide on suitable area-time trade-off. It isobserved that by using smaller address-lengths for DA-basedcomputing units, it is possible to reduce the memory-size buton the other hand that leads to increase of adder complexity andthe latency. For efficient DA-based realization of FIR filters ofdifferent orders, the flexible linear systolic design is implementedon a Xilinx Virtex-E XCV2000E FPGA using a hybrid combination of Handel-C and parameterizable VHDL cores. Variouskey performance metrics such as number of slices, maximumusable frequency, dynamic power consumption, energy densityand energy throughput are estimated for different filter ordersand address-lengths. Analysis of the results obtained indicatethat performance metrics of the proposed implementation isbroadly in line with theoretical expectations. It is found thatthe choice of addresslengthM=4yields the best of area-delaypowerefficient realizations of the FIR filter for various filterorders. Moreover, the proposed FPGA implementation is foundto involve significantly less area-delay complexity compared withthe existing DA-based implementations of FIR filter.
Examining Sources of Gender DIF Using Cross-Classification Multilevel IRT Models

A substantial amount of research has focused on the detection of differential item functioning (DIF) in the past. However, DIF detection and the estimation of DIF effect size do not explain why it occurs. Recent studies have investigated how or why DIF may occur. Improvements in DIF analysis models have made it possible to explore additional covariates as potential sources of DIF by measuring the extent to which these covariates account for variation in performance. The current study examines variability in math performance accounted for by gender, which is referred as gender DIF. This study then investigates how the presence of gender DIF is explained by both person predictors (i.e., opportunity to learn; OTL) and item characteristics (i.e., item format). A cross-classification multilevel IRT model framework is used to demonstrate the relationship among item difficulty, gender, OTL, and item format. Data come from three countries participating in an international study of pre-service math teachers, the Teacher Education and Development Study in Mathematics (TEDS-M). iii ACKNOWLEDGMENTS I would like to thank my adviser, Dr. Anthony Albano, for his encouragement and guidance throughout all stages of this thesis. I would also like to thank Dr. Kurt Geisinger for his constructive advice and feedback. My sincere gratitude also goes to my parents, sister, and brothers, for their unconditional love and constant support.
Force analysis using finger dummy for violin fingering

In this paper, we present a force analysis using finger dummy for violin fingering. The authors focus on musical instrument playing robots. Among the violin-playing robot systems developed by the authors, in the case of the robot finger, the angle of the joint is not considered when pressing the violin string. Therefore, the joint angle is considered to be perpendicular to the fingertip of the violin string, and a finger dummy is developed to reflect this. And we're experiments fingertip force analysis based on index finger dummy. Finger dummy fixes DIP joint and PIP joint, and only one degree of freedom of MCP joint. The force sensor system is configured inside the DP part of the finger dummy. Experiments are carried out to analyze the output value of the force sensor applied to the proposed finger dummy. In the experiment, the finger dummy analyzes the measured value from the force sensor by pressing the load cell with the setting force. At this time, the setting force is classified into six groups, and the finger dummy presses the load cell for 3 seconds with each force. The raw data obtained by ten attempts were fitted to a linear regression model. It also proved to be valid by calculating the bestfit of the calculated linear regression model.
Right ventricular contractile reserve in tetralogy of Fallot patients with pulmonary regurgitation

BACKGROUND The right ventricular (RV) contractile reserve is a measure of the dynamic function of the RV and is a sensitive indicator of volume load. This can be measured noninvasively using the tricuspid annular plane systolic excursion (TAPSE) during exercise. We studied the RV contractile reserve of patients after tetralogy of Fallot (TOF) repair with varying degree of RV dilation and pulmonary regurgitation (PR), and compared them to a control group. METHODS Twenty-six patients who had undergone TOF repair (mean age 29 ± 10 years) were identified and stratified into three group based on the presence and severity of RV dilation and PR. We recruited 13 age- and sex-matched controls with normal cardiac anatomy for comparison. After obtaining a baseline echocardiogram in the resting state, patients underwent exercise testing on a treadmill utilizing Bruce protocol. At maximal voluntary ability during the exercise testing, the patient was immediately laid down on an echocardiography couch, and a peak exercise echocardiogram was obtained. RESULTS TOF patients, regardless of RV size and PR severity, had significantly shorter exercise duration (685 vs 802 s, P = .02), lower TAPSE at rest (1.7 vs 2.3 cm, P < 0.001) and at peak exercise (1.6 ± 0.4 vs 2.6 ± 0.5 cm P < .001) when compared to the control group. Patients with RV dilation were more likely to have worse RV contractile reserve but increased TAPSE and tricuspid annular acceleration at rest when compared to patients without RV dilation. CONCLUSIONS TOF patients with dilated RV and PR have worse RV function at rest and during exercise, compared to TOF subjects without RV dilation. Long-axis RV contractile reserve as assessed by TAPSE, was lower in TOF subjects versus controls, and was worse in those with significant RV dilation, suggesting a decline in contractile reserve with an increase in RV volume.
A Simple Algorithm for Three Phase Three Level Space Vector PWM

Abstract— All renewable sources use power electronic devices specially multilevel inverters to convert DC source obtained from renewable sources into AC. These inverters can give their best results when the proper scheme is applied for obtaining desired output. There are many PWM schemes available for inverter operation to obtain variable voltage and frequency supply for inverter. The carrier based sinusoidal and space vector PWM(SVPWM) is the most widely used scheme for the voltage source inverters due to their easier digital realization and better DC link utilization. A simplified algorithm for the three-level SVPWM is proposed. Due to the geometrical symmetry of six sectors, there exist the close relationships in on time calculations and on time arrangement for switches between them. So it can complete the computation of the three-level SVPWM in one sector.
Educational system based on simulation and intelligent conversation

This paper explores the use of intelligent conversation and simulation to increase learning outcomes of science topics, i.e. Physics in our case. The underlying theory is that educational 3D interactive simulations can enhance learning by, first of all, adding a playful element to the learning process which boosts motivation, and, second, by creating an opportunity to "learn by doing" when enabling the learner to explore the simulated phenomena by themselves. Furthermore, conversational agents can enhance and strengthen the simulation-based learning environment by playing the role of guide or tutor, e.g. offering hints when the student is struggling, as well as evaluator, which is needed to provide appropriate feedback and guidance. Subsequently, a prototype of an educational system that combines a Physics simulation and a conversational agent was developed and then evaluated. In order to evaluate the effectiveness of the proposed educational system to induce learning gains, we conducted a randomized controlled trial experiment. In particular, we compared the following experimental conditions: using a simulation alone followed by a full system that included both the simulation and a conversational agent versus the conversational agent alone followed by the full system. In both conditions, students were exposed to the same content.
Research Project for INFN grant n. 16555: OptoTracker

The project OptoTracker aims to investigate a new approach to track charged particles in a scintillating material, by using the optical signal. Our idea is to reconstruct the trajectory of a charged particle by collecting the scintillation light emitted along the path with pixelized photo-detectors. This would permit to obtain an image of the track, similarly to what is done in a photographic camera. Full 3D reconstruction is performed by using both the charge distribution and the hit time information folded in a sophisticated reconstruction algorithm. This solution, compared to “traditional” tracking methods, exploits the fastest information carrier within a material: the light. Therefore, an optical tracking detector would be intrinsically capable of sustaining a very high interaction rate. Moreover, the intrinsic resolution would not be limited by carriers diffusion, as happens in charge-transport based detectors. This new technology could have a very large impact both on beam experiments, thanks to the possible increase in the acquisition rate, and in rare-physics experiments (double-β decay, dark-matter search, neutrino oscillation search), where the enhanced particle-id and directionality capability can provide significant background reduction. ∗andrea.celentano@ge.infn. it
cient Bayesian Optimization for Target Vector Estimation

We consider the problem of estimating a target vector by querying an unknown multioutput function which is stochastic and expensive to evaluate. Through sequential experimental design the aim is to minimize the squared Euclidean distance between the output of the function and the target vector. Applying standard single-objective Bayesian optimization to this problem is both wasteful, since individual output components are never observed, and imprecise since the predictive distribution for new inputs will be symmetric and have support in the negative domain. We address this issue by proposing a Gaussian process model that takes into account the individual function outputs and derive a distribution over the resulting 2norm. Furthermore, we derive computationally e cient acquisition functions and evaluate the resulting optimization framework on several synthetic benchmark functions and a real-world problem. The results demonstrate a significant improvement over standard Bayesian optimization methods based on both standard and Warped Gaussian processes.
Architecture and applications of the Parallel Distributed Image Search Engine (ParaDISE)

Medical images are important to physicians for diagnosis and treatment planning. The number of images produced in medical institutions bas been increasing rapidly over the past years. Making these imaging data available and allowing medical professionals to perform retrieval based on visual characteristics of images is the challenge that content–based image retrieval systems are tackling. In this paper we propose a system called ParaDISE (Parallel Distributed Image Search Engine). It allows indexing and retrieving images using both visual features as well as text. It is designed to scale well with big data sets, and it should be easily extensible with new features. The architecture of the system is described and its use in medical applications is presented. An open source release of the system is in progress.
A survey on real time processing with spiking neural networks

Neuromorphic computing is an emerging architecture to address the issues of parallel computing like energy efficiency, size and speed. In standard neural network the basic computation is matrix multiplication between inputs to the neurons and their weights. This type of heavy computation can be handled conventionally with high end Graphics Processing Units (GPUs) effectively. The current technologies like Parallel CPUs and GPUs can provide parallel computation, but by incurring heavy computation and power consumption overheads. This has motivated research in hardware implementation of spiking neural networks with silicon technologies. The hardware implementation of event driven networks can drastically reduce the computational load and hence the power consumption. This survey paper discusses the various hardware implementations of spike neural networks (SNNs) and how they address different issues related to parallel computation of neural network functions.
Extracellular Mitochondrial DNA Is Generated by Fibroblasts and Predicts Death in Idiopathic Pulmonary Fibrosis

Rationale: Idiopathic pulmonary fibrosis (IPF) involves the accumulation of &agr;‐smooth muscle actin‐expressing myofibroblasts arising from interactions with soluble mediators such as transforming growth factor‐&bgr;1 (TGF‐&bgr;1) and mechanical influences such as local tissue stiffness. Whereas IPF fibroblasts are enriched for aerobic glycolysis and innate immune receptor activation, innate immune ligands related to mitochondrial injury, such as extracellular mitochondrial DNA (mtDNA), have not been identified in IPF. Objectives: We aimed to define an association between mtDNA and fibroblast responses in IPF. Methods: We evaluated the response of normal human lung fibroblasts (NHLFs) to stimulation with mtDNA and determined whether the glycolytic reprogramming that occurs in response to TGF‐&bgr;1 stimulation and direct contact with stiff substrates, and spontaneously in IPF fibroblasts, is associated with excessive levels of mtDNA. We measured mtDNA concentrations in bronchoalveolar lavage (BAL) from subjects with and without IPF, as well as in plasma samples from two longitudinal IPF cohorts and demographically matched control subjects. Measurements and Main Results: Exposure to mtDNA augments &agr;‐smooth muscle actin expression in NHLFs. The metabolic changes in NHLFs that are induced by interactions with TGF‐&bgr;1 or stiff hydrogels are accompanied by the accumulation of extracellular mtDNA. These findings replicate the spontaneous phenotype of IPF fibroblasts. mtDNA concentrations are increased in IPF BAL and plasma, and in the latter compartment, they display robust associations with disease progression and reduced event‐free survival. Conclusions: These findings demonstrate a previously unrecognized and highly novel connection between metabolic reprogramming, mtDNA, fibroblast activation, and clinical outcomes that provides new insight into IPF.