Miracle-dz/UniFAR-FTUs huggingface dataset:
Priorities setting in mental health research: a scoping review

Objective Research processes are opening to stakeholders beyond the scientific community. We analyse the user involvement in the definition of research priorities in the field of mental health. Mental disorders represent a significant disease burden at a global scale and their identification and treatment involves caregivers, patients and related social groups such as family and friends. Therefore it is an area conducive to the application of participatory methods in priority setting. We present a scoping review of participatory methods in mental health priority setting for the period 2010-2020 to shed light on their spread and characteristics, the types of groups involved and the link with the priorities identified. First we describe the eligibility criteria for the scoping review. We selected peer-reviewed documents published between 2010 and 2020 using MEDLINE/PubMed, PsycINFO, the Core Collection of the Web of Science and Scopus, applying controlled terms of search. We initially identified 330 documents from which we selected seventy-four after further discarding studies that were not specifically addressing priority setting in mental disorders research. We noted and classified the interest groups participating in every study. We and When is to scientists, standards, receive The scoping review results on the participation of social groups in priority setting in mental disorders research show that different groups prioritise different health areas.
Composants à semiconducteurs pour hyperfréquences

Les composants semiconducteurs utilises dans les systemes hyperfrequences peuvent etre divises en quatre groupes : les composants discrets non lineaires, les composants discrets permettant la generation de puissance hyperfrequence, les composants discrets tripoles et les circuits integres monolithiques micro-ondes. Les principes physiques regissant le fonctionnement de ces divers composants sont precises, ainsi qu’un etat de l’art des performances obtenues a ce jour en laboratoire et en production de serie.
Fast and Accurate Prediction of Light Scattering from Plasmonic Nanoarrays in Multiple Directions

The method of moments (MoM) is an efficient electromagnetic numerical method for the accurate prediction of light scattering from plasmonic nanostructures. In practice, the light-scattering properties in different incident directions are often concerning. However, traditional MoM generally resorts to the iterative method, which suffers from the problems of convergence rate and redundant computations for multiple incident excitations. Nanoarray structures will further aggravate these problems due to a large number of unknowns. In this article, an efficient numerical method based on MoM and a hierarchical matrix (H-matrix) algorithm is proposed to solve these problems. Numerical experiments demonstrate the efficiency and accuracy of the proposed method for the prediction of light scattering from plasmonic nanoarrays in multiple directions.
E-health enabled integrated care teams, what makes them effective? - Interdisciplinary collaboration

s screened (n = 5122) Duplicates removed (n = 529) Papers excluded (n = 5057) Full-text papers assessed for eligibility (n = 65) Papers excluded after full-text assessment (n = 34) Papers included for qualitative synthesis (n = 31) Extended search (n = 21) Snowball search (n = 20) Handsearch (n = 1) Id en ti fi ca ti o n
Vibration Measurement And Analysis Of Knee-Ankle-Foot Orthosis (Kafo) Plastic-Metal Type

The aim of present paper is to measure the stresses that results from vibration for knee-Ankle-Foot-orthosis (KAFO) at different position during dress up by patient by measuring of accelerometer of (KAFO). Also the pressure between leg and knee -AnkleFoot-orthosis was measured by using piezoelectric sensor. Total stresses result from applied pressure and acceleration on the KAFO numerically calculated with ANSYS 14 package. The results showed that the values of stresses at the upper KAFO (thigh) are greater than the lower KAFO(shank). this is certainly because of the values of pressure in the upper leg
The cognitive phenotype of juvenile absence epilepsy and its heritability: an investigation of patients and unaffected siblings

Objective: The cognitive profile of juvenile absence epilepsy (JAE) remains uncharacterized. This study aimed to: (i) elucidate the neuropsychological profile of JAE; (ii) assess cognitive trait heritability, by investigating unaffected JAE siblings; (iii) determine whether cognitive traits across the idiopathic generalized epilepsy (IGE) spectrum are shared or syndrome-specific, by comparing JAE to JME. Methods: We investigated 123 participants with a comprehensive neuropsychological test battery: 23 JAE patients, 16 unaffected siblings of JAE patients, 45 healthy controls, and 39 JME patients. We correlated clinical measures with cognitive performance data to decode effects of age of onset and duration of epilepsy. Results: JAE patients performed worse than controls on tests of verbal comprehension, working memory, verbal fluency, psychomotor speed, mental flexibility, and learning. Patients and siblings were similarly impaired on measures of verbal comprehension, phonemic and semantic fluency compared to controls. Receiver operating characteristic curves indicated successful discrimination of JAE patients and siblings from controls via linguistic measures (area under the curve [≥] .77, p [≤] .0001). Individuals with JME were more impaired on response inhibition than those with JAE. Across all patients, those with older age at onset had better performance on psychomotor speed and executive function tests. Conclusions: JAE is associated with wide-ranging cognitive difficulties that encompass domains reliant on frontal lobe processing, including expressive language, working memory, and executive function. JAE siblings demonstrate shared impairment with patients on linguistic measures, indicative of a familial trait. Executive function subdomains may be differentially affected across the IGE spectrum.
PENGARUH CONSUMER LEVEL FACTORS TERHADAP KEPUTUSAN PRIVATE LABEL (Survei pada Pelanggan Nevada di Matahari Deparment Store Malang Town Square)

This research is based on strategy of company to expand by means of use consumer level factors that is able to compare between one product is exsist in public. The strategy enable costumer to choose between national brand and Private label which is able as alternative choice for functional thing. The type of this research is explanatory research. The sample of this study were 119 respondents who are costumers of nevada in Matahari Department Store, Malang Town Square.Analysis of the data used is descriptive analysis, multiple linear regression analysis and hypothesis testing. The sampling technique used was purposive sampling technique. The conclusion of this study, there are four variables that make up the costumer level factors, namely Consumer risk perception, price consciousness, price quality . Companies can harness the power of brand nevada to create its own Brand Loyalytiy on the costumer of Matahari Department Store that can influence the sales level of nevada. Keywords: Consumer Level Factors, Decision Purchasing, Private Label ABSTRAK Penelitian.ini dilatarbelakangi oleh strategi perusahaan untuk mengembangkan usahanya dengan cara memanfaatkan Consumer level factors yang bisa membandingkan antara satu produk dengan produk yang ada di pasar itu sendiri. Strategi tersebut memungkinkan konsumen untuk memilih antara Brand Nasional dan Private Label yang menjadi alternatif memilih barang fungsional. Sampel dalam penelitian ini sebanyak 119 orang responden yang merupakan pelanggan nevada di Matahari department store malang town square. Teknik pengambilan sampel yang dipakai adalah teknik purposive sampling. Kesimpulan dari penelitian ini, ada 4 variabel yang membentuk consumer level factors, yaitu Consumer risk perception, price consciousness, price quality association dan Brand Loyalytiy . Perusahaan dapat memanfaatkan kekuatan merek nevada untuk mewujudkan loyalitas merek tersendiri pada pelanggan Matahari department store yang dapat memberikan pengaruh terhadap tingkat penjualan nevada. Kata kunci : Consumer Level Factors , Keputusan , Private Label
Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high-frequency acoustic emission signals

Typical railway wheelsets consist of wheels, axle and axle bearings. Faults can develop on any of the aforementioned components, but the most common are related to wheel and axle bearing defects. The continuous increase in train operating speeds means that failure of an axle bearing can lead to serious derailments, causing loss of life and severe disruption in the operation of the network, damage to the track and loss of confidence in rail transport by the general public. The rail industry has focused on the improvement of maintenance and remote condition monitoring of rolling stock to reduce the probability of failure as much as realistically possible. Current wayside systems such as hot axle box detectors and acoustic arrays may fail to detect defective bearings. This article discusses the results of wayside high-frequency acoustic emission measurements performed on freight rolling stock with artificially induced damage in axle bearings in Long Marston, UK. Time spectral kurtosis is applied for the analysis of the acoustic emission data. From the results obtained, it is evident that time spectral kurtosis is capable of distinguishing the axle bearing defects from the random noises produced by different sources such as the wheel–rail interaction, braking and changes in train speed.
Structural design of high-performance capacitive accelerometers using parametric optimization with uncertainties

ABSTRACT Electrostatic or capacitive accelerometers are among the highest volume microelectromechanical systems (MEMS) products nowadays. The design of such devices is a complex task, since they depend on many performance requirements, which are often conflicting. Therefore, optimization techniques are often used in the design stage of these MEMS devices. Because of problems with reliability, the technology of MEMS is not yet well established. Thus, in this work, size optimization is combined with the reliability-based design optimization (RBDO) method to improve the performance of accelerometers. To account for uncertainties in the dimensions and material properties of these devices, the first order reliability method is applied to calculate the probabilities involved in the RBDO formulation. Practical examples of bulk-type capacitive accelerometer designs are presented and discussed to evaluate the potential of the implemented RBDO solver.
Motivating cascade testing for familial hypercholesterolemia: applying the extended parallel process model for clinician communication

Abstract Motivating at-risk relatives to undergo cascade testing for familial hypercholesterolemia (FH) is critical for diagnosis and lifesaving treatment. As credible sources of information, clinicians can assist in family communication about FH and motivate cascade testing uptake. However, there are no guidelines regarding how clinicians should effectively communicate with probands (the first person diagnosed in the family) and at-risk relatives. Individuals and families with FH can inform our understanding of the most effective communications to promote cascade testing. Guided by the extended parallel process model (EPPM), we analyzed the perspectives of individuals and families with FH for effective messaging clinicians can use to promote cascade testing uptake. We analyzed narrative data from interviews and surveys collected as part of a larger mixed-methods study. The EPPM was used to identify message features recommended by individuals and families with FH that focus on four key constructs (severity, susceptibility, response efficacy, self-efficacy) to promote cascade testing. Participants included 22 individuals from 11 dyadic interviews and 98 survey respondents. Participants described prioritizing multiple messages that address each EPPM construct to alert relatives about their risk. They illustrated strategies clinicians could use within each EPPM construct to communicate to at-risk relatives about the importance of pursuing diagnosis via cascade testing and subsequent treatment for high cholesterol due to FH. Findings provide guidance on effective messaging to motivate cascade testing uptake for FH and demonstrates how the EPPM may guide communication with at-risk relatives about genetic risk and motivate cascade testing broadly.
Multicenter Real-World Study on Effectiveness and Early Discontinuation Predictors in Patients With Non-small Cell Lung Cancer Receiving Nivolumab

Abstract Background Real-world (RW) evidence on nivolumab in pretreated patients with non-small cell lung cancer (NSCLC) by matching data from administrative health flows (AHFs) and clinical records (CRs) may close the gap between pivotal trials and clinical practice. Methods This multicenter RW study aims at investigating median time to treatment discontinuation (mTTD), overall survival (mOS) of nivolumab in pretreated patients with NSCLC both from AHF and CR; clinical-pathological features predictive of early treatment discontinuation (etd), budget impact (BI), and cost-effectiveness analysis were investigated; mOS in patients receiving nivolumab and docetaxel was assessed. Results Overall, 237 patients with NSCLC treated with nivolumab were identified from AHFs; mTTD and mOS were 4.2 and 9.8 months, respectively; 141 (59%) received at least 6 treatment cycles, 96 (41%) received < 6 (etd). Median overall survival in patients with and without etd were 3.3 and 19.6 months, respectively (P < .0001). Higher number, longer duration, and higher cost of hospitalizations were observed in etd cases. Clinical records were available for 162 patients treated with nivolumab (cohort 1) and 83 with docetaxel (cohort 2). Median time to treatment discontinuation was 4.8 and 2.6 months, respectively (P < .0001); risk of death was significantly higher in cohort 2 or cohort 1 with etd compared with cohort 1 without etd (P < .0001). Predictors of etd were body mass index <25, Eastern Cooperative Oncology Group performance status >1, neutrophile-to-lymphocyte ratio >2.91, and concomitant treatment with antibiotics and glucocorticoids. The incremental cost-effectiveness ratio of nivolumab was 3323.64 euros ($3757.37) in all patients and 2805.75 euros ($3171.47) for patients without etd. Finally, the BI gap (real-theoretical) was 857 188 euros ($969 050.18). Conclusion We defined predictors and prognostic-economic impact of nivolumab in etd patients.
Spatial Geometry of the Large-Scale Universe: The Role of Quantum Gravity, Dark Energy and other Unknowns

It has been known for some time that the usual inference drawn from the observed near-ﬂatness of the large-scale Universe - namely the existence of a cosmological “ﬂatness problem”, which is then taken as a partial, but key motivation for assuming the existence of an ultra-short, inﬂationary expansion of the very early Universe - is in itself deeply problematic. The present contribution consolidates the earlier results regarding the absence of a cosmological ﬂatness problem of the sort that could potentially be resolved by inﬂation, by clearing up some common misunderstandings and by presenting some arguments in more detail.
Helicity-conservative Physics-informed Neural Network Model for Navier-Stokes Equations

We design the helicity-conservative physics-informed neural network model for the Navier-Stokes equation in the ideal case. The key is to provide an appropriate PDE model as loss function so that its neural network solutions produce helicity conservation. Physics-informed neural network model is based on the strong form of PDE. We compare the proposed Physics-informed neural network model and a relevant helicity-conservative ﬁnite element method. We arrive at the conclusion that the strong form PDE is better suited for conservation issues. We also present theoretical justiﬁcations for helicity conservation as well as supporting numerical calculations.
The importance of landscape features for performance prediction of modular CMA-ES variants

Selecting the most suitable algorithm and determining its hyperparameters for a given optimization problem is a challenging task. Accurately predicting how well a certain algorithm could solve the problem is hence desirable. Recent studies in single-objective numerical optimization show that supervised machine learning methods can predict algorithm performance using landscape features extracted from the problem instances. Existing approaches typically treat the algorithms as black-boxes, without consideration of their characteristics. To investigate in this work if a selection of landscape features that depends on algorithms' properties could further improve regression accuracy, we regard the modular CMA-ES framework and estimate how much each landscape feature contributes to the best algorithm performance regression models. Exploratory data analysis performed on this data indicate that the set of most relevant features does not depend on the configuration of individual modules, but the influence that these features have on regression accuracy does. In addition, we have shown that by using classifiers that take the features' relevance on the model accuracy, we are able to predict the status of individual modules in the CMA-ES configurations.
AID: Accuracy Improvement of Analog Discharge-Based in-SRAM Multiplication Accelerator

This paper presents a novel circuit (AID) to improve the accuracy of an energy-efficient in-memory multiplier using a standard 6T -SRAM. The state-of-the-art discharge-based in-SRAM multiplication accelerators suffer from a non-linear behavior in their bit-line (BL, BLB) due to the quadratic nature of the access transistor that leads to a poor signal-to-noise ratio (SNR). In order to achieve linearity in the BLB voltage, we propose a novel root function technique on the access transistor's gate that results in accuracy improvement of on average 10.77 dB SNR compared to state-of-the-art discharge-based topologies. Our analytical methods and a circuit simulation in a 65 nm CMOS technology verify that the proposed technique consumes 0.523 pJ per computation (multiplication, accumulation, and preset) from a power supply of 1V, which is 51.18% lower compared to other state-of-the-art techniques. We have performed an extensive Monte Carlo based simulation for a 4×4 multiplication operation, and our novel technique presents less than 0.086 standard deviations for the worst-case incorrect output scenario.
Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models

With many real-world applications of Natural Language Processing (NLP) comprising of long texts, there has been a rise in NLP benchmarks that measure the accuracy of models that can handle longer input sequences. However, these benchmarks do not consider the trade-offs between accuracy, speed, and power consumption as input sizes or model sizes are varied. In this work, we perform a systematic study of this accuracy vs. efficiency trade-off on two widely used long-sequence models - Longformer-Encoder-Decoder (LED) and Big Bird - during fine-tuning and inference on four datasets from the SCROLLS benchmark. To study how this trade-off differs across hyperparameter settings, we compare the models across four sequence lengths (1024, 2048, 3072, 4096) and two model sizes (base and large) under a fixed resource budget. We find that LED consistently achieves better accuracy at lower energy costs than Big Bird. For summarization, we find that increasing model size is more energy efficient than increasing sequence length for higher accuracy. However, this comes at the cost of a large drop in inference speed. For question answering, we find that smaller models are both more efficient and more accurate due to the larger training batch sizes possible under a fixed resource budget.
Causal Transformer for Estimating Counterfactual Outcomes

Estimating counterfactual outcomes over time from observational data is relevant for many applications (e.g., personalized medicine). Yet, state-of-the-art methods build upon simple long short-term memory (LSTM) networks, thus rendering inferences for complex, long-range dependencies challenging. In this paper, we develop a novel Causal Transformer for estimating counterfactual outcomes over time. Our model is specifically designed to capture complex, long-range dependencies among time-varying confounders. For this, we combine three transformer subnetworks with separate inputs for time-varying covariates, previous treatments, and previous outcomes into a joint network with in-between cross-attentions. We further develop a custom, end-to-end training procedure for our Causal Transformer. Specifically, we propose a novel counterfactual domain confusion loss to address confounding bias: it aims to learn adversarial balanced representations, so that they are predictive of the next outcome but non-predictive of the current treatment assignment. We evaluate our Causal Transformer based on synthetic and real-world datasets, where it achieves superior performance over current baselines. To the best of our knowledge, this is the first work proposing transformer-based architecture for estimating counterfactual outcomes from longitudinal data.
Guided Co-Modulated GAN for 360° Field of View Extrapolation

We propose a method to extrapolate a 360° field of view from a single image that allows for user-controlled synthesis of the out-painted content. To do so, we propose improvements to an existing GAN-based in-painting architecture for out-painting panoramic image representation. Our method obtains state-of-the-art results and outperforms previous methods on standard image quality metrics. To allow controlled synthesis of out-painting, we introduce a novel guided co-modulation framework, which drives the image generation process with a common pretrained discriminative model. Doing so maintains the high visual quality of generated panoramas while enabling user-controlled semantic content in the extrapolated field of view. We demonstrate the state-of-the-art results of our method on field of view extrapolation both qualitatively and quantitatively, providing thorough analysis of our novel editing capabilities. Finally, we demonstrate that our approach benefits the photorealistic virtual insertion of highly glossy objects in photographs.
Saga: A Platform for Continuous Construction and Serving of Knowledge at Scale

We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremental design to continuously integrate billions of facts about real-world entities and construct a central knowledge graph that supports multiple production use cases with diverse requirements around data freshness, accuracy, and availability. In this paper, we discuss the unique challenges associated with knowledge graph construction at industrial scale, and review the main components of Saga and how they address these challenges. Finally, we share lessons-learned from a wide array of production use cases powered by Saga.