Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference

Although a vast body of literature relates to image segmentation methods that use deep neural networks (DNNs), less attention has been paid to assessing the statistical reliability of segmentation results. In this study, we interpret the segmentation results as hypotheses driven by DNN (called DNN-driven hypotheses) and propose a method by which to quantify the reliability of these hypotheses within a statistical hypothesis testing framework. Specifically, we consider a statistical hypothesis test for the difference between the object and background regions. This problem is challenging, as the difference would be falsely large because of the adaptation of the DNN to the data. To overcome this difficulty, we introduce a conditional selective inference (SI) framework -- a new statistical inference framework for data-driven hypotheses that has recently received considerable attention -- to compute exact (non-asymptotic) valid p-values for the segmentation results. To use the conditional SI framework for DNN-based segmentation, we develop a new SI algorithm based on the homotopy method, which enables us to derive the exact (non-asymptotic) sampling distribution of DNN-driven hypothesis. We conduct experiments on both synthetic and real-world datasets, through which we offer evidence that our proposed method can successfully control the false positive rate, has good performance in terms of computational efficiency, and provides good results when applied to medical image data.
A new approach to the design of dual-mode rectangular waveguide filters with distributed coupling

A synthesis procedure for a new configuration of four-pole dual-mode waveguide filters with distributed coupling is presented. In this configuration the dual-mode coupling is obtained by exploiting the well-known fact that the diagonal polarizations are coupled in an almost square waveguide. The obvious advantage of this type of structure is that the cross sections of all the discontinuities are rectangular, hence the global prediction of the filter is more accurate. The synthesis approach is based on a scattering matrix formulation where the dual-mode coupling is described in terms of a 2/spl times/2 propagation matrix. Universal design charts, which directly yield the scattering parameters of the various junctions, have been obtained. Comparisons with experimental results of a four-pole filter are also presented.
Outsource to India: The Impact of Service Outsourcing to India on the Labor Market in the US

While the impact of outsourcing in manufacturing industries is well-documented, relatively little is known about service outsourcing despite its growing importance in the economy. This paper is one of the few papers studying the effect of service outsourcing on the US’s labor market, focusing on India, one of the most popular outsourcing destination countries. I examine whether the increase in service outsourcing to India has reduced the employment of the occupations with greater exposure to Indian service Imports. To account for endogeneity, I instrument for the growth of the US’s service import from India exploiting the change in Indian import in European countries. The occupation level analysis gives a mixed result. An increase in service imports reduces the total employment from 2000 to 2007; however, this effect attenuates in the later period of 2007 to 2017. The change is skill-biased: the reduction in employment is smaller for collegeeducated workers in the first period, and the sign reverses later.
Efficient Online Cough Detection with a Minimal Feature Set Using Smartphones for Automated Assessment of Pulmonary Patients

An automated monitoring of chronic diseases may help in the early identification of exacerbation, reduction of healthcare expenditure, as well as improve patient's healthrelated quality of life. Cough monitoring provides valuable information in the assessment of asthma and Chronic Obstructive Pulmonary Disease (COPD). In this multi-cohort study, we have used every-day wearables such as smartphone and smartwatch to collect cough instances from 131 subjects including 69 asthma patients, 9 COPD patients, 13 patients with a co-morbidity of asthma and COPD and 40 healthy controls. For online cough detection we have identified the audio features suitable for resource-constrained platforms (e.g., smartphone), ranked the features and identified top 9 features to obtain an F-1 score of 99.8% in the offline classification of 23,884 cough instances from non-cough (speech/silence, etc.) events using Random Forest classifier. Finally, a power and time-efficient scheme for continuous online cough detection from the audio stream has been illustrated. The proposed model has an online cough detection sensitivity of 93.3%, specificity of 98.8% and accuracy of 98.8%. In addition, a good improvement in reducing the on-device execution (feature extraction and classification) time and power consumption has been achieved compared to the current state of the art algorithms. The proposed on-device cough detector has been implemented to meet the criteria for integration in the passive monitoring and online assessment of asthma/COPD patients. Keywordscough; online detection; pulmonary disease; random forest; streaming audio.
Single-mode optical fiber with arbitrary refractive index profile: Propagation solution by the Numerov method

The Numerov integration scheme is discussed in its application to calculation of the propagation characteristics of single-mode optical fibers with arbitrary refractive-index profiles. The technique can be made highly accurate, with modest computational requirements. Examples are given for simple step fiber, W-fiber with rectangular central dip, and for depressed-clad fibers. For the depressed-clad fibers, experimental results are compared with predictions for dispersion from the Numerov method.
Multiphase flow measurement techniques -- A review

This paper is a review of current techniques available for measuring the velocity and composition in multiphase streams, to obtain the mass flow rate of the individual phases. An extensive literature search was conducted on the topic and related areas of interest. The major difficulty in measuring both the velocity and composition of multiphase streams is in dealing with the wide variety of flow regimes which are possible in multiphase flow in pipes. A device which is suitable for accurate velocity measurement in multiphase flows is not commercially available. However, if the flow is well mixed, it should be possible to calibrate a simple device, such as a nozzle or a venturi, to provide accurate total volumetric flow rates. Several commercial in-line static mixing devices are suitable for low gas concentrations ([le] 10 percent) and with superficial gas velocities higher than 10 m/s. For lower gas velocities and high gas concentrations, the suitability of these in-line mixers will have to be further assessed experimentally. Other techniques such as cross-correlation are known for two-phase flow velocity measurements, and the results of these applications look promising. A multiphase compositional meter to monitor the concentration of oil, water, and gas phases flowing inmore » a pipeline, used in combination with a suitable homogenizer and a velocity meter, would facilitate measurement of the mass flow rates of the individual phases. Further work must be done to develop this concept. « less
Cis-regulatory hubs: a new 3D model of complex disease genetics with an application to schizophrenia

Genes and their regulatory elements are organized in neurons within 3D networks which model functional structures and explain schizophrenia genetic etiology. The 3D conformation of the chromatin creates complex networks of noncoding regulatory regions (distal elements) and promoters impacting gene regulation. Despite the importance of the role of noncoding regions in complex diseases, little is known about their interplay within regulatory hubs and implication in multigenic diseases such as schizophrenia. Here we show that cis-regulatory hubs (CRHs) in neurons highlight functional interactions between distal elements and promoters, providing a model to explain epigenetic mechanisms involved in complex diseases. CRHs represent a new 3D model, where distal elements interact to create a complex network of active genes. In a disease context, CRHs highlighted strong enrichments in schizophrenia-associated genes, schizophrenia-associated SNPs, and schizophrenia heritability compared with equivalent structures. Finally, CRHs exhibit larger proportions of genes differentially expressed in schizophrenia compared with promoter-distal element pairs or TADs. CRHs thus capture causal regulatory processes improving the understanding of complex disease etiology such as schizophrenia. These multiple lines of genetic and statistical evidence support CRHs as 3D models to study dysregulation of gene expression in complex diseases more generally.
Integration of passive and active components into build-up layers

In the last few years, an increasing number of mobile electronic products have been launched to the market, like mobile communicators, combining the RF functions of a mobile phone and the complexity of a palm top computer. Inside these devices there is less and less space for the electronic system, while packaging density and operating frequencies are constantly increasing. Additionally, the product life time is going down, requiring short design cycles and a production at low-cost based on well established technologies. These trends are a strong challenge for microelectronic packaging and assembly technology. An advanced packaging concept is presented, the chip in polymer technology, which aims to face the above mentioned challenges. It is based on the embedding of ultra thin semiconductor chips into the build-up layers of printed circuit boards together with integrated passive components. Processing issues with regard to registration tolerances between embedded chips and metal lines are discussed. First results of the formation of integrated resistors are shown, using electrolessly deposited NiP layers. The embedding technology is put into relation to common interconnection technologies regarding expected RF properties and system design aspects. Basic thermomechanical properties of a first technology demonstrator, a stackable chip scale package, is discussed using finite element modeling.
Electrical resistance standards and the quantum Hall effect

This review of electrical resistance standards begins with a description of classical standard resistors and their limitations. Methods of comparing resistance are described; these include bridges based on cryogenic current comparators capable of achieving statistical uncertainties approaching one part in 1010 in the measurement of resistance ratios. Such reproducibility is nearly two orders of magnitude smaller than the overall uncertainty of the most accurate determinations of the ohm from its SI definition via the calculable capacitor. The quantum Hall effect can provide an invariable reference standard of resistance linked to the fundamental physical constants. Many factors, however, limit the accuracy of practical realizations of quantized Hall resistance standards. Ultimately, the accuracy of a specific realization must be confirmed by comparison with similar standards; methods for doing this and the resulting agreement are presented. The ac techniques used in the determinations of the SI ohm by mea.
Anthropometric failures and its associated factors among preschool-aged children in a rural community in southwest Ethiopia

Background In 2019, 144 million under-five-year-old children were stunted, and 47 million were wasted globally. In Ethiopia, approximately 350,000 children are estimated to die each year. Preschool aged children need focused attention because this age group not only has special needs, but also forms the platform for growth and development of all children. Under nutrition among preschool children is the result of a complex interplay of diverse elements, such as birth weight, household access to food, availability and use of drinking water. This study aimed at determining the anthropometric failures and associated factors using composite indictors. Methods A community-based cross-sectional study design was used among randomly selected 588 caregivers with pre-school aged children. Under-nutrition of pre-school aged children was computed by using the composite index of anthropometric failure. A multi-stage sampling technique followed by a systematic random sampling technique was used to select study participants. Structured questionnaires were used to collect data. WHO Anthro software was used to calculate height for age, weight for age and weight for height. The overall prevalence of anthropometric failure (CIAF). Both bivariable and multivariable binary logistic regressions were used to identify factors associated with under-nutrition. Results The overall prevalence of under-nutrition among pre-school children was 50.8%, which was significantly associated with being a female (AOR = 1.51, CI: 1.076, 2.12), being from a large family (AOR = 1.78, CI: 1.19, 2.663), having acute respiratory infection (AOR = 1.767, CI: 1.216, 2.566), lack of improved source of drinking water (AOR = 1.484 CI: 1.056, 2.085) and poor dietary diversity score (AOR = 1.5, CI: 1.066, 2.112). Conclusions The study area has a high prevalence of CIAF in pre-school aged children. The CIAF was found to be significantly associated with the sex of the child, family size, ARI within the last two weeks, and dietary diversity score. To promote the use of family planning and the prevention of infectious diseases, health education is required. The government should adapt CIAF as a metric for assessing children’s nutritional status.
Optimization for error-canceling loop of the feedforward amplifier using a new system-level mathematical model

We present a system-level mathematical model to simulate the linearity of the feedforward amplifier. The model has fundamental and distortion signal sub-paths constructed from the data extracted by baseband system simulation. The calculated linearization performances using the model have been validated by comparison with the baseband system simulation results. From the calculated results, the optimum design of the error amplifier size and coupling factor of the output coupler for a down-link WCDMA signal with 16 data channels are provided to maximally achieve output linearity with a fixed power capacity of the amplifiers. Our data shows that the optimum size of the error amplifier is 0.45 times that of the main amplifier with the coupling factor of 5.5 dB.
Switching‐capacitor current‐reused VCO with symmetrical differential outputs

In this article, we present two switching‐capacitor current‐reused voltage‐controlled oscillators with two sub‐bands or four sub‐bands. The degeneration source resistors are used to improve magnitude symmetry of output signals. Tuning frequency range is from 2.16 to 2.74GHz. The phase noise is better than −104 dBc/Hz at 100 kHz frequency offset. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 600–604, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24994
No-go theorem based on incomplete information of Wigner about his friend

The notion of measurements is central for many debates in quantum mechanics. One critical point is whether a measurement can be regarded as an absolute event, giving the same result for any observer in an irreversible manner. Using ideas from the gedankenexperiment of Wigner's friend it has been argued that, when combined with the assumptions of locality and no-superdeterminism, regarding a measurement as an absolute event is incompatible with the universal validity of quantum mechanics. We consider a weaker assumption: is the measurement event realised relatively to the observer when he only partially observed the outcome. We proposed a protocol to show that this assumption putting in conjunction with the natural assumptions of no-superdeterminism and locality is also not compatible with the universal validity of quantum mechanics.
Ivabradine–Flecainide as Breakthrough Drug Combination for Congenital Junctional Ectopic Tachycardia: A Case Report and Literature Review

Congenital junctional ectopic tachycardia (CJET) is a rare tachyarrhythmia that remains difficult to manage, with suboptimal control in most cases. Here, we report literature research on the use of ivabradine in the treatment of pediatric junctional ectopic tachycardia (JET), both congenital and postoperative, and describe the successful use of ivabradine–flecainide association for CJET therapy resistant to other antiarrhythmic agents. This new drug combination was effective in completely suppressing JET. Ivabradine–flecainide combination may be considered a new therapeutic strategy of CJET with a satisfactory efficacy/tolerability ratio in patients resistant to conventional drug combinations.
Wound coverage by the linen dressing accelerates ulcer healing

Introduction Chronic ulcers are the main cause of morbidity and mortality, and the incidence of chronic wounds is expected to increase given that people live longer and that there are civil diseases. Aim Much attention in the treatment of wounds concerns a dressing that involves wound cleansing, bacterial balance, exudate management and local tissue in a wound environment. These important elements of the evaluation led to the development of an interactive dressing based entirely on flax raw materials. Material and methods The complete dressing for wound coverage was prepared from plant (flax) row products: seedcakes, oil, fiber. The content of bioactive compounds (qualitatively and quantitatively) was tested using chromatographic techniques, and their biological activity during tests on fibroblast cell cultures (NHDF). As a final step the clinical trial were performed. Results The dressings, which help control the microenvironment, combining with exudate using hydrophilic fibre, controlling the flow of exudate from the wound to the dressing were generated. They stimulate the activity in the healing cascade and accelerate the healing process by combining lignocellulose fibre with higher amounts of phenolic compounds, sterols, cannabidiol and unsaturated fatty acids simultaneously with the 3-hydroxybutyrate polymer. All constituents of linen dressing are natural, originate from two types of the engineered flax plant. Pre-clinical data reveal a reasonable reduction in wound size in patients with chronic leg ulcers treated with a linen dressing. Conclusions For the first time, a successful application of the innovative interactive linen dressing in the treatment of chronic wounds was noted.
Linearisation of the Travel Time Functional in Porous Media Flows

The travel time functional measures the time taken for a particle trajectory to travel from a given initial position to the boundary of the domain. Such evaluation is paramount in the post-closure safety assessment of deep geological storage facilities for radioactive waste where leaked, non-sorbing, solutes can be transported to the surface of the site by the surrounding groundwater. The accurate simulation of this transport can be attained using standard dual-weighted-residual techniques to derive goal-oriented $a$ $posteriori$ error bounds. This work provides a key aspect in obtaining a suitable error estimate for the travel time functional: the evaluation of its G\^ateaux derivative. A mixed finite element method is implemented to approximate Darcy's equations and numerical experiments are presented to test the performance of the proposed error estimator. In particular, we consider a test case inspired by the Sellafield site located in Cumbria, in the UK.
An Improved Reinforcement Learning Model Based on Sentiment Analysis

With the development of artificial intelligence technology, quantitative trading systems represented by reinforcement learning have emerged in the stock trading market. The authors combined the deep Q network in reinforcement learning with the sentiment quantitative indicator ARBR to build a high-frequency stock trading model for the share market. To improve the performance of the model, the PCA algorithm is used to reduce the dimensionality feature vector while incorporating the influence of market sentiment on the long-short power into the spatial state of the trading model and uses the LSTM layer to replace the fully connected layer to solve the traditional DQN model due to limited empirical data storage. Through the use of cumulative income, Sharpe ratio to evaluate the performance of the model and the use of double moving averages and other strategies for comparison. The results show that the improved model proposed by authors is far superior to the comparison model in terms of income, achieving a maximum annualized rate of return of 54.5%, which is proven to be able to increase reinforcement learning performance significantly in stock trading.
Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric Learning

Human affect recognition is a well-established research area with numerous applications, e.g. in psychological care, but existing methods assume that all emotions-of-interest are given a priori as annotated training examples. However, the rising granularity and refinements of the human emotional spectrum through novel psychological theories and the increased consideration of emotions in context brings considerable pressure to data collection and labeling work. In this paper, we conceptualize one-shot recognition of emotions in context - a new problem aimed at recognizing human affect states in finer particle level from a single support sample. To address this challenging task, we follow the deep metric learning paradigm and introduce a multi-modal emotion embedding approach which minimizes the distance of the same-emotion embeddings by leveraging complementary information of human appearance and the semantic scene context obtained through a semantic segmentation network. All streams of our context-aware model are optimized jointly using weighted triplet loss and weighted cross entropy loss. We conduct thorough experiments on both, categorical and numerical emotion recognition tasks of the Emotic dataset adapted to our one-shot recognition problem, revealing that categorizing human affect from a single example is a hard task. Still, all variants of our model clearly outperform the random baseline, while leveraging the semantic scene context consistently improves the learnt representations, setting state-of-the-art results in one-shot emotion recognition. To foster research of more universal representations of human affect states, we will make our benchmark and models publicly available to the community under https://github.com/KPeng9510/Affect-DML.
The Power of Communication in a Distributed Multi-Agent System

Single-Agent (SA) Reinforcement Learning systems have shown outstanding re-sults on non-stationary problems. However, Multi-Agent Reinforcement Learning(MARL) can surpass SA systems generally and when scaling. Furthermore, MAsystems can be super-powered by collaboration, which can happen through ob-serving others, or a communication system used to share information betweencollaborators. Here, we developed a distributed MA learning mechanism withthe ability to communicate based on decentralised partially observable Markovdecision processes (Dec-POMDPs) and Graph Neural Networks (GNNs). Minimis-ing the time and energy consumed by training Machine Learning models whileimproving performance can be achieved by collaborative MA mechanisms. Wedemonstrate this in a real-world scenario, an offshore wind farm, including a set ofdistributed wind turbines, where the objective is to maximise collective efficiency.Compared to a SA system, MA collaboration has shown significantly reducedtraining time and higher cumulative rewards in unseen and scaled scenarios.