Miracle-dz/UniFAR-FTUs huggingface dataset:
Tracking and Segmentation of the Endocardium of the Left Ventricle in 2D Ultrasound using Deep Learning Architectures and Monte Carlo Sampling

The tracking and segmentation of the left ventricle (LV) of the heart from ultrasound data still deserves the attention in medical image community, being a commonly used method in practical clinical setup nowadays. The goal above stated can be formulated as a sequential state estimation problem, which is essential to the study of linear and non-linear dynamical systems. In this work, we present a particle ﬁlter based approach, rooted in Bayesian estimation and Monte Carlo procedures, for tracking and segmenting the LV. Two main ingredients characterize this formalism: the prediction that models the dynamic of the object in consecutive frames and ﬁltering that collects information in the present frame. This methodology allows the computation of the expected segmentation value at the current time instant of the object, given all previous and current observations. Although the probability values of the ﬁltering distribution can be straightforwardly computed, sampling from it is challenging, so this means that a proposal distribution is needed since it provides an easier way for sampling the ﬁltering distribution. In this work, we present an algorithm for tracking and segmenting the LV in 2D Ultrasound data based on the observations made above. The contributions are as follows: ( i ) a new transition model (prediction) that combines different motion regimes presented in the systole and diastole phases of the cardiac cycle; ( ii ) a new observation model (ﬁltering) built with a deep neural network, and ( iii ) a new proposal distribution for efﬁcient sampling mechanism. The usefulness of our approach is evaluated using a database of disease cases and another dataset of normal cases, where both datasets present long axis views of the left ventricle. Using a training set comprising diseased and healthy cases, we show that our approach produces accurate results for tracking the endocardium. Also, we show that our method correlates well with inter-user statistics produced by four cardiologists.
Semantic Interoperability to Enable Smart, Grid-Interactive Efficient Buildings

Achieving a widespread transition to grid-interactive, efficient buildings (GEBs) depends critically on there being sufficient interoperability among connected building systems. While many critical elements already exist at the technical interoperability level (TCP/IP, BACnet, etc.), a lack of interoperability in the semantic level hinders streamlined integration of interdependent applications. Semantics refers to expressing information about “things” in a way that can be consistently understood by applications. Key components of formalized semantics include identifying what a “thing” is (its “type”), defining general information about that “thing” (its characteristics or properties), and defining the appropriate relationships of that “thing” to other “things” (its function or role in a larger system). Although this might seem initially trivial, the success of smart building applications is highly dependent on maintaining consistent self-descriptive notions of the “things”. Without semantic interoperability, it is technically difficult, labor-intensive, and cost-prohibitive to enable three key objectives of GEBs: optimizing performance, automatically identifying and diagnosing faults, and delivering grid services. Industry, academia, and standards bodies have invested effort in developing information models to facilitate semantic interoperability, however, they have not been widely adopted across the U.S. commercial building portfolio. This paper will present a pathway to drive semantic interoperability through a three-pronged approach to be led by the DOE Building Technologies Office in partnership with NIST and multiple national laboratories comprising: 1) industry engagement and coordination across existing efforts; 2) a semantic interoperability standard that empowers building owners to identify and require interoperable attributes when procuring equipment and applications; 3) tools to assist in implementation and a test framework to verify compliance of products with semantic interoperability specifications. This approach is designed to accelerate the timeline for adoption of semantic interoperability specifications. The intent is to reduce soft costs associated with implementing advanced controls, fault detection and diagnostics, and other smart building technologies and use cases as a necessary step in achieving an energy efficient smart grid future.
Comparative Research of Index Frequency - morphological Methods of Automatic Text Summarisation ∗

Automatic analyses perspective and natural language processing (NLP) is being re-searched in the subject paper. The modiﬁed quantitative approach using a collocation algorithm is presented. This approach eliminates previously discovered issues of text processing using the vector model in the number of cases of thematic classiﬁcation. The author’s frequency method of extracting a set of numerical indicators taking into account the morphological features of words, as well as collocation between terms in text is proposed. The quantitative technology of automatic thematic classiﬁcation using indicators which reﬂect morphological and parsing text features, methods of parameterization, text indexing, algorithms of artiﬁcial intelligence analysis and knowledge extraction is presented. The eﬃciency and advantage of the regression decision tree method in the tasks of ﬁnding of signiﬁcant frequency indexes and their logical representation are demonstrated. The results of comparative experiments to assess the eﬀectiveness of classiﬁcation of natural language text data, using the author’s, vector and set-theoretic models of text representation are stated.
Membrane protein degradation is intrinsic to vacuole fusion

In eukaryotic cells, integral membrane protein (IMP) turnover is an important contributor to cell physiology. Whereas the only known mechanism for IMP degradation is the multivesicular body (MVB) pathway, there are examples of IMPs that eschew this pathway and travel directly to the vacuole surface, suggesting that a second mechanism acts at the vacuole. Previously, it was shown that homotypic vacuole fusion yields an intralumenal membrane fragment within the fusion product, providing the cell with a unique opportunity to internalize (and thus, degrade) a portion of vacuolar membrane and its protein cargoes. Based on this result, we hypothesized that vacuole IMPs are sorted for internalization during the sub-reactions that lead to vacuole fusion. Using epifluorescence microscopy, I confirmed that Fth1-GFP and Ybt1-GFP localize exclusively to the vacuole membrane, and are sorted into, or out of, respectively, the membrane domain that is internalized upon homotypic vacuole fusion. Additionally, I demonstrated that Mup1, a surface methionine transporter, colonizes the vacuole limiting membrane upon disruption of the MVB pathway, and also becomes internalized during homotypic vacuole fusion. This study provides the first evidence of a novel selective IMP degradation mechanism, which works independently or complementarily to the MVB pathway. Further studies are required to confirm whether vacuole IMPs are sorted based on their ubiquitylation status (as in the MVB pathway), or whether the vacuole fusion protein machinery has a role within this process.
Projecting Unemployment Durations: A Factor-Flows Simulation Approach with Application to the Covid-19 Recession

Abstract We propose a three-step factor-flows simulation-based approach to forecast the duration distribution of unemployment. Step 1: estimate individual transition hazards across employment, temporary layoff, permanent layoff, quitter, entrant, and out of the labor force, with each hazard depending on an aggregate component as well as an individual’s labor force history. Step 2: relate the aggregate components to the overall unemployment rate using a factor model. Step 3: combine the individual duration dependence, factor structure, and an auxiliary forecast of the unemployment rate to simulate a panel of individual labor force histories. Applying our approach to the November Blue Chip forecast of the COVID-19 recession, we project that 750,000 workers laid off in April 2020 remain unemployed eight months later. Total long-term unemployment rises thereafter and eventually reaches 4.2 million individuals unemployed for more than 26 weeks and 1.4 million individuals unemployed for more than 46 weeks. Long-term unemployment rises even more in a more pessimistic recovery scenario, but remains below the level in the Great Recession due to a high amount of labor market churn.
The importance of scale in evaluating herbivory impacts

Interactions between plants and herbivores may be analyzed at many levels. As we move from one level of organization to another, the impacts of herbivory change, and so do the meanings of the words we use to describe those impacts. The hypothesis that plants may benefit from herbivory through compensatory growth has been tested at several levels. Although the concept of compensation is potentially useful, its meaning is measurement dependent and level specific. The importance of scaling considerations emerges from a hierarchical approach to the study of herbivory.
Preparing to Measure Vacuum Magnetic Birefringence: Measuring Cavity Birefringence

Vacuum magnetic birefringence (VMB) is predicted by quantum electrodynamics (QED), but, because of the exceedingly small scale of the predicted effect, has yet to be experimentally confirmed. The anisotropy of the QED vacuum structure causes a change in the polarization of light propagating in a high magnetic field. The proposed experiment to find axion like particles in the Any Light Particle Search (ALPS) can be used to measure VMB. This paper describes a sensing method to measure the birefringence of an optical cavity, and a table-top experimental test-bed to study systematic, non-VMB signals that might limit the detection sensitivity . A Fabry-Perot cavity was built and aligned. Light from two 1064 nm lasers was coupled into a single mode fiber and mode matched with lenses to the cavity. Time dependent birefringence was injected in the cavity using an auxillary short Fabry-Perot cavity with a piezo actuated end mirror. One laser was locked to the cavity using Poind-Drever-Hall (PDH) locking, while the second laser was locked to a 25MHz frequency offset using a heterodyne phase lock loop (PLL). Preliminary testing showed that the time dependent birefringence was generated at the expected frequency . Future testing will find the relationship between the modulated birefringence in the cavity and the measured signal.
Dialogue Systems in a Low-resource Scenario PhD. Thesis Proposal

Dialogue system research is a very active topic nowadays. However, deployed systems are usually very speciﬁcally focused and are not able to hold a complex conversation. One of the reasons is that there aren’t a lot of meth-ods that would allow us to extend a trained system in a simple way. Moreover, training a system usually requires a large amount of well-annotated training data. Data annotation is especially problematic for dialogue systems due to its relatively high abstraction and complexity. Therefore, we propose to invent new learning methods for dialogue systems which would lead to quality improvement and widen dialogue systems use cases. We plan to use unsupervised methods that bring us the possibility of using much larger unannotated corpora and hence more eﬀective training of statistical models. We further focus on exploiting weekly annotated data using methods such as transfer learning or meta-learning (learning from similar tasks). These techniques would enable usage of partially annotated data for dialogue sys-tem training. These methods are still severely underexplored in the area of dialogue systems.
Restricted-dimension subgradient descent: asymptotic bounds on error

Convex optimization, the study of minimizing convex functions over convex sets, is host to a multitude of methods with far-reaching applications in machine learning. For methods in convex optimization, it is often of interest to analyze the asymptotic bounds of the error of the method, or in other words, how close the result of the method gets to the minimum value after some set period of time. Gradient descent, an iterative procedure that involves taking the gradients of convex functions at a sequence of points, is one of the most important algorithms in the ﬁeld of convex optimization. Subgradient descent refers to gradient descent that can be applied to functions that need not be smooth. The primary focus of this text is this particular type of gradient descent. This text will explore error estimates of the asymptotic bounds of the ﬁnal iterate error of subgradient descent; that is, the error between the result of the subgradient descent procedure after a set number of steps T . Prior work has established tight asymptotic error bounds that are independent of the dimension of the underlying set; this work explores the possibility of there existing tighter bounds when the dimension of the underlying set is restricted to a ﬁnite constant d that is independent of T . In this work we have proven that in the case of d = 1, the ﬁnal iterate error has an upper bound of O (cid:16) 1 √ T (cid:17) .
Index Pricing Model Embedding Investor Sentiment: Based on Path Converged Design

This paper proposes an index pricing method to model the risk premium of market, industry and area for individual stocks. The individual stocks of Shanghai Index 50 in China’s stock market are selected. Path converged design is employed to construct three time-varying coefficient semi-parameter regression models for each stock. The three models are base model excluding investor sentiment, path model I including investor sentiment of each individual stock and path model II including investor sentiments of indexes. As the empirical result, the path models explain the fluctuation of individual stocks better than the base model, which means that investor sentiment is an important factor for asset pricing. Furthermore, pricing errors analysis is carried out. The result shows that index pricing model plays an important role in the determination of individual stock’s rational prices and it may contribute to trading strategy construction for programming trading. The paper is an effective modeling trial of asset pricing in the nonparametric frame. Key–Words: Investor sentiment, Index pricing model, Path converged design, Pricing error analysis, Nonparametric estimation
Elucidating the Joint Genetic Architecture of Mood Disorder and Schizophrenia

Introduction: Recent advances in psychiatric genomics have enabled large-scale genome-wide scans that elucidated genetic architecture both in mood disorder and schizophrenia across individuals of East Asian and European descent. Investigating joint genetic architecture of these psychiatric traits enables the identification of common and diverging etiological mechanisms underlying these psychiatric illnesses. Here, we leverage on the largest GWAS of schizophrenia and mood disorder conducted to date in East Asian and European descent samples to elucidate the joint genetic architecture that underlie these psychiatric disorders. Methodology: We carried out GWAS meta-analysis on both European (EUR) and East Asian (EAS) Ancestry summary statistics for Major Depressive Disorder (MDD) and Schizophrenia via Multi-Trait Analysis of GWAS. Downstream pathway, eQTL, chromatin interaction analysis were carried out to characterize genome-wide results. In addition we carried out genetic correlations and polygenic risk prediction analysis to further study the joint genetic architectures of mood disorder and schizophrenia. Results: There were 308 loci that was significantly associated with at least one trait. Specifically, there were 98 independent loci in EUR-MDD, 5 loci for MTAGx-EAS-MDD, 121 loci for MTAGx-EUR-MDD, 8 independent loci for EAS-SZ, 171 independent loci for EUR-SZ, 124 independent loci for MTAGx-EAS-SZ, and 159 independent loci for MTAGx-EUR-SZ. In all, 61 loci were novel across traits. SOAT1 and FOXO3 genes were implicated based on genome-wide associations. 114 gene(s) were implicated in eQTL analysis of gene expression in brain tissue. Gene-set analysis show support for GABA-egic pathways implicated in MDD, driven by several GABA-alpha receptor genes as well as more peripheral PLCL1 and NISCH genes that are responsible for endocytosis and neuronal trafficking. Cross-Ancestry genetic correlations ascertained that the CONVERGE MDD phenotype generally holds higher SNP based heritability and is likely driven by case-ascertainment procedures. Finally, polygenic risk score modelling indicates that MTAGx procedures were effective in enriching GWAS signals in the EAS-MDD for prediction in an independent case-control sample. Discussion: Here we are able to demonstrate that cross-trait cross-ancestry approaches in schizophrenia and MDD not only yields new discoveries to the genetic architecture of these illnesses; we were able to identify new biological underpinnings within the GABA pathways for depressive disorders. The evidence in the current report underscores the importance of taking into consideration both phenotype and ancestry complexities in genome-wide studies.
Does Algorithmic Trading Improve Liquidity in Emerging Markets? Empirical Evidence from Thailand

This study explores the impact of algorithmic trading (AT) on liquidity in Thailand, as it affects both investors’ welfare and the cost of capital for firms. Empirical studies on this topic in emerging markets are scarce. A panel data analysis and two-stage least square regressions on the stocks of the SET100 listed on the Stock Exchange of Thailand from March to December 2016, were used to establish the relationship between AT and liquidity. The results showed that AT causes liquidity to deteriorate by enlarging the effective half spread, decreasing share turnover, increasing Amihud’s illiquidity estimate, and lowering the liquidity ratio. Various methods were employed to alleviate endogeneity issues. The results indicated that liquidity declines due to information asymmetry. This study was the first to investigate the effect of AT (initiated by institutional vs. foreign investors) on liquidity, finding that AT initiated by foreign investors plays a larger role in decreasing liquidity in the short term, while AT initiated by institutional investors has a more long term effect.
Hardware prototype demonstration of a cognitive radar with sparse array antennas

As a typical signal processing problem, direction-of-arrival (DOA) estimation has been adapted to a wide range of applications in radar-based systems. A high DOA resolution requires a large number of antenna elements which increases the overall cost. To minimise the cost, it is desirable to choose an optimum sub-array from a full array. To enable cognition, the subarrays are selected based on the present target scenario. By using deep learning (DL) based techniques, the authors show a cognitive sparse array selection technique. By using hardware simulations, they demonstrate the applicability of the deep learning (DL)-based sparse antenna selection network in direction-of-arrival (DOA) estimation problems. They show that the DL-based sub-arrays lead to a higher direction-of-arrival (DOA) estimation accuracy by 6 dB over random array selection.
Effects of dietary supplementation of chelated water-soluble mineral mixture on growth performance, nutrient digestibility, blood profiles and faecal micro flora in weanling pigs

ABSTRACT A total of 105 [(Landrace × Yorkshire) × Duroc] pigs with an initial body weight (BW) of 7 ± 1.58 kg were used in a 5-week experiment in two phases to test the efficacy of supplementation of chelated mineral mixture on piglet performance. Pigs were randomly allocated to one of three treatments [five pigs per pen (three barrows and two gilts); seven pens per treatment]. Treatments consisted of: CON; basal diet, TRT1; basal diet + 0.89% chelated mineral mixture and TRT2; basal diet + 0.55% chelated mineral mixture. Pigs fed TRT1 and TRT2 had increased (P < .05) gain/feed ratio during the first phase and overall and had greater (P < .05) average daily gain (ADG) during the second phase compared with CON. Pigs fed TRT1 had greater (P < .05) ADG overall and had higher (P < .05) dry matter (DM) digestibility during the first phase. On day 35, the concentrations of Fe and Cu in blood serum were higher (P < .05) in TRT1 and TRT2 but Ca concentration increased (P < .05) and faecal Escherichia coli counts reduced in TRT1 compared with CON. In conclusion, our results indicated that chelated mineral mixture have beneficial effects in pigs.
Monitoring point optimization in lake waters

In order to grasp the distribution of water quality index in lake water, taking Jinghu Lake of Guangxi University as the experimental object, an radial basis function (RBF) neural network was combined with a genetic algorithm on the basis of an unmanned ship to study the optimal selection of monitoring points. The single-objective and multi-objective optimization of water quality parameters were tested respectively and used to make the fitting distribution map. The results show that the genetic neural network has obvious advantages over the traditional isometric monitoring in the distribution error of water quality parameters, and the data reflected by the results are still accurate and effective at least six weeks after optimization. The results show that a genetic neural network can significantly improve the efficiency of water quality monitoring. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). doi: 10.2166/ws.2020. 147 om http://iwaponline.com/ws/article-pdf/20/6/2348/767017/ws020062348.pdf er 2021 Gaoxuan Liu Jiaoyan Ai (corresponding author) Jun Xu Jianwu Zheng Dongyi Yao School of electrical engineering, Guangxi University, Nanning, Guangxi 530000, China E-mail: shinin@vip.163. com
Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.