Miracle-dz/UniFAR-FTUs huggingface dataset:
The protective effect of trimetazidine on cardiac myocardial cells in rats with heart failure

Trimetazidine is a drug that promotes myocardial energy metabolism. This study established a heart failure rat model and analyzed the impact of trimetazidine on myocardial energy metabolism and structure in a heart failure rat model. Abdominal aortic contraction was applied to establish the pressure overload heart failure rat model, followed by an analysis of the general conditions, including eating, drinking, breathing, activity, and hair. In addition, myocardial tissue structure changes were observed under lighted and electron microscopes. Cardiac hemodynamic changes were tested using cardiac ultrasonography. Serum ATP, ADP, AMP, and lactic acid content were detected using the perchlorate extraction enzyme reaction method. Myocardial cell apoptosis was determined using a TUNEL assay. The rats in the experimental group presented slight cardiac failure symptoms and a degree of mild myocardial tissue damage. The thickness of the interventricular septum (IVS), the left ventricular posterior wall thickness (LVPW), the left ventricular end-diastolic diameter (LVD), and the left ventricular end systolic diameter (LVS) in the experimental group were significantly lower than they were in the model group but were higher than they were in the control group. The left ventricular ejection fraction (LVEF) and the left ventricular short axis fractional shortening (FS) were significantly elevated in the model group but lower compared with the control group (P < 0.05). The ATP, ADP, AMP, and lactic acid levels in the experimental group were significantly higher than the levels in the model group but lower than the levels in the control group (P < 0.05). Myocardial AI in the experimental group was significantly lower than it was in the model group, but it was higher than it was in the control group (P < 0.05). The application of trimetazidine can treat heart failure, possibly by reducing myocardial apoptosis.
Unified Language-Independent DNN-Based G2P Converter

We introduce a unified Grapheme-to-phoneme conversion framework based on the composition of deep neural networks. In contrary to the usual approaches building the G2P frameworks from the dictionary, we use whole phrases, which allows us to capture various language properties, e.g. cross-word assimilation, without the need for any special care or topology adjustments. The evaluation is carried out on three different languages – English, Czech and Russian. Each requires dealing with specific properties, stressing the proposed framework in various ways. The very first results show promising performance of the proposed framework, dealing with all the phenomena specific to the tested languages. Thus, we consider the framework to be language-independent for a wide range of languages.
E-Availability and E-Accessibility of Financial Documents : A Cross-State Examination of U . S . County Websites

This article examines the e-availability and e-accessibility of financial documents through county websites in the United States (U.S.). E-availability and e-accessibility of financial documents supports a stakeholder-centric approach for evaluating performance and fiscal conditions of county governments, while also promoting democratic values of transparency and accountability. Previous research addresses e-availability and e-accessibility for cities, only one analysis reviews popular reports in both cities and counties, and only one study exists that exclusively focuses on the 100 most populous U.S. counties. Our review extends earlier research by examining 237 U.S. counties, up to five in each of the 48 states with county governments. Additionally, this research makes a limited comparison with an earlier study to indicate changes in the e-availability and e-accessibility of financial documents over a four-year period. Using systematic sampling and content analysis, this study contributes to fuller understanding of the significance of financial documents as a feature of e-government, while reviewing more counties and highlighting variations among counties of differing population sizes. This research also conducted Chi-square tests to examine the relationship of the variables, and the value of Cramér's V was calculated to measure the strength of the relationship between the variables. In addition to finding variations in the eavailability and e-accessibility of financial documents among counties of different population sizes, this analysis also demonstrates dramatic e-availability improvements for two of the three selected financial documents while noting a modest decrease in the overall e-accessibility of financial documents on county websites. After reporting and analyzing the findings, research limitations are disclosed, and recommendations are offered to advance the state-of-practice and for further studies. This form of benchmarking may assist other local governments in the U.S. with improving their websites, while internationally this analysis supports developing countries with refining their e-government strategies by improving online information disclosure.
NATIVE, NAÏVE, AND EXEMPLAR-BASED PERCEPTION OF STATEMENT AND QUESTION INTONATION IN CANTONESE AND MANDARIN

This study investigated whether an exemplar model of speech perception could account for the intonation-based classification of statements and questions in Cantonese and Mandarin. Both native and naïve listeners of each language performed a sentence-type identification task. They were presented with gated forms of 80 pairs of statements and questions that ended in all the lexical tones in each language. An exemplar-based model also simulated the listening task by classifying the same tokens based on the Euclidean distance of the F0 values between new and previously presented tokens. Results showed that the naïve listeners and the model performed worse than native listeners on whole-utterance stimuli, but all groups performed similarly well on gated utterances that comprised the final syllable only. Both naïve listeners and the model performed at similar above-chance levels, suggesting that a “naïve” exemplar-based model could account for the naïve listeners’ perception of intonation.
An evolutionary medicine perspective on pain and its disorders

Enormous progress in understanding the mechanisms that mediate pain can be augmented by an evolutionary medicine perspective on how the capacity for pain gives selective advantages, the trade-offs that shaped the mechanisms, and evolutionary explanations for the system's vulnerability to excessive and chronic pain. Syndromes of deficient pain document tragically the utility of pain to motivate escape from and avoidance of situations causing tissue damage. Much apparently excessive pain is actually normal because the cost of more pain is often vastly less than the cost of too little pain (the smoke detector principle). Vulnerability to pathological pain may be explained in part because natural selection has shaped mechanisms that respond adaptively to repeated tissue damage by decreasing the pain threshold and increasing pain salience. The other half of an evolutionary approach describes the phylogeny of pain mechanisms; the apparent independence of different kinds of pain is of special interest. Painful mental states such as anxiety, guilt and low mood may have evolved from physical pain precursors. Preliminary evidence for this is found in anatomic and genetic data. Such insights from evolutionary medicine may help in understanding vulnerability to chronic pain. This article is part of the Theo Murphy meeting issue ‘Evolution of mechanisms and behaviour important for pain’.
The Role of Precautionary Labelling for Food Allergens and the Care of Children with Food Allergies

There is no current cure for food allergy; therefore consumers with food allergy rely on accurate and detailed information on food labels in order to prevent an adverse reaction. Manufacturers cannot guarantee that food products are free from allergens as cross contamination can occur in several situations including but not limited to raw materials, the actual premises, storage and distribution, manufacturing processes and cleaning procedures. In order to alert the allergic consumer to the possible presence of trace allergens, manufacturers have voluntarily added precautionary labelling to processed foods. There are several variations to these statements, for example: “may contain traces of”, “may be present “and “made on the same production line”. The main purpose of this thesis is to understand the role of precautionary labelling in the care of children with food allergies.
Mortality Analysis of Adult Burn Patients

Backgrounds: Among burn patient, adult patient in productive age took a large number in statistic. Fire related to domestic accident and in workplace are the main source of burn incidences. A careful review of burn mortality of adult patient in Burn Unit of Ciptomangunkusumo Hospital is presented in this paper. The aim is to make a critical analysis of mortality in our burn centre with a view to fi nding possible ways of improving the care given to our patients and suggesting ways of reducing mortality. Patients and Method: A descriptive analysis study was done to analyze data collected by the review of medical records of the patients hospitalized to our Burn Center for burn injury from Januari 2011– Desember 2012. Parameters investigated include patients’ demographics, data for etiology and extent of burn injury, cause of death and the mortality were tabulated, computerized and analyzed. Result: During this 2-year period, 275 patients were admitted, 203 patients were adults. Number of death in adult patient were 76 patients (27,6%). Among the died patients, seventy-eight percent caused by fl ame, electrical burn injury (14%), hot water (4%), chemical (3%), metal (1%). Almost all burn extent was deep dermal (2nd degree) and full thickness (3rd degree). The cause of death including septicaemia (42,1%), multiple organ failure (31,6%), systemic in fl ammatory response syndrome (17,6%), and acute respiratory distress syndrome (8,7%). Conclusion: In order to implement a successful burn injury prevention program, it is important to focus on selected issues. A few speci fi c recommendations can be suggested based on these epidemiological features. Our ongoing efforts are to promote and support prevention program and look for changes in the incidence of burn injury.
The ASKAP EMU Early Science Project: radio continuum survey of the Small Magellanic Cloud

We present two new radio continuum images from the Australian Square Kilometre Array Pathfinder (ASKAP) survey in the direction of the Small Magellanic Cloud (SMC). These images are part of the Evolutionary Map of the Universe (EMU) Early Science Project (ESP) survey of the Small and Large Magellanic Clouds. The two new source lists produced from these images contain radio continuum sources observed at 960 MHz (4489 sources) and 1320 MHz (5954 sources) with a bandwidth of 192 MHz and beam sizes of 30.0 × 30.0 arcsec2 and 16.3 × 15.1 arcsec2, respectively. The median root mean square (RMS) noise values are 186 $\mu$Jy beam−1 (960 MHz) and 165 $\mu$Jy beam−1 (1320 MHz). To create point source catalogues, we use these two source lists, together with the previously published Molonglo Observatory Synthesis Telescope (MOST) and the Australia Telescope Compact Array (ATCA) point source catalogues to estimate spectral indices for the whole population of radio point sources found in the survey region. Combining our ASKAP catalogues with these radio continuum surveys, we found 7736 point-like sources in common over an area of 30 deg2. In addition, we report the detection of two new, low surface brightness supernova remnant candidates in the SMC. The high sensitivity of the new ASKAP ESP survey also enabled us to detect the bright end of the SMC planetary nebula sample, with 22 out of 102 optically known planetary nebulae showing point-like radio continuum emission. Lastly, we present several morphologically interesting background radio galaxies.
Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference

We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an important instance of the simultaneously structured model – an actively studied topic in statistics and machine learning. In the noiseless case, matching upper and lower bounds on sample complexity are established for the exact recovery of sparse vectors and for stable estimation of approximately sparse vectors, respectively. In the noisy case, upper and matching minimax lower bounds for estimation error are obtained. We also consider the debiased sparse group Lasso and investigate its asymptotic property for the purpose of statistical inference. Finally, numerical studies are provided to support the theoretical results.
Inference for Linear Conditional Moment Inequalities

We show that moment inequalities in a wide variety of economic applications have a particular linear conditional structure. We use this structure to construct uniformly valid confidence sets that remain computationally tractable even in settings with nuisance parameters. We first introduce least favorable critical values which deliver non-conservative tests if all moments are binding. Next, we introduce a novel conditional inference approach which ensures a strong form of insensitivity to slack moments. Our recommended approach is a hybrid technique which combines desirable aspects of the least favorable and conditional methods. The hybrid approach performs well in simulations calibrated to Wollmann (2018), with favorable power and computational time comparisons relative to existing alternatives.
Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons

Advances in neural recording methods enable sampling from populations of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to the theoretical models of computations underlying performance. In the context of decision making in rodents, patterns of functional connectivity between choice-selective cortical neurons, as well as broadly distributed choice information in both excitatory and inhibitory populations, were recently reported [1]. The straightforward interpretation of these data suggests a mechanism relying on specific patterns of anatomical connectivity to achieve selective pools of inhibitory as well as excitatory neurons. We investigate an alternative mechanism for the emergence of these experimental observations using a computational approach. We find that a randomly connected network of excitatory and inhibitory neurons generates single-cell selectivity, patterns of pairwise correlations, and indistinguishable excitatory and inhibitory readout weight distributions, as observed in recorded neural populations. Further, we make the readily verifiable experimental predictions that, for this type of evidence accumulation task, there are no anatomically defined sub-populations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. This work suggests that distributed stimulus selectivity and patterns of functional organization in population codes could be emergent properties of randomly connected networks. Author summary What can we learn about neural circuit organization and function from recordings of large populations of neurons? For example, in population recordings in the posterior parietal cortex of mice performing an evidence integration task, particular patterns of selectivity and correlations between cells were observed. One hypothesis for an underlying mechanism generating these patterns is that they follow from intricate rules of connectivity between specific neurons, but this raises the question of how such intricate patterns arise during learning or development. An alternative hypothesis, which we explore here, is that such patterns emerge from networks with broad spectra of eigenvalues, which is a generic property of certain random networks. We find that a random network model matches many features of experimental recordings, from single cells to populations. We suggest that such emergent selectivity could be an important principle in brain areas, in which a broad distribution of selectivity is observed.
Self-supervised 6D Object Pose Estimation for Robot Manipulation

To teach robots skills, it is crucial to obtain data with supervision. Since annotating real world data is timeconsuming and expensive, enabling robots to learn in a self- supervised way is important. In this work, we introduce a robot system for self-supervised 6D object pose estimation. Starting from modules trained in simulation, our system is able to label real world images with accurate 6D object poses for self-supervised learning. In addition, the robot interacts with objects in the environment to change the object configuration by grasping or pushing objects. In this way, our system is able to continuously collect data and improve its pose estimation modules. We show that the self-supervised learning improves object segmentation and 6D pose estimation performance, and consequently enables the system to grasp objects more reliably. A video showing the experiments can be found at https://youtu.be/W1Y0Mmh1Gd8.
Optimal Learning of Joint Alignments with a Faulty Oracle

We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover n discrete variables gi ∊ {0,…,k – 1} (up to some global offset) given noisy observations of a set of their pairwise differences {(gi – gj) mod k}; specifically, with probability $\frac{1}{k} + \delta $ for some δ > 0 one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer. We consider a learning-based formulation where one can perform a query to observe a pairwise difference, and the goal is to perform as few queries as possible while obtaining the exact joint alignment. We provide an easy-to-implement, time efficient algorithm that performs $O\left( {\frac{{n\lg n}}{{k{\delta ^2}}}} \right)$ queries, and recovers the joint alignment with high probability. We also show that our algorithm is optimal by proving a general lower bound that holds for all non-adaptive algorithms. Our work improves significantly the recent work by Chen and Candés [CC16], who view the problem as a constrained principal components analysis problem that can be solved using the power method. Specifically, our approach is simpler both in the algorithm and the analysis, and provides additional insights into the problem structure.
Do Compressed Representations Generalize Better?

One of the most studied problems in machine learning is finding reasonable constraints that guarantee the generalization of a learning algorithm. These constraints are usually expressed as some simplicity assumptions on the target. For instance, in the Vapnik-Chervonenkis (VC) theory the space of possible hypotheses is considered to have a limited VC dimension. In this paper, the constraint on the entropy $H(X)$ of the input variable $X$ is studied as a simplicity assumption. It is proven that the sample complexity to achieve an $\epsilon$-$\delta$ Probably Approximately Correct (PAC) hypothesis is bounded by $\frac{2^{ \left.6H(X)\middle/\epsilon\right. }+\log{\frac{1}{\delta}}}{\epsilon^2}$ which is sharp up to the $\frac{1}{\epsilon^2}$ factor. Morever, it is shown that if a feature learning process is employed to learn the compressed representation from the dataset, this bound no longer exists. These findings have important implications on the Information Bottleneck (IB) theory which had been utilized to explain the generalization power of Deep Neural Networks (DNNs), but its applicability for this purpose is currently under debate by researchers. In particular, this is a rigorous proof for the previous heuristic that compressed representations are exponentially easier to be learned. However, our analysis pinpoints two factors preventing the IB, in its current form, to be applicable in studying neural networks. Firstly, the exponential dependence of sample complexity on $\frac{1}{\epsilon}$, which can lead to a dramatic effect on the bounds in practical applications when $\epsilon$ is small. Secondly, our analysis reveals that arguments based on input compression are inherently insufficient to explain generalization of methods like DNNs in which the features are also learned using available data.
Lift-up, Kelvin–Helmholtz and Orr mechanisms in turbulent jets

Three amplification mechanisms present in turbulent jets, namely lift-up, Kelvin–Helmholtz and Orr, are characterized via global resolvent analysis and spectral proper orthogonal decomposition (SPOD) over a range of Mach numbers. The lift-up mechanism was recently identified in turbulent jets via local analysis by Nogueira et al. (J. Fluid Mech., vol. 873, 2019, pp. 211–237) at low Strouhal number ($St$) and non-zero azimuthal wavenumbers ($m$). In these limits, a global SPOD analysis of data from high-fidelity simulations reveals streamwise vortices and streaks similar to those found in turbulent wall-bounded flows. These structures are in qualitative agreement with the global resolvent analysis, which shows that they are a response to upstream forcing of streamwise vorticity near the nozzle exit. Analysis of mode shapes, component-wise amplitudes and sensitivity analysis distinguishes the three mechanisms and the regions of frequency–wavenumber space where each dominates, finding lift-up to be dominant as $St/m\rightarrow 0$. Finally, SPOD and resolvent analyses of localized regions show that the lift-up mechanism is present throughout the jet, with a dominant azimuthal wavenumber inversely proportional to streamwise distance from the nozzle, with streaks of azimuthal wavenumber exceeding five near the nozzle, and wavenumbers one and two most energetic far downstream of the potential core.
(Highlight Report) Patient-Centeredness in Sweden's Health System -- An External Assessment and Six Steps for Progress

Today, patient-centered care is recognized as an important aspect of health care quality, but remains the least defined, conceptualized and explored. Strengthening the position of patients has been a stated policy aim in Swedish health care for more than three decades, yet we lack sufficient knowledge of the position of patients, and how we are making progress towards that goal.Against this background, the Swedish Agency for Health and Care Services (Vardanalys) commissioned an external evaluation of patient-centeredness in the Swedish health care system.The external study was charged to answer three questions:1. What type of analytical framework can be used to assess the extent to which Sweden’s health care system is patient-centered? 2. To what extent is Sweden’s health care system patient-centered? 3. What changes in policy could help to strengthen the degree of patient-centeredness in Sweden’s health care system?