Miracle-dz/UniFAR-FTUs huggingface dataset:
Making Invisible Visible: Data-Driven Seismic Inversion With Spatio-Temporally Constrained Data Augmentation

Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this article, we develop new data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO2 leakage data. Our interest is to invert for subsurface velocity models associated with very small CO2 leakage. We validate the performance of our methods using comprehensive numerical tests. Via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques.
A Turing Test for Transparency

A central goal of explainable artificial intelligence (XAI) is to improve the trust relationship in human-AI interaction. One assumption underlying research in transparent AI systems is that explanations help to better assess predictions of machine learning (ML) models, for instance by enabling humans to identify wrong predictions more efficiently. Recent empirical evidence however shows that explanations can have the opposite effect: When presenting explanations of ML predictions humans often tend to trust ML predictions even when these are wrong. Experimental evidence suggests that this effect can be attributed to how intuitive, or human, an AI or explanation appears. This effect challenges the very goal of XAI and implies that responsible usage of transparent AI methods has to consider the ability of humans to distinguish machine generated from human explanations. Here we propose a quantitative metric for XAI methods based on Turing's imitation game, a Turing Test for Transparency. A human interrogator is asked to judge whether an explanation was generated by a human or by an XAI method. Explanations of XAI methods that can not be detected by humans above chance performance in this binary classification task are passing the test. Detecting such explanations is a requirement for assessing and calibrating the trust relationship in human-AI interaction. We present experimental results on a crowd-sourced text classification task demonstrating that even for basic ML models and XAI approaches most participants were not able to differentiate human from machine generated explanations. We discuss ethical and practical implications of our results for applications of transparent ML.
Model-based Pre-clinical Trials for Medical Devices Using Statistical Model Checking

Clinical trials are considered as the golden standard for medical device validation. However, many sacrifices have to be made during the design and conduction of the trials due to cost considerations and partial information, which may compromise the significance of the trial results. In this paper, we proposed a model-based pre-clinical trial framework using statistical model checking. Physiological models represent disease mechanism, which enable automated adjudication of simulation results. Sampling of the patient parameters and hypothesis testing are performed by statistical model checker. The framework enables a broader range of hypothesis to be tested with guaranteed statistical significance, which are useful complements to the clinical trials. We demonstrated our framework with a pre-clinical trial on implantable cardioverter defibrillators.
D ISCOVERY OF INTERSTELLAR GLYCINE IN THE HOT MOLECULAR CORE G 10 . 47 + 0 . 03

Amino acids are the essential keys in chemistry that contribute to the study of the formation of life. The complex organic molecule glycine (NH2CH2COOH) is the simplest amino acid that has been investigated in the interstellar medium for a long period to search for a potential connection between the Universe and the origin of life. Several attempts have failed to search for glycine in the last forty years, which made the researcher look for some glycine precursor in the interstellar medium as an alternative approach[1]. We report the successful detection of the rotational emission lines of interstellar glycine with confirmer I and II in the hot molecular core G10.47+0.03 between the frequency range of ν = 158.6–160.4 GHz with Atacama Large Millimeter/Submillimeter Array (ALMA) observation. In hot molecular core G10.47+0.03, the fractional abundance of glycine is found between the range of (4.01–4.61)×10−10 which refers to the “medium warm-up" case[2]. The detection of glycine in the interstellar medium is very complicated but many theoretical and laboratory studies indicated the possibilities of the presence of glycine and its precursors in hot molecular cores[2]. We also detected the emission lines of complex organic molecules CHOCHOHCH2OH, CH2OHCHO, CHD(OH)CHO, CH2OHCHO, cis–CH2OHCHO, GGg–CH2(OH)CH(OH)CH2OH, and CH2DOH in the hot molecular core G10.47+0.03.
Quantifying the Impact of Human Capital, Job History, and Language Factors on Job Seniority with a Large-scale Analysis of Resumes

As job markets worldwide have become more competitive and applicant selection criteria have become more opaque, and different (and sometimes contradictory) information and advice is available for job seekers wishing to progress in their careers, it has never been more difficult to determine which factors in a r\'esum\'e most effectively help career progression. In this work we present a novel, large scale dataset of over half a million r\'esum\'es with preliminary analysis to begin to answer empirically which factors help or hurt people wishing to transition to more senior roles as they progress in their career. We find that previous experience forms the most important factor, outweighing other aspects of human capital, and find which language factors in a r\'esum\'e have significant effects. This lays the groundwork for future inquiry in career trajectories using large scale data analysis and natural language processing techniques.
Total Least Squares for Optimal Pose Estimation

This work provides a theoretical framework for the pose estimation problem using total least squares for vector observations from landmark features. First, the optimization framework is formulated with observation vectors extracted from point cloud features. Then, error-covariance expressions are derived. The attitude and position solutions obtained via the derived optimization framework are proven to reach the bounds defined by the Cram\'er-Rao lower bound under the small-angle approximation of attitude errors. The measurement data for the simulation of this problem is provided through a series of vector observation scans, and a fully populated observation noise-covariance matrix is assumed as the weight in the cost function to cover the most general case of the sensor uncertainty. Here, previous derivations are expanded for the pose estimation problem to include more generic correlations in the errors than previous cases involving an isotropic noise assumption. The proposed solution is simulated in a Monte-Carlo framework to validate the error-covariance analysis.
Linear Logic, the π-calculus, and their Metatheory: A Recipe for Proofs as Processes

The Curry-Howard correspondence between natural deduction and the _-calculus has provided a canonical foundation for the study of typed functional languages. Initiated by Abramsky [1994], the Proofs as Processes agenda is to establish a similar foundation for the study of concurrent languages, by researching the connection between linear logic [Girard 1987] and the c-calculus [Milner et al. 1992]. To date, Proofs as Processes is still a partial success. Caires and Pfenning [2010] andWadler [2014] showed that linear propositions correspond to session types: structured communication protocols that prescribe the observable behaviour of processes [Honda 1993]. Further, Carbone et al. [2018b], Montesi and Peressotti [2018], and Kokke et al. [2019] demonstrated that adopting devices from hypersequents [Avron 1991] shapes proofs (typing derivations) such that they correspond to the expected syntactic structure of processes in the c-calculus. What remains is reconstructing the expected metatheory of session types and the c-calculus. In particular, the hallmark of session types is session fidelity: an operational correspondence between the observable behaviours of processes and their session types, formulated via labelled transition systems. To date, this result still has to be reconstructed for Proofs as Processes, because it is unclear how a transition system for session types can arise from linear logic. In this article, we present cLL, a new process calculus rooted in linear logic. The key novelty of cLL is that it comes with a carefully formulated design recipe for Proofs as Processes, which is based on a dialgebraic view of labelled transition systems and their homomorphisms [Ciancia 2013]. Thanks to our recipe, cLL offers the first reconstruction of the expected labelled transition systems of session types based on linear logic, which we use to establish session fidelity for Proofs as Processes. In general, we use cLL to carry out the first thorough investigation of the metatheoretical properties enforced by linear logic on the observable behaviour of processes, uncovering connections with standard relations such as similarity and bisimilarity. We also prove that cLL and our recipe offer a robust basis for the further exploration of Proofs as Processes, by considering different features and extensions: polymorphism, higher-order communications (code mobility), and recursion. Our higher-order extension of cLL, called HOcLL, operates as a higher-order linear logic that enforces linear usage of process variables.
Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networks

Multiple organ failure (MOF) is a severe syndrome with a high mortality rate among Intensive Care Unit (ICU) patients. Early and precise detection is critical for clinicians to make timely decisions. An essential challenge in applying machine learning models to electronic health records (EHRs) is the pervasiveness of missing values. Most existing imputation methods are involved in the data preprocessing phase, failing to capture the relationship between data and outcome for downstream predictions. In this paper, we propose classifier-guided generative adversarial imputation networks Classifier-GAIN) for MOF prediction to bridge this gap, by incorporating both observed data and label information. Specifically, the classifier takes imputed values from the generator(imputer) to predict task outcomes and provides additional supervision signals to the generator by joint training. The classifier-guide generator imputes missing values with label-awareness during training, improving the classifier's performance during inference. We conduct extensive experiments showing that our approach consistently outperforms classical and state-of-art neural baselines across a range of missing data scenarios and evaluation metrics.
A survey on effective webmining algorithms

This paper presents three algorithms and one proposed algorithm to find relevant pages for a given Web page (URL). The first algorithm comes from the Pagerank analysis of the Web pages. It is intuitive and easy to implement. The second one takes advantage of HITS to identify relevant pages more precisely and effectively. The third one shows the advantages and limitations of Weighted Pagerank algorithm and the final proposed algorithm based on Topic Sensitive Pagerank algorithm. These algorithms could be used for various Web applications, such as enhancing Web search. The ideas and techniques in this work would be helpful to other Web-related researches.
Decoupling of lattice and orbital degrees of freedom in an iron-pnictide superconductor

The interplay between structural and electronic phases in iron-based superconductors is a central theme in the search for the superconducting pairing mechanism. While electronic nematicity is competing with superconductivity, the effect of purely structural orthorhombic order is unexplored. Here, using x-ray diffraction and angle-resolved photoemission spectroscopy, we reveal a structural orthorhombic phase in the electron-doped iron-pnictide superconductor Pr 4 Fe 2 As 2 Te 0 . 88 O 4 ( T c = 25 K), which is distinct from orthorhombicity in the nematic phase in underdoped pnictides. Despite the high electron doping we ﬁnd an exceptionally high orthorhombic onset temperature ( T ort ∼ 250 K), no signatures of phase competition with superconductivity, and absence of electronic nematic order as the driving mechanism for orthorhombicity. Combined, our results establish a high-temperature phase in the phase diagram of iron-pnictide superconductors and impose strong constraints for the modeling of their superconducting pairing mechanism.
Indexing and Optimization Techniques in Biomedical Industry

The unpredictable amount of data generated everyday by smart phones, social networks, health care systems etc. is really mind blowing. Smart phones alone generate 335exabytes of data ineveryyear that is really big data.Thus, the storage industry is facing several challenges in providing high magnitude of storage and retrieval devices at lowest costs which help to fulfill the requirements of big data and even technologies like de-duplication on storage devices are also becoming very important. Similarly, in recent days storing and retrieving the health care information in biomedical area is also becoming a great challenge in providing the best optimum data because of its huge amount of biomedical datasets. In order to achieve efficiency in providing highest quality health care information, an optimized index scheme is needed for big data which is based on accuracy and timelines. The existing indexing and optimization solutions are not enough to meet the emerging grow of index size and seek time. The objective of this paper is to identify better indexing solutions by investigating the basic big data requirements on indexing and optimization. This also includes a comparative study of various indexing and optimization techniques along with a taxonomy which contains Artificial Intelligence (AI) and Non Artificial Intelligence (NAI) based indexing techniques, optimization enhancement techniques which improves the performance efficiency of big data health care informatics.
Research Inferences In Video Compression Domain

Video Compression (VC) is a painstaking investigation area in the modernization of digital existence because of more evolution being emerged in web relevance. In this direction, video compression is grown-up rapdily and confirmed by the large number of myriad applications in Video streaming, Computer Vision like Video Tampering Detection, Video Surveillance and Camera Moving etc., which collectively use of this compression technology. This paper attributes / opensup / endeavours the whole possible and recent research directions and its challenges by focusing the potential investigations on compression domain of MPEG, H.264, H.265 compression coding standard with latest computing techniques in learning videos via Machine Learning.
Association of elevated in fl ammatory markers and severe COVID-19 A meta-analysis

Our study aimed to assess the existing evidence on whether severe coronavirus disease 2019 (COVID-19) is associated with elevated inflammatory markers. The PubMed, Embase, Web of Science, Scopus, Chinese National Knowledge Infrastructure, WanFang, and China Science and Technology Journal databases were searched to identify studies published between January 1 and April 21, 2020 that assayed inflammatory markers in COVID-19 patients. Three reviewers independently examined the literature, extracted relevant data, and assessed the risk of publication bias before including the meta-analysis studies. Fifty-six studies involving 8719 COVID-19 patients were identified. Meta-analysis showed that patients with severe disease showed elevated levels of white blood cell count (WMD: 1.15, 95% CI: 0.78–1.52), C-reactive protein (WMD: 38.85, 95% CI: 31.19– 46.52), procalcitonin (WMD: 0.08, 95% CI: 0.06–0.11), erythrocyte sedimentation rate (WMD: 10.15, 95% CI: 5.03–15.46), interleukin-6 (WMD: 23.87, 95%CI: 15.95–31.78), and interleukin-10 (WMD: 2.12, 95%CI: 1.97–2.28). Similarly, COVID-19 patients who died during follow-up showed significantly higher levels of white blood cell count (WMD: 4.11, 95% CI: 3.25–4.97), C-reactive protein (WMD: 74.18, 95% CI: 56.63–91.73), procalcitonin (WMD: 0.26, 95% CI: 0.11–0.42), erythrocyte sedimentation rate (WMD: 10.94, 95% CI: 4.79–17.09), and interleukin-6 (WMD: 59.88, 95% CI: 19.46–100.30) than survivors. Severe COVID-19 is associated with higher levels of inflammatory markers than a mild disease, so tracking these markers may allow early identification or even prediction of disease progression. Abbreviations: CI = confidence interval, COVID-19 = coronavirus disease 2019, CRP = C-reactive protein, ESR = erythrocyte sedimentation rate, IL-10 = interleukin-10, IL-6 = interleukin-6, = not reported, PCT = procalcitonin, TNF-a = tumor necrosis factora, WBC count = white blood cell count, WMD = weighted mean difference.
: From Tensorflow to FPGA-based accelerator with reuse-aware memory allocation for shortcut data

—Residual block is a very common component in recent state-of-the art CNNs such as EfficientNet/EfficientDet. Shortcut data accounts for nearly 40% of feature-maps access in ResNet152 [8]. Most of the previous DNN compilers/accelerators ignore the shortcut data optimization. This paper presents ShortcutFusion , an optimization tool for FPGA-based accelerator with a reuse-aware static memory allocation for shortcut data, to maximize on-chip data reuse given resource constraints. From TensorFlow DNN models, the proposed design generates instruction sets for a group of nodes which uses an optimized data reuse for each residual block. The accelerator design implemented on the Xilinx KCU1500 FPGA card significantly outperforms NVIDIA RTX 2080 Ti, Titan Xp, and GTX 1080 Ti for the EfficientNet inference. Compared to RTX 2080 Ti, the proposed design is 1.35-2.33  faster and 6.7-7.9  more power efficient. Compared to the result from baseline, in which the weights/inputs/outputs are accessed from the off-chip memory exactly once per each layer, ShortcutFusion reduces the DRAM access by 47.8-84.8% for RetinaNet, Yolov3, ResNet152, and EfficientNet. Given a similar buffer size to ShortcutMining [8], which also “mine” the shortcut data in hardware, the proposed work reduces off-chip access for feature-maps 5.27  while accessing weight from off-chip memory exactly once.
The Management of Coronary Artery Disease in Ethiopia: Emphasis on Revascularization

Cardiovascular diseases are number one cause of death worldwide. Over half of the cardiovascular diseases, 51%, are due to coronary artery disease. Coronary artery disease is a pathological process characterized by atherosclerotic plaque accumulation in the epicardial coronary arteries. Rupture of the fibrous cap of the plaque causes the majority of the deaths due to myocardial infarction. Angina pectoris is a discomfort in the chest or adjacent areas caused by myocardial ischemia usually precipitated by exertion. In acute coronary syndrome, the chest discomfort is either of low threshold or appears at rest and when it evolves on the background of established angina pectoris, the discomfort becomes more frequent and prolonged. Exercise electrocardiography which has been the most frequently used non-invasive test to diagnose obstructive coronary artery disease is currently shown to have inferior diagnostic performance compared with diagnostic imaging tests. The pivotal tests in patients presenting with clinical features of acute coronary syndrome are electrocardiography and determination of serum troponin I and/or T. Revascularization is the mainstay of treatment in patients with acute coronary syndrome. In chronic coronary syndrome, on top of optimal medical treatment, revascularization reduces mortality in:- 1) left main stenosis, 2) three-vessel coronary artery disease, particularly with ejection fraction of less than 40%, 3) two vessel disease with more than 75% stenosis of the proximal left anterior descending coronary artery disease.
Parafibromin Abnormalities in Ossifying Fibroma

Abstract Ossifying fibromas are very rare tumors that are sometimes seen as part of the hyperparathyroidism-jaw tumor syndrome (HPT-JT), which is caused by inactivating mutations of the HRPT2/CDC73 tumor suppressor gene. CDC73 mutations have been identified in a subset of sporadic cases but aberrant expression of the encoded protein, parafibromin, has not been demonstrated in ossifying fibroma. We sought to determine if loss of parafibromin regularly contributes to the development of sporadic, nonsyndromic ossifying fibroma. We examined a series of 9 ossifying fibromas, including ossifying, cemento-ossifying, and juvenile active variants, for parafibromin protein expression by immunohistochemistry and for CDC73 sequence abnormalities by Sanger sequencing and/or targeted AmpliSeq panel sequencing. Four ossifying fibromas showed a complete absence of nuclear parafibromin expression; loss of parafibromin expression was coupled with aberrant cytoplasmic parafibromin expression in 1 case. CDC73 mutations were detected in 2 cases with aberrant parafibromin expression. These results provide novel evidence, at the level of protein expression, that loss of the parathyroid CDC73/parafibromin tumor suppressor may play a role in the pathogenesis of a subset of ossifying fibromas.