Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined detection of homocysteine and hypersensitive c-reactive protein in the risk assessment of coronary heart disease

Objective: The goal of this study was to determine the clinical value of combined detection of homocysteine (Hcy) and hypersensitive C-reactive protein (hs-CRP) in risk assessment of coronary heart disease (CHD). Methods: A total of 182 patients with CHD were enrolled as the CHD group, and 213 healthy people who came to Shenzhen Bao’an Hospital of Traditional Chinese Medicine (Group) for physical examination during the same period were selected as the control group. Baseline data for both groups were analyzed retrospectively, including gender, age, body mass index, systolic blood pressure, diastolic blood pressure, triglyceride, total cholesterol, blood glucose, smoking history, and drinking history. The venous plasma parameters of the two groups such as red blood cells, white blood cells, platelets, low density lipoprotein, uric acid, Hcy and hs-CRP were compared. ROC curve and logistic regression analysis were used to evaluate the results. Results: The baseline data of the two groups were equally comparable (P > 0.05). Compared with the control group, the levels of white blood cells, low density lipoprotein, uric acid, Hcy and hs-CRP in the CHD group were significantly increased (all P < 0.05). Multivariate logistic regression analysis indicated that low density lipoprotein, Hcy and hs-CRP were independent risk factors for CHD (P < 0.05). The area under the ROC curve of the serum Hcy level was 0.791 while the hs-CRP was 0.850, and the combined detection of the two was 0.946. The specificity (85%) and sensitivity (97%) of Hcy combined with hs-CRP detection were better than the single detection, and the differences were statistically significant (P < 0.05). Conclusion: Serum Hcy and hs-CRP levels are closely related to CHD. Combined detection of Hcy and hs-CRP can be used as an important indicator for clinical risk assessment of patients with CHD, thereby providing a basis for clinical early diagnosis and contributing to the improvement of the prevention, treatment and prognosis of CHD.
Characterizing copper nanoparticle DC sputtering with oxygen interference

The properties of a material are directly dictated by its size, shape and internal structure. In a time where research on nanoscale is widely practiced and the demand for the smallest possible electronics grows, new challenges emerge in the creating and usage of nanomaterials. In this thesis the workings of a new Nanosys550 Deposition System from MANTIS Deposition LTD for creation of nanoparticles is described with in the present work a specific focus on copper clusters. First the basics of direct current (DC) magnetron sputtering are explained. This form of sputtering works by accelerating argon plasma against a copper target, thereby breaking of single copper atoms which form a gas. By cooling this gas into supersaturation, copper nanoclusters are formed by condensation processes. Although, these processes are controlled by only a few system settings, together they comprise a complex system. These settings and their effects are discussed and have been investigated by experimentation. The latter has been done in a first experiment series by Half Factorial Design (HFD) with Center Point Experiments (CPE) to characterize the standard operation window of the new system. A second experiment series on Energetic Cluster Impact (ECI) has been performed to examine the charge character of the sputtered copper clusters by attempting cluster acceleration with an applied bias voltage to the substrate holder. The copper clusters were deposited on TEM grids and silicon wafer pieces for both experiment series. The results of the HFD experiments were imaged with the use of a Transmission Electron Microscope (TEM) and analyzed by statistical software, which calculated the effects of the separate system variables. Assessment of these effects revealed that they behaved according to a-priori (qualitative) expectations but were non-significant. This led to the discovery of oxygen leakage into the vacuum system and several attempts to prevent this problem. At the end, the oxygen problems have been solved and the observed HFD results are explained by literature research. Simultaneous with the preventive measures the ECI experiments were performed. The results were imaged also by TEM and an Atomic Force Microscope (AFM). The obtained data from both imaging techniques revealed no support or proof for ECI, although the absence of cluster charge seems unlikely from literature research. Master thesis for the study of Applied Physics Student: Bsc. Arjan Bijlsma (s1398245) Supervisor: ing. G.H. ten Brink Group leader: prof. dr. ir. B.J. Kooi Dept. Nanostructured Materials and Interfaces Zernike Institute for Advanced Materials University of Groningen Nijenborgh 4 9747 AG Groningen The Netherlands
Extending SUMO to Geological Times

One of the challenges of Natural Language Processing of Oil&Gas domain is reasoning with geological times. Although there are some initiatives for specifying the vocabulary of this information, they fall short on enforcement of expected properties, such as no overlapping between Ages (Epoch, Eras etc) and hierarchy compliance. We used the Suggested Upper Merged Ontology (SUMO) and its associated automated reasoning tools to tackle these matters and uncovered some inconsistencies on geological time International Chronos-tratigraphic Chart (ICC) ofﬁcial published material.
Target-Adapted Subspace Learning for Cross-Corpus Speech Emotion Recognition

For cross-corpus speech emotion recognition (SER), how to obtain effective feature representation for the discrepancy elimination of feature distributions between source and target domains is a crucial issue. In this paper, we propose a Target-adapted Subspace Learning (TaSL) method for cross-corpus SER. The TaSL method trys to find a projection subspace, where the feature regress the label more accurately and the gap of feature distributions in target and source domains is bridged effectively. Then, in order to obtain more optimal projection matrix, 1 norm and 2,1 norm penalty terms are added to different regularization terms, respectively. Finally, we conduct extensive experiments on three public corpuses, EmoDB, eNTERFACE and AFEW 4.0. The experimental results show that our proposed method can achieve better performance compared with the state-of-the-art methods in the cross-corpus SER tasks. key words: cross-corpus speech emotion recognition, transfer learning, domain adaptation, subspace learning
REVISION 2 1 2 Two generations of exsolution lamellae in pyroxene from Asuka 09545 : clues to the 3 thermal evolution of silicates in mesosiderite

23 Mesosiderite meteorites consist of a mixture of crustal basaltic or gabbroic material and 24 metal. Their formation process is still debated due to their unexpected combination of 25 crust and core materials, possibly derived from the same planetesimal parent body, and 26 lacking an intervening mantle component. Mesosiderites have experienced an extremely 27 slow cooling rate from ca. 550°C, as recorded in the metal (0.25-0.5°C/Ma). Here we 28 present a detailed investigation of exsolution features in pyroxene from the Antarctic 29 mesosiderite Asuka (A) 09545. Geothermobarometry calculations, lattice parameters, 30 lamellae orientation, and the presence of clinoenstatite as the host were used in an at31 tempt to constrain the evolution of pyroxene from 1150°C to 570°C and the formation of 32 two generations of exsolution lamellae. After pigeonite crystallization at ca. 1150°C, the 33 first exsolution process generated the thick augite lamellae along (100) in the 34 temperature interval 1000-900°C. By further cooling, a second order of exsolution 35 lamellae formed within augite along (001), consisting of monoclinic low-Ca pyroxene, 36 equilibrated in the temperature range 900-800°C. The last process, occurring in the 60037 500°C temperature range, was likely the inversion of high to low pigeonite in the host 38 crystal, lacking evidence for nucleation of orthopyroxene. 39 The formation of two generations of exsolution lamellae, as well as of likely metastable 40 pigeonite, suggest non-equilibrium conditions. Cooling was sufficiently slow to allow the 41 formation of the lamellae, their preservation, and the transition from high to low pigeon42 ite. In addition, the preservation of such fine-grained lamellae limits long lasting, impact 43 reheating to a peak temperature lower than 570°C. These features, including the presence 44
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations

Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task. While current verification methods mainly focus on the $\ell_p$-norm threat model of the input instances, robustness verification against semantic adversarial attacks inducing large $\ell_p$-norm perturbations, such as color shifting and lighting adjustment, are beyond their capacity. To bridge this gap, we propose \textit{Semantify-NN}, a model-agnostic and generic robustness verification approach against semantic perturbations for neural networks. By simply inserting our proposed \textit{semantic perturbation layers} (SP-layers) to the input layer of any given model, \textit{Semantify-NN} is model-agnostic, and any $\ell_p$-norm based verification tools can be used to verify the model robustness against semantic perturbations. We illustrate the principles of designing the SP-layers and provide examples including semantic perturbations to image classification in the space of hue, saturation, lightness, brightness, contrast and rotation, respectively. In addition, an efficient refinement technique is proposed to further significantly improve the semantic certificate. Experiments on various network architectures and different datasets demonstrate the superior verification performance of \textit{Semantify-NN} over $\ell_p$-norm-based verification frameworks that naively convert semantic perturbation to $\ell_p$-norm. The results show that \textit{Semantify-NN} can support robustness verification against a wide range of semantic perturbations. Code available this https URL
Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical parameter estimation is an ill-posed inverse problem, and thus it is sensitive to measurement and modeling errors. In this work, we propose a novel way to solve the inverse problem of quantitative photoacoustic tomography based on the perturbation Monte Carlo method. Monte Carlo method for light propagation is a stochastic approach for simulating photon trajectories in a medium with scattering particles. It is widely accepted as an accurate method to simulate light propagation in tissues. Furthermore, it is numerically robust and easy to implement. Perturbation Monte Carlo maintains this robustness and enables forming gradients for the solution of the inverse problem. We validate the method and apply it in the framework of Bayesian inverse problems. The simulations show that the perturbation Monte Carlo method can be used to estimate spatial distributions of both absorption and scattering parameters simultaneously. These estimates are qualitatively good and quantitatively accurate also in parameter scales that are realistic for biological tissues.
A Prospective, Randomized Trial of Autologous Bone Marrow Transplantation and Chemotherapy in Multiple Myeloma

Background The median survival of patients with myeloma after conventional chemotherapy is three years or less. Promising results have been reported with high-dose therapy supported by autologous bone marrow transplantation. We conducted a randomized study comparing conventional chemotherapy and high-dose therapy. Methods Two hundred previously untreated patients under the age of 65 years who had myeloma were randomly assigned at the time of diagnosis to receive either conventional chemotherapy or high-dose therapy and autologous bone marrow transplantation. Results The response rate among the patients who received high-dose therapy was 81 percent (including complete responses in 22 percent and very good partial responses in 16 percent), whereas it was 57 percent (complete responses in 5 percent and very good partial responses in 9 percent) in the group treated with conventional chemotherapy (P<0.001). The probability of event-free survival for five years was 28 percent in the high-dose group and 10 perce.
Distribution of tree parameters by martingale approach

For a uniform random labelled tree, we find the limiting distribution of tree parameters which are stable (in some sense) with respect to local perturbations of the tree structure. The proof is based on the martingale central limit theorem and the Aldous–Broder algorithm. In particular, our general result implies the asymptotic normality of the number of occurrences of any given small pattern and the asymptotic log-normality of the number of automorphisms.
Features or Shape? Tackling the False Dichotomy of Time Series Classification

Time series classification is an important task in its own right, and it is often a precursor to further downstream analytics. To date, virtually all works in the literature have used either shape-based classification using a distance measure or feature-based classification after finding some suitable features for the domain. It seems to be underappreciated that in many datasets it is the case that some classes are best discriminated with features, while others are best discriminated with shape. Thus, making the shape vs. feature choice will condemn us to poor results, at least for some classes. In this work, we propose a new model for classifying time series that allows the use of both shape and feature-based measures, when warranted. Our algorithm automatically decides which approach is best for which class, and at query time chooses which classifier to trust the most. We evaluate our idea on real world datasets and demonstrate that our ideas produce statistically significant improvement in classification accuracy.
Photo‐Effect on Ion Transport in Mixed Cation and Halide Perovskites and Implications for Photo‐Demixing

Abstract Lead halide perovskites are considered to be most promising photovoltaic materials. Highest efficiency and improved stability of perovskite solar cells have been achieved by using cation and anion mixtures. Experimental information on electronic and ionic charge carriers is key to evaluate device performance, as well as processes of photo‐decomposition and photo‐demixing which are observed in these materials. Here, we measure ionic and electronic transport properties and investigate various cation and anion substitutions with a special eye on their photo‐ionic effect, following our previous study on CH3NH3PbI3, where we found that light enhances not only electronic but also ionic conductivities. We find that this phenomenon is very sensitive to the nature of the halide, while the cationic substitutions are less relevant. Based on the observation that the ionic conductivity enhancement found for iodide perovskites is significantly weakened by bromide substitution, we provide a chemical rationale for the photo‐demixing in mixed halide compositions.
Erratum: The Ep,i–Eiso correlation: type I gamma-ray bursts and the new classification method

We present the most extensive sample of 45 type I (short) and 275 type II (long) gamma-ray bursts (GRBs) with known redshift to investigate the correlation between the rest-frame peak energy, Ep,i and the total isotropic equivalent energy, Eiso of the prompt emission (Amati relation). The Ep,i–Eiso correlation for type I bursts is found to be well distinguished from the one constructed for type II bursts and has a similar power-law index value, Ep,i$\propto E_\text{iso}^{~0.4}$, which possibly indicates the same emission mechanism of both GRB types. We show that the initial pulse complex (IPC) of type I bursts with an extended emission and regular type I bursts follow the same correlation. We obtain similar results for type II bursts associated with Ic supernovae and for regular type II bursts. Three possible outliers from the Ep,i–Eiso correlation for type II subsample are detected. Significant evolution of the Ep,i–Eiso correlation with redshift for type II bursts is not found. We suggest the new classification method, based on the Ep,i–Eiso correlation and introduce two parameters, $EH = E_\text{p,i,2} ~ E_\text{iso,51}^{~-0.4}$ and $EHD = E_\text{p,i,2} ~ E_\text{iso,51}^{~-0.4} ~ T_\text{90,i}^{~-0.5}$, where Ep,i,2 is the value of Ep,i parameter in units of 100 keV, Eiso,51 is the value of Eiso parameter in units of 1051 erg, and T90,i is the rest-frame duration in units of seconds. EHD is found to be the most reliable parameter for the blind type I/type II classification, which can be used to classify GRBs with no redshift.
A Framework for Explainable Text Classification in Legal Document Review

Companies regularly spend millions of dollars producing electronically-stored documents in legal matters. Over the past two decades, attorneys have been using a variety of technologies to conduct this exercise, and most recently, parties on both sides of the ‘legal aisle’ are accepting the use of machine learning techniques like text classification to cull massive volumes of data and to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs in legal matters, text classification also faces a peculiar perception challenge: amongst lawyers, this technology is sometimes looked upon as a black box Put simply, very little information is provided for attorneys to understand why documents are classified as responsive. In recent years, a group of AI and Machine Learning researchers have been actively researching Explainable AI. In an explainable AI system, actions or decisions are human understandable. In legal ‘document review’ scenarios, a document can be identified as responsive, as long as one or more of the text snippets (small passages of text) in a document are deemed responsive. In these scenarios, if text classification can be used to locate these responsive snippets, then attorneys could easily evaluate the model’s document classification decision. When deployed with defined and explainable results, text classification can drastically enhance the overall quality and speed of the document review process by reducing the time it takes to review documents. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. This paper describes a framework for explainable text classification as a valuable tool in legal services: for enhancing the quality and efficiency of legal document review and for assisting in locating responsive snippets within responsive documents. This framework has been implemented in our legal analytics product, which has been used in hundreds of legal matters. We also report our experimental results using the data from an actual legal matter that used this type of document review.
A Survey on Distributed Machine Learning

The demand for artificial intelligence has grown significantly over the past decade, and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a substantial amount of training data is required. Although small machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across multiple machines, and turning the centralized into a distributed system. These distributed systems present new challenges: first and foremost, the efficient parallelization of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.
Some functional transcendence results around the Schwarzian differential equation.

This paper centers around proving variants of the Ax-Lindemann-Weierstrass (ALW) theorem for analytic functions which satisfy Schwarzian differential equations. In previous work, the authors proved the ALW theorem for the uniformizers of genus zero Fuchsian groups, and in this work, we generalize that result in several ways using a variety of techniques from model theory, galois theory and geometry.
Approaching geoscientific inverse problems with adversarial vector-to-image domain transfer networks

We present vec2pix, a deep neural network designed to predict categorical or continuous 2D subsurface property fields from one-dimensional measurement data (e.g., time series), thereby, offering a new way to solve inverse problems. The performance of the method is investigated through two types of synthetic inverse problems: (a) a crosshole ground penetrating radar (GPR) tomography experiment with GPR travel times being used to infer a 2D velocity field, and (2) a multi-well pumping experiment within an unconfined aquifer with time series of transient hydraulic heads being used to retrieve a 2D hydraulic conductivity field. For each type of problem, both a multi-Gaussian and a binary channelized subsurface domain with long-range connectivity are considered. Using a training set of 20,000 examples, the method is found to recover a 2D model that is in much closer agreement with the true model than the closest training model in the forward-simulated data space. Further testing with smaller training sample sizes shows that despite a moderate reduction in performance, this remains the case when using 5000 training examples only. Even if the recovered models obtained by vec2pix are visually close to the true ones, the data misfits associated with their forward responses are generally larger than the noise level used to contaminate the true data. If finding a model that fits the data noise level is required, then vec2pix-based inversion models can be used as initial inputs for more traditional multiple-point statistics inversion. Uncertainty of the inverse solution is partially assessed using deep ensembles, in which the network is trained repeatedly with random initialization. Overall, this study opens up a promising research avenue on how to use deep learning to infer subsurface models from indirect measurement data.
Design and Implementation of a Blockchain-based Consent Management System

A blockchain is a distributed ledger forming a distributed consensus on a history of transactions. It is the underlying technology for the Bitcoin cryptocurrency, but there are many applications beyond the financial sector. With built-in security and removal of the need for third party trust, blockchain has started to see some use within contract applications among other things. In this paper, we present the design and implementation of a permissioned-based blockchain third party consent management system, whose policy can be decided by a government agency. We have constructed a proof of concept implementation using Hyperledger Fabric to provide a service that allows end-users to control and consent to who manages their private information. We believe our solution meets the guiding principles of EU General Data Protection Regulation or GDPR. While our performance and usability evaluation are limited, our solution design and its implementation meet the 7 foundational principles of privacy by design.