Miracle-dz/UniFAR-FTUs huggingface dataset:
Pathological interpretation of connective tissue disease-associated lung diseases

Connective tissue diseases (CTDs) can affect all compartments of the lungs, including airways, alveoli, interstitium, vessels, and pleura. CTD-associated lung diseases (CTD-LDs) may present as diffuse lung disease or as focal lesions, and there is significant heterogeneity between the individual CTDs in their clinical and pathological manifestations. CTD-LDs may presage the clinical diagnosis a primary CTD, or it may develop in the context of an established CTD diagnosis. CTD-LDs reveal acute, chronic or mixed pattern of lung and pleural manifestations. Histopathological findings of diverse morphological changes can be present in CTD-LDs airway lesions (chronic bronchitis/bronchiolitis, follicular bronchiolitis, etc.), interstitial lung diseases (nonspecific interstitial pneumonia/fibrosis, usual interstitial pneumonia, lymphocytic interstitial pneumonia, diffuse alveolar damage, and organizing pneumonia), pleural changes (acute fibrinous or chronic fibrous pleuritis), and vascular changes (vasculitis, capillaritis, pulmonary hemorrhage, etc.). CTD patients can be exposed to various infectious diseases when taking immunosuppressive drugs. Histopathological patterns of CTD-LDs are generally nonspecific, and other diseases that can cause similar lesions in the lungs must be considered before the diagnosis of CTD-LDs. A multidisciplinary team involving pathologists, clinicians, and radiologists can adequately make a proper diagnosis of CTD-LDs.
The role of FDG-PET in predicting prognosis for patients with advanced signet ring cell carcinoma

The role of FDG-PET in predicting prognosis for patients with advanced signet ring cell carcinoma Kyung Ho Pak Department of Medicine The Graduate School, Yonsei University (Directed by Professor Sung Hoon Noh) Background and Objectives. The purpose of this study was to investigate the role of the standardized uptake values (SUV), a semi-quantitative assessment of tumor FDG uptake, as a prognosticator for advanced signet ring cell carcinoma (SRC). Methods. We reviewed the files of 41 patients who underwent curative gastrectomy for advanced SRC and who had an FDG-PET at initial presentation between September 2003 and December 2007. We used the maximal SUV for the analysis. Results. The median follow-up period was 30.9 months (range: 4.0-70.3). We found a correlation between the SUVmax and TNM stage. When the patients were divided into two groups (low vs. high SUVmax) based on a median
The WANDAML markup language for digital document annotation

WANDAML is an XML-based markup language for the annotation and filter journaling of digital documents. It addresses in particular the needs of forensic handwriting data examination, by allowing experts to enter information about writer, material (pen, paper), script and content, and to record chains of image filtering and feature extraction operations applied to the data. Annotations may be organized in a structure that reflects the document layout via a hierarchy of document regions. WANDAML lends itself to a variety of applications, including the annotation all kinds of handwriting documents (on-line or off-line), images of printed text, medical images, and satellite images.
Preliminary design of JML: a behavioral interface specification language for java

JML is a behavioral interface specification language tailored to Java(TM). Besides pre- and postconditions, it also allows assertions to be intermixed with Java code; these aid verification and debugging. JML is designed to be used by working software engineers; to do this it follows Eiffel in using Java expressions in assertions. JML combines this idea from Eiffel with the model-based approach to specifications, typified by VDM and Larch, which results in greater expressiveness. Other expressiveness advantages over Eiffel include quantifiers, specification-only variables, and frame conditions.This paper discusses the goals of JML, the overall approach, and describes the basic features of the language through examples. It is intended for readers who have some familiarity with both Java and behavioral specification using pre- and postconditions.
A novel feature-based approach to extract drug-drug interactions from biomedical text

MOTIVATION Knowledge of drug-drug interactions (DDIs) is crucial for health-care professionals to avoid adverse effects when co-administering drugs to patients. As most newly discovered DDIs are made available through scientific publications, automatic DDI extraction is highly relevant. RESULTS We propose a novel feature-based approach to extract DDIs from text. Our approach consists of three steps. First, we apply text preprocessing to convert input sentences from a given dataset into structured representations. Second, we map each candidate DDI pair from that dataset into a suitable syntactic structure. Based on that, a novel set of features is used to generate feature vectors for these candidate DDI pairs. Third, the obtained feature vectors are used to train a support vector machine (SVM) classifier. When evaluated on two DDI extraction challenge test datasets from 2011 and 2013, our system achieves F-scores of 71.1% and 83.5%, respectively, outperforming any state-of-the-art DDI extraction system. AVAILABILITY AND IMPLEMENTATION The source code is available for academic use at http://www.biosemantics. org/uploads/DDI.zip.
Glycemic control, medication adherence, and injection practices among diabetic patients treated in the 3 tertiary referral hospitals in Bhutan: a call for more action

Abstract Background The burden of diabetes has increased rapidly with an increasing cost of treatment. Objectives To describe the glycemic control, injection practices, and treatment adherence among diabetic patients treated with insulin. Methods This cross-sectional study was conducted using a convenience sampling method at the 3 tertiary referral hospitals in Bhutan. Sociodemographic, injection practices, and clinical details were collected. Good glycemic control was defined as glycated hemoglobin A (HbA1c) <7% if available or fasting blood sugar 70–130 mg/dL and 2 h postprandial blood sugar <180 mg/dL if HbA1c values were unavailable. Medication adherence was assessed using the Morisky, Green and Levine (MGL) scale. The injection technique was assessed using a 10-item checklist. Results We studied 207 patients. Good glycemic control was achieved by only 58 (28.0%) of patients. Using the MGL scale score, the objective adherence with insulin therapy was mostly low to medium and a gross discordance was with self-declared adherence (P < 0.001). The injection technique was fair to poor in half of the participants. Those with good injection techniques also had good adherence to medication (P = 0.025, adjusted odds ratio = 4.4, 95% confidence interval 1.2–16.4). The majority (154, 74.4%) had self-injected insulin, while the remaining were dependent on their home caregivers. Forty percent of the participants used storage practices that were not recommended. The disposal of the used insulin needles was generally unsafe. Conclusions Glycemic control and adherence to insulin administration recommendations were poor. The injection technique needs to be improved and standardized, and methods of safe disposal of sharps need to be developed.
GPU-boosted online image matching

Matching feature points between images is a key point in many computer vision tasks. As the number of images increases, this rapidly becomes a bottleneck. We here present how to use the power of GPUs to obtain image matching in typically 20 ms for one thousand points. This speedup makes applications like interactive image matching possible. Such a portable system, dedicated to 3D large scale reconstruction, is reported.
Semantics and logic for security protocols

This paper presents a sound BAN-like logic for reasoning about security protocols with theorem prover support. The logic has formulas for sending and receiving messages (with nonces, public and private encryptions, etc.), and has both temporal and epistemic operators (describing the knowledge of participants). The logic's semantics is based on strand spaces. Several (secrecy or authentication) formulas are proven in general and are applied to the Needham-Schroeder(-Lowe), bilateral key exchange and the Otway-Rees protocols, as illustrations.
Context-dependent kernel design for object matching and recognition

The success of kernel methods including support vector networks (SVMs) strongly depends on the design of appropriate kernels. While initially kernels were designed in order to handle fixed-length data, their extension to unordered, variable-length data became more than necessary for real pattern recognition problems such as object recognition and bioinformatics. We focus in this paper on object recognition using a new type of kernel referred to as ldquocontext-dependentrdquo. Objects, seen as constellations of local features (interest points, regions, etc.), are matched by minimizing an energy function mixing (1) a fidelity term which measures the quality of feature matching, (2) a neighborhood criteria which captures the object geometry and (3) a regularization term. We will show that the fixed-point of this energy is a ldquocontext-dependentrdquo kernel (ldquoCDKrdquo) which also satisfies the Mercer condition. Experiments conducted on object recognition show that when plugging our kernel in SVMs, we clearly outperform SVMs with ldquocontext-freerdquo kernels.
Detecting Wikipedia vandalism via spatio-temporal analysis of revision metadata?

Blatantly unproductive edits undermine the quality of the collaboratively-edited encyclopedia, Wikipedia. They not only disseminate dishonest and offensive content, but force editors to waste time undoing such acts of vandalism. Language-processing has been applied to combat these malicious edits, but as with email spam, these filters are evadable and computationally complex. Meanwhile, recent research has shown spatial and temporal features effective in mitigating email spam, while being lightweight and robust. In this paper, we leverage the spatio-temporal properties of revision metadata to detect vandalism on Wikipedia. An administrative form of reversion called rollback enables the tagging of malicious edits, which are contrasted with non-offending edits in numerous dimensions. Crucially, none of these features require inspection of the article or revision text. Ultimately, a classifier is produced which flags vandalism at performance comparable to the natural-language efforts we intend to complement (85% accuracy at 50% recall). The classifier is scalable (processing 100+ edits a second) and has been used to locate over 5,000 manually-confirmed incidents of vandalism outside our labeled set.
Hepatic Reperfusion Injury following Remote Ischemia Experimental Study on Orygtalogus caniculus

Introduction . Ischemia/reperfusion (I/RI) injury following limb ischemia is realized to be responsible for remote organs injury which is found in vary, commence with mild injury to a severe one. Nevertheless, liver is an organ susceptible to such an injury. There were studies on I/RI, where ischemia in those studies were induced by direct ligation of hepatic vessels. However, study of remote ischemia was infrequently found. Thus, we run a study aimed to find out hepatic injury following ischemia induced by ligation of an artery with a significant anatomical distance. Method. An experimental study was conducted on New Zealand white rabbit. Ischemia was induced by ligation of right common femoral artery under anesthesia. Ligation was maintained for four hours period. Afterwards, ligation was released, and rabbit was set free in the cage for eight hours period. Laparotomy was carried out to take liver specimens of three different area, namely central, midzonal, and peripheral. These specimens were subjected to study histopathology and biochemical examination for malondialdehyde as well as HIF–1α. In addition, liver function test was carried out for serum bilirubin and transaminases. Results. Histomorphological study showed hepatic injury of central, midzonal and peripheral of the ischemic/reperfusion injury group, which was mostly sinusoidal dilatation. There was a significant statistical different of the three hepatics–zones (central, p = 0.028, midzonal, p = 0.012, and peripheral, p = 0.030). MDA levels showed a significant increase in the ischemic/reperfusion group (p = 0.012, sig α <0.05). Tissue HIF–1α level increased denoted tissue hypoxia in the treatment group. Liver function test showed no abnormality. Conclusion. Oxidative stress and sinusoidal changes were found in three zones, i.e. central, midzonal and peripheral following ischemic of a significant anatomical distance. Keywords : hepatic ischemia/reperfusion injury, sinusoidal dilatation, oxidative stress, HIF–1α
Interdisciplinary Journal of E-learning and Learning Objects Modalities of Using Learning Objects for Intelligent Agents in Learning

This article details some common characteristics of applications using Intelligent Agents as they relate to Learning Objects as software systems in delivering education. Readapting Learning Objects to different categories of learners constitutes a challenge for Intelligent Agents in their effort to provide a large scale of collaboration between different e-learning organizations. In order not only to have efficient access to Learning Objects, but also to offer to learners tutoring and men-toring help, collaborative and cooperative learning strategies, learning advancements, and social interactions, Intelligent Agents have been highly recommended by a number of researchers. This study investigates how these e-learning applications are designed, how students' differences are explored, and how these software systems are able to improve learning and teaching performances .
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

Local binary pattern (LBP) is a nonparametric descriptor, which efficiently summarizes the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as diverse as face detection, face recognition, facial expression analysis, and demographic classification. This paper presents a comprehensive survey of LBP methodology, including several more recent variations. As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial image analysis, are also highlighted.
Two Weeks of Metformin Treatment Enhances Mitochondrial Respiration in Skeletal Muscle of AMPK Kinase Dead but Not Wild Type Mice

Metformin is used as an anti-diabetic drug. Metformin ameliorates insulin resistance by improving insulin sensitivity in liver and skeletal muscle. Reduced mitochondrial content has been reported in type 2 diabetic muscles and it may contribute to decreased insulin sensitivity characteristic for diabetic muscles. The molecular mechanism behind the effect of metformin is not fully clarified but inhibition of complex I in the mitochondria and also activation of the 5′AMP activated protein kinase (AMPK) has been reported in muscle. Furthermore, both AMPK activation and metformin treatment have been associated with stimulation of mitochondrial function and biogenesis. However, a causal relationship in skeletal muscle has not been investigated. We hypothesized that potential effects of in vivo metformin treatment on mitochondrial function and protein expressions in skeletal muscle are dependent upon AMPK signaling. We investigated this by two weeks of oral metformin treatment of muscle specific kinase dead α2 (KD) AMPK mice and wild type (WT) littermates. We measured mitochondrial respiration and protein activity and expressions of key enzymes involved in mitochondrial carbohydrate and fat metabolism and oxidative phosphorylation. Mitochondrial respiration, HAD and CS activity, PDH and complex I-V and cytochrome c protein expression were all reduced in AMPK KD compared to WT tibialis anterior muscles. Surprisingly, metformin treatment only enhanced respiration in AMPK KD mice and thereby rescued the respiration defect compared to the WT mice. Metformin did not influence protein activities or expressions in either WT or AMPK KD mice. We conclude that two weeks of in vivo metformin treatment enhances mitochondrial respiration in the mitochondrial deficient AMPK KD but not WT mice. The improvement seems to be unrelated to AMPK, and does not involve changes in key mitochondrial proteins.
Evaluation of Knee Activities Using EMG Signals for Pre-predicting Lower Limb Dystonia Diseases

Electromyography (EMG) signal is one of the important tools for the detection of skeletal related muscular information. In this scenario, we analyse the normality and abnormality of knee movements for pre-predicting the knee dystonia diseases. It is characterized by involuntary sustained muscle contractions affecting one or more sides of the body, frequently causing twisting and repetitive movements or abnormal postures. In this paper, EMG based knee dystonia pre-prediction is assessed by employing EMG-Lower Limb dataset. Then, transformation is done by using discrete wavelet Transformation (DWT), which delivers a superior time and frequency localization ability. After transformation, hybrid feature extraction is performed by employing yule-walker, burg’s, voltage level (minimum and maximum), Renyi entropy, and Peak-Magnitude to Root Mean Square Ratio (PMRS) for achieving optimal feature subsets and also to reject the redundant and irrelevant features. In most of the existing studies, an individual feature or two different features are combined for extracting the feature values from the acquired signal. In this research, five effective entropy and autoregressive features are combined to obtain more active features. After obtaining the active features, a superior binary classifier: Kernel Nearest Neighbour (KNN) is implemented for classifying the normality and abnormality of knee movements. The experimental outcome proves that the proposed methodology effectively distinguishes the normal and abnormal knee movement in terms of sensitivity, specificity, recall, precision, accuracy and E-rate. The proposed methodology improves the classification accuracy in knee movement detection upto 0.7851. 65% compared to the existing methods.
FAST-Hex—A Morphing Hexarotor: Design, Mechanical Implementation, Control and Experimental Validation

We present FAST-Hex, a micro aerial hexarotor platform that allows to seamlessly transit from an underactuated to a fully actuated configuration with only one additional control input, a motor that synchronously tilts all propellers. The FAST-Hex adapts its configuration between the more efficient but underactuated, collinear multirotors, and the less efficient but full pose tracking, which is attained by noncollinear multirotors. On the basis of prior work on minimal input configurable micro aerial vehicle, we mainly stress three aspects: Mechanical design, motion control, and experimental validation. Specifically, we present the lightweight mechanical structure of the FAST-Hex that allows to only use one additional input to achieve configurability and full actuation in a vast state space. The motion controller receives as input any reference pose in $\mathbb {R}^3\times \mathrm{SO}(3)$ (3D position + 3D orientation). Full pose tracking is achieved if the reference pose is feasible with respect to actuator constraints. In case of unfeasibility, a new feasible desired trajectory is generated online giving priority to the position tracking over the orientation tracking. Finally, we present a large set of experimental results shading light on all aspects of the control of the FAST-Hex.
Divergence-Based Adaptive Extreme Video Completion

Extreme image or video completion, where, for instance, we only retain 1% of pixels in random locations, allows for very cheap sampling in terms of the required pre-processing. The consequence is, however, a reconstruction that is challenging for humans and inpainting algorithms alike.We propose an extension of a state-of-the-art extreme image completion algorithm to extreme video completion. We analyze a color-motion estimation approach based on color KL-divergence that is suitable for extremely sparse scenarios. Our algorithm leverages the estimate to adapt between its spatial and temporal filtering when reconstructing the sparse randomly-sampled video. We validate our results on 50 publicly-available videos using reconstruction PSNR and mean opinion scores.
Topology optimization for incremental elastoplasticity

We discuss a topology optimization problem for an elastoplastic medium. The distribution of material in a region is optimized with respect to a given target functional taking into account compliance. The incremental elastoplastic problem serves as state constraint. We prove that the topology optimization problem admits a solution. First-order optimality conditions are obtained by considering a regularized problem and passing to the limit.
Extraction of Three-dimensional Information from Images - Application to Computer Graphics. (Extraction d'informations tridimensionnelles à partir d'images - Application à l'informatique graphique)

This dissertation focuses on the creation of the information used in Computer Graphics. We especially concentrate on the data needed to render images. We do not ask the user to work directly on the object modeling but we rely on her to provide one or several images of the targeted object. These pictures are then automatically analyzed to extract the sought data. From this approach, we expect data more faithful to the original object and a shorter creation time for the user. Our work is centered on three case studies that have useful applications. We first recover the surface of a matte object from a short image sequence whose viewpoint is moving. We then show how the appearance of a human face is retrieved from a single image and how the extracted data are used to render the original face under a new lighting environment. We end with a technique to capture the hair geometry using multiple images from a fixed viewpoint and a moving light. We introduce several theoretical and technical contributions that enhance both precision and robustness of the capture. Results are provided to illustrate these improvements.
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines. Recent ``deep-learning-style'' implementations of PCs strive for a better scalability, but are still difficult to train on real-world data, due to their sparsely connected computational graphs. In this paper, we propose Einsum Networks (EiNets), a novel implementation design for PCs, improving prior art in several regards. At their core, EiNets combine a large number of arithmetic operations in a single monolithic einsum-operation, leading to speedups and memory savings of up to two orders of magnitude, in comparison to previous implementations. As an algorithmic contribution, we show that the implementation of Expectation-Maximization (EM) can be simplified for PCs, by leveraging automatic differentiation. Furthermore, we demonstrate that EiNets scale well to datasets which were previously out of reach, such as SVHN and CelebA, and that they can be used as faithful generative image models.