Miracle-dz/UniFAR-FTUs huggingface dataset:
SDM-Net: A simple and effective model for generalized zero-shot learning

Zero-Shot Learning (ZSL) is a classiﬁcation task where some classes referred to as unseen classes have no training images. Instead, we only have side information about seen and unseen classes, often in the form of semantic or descriptive attributes. Lack of training images from a set of classes re-stricts the use of standard classiﬁcation techniques and losses, including the widespread cross-entropy loss. We introduce a novel Similarity Distribution Matching Network (SDM-Net) which is a standard fully connected neural network architecture with a non-trainable penultimate layer consisting of class attributes. The output layer of SDM-Net consists of both seen and unseen classes. To enable zero-shot learning, during training, we regularize the model such that the predicted distribution of unseen class is close in KL divergence to the distribution of similarities between the correct seen class and all the unseen classes. We evaluate the proposed model on ﬁve benchmark datasets for zero-shot learning, AwA1, AwA2, aPY, SUN, and CUB datasets. We show that, despite the simplicity, our approach achieves competitive performance with state-of-the-art methods in Generalized-ZSL setting for all of these datasets.
Dynamic Plasticity Model for Rapidly Heated 1045 Steel Up to 1000 °C

The National Institute of Standards and Technology (NIST) developed an experimental technique to measure the dynamic flow stress of metals under rapid heating to study their time-dependent plastic response when heating times are short enough to interrupt or bypass thermally driven microstructural evolution. Such conditions may exist as chips are formed in the machining process. Measurements of American Iron and Steel Institute1045 steel behavior up to 1000 °C showed complex thermal softening due to dynamic strain aging effects and the diffusion-limited austenite transformation process beginning at the A1 temperature (712 °C). This paper proposes a constitutive model to capture the flow stress and hardening evolution of 1045 steel under rapidly heated conditions for simulating metal cutting. The model combines the Preston-TonksWallace plasticity model, which uses five parameters to capture complex rate- and temperature-sensitive strain hardening, with a dual-ratesensitivity model to capture the response of rapidly heated 1045 steel. Finally, a strain-rate-dependent Gaussian function is introduced to capture dynamic strain aging effects, which act over a narrow range of temperatures that change with strain rate. The proposed model is compared to existing plasticity models for 1045 steel over the range of data available and at a representative machining condition.
GuideMe: A system for Indoor Orientation and Guidance

The demand for indoor navigational systems is increasing daily. The use of navigational systems is ranging from smart cities and robots to visually impaired people support to navigate safely. The GuideMe Project aims to provide guidance and security for people suffering from blindness, and in the time being, it is in the final development stage. This paper presents GuideMe Project goals GuideMe Project architecture and GuideMe final prototype. GuideMe Project architecture consists of the wearable device curried the user has, the anchors with which the wearable device works to determine the position of the user, the smartphone that is informed by the system for the route the user must follow and converts the message into audio information through the user’s headset. The headset in order to guide the user in indoor places and the local server who controls the protocols and the information, are the last parts of the architecture. It is clear that the system is quite complex, it consists of several entities and requires them to work together harmoniously to provide the prescribed functionality, in real-time. All the technologies developed to this final system, each of which has multiple subentities as mentioned, handle the required functionality that is the provision orientation of users who are located and moving
CMAR_A_238295 3623..3629

Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, People’s Republic of China Introduction: PLAC 2 is a tumor-suppressive lncRNA in glioma, while its roles in other types of cancer remain unclear. This study was carried out to explore the potential involvement of PLAC 2 in osteosarcoma (OS). Methods: Expression levels of PLAC 2 in OS and paired non-tumor tissues from OS patients were determined by RT-qPCR. A follow-up study was performed to analyze the prognostic value of PLAC 2 for OS. Interactions between PLAC 2 and miR-93 were assessed by cell transfection, followed by RT-qPCR. Cell proliferation assay was performed to analyze cell proliferation. Results: Our results showed that PLAC 2 was downregulated in OS tissues, and the high expression levels of PLAC 2 were associated with favorable overall survival of OS patients. MiR-93 was upregulated in OS tissues and its expression was inversely correlated with the expression of PLAC 2. In OS cells, overexpression of PLAC 2 resulted in downregulated miR-93, while overexpression of miR-93 did not affect the expression of PLAC 2. Overexpression of PLAC 2 led to decreased proliferation rate of OS cells, while overexpression of miR-93 showed opposite roles and reduced the overexpressing effects of PLAC 2. Conclusion: PLAC 2 is downregulated in OS and regulates cancer cell proliferation through miR-93.
Translation Invariance (TI) based Novel Approach for better De-noising of Digital Images

– Image de-noising is an important image processing task, both as a process itself, and as a component in other processes. Image de-noising means removing unwanted noised form a corrupted image to restore the original image. There are many ways to de-noise an image or a set of data of images, exist. Image De-noising has remained a fundamental problem in the field of image processing. Reducing noise from the original image is still a challenging task for researchers. There are several algorithms and methods each having assumptions, advantages, and disadvantages. Noise can be introduced by transmission errors and compression. In this paper we have introduced an innovative method of Translation Invariant (TI) algorithmic approach for de-noising images to improve SNR. We have applied this algorithm for various Digital Image Processing filters: convolution filter, imaging filter, wiener filter, cross correlation filter, Gaussian filter, order-static filter, wavelet de-noising filter, sliding neighborhood filter. We de-noised the images with all these filters without TI and with TI and we found with TI, the filters are working with better performance than without TI. It is also verified using edge detection by observing the preservation accuracy of edges. In this research we made some useful novel observations, specified as conclusions.
Clustering Algorithms for Data Stream

: Nowadays streaming data is delivered by more and more applications, due to this crucial method for data and knowledge engineering is considered to be clustering data streams. It is a two step process. A normal approach is to summarize the data stream in real-time with an online process into so called micro-clusters. Local density estimates are represented by micro-clusters by assembling the information of many data points which is defined in an area. A traditional clustering algorithm is used in a second offline step, in which larger final clusters are formed by reclustering the micro-clusters. For reclustering, the pseudo points which are used are actually coordinator of the micro-clusters with the weights which are density estimates. However, in the online process, information about density in the area between micro-clusters is not preserved and reclustering is based on possibly inaccurate assumptions about the distribution of data within and between micro-clusters (e.g., uniform or Gaussian). In this paper we depicted various algorithms that are used for clustering and their workings in brief.
The Relationship between Low-Grade Inflammation and Muscle Mass, Strength, and Physical Performance in a Geriatric Out-Patient Population

Background: Chronic low-grade inflammation is suggested to play a key role in the age-related decline in muscle mass, strength and physical performance also known as sarcopenia. The aim of this study was to investigate the potential associations between systemic markers of lowgrade inflammation (CRP, IL-6 and TNF-α) and muscle mass, muscle strength, and physical performance in a geriatric out-patient population. Methods: Geriatric patients were recruited from an outpatient fall clinic at Rigshospitalet Glostrup (n = 95, age +65 yrs.). Appendicular Lean mass (ALM) and ALM normalised to height (ALM/h2) were assessed by Dual energy X-ray Absorptiometry (DXA), muscle strength was evaluated by handgrip strength (HGS) and leg extension power (LEP) and physical performance by gait speed (GS) and 30s sitto-stand test (30s STS). Inflammatory markers (hsCRP, IL-6 and TNF-α) were analysed by ELISA (Multiplex) and plasma concentration of each inflammatory marker was categorized as low, medium, or high when compared to reference values from a group of healthy older individuals who participated in The Copenhagen Sarcopenia Study. Results: High levels of inflammatory markers were associated with poorer HGS also after adjusting for age and BMI. Men with high levels of IL-6 had 22.9% lower HGS than men with low levels of the cytokine, whereas women with high levels of hsCRP and TNF-α had 19.2% and 22.5% lower HGS, respectively. Conclusion: Male geriatric patients having high plasma concentrations of hsCRP and IL-6 and female geriatric patients with high plasma concentrations of hsCRP and TNF-α have poorer HGS compared to those with lower plasma concentrations. These finding indicate that lowgrade inflammation is associated with sarcopenia and underlines the need for focus on the area.
Perception of Autism Spectrum Disorder Children by Envisaging Emotions from the Facial Images

Image processing is a rapidly growing technology and is one among the thrust areas of research in Medical Fields, various Engineering disciplines, life Sciences and Scientific applications. Many technical applications have already adopted image processing and it plays a key role in predicting unknown or hidden facts easily and efficiently. Facial image processing is an innovative application of image processing and is being widely used in many applications successfully. Some of the applications are used for person identification, identifying authorized persons, identifying criminals and so on. As we all know that person’s emotion shows personality & behavior, moods where he or she expresses feelings by emotions maximum on face only. Facial expression can also be used in various fields like emotion recognition, market analysis, prediction neurological disorder percentage, psychological problems and so on. So, it has become an emerging research area to study. Neurological disorder is a more complicated disease because it affects both physical body and mental body. In this paper a new methodology is proposed using optimized deep learning methods to predict ASD in children of age 1 to 10 years. Proposed model performance is tested on ASD children and normal children facial image dataset collected from Kaggle datasets and also tested on dataset collected from autism parents’ face book group. Convolutional Neural Networks (CNN) is applied on extracted face landmarks using optimization techniques, dropout, batch normalization and parameter updating. Most significant six types of emotions are considered for analysis in predicting ASD children accurately.
Deep kernels with probabilistic embeddings for small-data learning

Gaussian Processes (GPs) are known to provide accurate predictions and uncertainty estimates even with small amounts of labeled data by capturing similarity between data points through their kernel function. However traditional GP kernels are not very effective at capturing similarity between high dimensional data points. Neural networks can be used to learn good representations that encode intricate structures in high dimensional data, and can be used as inputs to the GP kernel. However the huge data requirement of neural networks makes this approach ineffective in small data settings. To solves the conflicting problems of representation learning and data efficiency, we propose to learn deep kernels on probabilistic embeddings by using a probabilistic neural network. Our approach maps high-dimensional data to a probability distribution in a low dimensional subspace and then computes a kernel between these distributions to capture similarity. To enable end-to-end learning, we derive a functional gradient descent procedure for training the model. Experiments on a variety of datasets show that our approach outperforms the state-of-the-art in GP kernel learning in both supervised and semi-supervised settings. We also extend our approach to other small-data paradigms such as few-shot classification where it outperforms previous approaches on mini-Imagenet and CUB datasets.
Supplementary material to "On the energy budget of a low-Arctic snowpack"

Abstract. Arctic landscapes are covered in snow for at least six months of the year. The energy balance of the snow cover plays a key role in these environments, influencing the surface albedo, the thermal regime of the permafrost, and other factors. Our goal is to quantify all major heat fluxes above, within, and below a low Arctic snowpack at a shrub tundra site on the east coast of Hudson Bay in eastern Canada. The study is based on observations from a flux tower that uses the eddy covariance approach and from profiles of temperature and thermal conductivity in the snow and soil. Additionally, we compared the observations with simulations produced using the Crocus snow model. We found that radiative losses due to negative longwave radiation are mostly counterbalanced by the sensible heat flux, whereas the latent heat flux is minimal. At the snow surface, the heat flux into the snow is similar in magnitude to the sensible heat flux. Because the snow cover stores very little heat, the majority of the heat flux into the snow is used to cool the soil. Overall, the model was able to reproduce the observed energy balance, but due to the effects of atmospheric stratification, showed some deficiencies when simulating turbulent heat fluxes at an hourly time scale.
Utility of kinetic glomerular filtration rate for predicting severe persistent acute kidney injury in critically ill children and young adults

1. Pediatric Nephrology, Seattle Children's Hospital, University of Washington, Seattle, Washington 2. Pediatric Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia 3. Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois 4. Center for Acute Care Nephology, Cincinnati Childrens Hospital, Cincinnati, Ohio 5. Anschutz Medical Campus, University of Colorado, Aurora, Colorado
Celecoxib treatment improves neurological deficit and reduces selective neuronal loss and glial response in rats following transient middle cerebral artery occlusion

Affiliation: Área de Biología Celular. Instituto de Biomedicina. University of León. León. Spain (MSG, DPR, BAS, EFB, IFU, AFL). Neural Therapies SL. Edificio Institutos de Investigación. Campus de Vegazana s/n León. Spain (BAS). Departamento de Medicina, Cirugía y Anatomía Veterinaria. University of León. León. Spain (CCPG). This article has not been copyedited and formatted. The final version may differ from this version. JPET Fast Forward. Published on October 5, 2018 as DOI: 10.1124/jpet.118. 251264
Evaluation of children with disabilities: BMI indicators and difficulties experienced by parents

Yakup Çağ1, Esra Tunalı2, Cafer Ataş3, Ahmet Lütfullah Orhan4, Mustafa Tunalı5, Buğra Özen6 1Department of Pediatrics, University of Health Sciences, Kartal Dr. Lütfi Kırdar Training and Research Hospital, Istanbul 2Department of Orthodontics, University of Health Sciences, Faculty of Dentistry, Istanbul 3Department of Pediatric Dentistry, University of Health Sciences, Faculty of Dentistry, Istanbul 4Department of Cardiology, University of Health Sciences, Dr. Siyami Ersek Chest and Vascular Surgery Training and Research Hospital, Istanbul 5Department of Periodontology, Çanakkale Onsekiz Mart University, Faculty of Dentistry, Çanakkale 6Department of Pediatric Dentistry, Altınbas University, Faculty of Dentistry, Istanbul, Turkey Evaluation of disabled children Evaluation of children with disabilities: BMI indicators and difficulties experienced by parents
Current Updates on the Management of AL Amyloidosis

Systemic immunoglobulin light chain (AL) amyloidosis is a rare but fatal disease. It results from clonal proliferation of plasma cells with excessive production of insoluble misfolded proteins that aggregate in the extracellular matrix, causing damage to the normal architecture and function of various organs. For decades, treatment for AL amyloidosis was based mainly on therapeutic agents previously studied for its more common counterpart, multiple myeloma. As the prevalence and incidence of AL amyloidosis have increased, ongoing research has been conducted with treatments typically used in myeloma with varying success. In this review, we focus on current treatment strategies and updates to clinical guidelines and therapeutics for AL amyloidosis.
Warum gibt es in Deutschland keine großen medizinischen Zwillingskohorten ?

The worldwide development of twin cohorts began after World War II. These cohorts now include around 1.5 million twins, and more than 2748 twin studies have been published between 1950 and 2012. Each year, the number of twin publications increases by another 500 to 1000. The underrepresentation of German twin studies cannot be solely explained by the abuse of medical research under National Socialism. Developing and expanding large twin cohorts is a challenge in terms of both ethics and data protection. However, twin cohorts enable long-term and real-time research on many medical issues and contribute to answer the question of predisposition or environment as possible disease triggers – even after the sequencing of the human genome. There are currently two German twin cohorts: the biomedical cohort HealthTwiSt, with around 1500 pairs of twins, and TwinLife, a sociological–psychological cohort with around 4000 pairs of twins. There are also disease-specific cohorts. The TwinHealth Consortium in the Faculty of Medicine at the University of Tübingen was established in 2016 with the aim of enabling open-ended and sustainable twin research in Tübingen to answer various scientific questions. With the help of systematic literature research and medical history, this article gives an overview of the worldwide development of twin studies and databases over the last 100 years. The example of the Tübingen TwinHealth Initiative illuminates the structure of a twin cohort and its legal, ethical, and data protection aspects.
Probing the properties of superheavy dark matter annihilating or decaying into neutrinos with ultra-high energy neutrino experiments

The evidence for dark matter particles, $\chi$, is compelling based on Galactic to cosmological scale observations. Thus far, the promising weakly interacting massive particle scenario have eluded detection, motivating alternative models of dark matter. We consider scenarios involving superheavy dark matter (SHDM) that potentially can decay or annihilate to neutrinos and antineutrinos. In the mass range $m_\chi=10^7-10^{15}\,{\rm GeV}$, we evaluate the sensitivities of future observatories POEMMA and GRAND for indirect dark matter detection via the measurement of neutrino-induced extensive air showers (EAS), compute the Auger and ANITA limits using their last up-to-date sensitivities, and compare them with IceCube limits. We also show that the uncertainties related to the dark matter distribution in the Galactic halo have a large impact on the neutrino flux. We show that a ground-based radio detector such as GRAND can achieve high sensitivities due to its large effective area and high duty cycle. Space-based Cherenkov detectors such as POEMMA that measure the EAS optical Cherenkov signal have the advantage of full-sky coverage and rapid slewing, enabling an optimized SHDM observation strategy focusing on the Galactic Center. We show that increasing the field of view of the Cherenkov detectors can significantly enhance the sensitivity. Moreover, POEMMA's fluorescence observation mode that measures EAS above $20\,$EeV will achieve state-of-the-art sensitivity to SHDM properties at the highest mass scales.
Effect of Pravastatin as an Adjunctive Therapeutic for Mitral Insufficiency with Hyperlipidemia in a Dog

Pravastatin (PS) has been found to increase left ventricle (LV) expansion capacity and decrease LV constriction and left atrial pressure in healthy dogs. To date, there are no available reports on the effects of PS in dogs with hypercholesterolemia with chronic heart failure (CHF). This case report demonstrates a successful long-term treatment plan using PS in a dog suffering from mitral insufficiency with hyperlipidemia. A 12-year-old, castrated male Chihuahua dog had mitral insufficiency with hyperlipidemia. The dog presented with symptoms of chronic coughing. PS was orally administered (1 mg/kg, SID) in addition to general treatment for mitral insufficiency. The follow-up period was 375 days. PS administration decreased the heart rate (HR), vertebral heart size (VHS), and N-terminal probrain natriuretic peptide (NT-proBNP) concentration of the dog. In addition, PS administration also improved chronic cardiac failure induced by mitral insufficiency and hyperlipidemia. This report suggests that PS can be useful as an adjunctive therapeutic for dogs with hypercholesterolemia with mitral insufficiency.
Hypoalbuminemia – An Indicator of the Severity and Prognosis of COVID-19 Patients: A Multicentre Retrospective Analysis

Background Hypoalbuminemia has been reported in COVID-19 patients. Exploring the influencing factors and possible adverse consequences of albumin reduction may provide some guidance for the treatment of COVID-19 patients. Methods In this multicentre retrospective study, we collected information including demographics, comorbidities, clinical symptoms, complications, laboratory tests, treatment, and outcomes of patients diagnosed with COVID-19 from three hospitals in Wuhan, China. We compared the indexes between patients with hypoalbuminemia and normal albumin. Regression model was used to evaluate various influencing factors of patients with hypoalbuminemia and their relationship with clinical outcomes. We also compared the changes of particular laboratory indexes in patients with hypoalbuminemia before and after enteral nutrition therapy. Results A total of 482 patients were enrolled in the study. About 53.7% patients developed hypoalbuminemia during admission. Patients with hypoalbuminemia were older, had a higher proportion of combined diabetes mellitus, fever, dyspnea, and natriuresis, and had a relatively poorer prognosis than patients with normal albumin. Patients with hypoalbuminemia had higher levels of CRP, leukocytes, ALT, AST, total bilirubin, ALP, GGT, LDH, creatine kinase, D-dimer, globulin, and lower levels of lymphocytes and eosinophils. Severe, older, anorexia, elevated CRP, and decreased lymphocytes were the independent predictors for decreased albumin in COVID-19 patients. In addition, decreased albumin is correlated with adverse outcomes. Nutritional support therapy to correct serum albumin may improve patient outcomes. Conclusion COVID-19 patients with hypoalbuminemia tend to have more severe clinical manifestations and more abnormal biochemical tests, which may result in poorer clinical outcomes. Nutritional support therapy may improve the clinical outcome of these patients.