Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Biometrics System Design using Score Level Fusion Approach

: Despite substantial advances in recent years, there are still severe challenges like non-universality, noisy sensor data, Intra-class variations, Inter-class similarities, spoof attacks in obtaining reliable authentication through unimodal biometric systems. Multimodal biometrics based identification provide improved identification performance using biometric information obtained from multiple traits as compared to unimodal biometrics identification which relies on a single biometric trait of an individual. This research work proposes an efficient multimodal system using score level fusion which can be employed for security critical applications. In this paper the performance of score level fusion using sum rule has been evaluated. Proposed fusion approach integrates four biometric modalities: iris, fingerprint, face and signature. Normalization of scores is very important in score level fusion based multimodal biometrics. Proposed fusion approach performs score normalization using three methods z-score normalization, min– max normalization, and tanh-estimators normalization. A new efficient normalization has been proposed. Weighted sum rule based method has been employed for combination of these normalized scores. Experimental results on combining four biometric modalities are presented. With proposed score level fusion algorithm implementation we have got significant reduction in error rates. We have achieved interesting to point work with multimodal system as FAR=0% and FRR=1.66%.
Heart Disease Detection using Core Machine Learning and Deep Learning Techniques: A Comparative Study

: At the present time the load on an individual person has increased significantly due to an increase in one’s work. Because of the dire circumstance which cannot be avoided, there is a high probability that the person is going to suffer from heart disease. In India mostly people die due to heart disease so it is a great matter of concern. Since the ancient times humans have significantly improved in the domain of machine and health care. In the modern times after the entry of machine and AI in the field of medicine and health care there has been a significant development and improvement in the domain of medicine and health care. New diseases are now being discovered frequently and are being eliminated by the help of certain evolved technologies. Heart disease being a matter of primary concern this paper involves a predictive model which discovers whether a person is having heart disease or not by the use of various classification algorithms like logistic regression, decision trees, K-Nearest neighbors, SVM, Deep learning algorithm which is ANN etc. In this paper the authors has compared all the above algorithms and have found that ANN performs best out of all these algorithms.
Nuclei Segmentation in Cell Images using Fully Convolutional Neural Networks

: Nuclei detection in microscopy images is a major bottleneck in the discovery of new and effective drugs. Researchers need to test thousands of chemical compounds to find something of therapeutic efficacy. Nucleus being the most prominent part of a cell helps in the identification of individual cells in a sample and by analyzing the cell’s reaction to various treatments the researchers can infer the underlying biological process at work. Automating the process of nuclei detection can help unlock cures faster and speedup drug discovery. In this paper, we propose a custom encoder-decoder style fully-convolutional neural network architecture with residual blocks and skip connections which achieves state-of-the-art accuracy. We use spatial transformations for data augmentation to prevent our model from over-fitting. We also use a combination of binary cross-entropy and dice-loss for our loss function which handles the class imbalance problem of the dataset. The proposed model is capable of segmenting nuclei effectively across a wide variety of cell types and experimental systems. The proposed model achieved a mean IOU of 94.86 on the BBBC038v1 dataset. Automated nuclei detection is projected to improve throughput for research in the
Gata2 in Embryonic Hematopoiesis

textabstractHematopoiesis is a word originating from the two greek words αἷμα (haima) which means blood and the verb ποιeῖν (poien) which means to make/create. Hematopoiesis describes the process by which the organism creates and replenishes all the blood cell types that are required for the physiologic functions of the organism. The importance of the blood tissue can be highlighted by the many and discrete functions that it performs. These are accomplished through several different cell types forming the blood tissue (erythrocytes, platelets, macrophages, neutrophils, eosinophils, basophils, B-cells, T-cells, NK-cells). For example, the red blood cells or erythrocytes found in the circulating blood are mainly involved in the transport of O2 and CO2. Lymphocytes which are white blood cells are part of the immune system and actively participate in the defense of the organism against pathogens. In the adult organism hematopoietic cells are found not only in the blood but also in hematopoietic tissues such as the bone marrow, spleen, lymph nodes and thymus. Importantly, all mature hematopoietic cell types found in the blood tissues originate from rare hematopoietic stem cells (HSCs). These founder cells are quiescent, long-lived and are at the base of a well-studied cell differentiation hierarchy. HSCs robustly produce all the billions of mature blood cells that are required daily and throughout the entire life of the organism. HSCs are clinically relevant cells that have been used for over 50 years in transplantation and cell replacement therapies for leukemia and monogenic blood-related diseases.
On Efficient Low Distortion Ultrametric Embedding

A classic problem in unsupervised learning and data analysis is to find simpler and easy-to-visualize representations of the data that preserve its essential properties. A widely-used method to preserve the underlying hierarchical structure of the data while reducing its complexity is to find an embedding of the data into a tree or an ultrametric. The most popular algorithms for this task are the classic linkage algorithms (single, average, or complete). However, these methods on a data set of $n$ points in $\Omega(\log n)$ dimensions exhibit a quite prohibitive running time of $\Theta(n^2)$. In this paper, we provide a new algorithm which takes as input a set of points $P$ in $\mathbb{R}^d$, and for every $c\ge 1$, runs in time $n^{1+\frac{\rho}{c^2}}$ (for some universal constant $\rho>1$) to output an ultrametric $\Delta$ such that for any two points $u,v$ in $P$, we have $\Delta(u,v)$ is within a multiplicative factor of $5c$ to the distance between $u$ and $v$ in the "best" ultrametric representation of $P$. Here, the best ultrametric is the ultrametric $\tilde\Delta$ that minimizes the maximum distance distortion with respect to the $\ell_2$ distance, namely that minimizes $\underset{u,v \in P}{\max}\ \frac{\tilde\Delta(u,v)}{\|u-v\|_2}$. We complement the above result by showing that under popular complexity theoretic assumptions, for every constant $\varepsilon>0$, no algorithm with running time $n^{2-\varepsilon}$ can distinguish between inputs in $\ell_\infty$-metric that admit isometric embedding and those that incur a distortion of $\frac{3}{2}$. Finally, we present empirical evaluation on classic machine learning datasets and show that the output of our algorithm is comparable to the output of the linkage algorithms while achieving a much faster running time.
Sommerfeld Coefficient for Hole- And Electron- Doped Cuprates

We have reflected on the ratio of specific heat and temperature (γorCel) of selected electron and hole doped highTccuprates. The electronic specific heat rises slowly and there is a sudden drop at the critical temperature and then it gradually decreases for temperatures above TC. The behavior of electronic specific heat below TC is in good agreement with the BCS result for d-wave superconductor. The electronic contribution to the total specific heat of these materials is less than 1 %. The relevance to high-Tc superconductivity in the cupratesis
TanSat ACGS on-orbit spectral calibration by use of individual
solar lines and entire atmospheric spectra

Abstract. The spectra measured by the Atmospheric Carbon dioxide Grating Spectrometer (ACGS) carried by the China global carbon dioxide observation satellite (TanSat) in the band of 0.76 μm, 1.61 μm and 2.06 μm can be used for the retrieval of carbon dioxide (CO2) concentrations by fitting the observations and simulations using the optimal estimation algorithm. Accurately detecting the change of the center wavelength is highly important because of its very high spectral resolution and accuracy requirement for product retrieval. The variations of center wavelength for all three bands of ACGS have been calculated on the locations of the individual solar absorption lines by comparing the solar-viewing measurements and the high resolution solar reference spectrum. The variations with magnitudes less than 10 % of the spectral resolution for each band have been detected. The changes are probably caused by vibration and the instrument status difference between the ground and space, especially temperature variation on orbit. In addition to solar lines, the entire atmospheric spectra simulated by radiative transfer model can be used as the reference spectrum to determine the wavelength change by fitting the measured and simulated spectra. The change of wavelength determined by atmospheric spectra is closely consistent with that by solar lines. Both schemes described here can be used not only for monitoring spectral stability but also to gain spectral knowledge prior to the level-2 product processing. These minor temporal changes of wavelength on orbit should be corrected in the product retrieval.
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems

This paper studies the lower bound complexity for minimax optimization problem whose objective function is the average of n individual smooth convex-concave functions. We consider the al-gorithm which has access to gradient and proximal oracle for each individual component. For the strongly-convex-strongly-concave case, we prove such an algorithm can not reach an ε -saddle point in fewer than Ω (( n + κ ) log(1 /ε )) iterations, where κ is the condition number of the objective function. This lower bound matches the upper bound of the existing incremental ﬁrst-order oracle algorithm stochastic variance-reduced ex-tragradient. We develop a novel construction to show the above result, which partitions the tridiagonal matrix of classical examples into n groups. This construction is friendly to the analysis of incremental gradient and proximal oracle and we also extend the analysis to general convex-concave cases.
The Eﬀect of PCA Decomposition on Supervised Learning

We propose that dimensionality reduction may assist in different supervised machine learning tasks by eliminating some of the noise in a dataset. Our project seeks to analyze the effect of different levels of PCA on a simple neural network based classification task by analyzing the trade-offs with reduced images and accuracy as a function of number of epochs. We attempt to have the best of both worlds by combining unsupervised learning approaches with supervised learning ones. We also seek to understand the relationship between entropy and classification accuracy.
A Systematic Pharmacology and In Vitro Study to Identify the Role of the Active Compounds of Achyranthes bidentata in the Treatment of Osteoarthritis

Backgrounds Achyranthes bidentata is a Chinese traditional herbal medicine widely used to treat osteoarthritis (OA). This study aimed to identify active compounds from Achyranthes bidentata through systematic pharmacology and in vitro experiments to find the targets of Achyranthes bidentata in the treatment of OA. Material/Methods We screened the active compounds of Achyranthes bidentata from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Then, we used STITCH and Open Targets Platform databases to screen the active components and predict the potential targets of Achyranthes bidentata in the treatment of OA. Subsequently, we studied the compound-target network and protein interaction network and analyzed the enrichment of potential target proteins. Finally, we used Western blot analysis to verify the therapeutic effect of Achyranthes bidentata extract on the expression of OA-related target proteins. Results There were 7 active components in Achyranthes bidentata, which were strongly related to the 74 targets of OA. Quercetin, baicalein, and berberine are the critical active compounds of Achyranthes bidentata in the treatment of OA. Protein interaction analysis and in vitro experiments suggested that TNF, IL-6, and TP53 are the critical targets of Achyranthes bidentata in the treatment of OA. Functional enrichment analysis showed that Achyranthes bidentata plays a pharmacological role in OA through apoptosis, inflammation, and immune regulation. Conclusions Quercetin, baicalein, and berberine are the critical active compounds of Achyranthes bidentata in the treatment of OA. TNF, IL-6, and TP53 may be potential targets for the treatment of OA.
TRAINING METHOD USING CLASS-FRAME PSEUDO LABEL FOR WEAKLY LABELED DATASET ON DCASE2019

We propose a training method using class-frame pseudo label for weakly labeled datasets given by the IEEE AASP challenge on detection and classification of acoustic scenes and events 2019 (DCASE2019) task 4. Our model is constructed based on a residual network (ResNet) and trained by datasets including strong and weak labels. The strong label has event classes and their presences at each frame, and the weak label has only event classes. In order to train the model effectively, we propose class-frame pseudo labels for weakly labeled datasets. The class-frame pseudo label contributes to improvement of the event presence prediction at each frame by avoidance of overfitting to strongly labeled datasets. A result shows that F1-scores by our proposed method are 25.9% and 62.0% in the event-based and segment-based evaluations, respectively.
A Comparative Survey on Wireless Sensor Networks based Internet of Things (IoT)

: Internet of Things has been evolving at a much faster pace from the past few years, as it is being one of the most funded research areas. The development of varied sorts of wireless sensor networks and therefore the requirement of their being interconnected led to the event of the Internet of Things. This work provides an insight of varied Wireless Sensor Networks whose protocols and architecture forms a boon to the event of the existing Internet of Things. This study discusses Wireless HART, ZigBee, and ISA.100 which are some of these communication standards. Further, the purpose of the study is to represent a discussion of contrast and similarities of the Internet of Things (IoT) with the most effective wireless sensor networks. The comparison is made based on different work accomplished by different researchers in the field of the Internet of things. In this, application requirements such as quality of service, security, scalability etc. for WSN and IOT are compared.
Recent Trends in Natural Language Question Answering Systems: A Survey

________________________________________________________________________________ Abstract - The need to inquiry data content accessible in different organizations including organized and unstructured information has turned out to be progressively significant. Henceforth, Question Answering Systems (QAS) are essential to fulfill this kind of need. The QA systems are focused towards giving relevant answers in light of inquiries proposed in natural language. Question Answering system is an vital research area in IR. Research on the area of Question Answering system started in the year 1960 and at present lot of Question Answering systems have been developed. Question Answering system combines the research from different domains like Natural Language Processing, Artificial Intelligence, Information Retrieval and Information extraction. QA is made out of three particular modules. These three core segments are: question processing, document processing, and answer extraction. Question Processing plays an important role in QAS by classifying the submitted query according to its type. Information retrieval is important for question answering, because it find the answer relevant document from the corpora. Finally, answer extraction goal is to recover the response for a question posed by the user. In this paper we investigate various QAS. We give also statistics and analysis that can clear the way and help researchers to choose the appropriate solution to their issue. They can see the deficiency, so they can propose new systems for complex questions. They can likewise adjust or reuse QAS methods for specific research issues.
The Relationship Between Intermediate And Capital Goods Imports, Industrial Production And Economic Growth: The Case Of Turkey

In this study, the relationship between intermediate goods import (MI), capital goods imports (MC), industrial production (IP) and economic growth (GDP) in Turkey was explored by using the quarterly data consist of 32 observations between 2010Q1 – 2017Q4. VAR Granger causality analysis was employed to explore the short-term causality and the direction between variables. It is concluded: a) a bidirectional causality between GDP and MI b) a bidirectional causality between (IP) and (MI), c) a unidirectional causality from IP to GDP, d) a unidirectional causality from MC to MI and there is not a causality between IP and MC. Johansen Co-integration test results indicated a long-term relationship between, MC, MI, IP and GDP. Variance decomposition was conducted to evaluate the variability of the dependent variable in the process of time. As a result, while in the first period (quarter) GDP is explained by itself at 100%. However In the tenth period GDP is explained by 18% GDP, 18% MC, 32% MI and 32% IP. As a result, increases in MC and IP raise MI, or vice versa. IP and MC depend on MI. MI affects GDP both directly and indirectly through IP. The stability of growth both in the short-term and in the long-term depends on imports of intermediate goods in Turkey. Given the chronic foreign trade deficits of Turkey, this pattern of the economy cause to foreign exchange bottlenecks and could have a negative impact on economic stability. In this context, this study supports the claim that liberal policies, which emerged after the 1980s and proposed an export-based growth model, evolved into an importbased growth model in developing countries eventually.
Distance-Based Formation Control for Quadrotors with Collision Avoidance via Lyapunov Barrier Functions

In this paper, group formation control with collision avoidance is investigated for heterogeneous multiquadrotor vehicles. Specifically, the distance-based formation and tracking control problem are addressed in the framework of leader-follower architecture. In this scheme, the leader is assigned the task of intercepting a target whose velocity is unknown, while the follower quadrotors are arranged to set up a predefined rigid formation pattern, ensuring simultaneously interagent collision avoidance and relative localization. The adopted strategy for the control design consists in decoupling the quadrotor dynamics in a cascaded structure to handle its underactuated property. Furthermore, by imposing constraints on the orientation angles, the follower will never be overturned. Rigorous stability analysis is presented to prove the stability of the entire closed-loop system. Numerical simulation results are presented to validate the proposed control strategy.
A Network Pharmacological Approach to Investigate the Mechanism of Action of Active Ingredients of Epimedii Herba and Their Potential Targets in Treatment of Alzheimer’s Disease

Background Epimedii Herba is a traditional Chinese herbal medicine used to treat central nervous system diseases such as Alzheimer’s disease in China. However, the pharmacological mechanism is unclear. To investigate the mechanisms of Epimedii Herba in the treatment of Alzheimer’s disease, we assessed effective compounds, corresponding targets, and related pathways of Epimedii Herba in the treatment of Alzheimer’s disease based on network pharmacology. Material/Methods The active components and targets of Epimedii Herba were obtained through the TCMSP database and the DrugBank database. The DisGeNET database and GeneCards database were used to search for Alzheimer’s disease targets. The common targets of components and disease were obtained by Wayne diagram. Gene ontology (GO) analysis and enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed using the DAVID database. The component-target-pathway interaction network model was constructed using Cytoscape software. Auto Duck Vina software was used for molecular docking to analyze the affinity of the key ingredients and the main targets. Results We screened 17 active ingredients and 27 key targets of Epimedii Herba in the treatment of Alzheimer’s disease, which were related to the HIF-1 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, NF-κB signaling pathway, VEGF signaling pathway, and sphingolipid signaling pathway. Conclusions Based on network pharmacology, the multi-component, multi-target, and multi-pathway characteristics of Epimedii Herba in the treatment of Alzheimer’s disease were explored. Our results provide new ideas for future pharmacological and experimental research on Epimedii Herba in the treatment of Alzheimer’s disease.
Real-time Hand Pose Recognition using Depth Sensors combined with Spherical Shape Model Descriptor

Hand pose recognition is a hard problem due to the inherent structure complexity of the hand, that can show a great variety of dynamic con(cid:28)gurations and self occlusions. Some traditional approaches tackle this problem using RGB image information, which translates into a extremely hard problem given the ambiguities that arise from this representation. Depth sensor devices, namely the Kinect, have opened a new perspective for this problem since it deals with the 3D information of the object of interest, potentiating more robust and accurate approaches. In this work it is proposed a new light-weight spherical 3D descriptor model that encodes the information provided by the depth data sensor. The descriptor has been integrated in a selective hand data capture and classi(cid:28)cation pipeline capable of real-time hand pose recognition and successfully integrated with Oblong’s pose recognition framework.
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate

Generative adversarial imitation learning (GAIL) demonstrates tremendous success in practice, especially when combined with neural networks. Different from reinforcement learning, GAIL learns both policy and reward function from expert (human) demonstration. Despite its empirical success, it remains unclear whether GAIL with neural networks converges to the globally optimal solution. The major difficulty comes from the nonconvex-nonconcave minimax optimization structure. To bridge the gap between practice and theory, we analyze a gradient-based algorithm with alternating updates and establish its sublinear convergence to the globally optimal solution. To the best of our knowledge, our analysis establishes the global optimality and convergence rate of GAIL with neural networks for the first time.