Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Moderation of Intervention Efficacy: Dopaminergic Genes, The Incredible Years, and Externalizing Behavior in Children Chhangur,

Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva. nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.
Hierarchical Attentional Feature Fusion for Surgical Instrument Segmentation

Instrument segmentation is a crucial and challenging task for robot-assisted surgery operations. Recent commonly-used models extract feature maps in multiple scales and combine them via simple but inferior feature fusion strategies. In this paper, we propose a hierarchical attentional feature fusion scheme, which is efficient and compatible with encoder-decoder architectures. Specifically, to better combine feature maps between adjacent scales, we introduce dense pixel-wise relative attentions learned from the segmentation model; to resolve specific failure modes in predicted masks, we integrate the above attentional feature fusion strategy based on position-channel-aware parallel attention into the decoder. Extensive experimental results evaluated on three datasets from MICCAI 2017 EndoVis Challenge demonstrate that our model outperforms other state-of-the-art counterparts by a large margin.
Multi-Scale Aggregated-Dilation Network for ex-vivo Lung Cancer Detection with Fluorescence Lifetime Imaging Endomicroscopy

Multi-scale architectures at a granular level are characterised by separating input features into groups and applying multi-scale feature extractions to the split input features, and thus the correlations among the input features as global information are no longer retained. Moreover, they usually require more input features due to the separation, and therefore, more complexity is introduced. To retain the global information while utilising the advantages of feature-level hierarchical multi-scale architectures, we propose a multi-scale aggregated-dilation architecture (MSAD) to perform hierarchical fusion of features at a layer level, with the integration of dilated convolutions to overcome these issues. To evaluate the model, we integrate it into ResNet, and apply it to a unique dataset, containing over 60,000 fluorescence lifetime endomicroscopic images (FLIM) collected on ex-vivo lung normal/cancerous tissues from 14 patients, by a custom fibre-based FLIM system. To evaluate the performance of our proposal, we use accuracy, precision, recall, and AUC. We first compare our MSAD model with eight networks achieving a superiority over 6%. To illustrate the advantages and disadvantages of multi-scale architectures at layer and feature-level, we thoroughly compare our MSAD model with the state-of-the-art feature-level multiscale network, namely Res2Net, in terms of parameters, scales, and effective convolutions.
Cu‐doped α–β phase heterojunctions in Bi2O3 nanoparticles for enhanced photocatalytic degradation of organic dye rhodamine B Anuradha Sharma1 · Anuj Mittal1 · Shankar Sharma1 · Kavitha Kumari2 · Sanjeev Maken2 · Naveen Kumar1

α–β Bi2O3 modified with Cu2+ ions in various compositions have been synthesized by hydrothermal process. The crystallinity, surface structure, elemental compositions and optical characteristics were uncovered by relevant techniques including XRD, SEM, EDX, XPS, PL, and UVDRS. Total organic carbon (TOC) analysis was performed to determine the dye degradation efficiency of photocatalyst. Highly crystalline Bi2O3 nanoparticles consisting of α–β phase heterojunctions were observed possessing crystallite sizes in the range of 30–40 nm. The photocatalytic activity of Cu2+-modified Bi2O3 composites was studied using RhB dye under the illumination of visible light. The results reveal that addition of Cu2+ ions into Bi2O3 lattice significantly improved the degradation rate over the pure sample. The highest activity was obtained for sample containing 2 mol % Cu in Bi2O3 lattice where complete degradation of the pollutant was attained in 120 min. The increased activity is largely ascribed to the effective separation of charge carriers due to Cu2+ doping and formation of α–β phase heterojunctions in Bi2O3.The effect of pH conditions and role of active species on degradation kinetics were also examined.
Surgical instrument segmentation based on multi-scale and multi-level feature network

Surgical instrument segmentation is critical for the field of computer-aided surgery system. Most of deep-learning based algorithms only use either multi-scale information or multi-level information, which may lead to ambiguity of semantic information. In this paper, we propose a new neural network, which extracts both multi-scale and multilevel features based on the backbone of U-net. Specifically, the cascaded and double convolutional feature pyramid is input into the U-net. Then we propose a DFP (short for Dilation Feature-Pyramid) module for decoder which extracts multi-scale and multi-level information. The proposed algorithm is evaluated on two publicly available datasets, and extensive experiments prove that the five evaluation metrics by our algorithm are superior than other comparing methods.
SURVEY: GANS FOR DATASET GENERATION

: - Large amount of data is essential for any machine learning tasks for accurate and efficient results. But data may not always be available in huge numbers according to the requirement like medical data, aerospace data, and forensic data and so on. For this purpose, Generative Adversarial Networks are used which are a type of deep neural networks used for generating realistic datasets. It consists of two networks one the Generator and other the Discriminator whose task is to generate data samples and to differentiate between true and false sample respectively. This paper gives an overview of variants of GANs. Each type of GAN is trained differently according to the application of use, from which data can be generated and can be utilized for different purposes.
An Efficient and Accurate 3D Multiple-Contextual Semantic Segmentation Network for Medical Volumetric Images

Convolutional neural networks have become popular in medical image segmentation, and one of their most notable achievements is their ability to learn discriminative features using large labeled datasets. Two-dimensional (2D) networks are accustomed to extracting multiscale features with deep convolutional neural network extractors, i.e., ResNet-101. However, 2D networks are inefficient in extracting spatial features from volumetric images. Although most of the 2D segmentation networks can be extended to three-dimensional (3D) networks, extended 3D methods are resource and time intensive. In this paper, we propose an efficient and accurate network for fully automatic 3D segmentation. We designed a 3D multiple-contextual extractor (MCE) to simulate multiscale feature extraction and feature fusion to capture rich global contextual dependencies from different feature levels. We also designed a light 3D ResU-Net for efficient volumetric image segmentation. The proposed multiple-contextual extractor and light 3D ResU-Net constituted a complete segmentation network. By feeding the multiple-contextual features to the light 3D ResU-Net, we realized 3D medical image segmentation with high efficiency and accuracy. To validate the 3D segmentation performance of our proposed method, we evaluated the proposed network in the context of semantic segmentation on a private spleen dataset and public liver dataset. The spleen dataset contains 50 patients’ CT scans, and the liver dataset contains 131 patients’ CT scans.
An Innovative Flood Prediction System Using Improved Machine Learning Approach

Flood is a highly chaotic, strongly emergent, and naturally occurring pattern. Flood prediction is important for building resilience to natural disasters by proactively managing and minimizing the impacts of natural hazards on our everyday lives. This project attempts to create a model that can accurately and efficiently map water level as a function of climate conditions to forecast flood levels at a given point in the future. Machine learning, as one of the Artificial Intelligence methods, contributes highly to the advancements of prediction systems to provide better performance and cost-effective solutions. Most of the current flood prediction models are primarily concerned with heavy rainfall and hurricanes. However, flooding in New Brunswick, commonly referred to as the spring flood, is primarily caused by rapid snowmelt, heavy rainfall and ice jams occurring during the spring (Figures 1 and 2). To create an accurate prediction, we imple - mented machine learning to discover a new prediction model for this type of flooding, using local environmental data and considering different hydrology and climate variables.
Joint Segmentation and Pairing of Nuclei and Golgi in 3D Microscopy Images

Blood vessels provide oxygen and nutrients to all tissues in the human body, and their incorrect organisation or dysfunction contributes to several diseases. Correct organisation of blood vessels is achieved through vascular patterning, a process that relies on endothelial cell polarization and migration against the blood flow direction. Unravelling the mechanisms governing endothelial cell polarity is essential to study the process of vascular patterning. Cell polarity is defined by a vector that goes from the nucleus centroid to the corresponding Golgi complex centroid, here defined as axial polarity. Currently, axial polarity is calculated manually, which is time-consuming and subjective. In this work, we used a deep learning approach to segment nuclei and Golgi in 3D fluorescence microscopy images of mouse retinas, and to assign nucleus-Golgi pairs. This approach predicts nuclei and Golgi segmentation masks but also a third mask corresponding to joint nuclei and Golgi segmentations. The joint segmentation mask is used to perform nucleus-Golgi pairing. We demonstrate that our deep learning approach using three masks successfully identifies nucleus-Golgi pairs, outperforming a pairing method based on a cost matrix. Our results pave the way for automated computation of axial polarity in 3D tissues and in vivo.
Comparison of Microplastic Abundance between Blood Cockle (Tegilarca granosa) and Asiatic Hard Clam (Meretrix meretrix) in Banyuurip Coastal Water, Gresik

The quantity of microplastic in the marine environment has increased significantly and it could lead to the accumulation in the marine organisms such as mussels and clams. Blood cockle (Tegilarca granosa) and Asiatic hard clam (Meretrix meretrix) are benthic organisms and their feeding mode could expose them to microplastic in the sediment. This study aimed to analyze and compare microplastic accumulation in both mussels in Banyuurip coastal water, Gresik. Sediment and mussel samples were collected on March 2019 from five sampling stations: Bengawan Solo river mouth, seaward area, mangrove, fish landing station dan shrimp culture site. About 1 kg sediment samples and 10 individual mussels in the size of 4−6 cm from each species were collected from each sampling station. Microplastic analysis was conducted in the laboratory by digesting organic matter using H2O2 30 % and the identification was conducted visually using microscope. Three types of microplastic (fiber, film and fragment) was found from the sediment and mussel samples with the domination of fiber (75 %) followed by fragment (20 %) dan film (< 2 %). Fiber and fragment were observed in all sediment and mussel samples, whereas film was available only in certain samples. Statistical test revealed no significant difference in the concentrations of microplastic between the two mussels, however, the differences were observed in the concentrations of microplastic among the sampling stations (p < 0.01). Higher concentration of microplastic was found in the mussels from mangrove area due to its rooting system that could trap microplastics.
Self-consistent simulation and modelling of electrostatically deformed diaphragms

Design of MEMS devices which apply electrostatic forces to deformable structures is complicated by the fact that as the structure deforms, the charges redistribute, thereby modifying the mechanical loads. This paper reports three different levels of model used to simulate such structures: a very simple lumped parallel-plate-capacitor-plus-spring model, a one-dimensional numerical model based on beam and/or plate theory with loads derived from an incrementally parallel-plate capacitor, and a fully self-consistent three-dimensional numerical simulation combining finite-element structural modeling with multipole-accelerated boundary element capacitance analysis. The relative merits of each model are described, and several specific examples are examined. The lumped model has the advantage of simplicity plus a full analytic description, which permits assessment of stability of solutions. The one-dimensional numerical model, in the specific cases examined, agrees with the full 3-D model, and is computationally far less costly However, the simple structures examined here have nearly parallel conductors which remain in the small-deflection regime up to the pull-in voltage. For non-parallel conductors, and for structures which involve large deflections, the full 3-D model will be required. Furthermore, the full 3-D model is extremely valuable in identifying the limitations of the beam-theory simulation, some of which could be critical to a design.
Towards Scalable Content-based Publish-Subscribe Networks

— Architecture design for content-based publish-subscribe service networks is challenging for two reasons: first, communication in such systems is guided by the content of event publications and subscriptions, rather than by network addresses; second, while an event often matches subscriptions from multiple locations, the highly diversified matching patterns and hence communication needs imply that existing multicast techniques cannot be readily used for efficient event delivery. In this paper, we propose a new architectural approach called MEDYM – Match Early with DYnamic Multicast. Unlike existing approaches, MEDYM does not build static overlay networks for event delivery. Instead, an event is matched against subscriptions as early as possible, to identify destinations with matching subscriptions; then, a multicast tree is dynamically constructed to route the event to the destinations with high network efficiency. We evaluate the MEDYM architecture using detailed simulations, and compare it with the two major existing design approaches: Content-based Forwarding and Channelization. Experimental results show that MEDYM significantly improves efficiency of event delivery, and is highly flexible and robust. We also analyze potential overheads introduced in MEDYM, and found them to be well acceptable and more than outweighed by the benefits of the approach. We expect the basic MEDYM architecture to scale to pub-sub networks of thousands of servers, which we believe is adequate for many interesting applications.
ANALYSIS OF CAPITAL STRUCTURE OF SELECTED POWER (GENERATION AND DISTRIBUTION) COMPANIES IN DEVELOPING INDIA – AN EMPIRICAL STUDY

Power Generation and Distribution companies pulls the huge demand of it generation capacity along with technological enhancement in field of power and energy. Role of Energy and Power industry is very important and significant in India’s GDP. They are one of the biggest players who contribute in treasuries at state and national level. Capital structure is the topic which is raised but no firm equation has been brought forward and this is the reason of carrying out this study. In this research study an attempt has been made to examine the capital structure of the selected Power – Generation and Distribution companies of India. For the research, 10 companies are selected with the study duration of 5 years. In findings and analysis it is found that selected companies of energy and power (Generation and Distribution) industry are running with low debt fund. Few companies largely utilizing shareholder’s funds in their assets and few are with high degree of financial risk.
Benefit Assessment of Solar Photovoltaic Industry in China

Because of shortages of energy and environmental carrying capacity limits, China will launch a solar photovoltaic energy. In 2011, the Chinese Government provides the strong policy for the development of photovoltaic industry, including “Inter solar China”, “The Twelfth Five-Year Plan of PV Industry” and so on. Due to the policy encouragement, by the end of 2011 projects completed formally is up to 2.7GW. China’s PV market has finally officially launched, it is expected that China will become the world's leading PV market in 2012. Therefore, this paper made an original creative analysis on comprehensive benefits of solar energy photovoltaic industry in China, and evaluated based on comprehensive evaluation system, and finally got economic, social and ecological benefits of the solar PV industry. The conclusions show the ecological optimal efficiency, so seeing the PV industry is to the benefit of future generations, the power industry of the future.
Factors Influencing Museum Visits: An Empirical Study in Vietnam

This paper aims to investigate factors affecting museum visits of young people in Vietnam by sending questionnaires to 2106 young people aged between 14–30 in Vietnam. With the support of SPSS version 2016 and STATA version 22, this paper illustrates that the empirical findings are appropriate with previous literature reviews. Research hypotheses such as learning purposes, intrinsic motivation, extrinsic motivation, and museum architecture are significantly and positively correlated with museum visits measured through customer visiting intention, customer satisfaction, and customer loyalty. Furthermore, males have a stronger interest in visiting the museum but a low re-visit intention than females. People with a higher level of education and higher income are also more likely to visit the museum than those with a lower level of education and income. Finally, the research results suggest that the family class and the orientation of parents play an important role in encouraging children to increase museum visiting intention; however, children of high-class families are less likely to have a high intention, satisfaction, and loyalty toward museum visitation than children of lower-class families.
Learning to Generate Missing Pulse Sequence in MRI using Deep Convolution Neural Network Trained with Visual Turing Test

Magnetic resonance imaging (MRI) is widely used in clinical applications due to its ability to acquire a wide variety of soft tissues using multiple pulse sequences. Each sequence provides information that generally complements the other. However, factors like an increase in scan time or contrast allergies impede imaging with numerous sequences. Synthesizing images of such non acquired sequences is a challenging proposition that can suffice for corrupted acquisition, fast reconstruction prior, super-resolution, etc. This manuscript employed a deep convolution neural network (CNN) to synthesize multiple missing pulse sequences of brain MRI with tumors. The CNN is an encoder-decoder-like network trained to minimize reconstruction mean square error (MSE) loss while maximizing the adversarial attack. It inflicts on a relativistic Visual Turing Test discriminator (rVTT). The approach is evaluated through experiments performed with the Brats2018 dataset, quantitative metrics viz. MSE, Structural Similarity Measure (SSIM), and Peak Signal to Noise Ratio (PSNR). The Radiologist and MR physicist performed the Turing test with 76% accuracy, demonstrating our approach’s performance superiority over the prior art. We can synthesize MR images of missing pulse sequences at an inference cost of 350.71 GFlops/voxel through this approach.
Bank Customer Response Based on Data Mining

: The development of the era of big data has made China's banking industry face fierce external competition and huge internal limitations. Moreover, the current research on the banking industry mostly stays in the theoretical analysis, and rarely continues to quantitatively analyze the bank customer response problem. First based on the viewpoint of precision marketing, this paper reviews the research status of using data mining technology in precision marketing. Second, we use the telemarketing data of a bank in Europe in 2014, then the collected bank data is cleaned and converted, and the appropriate bank customer response model: logistic regression model is selected. Then the model is evaluated and predicted. At last, the modeling results show that the precise marketing based on data mining technology plays an important role in customer response of banking industry, and discusses the important role of precise marketing in improving marketing efficiency and reducing bank marketing costs.