Miracle-dz/UniFAR-FTUs huggingface dataset:
Skin cancer classification computer system development with deep learning

Melanoma is a deadly form of skin cancer that is often undiagnosed or misdiagnosed as a benign skin lesion. Its early detection is extremely important, since the life of patients with melanoma depends on accurate and early diagnosis of the disease. However, doctors often rely on personal experience and assess each patient’s injuries based on a personal examination. Clinical studies allow us to get the accuracy of the diagnosis of melatoma from 65 to 80 percents, which was a good result for some time. However, modern research claims that the use of dermoscopic images in diagnosis significantly increases the accuracy of diagnosis of skin lesions. The visual differences between melanoma and benign skin lesions can be very small, making diagnosis difficult even for an expert doctor. Recent advances in the use of artificial intelligence methods in the analysis of medical images have made it possible to consider the development of intelligent medical diagnostic systems based on visualization as a very promising direction that will help the doctor in making more effective decisions about the health of patients and making a diagnosis at an early stage and in adverse conditions. In this paper, we propose an approach to solving the problem of classification of skin diseases, namely, melanoma at an early stage, based on deep learning. In particular, a solution to the problem of classification of a dermoscopic image containing either malignant or benign skin lesions is proposed. For this purpose, the deep neural network architecture was developed and applied to image processing. Computer experiments on the ISIC data set have shown that the proposed approach provides 92% accuracy on the test sample, which is significantly higher than other algorithms in this data set have shown.
An Adaptive Selection of Filter Parameters: Defect Detection in Steel Image Using Wavelet Reconstruction Method

Vision-based defect-detection systems are currently being applied to many manufacturing processes and have contributed to factory automation and production of highquality goods through the development of image-analysis techniques. There are many image-analysis techniques used in surface defect detection such as statistical-, filter-, and model-based approaches.1) Among them, filter-based approach is highly advantageous for spatial-frequency analysis, which is more useful on surfaces with noisy and variations in intensity.2) Further, some of these approaches analyze the statistics and structure of a defect after filtering to improve the reliability of the defect-detection process. The general process of a filter-based approach is shown in Fig. 1. First, images are preprocessed, e.g., through segmentation to find regions of interest (ROIs), or undergo a smoothing process to eliminate noises, depending on the particular application. After preprocessing, the images are An Adaptive Selection of Filter Parameters: Defect Detection in Steel Image Using Wavelet Reconstruction Method
Prospective Evaluation of Postoperative Complications of Radical Cystectomy according to Clavien Dindo Classification: Laparoscopy Versus Conventional Surgery

Laparoscopy has now become a common surgical approach that is increasingly used in public and private hospitals. Indeed, it tends to achieve a real alternative to conventional surgery called «open». The main advantage of this approach compared to conventional surgery is the reproduction of what is done in conventional minimally invasive surgery. This means a significant reduction in parietal and visceral trauma, and therefore a reduction of postoperative pain. This saves time in hospitalization and convalescence, not to mention the aesthetic benefit of the scar.
Automatic Policy Decomposition through Abstract State Space Dynamic Specialization

Significant progress has been made recently in deep reinforcement learning in the development of options. This idea consists in learning policies (or macro of actions) for sub-goals. An important bottleneck of this approach is that these options are often available as actions everywhere in the state space, hence, potentially enlarging the action space to search for the optimal policy. In this paper, we propose to use the fact that the state space is rarely fully connected, but instead has regions of highly connected states with fewer links between those regions. Our proposed model extends deep Q-Learning network (DQN) by splitting the top layers into multiple heads each specializing in learning the dynamics of a particular region of the state space as well as the optimal policy for that region. The state prediction quality of each head is used to determine which head is the local expert, rating its contribution to the current state’s policy. We show that this approach is able to learn something similar to options and generalized value function, providing a promising alternative to the current approach.
Defining the mechanism of small molecule inhibition of amyloid fibril formation using ion mobility spectrometry-mass spectrometry

The study of protein/peptide folding, misfolding, structure, and interactions are vital to understanding complex biological problems. The work presented in this thesis describes the development and application of mass spectrometry -based techniques to investigate protein structure, aggregation and interference with aggregation, providing insights into the self-assembly and inhibition of several disease-related peptides. Mass spectrometry has evolved significantly over the past decade, its applications varying from small molecules to macromolecules. Travelling wave ion mobility spectrometry (TWIMS), when combined with mass spectrometry (MS), has the unique and unrivalled capability of separating molecular ions based on their collision cross-sectional area in addition to their mass-to-charge ratio, thus facilitating structural studies of co-populated protein conformations and structural isomers of protein complexes that cannot be separated by molecular weight alone. One biological system that has benefitted enormously from such advances is the study of in vitro amyloid formation. The ability of amyloidogenic protein/peptides to assemble into insoluble fibrils is the basis of a vast array of human disorders. Human islet amyloid polypeptide (hIAPP) is one such peptide able to readily assemble into amyloid fibrils in vitro at neutral pH, despite being intrinsically disordered. Identifying oligomeric states occupied between monomer and final fibrils creates an enormous challenge, given that few techniques are able to separate and characterise such lowly-populated and transient species. In addition to characterisation of fibril precursors, recent research has focussed on the identification of small molecule inhibitors of the amyloid cascade and understanding their mechanism of action is of great interest to this field. In the work presented here, the power of TWIMS-MS has been harnessed to achieve the separation and characterisation of oligomeric precursors of the type-2 diabetes-related peptide hIAPP along with IAPP mutants and peptides corresponding to its core sequence. In addition, the effects of small molecule inhibitors on oligomer population and fibril formation have been studied and described in detail. Further, an experimentally simple, in vitro MS-based screen has been developed and implemented that provides rapid and accurate analysis of protein aggregation and its inhibition. All of the results highlight the powers of MS to provide important insights into the mechanism of amyloid formation and demonstrate the potential of this approach for screening for novel inhibitors of disease-related amyloid assembly.
Effect of Inorganic Copper/Montmorillonite Nanomaterial on Growth Performance,Intestinal Microflora and Bacterial Enzyme Activities in Broilers

1-d-old Arbor Acres broiler chickens were used to investigate the effect of inorganic copper/montmorillonite nanoparticles (ICMN) on growth performance, intestinal microflora, and bacterial enzyme activities of broilers. The results showed that the addition of 1~2 g/kg ICMN to the diet increased significantly the body weight and feed efficiency. Further investigation indicated that dietary addition of ICMN decreased the numbers of Escherichia and Clostridium perfringens significantly in the small intestine and cecum, but a similar difference in the counts of total aerobes, total anaerobes, Lactobacilli and Bifidobacteria was not observed. The treatment with ICMN of feed depressed the activities of β-glucosidase and β-glucuronidase in the small intestine and cecum.
Power Line Communications Networking Method Based on Hybrid Ant Colony and Genetic Algorithm

When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.
DAMAGE DETECTION OF FRAME STRUCTURES SUBJECTED TO EARTHQUAKE LOADING

This paper deals with an application of wavelet analysis for damage detection of frame structure subjected to earthquake excitation. Non linear dynamic analysis has been performed and response data at each story are obtained which are used as simulation data. Damage in the frame is introducing by the non linear behaviour of columns and beams. In order the structural members go beyond the elastic limit and start yielding the earthquake excitation was scaled up with appropriate scale factor. Discrete wavelet analysis was performing in order to analyze the simulation response data for each floor because the dynamic behaviour of inelastic structures subjected to an earthquake is a non-stationary process. It was shown that structural damage, due to non linear behaviour of structural elements and the time when this damage occurred, can be clearly detected by spikes in the wavelet details. The effect of noise is taken into account by adding a white Gaussian noise to the simulation response data and discrete wavelet analysis was performing again. It was observed that if the level of details and the order of wavelets are increased, spikes can be detected again and damage would be recognized. Results show the great promise of the wavelet approach for damage detection and structural health monitoring.
Fast time integration of parabolic equations with variable coefficients

This paper proposes a fast time integration method for parabolic equations with a variable coefficient. The key idea of the proposed method is to approximate the differential operator using a constant coefficient operator, which provides an efficient mechanism to expedite the calculation of a solution of an algebraic equation in an implicit method. The method does not require to specify the constant as the constant is implicitly incorporated in the method. Without relying on an iterative solver, the computational complexity of the proposed method at each time step remains at O(N logN) for an N -dimensional solution vector. We analyze the accuracy and the stability of the new method and discuss its connection with other methods, including multiscale time integrators. The efficiency and the stability of the method are validated through a suite of numerical tests in 1D and 2D with multiscale random coefficients. The method is also applied to solve an elliptic problem and a quasilinear diffusion model in the semiconductor.
The Weakening of the Hegemon and the Future of the Liberal International Order

The hereby paper presents a theoretical approach to the U.S.–China geopolitical rivalry as the process of dispersion of power from the hegemon to the challenger [Allison] that may toward, through the sphere of influence fragmentation processes, to the polycentricity of the international relations system. In this work the author presents a new theoretical approach to the U.S.–China political rivalry understood as a key element of a process of changing the model of the global hegemonic leadership, shaped most fully since 1991. The paper presents the concept of two theoretical levels – the rational strategy and the political vibrancy – which are a necessary context for identifying the nature of given decision-making processes of the main subjects of contemporary international relations. Thus, through the abovementioned concept the sino-american relations are explained within the methods that are being used by states with particular emphasis on analyzing the operations of the People's Republic of China. Furthermore, the author reasons why China is withdrawing from the use of soft power – understood in the terms of J. S. Nye’s – and why it primarily uses the linking power and sharp power [Walker, Ludwig]. The article ends with a summary in which author, based on the information presented, tries to answer the question – why the liberal international order remains uncertain (or is about to fail).
1 1 1 Naïve Realism and the Science of Sensory Consciousness

We all start from “naive realism,” i.e., the doctrine that things are what they seem. We think that grass is green, that stones are hard, and that snow is cold. But science assures us that the greenness of grass, the hardness of stones, and the coldness of snow, are not the greenness, hardness, and coldness that we know in our own experience, but something very different. The observer, when he seems to himself to be observing a stone, is really, if physics is to be believed, observing the effects of the stone upon himself. Naive realism leads to science, and science, if true, shows that naive realism is false. Therefore, naive realism, if true, is false; therefore it is false. (1950, 15)
On drift parameter estimation for mean-reversion type nonlinear nonhomogeneous stochastic differential equations

This paper is concerned with the drift parameter estimation for meanreversion type nonlinear nonhomogeneous stochastic differential equations. Firstly, the Girsanov transformation is used to simplify the equation because of the expression of the drift coefficient. Secondly, we find a closed interval on which the likelihood function is continuous and does not attain the maximum at two endpoints of this interval. Then, we prove that the maximum likelihood estimator exists in the interval when the sample size is large enough. Finally, the strong consistency of the estimator and the asymptotic normality of the error of estimation are proved.
Numerical Simulation on Non-Darcy Flow in a Single Rock Fracture Domain Inverted by Digital Images

The influence of rock seepage must be considered in geotechnical engineering, and understanding the fluid flow in rock fractures is of great concern in the seepage effect investigation. This study is aimed at developing a model for inversion of rock fracture domains based on digital images and further study of non-Darcy flow. The visualization model of single rock fracture domain is realized by digital images, which is further used in flow numerical simulation. We further discuss the influence of fracture domain geometry on non-Darcy flow. The results show that it is feasible to study non-Darcy flow in rock fracture domains by inversion based on digital images. In addition, as the joint roughness coefficient (JRC) increases or the fracture aperture decreases, distortion of the fluid flow path increases, and the pressure gradient loss caused by the inertial force increases. Both coefficients of the Forchheimer equation decrease with increasing fracture aperture and increase with increasing JRC. Meanwhile, the critical Reynolds number tends to decrease when JRC increases or the fracture aperture decreases, indicating that the fluid tends to non-Darcy flow. This work provides a reference for the study of non-Darcy flow through rock fractures.
Myocardial Infarction Detection and Quantification Based on a Convolution Neural Network with Online Error Correction Capabilities

Myocardial infarction (MI), more commonly known as heart attack, occurs when the blood flow to the heart decreases or stops. Over 100,000 people each year in the UK suffer from an MI according to the report by British Heart Foundation. Following an MI, there is irreversible heart muscle damage that will become scar. The amount of scar following larger heart attacks, ST segment elevation myocardial infarction, drives enlargement of the heart and is associated with worse prognosis (increased risk of death and subsequent heart failure). Cardiac Magnetic Resonance Imaging (MRI) late gadolinium enhancement (LGE) has become the "gold standard" for the visualization of MI. However, to date, no "gold standard" fully automated methods exist for the quantification of MI from MRI.In this work, we propose an approach to construct such methods using Artificial Intelligence (AI) and Machine Learning (ML) technologies, in particular, Convolutional Neural Networks (CNN). Uncertainties, variability, and a possibility of bias inherent to any data imply that data-driven systems which are intended for use in clinical research and practice must be capable of learning from mistakes on-the-job. Here we develop and test a first deep learning CNN system with error correction capabilities (CNNEC) for the detection and quantification of MI. The system could be viewed as a proof-of-principle for the technology.
Interactive comment on “Vegetation, ground cover, soil, rainfall simulation, and overland flow experiments before and after tree removal in woodland-encroached sagebrush steppe: the hydrology component of the Sagebrush Steppe

1. Williams et al. provide an important and highly valuable contribution to plot-scale experiments on runoff and soil erosion in the semi-arid Great Basin, USA. Up to my best knowledge, this is the most extensive data set currently available. These data are important to parameterize commonly applied runoff and soil erosion models, such as RHEM. While I am convinced that this data set is highly relevant for a wide array of scientific disciplines for model applications and hypothesis testing, there are several suggestions [that] I’d like to point out.
A Prediction Model of Forest Preliminary Precision Fertilization Based on Improved GRA-PSO-BP Neural Network

The optimal amount of fertilizer application which was needed by the trees and the factors that influence the fertilization have an intricated nonlinear relationship. According to the problems that the traditional fertilization prediction model has, such as lacking of the scalability and practicality, this paper initiates an accurate fertilization prediction model that was based on the GRA-PSO-BP neural network which can make the accurate fertilization come true and improve the economic benefits of forest industry. This paper uses the GRA method to determine the input of the neural network as the site index and make the forest age, nutrient content of the advantage trees, biomass of the advantage trees, biomass of average trees, and target yield as the output numbers of the Actual amount of fertilizer applied. During the calculation process, the global particle swarm optimization algorithm is used to optimize the initial numbers and threshold numbers of BP neural network which build a phased GRA-PSO-BP accurate fertilization model. Compared with the prediction algorithm of full input variate that is based on the single BP neural network and the prediction algorithm of full input variate that is based on PSO-BP Neural Network, the GRA method can determine the key factors that influence the amount of fertilizer applied in different forest areas and modify the prediction model to improve the scalability and accuracy of the prediction and finally achieve the precision fertilization as the data of different forests updated, so we can see that the prediction result of this paper is more accurate. The result demonstrates that the GRA-PSO-BP neural network Segment fertilization model is more accurate than the traditional BP neural network and BP Neural Network that was optimized by the PSO algorithm, and specifically, the error of the predicted amount of fertilizer application and the actual amount of fertilizer application is less than 5%, which can effectively guide the fertilization in stages.
A New Classification Based Model for Malicious PE Files Detection

— Malware presents a major threat to the security of computer systems, smart devices, and applications. It can also endanger sensitive data by modifying or destroying them. Thus, electronic exchanges through different communicating entities can be compromised. However, currently used signature-based methods cannot provide accurate detection of zero-day attacks, polymorphic and metamorphic programs which have the ability to change their code during propagation. In order to solve this issue, static and dynamic malware analysis is being used along with machine learning algorithms for malware detection and classification. Machine learning methods play an important role in automated malware detection. Several approaches have been applied to classify and to detect malware. The most challenging task is selecting a relevant set of features from a large dataset so that the classification model can be built in less time with higher accuracy. The purpose of this work is firstly to make a general review on the existing classification and detection meth-ods, and secondly to develop an automated system to detect malicious Portable Executable files based on their headers with low performance and more efficiency. Experimental results will be presented for the best classifier selected in this study, namely Random Forest; accuracy and time performance will be discussed
Towards Real-time Video Content Detection in Resource Constrained Devices

Convolutional neural networks have been successfully applied for video content detection in the last years. However, such cognitive models usually demand the availability of several gigabytes of memory and present a low detection throughput, as a result, they are not feasible for resource-constrained devices, especially for real-time applications like video streaming. In this paper, we address real-time video content detection in resource-constrained devices in a threefold manner. First, we improve detection throughput by means of a frame sampling technique. Then, we propose a new evaluation measure towards proper deployment of convolutional neural networks in resource-constrained devices. Finally, we address the accuracy degradation caused by the porting of the convolutional neural network, applying a lightweight classification verification technique. The evaluation results, through a real-time demanding application, show that the proposed approach can detect up to 301 frames/sec, demanding only 9 megabytes of memory while reaching up to 89.3% of accuracy. Besides, we can increase the detection throughput by up to 10 times, with no effects on accuracy, and further increase accuracy without effects on processing demands.