다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

A morphable model for the synthesis of 3D faces

The hypo osmotic swelling test is a simple laboratory test which is used for the assessment of functional integrity of sperm membrane. When the sperm are put in hypo osmotic solution, water moves within cell membrane and swelling of membrane will take place. If the membrane is already damaged then there will be no swelling of membrane. The percentage of swollen spermatozoa is the measure of intact membrane. The semen of bull is evaluated on the basis of conventional parameters like motility, morphology and livability. To evaluate the semen on the basis of functional integrity, Fresh semen collected from 3 bulls was subjected to the hypo osmotic swelling (HOS) test in order to determine if the results could be correlated with conventional semen evaluation parameters. A total of 20 pooled samples from each bull were collected. Each semen sample was separated into two parts. One fraction was used for the evaluation of semen by usual method, while the other part was subjected to hypo-osmotic swelling test by using 150 mOsm/L sodium citrate and fructose solutions. The mean sperm positive to HOS test was 47% in Sahiwal cow bull semen, 40% was in Frisian semen and 27% in crossbred semen. On the statistical analysis of the data there was a significant (P

https://www.entomoljournal.com/archives/2013/vol1issue6/PartC/37.pdf

Intellectual Property Rights on the Impact of Biodiversity

Brain signals can be captured via electroencephalogram (EEG) and be used in various brain–computer interface (BCI) applications. Classifying motor imagery (MI) using EEG signals is one of the important applications that can help a stroke patient to rehabilitate or perform certain tasks. Dealing with EEG-MI signals is challenging because the signals are weak, may contain artefacts, are dependent on the patient’s mood and posture, and have low signal-to-noise ratio. This paper proposes a multi-branch convolutional neural network model called the Multi-Branch EEGNet with Convolutional Block Attention Module (MBEEGCBAM) using attention mechanism and fusion techniques to classify EEG-MI signals. The attention mechanism is applied both channel-wise and spatial-wise. The proposed model is a lightweight model that has fewer parameters and higher accuracy compared to other state-of-the-art models. The accuracy of the proposed model is 82.85% and 95.45% using the BCI-IV2a motor imagery dataset and the high gamma dataset, respectively. Additionally, when using the fusion approach (FMBEEGCBAM), it achieves 83.68% and 95.74% accuracy, respectively.

/pdf/electroencephalogram-based-motor-imagery-signals-2ngw3ycd.pdf

Electroencephalogram-Based Motor Imagery Signals Classification Using a Multi-Branch Convolutional Neural Network Model with Attention Blocks

Two oil emuisified vaccines of Newcaslte disease were prepared from Lasota strain. The virus was propagated in 9-day-old chicken embryos through allantoic route. The haemagglutination titre of the allantoic fluid used for vaccine preparation was 2048. The fluid was inactivated by 0.1 per cent f nal concentration of formalin at 37 oc for 6 hours. Inactivated allantoic fluid was emulsified with aqueous to oil (mineral oil) ratio of 1:2 (vaccine I) and 1:4 (vaccine II). The hydrophile-lipophile balance (HLB) of the vaccines was fixed at 7.0 using oil phase (Span 80) and aqueous phase (Tween 80) surfactants. Physical characteristics including colour, viscosity, stability and type of emulsion were studied. Sterility and safety were tested. Efficacy of the vaccine was evaluated on the basis of humoral antibody response and post challenge protection in layer chicks. One hundred and twenty, 4-week-old-layer, chicks were divided into 4 groups (A to D) comprising 30 birds each. Group A and B were injected with two experimental vaccines, C with imported oil emulsion vaccine and group D was kept as non-vaccinated control. Statistical analysis showed non-significant differen ce among four weeks cumulative haemagglutination inhibition mean titres of vaccine-I (256), vaccine-II (243) and imported vaccine (217). All the three vaccinated groups showed significantly higher titres than those of non-vaccinated control group. Both the experimental and imported vaccines gave 80 per cent protection against challenge with field virulent virus at 4 weeks post-vaccination.

Preparation and evaluation of newcastle disease oil emulsion vaccines at hydrophile-lipophile balance 7.0 using lasota strain-a preliminary trial

This preliminary report describes the occurrence of acute pulmonary emphysema cum pulmonary edema ensuing in extensive subcutaneous emphysematous swellings in a dairy buffalo ( Bubalus bubalis) apparently associated with a sudden shift from berseem ( Trifolium alexendrinum) to Brassica juncea fodder. Tachypnea, expiratory dyspnea, open-mouth breathing, loud expiratory grunt with abdominal lift, and crackles in ventral aspects of the lungs with normal rectal temperature characterized the condition clinically. A substantial improvement was noted 3 h after treatment with parenteral administration of prednisolone plus dexamethasone, diclofenac sodium, and furosemide. Per os administration of chlortetracycline was undertaken after dyspnea had abated. Extensive subcutaneous swellings developed as sequel on withers, back, axillae, and thorax, was deflated using a hypodermic needle. The buffalo completely recovered in 10 days. As far as could be ascertained, this is the first report dwelling on acute pulmonary emphysema cum pulmonary edema.

https://journals.tubitak.gov.tr/veterinary/issues/vet-10-34-3/vet-34-3-13-0806-1.pdf

Acute pulmonary emphysema cum pulmonary edema apparently associated with feeding of Brassica juncea in a dairy buffalo

With the growing popularity of social networking services and the rapid development of smart devices, an increasing number of people are now uploading media such as images and videos to social networks for sharing with acquaintances, and making that content available for public use. Given the flexibility of uploading and sharing media, a common question arises: can we trust all these images and videos? To address this issue, we propose a mobile cloud-based media uploading framework that checks images for authenticity (i.e., by detecting known forgery techniques). For images that are found to be genuine, the framework will allow public sharing. The authenticity check will be carried out on the private or local cloud to which the image is uploaded. For the check itself, we propose a curvelet transform and Weber local descriptor-based system to extract features from the image. Statistical features obtained from the system are then fed into a support vector machine-based classifier. Offline experiments show that the proposed system can achieve very high detection accuracy for image forgery. Online experiments show that the proposed framework also works in real time. To validate the suitability of this media uploading framework, workloads were measured in the Amazon Elastic Compute Cloud environment.

Ghulam Muhammad

Papers

Intellectual Property Rights on the Impact of Biodiversity

Electroencephalogram-Based Motor Imagery Signals Classification Using a Multi-Branch Convolutional Neural Network Model with Attention Blocks

Preparation and evaluation of newcastle disease oil emulsion vaccines at hydrophile-lipophile balance 7.0 using lasota strain-a preliminary trial

Acute pulmonary emphysema cum pulmonary edema apparently associated with feeding of Brassica juncea in a dairy buffalo

Authenticated media uploading framework for mobile cloud computing