scholarly journals Pulsating STM – The in-memory Optimistic Concurrency Control Technique for Multi Core Systems

2019 ◽  
Vol 9 (1) ◽  
pp. 1966-1972
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Embedded System ◽  
Concurrency Control ◽  
Control Technique ◽  
Software Transactional Memory ◽  
Optimistic Concurrency Control ◽  
The World ◽  
Data Source ◽  
Better Than

In the world of ever increasing parallelism, the problem of deadlock-free concurrency control is inevitable. As the number of processing cores is increasing, the number of processing threads is also increasing, and with this increase in the number of processing threads, there is a good chance of problems arising due to lack of proper concurrency control. The application areas under the domain of advanced graphics, cryptography, deep learning, embedded system programming, artificial intelligence and networking are prone to the problems of heavy uncontrolled concurrency of threads. This paper presents a novel Software Transactional Memory (STM) based optimistic concurrency control technique that is deadlock free for threads accessing the in-memory data structure for the purpose of reading as well as writing. The technique is lock free and is based upon timestamping. Threads involved in the proposed approach possess the transactional properties of atomicity, concurrency and isolation. Durability is not expected as the threads are working on an in-memory data source. The approach involves lazy conflict detection that ensures minimum aborts and restarts as well as maximum concurrency among transactions. Being lock free, the algorithm is better than the existing lock-based techniques. The technique is tested on Sniper-6.1 multi core simulator simulating 64 CPU cores and running 16, 32, 40 and 50 threads in our case. The results show significant improvement in throughput with the increasing number of threads over the existing lock-based techniques as well as other STM techniques based on optimistic concurrency control.

scholarly journals Multi-Version-Pulsating STM: A Multi-Version Optimistic Concurrency Control Scheme for Highly Parallel in-Memory Workload in a Multi Core Environment

2019 ◽  
Vol 9 (1) ◽  
pp. 1973-1979
Keyword(s):  
Data Structures ◽  
Energy Efficient ◽  
Concurrency Control ◽  
Control Algorithm ◽  
Software Transactional Memory ◽  
Optimistic Concurrency Control ◽  
Control Scheme ◽  
Memory Dynamic ◽  
Time And Energy ◽  
Better Than

Large in-memory data structures have a significant application in the fields of graphics, gaming, military and all the possible areas where Big Data can be employed. Their fame in the area of science and technology is attributable to fast in-memory access by the processor as compared to on-disk data structures. These enormous data structures can be accessed still fast and efficiently through parallel computing. For employing highly parallel computations, equally parallel algorithms are required. One of the most desirable attributes of such algorithms is their ability to control concurrency and avoid any deadlocks while being time and energy efficient. This paper presents a multi-version optimistic concurrency control algorithm based on timestamping. This algorithm is lock free and is tested on 64 simulated CPU cores on a multi core simulator. The algorithm is a Software Transactional Memory approach employing 16, 32, 40 and 50 threads in different tests running on the simulator. Half of the threads are doing reading and half are doing writing operation in each case while accessing an in-memory dynamic array. Being lock free and employing lazy timestamp calculations, this approach is better than other existing concurrency control approaches.

scholarly journals Design of a Microwave Lowpass – Bandpass Filter using Deep Learning and Artificial Intelligence

2021 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Saeed Roshani ◽  
◽  
Hossein Heshmati ◽  
Sobhan Roshani ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Insertion Loss ◽  
Dual Band ◽  
Transmission Zeros ◽  
Compact Size ◽  
Electrical Devices ◽  
Low Pass ◽  
Artificial Neural Network Ann ◽  
Better Than

In this paper, a lowpass – bandpass dual band microwave filter is designed by using deep learning and artificial intelligence. The designed filter has compact size and desirable pass bands. In the proposed filter, the resonators with Z-shaped and T-shaped lines are used to design the low pass channel, while coupling lines, stepped impedance resonators and open ended stubs are utilized to design the bandpass channel. Artificial neural network (ANN) and deep learning (DL) technique has been utilized to extract the proposed filter transfer function, so the values of the transmission zeros can be located in the desired frequency. This technique can also be used for the other electrical devices. The lowpass channel cut off frequency is 1 GHz, with better than 0.2 dB insertion loss. Also, the bandpass channel main frequency is designed at 2.4 GHz with 0.5 dB insertion loss in the passband.

A Review of Recent Deep Learning Models in COVID-19 Diagnosis

2021 ◽  
Vol 6 (5) ◽  
pp. 10-15
Author(s):  
Ela Bhattacharya ◽  
D. Bhattacharya
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Test Results ◽  
Learning Models ◽  
Future Directions ◽  
Human Contact ◽  
The World ◽  
Short Span

COVID-19 has emerged as the latest worrisome pandemic, which is reported to have its outbreak in Wuhan, China. The infection spreads by means of human contact, as a result, it has caused massive infections across 200 countries around the world. Artificial intelligence has likewise contributed to managing the COVID-19 pandemic in various aspects within a short span of time. Deep Neural Networks that are explored in this paper have contributed to the detection of COVID-19 from imaging sources. The datasets, pre-processing, segmentation, feature extraction, classification and test results which can be useful for discovering future directions in the domain of automatic diagnosis of the disease, utilizing artificial intelligence-based frameworks, have been investigated in this paper.

scholarly journals Use of artificial intelligence in imaging in rheumatology – current status and future perspectives

RMD Open ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. e001063 ◽  
Author(s):  
Berend Stoel
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Basic Research ◽  
Current Status ◽  
Future Perspectives ◽  
Clinical Imaging ◽  
The World ◽  
Human Interpretation ◽  
The Impact ◽  
Insight Into

After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock wave that probably exceeds the impact of the first AI victory of defeating the world chess champion in 1997, some reflection may be appropriate on the consequences for clinical imaging in rheumatology. In this narrative review, a short explanation is given about the various AI techniques, including ‘deep learning’, and how these have been applied to rheumatological imaging, focussing on rheumatoid arthritis and systemic sclerosis as examples. By discussing the principle limitations of AI and deep learning, this review aims to give insight into possible future perspectives of AI applications in rheumatology.

scholarly journals Plant Disease Detection and Localization using GRADCAM

2020 ◽  
Vol 8 (6) ◽  
pp. 3069-3075
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Plant Diseases ◽  
Financial Resources ◽  
Disease Detection ◽  
The World ◽  
Detection And Localization ◽  
Better Than ◽  
Made In

Plant diseases are diseases that change or disrupt its important functions. The reduction in the age at which a plant dies is the main danger of plant diseases. And farmers around the world have to face the challenge of identifying and classifying these diseases and changing their treatments for each disease. This task becomes more difficult when they have to rely on naked eyes to identify diseases due to the lack of proper financial resources. But with the widespread use of smartphones by farmers and advances made in the field of deep learning, researchers around the world are trying to find a solution to this problem. Similarly, the purpose of this paper is to classify these diseases using deep learning and localize them on their respective leaves. We have considered two main models for classification called resnet and efficientnet and for localizing these diseases we have used GRADCAM and CAM. GRADCAM was able to localize diseases better than CAM

scholarly journals A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules

2021 ◽  
Author(s):  
Oscar Méndez-Lucio ◽  
Mazen Ahmad ◽  
Ehecatl Antonio del Rio-Chanona ◽  
Jörg Kurt Wegner
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Molecular Target ◽  
Bioactive Molecules ◽  
Target Pair ◽  
Learning Approach ◽  
Scoring Functions ◽  
Structure Based Drug Design ◽  
Protein Targets ◽  
Better Than

Understanding the interactions formed between a ligand and its molecular target is key to guide the optimization of molecules. Different experimental and computational methods have been key to understand better these intermolecular interactions. Herein, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. Concretely, the model learns a statistical potential based on distance likelihood which is tailor-made for each ligand-target pair. This potential can be coupled with global optimization algorithms to reproduce experimental binding conformations of ligands. We show that the potential based on distance likelihood described in this paper performs similar or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design.

scholarly journals Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes? (Preprint)

2020 ◽  
Author(s):  
Joon Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Patient Outcome ◽  
Deep Learning ◽  
Patient Outcomes ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Challenging Problem ◽  
Imaging Diagnostics ◽  
Better Than

UNSTRUCTURED In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly, the performance of machine learning–based patient outcome prediction models has rarely been compared with that of human clinicians in the literature. Human intuition and insight may be sources of underused predictive information that AI will not be able to identify in electronic data. Both human and AI predictions should be investigated together with the aim of achieving a human-AI symbiosis that synergistically and complementarily combines AI with the predictive abilities of clinicians.

scholarly journals Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gang Yu ◽  
Kai Sun ◽  
Chao Xu ◽  
Xing-Hua Shi ◽  
Chong Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Deep Learning ◽  
Supervised Learning ◽  
Area Under The Curve ◽  
Patient Level ◽  
Significant Difference ◽  
The Mean ◽  
Whole Slide Images ◽  
Better Than

AbstractMachine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole slide images of colorectal cancer from 8803 subjects from 13 independent centers. SSL (~3150 labeled, ~40,950 unlabeled; ~6300 labeled, ~37,800 unlabeled patches) performs significantly better than the SL. No significant difference is found between SSL (~6300 labeled, ~37,800 unlabeled) and SL (~44,100 labeled) at patch-level diagnoses (area under the curve (AUC): 0.980 ± 0.014 vs. 0.987 ± 0.008, P value = 0.134) and patient-level diagnoses (AUC: 0.974 ± 0.013 vs. 0.980 ± 0.010, P value = 0.117), which is close to human pathologists (average AUC: 0.969). The evaluation on 15,000 lung and 294,912 lymph node images also confirm SSL can achieve similar performance as that of SL with massive annotations. SSL dramatically reduces the annotations, which has great potential to effectively build expert-level pathological artificial intelligence platforms in practice.

WHEN GOOGLE KNOWS US BETTER THAN WE KNOW OURSELVES, THE WORLD WILL CHANGE

2021 ◽  
Vol 1 (1) ◽  
pp. 66-76
Author(s):  
E. D. Popova ◽  
Keyword(s):  
Artificial Intelligence ◽  
Public Relations ◽  
Legal Regulation ◽  
The World ◽  
The Moment ◽  
Artificial Intelligence Systems ◽  
Better Than

In this paper, we will consider the challenges facing the regulation of public relations related to the system of using artificial intelligence. At the moment, there is no legal regulation in this area, or it is fragmentary and unsystematic, so in my work I will mostly assess the consequences of using artificial intelligence systems from the standpoint of ethical, moral and other informal norms. Is there a chance for humanity to avoid the scenario of an Orwellian dystopia?

scholarly journals Cigarette-Smokers’ Problem with STM

2017 ◽  
pp. 130-135
Author(s):  
Rup Kamal ◽  
Ryan Saptarshi Ray ◽  
Utpal Kumar Ray ◽  
Parama Bhaumik
Keyword(s):  
Parallel Processing ◽  
Concurrency Control ◽  
Transactional Memory ◽  
Performance Comparison ◽  
Critical Section ◽  
Parallel Programs ◽  
Software Transactional Memory ◽  
Complex Interactions ◽  
Processor Speed ◽  
Better Than

The past few years have marked the start of a historic transition from sequential to parallel computation. The necessity to write parallel programs is increasing as systems are getting more complex while processor speed increases are slowing down. Current parallel programming uses low-level programming constructs like threads and explicit synchronization using locks to coordinate thread execution. Parallel programs written with these constructs are difficult to design, program and debug. Also locks have many drawbacks which make them a suboptimal solution. One such drawback is that locks should be only used to enclose the critical section of the parallel-processing code. If locks are used to enclose the entire code then the performance of the code drastically decreases. Software Transactional Memory (STM) is a promising new approach to programming shared-memory parallel processors. It is a concurrency control mechanism that is widely considered to be easier to use by programmers than locking. It allows portions of a program to execute in isolation, without regard to other, concurrently executing tasks. A programmer can reason about the correctness of code within a transaction and need not worry about complex interactions with other, concurrently executing parts of the program. If STM is used to enclose the entire code then the performance of the code is the same as that of the code in which STM is used to enclose the critical section only and is far better than code in which locks have been used to enclose the entire code. So STM is easier to use than locks as critical section does not need to be identified in case of STM. This paper shows the concept of writing code using Software Transactional Memory (STM) and the performance comparison of codes using locks with those using STM. It also shows why the use of STM in parallel-processing code is better than the use of locks.

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