Alzheimer’s Disease and Parkinson Dementia Distinguished by Cognitive Marker

Background Temporary memory binding (TMB) has been shown to be specifically affected by Alzheimer’s disease (AD) when it is assessed via free recall and titrating the task demands to equate baseline performance across patients. Methods Patients with Parkinson’s disease (PD) were subdivided into patients with and without cognitive impairment and compared with AD and amnestic mild cognitive impairment (aMCI) patients on their performance on the TMB. Results The results show that only patients with AD dementia present with impaired TMB performance. Receiver operating characteristic curve analyses showed that TMB holds high sensitivity and specificity for aMCI and AD relative to PD groups and healthy controls. Conclusion The TMB is sensitive to the neurodegenerative mechanisms leading to AD dementia but not to those underpinning PD dementia. As such, TMB task can aid the differential diagnosis of these common forms of dementia.

COMPARATIVE ANALYSIS OF SYSTEM LOGS AND STREAMING DATA ANOMALY DETECTION ALGORITHMS

This paper provides with the description, comparative analysis of multiple commonly used approaches of the analysis of system logs, and streaming data massively generated by company IT infrastructure with an unattended anomaly detection feature. An importance of the anomaly detection is dictated by the growing costs of system downtime due to the events that would have been predicted based on the log entries with the abnormal data reported. Anomaly detection systems are built using standard workflow of the data collection, parsing, information extraction and detection steps. Most of the document is related to the anomaly detection step and algorithms like regression, decision tree, SVM, clustering, principal components analysis, invariants mining and hierarchical temporal memory model. Model-based anomaly algorithms and hierarchical temporary memory algorithms were used to process HDFS, BGL and NAB datasets with ~16m log messages and 365k data points of the streaming data. The data was manually labeled to enable the training of the models and accuracy calculation. According to the results, supervised anomaly detection systems achieve high precision but require significant training effort, while HTM-based algorithm shows the highest detection precision with zero training. Detection of the abnormal system behavior plays an important role in large-scale incident management systems. Timely detection allows IT administrators to quickly identify issues and resolve them immediately. This approach reduces the system downtime dramatically.Most of the IT systems generate logs with the detailed information of the operations. Therefore, the logs become an ideal data source of the anomaly detection solutions. The volume of the logs makes it impossible to analyze them manually and requires automated approaches.

The reordered deblocking filter and SAO architecture for HEVC system

High Efficiency Video Coding (HEVC) used in broad band and wireless applications employs two in-loop filters to remove the blurring artifacts, blocking artifacts and ringing artifacts. The standard h.264/AVC deblocking filter which requires more memory initially filters the horizontal borders, followed by the vertical borders. The results of vertical borders filtering are utilized in the horizontal filtering process and the obtained results are further stored in temporary memory. The proposed system comprises of a reordering filter to reduce the order of the filter and a SAO to modify the decoded samples to a new offset value inorder to perform robust encryption mechanism. Hence reorder filter reduces the memory needed for this filtering process. In HEVC, SAO is an in-loop filter and located next to deblocking filter. The idea of SAO is to compensate renovated samples by adding an offset to each pixel, so that the distortion between renovated picture and original one can be reduced. Implementation of proposed simulation work is done by Verilog HDL and implemented using Virtex 6 FPGA to compute the power and hardware requirements in terms of LUT and slice registers.

Deep learning asan advanced concept of artificial intelligence

This paper represented on the Deep learning technique growing in the learning community of machines, as traditional learning architecture has proven incompetent for the machine learning challenging tasks and strong feature of artificial intelligence (AI). Increasing and widespread availability of computing power, along the use of efficient training and improvement algorithms, has made it possible to implement, until then, the concept of deep learning. These development events deep learning architecture and algorithms look at cognitive neuroscience and point to biologically inspired solutions for learning. This paper represented on the rule of Convolutional Neural Networks (CNNs), Neural Networks (SNNs) and Hierarchical Temporary Memory (HTM), and other related techniques to the least mature technique.

Sodium pump regulation of locomotor control circuits

Sodium pumps are ubiquitously expressed membrane proteins that extrude three Na+ ions in exchange for two K+ ions, using ATP as an energy source. Recent studies have illuminated additional, dynamic roles for sodium pumps in regulating the excitability of neuronal networks in an activity-dependent fashion. We review their role in a novel form of short-term memory within rhythmic locomotor networks. The data we review derives mainly from recent studies on Xenopus tadpoles and neonatal mice. The role and underlying mechanisms of pump action broadly match previously published data from an invertebrate, the Drosophila larva. We therefore propose a highly conserved mechanism by which sodium pump activity increases following a bout of locomotion. This results in an ultraslow afterhyperpolarization (usAHP) of the membrane potential that lasts around 1 min, but which only occurs in around half the network neurons. This usAHP in turn alters network excitability so that network output is reduced in a locomotor interval-dependent manner. The pumps therefore confer on spinal locomotor networks a temporary memory trace of recent network performance.

Ion-Molecular Memory Model. Retrieving Information and the Temporary Memory

The article is devoted to the issues of retrieving information and the temporary memory in frame of formation of the ion-molecular memory model. It is shown that the retrieving information, i.e., in everyday life language -remembering, in essence functioning close to the process of memorizing discussed in the previous article in the series. Memorizing and remembering are the essence of "antagonists" (plus or minus) with an almost identical mechanism of action. Here the main task memory to recall what exactly you need to remember. This isn´t a tautology, but the essence of the process. With this purpose, some image is formed as a mask search implicating the information about the image search, and the vector information retrieval. Obviously, the retrieval of information from memory and memorizing are carried out by means of images (fractals, wavelets, soliton-holograms etc). Scheme of retrieving information from the library memory is proposed and substantiated. Factor of the temporary memory is defined. The last point, the authors emphasize in the article because of its importance in the process of retrieving information from memory. The memory itself has many self-manifestations, but the most significant of them is the essence of short-term memory and long-term memory. There´s an emphasis: temporary, i.e., the primary question is about the characteristic time of storing information in memory. It is important to consider: long-term memory cannot exist without short-term memory, which operation is required the initial stage of memory formation in general, and short-term memory is considered as not completed memory in the sense that fixed in it facts-images weren´t recorded in the library memory and weren´t contained in it.