Ranking in information streams

2013 ◽  
Author(s):  
Steven Bourke ◽  
Michael O'Mahony ◽  
Rachael Rafter ◽  
Barry Smyth
Keyword(s):  
Information Streams

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

2017 ◽  
Vol 7 (10) ◽  
pp. 52
Author(s):  
LAKSHMI PRANEETHA
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Bloom Filter ◽  
Scientific Applications ◽  
Pruning Algorithm ◽  
Density Data ◽  
Data Points ◽  
Short Time ◽  
Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

scholarly journals Mouse entorhinal cortex encodes a diverse repertoire of self-motion signals

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Caitlin S. Mallory ◽  
Kiah Hardcastle ◽  
Malcolm G. Campbell ◽  
Alexander Attinger ◽  
Isabel I. C. Low ◽  
...  
Keyword(s):  
Entorhinal Cortex ◽  
Medial Temporal Lobe ◽  
Visual Cues ◽  
Neural Circuits ◽  
Sensory Cues ◽  
Medial Entorhinal Cortex ◽  
Representation Of Space ◽  
Self Motion ◽  
Information Streams ◽  
Representations Of Space

AbstractNeural circuits generate representations of the external world from multiple information streams. The navigation system provides an exceptional lens through which we may gain insights about how such computations are implemented. Neural circuits in the medial temporal lobe construct a map-like representation of space that supports navigation. This computation integrates multiple sensory cues, and, in addition, is thought to require cues related to the individual’s movement through the environment. Here, we identify multiple self-motion signals, related to the position and velocity of the head and eyes, encoded by neurons in a key node of the navigation circuitry of mice, the medial entorhinal cortex (MEC). The representation of these signals is highly integrated with other cues in individual neurons. Such information could be used to compute the allocentric location of landmarks from visual cues and to generate internal representations of space.

How Position, Velocity, and Temporal Information Combine in the Prospective Control of Catching: Data and Model

2005 ◽  
Vol 17 (4) ◽  
pp. 668-686 ◽  
Author(s):  
Joost C. Dessing ◽  
C. (Lieke) E. Peper ◽  
Daniel Bullock ◽  
Peter J. Beek
Keyword(s):  
Neural Control ◽  
Neural Model ◽  
Temporal Information ◽  
Behavioral Experiment ◽  
Position Information ◽  
Prospective Control ◽  
Ball Velocity ◽  
Velocity Information ◽  
Centered Ball ◽  
Information Streams

The cerebral cortex contains circuitry for continuously computing properties of the environment and one's body, as well as relations among those properties. The success of complex perceptuomotor performances requires integrated, simultaneous use of such relational information. Ball catching is a good example as it involves reaching and grasping of visually pursued objects that move relative to the catcher. Although integrated neural control of catching has received sparse attention in the neuroscience literature, behavioral observations have led to the identification of control principles that may be embodied in the involved neural circuits. Here, we report a catching experiment that refines those principles via a novel manipulation. Visual field motion was used to perturb velocity information about balls traveling on various trajectories relative to a seated catcher, with various initial hand positions. The experiment produced evidence for a continuous, prospective catching strategy, in which hand movements are planned based on gaze-centered ball velocity and ball position information. Such a strategy was implemented in a new neural model, which suggests how position, velocity, and temporal information streams combine to shape catching movements. The model accurately reproduces the main and interaction effects found in the behavioral experiment and provides an interpretation of recently observed target motion-related activity in the motor cortex during interceptive reaching by monkeys. It functionally interprets a broad range of neurobiological and behavioral data, and thus contributes to a unified theory of the neural control of reaching to stationary and moving targets.

Multiple side information streams for distributed video coding

2006 ◽  
Vol 42 (25) ◽  
pp. 1447 ◽  
Author(s):  
A.B.B. Adikari ◽  
W.A.C. Fernando ◽  
H. Kodikara Arachchi ◽  
W.A.R.J. Weerakkody
Keyword(s):  
Video Coding ◽  
Side Information ◽  
Distributed Video Coding ◽  
Information Streams

scholarly journals Will neuroimaging produce a clinical tool for psychiatry?

2018 ◽  
Author(s):  
Satrajit S Ghosh ◽  
Justin T. Baker
Keyword(s):  
Treatment Options ◽  
Mental Illnesses ◽  
Current Status ◽  
Computational Techniques ◽  
Policy Changes ◽  
Clinical Tool ◽  
The Past ◽  
Limited Success ◽  
Imaging Research ◽  
Information Streams

The increasing incidence, awareness, and social and economic impact of mental health disorders, the current status quo of treatment options and their limited success, and the extensive investment into brain imaging research raises an important question for the future behavioral medicine: will neuroimaging produce a clinical tool for psychiatry? Significant advances in neuroimaging over the past two decades allow psychiatric clinicians to peer into the living, functioning brain. Neuroimaging has been used to diagnose mental illnesses, to predict treatment outcomes, to find new stratifications of psychiatric disorders, and to provide therapy. The development of computational techniques, alongside several population neuroimaging efforts worldwide, increase the prospect for a first neuroimaging-based clinical tool. In this article, we describe the formidable challenges to creating such a tool and forecast how current institutions can solve them through social, technical, educational, and policy changes, improving data sharing practices, advances in technology, and integration between neuroimaging and other emerging information streams.

scholarly journals Identifying and Quantifying Critical Information Streams for Tactical Combat Decision Modeling

2016 ◽  
Vol 4 (2) ◽  
pp. 74-81
Author(s):  
Daniel Goodman ◽  
Shea Mullins ◽  
Josh McDonald ◽  
James Cho ◽  
Gregory Boylan
Keyword(s):  
Situational Awareness ◽  
Decision Processes ◽  
Decision Models ◽  
Test Cases ◽  
Decision Modeling ◽  
Critical Information ◽  
Domain Mapping ◽  
Available Information ◽  
Information Streams ◽  
Flow Of Information

It is often asserted that more information on the battlefield leads to greater situational awareness (SA) which, in turn, translates to enhanced mission performance and outcomes.  However, the volume of available information on the modern networked battlefield is extensive and growing, which induces risk of indecision due to cognitive overload.  The potential overload highlights the need to streamline the flow of information to those critical streams that provide the most value to a tactical leader’s decision process at particular points in time. The purpose of this study is to identify critical information streams required by tactical leaders within the various phases of a dismounted search and attack/react to contact scenario.  Domain Mapping Matrix methodology (DMM) is utilized to quantify the value of various information streams relative to the sub -phases within the scenario using a constructed nominal scale. The significance of the highlighted interactions is validated through the use of statistical analysis, with combat veterans serving as test cases. The findings of this study will facilitate the development of decision models that will eventually enable more accurate and realistic simulation of the leader’s decision processes that increased SA purportedly enhances.

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