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Search Results: 1 - 10 of 400817 matches for " M. Ghose "
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Gravity Induced New Topological Phase in Optics
Partha Ghose,M. K. Samal
Physics , 2001,
Abstract: It is shown that both classical and quantum light can acquire a topological phase shift induced by classical gravity, and the latter is detectable in a laboratory-scale experiment.
Lorentz Invariant Superluminal Tunneling
Partha Ghose,M. K. Samal
Physics , 2000, DOI: 10.1103/PhysRevE.64.036620
Abstract: It is shown that superluminal optical signalling is possible without violating Lorentz invariance and causality via tunneling through photonic band gaps in inhomogeneous dielectrics of a special kind.
EPR Type Nonlocality in Classical Electrodynamics!
Partha Ghose,M. K. Samal
Physics , 2001,
Abstract: It is shown that classical electrodynamics in its alternative Kemmer-Duffin-Petiau-Harish-Chandra formulation surprisingly reveals a Hilbert space structure leading to the possibility of entangled states of classical radiation, and this in turn implies the violation of Einstein-Bell locality in spite of Lorentz invariance.
Mining Spatial Gene Expression Data Using Negative Association Rules
M. Anandhavalli,M. K. Ghose,K. Gauthaman
International Journal of Computer Science and Information Security , 2009,
Abstract: Over the years, data mining has attracted most of the attention from the research community. The researchers attempt to develop faster, more scalable algorithms to navigate over the ever increasing volumes of spatial gene expression data in search of meaningful patterns. Association rules are a data mining technique that tries to identify intrinsic patterns in spatial gene expression data. It has been widely used in different applications, a lot of algorithms introduced to discover these rules. However Priori-like algorithms has been used to find positive association rules. In contrast to positive rules, negative rules encapsulate relationship between the occurrences of one set of items with absence of the other set of items. In this paper, an algorithm for mining negative association rules from spatial gene expression data is introduced. The algorithm intends to discover the negative association rules which are complementary to the association rules often generated by Priori like algorithm. Our study shows that negative association rules can be discovered efficiently from spatial gene expression data. Keywords- Spatial Gene expression data; Association Rule; Negative Association Rule;
Interestingness Measure for Mining Spatial Gene Expression Data using Association Rule
M. Anandhavalli,M. K. Ghose,K. Gauthaman
Computer Science , 2010,
Abstract: The search for interesting association rules is an important topic in knowledge discovery in spatial gene expression databases. The set of admissible rules for the selected support and confidence thresholds can easily be extracted by algorithms based on support and confidence, such as Apriori. However, they may produce a large number of rules, many of them are uninteresting. The challenge in association rule mining (ARM) essentially becomes one of determining which rules are the most interesting. Association rule interestingness measures are used to help select and rank association rule patterns. Besides support and confidence, there are other interestingness measures, which include generality reliability, peculiarity, novelty, surprisingness, utility, and applicability. In this paper, the application of the interesting measures entropy and variance for association pattern discovery from spatial gene expression data has been studied. In this study the fast mining algorithm has been used which produce candidate itemsets and it spends less time for calculating k-supports of the itemsets with the Boolean matrix pruned, and it scans the database only once and needs less memory space. Experimental results show that using entropy as the measure of interest for the spatial gene expression data has more diverse and interesting rules.
Mining Spatial Gene Expression Data Using Negative Association Rules
M. Anandhavalli,M. K. Ghose,K. Gauthaman
Computer Science , 2010,
Abstract: Over the years, data mining has attracted most of the attention from the research community. The researchers attempt to develop faster, more scalable algorithms to navigate over the ever increasing volumes of spatial gene expression data in search of meaningful patterns. Association rules are a data mining technique that tries to identify intrinsic patterns in spatial gene expression data. It has been widely used in different applications, a lot of algorithms introduced to discover these rules. However Priori like algorithms has been used to find positive association rules. In contrast to positive rules, negative rules encapsulate relationship between the occurrences of one set of items with absence of the other set of items. In this paper, an algorithm for mining negative association rules from spatial gene expression data is introduced. The algorithm intends to discover the negative association rules which are complementary to the association rules often generated by Priori like algorithm. Our study shows that negative association rules can be discovered efficiently from spatial gene expression data.
Temporal Production Signals in Parietal Cortex
Blaine A. Schneider,Geoffrey M. Ghose
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.1001413
Abstract: We often perform movements and actions on the basis of internal motivations and without any explicit instructions or cues. One common example of such behaviors is our ability to initiate movements solely on the basis of an internally generated sense of the passage of time. In order to isolate the neuronal signals responsible for such timed behaviors, we devised a task that requires nonhuman primates to move their eyes consistently at regular time intervals in the absence of any external stimulus events and without an immediate expectation of reward. Despite the lack of sensory information, we found that animals were remarkably precise and consistent in timed behaviors, with standard deviations on the order of 100 ms. To examine the potential neural basis of this precision, we recorded from single neurons in the lateral intraparietal area (LIP), which has been implicated in the planning and execution of eye movements. In contrast to previous studies that observed a build-up of activity associated with the passage of time, we found that LIP activity decreased at a constant rate between timed movements. Moreover, the magnitude of activity was predictive of the timing of the impending movement. Interestingly, this relationship depended on eye movement direction: activity was negatively correlated with timing when the upcoming saccade was toward the neuron's response field and positively correlated when the upcoming saccade was directed away from the response field. This suggests that LIP activity encodes timed movements in a push-pull manner by signaling for both saccade initiation towards one target and prolonged fixation for the other target. Thus timed movements in this task appear to reflect the competition between local populations of task relevant neurons rather than a global timing signal.
Temporal Production Signals in Parietal Cortex
Blaine A. Schneider,Geoffrey M. Ghose
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.1001413
Abstract: We often perform movements and actions on the basis of internal motivations and without any explicit instructions or cues. One common example of such behaviors is our ability to initiate movements solely on the basis of an internally generated sense of the passage of time. In order to isolate the neuronal signals responsible for such timed behaviors, we devised a task that requires nonhuman primates to move their eyes consistently at regular time intervals in the absence of any external stimulus events and without an immediate expectation of reward. Despite the lack of sensory information, we found that animals were remarkably precise and consistent in timed behaviors, with standard deviations on the order of 100 ms. To examine the potential neural basis of this precision, we recorded from single neurons in the lateral intraparietal area (LIP), which has been implicated in the planning and execution of eye movements. In contrast to previous studies that observed a build-up of activity associated with the passage of time, we found that LIP activity decreased at a constant rate between timed movements. Moreover, the magnitude of activity was predictive of the timing of the impending movement. Interestingly, this relationship depended on eye movement direction: activity was negatively correlated with timing when the upcoming saccade was toward the neuron's response field and positively correlated when the upcoming saccade was directed away from the response field. This suggests that LIP activity encodes timed movements in a push-pull manner by signaling for both saccade initiation towards one target and prolonged fixation for the other target. Thus timed movements in this task appear to reflect the competition between local populations of task relevant neurons rather than a global timing signal.
Rapid shape detection signals in area V4
Katherine F. Weiner,Geoffrey M. Ghose
Frontiers in Neuroscience , 2014, DOI: 10.3389/fnins.2014.00294
Abstract: Vision in foveate animals is an active process that requires rapid and constant decision-making. For example, when a new object appears in the visual field, we can quickly decide to inspect it by directing our eyes to the object's location. We studied the contribution of primate area V4 to these types of rapid foveation decisions. Animals performed a reaction time task that required them to report when any shape appeared within a peripherally-located noisy stimulus by making a saccade to the stimulus location. We found that about half of the randomly sampled V4 neurons not only rapidly and precisely represented the appearance of this shape, but they were also predictive of the animal's saccades. A neuron's ability to predict the animal's saccades was not related to the specificity with which the cell represented a single type of shape but rather to its ability to signal whether any shape was present. This relationship between sensory sensitivity and behavioral predictiveness was not due to global effects such as alertness, as it was equally likely to be observed for cells with increases and decreases in firing rate. Careful analysis of the timescales of reliability in these neurons implies that they reflect both feedforward and feedback shape detecting processes. In approximately seven percent of our recorded sample, individual neurons were able to predict both the delay and precision of the animal's shape detection performance. This suggests that a subset of V4 neurons may have been directly and causally contributing to task performance and that area V4 likely plays a critical role in guiding rapid, form-based foveation decisions.
Complete Security Framework for Wireless Sensor Networks
Kalpana Sharma,M. K. Ghose,Kuldeep
Computer Science , 2009,
Abstract: Security concern for a Sensor Networks and level of security desired may differ according to application specific needs where the sensor networks are deployed. Till now, most of the security solutions proposed for sensor networks are layer wise i.e a particular solution is applicable to single layer itself. So, to integrate them all is a new research challenge. In this paper we took up the challenge and have proposed an integrated comprehensive security framework that will provide security services for all services of sensor network. We have added one extra component i.e. Intelligent Security Agent (ISA) to assess level of security and cross layer interactions. This framework has many components like Intrusion Detection System, Trust Framework, Key Management scheme and Link layer communication protocol. We have also tested it on three different application scenarios in Castalia and Omnet++ simulator.
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