Abstract

Purpose: To model the modifiable risk factors by using the logistic regression (LR) and artificial neural network (ANN) models for prediction of progression of Age-related Macular Degeneration (AMD) and cross-validate these models for their predictive accuracies in a population in South India. Methods: The data (N= 3,723) were analyzed from Andhra Pradesh Eye Disease Study (APEDS) on participants aged≥ 40 years. Sub-population data from this sample were drawn by using Random under Sampling (RUS)(n= 213) and combination of RUS and Random over Sampling (ROS)(n= 1420) techniques. The modifiable and non-modifiable risk factors which were elicited as part of the study were used to derive the LR based risk score models and the model fit was assessed using bootstrap method for internal validity. The ANN model was built for three sets of data using the multi-layer feed-forward back propagation …

Abstract

Extreme learning machine (ELM) as a new learning approach has shown its good generalization performance in regression and classification applications. Clustering analysis is an important tool to explore the structure of data and has been employed in many disciplines and applications. In this paper, we present a method that builds on ELM projection of input data into a high-dimensional feature space and followed by unsupervised clustering using artificial bee colony (ABC) algorithm. While ELM projection facilitates separability of clusters, a metaheuristic technique such as ABC algorithm overcomes problems of dependence on initialization of cluster centers and convergence to local minima suffered by conventional algorithms such as K-means. The proposed ELM-ABC algorithm is tested on 12 benchmark data sets. The experimental results show that the ELM-ABC algorithm can effectively improve the …

Abstract

Voltage noise can lead to various errors such as dynamic and permanent, which are directly associated to circuit-level reliability issues. Variability in process parameters directly affects the probability of failures associated to voltage noise. Yet, the evaluation of the probability of failures by SPICE level Monte Carlo simulation is prohibitively time-consuming. This work proposes a technique to characterize the input noise and process variations in order to estimate failure probability in a logic circuit path composed of combinational cells and registers. The method allows to correctly estimate the order of magnitude of the probability of failures and to evidence the influence of process variations, while reaching >104 speedup versus SPICE.

Abstract

Aging phenomena, on top of process variations along with temperature and supply voltage variations, translate into complex degradation effects on the required performance and yield of nanoscale circuits. The proposed paper focuses on the development of mathematically optimal circuit sizing for yield maximization on the case study of a CMOS full adder circuit. The final cell design is robust against NBTI aging effects, impact of statistical (global and mismatch) and operating variation of temperature and supply voltage. Monte Carlo analysis has been carried out to verify the estimated yields. The demonstrated technique can be extended to a library of optimally designed digital cells

Abstract

Perception of temporal duration is subjective and is influenced by factors such as attention and context. For example, unexpected or emotional events are often experienced as if time subjectively expands, suggesting that the amount of information processed in a unit of time can be increased. Time dilation effects have been measured with an oddball paradigm in which an infrequent stimulus is perceived to last longer than standard stimuli in the rest of the sequence. Likewise, time compression for the oddball occurs when the duration of the standard items is relatively brief. Here, we investigated whether the amount of information processing changes when time is perceived as distorted. On each trial, an oddball stimulus of varying numerosity (1–14 items) and duration was presented along with standard items that were either short (70 ms) or long (1050 ms). Observers were instructed to count the number of dots within the oddball stimulus and to judge its relative duration with respect to the standards on that trial. Consistent with previous results, oddballs were reliably perceived as temporally distorted: expanded for longer standard stimuli blocks and compressed for shorter standards. The occurrence of these distortions of time perception correlated with perceptual processing; i.e. enumeration accuracy increased when time was perceived as expanded and decreased with temporal compression. These results suggest that subjective time distortions are not epiphenomenal, but reflect real changes in sensory processing. Such short-term plasticity in information processing rate could be evolutionarily advantageous in optimizing perception and action …

Abstract

This study reports dynamic functional network connectivity (dFNC) analysis on time courses of putative empathy networks—cognitive, emotional, and motor—and the default mode network (DMN) identified from independent components (ICs) derived by the group independent component analysis (ICA) method. The functional magnetic resonance imaging (fMRI) data were collected from 15 subjects watching movies of three genres, an animation (S1), Indian Hindi (S2), and a Hollywood English (S3) movie. The hypothesis of the study is that empathic engagement in a movie narrative would modulate the activation with the DMN. The clippings were individually rated for emotional expressions, context, and empathy self-response by the fMRI subjects post scanning and by 40 participants in an independent survey who rated at four time intervals in each clipping. The analysis illustrates the following: (a) the ICA method

Abstract

Promoters are DNA sequences containing regulatory elements required to guide and modulate the transcription initiation of the gene. Predicting promoter sequences in genomic sequences is a significant task in genome annotation and understanding transcriptional regulation. In the past decade many methods with many feature extraction schemes have been proposed for the prediction of eukaryotic and prokaryotic promoters. Still there is great need for more accurate and faster methods. In this paper we employed extreme learning machine algorithm (ELM), for promoter prediction in DNA sequences of H. sapiens, D. melanogaster, A. thaliana, C. elegans and E. coli. We extracted dinucleotide and CpG island features, and achieved accuracy above 90 % for all the five species. Performance is compared with the feed forward back propagation algorithm (BP) and support vector machines (SVM) and the …

Abstract

Task: The task is a probabilistic learning task as described in Pasquereau et al., 2007 where four visual cues are associated with different reward probabilities (0.00, 0.33, 0.66 & 1.00). A trial is made of the simultaneous presentation of two random cues with equal salience at two random positions. Some time after the presentation, a switch in the cortex activities is observed, representing the decision taken. After the model has chosen one cue or the other, a reward is given according to the probability associated with the chosen cue. Connections between the cortex and the striatum are then modified using a reinforcement learning rule based on the reward signal. The model is trained over 120 trials such that each combination of cues is presented equal number of times at uniformly sampled positions and the model performance reaches at least 0.9 measuring the ration of optimal choices. The decision switch and the performance are identical to the results when primates are tested with same task. Learning is then disabled and the model is tested using always the same pair of cues A (P (R)= 1) and B (P (R)= 0.33) in the presence of external factors. We study how, despite reward based learning, visual salience of the stimuli and the temporal difference between stimulus presentations affect the model to take a sub-optimal decision.

Abstract

Complex tasks are often handled through software implementation in combination with high performance processors. Taking advantage of hardware parallelism, FPGA is breaking the paradigm by accomplishing more per clock cycle with closely matched application requirements. With the aim to minimise computation delay with increase in map's size and geometric constraints, we present the FPGA based combinatorial architecture that allows multiple RRTs to work together to achieve accelerated, uniform exploration of the map. We also analyse our architecture against hardware implementation of other scalable RRT methods for motion planning. We observe notable furtherance of acceleration capabilities with the proposed architecture delivering a minimum 3X gain over the other implementations while maintaining uniformity in exploration

Abstract

In an object search scenario with several small objects spread over a large indoor environment, the robot cannot infer about all of them at once. Pruning the search space is highly desirable in such a case. It has to actively select a course of actions to closely examine a selected set of objects. Here, we model the inferences about far away objects and their viewpoint priors into a decision analytic abstraction to prioritize the waypoints. By selecting objects of interest, a potential field is built over the environment by using Composite Viewpoint Object Potential (CVOP) maps. A CVOP is built using VOP, a framework to identify discriminative viewpoints to recognize small objects having distinctive features only in specific views. Also, a CVOP helps to clearly disambiguate objects which look similar from far away. We formulate a Decision Analysis Graph (DAG) over the above information, to assist the robot in actively navigating and maximize the reward earned. This optimal strategy increases search reliability, even in the presence of similar looking small objects which induce confusion into the agent and simultaneously reduces both time taken and distance travelled. To the best of our knowledge, there is no current unified formulation which addresses indoor object search scenarios in this manner. We evaluate our system over ROS using a TurtleBot mounted with a Kinect.