Mechanism design for time critical and cost critical task execution via crowdsourcing

An exciting application of crowdsourcing is to use social networks in complex task execution. In this paper, we address the problem of a planner who needs to incentivize agents within a network in order to seek their help in executing an atomic task as well as in recruiting other agents to execute the task. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planner's goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We identify a set of desirable properties that should ideally be satisfied by a crowdsourcing mechanism. In particular, sybil-proofness and collapse-proofness are two complementary properties in our desiderata. We prove that no mechanism can satisfy all the desirable properties simultaneously. This leads us naturally to explore approximate versions of the critical properties. We focus our attention on approximate sybil-proofness and our exploration leads to a parametrized family of payment mechanisms which satisfy collapse-proofness. We characterize the approximate versions of the desirable properties in cost critical and time critical domain.

Multi-task learning using shared and task specific information

Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.

Reach task-associated excitatory overdrive of motor cortical neurons following infusion with ALS-CSF

Converging evidence from transgenic animal models of amyotrophic lateral sclerosis (ALS) and human studies suggest alterations in excitability of the motor neurons in ALS. Specifically, in studies on human subjects with ALS the motor cortex was reported to be hyperexcitable. The present study was designed to test the hypothesis that infusion of cerebrospinal fluid from patients with sporadic ALS (ALS-CSF) into the rat brain ventricle can induce hyperexcitability and structural changes in the motor cortex leading to motor dysfunction. A robust model of sporadic ALS was developed experimentally by infusing ALS-CSF into the rat ventricle. The effects of ALS-CSF at the single neuron level were examined by recording extracellular single unit activity from the motor cortex while rats were performing a reach to grasp task. We observed an increase in the firing rate of the neurons of the motor cortex in rats infused with ALS-CSF compared to control groups. This was associated with impairment in a specific component of reach with alterations in the morphological characteristics of the motor cortex. It is likely that the increased cortical excitability observed in the present study could be the result of changes in the intrinsic properties of motor cortical neurons, a dysfunctional inhibitory mechanism and/or an underlying structural change culminating in a behavioral deficit.

Cooperative Communication of UAV to Perform Multi-task Using Nature Inspired Techniques

In this paper, the approach for assigning cooperative communication of Uninhabited Aerial Vehicles (UAV) to perform multiple tasks on multiple targets is posed as a combinatorial optimization problem. The multiple task such as classification, attack and verification of target using UAV is employed using nature inspired techniques such as Artificial Immune System (AIS), Particle Swarm Optimization (PSO) and Virtual Bee Algorithm (VBA). The nature inspired techniques have an advantage over classical combinatorial optimization methods like prohibitive computational complexity to solve this NP-hard problem. Using the algorithms we find the best sequence in which to attack and destroy the targets while minimizing the total distance traveled or the maximum distance traveled by an UAV. The performance analysis of the UAV to classify, attack and verify the target is evaluated using AIS, PSO and VBA.

Lizards of the Thar Desert - Resource partitioning and community composition

How similar species co-exist in nature is a fundamental question in community ecology. Resource partitioning has been studied in desert lizard communities across four continents, but data from South Asia is lacking. We used area-constrained visual encounter surveys to study community composition and spatial and temporal resource partitioning in a lizard community during summer in the Thar Desert, western India, addressing an important biogeographic gap in knowledge. Twelve one-hectare grids divided into 25 m x 25 m plots were placed across four habitats barren dunes, stabilized dunes, grassland, and rocky hills. We recorded 1039 sightings of 12 species during 84 sampling sessions. Lizard abundance decreased in the order stabilized dunes > grassland > barren dunes > rocky hills; richness was in roughly the opposite order. Resource partitioning was examined for the seven commonest species. Overall spatial overlap was low (<0.6) between species pairs. Overlap was higher within habitats, but species showed finer separation through use of different microhabitat categories and specific spatial resources, as well as by positioning at different distances to vegetation. Diurnal species were also separated by peak time of activity. Space appears to be an important resource dimension facilitating coexistence in this desert lizard community. (C) 2015 Elsevier Ltd. All rights reserved.

Eye-hand coordination during a double-step task: evidence for a common stochastic accumulator

Many studies of reaching and pointing have shown significant spatial and temporal correlations between eye and hand movements. Nevertheless, it remains unclear whether these correlations are incidental, arising from common inputs (independent model); whether these correlations represent an interaction between otherwise independent eye and hand systems (interactive model); or whether these correlations arise from a single dedicated eye-hand system (common command model). Subjects were instructed to redirect gaze and pointing movements in a double-step task in an attempt to decouple eye-hand movements and causally distinguish between the three architectures. We used a drift-diffusion framework in the context of a race model, which has been previously used to explain redirect behavior for eye and hand movements separately, to predict the pattern of eye-hand decoupling. We found that the common command architecture could best explain the observed frequency of different eye and hand response patterns to the target step. A common stochastic accumulator for eye-hand coordination also predicts comparable variances, despite significant difference in the means of the eye and hand reaction time (RT) distributions, which we tested. Consistent with this prediction, we observed that the variances of the eye and hand RTs were similar, despite much larger hand RTs (similar to 90 ms). Moreover, changes in mean eye RTs, which also increased eye RT variance, produced a similar increase in mean and variance of the associated hand RT. Taken together, these data suggest that a dedicated circuit underlies coordinated eye-hand planning.

Divide and Conquer: Efficient Large-Scale Structure from Motion Using Graph Partitioning

Despite significant advances in recent years, structure-from-motion (SfM) pipelines suffer from two important drawbacks. Apart from requiring significant computational power to solve the large-scale computations involved, such pipelines sometimes fail to correctly reconstruct when the accumulated error in incremental reconstruction is large or when the number of 3D to 2D correspondences are insufficient. In this paper we present a novel approach to mitigate the above-mentioned drawbacks. Using an image match graph based on matching features we partition the image data set into smaller sets or components which are reconstructed independently. Following such reconstructions we utilise the available epipolar relationships that connect images across components to correctly align the individual reconstructions in a global frame of reference. This results in both a significant speed up of at least one order of magnitude and also mitigates the problems of reconstruction failures with a marginal loss in accuracy. The effectiveness of our approach is demonstrated on some large-scale real world data sets.

Segregating Discourse Segments from Engineering Documents for Knowledge Acquisition

The broader goal of the research being described here is to automatically acquire diagnostic knowledge from documents in the domain of manual and mechanical assembly of aircraft structures. These documents are treated as a discourse used by experts to communicate with others. It therefore becomes possible to use discourse analysis to enable machine understanding of the text. The research challenge addressed in the paper is to identify documents or sections of documents that are potential sources of knowledge. In a subsequent step, domain knowledge will be extracted from these segments. The segmentation task requires partitioning the document into relevant segments and understanding the context of each segment. In discourse analysis, the division of a discourse into various segments is achieved through certain indicative clauses called cue phrases that indicate changes in the discourse context. However, in formal documents such language may not be used. Hence the use of a domain specific ontology and an assembly process model is proposed to segregate chunks of the text based on a local context. Elements of the ontology/model, and their related terms serve as indicators of current context for a segment and changes in context between segments. Local contexts are aggregated for increasingly larger segments to identify if the document (or portions of it) pertains to the topic of interest, namely, assembly. Knowledge acquired through such processes enables acquisition and reuse of knowledge during any part of the lifecycle of a product.