Does linear separability really matter? Complex visual search is explained by simple search

Visual search in real life involves complex displays with a target among multiple types of distracters, but in the laboratory, it is often tested using simple displays with identical distracters. Can complex search be understood in terms of simple searches? This link may not be straightforward if complex search has emergent properties. One such property is linear separability, whereby search is hard when a target cannot be separated from its distracters using a single linear boundary. However, evidence in favor of linear separability is based on testing stimulus configurations in an external parametric space that need not be related to their true perceptual representation. We therefore set out to assess whether linear separability influences complex search at all. Our null hypothesis was that complex search performance depends only on classical factors such as target-distracter similarity and distracter homogeneity, which we measured using simple searches. Across three experiments involving a variety of artificial and natural objects, differences between linearly separable and nonseparable searches were explained using target-distracter similarity and distracter heterogeneity. Further, simple searches accurately predicted complex search regardless of linear separability (r = 0.91). Our results show that complex search is explained by simple search, refuting the widely held belief that linear separability influences visual search.

Adaptive selection of search space in look ahead orthogonal matching pursuit

Compressive Sensing theory combines the signal sampling and compression for sparse signals resulting in reduction in sampling rate and computational complexity of the measurement system. In recent years, many recovery algorithms were proposed to reconstruct the signal efficiently. Look Ahead OMP (LAOMP) is a recently proposed method which uses a look ahead strategy and performs significantly better than other greedy methods. In this paper, we propose a modification to the LAOMP algorithm to choose the look ahead parameter L adaptively, thus reducing the complexity of the algorithm, without compromising on the performance. The performance of the algorithm is evaluated through Monte Carlo simulations.

Diffraction tomography from intensity measurements: an evolutionary stochastic search to invert experimental data

We develop iterative diffraction tomography algorithms, which are similar to the distorted Born algorithms, for inverting scattered intensity data. Within the Born approximation, the unknown scattered field is expressed as a multiplicative perturbation to the incident field. With this, the forward equation becomes stable, which helps us compute nearly oscillation-free solutions that have immediate bearing on the accuracy of the Jacobian computed for use in a deterministic Gauss-Newton (GN) reconstruction. However, since the data are inherently noisy and the sensitivity of measurement to refractive index away from the detectors is poor, we report a derivative-free evolutionary stochastic scheme, providing strictly additive updates in order to bridge the measurement-prediction misfit, to arrive at the refractive index distribution from intensity transport data. The superiority of the stochastic algorithm over the GN scheme for similar settings is demonstrated by the reconstruction of the refractive index profile from simulated and experimentally acquired intensity data. (C) 2014 Optical Society of America

Features in visual search combine linearly

Single features such as line orientation and length are known to guide visual search, but relatively little is known about how multiple features combine in search. To address this question, we investigated how search for targets differing in multiple features ( intensity, length, orientation) from the distracters is related to searches for targets differing in each of the individual features. We tested race models (based on reaction times) and coactivation models ( based on reciprocal of reaction times) for their ability to predict multiple feature searches. Multiple feature searches were best accounted for by a co-activation model in which feature information combined linearly (r = 0.95). This result agrees with the classic finding that these features are separable i.e., subjective dissimilarity ratings sum linearly. We then replicated the classical finding that the length and width of a rectangle are integral features-in other words, they combine nonlinearly in visual search. However, to our surprise, upon including aspect ratio as an additional feature, length and width combined linearly and this model outperformed all other models. Thus, length and width of a rectangle became separable when considered together with aspect ratio. This finding predicts that searches involving shapes with identical aspect ratio should be more difficult than searches where shapes differ in aspect ratio. We confirmed this prediction on a variety of shapes. We conclude that features in visual search co-activate linearly and demonstrate for the first time that aspect ratio is a novel feature that guides visual search.

A perturbed martingale approach to global optimization

A new global stochastic search, guided mainly through derivative-free directional information computable from the sample statistical moments of the design variables within a Monte Carlo setup, is proposed. The search is aided by imparting to the directional update term additional layers of random perturbations referred to as `coalescence' and `scrambling'. A selection step, constituting yet another avenue for random perturbation, completes the global search. The direction-driven nature of the search is manifest in the local extremization and coalescence components, which are posed as martingale problems that yield gain-like update terms upon discretization. As anticipated and numerically demonstrated, to a limited extent, against the problem of parameter recovery given the chaotic response histories of a couple of nonlinear oscillators, the proposed method appears to offer a more rational, more accurate and faster alternative to most available evolutionary schemes, prominently the particle swarm optimization. (C) 2014 Elsevier B.V. All rights reserved.

Precise Slicing in Imperative Programs via Term-Rewriting and Abstract Interpretation

We propose a new approach for producing precise constrained slices of programs in a language such as C. We build upon a previous approach for this problem, which is based on term-rewriting, which primarily targets loop-free fragments and is fully precise in this setting. We incorporate abstract interpretation into term-rewriting, using a given arbitrary abstract lattice, resulting in a novel technique for slicing loops whose precision is linked to the power of the given abstract lattice. We address pointers in a first-class manner, including when they are used within loops to traverse and update recursive data structures. Finally, we illustrate the comparative precision of our slices over those of previous approaches using representative examples.

Group Testing-Based Spectrum Hole Search for Cognitive Radios

This paper investigates the use of adaptive group testing to find a spectrum hole of a specified bandwidth in a given wideband of interest. We propose a group testing-based spectrum hole search algorithm that exploits sparsity in the primary spectral occupancy by testing a group of adjacent subbands in a single test. This is enabled by a simple and easily implementable sub-Nyquist sampling scheme for signal acquisition by the cognitive radios (CRs). The sampling scheme deliberately introduces aliasing during signal acquisition, resulting in a signal that is the sum of signals from adjacent subbands. Energy-based hypothesis tests are used to provide an occupancy decision over the group of subbands, and this forms the basis of the proposed algorithm to find contiguous spectrum holes of a specified bandwidth. We extend this framework to a multistage sensing algorithm that can be employed in a variety of spectrum sensing scenarios, including noncontiguous spectrum hole search. Furthermore, we provide the analytical means to optimize the group tests with respect to the detection thresholds, number of samples, group size, and number of stages to minimize the detection delay under a given error probability constraint. Our analysis allows one to identify the sparsity and SNR regimes where group testing can lead to significantly lower detection delays compared with a conventional bin-by-bin energy detection scheme; the latter is, in fact, a special case of the group test when the group size is set to 1 bin. We validate our analytical results via Monte Carlo simulations.

Long-Term Sustained Release of Salicylic Acid from Cross-Linked Biodegradable Polyester Induces a Reduced Foreign Body Response in Mice

There has been a continuous surge toward developing new biopolymers that exhibit better in vivo biocompatibility properties in terms of demonstrating a reduced foreign body response (FBR). One approach to mitigate the undesired FBR is to develop an implant capable of releasing anti-inflammatory molecules in a sustained manner over a long time period. Implants causing inflammation are also more susceptible to infection. In this article, the in vivo biocompatibility of a novel, biodegradable salicylic acid releasing polyester (SAP) has been investigated by subcutaneous implantation in a mouse model. The tissue response to SAP was compared with that of a widely used biodegradable polymer, poly(lactic acid-co-glycolic acid) (PLGA), as a control over three time points: 2, 4, and 16 weeks postimplantation. A long-term in vitro study illustrates a continuous, linear (zero order) release of salicylic acid with a cumulative mass percent release rate of 7.34 x 10(-4) h(-1) over similar to 1.5-17 months. On the basis of physicochemical analysis, surface erosion for SAP and bulk erosion for PLGA have been confirmed as their dominant degradation modes in vivo. On the basis of the histomorphometrical analysis of inflammatory cell densities and collagen distribution as well as quantification of proinflammatory cytokine levels (TNF-alpha and IL-1 beta), a reduced foreign body response toward SAP with respect to that generated by PLGA has been unambiguously established. The favorable in vivo tissue response to SAP, as manifest from the uniform and well-vascularized encapsulation around the implant, is consistent with the decrease in inflammatory cell density and increase in angiogenesis with time. The above observations, together with the demonstration of long-term and sustained release of salicylic acid, establish the potential use of SAP for applications in improved matrices for tissue engineering and chronic wound healing.

Long-term effects of Macroternies termites, herbivores and annual early fire on woody undergrowth community in Sudanian woodland, Burkina Faso

Termites, herbivores and fire are recognized as major guilds that structure woody plant communities in African savanna and woodland ecosystems. An understanding of their interaction is crucial to design appropriate management regimes. The aim of this study was to evaluate the long-term impacts of herbivore, fire and termite activities on regeneration of trees. Permanent experimental quadrats were established in 1992 in the Sudanian woodland of Burkina Faso subjected to grazing by livestock and annual early fire and the control. Within the treatment quadrats, an inventory of the woody undergrowth community was conducted on termitaria occupied by Macrotermes subhyalinus, extended termitosphere (within 5 m radius from the mound base) and adjacent area (beyond 5 m from the mound base). Hierarchical analysis was performed to determine significant differences in species richness, abundance and diversity indices among vegetation patches within fire and herbivory treatments. Grazed quadrats had significantly (P < 0.001) more species and stem density of woody undergrowth than non-grazed quadrats but maintained similar level of species richness and stem density of woody undergrowth on termitaria. There were not significant differences (P>0.05) in species richness and stem density between burnt and unburnt quadrats. Termitaria supported a highly diverse woody undergrowth with higher stem density than either the extended termitosphere or rest of quadrats. The density of woody undergrowth was significantly related with mature trees of selected species on termitaria (R-2 = 0.593; P<0.001) than that on the extended termitosphere (R-2 = 0.333; P<0.001) and adjacent area (R-2 = 0.197; P<0.001). It can be concluded that termites facilitate the regeneration of woody species while grazing and annual early fire play a minor role in the regeneration of woody species. The current policy that prohibits grazing should be revised to accommodate the interests of livestock herders. (C) 2014 Elsevier GmbH. All rights reserved.

A search for chaos in the optical light curve of a blazar: W2R 1926+42

Aims. In this work we search for the signatures of low-dimensional chaos in the temporal behavior of the Kepler-field blazar W2R 1946+42. Methods. We use a publicly available, similar to 160 000-point-long and mostly equally spaced light curve of W2R 1946+42. We apply the correlation integral method to both real datasets and phase randomized surrogates. Results. We are not able to confirm the presence of low-dimensional chaos in the light curve. This result, however, still leads to some important implications for blazar emission mechanisms, which are discussed.

Robust Whisper Activity Detection Using Long-Term Log Energy Variation of Sub-Band Signal

The goal in the whisper activity detection (WAD) is to find the whispered speech segments in a given noisy recording of whispered speech. Since whispering lacks the periodic glottal excitation, it resembles an unvoiced speech. This noise-like nature of the whispered speech makes WAD a more challenging task compared to a typical voice activity detection (VAD) problem. In this paper, we propose a feature based on the long term variation of the logarithm of the short-time sub-band signal energy for WAD. We also propose an automatic sub-band selection algorithm to maximally discriminate noisy whisper from noise. Experiments with eight noise types in four different signal-to-noise ratio (SNR) conditions show that, for most of the noises, the performance of the proposed WAD scheme is significantly better than that of the existing VAD schemes and whisper detection schemes when used for WAD.

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

Active learning in keyword search-based data integration

The problem of scaling up data integration, such that new sources can be quickly utilized as they are discovered, remains elusive: Global schemas for integrated data are difficult to develop and expand, and schema and record matching techniques are limited by the fact that data and metadata are often under-specified and must be disambiguated by data experts. One promising approach is to avoid using a global schema, and instead to develop keyword search-based data integration-where the system lazily discovers associations enabling it to join together matches to keywords, and return ranked results. The user is expected to understand the data domain and provide feedback about answers' quality. The system generalizes such feedback to learn how to correctly integrate data. A major open challenge is that under this model, the user only sees and offers feedback on a few ``top-'' results: This result set must be carefully selected to include answers of high relevance and answers that are highly informative when feedback is given on them. Existing systems merely focus on predicting relevance, by composing the scores of various schema and record matching algorithms. In this paper, we show how to predict the uncertainty associated with a query result's score, as well as how informative feedback is on a given result. We build upon these foundations to develop an active learning approach to keyword search-based data integration, and we validate the effectiveness of our solution over real data from several very different domains.

Flow Cytometry Analysis of Cytotoxicity In Vitro and Long-Term Toxicity of HA-40wt% BaTiO3 Nanoparticles In Vivo

The development of new implantable biomaterials requires bone-mimicking physical properties together with desired biocompatible property. In continuation to our earlier published research to establish compositional dependent multifunctional bone-like properties and cytocompatibility response of hydroxyapatite (HA)-BaTiO3 composites, the toxicological property evaluation, both invitro and invivo, were conducted on HA-40wt% BaTiO3 and reported in this work. In particular, this work reports invitro cytotoxicity of mouse myoblast cells as well as invivo long-term tissue and nanoparticles interaction of intra-articularly injected HA-40wt% BaTiO3 and BaTiO3 up to the concentration of 25mg/mL in physiological saline over 12weeks in mouse model. The careful analysis of flow cytometry results could not reveal any statistically significant difference in terms of early/late apoptotic cells or necrotic cells over 8d in culture. Extensive histological analysis could not record any signature of cellular level toxicity or pronounced inflammatory response in vital organs as well as at knee joints of Balb/c mice after 12weeks. Taken together, this study establishes nontoxic nature of HA-40wt% BaTiO3 and therefore, HA-40wt% BaTiO3 can be used safely for various biomedical applications.