43 research outputs found

    Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?

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    Visual scene understanding often requires the processing of human-object interactions. Here we seek to explore if and how well Deep Neural Network (DNN) models capture features similar to the brain's representation of humans, objects, and their interactions. We investigate brain regions which process human-, object-, or interaction-specific information, and establish correspondences between them and DNN features. Our results suggest that we can infer the selectivity of these regions to particular visual stimuli using DNN representations. We also map features from the DNN to the regions, thus linking the DNN representations to those found in specific parts of the visual cortex. In particular, our results suggest that a typical DNN representation contains encoding of compositional information for human-object interactions which goes beyond a linear combination of the encodings for the two components, thus suggesting that DNNs may be able to model this important property of biological vision.Comment: 4 pages, 2 figures; presented at CCN 201

    Extracting low-dimensional psychological representations from convolutional neural networks

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    Deep neural networks are increasingly being used in cognitive modeling as a means of deriving representations for complex stimuli such as images. While the predictive power of these networks is high, it is often not clear whether they also offer useful explanations of the task at hand. Convolutional neural network representations have been shown to be predictive of human similarity judgments for images after appropriate adaptation. However, these high-dimensional representations are difficult to interpret. Here we present a method for reducing these representations to a low-dimensional space which is still predictive of similarity judgments. We show that these low-dimensional representations also provide insightful explanations of factors underlying human similarity judgments.Comment: Accepted to CogSci 202

    Development of a Composite Tailoring Technique for Airplane Wing

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    Development of a new composite beam modeling technique to represent the principal load-carrying member in the wing is reported along with the development of a formal design optimization procedure to investigate the effect of composite tailoring on aeroelastic stability and structural characteristics of airplane wings. The developed procedure is used to perform design optimization studies on realistic airplane configurations to investigate the various aeroelastic/structural/dynamic design issues

    Natural Language Processing in Biomedical Literature for Analysing the Effects of Neurodynamic in Pain and Disability in Carpal Tunnel Syndrome

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    Carpal tunnel syndrome (CTS) a most common peripheral neuropathy characterised by numbness, tingling in the sensory distribution area of the of the median nerve, particularly in the thumb , index finger ,middle finger and radial side of ring finger along with motor weakness, distal to wrist that results into decreased hand grip strength and hand function disability. CTS puts an economy burden on healthcare services as its incidence and prevalence are increasing day by day although a slight decline in numbers has been seen over a period time. Fuzzy logic retains expert information in an intelligent system that may be effectively utilized by others, simulating the cognitive decision-making abilities of the specialist, and helping junior doctors with less expertise make better diagnoses. Therefore, the use of such an expert system is advised to speed up and enhance the accuracy of the diagnosis in patients with suspected CTS by studying different literatures. To device, evidence based therapeutic protocol from biomedical literature for the treatment of pain and disability in CTS. To analyse the effect of openers, sliders, and tensioners on NPRS and disability in carpal tunnel syndrome, using biomedical literature. Therefore, we draw the very encouraging conclusion that further research on the application of such a fuzzy expert system for medical opinion prediction and diagnosis is warranted

    Evolution of Perturbation in Quiescent Medium

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    Here, the perturbation equation for a dissipative medium is derived from the first principle from the linearized compressible Navier-Stokes equation without Stokes's hypothesis. The dispersion relations of this generic governing equation are obtained for one and three-dimensional perturbations, which exhibit both the dispersive and dissipative nature of the perturbations traveling in a dissipative medium, depending upon the length scale. We specifically provide a theoretical cut-off wave number above which the perturbation equation represents diffusive and dissipative nature. Such behavior has not been reported before, as per the knowledge of the authors.Comment: 12 page 1 figure. arXiv admin note: substantial text overlap with arXiv:2212.1379

    Improvements in Sperm Motility Following Low- or High-Intensity Dietary Interventions in Men With Obesity

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    INTRODUCTION: Obesity increases risks of male infertility, but bariatric surgery does not improve semen quality. Recent uncontrolled studies suggest that a low-energy diet (LED) improves semen quality. Further evaluation within a randomized, controlled setting is warranted. METHODS: Men with obesity (18-60 years) with normal sperm concentration (normal count) (n = 24) or oligozoospermia (n = 43) were randomized 1:1 to either 800 kcal/day LED for 16 weeks or control, brief dietary intervention (BDI) with 16 weeks' observation. Semen parameters were compared at baseline and 16 weeks. RESULTS: Mean age of men with normal count was 39.4 ± 6.4 in BDI and 40.2 ± 9.6 years in the LED group. Mean age of men with oligozoospermia was 39.5 ± 7.5 in BDI and 37.7 ± 6.6 years in the LED group. LED caused more weight loss than BDI in men with normal count (14.4 vs 6.3 kg; P < .001) and men with oligozoospermia (17.6 vs 1.8 kg; P < .001). Compared with baseline, in men with normal count total motility (TM) increased 48 ± 17% to 60 ± 10% (P < .05) after LED, and 52 ± 8% to 61 ± 6% (P < .0001) after BDI; progressive motility (PM) increased 41 ± 16% to 53 ± 10% (P < .05) after LED, and 45 ± 8% to 54 ± 65% (P < .001) after BDI. In men with oligozoospermia compared with baseline, TM increased 35% [26] to 52% [16] (P < .05) after LED, and 43% [28] to 50% [23] (P = .0587) after BDI; PM increased 29% [23] to 46% [18] (P < .05) after LED, and 33% [25] to 44% [25] (P < .05) after BDI. No differences in postintervention TM or PM were observed between LED and BDI groups in men with normal count or oligozoospermia. CONCLUSION: LED or BDI may be sufficient to improve sperm motility in men with obesity. The effects of paternal dietary intervention on fertility outcomes requires investigation

    Inferring latent factors and states underlying behavior and neural dynamics

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    Rapid advancements in experimental neuroscience are now generating a wealth of high-resolution neural and behavioral datasets. This opens new avenues for understanding brain computations and observed behaviors. Neural and behavioral datasets are complex, often containing several dimensions. However, studies consistently show that a small number of underlying factors can explain much of the observed complexity and offer interpretable descriptions. Nonetheless, accurately extracting these latent factors poses several challenges, particularly given the limited samples in these high-dimensional datasets. This thesis addresses these challenges by presenting several novel statistical approaches that are data-efficient and are tailored for neuroscientific datasets. In the first half, we present methods to uncover and interpret the low-dimensional representations that underlie neural activity and behavior during different perceptual tasks. We also introduce a model class designed to extract the underlying dynamics of different neural populations engaged in sensory tasks. Notably, this framework enables testing the causal involvement of various neural circuits in observed behavior. The second half of this thesis focuses on animal behavior: given the challenge of data collection in neuroscience, we propose an approach aimed at accelerating the inference of internal states that describe observed animal behavior during decision-making. Finally, we develop a novel formulation to understand complex animal behavior from the perspective of an animal's goals and actions, using inverse reinforcement learning. Overall, this thesis highlights the efficacy of data-efficient methods and approaches with the tailored inductive biases towards obtaining a nuanced understanding of neural computations. It advocates for novel paradigms in animal behavior modeling, contributes to the expanding literature on dynamic models of behavior, and adds to recent endeavors in disentangling the roles of different neural populations during a task
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