Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model

A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competetive accuracy with respect to state-of-the-art non-spiking neural models.Comment: 13 figures (includes supplementary information

Relating the curvature of De Sitter Universe to Open Quantum Lamb Shift Spectroscopy

In this paper, we explore the connection between the curvature of the background De Sitter space-time with the spectroscopic study of entanglement of two atoms. Our set up is in the context of an Open Quantum System (OQS), where the two atoms, each having two energy levels and represented by Pauli spin tensor operators projected along any arbitrary direction. The system mimics the role of a pair of freely falling Unruh De-Witt detectors, which are allowed to non-adiabatically interact with a conformally coupled massless probe scalar field which has the role of background thermal bath. The effective dynamics of this combined system takes into account of the non-adiabatic interaction, which is commonly known as the Resonant Casimir Polder Interaction (RCPI) with the thermal bath. Our analysis reveals that the RCPI of two stable entangled atoms in the quantum vacuum states in OQS depends on the de Sitter space-time curvature relevant to the temperature of the thermal bath felt by the static observer. We also find that, in OQS, RCPI produces a new significant contribution appearing in the effective Hamiltonian of the total system and thermal bath under consideration. We find that the Lamb Shift is characterized by a decreasing inverse square power-law behavior, $L^{-2}$, when inter atomic Euclidean distance, $L$, is much larger than a characteristic length scale, $k$, which is the inverse surface gravity of the background De Sitter space. If the background space-time would have been Minkowskian this shift decreases as, $L^{-1}$, and is independent of temperature. Thus, we establish a connection between the curvature of the De Sitter space-time with the Lamb Shift spectroscopy.Comment: 65 pages, 3 figures, 1 Table, This project is the part of the non-profit virtual international research consortium "Quantum Structures of the Space-Time & Matter". Accepted for publication in European Physical Journal

A CONCEPTUAL PATHO-CLINICAL STUDY ON THE DISORDER OF MUTRABAHA SROTAS W. S. R. TO MUTR ASHMARI (UROLITHIASIS)

Now a days, several patients are reporting to the hospitals by regularly affecting with different Mutrabaha Srotas disorders like burning micturation, urinary stone diseases etc. Mutrashmari is one of the important diseases of Mutrabaha Srotas. This may caused due to the intake of adulterated food and polluted water. So, it becomes important to know the details about this disease in terms of Nidana (etiological factors), Samprapti (Pathogenesis), lakshyanas (symptomatology), Upadrava (complications), Sadhya-sadhyata (prognosis) and chikitsa (management) of this disease, in order to treat or prevent the disease. Here an effort was made to describe details about this disease both in Ayurvedic as well as modern view and interpreted each other. It helps physician to know about this disease in detail. So that, they can treat successfully to this disease for the benefits of mankind

Prediction of Heart Disease using Deep Convolutional Neural Networks

Heart disease is a very deadly disease. Worldwide, the majority of people are suffering from thisproblem. Many machine learning (ML) approaches are not sufficient to forecast the disease caused bythe virus. Therefore, there is a need for one system that predicts disease efficiently. The DeepLearning approach predicts the disease caused by the blocked heart. This paper proposes aConvolutional Neural Network (CNN) to predict the disease at an early stage. This paper focuses on acomparison between the traditional approaches such as Logistic Regression, K-Nearest Neighbors(KNN), Naïve-Bayes (NB), Support Vector Machine (SVM), Neural Networks (NN), and theproposed prediction model of CNN. The UCI machine learning repository dataset for experimentationand cardiovascular disease (CVD) predictions with 94% accuracy

Performance Analysis of HE Methods for Low Contrast Images

AbstractThe image enhancement is one of the important issues in image processing. The main purpose is to highlight certain characteristic of image such as: contrast, sharpening. Histogram equalization is the well-known method for image enhancement. Histogram equalization became a popular technique because it is simple and effective. However Histogram equalization cause excessive contrast enhancement which cause visual artifacts of processed image. In this paper new forms of histogram equalization are overviewed to overcome this drawback. The major difference among the methods is the way to divide the input histogram. Recursive exposure based sub-image histogram equalization (R_ESIHE) use average intensity value as the separating point. Median-mean based sub-image clipped histogram equalization (MMSICHE) and Quadrants dynamic histogram equalization for contrast enhancement (QDHE) use median intensity value as separating point. Here objective parameters are Peak signal to noise ratio (PSNR) and Absolute Mean Brightness Error (AMBE)used to compare the quality of enhancement

Design and Development of Cellular Structure for Additive Manufacturing

The demand for shorter product development time has resulted in the introduction of a new paradigm called Additive Manufacturing (AM). Due to its significant advantages in terms of cost effective, lesser build time, elimination of expensive tooling, design flexibility AM is finding applications in many diverse fields of the industry today. One of the recent applications of this technology is for fabrication of cellular structures. Cellular structures are designed to have material where it is needed for specific applications. Compared to solid materials, these structures can provide high strength-to-weight ratio, good energy absorption characteristics and good thermal and acoustic insulation properties to aerospace, medical and engineering products. However, due to inclusion of too many design variables, the design process of these structures is a challenge task. Furthermore, polymer additive manufacturing techniques, such as fused deposition modeling (FDM) process which shows the great capability to fabricate these structures, are still facing certain process limitations in terms of support structure requirement for certain category of cellular structures. Therefore, in this research, a computer-aided design (CAD) based method is proposed to design and develop hexagonal honeycomb structure (self-supporting periodic cellular structure) for FDM process. This novel methodology is found to have potential to create honeycomb cellular structures with different volume fractions successfully without any part distortion. Once designing process is complete, mechanical and microstructure properties of these structures are characterized to investigate effect of volume fraction on compressive strength of the part. Volume fraction can be defined as the volume percentage of the solid material inside the cellular structure and it is varied in this thesis by changing the cell size and wall thickness of honeycombs. Compression strength of the honeycomb structure is observed to increase with the increase in the volume fraction and this behavior is compared with an existing Wierzbicki expression, developed for predicting compression properties. Some differences are noticed in between experimentally tested and Wierzbicki model estimated compressive strength. These differences may be attributed to layer by layer deposition strategy and the residual stress inherent to the FDM-manufacturing process. Finally, as a design case study, resin transfer molding (RTM) mold internally filled with honeycomb is designed and tested instead of the regular FDM mold. Results show that our proposed methodology has the ability to generate honeycomb structures efficiently while reducing the expensive build material (Mold) consumption to near about 50%. However, due to complex geometry of the honeycomb pattern the build time increased about 65% compare to solid FDM mould. In this regard, FDM tool-path can be optimized in future, so that overall product cost will be minimized. As per the author’s knowledge, this design methodology will have a greatest contribution towards creating sustainable and green product development. Using this, in future, expensive build material and production time can also be minimized for some hydroforming and injection molding applications

Prevalence, Isolation, Characterisation and Antibiogram Study of Pathogenic Escherichia coli from Different Poultry Farms of Odisha

From 182 birds of different farms of odisha suspected for colibacillosis 317 swab samples containing 51 air sacs, 39 lungs, 53 livers, 36 heart bloods, 45 pericardial fluids, 19 yolk sacs and 74 intestine samples were processed for isolation and identification of pathogenic E. coli. and subjected to detail bacteriological and biochemical examination in the laboratory and 105 E. coli isolates were isolated following standard procedures. The percentage of isolation of E. coli isolates in decreasing order was yolk sac (52.6%) and heart blood (38.4%) in 0-4 week birds. In older birds (4-7week) the highest percentage of isolation was from pericardial fluid (35.8%) followed by heart blood (33.4%). The present study showed that the frequency of occurrence of O9 strain is highest (16.7%) followed by O1, O33 & O51 (13.3%), O23 & O119 (10%), O103 & 79 (6.7%) and serotype O90 (3.3%). The antibiogram study reveals that, E. coli isolates found were highly resistant to some of the classical drugs like chlortetracycline (88.58%), streptomycin (85.72%), penicillin-G (82.86%), amikacin (82.86%), furazolidone (77.14%), ampicillin (74.29%), Tetracycline(74.29%), Amoxicillin (71.43%) and cotrimoxazole (71.43%), while isolates were highly sensitive to ceftriaxone and tazobactum (94.29%), ceftriaxone (91.43%), gentamicin (85.72%), chloromphenicol (82.67%), cephotaxime (77.14%), cefixime (74.29%) and ciprofloxacin (74.29%)

Investigation of Ultrafast Demagnetization and Gilbert Damping and their Correlation in Different Ferromagnetic Thin Films Grown Under Identical Conditions

Following the demonstration of laser-induced ultrafast demagnetization in ferromagnetic nickel, several theoretical and phenomenological propositions have sought to uncover its underlying physics. In this work we revisit the three temperature model (3TM) and the microscopic three temperature model (M3TM) to perform a comparative analysis of ultrafast demagnetization in 20-nm-thick cobalt, nickel and permalloy thin films measured using an all-optical pump-probe technique. In addition to the ultrafast dynamics at the femtosecond timescales, the nanosecond magnetization precession and damping are recorded at various pump excitation fluences revealing a fluence-dependent enhancement in both the demagnetization times and the damping factors. We confirm that the Curie temperature to magnetic moment ratio of a given system acts as a figure of merit for the demagnetization time, while the demagnetization times and damping factors show an apparent sensitivity to the density of states at the Fermi level for a given system. Further, from numerical simulations of the ultrafast demagnetization based on both the 3TM and the M3TM, we extract the reservoir coupling parameters that best reproduce the experimental data and estimate the value of the spin flip scattering probability for each system. We discuss how the fluence-dependence of inter-reservoir coupling parameters so extracted may reflect a role played by nonthermal electrons in the magnetization dynamics at low laser fluences

Bringing Reconfigurability to the Network Stack

Reconfiguring the network stack allows applications to specialize the implementations of communication libraries depending on where they run, the requests they serve, and the performance they need to provide. Specializing applications in this way is challenging because developers need to choose the libraries they use when writing a program and cannot easily change them at runtime. This paper introduces Bertha, which allows these choices to be changed at runtime without limiting developer flexibility in the choice of network and communication functions. Bertha allows applications to safely use optimized communication primitives (including ones with deployment limitations) without limiting deployability. Our evaluation shows cases where this results in 16x higher throughput and 63% lower latency than current portable approaches while imposing minimal overheads when compared to a hand-optimized versions that use deployment-specific communication primitives.Comment: 12 pages, 10 figure