Towards a super-resolution based approach for improved face recognition in low resolution environment

Multimedia Tools and Applications - The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object...     

Effect of 100 KeV Ar+ ion beam irradiation on ZnO thin films – Influence of morphology vis-a-vis electrode/electrolyte interface and its impact on photoelectrochemical water splitting

The present study attempts quantitative determination of changes in the morphological surface features viz. fractal dimension, lower and upper cut off length scale through Power Spectral Density analysis prior to and after irradiation of 100 KeV Ar⁺ ion beam at incidence angles of 0°, 40° and 60° on ZnO thin films. All the unirradiated and irradiated samples are subjected to photoelectrochemical characterization and a correlation between photoelectrochemical performance and morphological parameters is established. Sample irradiated at 40° angle at the fluence of 5 × 10¹⁶ ions/cm² is found to possess maximum fractal dimension of 2.72, lower and upper cut off length scale of 3.16 nm and 63.00 nm respectively. This sample exhibits maximum photocurrent density of 3.19 mA/cm² and applied bias photon-to-current efficiency of 1.12% at 1.23 V/RHE. Hydrogen gas collected for duration of 1 h for the same sample was ~4.83 mLcm^?2.     

TopoAct: Visually Exploring the Shape of Activations in Deep Learning

Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e.combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present TopoAct , a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using TopoAct that provide valuable insights into learned representations of neural networks. We expect TopoAct to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.     

A Novel High Nitrogen Steel Powder Designed for Minimized Cr2N Precipitations

High nitrogen steels provide excellent mechanical properties and corrosion resistance but are prone to form precipitates which adversely affect the corrosion resistance and toughness. High nitrogen steel powders currently available in the market are not claimed to be precipitate free. It is critical to avoid these precipitates while retaining nitrogen in the dissolved form to realize the value of these powder alloys. However, retaining high level of dissolved nitrogen in steel powder during melt atomization process is very challenging. Instead, solid-state dissolution of nitrogen into the powder alloy followed by rapid cooling may provide a convenient approach to avoid precipitate formation compared to traditional melt processing. This study presents a solution treatment approach to achieve elevated dissolved nitrogen levels (~ 0.4 wt pct) in Fe–Mn–Cr powder alloy with negligible precipitation of nitrides. The influence of starting material, holding time, temperature and cooling rate on the resulting microstructure is presented. A fully austenite matrix with high dissolved nitrogen content resulted in powders with desired mechanical properties.

     

Visible‐light‐induced controlled/living radical polymerization of styrene with a phenyl seleno group at one terminal chain end: 1‐(Phenylseleno)ethyl benzene as a photoiniferter

The photopolymerization of styrene with a well‐defined molecular architecture and a low polydispersity index and with methyl and phenylseleno (? SePh) groups at α‐ and ω‐chain ends, respectively, was performed via a controlled/living radical polymerization with a new initiating system, 1‐(phenylseleno)ethyl benzene/tert‐butyl diphenyl (phenylseleno) silane, through the absorption of visible light at room temperature. A novel initiating living radical polymerization was examined. The yield and number‐average molecular weight (M_n) of the resulting polymer increased with the reaction time. Furthermore, a linear relationship was found in a plot of M_n versus the polymer yield. These results indicated that this polymerization proceeded through a living radical mechanism. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 93: 348–355, 2004     

Biodegradable polymers: An update

The use of polymeric materials for the administration of pharmaceuticals, and as biomedical devices has increased dramatically. This review focuses on synthetic biodegradable polymers of current interest for medical use, based on ester and anhydride bonds. Special attention is given to factors affecting biodegradation, including: polymer structure, morphology, molecular weight, radiation, and chemical treatment, as well as the effects of drugs and plasticizers added to the polymer mass. The toxicity and biocompatibility of the polymers and their current and future applications in medicine are also briefly reviewed.     

Alkaline peroxide generation using a novel perforated bipole trickle-bed electrochemical reactor

A novel perforated bipole trickle-bed electrochemical reactor is investigated for the electro-synthesis of alkaline peroxide. The process uses a relatively simple cell configuration in which a single electrolyte flows with oxygen gas in a flow-by graphite felt cathode, sandwiched between a micro-porous diaphragm and a perforated bipolar electrode plate. The graphite felt cathodes are 120 mm high by 25 mm wide and have a thickness of 3.2 mm. The reactor is operated at current densities in the range 1–5 kA m⁻², ca. 800 kPa (abs) pressure and temperature (In/Out) 20–45 °C with one and two-cells. The reactor shows good performance (current efficiency ∼78% at 2 kA m⁻² and a specific energy of 5 kWh per kg of peroxide generated) with peroxide concentrations from 0.02 to 0.15 M in 1 M NaOH.     

Random arithmetic formulas can be reconstructed efficiently

Informally stated, we present here a randomized algorithm that given black-box access to the polynomial f computed by an unknown/hidden arithmetic formula ? reconstructs, on the average, an equivalent or smaller formula \({\hat{\phi}}\) in time polynomial in the size of its output \({\hat{\phi}}\) . Specifically, we consider arithmetic formulas wherein the underlying tree is a complete binary tree, the leaf nodes are labeled by affine forms (i.e., degree one polynomials) over the input variables and where the internal nodes consist of alternating layers of addition and multiplication gates. We call these alternating normal form (ANF) formulas. If a polynomial f can be computed by an arithmetic formula μ of size s, it can also be computed by an ANF formula ?, possibly of slightly larger size s ^O(1). Our algorithm gets as input black-box access to the output polynomial f (i.e., for any point x in the domain, it can query the black box and obtain f(x) in one step) of a random ANF formula ? of size s (wherein the coefficients of the affine forms in the leaf nodes of ? are chosen independently and uniformly at random from a large enough subset of the underlying field). With high probability (over the choice of coefficients in the leaf nodes), the algorithm efficiently (i.e., in time s ^O(1)) computes an ANF formula \({\hat{\phi}}\) of size s computing f. This then is the strongest model of arithmetic computation for which a reconstruction algorithm is presently known, albeit efficient in a distributional sense rather than in the worst case.