G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

Nowadays, deep neural networks for object detection in images are very prevalent. However, due to the complexity of these networks, users find it hard to understand why these objects are detected by models. We proposed Gaussian Class Activation Mapping Explainer (G-CAME), which generates a saliency map as the explanation for object detection models. G-CAME can be considered a CAM-based method that uses the activation maps of selected layers combined with the Gaussian kernel to highlight the important regions in the image for the predicted box. Compared with other Region-based methods, G-CAME can transcend time constraints as it takes a very short time to explain an object. We also evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO 2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.Comment: 10 figure

Phlogacanthus cornutus: chemical profiles and antioxidant effects

Phlogacanthus cornutus is a rare species and the chemical profiles and the bioactivities of this plant are unknown. In present study, the chemical components of the acetone extract as well as the antioxidant activity of acetone extract and its fractions such as n-hexane, chloroform and ethyl acetate of P. cornutus were firstly reported. A total of 33 constituents were identify in the acetone extract of this plant using Gas Chromatography/Mass Spectrometry assay, in which trans-cinnamic acid (21.26%), neophytadiene (6.36%), linolenic acid (5.86%), dihydroagathic acid (5.71%), n-hexadecanoic acid (5.53%), phytol (4.14%) and cis-cinnamic acid (3.23%) were the major compounds. The acetone extract and its fractions such as n-hexane, chloroform and ethyl acetate of P. cornutus showed DPPH radical scavenging activity with IC50 value of 234.31, 185.95, 758.65 and 458.52 µg/mL respectively

FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.Comment: Accepted for presentation at the 51st International Conference on Parallel Processin

Application of the cut-off projection to solve a backward heat conduction problem in a two-slab composite system

The main goal of this paper is applying the cut-off projection for solving one-dimensional backward heat conduction problem in a two-slab system with a perfect contact. In a constructive manner, we commence by demonstrating the Fourier-based solution that contains the drastic growth due to the high-frequency nature of the Fourier series. Such instability leads to the need of studying the projection method where the cut-off approach is derived consistently. In the theoretical framework, the first two objectives are to construct the regularized problem and prove its stability for each noise level. Our second interest is estimating the error in -norm. Another supplementary objective is computing the eigen-elements. All in all, this paper can be considered as a preliminary attempt to solve the heating/cooling of a two-slab composite system backward in time. Several numerical tests are provided to corroborate the qualitative analysis.Peer reviewe

Growth of single crystals of methylammonium lead mixedhalide perovskites

We report the growth and characterization of different bulk single crystals of organo lead mixed halide perovskites CH3NH3PbI3−xBrx by two different crystal growth approaches: (i)anti-solvent diffusion, and (ii) inverse temperature crystallization. In order to control the size and the shape of crystals, we have investigated different experimental growth parameters such as temperature and precursor concentration. The morphology of obtained crystals was observed by optical microscope, whereas their intrinsic crystalline properties were characterized by single crystal as well as powder X-ray diffraction. The results illustrated that the growth and crystalline structure of mixed halide perovskites CH3NH3PbI3−xBrx could be easily tuned

OXIDIZED MAIZE STARCH: CHARACTERIZATION AND EFFECT OF IT ON THE BIODEGRADABLE FILMS I. CHARACTERIZATION OF MAIZE STARCH OXIDIZED BY SODIUM HYPOCHLORITE

The effects of hypochlorite level, i.e. 0.5; 1 and 2 active chlorine g/100g starch, on the structures and physicochemical properties of oxidized maize starch were investigated. The obtained results shown that oxidation degree grew up with increasing hypochlorite concentration, specially, the carboxyl content saw higher increased than the content of carbonyl. SEM images indicated that surface of oxidized maize starches were rougher than native starch. The surface of oxidized starches saw rougher with increasing hypochlorite level. However, the DSC results illustrated that there was no significant difference of gelatinization temperature between the native starch and oxidized starches