FPGA-based implementation of classification techniques: A survey

Recently, a number of classification techniques have been introduced. However, processing large dataset in a reasonable time has become a major challenge. This made classification task more complex and expensive in calculation. Thus, the need for solutions to overcome these constraints such as field programmable gate arrays (FPGAs). In this paper, we give an overview of the various classification techniques. Then, we present the existing FPGA based implementation of these classification methods. After that, we investigate the confronted challenges and the optimizations strategies. Finally, we highlight the hardware accelerator architectures and tools for hardware design suggested to improve the FPGA implementation of classification methods.     

Residual attention-based tracking-by-detection network with attention-driven data augmentation

Tracking-by-detection (TBD) is a significant framework for visual object tracking. However, current trackers are usually updated online based on random sampling with a probability distribution. The performance of the learning-based TBD trackers is limited by the lack of discriminative features, especially when the background is full of semantic distractors. We propose an attention-driven data augmentation method, in which a residual attention mechanism is integrated into the TBD tracking network as supplementary references to identify discriminative image features. A mask generating network is used to simulate changes in target appearances to obtain positive samples, where attention information and image features are combined to identify discriminative features. In addition, we propose a method for mining hard negative samples, which searches for semantic distractors with the response of the attention module. The experiments on the OTB2015, UAV123, and LaSOT benchmarks show that this method achieves competitive performance in terms of accuracy and robustness.     

Structural Aspects and Prediction of Calmodulin-Binding Proteins

Calmodulin (CaM) is an important intracellular protein that binds Ca²⁺ and functions as a critical second messenger involved in numerous biological activities through extensive interactions with proteins and peptides. CaM’s ability to adapt to binding targets with different structures is related to the flexible central helix separating the N- and C-terminal lobes, which allows for conformational changes between extended and collapsed forms of the protein. CaM-binding targets are most often identified using prediction algorithms that utilize sequence and structural data to predict regions of peptides and proteins that can interact with CaM. In this review, we provide an overview of different CaM-binding proteins, the motifs through which they interact with CaM, and shared properties that make them good binding partners for CaM. Additionally, we discuss the historical and current methods for predicting CaM binding, and the similarities and differences between these methods and their relative success at prediction. As new CaM-binding proteins are identified and classified, we will gain a broader understanding of the biological processes regulated through changes in Ca²⁺ concentration through interactions with CaM.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

PUL-Mediated Plant Cell Wall Polysaccharide Utilization in the Gut Bacteroidetes

Plant cell wall polysaccharides (PCWP) are abundantly present in the food of humans and feed of livestock. Mammalians by themselves cannot degrade PCWP but rather depend on microbes resident in the gut intestine for deconstruction. The dominant Bacteroidetes in the gut microbial community are such bacteria with PCWP-degrading ability. The polysaccharide utilization systems (PUL) responsible for PCWP degradation and utilization are a prominent feature of Bacteroidetes. In recent years, there have been tremendous efforts in elucidating how PULs assist Bacteroidetes to assimilate carbon and acquire energy from PCWP. Here, we will review the PUL-mediated plant cell wall polysaccharides utilization in the gut Bacteroidetes focusing on cellulose, xylan, mannan, and pectin utilization and discuss how the mechanisms can be exploited to modulate the gut microbiota.     

Technology enhanced learning of quantitative critical thinking

A Quantitative Critical Thinking (QCT) software tool was developed in this study to facilitate students’ learning of quantitative critical thinking via repeated practice by chemical engineering students reading a core module called fluid-solid systems. The software tool generated detailed calculation steps to typical engineering design problems encountered in this module that contained weaknesses, flaws or even errors. Students utilized the software tool to practice identifying these weaknesses, flaws or errors in the design solutions and then present a better or correct design by applying the concepts and knowledge acquired in the module. Since the QCT software tool was built upon an existing design software tool that was able to generate the correct, detailed design calculation steps to design problems, students were able to check their own design calculations against those presented by the software tool during this second learning step, thereby engaging in and learning quantitative critical thinking via a repeated practice approach. The software tool was successful in enhancing the performance of second-year undergraduate students in solving a question that required quantitative critical thinking in the final examination of the module. The average percentage scores achieved by students for the question who reported higher frequencies of usage of the software were generally higher than those who reported lower frequencies of usage or did not utilize the software tool throughout the semester.     

Box-Behnken Design a Key Tool to Achieve Optimized PCL/Gelatin Electrospun Mesh

Hybrid electrospun nanofibers of polycaprolactone (PCL)/gelatin are considered as drug-delivery systems for increasing the treatment efficacy in superficial (skin) wounds. Continuous delivery of therapeutic agents, skin extracellular matrix similarity, management of wound exudate, and antimicrobial barrier effect are the major advantages of electrospun nanofibers in skin applications. Additionally, combining the favorable properties of PCL and gelatin, regarding their biocompatibility, biodegradability and mechanical performance have been revealed promising parameters to be considered for blend in hybrid structures. However, the usual optimization protocol of nanofibers’ production in electrospinning is based on the observation of one-variable-at-time being this methodology expensive and time-consuming. Therefore, in this research work, a statistical model based on four input variables namely, the flow rate, the needle-working distance, the applied voltage, and the ratio of PCL in the solution, is developed to predict the behavior of nanofibers. The performance of nanofibers is monitored by measurements of fiber's diameter, mesh's thickness, and mesh's permeability. Overall, the model showed to be statistically significant (p-value < 0.05) and an independent analysis validated the predicted response for optimal condition. Finally, a delivery study is performed to evaluate the electrospun mesh performance as a drug carrier.     

Review on synthesis of porous TiO2-based catalysts for energy conversion systems

Porous TiO₂-based catalysts have recently received remarkable attention in the field of energy conversion systems, including hydrogen/oxygen evolution reaction, oxygen/nitrogen reduction reaction, and photodegradation of pollutants owing to their unique structure, large surface area, and good chemical stability. In this report, we review existing research on porous TiO₂-based catalysts for energy conversion systems during the past four years. First, the advantages of porous TiO₂-based catalysts are introduced. Next, the synthetic approaches in developing porous TiO₂-based catalysts are summarized. The different types of energy conversion systems based on porous TiO₂-based catalysts are then presented. Finally, the challenges and future perspectives in synthesizing porous TiO₂-based catalysts are discussed.     

Mechanisms of Reciprocal Regulation of Gonadotropin-Releasing Hormone (GnRH)-Producing and Immune Systems: The Role of GnRH,Cytokines and Their Receptors in Early Ontogenesis in Normal and Pathological Conditions

Different aspects of the reciprocal regulatory influence on the development of gonadotropin-releasing hormone (GnRH)-producing- and immune systems in the perinatal ontogenesis and their functioning in adults in normal and pathological conditions are discussed. The influence of GnRH on the development of the immune system, on the one hand, and the influence of proinflammatory cytokines on the development of the hypothalamic-pituitary-gonadal system, on the other hand, and their functioning in adult offspring are analyzed. We have focused on the effects of GnRH on the formation and functional activity of the thymus, as the central organ of the immune system, in the perinatal period. The main mechanisms of reciprocal regulation of these systems are discussed. The reproductive health of an individual is programmed by the establishment and development of physiological systems during critical periods. Regulatory epigenetic mechanisms of development are not strictly genetically controlled. These processes are characterized by a high sensitivity to various regulatory factors, which provides possible corrections for disorders.     

Past is important: Improved image captioning by looking back in time

A major development in the area of image captioning consists of trying to incorporate visual attention in the design of language generative model. However, most previous studies only emphasize its role in enhancing visual composition at the current moment, while neglect its role in global sequence reasoning. This problem appears not only in captioning model, but also in reinforcement learning structure. To tackle this issue, we first propose a Visual Reserved model that enables previous visual context to be considered for the current sequence reasoning. Next, a Attentional-Fluctuation Supervised model is also proposed in reinforcement learning structure. Compared against the traditional strategies that only take non-differentiable Natural Language Processing (NLP) metrics as the incentive standard, the proposed model regards the fluctuation of previous attention matrix as an important indicator to judge the convergence of the captioning model. The proposed methods have been tested on MS-COCO captioning dataset and achieve competitive results evaluated by the evaluation server of MS COCO captioning challenge.