GPT-NAS: Neural Architecture Search with the Generative Pre-Trained Model

Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have achieved human-level performances in several tasks, few of them are obtained from the NAS method. The main reason is the huge search space of neural architectures, making NAS algorithms inefficient. This work presents a novel architecture search algorithm, called GPT-NAS, that optimizes neural architectures by Generative Pre-Trained (GPT) model. In GPT-NAS, we assume that a generative model pre-trained on a large-scale corpus could learn the fundamental law of building neural architectures. Therefore, GPT-NAS leverages the generative pre-trained (GPT) model to propose reasonable architecture components given the basic one. Such an approach can largely reduce the search space by introducing prior knowledge in the search process. Extensive experimental results show that our GPT-NAS method significantly outperforms seven manually designed neural architectures and thirteen architectures provided by competing NAS methods. In addition, our ablation study indicates that the proposed algorithm improves the performance of finely tuned neural architectures by up to about 12% compared to those without GPT, further demonstrating its effectiveness in searching neural architectures

Indoor Particulate Matter Transfer in CNC Machining Workshop and The Influence of Ventilation Strategies—A Case Study

Particulate matter in Computer Numerical Control (CNC) machining workshop is harmful to workers’ health. This paper studies particulate matter transfer and the performance of various ventilation strategies in a CNC machining workshop. To obtain the boundary condition of the particle field, instruments were installed to obtain the particle size attenuation characteristics and source strength, respectively. The results show that the 99% cumulative mass concentration of particles is distributed within 1.5 μm, and the release rate of particles from the full enclosure. Next, the indoor flow field and particle field were simulated by numerical simulation with the measured boundary conditions. The working area’s age of air, particle concentration, and ventilation efficiency were compared between four displacement ventilation methods and one mixed ventilation method. The results show that the working area’s mean particle concentration and ventilation efficiency under longitudinal displacement ventilation is better than other methods. At the same time, the mean age of air is slightly worse. In addition, mixed ventilation can obtain lower mean age of air, but the particle concentration is higher in the working area. The bilateral longitudinal ventilation can be improved by placing axial circulation fans with vertical upward outlets in the center of the workshop

Analisis Strategi Penerapan Sistem Manajemen Keamanan Pangan HACCP (Hazard Analysis and Critical Control Points) Di PT. Sierad Produce Tbk. Parung

Quality and safety food products problem was usually after thought in the food industry development issues, accordance with the consumer\u27s desirability that understand the importance of product quality and food safety. Hazard Analysis and Critical Control Points (HACCP) certification is one way for company to implementing food safety. Sierad Produce Corp. at this moment has obtained HACCP certificate to produce chicken carcasses.But the implementation need to be controlled, as the case of foodborne illness and foodborne disease can occur easily if not properly controlled. The main objective of this research is to develop the best strategy to implement HACCP and to maintain the food safety quality system at Sierad Produce Corp. The information and data that has been collected within this research were covering both the primary and secondary data based on the date of September 2012 to December 2012. The methods used in this research are descriptive analysis, Internal Factor Evaluation (IFE), External Factor Evaluation (EFE), Internal External (IE), Strength Weakness Opportunity Threat (SWOT) and Analysis Hierarchy Process (AHP). Based on this research, the best strategy for implementing HACCP and sustain the system on Sierad Produce are Critical Control Points (CCP) evaluation and improvement of production room

Unveiling causal attention in dogs' eyes with smart eyewear

Our goals are to better understand dog cognition, and to support others who share this interest. Existing investigation methods predominantly rely on human-manipulated experiments to examine dogs’ behavioral responses to visual stimuli such as human gestures. As a result, existing experimental paradigms are usually constrained to in-lab environments and may not reveal the dog’s responses to real-world visual scenes. Moreover, visual signals pertaining to dog behavioral responses are empirically derived from observational evidence, which can be prone to subjective bias and may lead to controversies. We aim to overcome or reduce the existing limitations of dog cognition studies by investigating a challenging issue: identifying the visual signal(s) from dog eye motion that can be utilized to infer causal explanations of its behaviors, namely estimating causal attention. To this end, we design a deep learning framework named Causal AtteNtIon NEtwork (CANINE) to unveil the dogs’ causal attention mechanism, inspired by the recent advance in causality analysis with deep learning. Equipped with CANINE, we developed the first eyewear device to enable inference on the vision-related behavioral causality of canine wearers. We demonstrate the technical feasibility of the proposed CANINE glasses through their application in multiple representative experimental scenarios of dog cognitive study. Various in-field trials are also performed to demonstrate the generality of the CANINE eyewear in real-world scenarios. With the proposed CANINE glasses, we collect the first large-scale dataset, named DogsView, which consists of automatically generated annotations on the canine wearer’s causal attention across a wide range of representative scenarios. The DogsView dataset is available online to facilitate research

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods