Defect segmentation for multi-illumination quality control systems

Thanks to recent advancements in image processing and deep learning techniques, visual surface inspection in production lines has become an automated process as long as all the defects are visible in a single or a few images. However, it is often necessary to inspect parts under many different illumination conditions to capture all the defects. Training deep networks to perform this task requires large quantities of annotated data, which are rarely available and cumbersome to obtain. To alleviate this problem, we devised an original augmentation approach that, given a small image collection, generates rotated versions of the images while preserving illumination effects, something that random rotations cannot do. We introduce three real multi-illumination datasets, on which we demonstrate the effectiveness of our illumination preserving rotation approach. Training deep neural architectures with our approach delivers a performance increase of up to 51% in terms of AuPRC score over using standard rotations to perform data augmentation.

Memory for individual items is related to nonreinforced preference change

It is commonly assumed that memories contribute to value-based decisions. Nevertheless, most theories of value-based decision-making do not account for memory influences on choice. Recently, new interest has emerged in the interactions between these two fundamental processes, mainly using reinforcement-based paradigms. Here, we aimed to study the role memory processes play in preference change following the nonreinforced cue-approach training (CAT) paradigm. In CAT, the mere association of cued items with a speeded motor response influences choices. Previous studies with this paradigm showed that a single training session induces a long-lasting effect of enhanced preferences for high-value trained stimuli, that is maintained for several months. We hypothesized that CAT increases memory of trained items, leading to enhanced accessibility of their positive associative memories and in turn to preference changes. In two preregistered experiments, we found evidence that memory is enhanced for trained items and that better memory is correlated with enhanced preferences at the individual item level, both immediately and 1 mo following CAT. Our findings suggest that memory plays a central role in value-based decision-making following CAT, even in the absence of external reinforcements. These findings contribute to new theories relating memory and value-based decision-making and set the groundwork for the implementation of novel nonreinforced behavioral interventions that lead to long-lasting behavioral change.

Evaluating the role of Approach-Avoidance Training on action-tendencies in individuals with skin-picking disorder: A preliminary randomized experiment

Background and aimsPathological skin-picking (PSP) or excoriation disorder is a destructive behavior that affects 1-2% of the general population. The purpose of this pilot study was to evaluate the effect of a computerized behavior modification task on action-tendencies (i.e., approach or avoidance) in adults with PSP. We aimed to modify these action-tendencies by having participants with PSP complete the Approach-Avoidance Training (AAT) task, using a joystick to simulate an approach (=pull) or avoidance (=push) response.MethodForty-five participants diagnosed with PSP were randomized to one of three training conditions: (1) Avoidance Training (AvT; n = 15), (2) Approach Training (ApT; n = 15), or (3) Placebo Training (PT; n = 15). We hypothesized that after training, those in the AvT would have the greatest reduction in behavioral approach (i.e., their overall reaction time [RT] to approach pictures of irregular skin stimuli).ResultsResults of the pre-training assessment task revealed a positive correlation between behavioral approach to irregular skin stimuli and skin-picking severity as assessed by the Skin Picking Scale-Revised (SPS-R). After training, a lower behavioral approach and urges to pick were found in the AvT and PT groups, while those in the ApT reported higher behavioral approach and urges to pick. At two-week follow-up, no significant changes on the SPS-R were reported between groups.DiscussionOur preliminary data suggest that the AAT is a promising avenue of research to develop as a cognitive intervention to address an excessive behavioral approach tendency that characterizes skin-picking problems.

Green project management from employees' perspective in Hong Kong's engineering and construction sectors

PurposeThe study investigates employees’ perceptions of green project management in Hong Kong's engineering and construction sectors.Design/methodology/approachGreen project management attributes were identified and categorized in terms of organization and process aspects based on a literature review. A questionnaire was developed to collect responses from employees working in Hong Kong's engineering and construction sectors.FindingsRespondents perceived “using Design for Environment approach,” “training employees about green project management” and “recycling the used or excessive materials/components” as the most important attributes. Generally, females gave higher importance ratings than males to most attributes. Respondents who had higher education qualifications or held senior positions perceived green project management attributes as more important than their counterparts with lower education qualifications or in lower positions. Green project management was found to have four distinct factors: “Management Commitment,” “Green Technologies and Processes,” “Green Partnerships” and “External Communication.”Originality/valueThe study is one of the first empirical works on green project management in Hong Kong's engineering and construction sectors. It demonstrates that green project management should be characterized as a multidimensional concept.

Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques

Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension). We previously reported a machine-learning approach to identify polyHAM from FDA-approved human targeted drugs using a heterologous approach (training with peptides and non-peptide compounds). Here we discover that polyHAM are more likely to be found among antimicrobials displaying a broad-spectrum antibiotic activity and that topological, but not chemical features, are most informative to classify this activity. A heterologous machine-learning approach was trained with broad-spectrum antimicrobials and tested with human metabolites; these metabolites were labeled as antimicrobials or non-antimicrobials based on a naïve text-mining approach. Human metabolites are not commonly recognized as antimicrobials yet circulate in the human body where microbes are found and our heterologous model was able to classify those with antimicrobial activity. These results provide the basis to develop applications aimed to design human diets that purposely alter metabolic compounds proportions as a way to control human microbiome.

Main Scientific and Technological Problems in the Field of Architectural Solutions for Supercomputers

In this paper author describes creation of a domestic accelerator processor capable of replacing NVIDIA GPGPU graphics processors for solving scientific and technical problems and other tasks requiring high performance, but which are characterized by good or medium localization of the processed data. Moreover, this paper illustrates creation of a domestic processor or processors for solving the problems of creating information systems for processing big data, as well as tasks of artificial intelligence (deep learning, graph processing and others). Therefore, these processors are characterized by intensive irregular work with memory (poor and extremely poor localization of data), while requiring high energy efficiency. The author points out the need for a systematic approach, training of young specialists on supporting innovative research.