Compensating Data Shortages in Manufacturing with Monotonicity Knowledge

Systematic decision making in engineering requires appropriate models. In this article, we introduce a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints. Incorporating such information is particularly useful when the available datasets are small or do not cover the entire input space, as is often the case in manufacturing applications. We set up the regression subject to the considered monotonicity constraints as a semi-infinite optimization problem, and propose an adaptive solution algorithm. The method is applicable in multiple dimensions and can be extended to more general shape constraints. It was tested and validated on two real-world manufacturing processes, namely, laser glass bending and press hardening of sheet metal. It was found that the resulting models both complied well with the expert’s monotonicity knowledge and predicted the training data accurately. The suggested approach led to lower root-mean-squared errors than comparative methods from the literature for the sparse datasets considered in this work.

Gaze control during reaching is flexibly modulated to optimize task outcome

When reaching for an object with the hand, the gaze is usually directed at the target. In a laboratory setting, fixation is strongly maintained at the reach target until the reaching is completed, a phenomenon known as "gaze-anchoring". While conventional accounts of such tight eye-hand coordination have often emphasized the internal synergetic linkage between both motor systems, more recent optimal control theories regard motor coordination as the adaptive solution to task requirements. We here investigated to what degree gaze control during reaching is modulated by task demands. We adopted a gaze-anchoring paradigm in which participants had to reach for a target location. During the reach, they additionally had to make a saccadic eye movement to a salient visual cue presented at locations other than the target. We manipulated the task demands by independently changing reward contingencies for saccade reaction time (RT) and reaching accuracy. On average, both saccade RTs and reach error varied systematically with reward condition, with reach accuracy improving when the saccade was delayed. The distribution of the saccade RTs showed two types of eye movements: fast saccades with short RTs, and voluntary saccade with longer RTs. Increased reward for high reach accuracy reduced the probability of reflexive fast saccades, but left their latency unchanged. The results suggest that gaze-anchoring acts through a suppression of fast saccades, a mechanism that can be adaptively adjusted to the current task demands.

Inversion of pheromone preference optimizes foraging in C. elegans

Foraging animals have to locate food sources that are usually patchily distributed and subject to competition. Deciding when to leave a food patch is challenging and requires the animal to integrate information about food availability with cues signaling the presence of other individuals (e.g., pheromones). To study how social information transmitted via pheromones can aid foraging decisions, we investigated the behavioral responses of the model animal Caenorhabditis elegans to food depletion and pheromone accumulation in food patches. We experimentally show that animals consuming a food patch leave it at different times and that the leaving time affects the animal preference for its pheromones. In particular, worms leaving early are attracted to their pheromones, while worms leaving later are repelled by them. We further demonstrate that the inversion from attraction to repulsion depends on associative learning and, by implementing a simple model, we highlight that it is an adaptive solution to optimize food intake during foraging.

The neurocomputational architecture of explore-exploit decision making

People often make the difficult decision to try new options (exploration) and forego immediate rewards (exploitation). Novelty-seeking is an adaptive solution to this explore-exploit dilemma that has been studied using targeted recordings in monkeys, but our understanding of the neural computations supporting novelty-seeking in humans is limited. Here, we show homologous computations supporting novelty-seeking across humans and monkeys, and reveal a previously unidentified cortico-subcortical architecture mediating explore-exploit behavior in humans.

Nothing in Cognitive Neuroscience Makes Sense Except in the Light of Evolution

Evolutionary theory should be a fundamental guide for neuroscientists. This would seem a trivial statement, but I believe that taking it seriously is more complicated than it appears to be, as I argue in this article. Elsewhere, I proposed the notion of “bounded functionality” As a way to describe the constraints that should be considered when trying to understand the evolution of the brain. There are two bounded-functionality constraints that are essential to any evolution-minded approach to cognitive neuroscience. The first constraint, the bricoleur constraint, describes the evolutionary pressure for any adaptive solution to re-use any relevant resources available to the system before the selection situation appeared. The second constraint, the satisficing constraint, describes the fact that a trait only needs to behave more advantageously than its competitors in order to be selected. In this paper I describe how bounded-functionality can inform an evolutionary-minded approach to cognitive neuroscience. In order to do so, I resort to Nikolaas Tinbergen’s four questions about how to understand behavior, namely: function, causation, development and evolution. The bottom line of assuming Tinbergen’s questions is that any approach to cognitive neuroscience is intrinsically tentative, slow, and messy.

Direct Method for Solving Bilinear Programming Problem

The bilinear programming problem is considered, where a column, which corresponds to one of the variables, is not fixed but can be chosen from a convex set. This problem is known as the Dantzig – Wolfe problem. Earlier, a modified support method was proposed to solve the problem, using the decomposition of the problem constraints of the Dantzig – Wolfe method. The author of the paper has developed a direct exact method for solving the formulated problem. The method is based on the idea of the solving a linear programming problem with generalized direct constraints and a general concept of an adaptive solution method. The notions of support, support plan, optimal and suboptimal (e-optimal) plan are introduced which is a given approximation of the objective function to the optimal plan of the problem. Criteria for optimality and suboptimality of the support plan have been formulated and have been proved in the paper. The search for the optimal solution is based on the idea of maximizing the increment of the objective function. This approach allows more fully to take into account the main target and structure of the problem. Improving a support plan consists of two parts: replacing the plan and replacing the support. To find a suitable direction, a special derived problem is solved while taking into account the main constraints of the problem. The replacement of the support is based on the search for the optimal plan of the dual problem. The method leads to an optimal solution to the problem in a finite number of iterations (in the case of a non-degenerate value).

Optimal Reconfiguration in Distribution Systems with Distributed Generations Based on Modified Sequential Switch Opening and Exchange

The distribution systems operate radially with meshed topologies thanks to tie-switches and sectionalizing switches consisting of the systems. The power distribution systems are undergoing evolutions strongly toward active distribution systems for reliability and quality of service enhancements through reconfiguration. The distributed generations are also one effective solution for the above objectives. A consumption time is one of the considerable issues that many existing methods have not been achieved for reconfiguration purpose. This paper brings an adaptive solution called modified sequential switch opening and exchange (MSSOE), which is carried out with MATLAB and MATPOWER tool, to overcome these issues. The fundamental loop has been proposed for the MSSOE method to reduce search space in the iteration process. In each step of the searching process, MSSOE observes that if any switches within the same loop of the selected tie-switch are deleted to avoid opening those switches in the next iteration. The process of MSSOE is done when the radial topology is given. The proposed method is tested with the standard IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus distribution systems to observe the effectiveness of the MSSOE algorithm. Comparison to existing algorithms in terms of global solution and computation time conclude that the MSSOE is the best method.