ESTIMATES OF DERIVATIVES OF (LOG) DENSITIES AND RELATED OBJECTS

We estimate the density and its derivatives using a local polynomial approximation to the logarithm of an unknown density function f. The estimator is guaranteed to be non-negative and achieves the same optimal rate of convergence in the interior as on the boundary of the support of f. The estimator is therefore well-suited to applications in which non-negative density estimates are required, such as in semiparametric maximum likelihood estimation. In addition, we show that our estimator compares favorably with other kernel-based methods, both in terms of asymptotic performance and computational ease. Simulation results confirm that our method can perform similarly or better in finite samples compared to these alternative methods when they are used with optimal inputs, that is, an Epanechnikov kernel and optimally chosen bandwidth sequence. We provide code in several languages.

Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques

Estimating the bearing capacity of piles is an essential point when seeking for safe and economic geotechnical structures. However, the traditional methods employed in this estimation are time-consuming and costly. The current study aims at elaborating a new alternative model for predicting the pile-bearing capacity based on eleven new advanced machine-learning methods in order to overcome these limitations. The modeling phase used a database of 100 samples collected from different countries. Additionally, eight relevant factors were selected in the input layer based on the literature recommendations. The optimal inputs were modeled using the machine-learning methods and their performance was assessed through six performance measures using a K-fold cross-validation approach. The comparative study proved the effectiveness of the DNN model, which displayed a higher performance in predicting the pile-bearing capacity. This elaborated model provided the optimal prediction, i.e., the closest to the experimental values, compared to the other models and formulae proposed by previous studies. Finally, a reliable and easy-to-use graphical interface was generated, namely “BeaCa2021”. This will be very helpful for researchers and civil engineers when estimating the pile-bearing capacity, with the advantage of saving time and money.

Optimal information loading into working memory in prefrontal cortex

Working memory involves the short-term maintenance of information and is critical in many tasks. The neural circuit mechanisms underlying this information maintenance are thought to rely on persistent activities resulting from attractor dynamics. However, how information is loaded into working memory for subsequent maintenance remains poorly understood. A pervasive assumption is that information loading requires inputs that are similar to the persistent activities expressed during maintenance. Here, we show through mathematical analysis and numerical simulations that optimal inputs are instead largely orthogonal to persistent activities and naturally generate the rich transient dynamics that are characteristic of prefrontal cortex (PFC) during working memory. By analysing recordings from monkeys performing a memory-guided saccade task, and using a novel, theoretically principled metric, we show that PFC exhibits the hallmarks of optimal information loading. Our theory unifies previous, seemingly conflicting theories of memory maintenance based on attractor or purely sequential dynamics, and reveals a normative principle underlying the widely observed phenomenon of dynamic coding in PFC. These results suggest that optimal information loading may be a key component of attractor dynamics characterising various cognitive functions and cortical areas, including long-term memory and navigation in the hippocampus, and decision making in the PFC.

Real-Time Velocity Optimization for Energy-Efficient Control of Connected and Automated Vehicles

This paper presents a fast numerical algorithm for velocity optimization based on the Pontryagin' minimum principle (PMP). Considering the difficulties in the application of the PMP when state constraints exist, the penalty function approach is proposed to convert the state-constrained problem into an unconstrained one. Then this paper proposes an iterative numerical algorithm by using the explicit solution to find the optimal solution. The proposed numerical algorithm is applied to the velocity trajectory optimization for energy-efficient control of connected and automated vehicles (CAVs). Simulation results indicate that the algorithm can generate the optimal inputs in milliseconds, and a significant improvement in computational efficiency compared with traditional methods (a few seconds). Hardware in the Loop test for experimental validation is given to further verify the real-time performance of the proposed algorithm.

OPTIMALISASI PENGGUNAAN INPUT USAHATANI BAWANG MERAH DI DESA SUNGAI GERINGGING KECAMATAN KAMPAR KIRI KABUPATEN KAMPAR PROPINSI RIAU

ABSTRACT Shallots are a strategic commodity but still require attention in the use of optimal inputs to achieve maximum results. All farm inputs are still limited in number while high production is highly expected by farmers. The purpose of this study was to determine the use of inputs, analyze the use of inputs in order to achieve optimal conditions and analyze the effect of input price changes on the optimal solution on red onion farming in Sungai Geringging Village, Kampar Kiri District, Kampar Regency. Descriptive qualitative and quantitative analysis methods using Linear Programming. The results of input use research state that the area of the land is narrow that is an average of 0.25 hectares, the seeds are superior but the number of uses is still below standard, more use of labor outside the family, dominant farmers use inorganic fertilizers, use pesticides to repel pests and work equipment simple one. The use of farming inputs is not optimal so that the reduction or addition of input availability will not affect the total profit in optimal conditions. The effect of changes when an input price increases and decreases by 3.25 percent does not show a difference when compared to the initial optimal conditions. Keywords: Optimization, Input, Shallot, Linear Programming, LINDO

An efficient methodology to generate optimal inputs for the preliminary design of centrifugal compressor impellers

Preliminary design of centrifugal compressors is an emphasized step, which initiates the design process. Even with the use of quick analysis methods such as one-dimensional models, preliminary design still occupies a substantial part of the total design time. Among the factors that can lengthen this time or even cause the design failure is the inappropriate selection of the design input parameters. The present paper proposes a methodology to generate optimal inputs for the preliminary design, which reduces the design time and optimizes its overall performance. This is achieved, firstly, by performing an aerothermodynamic analysis that defines the appropriate input parameters, which will be used by a preliminary design code. This analysis has allowed identifying three pilot parameters (inlet relative Mach number, work input factor, and slip factor) to guide the generation of adequate input parameters. This input parameter generator, which reduces considerably the failure rate, is then exploited efficiently in an optimization process considering the pilot parameters as decision variables. Therefore, the proposed input parameter generator is coupled with a preliminary design code and an optimization algorithm. The proposed input parameter generator was validated on four existing compressors, showing a gain of more than 90% of the design time. Mainly, the proposed optimization has created a preliminary design trade-off having the target requirements and with optimized off-design performance.

Path Planning and Control of Redundant Manipulators Using Bilevel Optimization

Redundancy is a useful feature in dynamic systems which can be exploited to enhance performance in various tasks. In this work, redundancy will be utilized to minimize the energy consumption of a linear manipulator, while in some cases an additional task of end-effector tracking will also be required and achieved. Optimal control theory has been extensively used for the optimization of dynamic systems; however, complex tasks and redundancy make these problems computationally expensive, numerically difficult to solve, and in many cases, ill-defined. In this paper, evolutionary bilevel optimization for the problem is presented. This is done by setting up an upper level optimization problem for a set of decision variables and a lower level one that actually calculates the optimal inputs and trajectories. The upper level problem is solved by a genetic algorithm (GA), whereas the lower level problem uses classical optimal control. As a result, the proposed algorithm allows the optimization of complex tasks that usually cannot be solved in practice using standard optimal control tools. In addition, despite the use of penalty functions to enforce saturation constraints, the algorithm leads to global energy minimization. Illustrative examples of a redundant x-y robotic manipulator with complex overall tasks will be presented, solved, and discussed.

On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Views

This tutorial paper focuses on the variants of the bottleneck problem taking an information theoretic perspective and discusses practical methods to solve it, as well as its connection to coding and learning aspects. The intimate connections of this setting to remote source-coding under logarithmic loss distortion measure, information combining, common reconstruction, the Wyner–Ahlswede–Korner problem, the efficiency of investment information, as well as, generalization, variational inference, representation learning, autoencoders, and others are highlighted. We discuss its extension to the distributed information bottleneck problem with emphasis on the Gaussian model and highlight the basic connections to the uplink Cloud Radio Access Networks (CRAN) with oblivious processing. For this model, the optimal trade-offs between relevance (i.e., information) and complexity (i.e., rates) in the discrete and vector Gaussian frameworks is determined. In the concluding outlook, some interesting problems are mentioned such as the characterization of the optimal inputs (“features”) distributions under power limitations maximizing the “relevance” for the Gaussian information bottleneck, under “complexity” constraints.

Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets” (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices.