Working with OpenCL to Speed Up a Genetic Programming Financial Forecasting Algorithm: Initial Results

The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device

Learning of Weighted Dynamic Multi-layer Networks via Latent Gaussian Processes

International audienc

Situación actual de la ganadería de bovinos en el municipio de Tejupilco

La ganadería constituye una actividad importante en zonas rurales a pesar del acelerado crecimiento de actividades alternativas. El objetivo de este trabajo fue caracterizar unidades de producción (UP) de ganado bovino en el municipio de Tejupilco, Estado de México, desde un enfoque sistémico. Se recopiló información primaria mediante encuestas estructuradas y entrevista directa a 55 ganaderos. Las UP se agruparon en cuatro estratos en función del tamaño del hato (E1 = UP entre 1 y 10 unidades ganaderas totales; E2 = UP entre 11 y 21 UGT; E3 = UP entre 22 y 31 UGT; y E4 = UP con más de 32 UGT). La estructura de las UP, el manejo del ganado y la orientación de la producción depende del tamaño del hato y de la disponibilidad de tierra. UP de menor tamaño tienen mayor orientación a la producción de leche, mientras que UP grandes se orientan a la producción de becerros para abasto en un sistema extensivo, aunque también se observa la engorda de animales. Se destacó la diversidad de UP y se evidenció que hatos de menor tamaño aprovechan eficientemente la tierra, y UP de mayor tamaño hacen eficiente el uso de la mano de obraUNIVERSIDAD AUTÓNOMA DEL ESTADO DE MÉXICO CONSEJO NACIONAL DE CIENCIA Y TECNOLOGÍ

A network characterization of the interbank exposures in Peru

After the Global Financial Crisis (GFC), systemic risk measurement became crucial for policy makers as well as for academics. We have witnessed an important increase in the number of methodologies proposed. Among such proposals, DebtRank arose as perhaps one of the most relevant in this context, as it resorts to network modeling and captures the all-important aspect of interconnectedness in the financial system. Additionally, within the network modeling approach, there is the multilayer approach, which provides additional insights on the decomposition of systemic risk. In this paper, we apply a multilayer network analysis to study systemic risk in the Peruvian banking system by utilizing DebtRank centrality. The main contributions of this work are as follows: i) It fully characterizes the multilayer exposure network of the Peruvian banking system, and ii) it obtains the systemic risk profile of the banking system according to different types of exposures

Virtual environment for assistant mobile robot

This paper shows the development of a virtual environment for a mobile robotic system with the ability to recognize basic voice commands, which are oriented to the recognition of a valid command of bring or take an object from a specific destination in residential spaces. The recognition of the voice command and the objects with which the robot will assist the user, is performed by a machine vision system based on the capture of the scene, where the robot is located. In relation to each captured image, a convolutional network based on regions is used with transfer learning, to identify the objects of interest. For human-robot interaction through voice, a convolutional neural network (CNN) of 6 convolution layers is used, oriented to recognize the commands to carry and bring specific objects inside the residential virtual environment. The use of convolutional networks allowed the adequate recognition of words and objects, which by means of the associated robot kinematics give rise to the execution of carry/bring commands, obtaining a navigation algorithm that operates successfully, where the manipulation of the objects exceeded 90%. Allowing the robot to move in the virtual environment even with the obstruction of objects in the navigation path.&lt

Vision-Based Safety System for Barrierless Human-Robot Collaboration

Human safety has always been the main priority when working near an industrial robot. With the rise of Human-Robot Collaborative environments, physical barriers to avoiding collisions have been disappearing, increasing the risk of accidents and the need for solutions that ensure a safe Human-Robot Collaboration. This paper proposes a safety system that implements Speed and Separation Monitoring (SSM) type of operation. For this, safety zones are defined in the robot's workspace following current standards for industrial collaborative robots. A deep learning-based computer vision system detects, tracks, and estimates the 3D position of operators close to the robot. The robot control system receives the operator's 3D position and generates 3D representations of them in a simulation environment. Depending on the zone where the closest operator was detected, the robot stops or changes its operating speed. Three different operation modes in which the human and robot interact are presented. Results show that the vision-based system can correctly detect and classify in which safety zone an operator is located and that the different proposed operation modes ensure that the robot's reaction and stop time are within the required time limits to guarantee safety.Comment: Accepted for publication at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

1D and 3D supramolecular structures exhibiting weak ferromagnetism in three Cu(II) complexes based on malonato and di-alkyl-2,2’-bipyridines

Abstract Manganese coordination polymers {Mn(- fum)(5dmb)(H2O)2}n (1) and {[Mn2(fum)2(4dmb)2] H2O}n (2) (fum= fumarato; 5dmb = 5,50-dimethyl-2,20-bipyridine; 4dmb = 4,40-dimethyl-2,20-bipyridine) were obtained from one-pot, solution reactions under ambient conditions. The fum ligand acquires different coordination modes in the presence of the different dmb ancillary ligands, promoting distinctive crystal structures, including divergent dimensionalities. Thus, X-ray single-crystal data reveal that complex 1 crystallizes in a monoclinic system with C2/c space group and forms an infinite one-dimensional polymer. The Mn(II) center is six-coordinated and displays a distorted octahedral configuration. In addition, the solid-state selfassembly of the polymeric structure of 1 gives rise to a twodimensional (2D) supramolecular framework, mainly through hydrogen bonding. In contrast, complex 2 crystallizes in a monoclinic system with a Cc space group and forms an infinite 2D coordination polymer having dinuclear units. The Mn(II) center has a distorted octahedral configuration. The thermal stabilities of both coordination polymers were investigated. Variable-temperature magnetic measurements show that complex 1 is paramagnetic, while complex 2 exhibits weak antiferromagnetic coupling between adjacent Mn(II) centers.supported by CONACyT project 129293, DGAPA-UNAM project IN106014, and ICYTDF, project PICCO