The Markov-Dubins Problem with Free Terminal Direction in a Nonpositively Curved Cube Complex

State complexes are nonpositively curved cube complexes that model the state spaces of reconfigurable systems. The problem of determining a strategy for reconfiguring the system from a given initial state to a given goal state is equivalent to that of finding a path between two points in the state complex. The additional requirement that allowable paths must have a prescribed initial direction and minimal turning radius determines a Markov-Dubins problem with free terminal direction (MDPFTD). Given a nonpositively curved, locally finite cube complex X, we consider the set of unit-speed paths which satisfy a certain smoothness condition in addition to the boundary conditions and curvature constraint that define a MDPFTD. We show that this set either contains a path of minimal length, or is empty. We then focus on the case that X is a surface with a nonpositively curved cubical structure. We show that any solution to a MDPFTD in X must consist of finitely many geodesic segments and arcs of constant curvature, and we give an algorithm for determining those solutions to the MDPFTD in X which are CL paths, that is, made up of an arc of constant curvature followed by a geodesic segment. Finally, under the assumption that the 1-skeleton of X is d-regular, we give sufficient conditions for a topological ray in X of constant curvature to be a rose curve or a proper ray

Modeling truncated pixel values of faint reflections in MicroED images.

The weak pixel counts surrounding the Bragg spots in a diffraction image are important for establishing a model of the background underneath the peak and estimating the reliability of the integrated intensities. Under certain circumstances, particularly with equipment not optimized for low-intensity measurements, these pixel values may be corrupted by corrections applied to the raw image. This can lead to truncation of low pixel counts, resulting in anomalies in the integrated Bragg intensities, such as systematically higher signal-to-noise ratios. A correction for this effect can be approximated by a three-parameter lognormal distribution fitted to the weakly positive-valued pixels at similar scattering angles. The procedure is validated by the improved refinement of an atomic model against structure factor amplitudes derived from corrected micro-electron diffraction (MicroED) images

PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. This work is an important first step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.Comment: 14 pages, 5 figures, submitted for review to JML

Propuesta de mejora de la gestión de mantenimiento en el área de pozos basado en el TPM para mejorar la productividad de los equipos de bombeo de la empresa Agroindustrias San Jacinto S. A. A.

La presente investigación tuvo como objetivo general el desarrollo de una propuesta de mejora en la gestión de mantenimiento en el área de pozos basada en el TPM para incrementar la productividad de los equipos de bombeo en la empresa Agroindustrias San Jacinto S.A.A. Se realizó el diagnóstico de la situación actual de los procesos de mantenimiento de los equipos del área de pozos encontrando los principales problemas que afectan a la productividad actual, las cuales se deben a la falta de un programa de mantenimiento preventivo ya que en el año 2016 se tuvo 254 paradas por mantenimiento correctivo dejando de producir un total de 14,415.36 bolsas de azúcar valorizadas en S/.1,263,243.78. Se tuvo un total de 9,745 horas en reparaciones (TTR) por las 254 paradas por fallas correctivas y se tuvo un MTTR promedio de todos los equipos de 40 horas y un MTBF de 336 horas. Además se detectó falta de personal especializado para algunas ordenes de trabajo y falta de capacitación, esto se vió reflejado en el monto de servicio externo de mantenimiento que asciende en S/. 64,575.00, además se detectó falta de herramientas las cuales muchas ya están obsoletas produciendo demoras en los mantenimientos, falta de equipos predictivos para detección de fallas, falta de orden y limpieza y por último no se contaba con un manual de procedimiento de mantenimiento donde se detectaron % demoras que generaban grandes pérdidas que ascienden a S/.257,427.64 monto significativo que pudo ser reducido a S/. 188,489.39 al implementar procedimientos para reducir los tiempos de mantenimiento de los equipos de bombeo. Finalmente se desarrollaron 6 fases para implementación de la metodología TPM en el área de Pozos a través de charlas, capacitaciones, temas de discusión y desarrollo en general; con el fin de educar y crear una cultura basada en el TPM lo cual tiene como objetivo generar la máxima eficiencia de los equipos de bombeo de la empresa Agroindustrias San Jacinto S.A.A reduciendo tiempos de demoras por paradas y lograr obtener personal más productivo. Para culminar, se realizó una evaluación económica financiera obteniéndose un VAN de S/. 624,133 y un TIR de 59,10% y un retorno de inversión de 1.98 años.The general objective of this research was the development of a proposal forimprovement in maintenance management in the wells area based on the TPM toincrease the productivity of the pumping equipment in the company AgroindustriasSan Jacinto S.A.A. The diagnosis of the current situation of the maintenance processes of the wellarea equipment was made, finding the main problems that affect the currentproductivity, which are due to the lack of a preventive maintenance program sincein 2016 There were 254 stops for corrective maintenance, leaving a total of 14,415.36 bags of sugar valued at S /. 1, 263,243.78. A total of 9,745 hours in repairs (TTR) were taken for the 254 corrective failureshutdowns and there was an average MTTR of all the equipment of 40 hours andan MTBF of 336 hours. In addition, a lack of specialized personnel was detected forsome work orders and lack of training, this was reflected in the amount of externalmaintenance service that amounts to S /. 64,575.00, it was also detected a lack of tools, many of which are already obsolete, causing delays in maintenance, lack of predictive equipment for fault detection, lack of order and cleaning and finally, therewas no maintenance procedure manual where they were detected. % delays thatgenerated large losses amounting to S / .257, 427.64 significant amount that couldbe reduced to S /. 188,489.39 when implementing procedures to reduce themaintenance times of the pumping equipment. Finally, six phases were developed for the implementation of the TPM methodologyin the Wells area through talks, trainings, discussion topics and development ingeneral; in order to educate and create a culture based on the TPM which aims togenerate maximum efficiency of the pumping equipment of the companyAgroindustrias San Jacinto S.A.A reducing downtime due to stops and achievemore productive personnel. To complete it, an economic financial evaluation was carried out, obtaining a VPN of S /. 624,133 and an IRR of 59.10% and aninvestment return of 1.98 years