Factorization of weakly continuous holomorphic mappings

We prove a basic property of continuous multilinear mappings between topological vector spaces, from which we derive an easy proof of the fact that a multilinear mapping (and a polynomial) between topological vector spaces is weakly continuous on weakly bounded sets if and only if it is weakly {\it uniformly\/} continuous on weakly bounded sets. This result was obtained in 1983 by Aron, Herv\'es and Valdivia for polynomials between Banach spaces, and it also holds if the weak topology is replaced by a coarser one. However, we show that it need not be true for a stronger topology, thus answering a question raised by Aron. As an application of the first result, we prove that a holomorphic mapping $f$ between complex Banach spaces is weakly uniformly continuous on bounded subsets if and only if it admits a factorization of the form $f=g\circ S$, where $S$ is a compact operator and $g$ a holomorphic mapping

TMbarrier: speculative barriers using hardware transactional memory

Barrier is a very common synchronization method used in parallel programming. Barriers are used typically to enforce a partial thread execution order, since there may be dependences between code sections before and after the barrier. This work proposes TMbarrier, a new design of a barrier intended to be used in transactional applications. TMbarrier allows threads to continue executing speculatively after the barrier assuming that there are not dependences with safe threads that have not yet reached the barrier. Our design leverages transactional memory (TM) (specifically, the implementation offered by the IBM POWER8 processor) to hold the speculative updates and to detect possible conflicts between speculative and safe threads. Despite the limitations of the best-effort hardware TM implementation present in current processors, experiments show a reduction in wasted time due to synchronization compared to standard barriers.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Planeacion Financiera: Aplicación de las herramientas de la planeacion financiera en la Empresa Químicas Veterinarias S,A. en el periodo 2015-2016

Con esta investigación abordamos problemas de liquidez y rentabilidad que se estaban presentando en la empresa Químicas Veterinarias, S.A. con el objetivo de aplicar las principales herramientas de planeación financiera y explicar la importancia y utilidad de las mismas en toda empresa. Para llevar a cabo esta investigación, se utilizó la técnica de investigación documental y bibliográfica, además se revisó los principales Documentos de Gestión e información financiera de la empresa Químicas Veterinarias, S.A que se abordó en el caso práctico. Se logró así comprender en más detalle la problemática presentada por la Empresa. También se determinó las causas que generaron el problema y las posibles soluciones. Como solución, se implementaron las principales herramientas de planeación financiera como son: Presupuesto Maestro, Flujo de Caja Presupuestado y Estados Financieros Proforma, logrando así dar solución a los problemas de Liquidez con la Planeación del Efectivo y mejorar la Rentabilidad con la Planeación de las Utilidades a través del control y planificación de los gastos al analizar el efecto e importancia que los mismos presentaban en los Estados Financieros Proforma. Se puede concluir que las herramientas de planeación financiera son un instrumento de vital importancia en toda empresa si se quiere prolongar la existencia de la misma y si se quiere mantener un crecimiento y desarrollo sostenibles, puesto que con la planeación financiera adecuada se pueden alcanzar los objetivos estratégicos y mantener la “salud” financiera de empresa

A Similarity Measure for Material Appearance

We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.Comment: 12 pages, 17 figure

Predicting real-time scientific experiments using transformer models and reinforcement learning

Life and physical sciences have always been quick to adopt the latest advances in machine learning to accelerate scientific discovery. Examples of this are cell segmentation or cancer detection. Nevertheless, these exceptional results are based on mining previously created datasets to discover patterns or trends. Recent advances in AI have been demonstrated in real-time scenarios like self-driving cars or playing video games. However, these new techniques have not seen widespread adoption in life or physical sciences because experimentation can be slow. To tackle this limitation, this work aims to adapt generative learning algorithms to model scientific experiments and accelerate their discovery using in-silico simulations. We particularly focused on real-time experiments, aiming to model how they react to user inputs. To achieve this, here we present an encoder-decoder architecture based on the Transformer model to simulate real-time scientific experimentation, predict its future behaviour and manipulate it on a step-by-step basis. As a proof of concept, this architecture was trained to map a set of mechanical inputs to the oscillations generated by a chemical reaction. The model was paired with a Reinforcement Learning controller to show how the simulated chemistry can be manipulated in real-time towards user-defined behaviours. Our results demonstrate how generative learning can model real-time scientific experimentation to track how it changes through time as the user manipulates it, and how the trained models can be paired with optimisation algorithms to discover new phenomena beyond the physical limitations of lab experimentation. This work paves the way towards building surrogate systems where physical experimentation interacts with machine learning on a step-by-step basis.Comment: 8 pages, 5 figures, conferenc

Towards heterotic computing with droplets in a fully automated droplet-maker platform

The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations