A Simple Label Switching Algorithm for Semisupervised Structural SVMs

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

MOMENT ANALYSIS OF A SCROLL EXPANDER USED IN AN ORGANIC RANKINE CYCLE

Recent studies on small-scale power generation with the organic Rankine cycle suggest superior performance of positive displacement type of expanders compared to turbines. Scroll expanders in particular achieve high isentropic efficiencies due to lower leakage and frictional losses. Performance of scroll machines may be enhanced by the use of non-circular involute curves in place of the circular involutes resulting non-uniform wall thickness. In this paper, a detailed moment analysis is performed for such an expander having volumetric expansion ratio of 5 using thermodynamic models proposed earlier by one of the present authors. The working fluid considered in the power cycle is R-245fa with scroll inlet temperature of 125 degrees C for a gross power output of similar to 3.5 kW. The model developed in this paper is verified with an air scroll compressor available in the literature and then applied to an expander Prediction of small variation of moment with scroll motion recommends use of scroll expander without a flywheel over other positive displacement type of expanders, e.g. reciprocating, where a flywheel is an essential component.

Hybrid Working Set Algorithm for SVM Learning With a Kernel Coprocessor on FPGA

Support vector machines (SVM) are a popular class of supervised models in machine learning. The associated compute intensive learning algorithm limits their use in real-time applications. This paper presents a fully scalable architecture of a coprocessor, which can compute multiple rows of the kernel matrix in parallel. Further, we propose an extended variant of the popular decomposition technique, sequential minimal optimization, which we call hybrid working set (HWS) algorithm, to effectively utilize the benefits of cached kernel columns and the parallel computational power of the coprocessor. The coprocessor is implemented on Xilinx Virtex 7 field-programmable gate array-based VC707 board and achieves a speedup of upto 25x for kernel computation over single threaded computation on Intel Core i5. An application speedup of upto 15x over software implementation of LIBSVM and speedup of upto 23x over SVMLight is achieved using the HWS algorithm in unison with the coprocessor. The reduction in the number of iterations and sensitivity of the optimization time to variation in cache size using the HWS algorithm are also shown.

Design of a manual press for the production of compacted stabilized soil blocks

In this paper, we discuss the design of a manually operated soil compaction machine that is being used to manufacture stabilized soil blocks (SSB). A case study of manufacturing more than three million blocks in a housing project using manually operated machines is illustrated. The paper is focussed on the design, development, and evaluation of a manually operated soil compaction machine for the production of SSB. It also details the machine design philosophy, compaction characteristics of soils, employment generation potential of small-scale stabilized soil block productions systems, and embodied energy. Static compaction of partially saturated soils was performed to generate force-displacement curves in a confined compaction process were generated. Based on the soil compaction data engineering design aspects of a toggle press are illustrated. The results of time and motion study on block production operations using manual machines are discussed. Critical path network diagrams were used for small-scale SSB production systems. Such production systems generate employment at a very low capital cost.

Empirical Analysis of Sampling Based Estimators for Evaluating RBMs

The Restricted Boltzmann Machines (RBM) can be used either as classifiers or as generative models. The quality of the generative RBM is measured through the average log-likelihood on test data. Due to the high computational complexity of evaluating the partition function, exact calculation of test log-likelihood is very difficult. In recent years some estimation methods are suggested for approximate computation of test log-likelihood. In this paper we present an empirical comparison of the main estimation methods, namely, the AIS algorithm for estimating the partition function, the CSL method for directly estimating the log-likelihood, and the RAISE algorithm that combines these two ideas.

七氟丁酸酐有序超薄膜的制备与结构

在涂敷有聚合物PEI涂层的单晶硅表面上制备了HFBA单层分子膜,接触角测量及XPS结果表明,HFBA在PEI表面产生了化学吸附发生了化学键合(酰胺键),形成了低表面能的HFBA单分子层膜.这一吸附反应的动力学行为可能表现为Langmuir单分子层化学吸附.

Carne e bits : reflexões sobre a indiscernibilidade das fronteiras entre mentes e máquinas e os sistemas cognitivos híbridos

Faz reflexões sobre as várias formas de relação entre sujeitos e objetos-técnicos, com ênfase para a utilização dos computadores digitais e, particularmente, os softwares chamados agentes inteligentes. Analisa o espaço e suas mudanças qualitativas na atualidade, a partir do conceito do espaço como produção humana, analisando como as transformações em curso no ambiente afetam nossas subjetividades e, reciprocamente, como afetamos nossos ambientes. Discutidas as possibilidades de sobrevivência do homem nu nesses novos espaços, sem que esteja devidamente atualizado com as últimas novidades tecnológicas - próteses sensoriais e motoras. Perpassa a discussão sobre o pensamento que se utiliza do espaço como elemento constituinte do próprio pensamento e reflete sobre o espaço abstrato por excelência, os mundos virtuais. Discute o padrão de apropriação de artefatos pelo homem e seus efeitos na subjetividade, a manutenção do padrão de apropriação dos objetos-técnicos materiais em relação às formas de apropriação dos objetos-técnicos intangíveis (softwares). Traz reflexões sobre a possibilidade de autonomização completa dos agentes inteligentes e a sua instituição, ipso facto, como agentes - a chamada Inteligência Artificial.

Psicología Evolutiva y Psicología Evolucionista. Claves para la discusión

[Es] La teoría creacionista, universalmente en vigor antes de Darwin (y aún hoy sostenida por más de uno) sostenía que había habido una mente (divina), encargada de diseñar al hombre; que le puso ojos para que viera, oídos, mente para que pensara...Desde Darwin, por el contrario, se opina que la necesidad de enfrentarse a problemas diversos hizo que la mente fuera diversificándose, a través de sucesivas selecciones de las mentes más adaptativas. Según esto: 1.-¿Existen maquinarias biológicas “especializadas” en nuestro cerebro?. Y si existieran, 2.-¿Se desarrollan de forma espontánea?; es decir, sin esfuerzo ni instrucción formal? 3.-¿Se despliegan de forma inconsciente, autónoma y similar en todos los seres humanos? En definitiva: ¿Existe algo (facultad psicológica, órgano mental, sistema neurológico, módulo computacional…) que permita decir que las personas saben esto… o aquello… en el mismo sentido que las arañas saben tejer o los pájaros construir sus nidos?

¿Bancos con Problemas? Un Sistema de Alerta Temprana para la Prevención de Crisis Bancarias

[ES] Las autoridades reguladoras y supervisoras de los sistemas financieros han probado diversos métodos para intentar encontrar un procedimiento eficaz en la elaboración de un sistema de alerta temprana de las crisis bancarias. Los Modelos de Regresión Logística han sido usados aunque han mostrado algunas debilidades, por lo que se necesitan nuevos y mejores métodos. La crisis bancaria ocurrida en la República Dominicana entre los años 2002 y 2004 se ha usado para comparar la eficacia de la Regresión Logística frente al uso del método Support Vector Machines (SVM) para la detección de crisis bancarias.