A unified learning framework for object detection and classification using nested cascades of boosted classifiers

In this paper a unified learning framework for object detection and classification using nested cascades of boosted classifiers is proposed. The most interesting aspect of this framework is the integration of powerful learning capabilities together with effective training procedures, which allows building detection and classification systems with high accuracy, robustness, processing speed, and training speed. The proposed framework allows us to build state of the art face detection, eyes detection, and gender classification systems. The performance of these systems is validated and analyzed using standard face databases (BioID, FERET and CMU-MIT), and a new face database (UCHFACE).

On the use of one bit quantizers in networked control

This paper addresses the following problem in networked control: “If a control law is implemented over a channel that supports a certain fixed bit rate what is the best choice for the control update rate and, consequently, the number of bits carried in each sample?” A restricted architecture in which linear filters are used for the encoder/decoder is considered and a quantizer with linear feedback is deployed. Subject to these restrictions, a procedure for designing the controller and associated filters is presented. These filters are then deployed to choose the best number of bits per control update. It is shown, subject to the above restrictions, that it is generally best to use one bit per sample, in which case, the control update rate is equal to the bit rate. Our analysis has two points of departure from contemporary literature in this area. Firstly, we focus on bits per unit time, as opposed to bits per sample. Secondly we use a fixed number of bits in every time period as opposed to an average bit rate.     

An extended Akers graphical method with a biased random‐key genetic algorithm for job‐shop scheduling

This paper presents a local search, based on a new neighborhood for the job‐shop scheduling problem, and its application within a biased random‐key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based on an extension of the 1956 graphical method of Akers, is applied to improve the solution. The new heuristic is tested on a set of 205 standard instances taken from the job‐shop scheduling literature and compared with results obtained by other approaches. The new algorithm improved the best‐known solution values for 57 instances.     

Multi-ellipses detection on images inspired by collective animal behavior

This paper presents a novel and effective technique for extracting multiple ellipses from an image. The approach employs an evolutionary algorithm to mimic the way animals behave collectively assuming the overall detection process as a multi-modal optimization problem. In the algorithm, searcher agents emulate a group of animals that interact with each other using simple biological rules which are modeled as evolutionary operators. In turn, such operators are applied to each agent considering that the complete group has a memory to store optimal solutions (ellipses) seen so far by applying a competition principle. The detector uses a combination of five edge points as parameters to determine ellipse candidates (possible solutions), while a matching function determines if such ellipse candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate ellipses are evolved through the evolutionary algorithm so that the best candidates can be fitted into the actual ellipses within the image. Just after the optimization process ends, an analysis over the embedded memory is executed in order to find the best obtained solution (the best ellipse) and significant local minima (remaining ellipses). Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed, and robustness.     

High temperature stability of electrically conductive Pt–Rh/ZrO2 and Pt–Rh/HfO2 nanocomposite thin film electrodes

Nanocomposite films made up of either Pt–Rh/ZrO₂ or Pt–Rh/HfO₂ materials were co-deposited using multiple e-beam evaporation sources onto langasite (La₃Ga₅SiO₁₄) substrates, both as blanket films and as patterned interdigital transducer electrodes for surface acoustic wave sensor devices. The films and devices were tested after different thermal treatments in a tube furnace up to 1,200 °C. X-ray diffraction and electron microscopy results indicate that Pt–Rh/HfO₂ films are stabilized by the formation of monoclinic HfO₂ precipitates after high temperature exposure, which act as pinning sites to retard grain growth and prevent agglomeration of the conductive cubic Pt–Rh phase. The Pt–Rh/ZrO₂ films were found to be slightly less stable, and contain both tetragonal and monoclinic ZrO₂ precipitates that also helps prevent Pt–Rh agglomeration. Film conductivities were measured versus temperature for Pt–Rh/HfO₂ films on a variety of substrates, and it was concluded that La and/or Ga diffusion from the langasite substrate into the nanocomposite films is detrimental to film stability. An Al₂O₃ diffusion barrier grown on langasite using atomic layer deposition was found to be more effective than a SiAlON barrier layer in minimizing interdiffusion between the nanocomposite film and the langasite crystal at temperatures above 1,000 °C.     

Hydrothermal Strontium Titanate Films on Titanium: An XPS and AES Depth-Profiling Study

SrTiO₃ films in the 100-nm thickness range were grown on Ti foils by a 2–4 h hydrothermal treatment at 225°C in an aqueous solution of Sr(OH)₂. X-ray diffraction showed the principal reflections of cubic SrTiO₃. Photoelectron analysis revealed the presence of carbon contamination and suggested the presence of hydroxyl groups on the surface, which disappeared in the film bulk. The Auger depth profiles showed three distinct zones. The outermost layer, besides the common carbon contamination, had a large number of OH groups. The second zone was a clean, almost carbon-free SrTiO₃ film. The third zone was a diffuse interface region where the Sr and O concentrations slowly decreased toward the substrate.     

重新思考包装生产、消费和循环经济:以可分解木薯淀粉作基础材料为例

在主导的工业经济中,包装废物在城市固体废物产生中占有相当大的比例(～20.0%在欧洲和美国),对环境造成负面影响。从主导的线性经济向以意图和设计为基础的循环经济模式的转变,可以为市场经济和包装利用奠定新的基础。本研究探讨包装生产与消费相对于循环经济的关系。为实现这一目标,它包括(i)硬包装产品的生命周期库存分析,讨论原材料和产品的产量,水和能源的使用,以及温室气体的排放;(ii)木薯淀粉基材料的案例研究,以及(iii)石油基包装和木薯淀粉基包装的比较分析。结果表明,木薯淀粉的可降解包装比石油包装具有更好的社会和环境效益。从线性的(获取制造-使用-处理)模式到循环的(增长-制造-使用-恢复)模式的转变为技术之外的创新创造了新的机会,因为它不可避免地重新定义了浪费、产品、服务、市场、自然资本和增长的重要性。解决以石油为基础的包装工业的温室气体排放问题,社会采用以生物为基础的木薯淀粉包装是一项有效和有前途的减缓气候变化战略。     

Tunable Fano Resonance and Plasmon–Exciton Coupling in Single Au Nanotriangles on Monolayer WS2 at Room Temperature

Tunable Fano resonances and plasmon–exciton coupling are demonstrated at room temperature in hybrid systems consisting of single plasmonic nanoparticles deposited on top of the transition metal dichalcogenide monolayers. By using single Au nanotriangles (AuNTs) on monolayer WS₂ as model systems, Fano resonances are observed from the interference between a discrete exciton band of monolayer WS₂ and a broadband plasmonic mode of single AuNTs. The Fano lineshape depends on the exciton binding energy and the localized surface plasmon resonance strength, which can be tuned by the dielectric constant of surrounding solvents and AuNT size, respectively. Moreover, a transition from weak to strong plasmon–exciton coupling with Rabi splitting energies of 100–340 meV is observed by rationally changing the surrounding solvents. With their tunable plasmon–exciton interactions, the proposed WS₂–AuNT hybrids can open new pathways to develop active nanophotonic devices.     

Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem

The problem of merging multiple databases of information about common entities is frequently encountered in KDD and decision support applications in large commercial and government organizations. The problem we study is often called the Merge/Purge problem and is difficult to solve both in scale and accuracy. Large repositories of data typically have numerous duplicate information entries about the same entities that are difficult to cull together without an intelligent equational theory that identifies equivalent items by a complex, domain-dependent matching process. We have developed a system for accomplishing this Data Cleansing task and demonstrate its use for cleansing lists of names of potential customers in a direct marketing-type application. Our results for statistically generated data are shown to be accurate and effective when processing the data multiple times using different keys for sorting on each successive pass. Combing results of individual passes using transitive closure over the independent results, produces far more accurate results at lower cost. The system provides a rule programming module that is easy to program and quite good at finding duplicates especially in an environment with massive amounts of data. This paper details improvements in our system, and reports on the successful implementation for a real-world database that conclusively validates our results previously achieved for statistically generated data.