A tabu search heuristic for a sequence-dependent and time-dependent scheduling problem on a single machine

This paper introduces a tabu search heuristic for a production scheduling problem with sequence-dependent and time-dependent setup times on a single machine. The problem consists in scheduling a set of dependent jobs, where the transition between two jobs comprises an unrestricted setup that can be performed at any time, and a restricted setup that must be performed outside of a given time interval which repeats daily in the same position. The setup time between two jobs is thus a function of the completion time of the first job. The tabu search heuristic relies on shift and swap moves, and a surrogate objective function is used to speed-up the neighborhood evaluation. Computational experiments show that the proposed heuristic consistently finds better solutions in less computation time than a recent branch-and-cut algorithm. Furthermore, on instances where the branch-and-cut algorithm cannot find the optimal solution, the heuristic always identifies a better solution.     

A Genre-Aware Approach to Focused Crawling

Focused crawlers have as their main goal to crawl Web pages that are relevant to a specific topic or user interest, playing an important role for a great variety of applications. In general, they work by trying to find and crawl all kinds of pages deemed as related to an implicitly declared topic. However, users are often not simply interested in any document about a topic, but instead they may want only documents of a given type or genre on that topic to be retrieved. In this article, we describe an approach to focused crawling that exploits not only content-related information but also genre information present in Web pages to guide the crawling process. This approach has been designed to address situations in which the specific topic of interest can be expressed by specifying two sets of terms, the first describing genre aspects of the desired pages and the second related to the subject or content of these pages, thus requiring no training or any kind of preprocessing. The effectiveness, efficiency and scalability of the proposed approach are demonstrated by a set of experiments involving the crawling of pages related to syllabi of computer science courses, job offers in the computer science field and sale offers of computer equipments. These experiments show that focused crawlers constructed according to our genre-aware approach achieve levels of F1 superior to 88%, requiring the analysis of no more than 65% of the visited pages in order to find 90% of the relevant pages. In addition, we experimentally analyze the impact of term selection on our approach and evaluate a proposed strategy for semi-automatic generation of such terms. This analysis shows that a small set of terms selected by an expert or a set of terms specified by a typical user familiar with the topic is usually enough to produce good results and that such a semi-automatic strategy is very effective in supporting the task of selecting the sets of terms required to guide a crawling process.     

Metamodeling live sequence charts for code generation

This article presents a metamodeling study for Live Sequence Charts (LSCs) and Message Sequence Charts (MSCs) with an emphasis on code generation. The article discusses specifically the following points: the approach to building a metamodel for MSCs and LSCs, a metamodel extension from MSC to LSC, support for model-based code generation, and finally action model and domain-specific data model integration. The metamodel is formulated in metaGME, the metamodel language for the Generic Modeling Environment.

Wide band resonant ultrasound spectroscopy of spheroidal modes for high accuracy estimation of Poisson coefficient of balls

An original inversion method specifically adapted to the estimation of Poisson coefficient of balls by using their resonance spectra is described. From the study of their elastic vibrations, it is possible to accurately characterize the balls. The proposed methodology can create both spheroidal modes in the balls and detect such vibrations over a large frequency range. Experimentally, by using both an ultrasonic probe for the emission (piezoelectric transducer) and a heterodyne optic probe for the reception (interferometer), it was possible to take spectroscopic measurements of spheroidal vibrations over a large frequency range (100 kHz-45 MHz) in a continuous regime. This method, which uses ratios between wave resonance frequencies, allows the Poisson coefficient to be determined independently of Young's modulus and the ball's radius and density. This has the advantage of providing highly accurate estimations of Poisson coefficient (+/-4.3 x 10(-4)) over a wide frequency range.     

Incidence of different pork quality categories in a Portuguese slaughterhouse: A survey

This study was designed to evaluate the incidence of the PSE/DFD status in a Portuguese pig slaughterhouse, covering two seasons of the year (spring and summer) in order to find out if the proportions of those poor meat quality categories were sufficient to concern the meat industry. Meat quality classification was based on the measurements of the pH(60), pH(24), drip losses and colour (L, a, b) in the longissimus dorsi muscle (between the last third and fourth ribs) of 380 pigs randomly chosen from the line. The high global incidence of PSE and likely PSE (30%) as well as DFD (10%) carcasses and the enormous variation of the meat quality between the different days of analysis proves that the pig population presents a great variation of halothane genotype and that handling procedures have to be optimized in order to decrease stress and glycogen store depletion. The percentage of PSE carcasses during the summer season was double that found in the spring, probably due to a higher environmental temperature and relative humidity.     

Dynamique des lasers à fibre dopée à I’erbium

The dynamics of the erbium doped fiber laser is analysed both experimentally and theoretically. This laser operates spontaneously in self-pulsing, in sinusoidal or in CW regime. Experiments have allowed to determine the control parameters : the cavity losses, the pumping rate and the ion pairs concentration. An antiphase dynamics is observed when the laser operates simultaneously at 1.55 µn and 1.536 µm. The self-pulsing behavior is attributed to a fast energy transfer which occurs between two neighboring ions (pair induced quenching). A simple model considering the active medium as a mixture of isolated ions and ions pairs is developped and leads to a good qualitative agreement with the experimental results.     

Impact of white-matter mask selection on DTI histogram-based metrics as potential biomarkers in cerebral small vessel disease

Magnetic Resonance Materials in Physics, Biology and Medicine - Histogram-based metrics extracted from diffusion-tensor imaging (DTI) have been suggested as potential biomarkers for cerebral small...     

基于卡口上下文和深度置信网络的车辆轨迹预测模型研究

针对车辆轨迹预测中节点序列的时序特性和实际路网中的空间关联性,该文提出一种基于深度置信网络和SoftMax (DBN-SoftMax)轨迹预测方法.首先,考虑到轨迹在节点集合中的强稀疏性和一般特征学习方法对新特征的泛化能力不足,该文利用深度置信网络(DBN)较强的无监督特征学习能力,达到提取轨迹局部空间特性的目的;然后,针对轨迹的时序特性,该文采用逻辑回归的预测思路,用当前轨迹集在路网特征空间中的线性组合来预测轨迹;最后,结合自然语言处理领域中的词嵌入的思想,基于实际轨迹中节点存在的上下文关系,运用节点的向量集表征了节点间的交通时空关系.实验结果表明该模型不仅能够有效地提取轨迹特征,并且在拓扑结构复杂的路网中也能得到较好的预测结果.     

Evolutionary design of neural network architectures: a review of three decades of research

We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.