Miracle-dz/UniFAR-FTUs huggingface dataset:
An analysis of cooperative coevolutionary algorithms

Coevolutionary algorithms behave in very complicated, often quite counterintuitive ways. Researchers and practitioners have yet to understand why this might be the case, how to change their intuition by understanding the algorithms better, and what to do about the differences. Unfortunately, there is little existing theory available to researchers to help address these issues. Further, little empirical analysis has been done at a component level to help understand intrinsic differences and similarities between coevolutionary algorithms and more traditional evolutionary algorithms. Finally, attempts to categorize coevolution and coevolutionary behaviors remain vague and poorly defined at best. The community needs directed investigations to help practitioners understand what particular coevolutionary algorithms are good at, what they are not, and why. This dissertation improves our understanding of coevolution by posing and answering the question: “Are cooperative coevolutionary algorithms (CCEAs) appropriate for static optimization tasks? ” Two forms of this question are “How long do they take to reach the global optimum? ” and “How likely are they to get there? ” The first form of the question is addressed by analyzing their performance as optimizers, both theoretically and empirically. This analysis includes investigations into the effects of coevolution-specific parameters on optimization performance in the context of particular properties of potential problem domains. The second leg of this dissertation considers the second form of the question by looking at the dynamical properties of these algorithms, analyzing their limiting behaviors again from theoretical and empirical points of view. Two common cooperative coevolutionary pathologies are explored and illustrated, in both formal and practical settings. The result is a better understanding of, and appreciation for, the fact that CCEAs are not generally appropriate for the task of static, single-objective optimization. In the end a new view of the CCEA is offered that includes analysis-guided suggestions for how a traditional CCEA might be modified to be better suited for optimization tasks, or might be applied to more appropriate tasks, given the nature of its dynamics.
Adaptive output-feedback control of a class of nonlinear systems

For a class of single-input-single-output nonlinear systems with unknown constant parameters, the authors construct a novel systematic procedure for adaptive nonlinear control design, which requires only output, rather than full-state, measurement and which yields global boundedness and tracking properties without imposing any type of growth constraints on the nonlinearities. The proposed procedure is applicable to nonlinear systems which can be expressed in the output-feedback canonical form. The authors give a coordinate-free characterization of this class of systems, and show that a single-link robotic manipulator with an elastically coupled DC-motor actuator belongs to this class, and can thus be adaptively controlled via the design procedure using only position measurement. <<ETX>>
Tracking attention in space and time : the dynamics of human visual attention

ii TABLE OF CO TE TS . iv LIST OF FIGURES . viii LIST OF TABLES . x ACK OWLEDGEME TS . xii DEDICATIO . xiii CHAPTER 1: GE ERAL I TRODUCTIO . 1 I.I A Zoom-Lens Conception of Focal Attention . 2 I.2 Unitary vs. Divided Focus of Attention . 5 I.2.1 Evidence for a Unitary Focus of Attention . 6 I.2. 2 Evidence for a Divided Focus of Attention . 11 I.3 Dissertation Objectives . 18 References . 20 CHAPTER 2: LI EAR CHA GES I THE SPATIAL EXTE T OF THE FOCUS OF ATTE TIO ACROSS TIME . 24 Lag-1 Sparing Across Space . 26 Control of Attention in the Spatiotemporal Domain . 27 Experiment 2.1 . 31 Method . 31 Observers . 31 Apparatus and Stimuli . 31 Procedure . 32 Results and Discussion . 34 Second-target Accuracy . 34
Classification-based fusion of IKONOS 1-m high-resolution panchromatic image and 4-m multi-spectral images

Presents a new classification-based fusion (CL-based fusion) scheme, which is used throughout this paper as a synonym of "pixel-level fusion". It can be applied to merge low-resolution images and co-registered high-resolution images. The key of this trial is as follows: (1) geometrical co-registration of an IKONOS 1-m high-resolution panchromatic image and 4-m multi-spectral images, (2) classification of the high-resolution image together with the low-resolution images using supervised classification algorithm, (3) according to each class histogram of the high-resolution image, adjustment of the corresponding spectral values of single multi-spectral image, and (4) HIS transform of the adjusted multi-spectral images using cylindrical coordinates and acquirement of the fusion images. The proposed method can merge the class with a high-resolution image and multi-spectral images, accounting for the importance of prior knowledge, and try to add classification as prior knowledge to fusion. In this trial, a selected IKONOS image of Peking is used.
Role of brain-derived neurotrophic factor for the treatment of glaucoma and retinitis pigmentosa

Primary open-angle glaucoma (POAG) is the most common form of glau co ma, representing up to 90% of cases. POAG is described as a multifactorial optic neuropathy that is chronic and progressive with a characteristic ac quired loss of optic nerve fibres. This loss develops in the presence of open anterior chamber angles, characteristic visual field abnormalities, and in creased intraocular pressure (IOP). POAG manifests by cupping and atrophy of the optic disc, in the absence of other known causes of glauco ma tous disease [1,2]. The exact cause of glaucomatous optic neuropathy is not known, although many risk factors have been identified, including the follo wing: elevated IOP, family history, race, age older than 40 years and myo pia. Elevated IOP is the most studied of these risk factors because it is the main clinically treatable risk factor for glaucoma and may cause neuro pathy via vascular dysfunction causing ischemia to the optic nerve or cribriform plate compression of the axons. Other factors may include excitotoxic damage from excessive retinal glutamate, deprivation of neuro nal growth factors, peroxynitrite toxicity from increased nitric oxide syn thase activity, im mune-mediated nerve damage and oxidative stress. The exact role that IOP plays in combination with these other factors and their significance to the initiation and progression of subsequent glaucomatous neuronal damage and cell death over time is still under debate [3,4].
Digital Business Strategy: Toward a Next Generation of Insights

Over the last three decades, the prevailing view of information technology strategy has been that it is a functional-level strategy that must be aligned with the firm's chosen business strategy. Even within this so-called alignment view, business strategy directed IT strategy. During the last decade, the business infrastructure has become digital with increased interconnections among products, processes, and services. Across many firms spanning different industries and sectors, digital technologies (viewed as combinations of information, computing, communication, and connectivity technologies) are fundamentally transforming business strategies, business processes, firm capabilities, products and services, and key interfirm relationships in extended business networks. Accordingly, we argue that the time is right to rethink the role of IT strategy, from that of a functional-level strategy--aligned but essentially always subordinate to business strategy--to one that reflects a fusion between IT strategy and business strategy. This fusion is herein termed digital business strategy. We identify four key themes to guide our thinking on digital business strategy and help provide a framework to define the next generation of insights. The four themes are (1) the scope of digital business strategy, (2) the scale of digital business strategy, (3) the speed of digital business strategy, and (4) the sources of business value creation and capture in digital business strategy. After elaborating on each of these four themes, we discuss the success metrics and potential performance implications from pursuing a digital business strategy. We also show how the papers in the special issue shed light on digital strategies and offer directions to advance insights and shape future research.
Auditory parallax effects in the HRTF for nearby sources

When a sound source is close, the angle of the source relative to the center of the head can differ substantially from the angle of the source relative to the ear. Since the high-frequency features of the HRTF (head related transfer function) are known to depend on angle of the source relative to the ear, this "acoustic parallax" should produce a systematic remapping of high-frequency features in the far-field ipsilateral HRTF to more lateral locations in the near-field HRTF. HRTFs measured on an acoustic manikin indicate that this type of remapping does occur in the near field, and that the frequency response of the pinna is roughly independent of distance when the source is more than 5 cm from the ear. The perceptual relevance of the acoustic parallax effect is briefly discussed, along with its potential application to near-field virtual audio displays.
Numerical Optimization Methods based on Discrete Structure for Text Summarization and Relational Learning

Numerical optimization problems have an important role in natural language processing (NLP) and many machine learning (ML) tasks, which are accomplished by first making a mathematical model, and then solving certain optimization problems based on the developed model. The difficulty in solving a numerical optimization problem affects the efficiency of a task, and it is therefore important to design a model such that it can formalize the task well, thereby making the optimization problem easy to solve. In this thesis, we propose novel numerical optimization methods for text summarization and relational learning, both of which are typical ML and NLP tasks that can be solved using numerical optimization methods. The key point to our methods is exploiting the discrete structure inherent to the problem domains for decomposing them into small subproblems. This approach has two merits. The first merit is efficiency, i.e., we can use a discrete structure inherent to a problem by decomposing the problem into small subproblems, thereby solving the original problem efficiently. The second merit is flexibility in the design of the optimization problems. We have to be careful when making a new optimization problem for a given NLP or ML task because an easy problem may become a far more difficult problem if we extend it by adding new constraints or objectives. Our technique is flexible from the viewpoint that it can extend existing optimization problems without making the new problem difficult to solve. In the thesis, we treat three different tasks for text summarization and relational learning, and present a concrete optimization method for each of them.
MicroRNAs IN LUNG CANCER: FROM GENOMICS TO CLINICAL APPLICATIONS

Lung cancer is the neoplasia with the highest incidence and mortality in men and women worldwide. Lung cancer is classifi ed into two large histologic subgroups: small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC) according to cellular origin, molecular changes, clinical-pathological features, and response to treatments. NSCLC constitutes 80% of the all cases of lung cancer; nevertheless the molecular mechanisms underlying the tumoral etiology still remain poorly understood. MicroRNAs (miRNAs) are evolutionarily conserved small noncoding RNAs that negatively regulate gene expression at the post-transcriptional level by repressing translation or decreasing mRNA stability in numerous biological processes. In cancer, miRNAs have differential spatial and temporal expression, which is related to several clinical, biological, molecular and genomic features of tumors. miRNA Authors’ affi liations given at the end of the chapter. MicroRNAs in Lung Cancer: From Genomics to Clinical Applications 221 expression profi les, polymorphisms and epigenetic modifi cation in NSCLC have been studied, and these studies have improved NSCLC diagnosis and classifi cation, as well as provided prognostic information. In this review, we describe some of the better-characterized miRNAs in NSCLC and how they could improve lung cancer prognosis and therapy. We summarize the current understanding in expression of miRNAs and their involvement in process biology of NSCLC, as well as their potential as biomarkers for risk stratifi cation, outcome prediction and classifi cation of histologic subtypes, in addition to circulating biomarkers and miRNA-based NSCLC therapy.
Dimensionality, noise separation and full frequency band perspectives of ICA in resting state fMRI : investigations into ICA in resting state fMRI

The concept of resting state functional magnetic resonance imaging (fMRI) is built onto an original finding in 1995 that brain hemispheres present synchronous signal fluctuations with distinct patterns. fMRI measurements rely on blood oxygenation changes that indirectly mirror neural activity. Therefore, the origin of certain functional connectivity patterns, resting state networks (RSNs), has been a widely debated research question and numerous contributing factors have been identified. According to current understanding the fluctuations reflect maintenance of the system integrity in addition to spontaneous thought and action processes in the resting state. A popular method to study the functional connectivity in resting state fMRI is spatial independent component analysis (ICA) that decomposes signal sources into statistically independent components. The dichotomy of functional specialization versus functional integration has a correspondence in fMRI studies where RSNs play the integrative viewpoint of brain function. Although canonical large-scale RSNs are broadly distributed they also express modularity that can be accomplished by ICA with a high number of estimated components. The characteristics of high ICA dimensionality are broadly investigated in the thesis. An enduring issue in resting state research has been the confounding noise sources like motion and cardiorespiratory processes which may hamper the analysis. In this thesis the ability of ICA to separate these noise sources from the default mode network, a major RSN, is studied. Additionally, the suitability of ICA for full frequency spectrum analysis, a relatively rare setting in biosignal analysis, is investigated. The results of the thesis support the viewpoint of ICA as a robust analysis method for functional connectivity analysis. Cardiorespiratory and motion induced noise did not confound the functional connectivity analyses with ICA. High dimensional ICA provided better signal source separation, revealed the modular structure of the RSNs and pinpointed the specific aberrations in the autism spectrum disorder population. ICA was also found applicable for fully explorative analysis in both the spatial and temporal domains and indicated functional connectivity changes induced by transcranial bright light stimulation.
Multi-sensor data fusion for helicopter guidance using neuro-fuzzy estimation algorithms

The main objective of this paper is to present some algorithms to fuse information about obstacles, whose dynamics are a-priori unknown, in a helicopter's environment, provided by multiple spatially separate sensors. The fused information can then be used to help helicopters locate obstacles in hazardous conditions so that it can avoid them. Obstacle track estimation has been commonly carried out using the Kalman filter (KF), a linear estimator, or one of its variations. The extended Kalman filter, one such variation designed for use on non-linear problems, produces the best linear approximation to the object track. However certain assumptions made in the derivation of these algorithms render them suboptimal for aerial obstacle track estimation. Work produced by the University of Southampton has highlighted a link between fuzzy networks and associative memory neural networks. This link is important as it allows new learning rules to be developed for training fuzzy rules, and the conditions under which convergence can be proved to be derived. This paper explores methods for the fusion of estimates using these neurofuzzy models, and also addresses some of the weaknesses of the Kalman filter approximation introduced by the assumptions made in its derivation.
The Microinnovation Approach in Design

Engineering design is here understood as a multi-phased thinking and problem solving process which creates new information and constructs. This perspective is referred to by us as the microinnovation approach. It forms the basis of this study, the aim of which is to analyze design errors and to identify thought errors behind them. We have interviewed professional engineering designers to search for the reasons for design errors. We found evidence of some such reasons including: 1) problems in organizational coordination, 2) haste 3) communication failures, 4) unavailability of and lack of access to knowledge, and 5) problems in professionalism. The main thought errors behind these problems were lack of long-term thinking and narrowness of perspective. By eliminating these problems and shortcomings, organizations could decrease the number of risk factors for design errors and hence increase innovative thinking.
The Effect of Student-Related and Text-Related Characteristics on Student’s Reading Behaviour and Text Comprehension: An Eye Movement Study

ABSTRACT The focus of the present study was on the mediation and moderation effects of reading processes as evidenced from eye movements on the relation between cognitive and linguistic student characteristics (word decoding, vocabulary, comprehension skill, short-term memory, working memory, and nonverbal intelligence) and text comprehension. Forty 4th graders read 4 explanatory texts and afterward answered text comprehension questions. During their reading, eye-movements of gaze, look back, and second pass duration were examined for the heading, first sentence, and final sentence. The result show differential effects of reader and text characteristics on skipping probability, driven by decoding and nonverbal intelligence. Regression probability and regression path duration are also influenced by decoding. Concluding, this study shows reading behaviour to be related to both students’ skills and text comprehension measures.
The external larval morphology of aquatic and terrestrial Luciolinae fireflies (Coleoptera: Lampyridae)

The external morphology of aquatic, semiaquatic and terrestrial lucioline larvae was investigated in order to provide anoverview of what traits constitute the extremely ecologically diverse Luciolinae (Coleoptera: Lampyridae). The aquaticspecies, Aquatica ficta (Olivier), A. leii (Fu et Ballantyne), A. hydrophila (Jeng et al.), A. lateralis (Motschulsky), A. wu- hana Fu et Ballantyne, Luciola cruciata Motschulsky and L. owadai Sato et Kimura cannot swim, but instead crawl onthe substrate. They have soft bodies, lateral abdominal tracheal gills and glands on eversible structures that secrete repel-lent substances. The back-swimming species, Luciola substriata Gorham and L. aquatilis Thancharoen, which inhabit thesurface of ponds, have hardened exoskeletons, and lack gills and eversible glands. Unlike the crawling species, the back-swimmers have sense organs along the ventral surface of the apical maxillary and labial palpomeres, and are metapneusticin their later instars. The larval morphology of the aquatic species is contrasted with Pygoluciola qingyu Fu et Ballantyne, whose larvae are semiaquatic and lack gills, and with the terrestrial larvae of Asymmetricata circumdata (Motsch.) (newly
The opinion-changing power of computer-based multimedia presentations

This study investigated the persuasive impact of information presented in formats possessing varying degrees of technological sophistication on individuals who were novices and experts in the domain of the information. Participants reviewed a presentation of a football scout’s favorable report on a potential recruit and evaluated the recruit’s projected success. The experimental design wa sa2( participant football expertise: expert vs. novice)! 3 (technological sophistication of presentation: low [typed summary of statistics] vs. moderate [printed PowerPoint charts] vs. high [computer-based animated PowerPoint charts]) factorial. Based on predictions derived from Dual Process Models of persuasion, we expected that the recruit would be rated higher in the PowerPoint presentation condition but that experts would be less swayed by the difference in communication modality than would novices. We found support for these predictions. For both novice and expert participants, the greater the technological sophistication of the presentation, the more highly they rated the projected success of the football recruit. However, this effect was marked for football novices relative to football experts.
Technologies Related with the Artificial Insemination in Buffalo

In buffalo oestrus behaviour has a lower intensity than in cows and is much more difficult to detect, limiting the application of artificial insemination (AI) program. Several methods of heat detection have been developed for use in cattle; these include visual observation, heat mount detectors, tail paint, chin-ball markers, teaser animals and electronic devices. In buffalo, unlike cattle, the female are receptive to mounting activity mainly by the bull and occasionally by other cows. Consequently unless a buffalo bull is to be left running with the herd it can be difficult to know when oestrus is occurring. The presence of a teaser bull is helpful to identify buffaloes in heat; in this case the standing oestrus is the most reliable sign referable to a next ovulation. Other heat detection aids utilized in buffalo include: pedometers; vaginal probes; pressure sensitive telemetry device (Heat Watch ® ). In order to increase the use of AI easy management schemes, that not require the identification of oestrus, have been studied. These schemes are based on the manipulation of the hormonal events occurring during the oestrous cycle as: manipulate peripheral progesterone concentration (by PGF2￻ or progesterone releasing device); manipulate follicular growth and timing of ovulation (by GnRH and PGF2￻ ). A brief description of these technologies, with special reference to synchronization protocols to apply fixed time AI in buffalo, are presented in this review. The potential application of predetermining the sex of offspring will be also discussed, with reference to the techniques available for commercial practice in buffalo.
A bottom-up methodology to characterize delay faults

Inductive Fault Analysis is extended to consider localized spot defects in CMOS VLSI circuits which impact circuit timing performance. A scheme of deterministic introduction of spot defects into layouts of combinational logic circuits is described. A methodology and tool is developed to characterize delay defects resulting from missing and extra spot defects and to generate realistic delay defect distribution. <<ETX>>