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Foreground map evaluation is crucial for gauging the progress of object segmentation algorithms, in particular in the field of salient object detection where the purpose is to accurately detect and segment the most salient object in a scene. Several measures (e.g., area-under-the-curve, F1-measure, average precision, etc.) have been used to evaluate the similarity between a foreground map and a ground-truth map. The existing measures are based on pixel-wise errors and often ignore the structural similarities. Behavioral vision studies, however, have shown that the human visual system is highly sensitive to structures in scenes. Here, we propose a novel, efficient (0.005 s per image), and easy to calculate measure known as S-measure (structural measure) to evaluate foreground maps. Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a foreground map and a ground-truth map. We demonstrate superiority of our measure over existing ones using 4 meta-measures on 5 widely-used benchmark datasets. Furthermore, we conduct a behavioral judgment study over a new database. Data from 45 subjects shows that on average they preferred the saliency maps chosen by our measure over the saliency maps chosen by the state-of-the-art measures. Our experimental results offer new insights into foreground map evaluation where current measures fail to truly examine the strengths and weaknesses of models. Code: https://github.com/DengPingFan/S-measure.

An enhanced method called FreeViBe+ for moving target segmentation is proposed in this paper, addressing limitations in the ViBe algorithm such as ghosting, shadows, and holes. To eliminate ghosts, multi-frame background modeling is introduced. Shadows are detected and removed based on their characteristics in the HSV color space, while holes are filled by merging GrabCut segmentation results with the ViBe extraction output. Furthermore, the Structure-measure is tuned to optimize image fusion, enabling improved foreground–background separation. Comprehensive experiments on the UCF101 and Weizmann datasets demonstrate the effectiveness of FreeViBe+ in comparison with Finite Difference, Gaussian Mixture Model, and ViBe methods. Ablation studies confirm the individual contributions of multi-frame modeling, shadow removal, and GrabCut refinement, while sensitivity analysis verifies the robustness of key parameters. Quantitative evaluations show that FreeViBe+ achieves superior performance in precision, recall, and F-measure compared with existing approaches.

This volume contains a collection of survey and research articles from the special program and international conference on Dynamics and Numbers held at the Max-Planck Institute for Mathematics in Bonn, Germany in 2014. The papers reflect the great diversity and depth of the interaction between number theory and dynamical systems and geometry in particular. Topics covered in this volume include symbolic dynamics, Bratelli diagrams, geometry of laminations, entropy, Nielsen theory, recurrence, topology of the moduli space of interval maps, and specification properties.

This volume contains the proceedings of the International Research Conference ``Probability on Algebraic and Geometric Structures'', held from June 5-7, 2014, at Southern Illinois University, Carbondale, IL, celebrating the careers of Philip Feinsilver, Salah-Eldin A. Mohammed, and Arunava Mukherjea.These proceedings include survey papers and new research on a variety of topics such as probability measures and the behavior of stochastic processes on groups, semigroups, and Clifford algebras; algebraic methods for analyzing Markov chains and products of random matrices; stochastic integrals and stochastic ordinary, partial, and functional differential equations.

This investigation applies two approaches for representing and comparing text structures as undirected network graphs to describe the influence of narrative and expository lesson texts on readers' knowledge structure elicited as free recall. Narrative and expository lesson texts and undergraduate participants' free recall essays (n = 90) from a study by Wolfe and Mienko (Br J Educ Psychol 77, 541–564, 2007) were reanalyzed for lexical proximity as sequential occurrence of selected important terms in the text and as actual minimum distances between these terms. The proximity data were then rendered as Pathfinder networks for analysis. Compared to human-rater benchmark measures, the convergent validity of the sequential approach (range of r = .53 to .83, median r = .70) was a little better than that of the minimum distance approach (.51 to .80, median r = .67). Further, we anticipated that the lesson text structure would be reflected in the text structure of the free recall essays, but this was not observed. On average, the essays in all three lesson conditions tended to converge on a sequential expository structure. Further, compared to the expository lesson texts, the narrative lesson text had a distinctly different influence on posttest recall essay text structures. Overall then, the sequential occurrence approach appears to provide a reasonably good, automatically derived method for representing and comparing lesson texts and participants' essays as network graphs. If further confirmed and fully automated, there is a wide range of application of such measurement approaches for learning and research.

In order to further validate and extend the application of recent knowledge structure (KS) measures to second language settings, this investigation explores how second language (L2, English) situation models are influenced by first language (L1, Korean) translation tasks. Fifty Korean low proficient English language learners were asked to read an L2 story and then complete L2 concept map and summary writing tasks, with or without an intervening L1 production tasks (Translated versus Directed conditions). Posttest comprehension was measured using the TOEFL multiple-choice items associated with the story (both in L2). KS elicited as concept maps and as text summaries were used to represent the situation models before, during, and after writing. For analysis, all of the participants’ maps and writing artifacts were converted into Pathfinder Networks ( PFNets ) that were analyzed using two distinctly different approaches, correlation of the raw proximity data and also degree centrality of the PFNets , in order to analyze the PFNets statistically and to visually describe KS cognitive state changes over time. The correlation results showed that the Translated Writing participants’ L2 KS relative to the Directed Writing condition are more similar to that of an expert and are significantly correlated with comprehension posttest scores. Including L1 tasks substantially improved the qualities of the L2 KS artifacts and underlying mental structures related to reading comprehension. In addition, the average centrality results showed that the KS ‘form’ of the participants’ PFNets who translated were relational network structures relative to the Directed Writing group’s (English only) PFNets that had a more linear structure that matched the text surface structure, suggesting a fundamental way that L1 and L2 cognitive processing differs.

Der Nachwuchsmangel in der Medizin, insbesondere in den operativen Fachgebieten, u. a. der Orthopädie und Unfallchirurgie, ist ein allgegenwärtiges und aktuelles Diskussionsthema geworden. Neben gesellschafts- und gesundheitspolitischen Ursachen wird den großen operativen Disziplinen ein Attraktivitätsproblem bescheinigt, das zum einen in der starken Arbeitsbelastung sowie ungünstigen Dienstzeiten – insbesondere in Fächern mit hohem Notfallaufkommen wie in der Unfallchirurgie – zum anderen in der mangelnden Strukturierung und Kalkulierbarkeit der Weiterbildung begründet ist. Zur Abwendung eines Versorgungsengpasses durch den drohenden Nachwuchsmangel muss eine Reihe struktureller und inhaltlicher Maßnahmen zur Optimierung der Lehre, Aus- und Weiterbildung erfolgen. Im vorliegenden Artikel werden aufgrund der zahlreichen Facetten der Thematik die Analyse und Empfehlungen zum Vorgehen zeitlich orientiert zunächst im ersten Teil auf die Periode bis zur Wahl des speziellen Weiterbildungsfachs beschränkt.Für ein fundamentales Verständnis und das Interesse für das Gebiet der Orthopädie und Unfallchirurgie mit dem Tätigkeitsschwerpunkt Unfallchirurgie kann der Grundstein bereits in der Phase vor dem Studium gelegt werden. Im Studium gilt es, die traditionellen Strukturen im Rahmen der aktuell gültigen Approbationsordnung kreativ umzugestalten und den besonderen Reiz des Zusammenspiels praktischer Fähigkeiten und theoretischem Wissen für die verbesserte Wahrnehmung unseres Fachs herauszuarbeiten. Dies betrifft gleichermaßen Veranstaltungen in der Vorklinik (Klinikerseminare) als auch curriculare Praktika des klinischen Studienabschnitts (Querschnittsbereich Notfallmedizin, Blockpraktikum Chirurgie) mit praxisorientierten Kursen (Nahtkurs, TEAM-Training) in sog. SkillsLabs. Darüber hinaus gehende extracurriculare Wahlangebote (AO-Kurs, Doktorandenseminare), ggf. unter Generierung eines Mentorenkonzepts, können das Fach entsprechend darstellen. Eine gleichermaßen inhaltliche wie strukturelle Qualitätsverbesserung des Praktischen Jahres erscheint unabdingbar für eine Stimmungsumkehr der Studierenden. Gemeinsam mit den aktuellen Angeboten der Fachgesellschaft für Studierende, wie der strukturierten Begleitung auf dem Jahreskongress, Stipendien und der inaugurierten „summer school“, können diese Optionen den Rahmen für eine umfassende Information des Studierenden und Anreiz für einen Übergang in die Weiterbildungsphase unseres Fachs bieten.

Given a n-dimensional lamination endowed with a Riemannian metric, the author introduces the notion of a multiplicative cocycle of rank d, where n and d are arbitrary positive integers. The holonomy cocycle of a foliation and its exterior powers as well as its tensor powers provide examples of multiplicative cocycles. Next, the author defines the Lyapunov exponents of such a cocycle with respect to a harmonic probability measure directed by the lamination. He also proves an Oseledec multiplicative ergodic theorem in this context. This theorem implies the existence of an Oseledec decomposition almost everywhere which is holonomy invariant. Moreover, in the case of differentiable cocycles the author establishes effective integral estimates for the Lyapunov exponents. These results find applications in the geometric and dynamical theory of laminations. They are also applicable to (not necessarily closed) laminations with singularities. Interesting holonomy properties of a generic leaf of a foliation are obtained. The main ingredients of the author's method are the theory of Brownian motion, the analysis of the heat diffusions on Riemannian manifolds, the ergodic theory in discrete dynamics and a geometric study of laminations.

The study of the mapping class group Mod(S) is a classical topic that is experiencing a renaissance. It lies at the juncture of geometry, topology, and group theory. This book explains as many important theorems, examples, and techniques as possible, quickly and directly, while at the same time giving full details and keeping the text nearly self-contained. The book is suitable for graduate students. A Primer on Mapping Class Groups begins by explaining the main group-theoretical properties of Mod(S), from finite generation by Dehn twists and low-dimensional homology to the Dehn-Nielsen-Baer theorem. Along the way, central objects and tools are introduced, such as the Birman exact sequence, the complex of curves, the braid group, the symplectic representation, and the Torelli group. The book then introduces Teichmüller space and its geometry, and uses the action of Mod(S) on it to prove the Nielsen-Thurston classification of surface homeomorphisms. Topics include the topology of the moduli space of Riemann surfaces, the connection with surface bundles, pseudo-Anosov theory, and Thurston's approach to the classification.

Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed. Based on the Econometric and Tinbergen Institutes Lectures,Yield Curve Modeling and Forecastingcontains essential tools with enhanced utility for academics, central banks, governments, and industry.