The recently found map of the world (Kunyutu 坤輿圖): a philological survey (Part II)

This is Part II following on from the first presentation of the Chinese Map of the World or Kunyutu 坤輿圖 published in vol. 75 of this journal. The large wall map in silk, found in a federal library in Berne, Switzerland, is anonymous and purports to represent the geopolitical situation during the period 1700–1730. After a short general, geographical overview containing cartographic remarks and findings, this article will focus on the translation of all 112 text blocks on the map, which are found in Appendix II. These text blocks contain information on the customs and products of a given place, as well as describing other curiosities associated with it. I compared these texts with other maps and textual sources, in particular with The Complete World Map (Kunyu quantu 坤輿全圖) and The Explanations of the World Map (Kunyutu shuo 坤輿圖說) by Verbiest in 1674. A Venn diagram reveals which source was used most frequently. I detected some geographical assumptions the creator of the Bernese map has drawn from previous sources or copied from earlier maps. These offer valuable pointers to the source language and enable the identification of specific misunderstandings, original creations and uncertainties or signs of ignorance. My translation of the text blocks helps to pinpoint certain anachronisms and inaccuracies, although the map essentially reflects the political situation in the years 1700–1730. These various pieces of evidence suggest that the map was in fact made much later by a copyist and is not only a cartographic composite of earlier maps, but also a fusion based on Northern and Southern Mandarin sources. I presume that the translations, drawn from French sources into Southern Mandarin – the language of the late Ming officials (guanhua 官話) – as well as a variety of Southern Chinese dialects, suggest the underlying influence of a Jesuit tradition

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety of existing data sources, the satellites continuously observing the surface of the Earth are key to the monitoring of our environment. The new generation of satellite sensors are tremendously increasing the possibilities of applications but also increasing the need for efficient processing methodologies in order to extract information relevant to the users' needs in an automatic or semi-automatic way. This is where machine learning comes into play to transform complex data into simplified products such as maps of land-cover changes or classes by learning from data examples annotated by experts. These annotations, also called labels, may actually be difficult or costly to obtain since they are established on the basis of ground surveys. As an example, it is extremely difficult to access a region recently flooded or affected by wildfires. In these situations, the detection of changes has to be done with only annotations from unaffected regions. In a similar way, it is difficult to have information on all the land-cover classes present in an image while being interested in the detection of a single one of interest. These challenging situations are called novelty detection or one-class classification in machine learning. In these situations, the learning phase has to rely only on a very limited set of annotations, but can exploit the large set of unlabeled pixels available in the images. This setting, called semi-supervised learning, allows significantly improving the detection. In this Thesis we address the development of methods for novelty detection and one-class classification with few or no labeled information. The proposed methodologies build upon the kernel methods, which take place within a principled but flexible framework for learning with data showing potentially non-linear feature relations. The thesis is divided into two parts, each one having a different assumption on the data structure and both addressing unsupervised (automatic) and semi-supervised (semi-automatic) learning settings. The first part assumes the data to be formed by arbitrary-shaped and overlapping clusters and studies the use of kernel machines, such as Support Vector Machines or Gaussian Processes. An emphasis is put on the robustness to noise and outliers and on the automatic retrieval of parameters. Experiments on multi-temporal multispectral images for change detection are carried out using only information from unchanged regions or none at all. The second part assumes high-dimensional data to lie on multiple low dimensional structures, called manifolds. We propose a method seeking a sparse and low-rank representation of the data mapped in a non-linear feature space. This representation allows us to build a graph, which is cut into several groups using spectral clustering. For the semi-supervised case where few labels of one class of interest are available, we study several approaches incorporating the graph information. The class labels can either be propagated on the graph, constrain spectral clustering or used to train a one-class classifier regularized by the given graph. Experiments on the unsupervised and oneclass classification of hyperspectral images demonstrate the effectiveness of the proposed approaches

Promotion commerciale de produits artisanaux en jute du Bangladesh : pour un élargissement de la politique d’OS3 ?

Die Organisation Schweiz Dritte Welt (OS3) will Exportgüter aus der Dritten Welt zugunsten der ärmsten Schichten der Bevölkerung fördern. Die Schweizer Konsumenten sollen dadurch auch mehr über die Lebensverhältnisse der Erzeuger jener Produkte erfahren und einen Einblick in die Realität der Dritten Welt erhalten. Die Zusammenarbeit mit den Frauengenossenschaften in Bangladesh begann mit der Aktion Jute, die von der Erklärung von Bern unter dem Motto « Solidarität – Jute – Ökologie », veranstaltet wurde. Diese Aktion ermöglichte, in weniger als zwei Jahren ab Ende 1976 bis Mitte 1978, den Verkauf in der ganzen Schweiz von 240.000 Taschen. Die mögliche Erweiterung dieses Versuches auf den ganzen schweizerischen Markt wird jetzt dank der finanziellen Unterstützung des Bundes bis Mai 1983 überprüft. Man erhofft sich dadurch, falls das Ergebnis der Studie positiv sein wird, eine grössere Mobilisierung lokaler, natürlicher, menschlicher und finanzieller Mittel, damit die betroffene Bevölkerung ihre eigene Zukunft in die Hand nehmen kann. In der Schweiz soll gezeigt werden, ob der Markt vom Preis und von der Qualität her gesehen Interesse zeigt. Für OS3 ist zweierlei besonders wichtig : Wird der “gerechte” Preis als Massregel weiterhin aufrechthalten ? Kommt die dazugehörige Information sowohl beim privaten Verkäufer, als auch beim Käufer, durch 

Förderung Handwerklicher Jute Produkte aus Bangladesh : eine Prüfung der möglichen Erweiterung der Politik OS3 ?

Die Organisation Schweiz Dritte Welt (OS3) will Exportgüter aus der Dritten Welt zugunsten der ärmsten Schichten der Bevölkerung fördern. Die Schweizer Konsumenten sollen dadurch auch mehr über die Lebensverhältnisse der Erzeuger jener Produkte erfahren und einen Einblick in die Realität der Dritten Welt erhalten. Die Zusammenarbeit mit den Frauengenossenschaften in Bangladesh begann mit der Aktion Jute, die von der Erklärung von Bern unter dem Motto « Solidarität – Jute – Ökologie », veranstaltet wurde. Diese Aktion ermöglichte, in weniger als zwei Jahren ab Ende 1976 bis Mitte 1978, den Verkauf in der ganzen Schweiz von 240.000 Taschen. Die mögliche Erweiterung dieses Versuches auf den ganzen schweizerischen Markt wird jetzt dank der finanziellen Unterstützung des Bundes bis Mai 1983 überprüft. Man erhofft sich dadurch, falls das Ergebnis der Studie positiv sein wird, eine grössere Mobilisierung lokaler, natürlicher, menschlicher und finanzieller Mittel, damit die betroffene Bevölkerung ihre eigene Zukunft in die Hand nehmen kann. In der Schweiz soll gezeigt werden, ob der Markt vom Preis und von der Qualität her gesehen Interesse zeigt. Für OS3 ist zweierlei besonders wichtig : Wird der “gerechte” Preis als Massregel weiterhin aufrechthalten ? Kommt die dazugehörige Information sowohl beim privaten Verkäufer, als auch beim Käufer, durch 

Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise unsupervised (UND). SSND can be seen as an unbalanced classification between labeled and unlabeled samples using the Cost-Sensitive Support Vector Machine (CS-SVM). UND assumes novelties in low density regions and can be approached using the One-Class SVM (OC-SVM). We propose here to use nested entire solution path algorithms for the OC-SVM and CS-SVM in order to accelerate the parameter selection and alleviate the dependency to labeled ``changed'' samples. Experiments are performed on two multitemporal change detection datasets (flood and fire detection) and the performance of the two methods proposed compared

Semi-Supervised Novelty Detection using SVM entire solution path

Very often, the only reliable information available to perform change detection is the description of some unchanged regions. Since sometimes these regions do not contain all the relevant information to identify their counterpart (the changes), we consider the use of unlabeled data to perform Semi-Supervised Novelty detection (SSND). SSND can be seen as an unbalanced classification problem solved using the Cost-Sensitive Support Vector Machine (CS-SVM), but this requires a heavy parameter search. We propose here to use entire solution path algorithms for the CS-SVM in order to facilitate and accelerate the parameter selection for SSND. Two algorithms are considered and evaluated. The first one is an extension of the CS-SVM algorithm that returns the entire solution path in a single optimization. This way, the optimization of a separate model for each hyperparameter set is avoided. The second forces the solution to be coherent through the solution path, thus producing classification boundaries that are nested (included in each other). We also present a low density criterion for selecting the optimal classification boundaries, thus avoiding the recourse to cross-validation that usually requires information about the ``change'' class. Experiments are performed on two multitemporal change detection datasets (flood and fire detection). Both algorithms tracing the solution path provide similar performances than the standard CS-SVM while being significantly faster. The low density criterion proposed achieves results that are close to the ones obtained by cross-validation, but without using information about the changes

Unsupervised Change Detection via Hierarchical Support Vector Clustering

When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of nested hierarchical support vector clustering descriptions, select the appropriate one using a cluster validity measure and finally merge the clusters into two classes, corresponding to changed and unchanged areas. Experiments on two multispectral datasets confirm the power and appropriateness of the proposed system

Robust Phase-Correlation based Registration of Airborne Videos using Motion Estimation

This paper presents a robust algorithm for the registration of airborne video sequences with reference images from a different source (airborne or satellite), based on phase-correlation. Phase-correlations using Fourier-Melin Invariant (FMI) descriptors allow to retrieve the rigid transformation parameters in a fast and non-iterative way. The robustness to multi-sources images is improved by an enhanced image representation based on the gradient norm and the extrapolation of registration parameters between frames by motion estimation. A phase-correlation score, indicator of the registration quality, is introduced to regulate between motion estimation only and frame-toreference image registration. Our Robust Phase-Correlation registration algorithm using Motion Estimation (RPCME) is compared with state-of-the-art Mutual Information (MI) algorithm on two different airborne videos. RPCME algorithm registered most of the frames accurately, retrieving much better orientation than MI. Our algorithm shows robustness and good accuracy to multisource images with the advantage of being a direct (non-iterative) method

Congenital Hypogonadotropic Hypogonadism and Kallmann Syndrome: Past, Present, and Future

The proper development and coordination of the hypothalamic-pituitary-gonadal (HPG) axis are essential for normal reproductive competence. The key factor that regulates the function of the HPG axis is gonadotrophin-releasing hormone (GnRH). Timely release of GnRH is critical for the onset of puberty and subsequent sexual maturation. Misregulation in this system can result in delayed or absent puberty and infertility. Congenital hypogonadotropic hypogonadism (CHH) and Kallmann syndrome (KS) are genetic disorders that are rooted in a GnRH deficiency but often accompanied by a variety of non-reproductive phenotypes such as the loss of the sense of smell and defects of the skeleton, eye, ear, kidney, and heart. Recent progress in DNA sequencing technology has produced a wealth of information regarding the genetic makeup of CHH and KS patients and revealed the resilient yet complex nature of the human reproductive neuroendocrine system. Further research on the molecular basis of the disease and the diverse signal pathways involved will aid in improving the diagnosis, treatment, and management of CHH and KS patients as well as in developing more precise genetic screening and counseling regime