BREAKING CODES: MAKING THEORIES ON MENSTRUATION ACCESSIBLE THROUGH THIRD-WAVE FEMINISM

Men and women respond to situations according to their community’s social codes. With menstruation, people adhere to “menstrual codes”. Within academic communities, people adhere to “academic codes”. This report paper investigates performances of academic codes and menstrual codes. Implications of gender identity and race are missing and/or minimal in past feminist work regarding menstruation. This paper includes considerations for gender identity and race. Within the examination of academic codes, this paper discusses the inhibitive process of idea creation within the academic sphere, and the limitations to the predominant ways of knowledge sharing within, and outside of, the academic community. The digital project (www.hu.mtu.edu/~creynolds) is one example of how academic and menstrual codes can be broken. The report and project provide a broadly accessible deconstruction of menstrual advertising and academic theories while fostering conversations on menstruation through the sharing of knowledge with others, regardless of gender, race, or academic standing.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

OBJECTIVES: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. METHODS: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. RESULTS: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. CONCLUSION: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

„RFID - Systematische Versuche für den zuverlässigen Einsatz in der Logistik“

Mit zunehmender Komplexität und Vielfalt der Logistikprozesse steigt der Stellenwert der eingesetzten Informationstechnologien. Die den Warenfluss begleitenden bzw. vorhereilenden Informationen sind erforderlich, um Waren identifizieren und Unternehmensressourcen optimal einsetzen zu können. Als Beispiel ist der klassische Wareneingang zu nennen. Durch die Avisierung von Menge und Art eingehender Waren können der Einsatz des Personals zur Entladung und Vereinnahmung sowie die erforderlichen Ressourcen (Ladehilfsmittel, Flurförderzeuge, usw.) im Vorfeld geplant und bereitgestellt werden. Der Informationsfluss ist demnach als Qualitätsmerkmal und als Wirtschaftlichkeitsfaktor zu verstehen. Die Schnittstelle zwischen dem physischen Warenfluss und dem Informationsfluss auf EDV-Basis bildet die Identifikationstechnologien. In der Industrie verbreitete Identifikationstechnologien bestehen in der Regel aus einem Datenträger und einem Erfassungsgerät. Der Datenträger ist am physischen Objekt fixiert. Das Erfassungsgerät liest die auf dem Datenträger befindlichen Objektinformationen und wandelt sie in einen Binär-Code um, der durch nachgelagerte EDV weiterverarbeitet wird. Die momentan in der Industrie und im Handel am häufigsten verwendete Identifikationstechnologie ist der Barcode. In den letzten Jahren tritt die RFID-Technologie in den Fokus der Industrie und des Handels im Bereich Materialfluss und Logistik. Unter „Radio Frequency IDentification“ wird die Kommunikation per Funkwellen zwischen Datenträger (Transponder) und Lesegerät verstanden. Mittels der RFID-Technologie ist der Anwender, im Gegensatz zum Barcode, in der Lage, Informationen auf dem Transponder ohne Sichtkontakt zu erfassen. Eine Ausrichtung der einzelnen Artikel ist nicht erforderlich. Zudem können auf bestimmten Transpondertypen weitaus größere Datenmengen als auf einem Barcode hinterlegt werden. Transponder mit hoher Speicherkapazität eignen sich in der Regel, um die auf ihnen hinterlegten Daten bei Bedarf aktualisieren zu k��nnen. Eine dezentrale Datenorganisation ist realisierbar. Ein weiterer Vorteil der RFID-Technologie ist die Möglichkeit, mehrere Datenträger im Bruchteil einer Sekunde zu erfassen. In diesem Fall spricht man von einer Pulkerfassung. Diese Eigenschaft ist besonders im Bereich Warenein- und -ausgang von Interesse. Durch RFID ist es möglich, Ladeeinheiten, z. B. Paletten mit Waren, durch einen Antennenbereich zu fördern, und die mit Transpondern versehenen Artikel zu identifizieren und in die EDV zu übertragen. Neben der Funktionalität einer solchen Technologie steht in der Industrie vor allem die Wirtschaftlichkeit im Vordergrund. Transponder sind heute teuerer als Barcodes. Zudem müssen Investitionen in die für den Betrieb von RFID erforderliche Hard- und Software einkalkuliert werden. Daher muss der Einsatz der RFID-Technologie Einsparungen durch die Reorganisation der Unternehmensprozesse nach sich ziehen. Ein Schwachpunkt der RFID-Technologie ist momentan je nach Anwendung die mangelnde Zuverlässigkeit und Wiederholgenauigkeit bei Pulklesungen. Die Industrie und der Handel brauchen Identifikationstechnologien, deren Erfassungsrate im Bereich nahe 100 % liegt. Die Gefahr besteht darin, dass durch ein unzuverlässiges RFID-System unvollständige bzw. fehlerhafte Datensätze erzeugt werden können. Die Korrektur der Daten kann teurer sein als die durch die Reorganisation der Prozesse mittels RFID erzielten Einsparungen. Die Erfassungsrate der Transponder bei Pulkerfassungen wird durch mehrere Faktoren beeinflusst, die im Folgenden detailliert dargestellt werden. Das Institut für Fördertechnik und Logistik (IFT) in Stuttgart untersucht m��gliche Einflussgrößen auf die Erkennungsraten bei Pulkerfassungen. Mit den gewonnenen Erkenntnissen sollen mögliche Schwachstellen bei der Erkennung mehrerer Transponder im Vorfeld einer Implementierung in die Logistikprozesse eines Unternehmens eliminiert werden. With increasing complexity and variety of the logistics processes the significance of the used information technologies increases. The information accompanying the material flow is necessary in order to be able to identify goods and to be able to use corporate resources optimally. The classical goods entrance is to be mentioned as an example. The notification of amount and kind of incoming goods can be used for previously planning and providing of the personnel and necessary resources. The flow of information is to be understood accordingly as a high-quality feature and as an economic efficiency factor. With increasing complexity and variety of the logistics processes the significance of the used information technologies increases. The information accompanying the material flow is necessary in order to be able to identify goods and to be able to use corporate resources optimally. The classical goods entrance is to be mentioned as an example. The notification of amount and kind of incoming goods can be used for previously planning and providing of the personnel and necessary resources. The flow of information is to be understood accordingly as a high-quality feature and as an economic efficiency factor.

Code-specific learning rules improve action selection by populations of spiking neurons

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Evaluation of the accuracy of multilevel analysis parameter estimates for intra-cluster correlation in correlated binary data

Many studies in biostatistics deal with binary data. Some of these studies involve correlated observations, which can complicate the analysis of the resulting data. Studies of this kind typically arise when a high degree of commonality exists between test subjects. If there exists a natural hierarchy in the data, multilevel analysis is an appropriate tool for the analysis. Two examples are the measurements on identical twins, or the study of symmetrical organs or appendages such as in the case of ophthalmic studies. Although this type of matching appears ideal for the purposes of comparison, analysis of the resulting data while ignoring the effect of intra-cluster correlation has been shown to produce biased results.^ This paper will explore the use of multilevel modeling of simulated binary data with predetermined levels of correlation. Data will be generated using the Beta-Binomial method with varying degrees of correlation between the lower level observations. The data will be analyzed using the multilevel software package MlwiN (Woodhouse, et al, 1995). Comparisons between the specified intra-cluster correlation of these data and the estimated correlations, using multilevel analysis, will be used to examine the accuracy of this technique in analyzing this type of data. ^

Growth Codes: Intermediate Performance Analysis and Application to Video

Growth codes are a subclass of Rateless codes that have found interesting applications in data dissemination problems. Compared to other Rateless and conventional channel codes, Growth codes show improved intermediate performance which is particularly useful in applications where partial data presents some utility. In this paper, we investigate the asymptotic performance of Growth codes using the Wormald method, which was proposed for studying the Peeling Decoder of LDPC and LDGM codes. Compared to previous works, the Wormald differential equations are set on nodes' perspective which enables a numerical solution to the computation of the expected asymptotic decoding performance of Growth codes. Our framework is appropriate for any class of Rateless codes that does not include a precoding step. We further study the performance of Growth codes with moderate and large size codeblocks through simulations and we use the generalized logistic function to model the decoding probability. We then exploit the decoding probability model in an illustrative application of Growth codes to error resilient video transmission. The video transmission problem is cast as a joint source and channel rate allocation problem that is shown to be convex with respect to the channel rate. This illustrative application permits to highlight the main advantage of Growth codes, namely improved performance in the intermediate loss region.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

Cancer intelligence acquired (CIA): tumor glycosylation and sialylation codes dismantling antitumor defense

Aberrant glycosylation is a key feature of malignant transformation and reflects epigenetic and genetic anomalies among the multitude of molecules involved in glycan biosynthesis. Although glycan biosynthesis is not template bound, altered tumor glycosylation is not random, but associated with common glycosylation patterns. Evidence suggests that acquisition of distinct glycosylation patterns evolves from a ‘microevolutionary’ process conferring advantages in terms of tumor growth, tumor dissemination, and immune escape. Such glycosylation modifications also involve xeno- and hypersialylation. Xeno-autoantigens such as Neu5Gc-gangliosides provide potential targets for immunotherapy. Hypersialylation may display ‘enhanced self’ to escape immunosurveillance and involves several not mutually exclusive inhibitory pathways that all rely on protein–glycan interactions. A better understanding of tumor ‘glycan codes’ as deciphered by lectins, such as siglecs, selectins, C-type lectins and galectins, may lead to novel treatment strategies, not only in cancer, but also in autoimmune disease or transplantation.