Molecular Computing: from conformational pattern recognition to complex processing networks

Natural biomolecular systems process information in a radically different manner than programmable machines. Conformational interactions, the basis of specificity and self-assembly, are of key importance. A gedanken device is presented that illustrates how the fusion of information through conformational self-organization can serve to enhance pattern processing at the cellular level. The device is used to highlight general features of biomolecular information processing. We briefly outline a simulation system designed to address the manner in which conformational processing interacts with kinetic and higher level structural dynamics in complex biochemical networks. Virtual models that capture features of biomolecular information processing can in some instances have artificial intelligence value in their own right and should serve as design tools for future computers built from real molecules

Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification

We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have considerably improved the performance of object recognition. However, to date there has been limited work using these deep CNNs as local feature extractors. This partly stems from CNNs having internal representations which are high dimensional, thereby making such representations difficult to model using stochastic models. To overcome this issue, we propose to reduce the dimensionality of one of the internal fully connected layers, in conjunction with layer-restricted retraining to avoid retraining the entire network. The distribution of low-dimensional features obtained from the modified layer is then modelled using a Gaussian mixture model. Comparative experiments show that considerable performance improvements can be achieved on the challenging Fish and UEC FOOD-100 datasets.Comment: 5 pages, three figure

Subset Feature Learning for Fine-Grained Category Classification

Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy

Comment: Judicial Accountability and Discipline

The judicial disciplinary process and the specter of politically motivated misconduct allegations against state judges poses an important challenge to judicial independence

Use of Motorcycle Helmets in YOGYAKARTA : Some Observations and Comments

Cedera kepala merupakan sebab utama kematian dalam kecelakaan sepeda motor. Penelitian di Ame­rika Serikat, menunjukkan pemakaian helm mengurangi risiko cedera dan kematian. Penelitian ini mene­liti ketaatan terhadap peraturan pemakaian helm di Yogyakarta. Data dikumpulkan melalui observasi sistematik (N=9242) dan wawancara terbuka (n=150) di lima jalan utama yang berbeda di seluruh kota. Ketaatan umum terhadap peraturan pemakaian helm adalah 87% untuk pengemudi, dengan variasi kepen­tingan terhadap waktu dan tempat.Hanya 55% pengemudi memakai helm dengan baik (dengan tali di ikatkan) dan hanya 20% penumpang memakai helm. Jadi hanya 50% orang yang naik sepeda motor terlindungi secara maksimum. Di dalam wawancara, responden mengatakan ketidak-enakan fisik dan "malas" sebagai alasan paling umum untuk tidak memakai helm; beberapa orang menyatakan helm tidak perlu di jalan-jalan kota dan di waktu malam. Wawancara mengisyaratkan bahwa orang yang naik sepeda motor memakai helm kebanyakan karena takut di tegur polisi dan responden hanya tahu sedikit tentang nilai keselamatan helm. Banyaknya pemakaian helm sekarang ini merupakan ketaatan semu ("token compliance") terhadap peraturan. Dari hasil studi diusulkan cara-cara agar keselamatan pemakaian helm di Indonesia bisa di­tingkatkan

Graded potential of neural crest to form cornea, sensory neurons and cartilage along the rostrocaudal axis

Neural crest cells arising from different rostrocaudal axial levels form different sets of derivatives as diverse as ganglia, cartilage and cornea. These variations may be due to intrinsic properties of the cell populations, different environmental factors encountered during migration or some combination thereof. We test the relative roles of intrinsic versus extrinsic factors by challenging the developmental potential of cardiac and trunk neural crest cells via transplantation into an ectopic midbrain environment. We then assess long-term survival and differentiation into diverse derivatives, including cornea, trigeminal ganglion and branchial arch cartilage. Despite their ability to migrate to the periocular region, neither cardiac nor trunk neural crest contribute appropriately to the cornea, with cardiac crest cells often forming ectopic masses on the corneal surface. Similarly, the potential of trunk and cardiac neural crest to form somatosensory neurons in the trigeminal ganglion was significantly reduced compared with control midbrain grafts. Cardiac neural crest exhibited a reduced capacity to form cartilage, contributing only nominally to Meckle's cartilage, whereas trunk neural crest formed no cartilage after transplantation, even when grafted directly into the first branchial arch. These results suggest that neural crest cells along the rostrocaudal axis display a graded loss in developmental potential to form somatosensory neurons and cartilage even after transplantation to a permissive environment. Hox gene expression was transiently maintained in the cardiac neural tube and neural crest at 12 hours post-transplantation to the midbrain, but was subsequently downregulated. This suggests that long-term differences in Hox gene expression cannot account for rostrocaudal differences in developmental potential of neural crest populations in this case

Transitions : individuelle Handhabung und Verarbeitungsformen institutionellen Wandels

Unter Transitionen werden hier allgemein unstete, diskontinuierliche Übergangsprozesse verstanden. In der Selbstbeobachtung erscheinen sie z.B. als Brüche, überraschende Ereignisse, ungeahnte Chancen oder nie für möglich gehaltene Schocks. Retrospektiv jedenfalls – positiv wie negativ bewertet – als entscheidende Weichenstellungen, die später nachfolgende Entscheidungen in einschneidendem Umfang wenn schon nicht determinieren, so jedenfalls aber nachhaltig oder dauerhaft prägen. Als unstet werden sie deswegen eingeschätzt, weil Akteure heute davon zunehmend „unvorhergesehen“ und nicht planbar betroffen sind

Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126

Autonomous rendezvous and docking: A commercial approach to on-orbit technology validation

The Space Automation and Robotics Center (SpARC), a NASA-sponsored Center for the Commercial Development of Space (CCDS), in conjunction with its corporate affiliates, is planning an on-orbit validation of autonomous rendezvous and docking (ARD) technology. The emphasis in this program is to utilize existing technology and commercially available components whenever possible. The primary subsystems that will be validated by this demonstration include GPS receivers for navigation, a video-based sensor for proximity operations, a fluid connector mechanism to demonstrate fluid resupply capability, and a compliant, single-point docking mechanism. The focus for this initial experiment will be expendable launch vehicle (ELV) based and will make use of two residual Commercial Experiment Transporter (COMET) service modules. The first COMET spacecraft will be launched in late 1992 and will serve as the target vehicle. The ARD demonstration will take place in late 1994, after the second COMET spacecraft has been launched. The service module from the second COMET will serve as the chase vehicle

Fine-grained classification via mixture of deep convolutional neural networks

We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification problem is characterised by large intra-class variations and small inter-class variations. To overcome these problems our proposed MixDCNN system partitions images into K subsets of similar images and learns an expert DCNN for each subset. The output from each of the K DCNNs is combined to form a single classification decision. In contrast to previous techniques, we provide a formulation to perform joint end-to-end training of the K DCNNs simultaneously. Extensive experiments, on three datasets using two network structures (AlexNet and GoogLeNet), show that the proposed MixDCNN system consistently outperforms other methods. It provides a relative improvement of 12.7% and achieves state-of-the-art results on two dataset