Decision Forests, Convolutional Networks and the Models in-Between

This paper investigates the connections between two state of the art classifiers: decision forests (DFs, including decision jungles) and convolutional neural networks (CNNs). Decision forests are computationally efficient thanks to their conditional computation property (computation is confined to only a small region of the tree, the nodes along a single branch). CNNs achieve state of the art accuracy, thanks to their representation learning capabilities. We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency. We call this new family of hybrid models conditional networks. Conditional networks can be thought of as: i) decision trees augmented with data transformation operators, or ii) CNNs, with block-diagonal sparse weight matrices, and explicit data routing functions. Experimental validation is performed on the common task of image classification on both the CIFAR and Imagenet datasets. Compared to state of the art CNNs, our hybrid models yield the same accuracy with a fraction of the compute cost and much smaller number of parameters

Using Noun Phrases for Navigating Biomedical Literature on Pubmed: How Many Updates Are We Losing Track of?

Author-supplied citations are a fraction of the related literature for a paper. The “related citations” on PubMed is typically dozens or hundreds of results long, and does not offer hints why these results are related. Using noun phrases derived from the sentences of the paper, we show it is possible to more transparently navigate to PubMed updates through search terms that can associate a paper with its citations. The algorithm to generate these search terms involved automatically extracting noun phrases from the paper using natural language processing tools, and ranking them by the number of occurrences in the paper compared to the number of occurrences on the web. We define search queries having at least one instance of overlap between the author-supplied citations of the paper and the top 20 search results as citation validated (CV). When the overlapping citations were written by same authors as the paper itself, we define it as CV-S and different authors is defined as CV-D. For a systematic sample of 883 papers on PubMed Central, at least one of the search terms for 86% of the papers is CV-D versus 65% for the top 20 PubMed “related citations.” We hypothesize these quantities computed for the 20 million papers on PubMed to differ within 5% of these percentages. Averaged across all 883 papers, 5 search terms are CV-D, and 10 search terms are CV-S, and 6 unique citations validate these searches. Potentially related literature uncovered by citation-validated searches (either CV-S or CV-D) are on the order of ten per paper – many more if the remaining searches that are not citation-validated are taken into account. The significance and relationship of each search result to the paper can only be vetted and explained by a researcher with knowledge of or interest in that paper

Organising multi-dimensional biological image information: The BioImage Database

Nowadays it is possible to unravel complex information at all levels of cellular organization by obtaining multi-dimensional image information. at the macromolecular level, three-dimensional (3D) electron microscopy, together with other techniques, is able to reach resolutions at the nanometer or subnanometer level. The information is delivered in the form of 3D volumes containing samples of a given function, for example, the electron density distribution within a given macromolecule. The same situation happens at the cellular level with the new forms of light microscopy, particularly confocal microscopy, all of which produce biological 3D volume information. Furthermore, it is possible to record sequences of images over time (videos), as well as sequences of volumes, bringing key information on the dynamics of living biological systems. It is in this context that work on bioimage started two years ago, and that its first version is now presented here. In essence, Bioimage is a database specifically designed to contain multi-dimensional images, perform queries and interactively work with the resulting multi-dimensional information on the World Wide Web, as well as accomplish the required cross-database links. Two sister home pages of bioimage can be accessed at http://www.bioimage.org and http://www-embl.bioimage.or

Evidence for a nuclear compartment of transcription and splicing located at chromosome domain boundaries

The nuclear topography of splicing snRNPs, mRNA transcripts and chromosome domains in various mammalian cell types are described. The visualization of splicing snRNPs, defined by the Sm antigen, and coiled bodies, revealed distinctly different distribution patterns in these cell types. Heat shock experiments confirmed that the distribution patterns also depend on physiological parameters. Using a combination of fluorescencein situ hybridization and immunodetection protocols, individual chromosome domains were visualized simultaneously with the Sm antigen or the transcript of an integrated human papilloma virus genome. Three-dimensional analysis of fluorescence-stained target regions was performed by confocal laser scanning microscopy. RNA transcripts and components of the splicing machinery were found to be generally excluded from the interior of the territories occupied by the individual chromosomes. Based on these findings we present a model for the functional compartmentalization of the cell nucleus. According to this model the space between chromosome domains, including the surface areas of these domains, defines a three-dimensional network-like compartment, termed the interchromosome domain (ICD) compartment, in which transcription and splicing of mRNA occurs

Utjecaj sadržaja lijeka i veličine aglomerata na tabletiranje i oslobađanje bromheksin hidroklorida iz aglomerata s talkom pripremljenih kristalokoaglomeracijom

The objective of the investigation was to study the effect of bromhexine hydrochloride (BXH) content and agglomerate size on mechanical, compressional and drug release properties of agglomerates prepared by crystallo-co-agglomeration (CCA). Studies on optimized batches of agglomerates (BXT1 and BXT2) prepared by CCA have showed adequate sphericity and strength required for efficient tabletting. Trend of strength reduction with a decrease in the size of agglomerates was noted for both batches, irrespective of drug loading. However, an increase in mean yield pressure (14.189 to 19.481) with an increase in size was observed for BXT2 having BXH-talc (1:15.7). Surprisingly, improvement in tensile strength was demonstrated by compacts prepared from BXT2, due to high BXH load, whereas BXT1, having a low amount of BXH (BXH-talc, 1:24), showed low tensile strength. Consequently, increased tensile strength was reflected in extended drug release from BXT2 compacts (Higuchi model, R2 = 0.9506 to 0.9981). Thus, it can be concluded that interparticulate bridges formed by BXH and agglomerate size affect their mechanical, compressional and drug release properties.Cilj rada bio je praćenje utjecaja sadržaja bromheksidin hidroklorida (BXH) i veličine aglomerata na mehanička svojstva, kompresivnost i oslobađanje ljekovite tvari iz aglomerata pripravljenih kristalokoaglomeracijom (CCA). Optimizirani pripravci aglomerata (BXT1 i BXT2) pripravljeni CCA metodom pokazuju adekvatnu sferičnost i čvrstoću potrebnu za učinkovito tabletiranje. U oba pripravka se smanjenjem veličine aglomerata smanjivala i čvrstoća, neovisno o količini ljekovite tvari. Međutim, povećanje prosječnog tlaka s povećanjem veličine čestica primijećeno je u pripravku BXT2 s omjerom BXH-talk 1:15,7. Iznenađuje da su kompakti pripravljeni iz BXT2, s visokim sadržajem BXH, imali veću vlačnu čvrstoću, dok su BXT1 s niskim sadržajem BXH (BXH-talk, 1:24) imali manju čvrstoću. Veća vlačna čvrstoća imala je za posljedicu produljeno oslobađanje ljekovite tvari iz BXT2 (Higuchijev model, R2 = 0,9506 do 0,9981). Može se zaključiti da mostovi među česticama BXH i veličina aglomerata utječu na njihova mehanička i kompresivna svojstva te na oslobađanje ljekovite tvari

Knowledge sharing and collaboration in translational research, and the DC-THERA Directory

Biomedical research relies increasingly on large collections of data sets and knowledge whose generation, representation and analysis often require large collaborative and interdisciplinary efforts. This dimension of ‘big data’ research calls for the development of computational tools to manage such a vast amount of data, as well as tools that can improve communication and access to information from collaborating researchers and from the wider community. Whenever research projects have a defined temporal scope, an additional issue of data management arises, namely how the knowledge generated within the project can be made available beyond its boundaries and life-time. DC-THERA is a European ‘Network of Excellence’ (NoE) that spawned a very large collaborative and interdisciplinary research community, focusing on the development of novel immunotherapies derived from fundamental research in dendritic cell immunobiology. In this article we introduce the DC-THERA Directory, which is an information system designed to support knowledge management for this research community and beyond. We present how the use of metadata and Semantic Web technologies can effectively help to organize the knowledge generated by modern collaborative research, how these technologies can enable effective data management solutions during and beyond the project lifecycle, and how resources such as the DC-THERA Directory fit into the larger context of e-science

Free-hand sketch synthesis with deformable stroke models

We present a generative model which can automatically summarize the stroke composition of free-hand sketches of a given category. When our model is fit to a collection of sketches with similar poses, it discovers and learns the structure and appearance of a set of coherent parts, with each part represented by a group of strokes. It represents both consistent (topology) as well as diverse aspects (structure and appearance variations) of each sketch category. Key to the success of our model are important insights learned from a comprehensive study performed on human stroke data. By fitting this model to images, we are able to synthesize visually similar and pleasant free-hand sketches

A 3D Human Posture Approach for Activity Recognition Based on Depth Camera

Human activity recognition plays an important role in the context of Ambient Assisted Living (AAL), providing useful tools to improve people quality of life. This work presents an activity recognition algorithm based on the extraction of skeleton joints from a depth camera. The system describes an activity using a set of few and basic postures extracted by means of the X-means clustering algorithm. A multi-class Support Vector Machine, trained with the Sequential Minimal Optimization is employed to perform the classification. The system is evaluated on two public datasets for activity recognition which have different skeleton models, the CAD-60 with 15 joints and the TST with 25 joints. The proposed approach achieves precision/recall performances of 99.8 % on CAD-60 and 97.2 %/91.7 % on TST. The results are promising for an applied use in the context of AAL

Hierarchical colour image segmentation by leveraging RGB channels independently

In this paper, we introduce a hierarchical colour image segmentation based on cuboid partitioning using simple statistical features of the pixel intensities in the RGB channels. Estimating the difference between any two colours is a challenging task. As most of the colour models are not perceptually uniform, investigation of an alternative strategy is highly demanding. To address this issue, for our proposed technique, we present a new concept for colour distance measure based on the inconsistency of pixel intensities of an image which is more compliant to human perception. Constructing a reliable set of superpixels from an image is fundamental for further merging. As cuboid partitioning is a superior candidate to produce superpixels, we use the agglomerative merging to yield the final segmentation results exploiting the outcome of our proposed cuboid partitioning. The proposed cuboid segmentation based algorithm significantly outperforms not only the quadtree-based segmentation but also existing state-of-the-art segmentation algorithms in terms of quality of segmentation for the benchmark datasets used in image segmentation. © 2019, Springer Nature Switzerland AG