The
Pascal
Visual Object Classes (VOC) challenge consists of two components: (i) a publicly available
dataset
of images together with ground truth annotation and standardised evaluation software; and ...(ii) an annual
competition
and workshop. There are five challenges: classification, detection, segmentation, action classification, and person layout. In this paper we provide a review of the challenge from 2008–2012. The paper is intended for two audiences:
algorithm designers
, researchers who want to see what the state of the art is, as measured by performance on the VOC datasets, along with the limitations and weak points of the current generation of algorithms; and,
challenge designers
, who want to see what we as organisers have learnt from the process and our recommendations for the organisation of future challenges. To analyse the performance of submitted algorithms on the VOC datasets we introduce a number of novel evaluation methods: a bootstrapping method for determining whether differences in the performance of two algorithms are significant or not; a normalised average precision so that performance can be compared across classes with different proportions of positive instances; a clustering method for visualising the performance across multiple algorithms so that the hard and easy images can be identified; and the use of a joint classifier over the submitted algorithms in order to measure their complementarity and combined performance. We also analyse the community’s progress through time using the methods of Hoiem et al. (Proceedings of European Conference on Computer Vision,
2012
) to identify the types of occurring errors. We conclude the paper with an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
The Pascal Visual Object Classes (VOC) Challenge Everingham, Mark; Van Gool, Luc; Williams, Christopher K. I. ...
International journal of computer vision,
06/2010, Letnik:
88, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The
Pascal
Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of ...images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as
the
benchmark for object detection.
This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
In this work, four methods are described and validated for generating in silico ensembles of PROTAC-mediated ternary complexes. Filters based on characteristics of the proposed ternary complexes are ...developed to identify those that resemble known crystal structures. We then show how to use these modeling techniques a priori to discriminate the PROTAC-mediated degradation behavior of a mutant protein vs its wild type, of three closely related targets, and among three different PROTAC molecules.
Extending upon our previous publication Drummond, M. ; J. Chem. Inf. Model. 2019, 59, 1634−1644 , two additional computational methods are presented to model PROTAC-mediated ternary complex ...structures, which are then used to predict the efficacy of any accompanying protein degradation. Method 4B, an extension to one of our previous approaches, incorporates a clustering procedure uniquely suited for considering ternary complexes. Method 4B yields the highest proportion to date of crystal-like poses in modeled ternary complex ensembles, nearing 100% in two cases and always giving a hit rate of at least 10%. Techniques to further improve this performance for particularly troublesome cases are suggested and validated. This demonstrated ability to reliably reproduce known crystallographic ternary complex structures is further established through modeling of a newly released crystal structure. Moreover, for the far more common scenario where the structure of the ternary complex intermediate is unknown, the methods detailed in this work nonetheless consistently yield results that reliably follow experimental protein degradation trends, as established through seven retrospective case studies. These various case studies cover challenging yet common modeling situations, such as when the precise orientation of the PROTAC binding moiety in one (or both) of the protein pockets has not been experimentally established. Successful results are presented for one PROTAC targeting many proteins, for different PROTACs targeting the same protein, and even for degradation effected by an E3 ligase that has not been structurally characterized in a ternary complex. Overall, the computational modeling approaches detailed in this work should greatly facilitate PROTAC screening and design efforts, so that the many advantages of a PROTAC-based degradation approach can be effectively utilized both rapidly and at reduced cost.
This paper presents and critically analyses the current waste electrical and electronic equipment (WEEE) management practices in various countries and regions. Global trends in (i) the quantities and ...composition of WEEE; and (ii) the various strategies and practices adopted by selected countries to handle, regulate and prevent WEEE are comprehensively examined. The findings indicate that for (i), the quantities of WEEE generated are high and/or on the increase. IT and telecommunications equipment seem to be the dominant WEEE being generated, at least in terms of numbers, in Africa, in the poorer regions of Asia and in Latin/South America. However, the paper contends that the reported figures on quantities of WEEE generated may be grossly underestimated. For (ii), with the notable exception of Europe, many countries seem to be lacking or are slow in initiating, drafting and adopting WEEE regulations. Handling of WEEE in developing countries is typified by high rate of repair and reuse within a largely informal recycling sector. In both developed and developing nations, the landfilling of WEEE is still a concern. It has been established that stockpiling of unwanted electrical and electronic products is common in both the USA and less developed economies. The paper also identifies and discusses four common priority areas for WEEE across the globe, namely: (i) resource depletion; (ii) ethical concerns; (iii) health and environmental issues; and (iv) WEEE takeback strategies. Further, the paper discusses the future perspectives on WEEE generation, treatment, prevention and regulation. Four key conclusions are drawn from this review: global amounts of WEEE will continue unabated for some time due to emergence of new technologies and affordable electronics; informal recycling in developing nations has the potential of making a valuable contribution if their operations can be changed with strict safety standards as a priority; the pace of initiating and enacting WEEE specific legislation is very slow across the globe and in some cases non-existent; and globally, there is need for more accurate and current data on amounts and types of WEEE generated.
We introduce a generative model of part‐segmented 3D objects: the shape variational auto‐encoder (ShapeVAE). The ShapeVAE describes a joint distribution over the existence of object parts, the ...locations of a dense set of surface points, and over surface normals associated with these points. Our model makes use of a deep encoder‐decoder architecture that leverages the part‐decomposability of 3D objects to embed high‐dimensional shape representations and sample novel instances. Given an input collection of part‐segmented objects with dense point correspondences the ShapeVAE is capable of synthesizing novel, realistic shapes, and by performing conditional inference enables imputation of missing parts or surface normals. In addition, by generating both points and surface normals, our model allows for the use of powerful surface‐reconstruction methods for mesh synthesis. We provide a quantitative evaluation of the ShapeVAE on shape‐completion and test‐set log‐likelihood tasks and demonstrate that the model performs favourably against strong baselines. We demonstrate qualitatively that the ShapeVAE produces plausible shape samples, and that it captures a semantically meaningful shape‐embedding. In addition we show that the ShapeVAE facilitates mesh reconstruction by sampling consistent surface normals.
The Female Athlete Triad represents three interrelated conditions of (i) low energy availability (energy deficiency), presenting with or without disordered eating, (ii) menstrual dysfunction, and ...(iii) poor bone health, each of which can exist along a continuum of severity ranging from mild and moderate subclinical health concerns to severe clinical outcomes, including eating disorders, amenorrhea, and osteoporosis. This review provides a brief overview of the Female Athlete Triad, including updating the current thinking regarding energy availability and how it relates to reproductive function, and sets the stage for an initial working model of a similar syndrome in males that will be based on currently available evidence and will later be defined and referred to as a Male Athlete Triad by the newly re-named Female and Male Athlete Triad Coalition. A primary focus of this paper will be on the physiology of each Triad model with an emphasis on low energy availability and its role in reproductive function, with a brief introduction on its effects on bone health in men. From the data reviewed, (i) a specific threshold of energy availability below which menstrual disturbances are induced is not supported; (ii) it appears that the energetic, reproductive, and bone systems in men are more resilient to the effects of low energy availability compared to those of women, requiring more severe energetic perturbations before alterations are observed; and (iii) it appears that recovery of the hypothalamic pituitary gonadal axis can be observed more quickly in men than in women.
The transcytosis of antigens across the gut epithelium by microfold cells (M cells) is important for the induction of efficient immune responses to some mucosal antigens in Peyer's patches. Recently, ...substantial progress has been made in our understanding of the factors that influence the development and function of M cells. This review highlights these important advances, with particular emphasis on: the host genes which control the functional maturation of M cells; how this knowledge has led to the rapid advance in our understanding of M-cell biology in the steady state and during aging; molecules expressed on M cells which appear to be used as "immunosurveillance" receptors to sample pathogenic microorganisms in the gut; how certain pathogens appear to exploit M cells to infect the host; and finally how this knowledge has been used to specifically target antigens to M cells to attempt to improve the efficacy of mucosal vaccines.
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past ...decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
The results of cognate docking with the prepared Astex dataset provided by the organizers of the “Docking and Scoring: A Review of Docking Programs” session at the 241st ACS national meeting are ...presented. The MOE software with the newly developed GBVI/WSA dG scoring function is used throughout the study. For 80 % of the Astex targets, the MOE docker produces a top-scoring pose within 2 Å of the X-ray structure. For 91 % of the targets a pose within 2 Å of the X-ray structure is produced in the top 30 poses. Docking failures, defined as cases where the top scoring pose is greater than 2 Å from the experimental structure, are shown to be largely due to the absence of bound waters in the source dataset, highlighting the need to include these and other crucial information in future standardized sets. Docking success is shown to depend heavily on data preparation. A “dataset preparation” error of 0.5 kcal/mol is shown to cause fluctuations of over 20 % in docking success rates.
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