Discovering multi-purpose modules through deep multitask learning

Machine learning scientists aim to discover techniques that can be applied across diverse sets of problems. Such techniques need to exploit regularities that are shared across tasks. This begs the question: What shared regularity is not yet being exploited? Complex tasks may share structure that is difficult for humans to discover. The goal of deep multitask learning is to discover and exploit this structure automatically by training a joint model across tasks. To this end, this dissertation introduces a deep multitask learning framework for collecting generic functional modules that are used in different ways to solve different problems. Within this framework, a progression of systems is developed based on assembling shared modules into task models and leveraging the complementary advantages of gradient descent and evolutionary optimization. In experiments, these systems confirm that modular sharing improves performance across a range of application areas, including general video game playing, computer vision, natural language processing, and genomics; yielding state-of-the-art results in several cases. The conclusion is that multi-purpose modules discovered by deep multitask learning can exceed those developed by humans in performance and generality.Computer Science

Semi-Supervised and Unsupervised Deep Visual Learning: A Survey

State-of-the-art deep learning models are often trained with a large amountof costly labeled training data. However, requiring exhaustive manualannotations may degrade the model's generalizability in the limited-labelregime. Semi-supervised learning and unsupervised learning offer promisingparadigms to learn from an abundance of unlabeled visual data. Recent progressin these paradigms has indicated the strong benefits of leveraging unlabeleddata to improve model generalization and provide better model initialization.In this survey, we review the recent advanced deep learning algorithms onsemi-supervised learning (SSL) and unsupervised learning (UL) for visualrecognition from a unified perspective. To offer a holistic understanding ofthe state-of-the-art in these areas, we propose a unified taxonomy. Wecategorize existing representative SSL and UL with comprehensive and insightfulanalysis to highlight their design rationales in different learning scenariosand applications in different computer vision tasks. Lastly, we discuss theemerging trends and open challenges in SSL and UL to shed light on futurecritical research directions.<br

Ecological and molecular insights into the function of colourful signals in aquatic environments

Discovering the processes that drive the emergence of new species and connecting it to biodiversity in its past, present, and future form has been one of the central questions of natural scientists for over a century. Two ways in which we can start to unravel the mechanisms that have created such diversity is to investigate: 1) the selective pressures that can initiate/drive and 2) the molecular capacities allowing evolutionary changes to happen. A convenient approach to study how environmental cues and molecular processes are linked to appearance is by investigating the emergence of similar phenotypes, whether they evolve in response to likewise selective pressures and/or in response to molecular or developmental constraints. Here, mimics because they imitate unrelated species (the model), are a classical example in which to study phenotypic convergence. CHAPTER1 (Published in Current Biology, 2015); Using a combination of behavioural, cell histological, neurophysiological and molecular approaches, the first chapter of my PhD thesis aimed to uncover the triggers for colour change in a small coral reef fish mimic, the dottyback, Pseudochromis fuscus. Yellow morphs are mainly found on live coral in association with yellow damselfish species such as the ambon- (Pomacentrus amboinensis) and the lemon damselfish (P. moluccensis), while brown morphs occur mainly on coral rubble in association with brown damselfishes such as the whitetail damselfish (P. chrysurus). Potential environmental cues that could be associated with colour change therefore included: i) aggressive mimicry, dottyback morphs associate with similarly coloured damselfishes to increase foraging success by preventing detection by juvenile fish prey; ii) social mimicry, differently coloured morphs hide among similarly coloured damselfish to reduce detection and predation risk from their own predators; and iii) crypsis, different coloured morphs match the colour of their background habitat to prevent detection from predators or potential prey. CHAPTER 2 (Published in The Journal of Experimental Biology, 2016); The second chapter aimed at investigating the triggers for ontogenetic colour changes and how these interrelate to the development of the visual system in dottybacks. Although adult dottybacks were found to be aggressive mimics that change colour to impersonate the colouration of the prevalent damselfish community, little was known about the early life stages of this fish. Using a developmental time series in combination with wild caught dottyback specimens I show multiple colour changes during dottyback ontogeny and link them to crucial life history transitions of dottybacks. Moreover, changes in the visual system were found to precede ontogenetic colour changes, and theoretical fish visual models were subsequently used to investigate the potential benefits of this pattern. CHAPTER 3 (Published in PNAS, 2015); Work for chapter 3 was done in collaboration with Dr. Zuzana Musilová at the University of Basel and was based on the discovery of multiple novel visual genes (opsins) in the dottyback (Chapter 2), which arose through gene duplications. One of these novel gene duplicates was found in the violet-blue opsin sub-family (SWS2); however, initial reconstruction of the SWS2 phylogeny suggested a much older, non- dottyback specific origin of the duplication event. Therefore, in chapter three we performed a thorough investigation of SWS2 by exploring the evolutionary history of this family in close to 100 fish species representing most fish lineages across the modern fish phylogeny

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,873 new measurements from 758 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 118 reviews are many that are new or heavily revised, including a new review on Neutrinos in Cosmology.Starting with this edition, the Review is divided into two volumes. Volume 1 includes the Summary Tables and all review articles. Volume 2 consists of the Particle Listings. Review articles that were previously part of the Listings are now included in volume 1.The complete Review (both volumes) is published online on the website of the Particle Data Group (http://pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is also available.The 2018 edition of the Review of Particle Physics should be cited as: M. Tanabashi (Particle Data Group), Phys. Rev. D 98, 030001 (2018)

Review of particle physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,062 new measurements from 721 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 117 reviews are many that are new or heavily revised, including those on Pentaquarks and Inflation. The complete Review is published online in a journal and on the website of the Particle Data Group (http://pdg.lbl.gov). The printed PDG Book contains the Summary Tables and all review articles but no longer includes the detailed tables from the Particle Listings. A Booklet with the Summary Tables and abbreviated versions of some of the review articles is also available

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,062 new measurements from 721 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 117 reviews are many that are new or heavily revised, including new reviews on Pentaquarks and Inflation. The complete Review is published online in a journal and on the website of the Particle Data Group (http://pdg.lbl.gov). The printed PDG Book contains the Summary Tables and all review articles but no longer includes the detailed tables from the Particle Listings. A Booklet with the Summary Tables and abbreviated versions of some of the review articles is also available.The publication of the Review of Particle Physics is supported by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under Contract No. DE–AC02–05CH11231; by the European Laboratory for Particle Physics (CERN); by an implementing arrangement between the governments of Japan (MEXT: Ministry of Education, Culture, Sports, Science and Technology) and the United States (DOE) on cooperative research and development; by the Institute of High Energy Physics, Chinese Academy of Sciences; and by the Italian National Institute of Nuclear Physics (INFN).The authors are grateful to Vincent Vennin for his careful reading of this manuscript and preparing Fig. 23.3 for this review. The work of J.E. was supported in part by the London Centre for Terauniverse Studies (LCTS), using funding from the European Research Council via the Advanced Investigator Grant 267352 and from the UK STFC via the research grant ST/L000326/1. The work of D.W. was supported in part by the UK STFC research grant ST/K00090X/1