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Iterative Refinement of Approximate Posterior for Training Directed Belief NetworksNov 19 2015Mar 14 2016Recent advances in variational inference that make use of an inference or recognition network for training and evaluating deep directed graphical models have advanced well beyond traditional variational inference and Markov chain Monte Carlo methods. ... More

Learning Representations by Maximizing Mutual Information Across ViewsJun 03 2019Jul 08 2019We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context ... More

Batch weight for domain adaptation with mass shiftMay 29 2019Unsupervised domain transfer is the task of transferring or translating samples from a source distribution to a different target distribution. Current solutions unsupervised domain transfer often operate on data on which the modes of the distribution ... More

Learning Representations by Maximizing Mutual Information Across ViewsJun 03 2019We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, a context could be an image from ImageNet, and multiple views of ... More

Variance Regularizing Adversarial LearningJul 02 2017Aug 19 2018We introduce a novel approach for training adversarial models by replacing the discriminator score with a bi-modal Gaussian distribution over the real/fake indicator variables. In order to do this, we train the Gaussian classifier to match the target ... More

Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic Networks from fMRI DataNov 03 2016Functional magnetic resonance imaging (fMRI) of temporally-coherent blood oxygenization level-dependent (BOLD) signal provides an effective means of analyzing functionally coherent patterns in the brain. Intrinsic networks and functional connectivity ... More

Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging DataMar 21 2016Independent component analysis (ICA), as an approach to the blind source-separation (BSS) problem, has become the de-facto standard in many medical imaging settings. Despite successes and a large ongoing research effort, the limitation of ICA to square ... More

A new and flexible method for constructing designs for computer experimentsOct 02 2010We develop a new method for constructing "good" designs for computer experiments. The method derives its power from its basic structure that builds large designs using small designs. We specialize the method for the construction of orthogonal Latin hypercubes ... More

Negative Learning Rates and P-LearningMar 27 2016Mar 29 2016We present a method of training a differentiable function approximator for a regression task using negative examples. We effect this training using negative learning rates. We also show how this method can be used to perform direct policy learning in ... More

Iterative Refinement of Approximate Posterior for Training Directed Belief NetworksNov 19 2015Oct 29 2016Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods. Recent advances that exploit the capacity and flexibility in this approach have ... More

Deep learning for neuroimaging: a validation studyDec 20 2013Feb 19 2014Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's ... More

Prediction of Progression to Alzheimer`s disease with Deep InfoMaxApr 24 2019Apr 30 2019Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a promising tool for exploring brain structure in a flexible non-linear ... More

Unsupervised State Representation Learning in AtariJun 19 2019Jun 26 2019State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is ... More

Adversarial Mixup ResynthesizersMar 07 2019In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained ... More

Maximum-Likelihood Augmented Discrete Generative Adversarial NetworksFeb 26 2017Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted. The fundamental reason is the difficulty of back-propagation ... More

Prediction of Progression to Alzheimer's disease with Deep InfoMaxApr 24 2019May 01 2019Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a promising tool for exploring brain structure in a flexible non-linear ... More

Spatio-Temporal Deep Graph InfomaxApr 12 2019Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time. To address these issues, we introduce Spatio-Temporal Deep Graph ... More

Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural NetworksNov 03 2016Aug 27 2018We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal dynamics through recurrent ... More

Leveraging exploration in off-policy algorithms via normalizing flowsMay 16 2019Exploration is a crucial component for discovering approximately optimal policies in most high-dimensional reinforcement learning (RL) settings with sparse rewards. Approaches such as neural density models and continuous exploration (e.g., Go-Explore) ... More

Adversarial Mixup ResynthesizersMar 07 2019Apr 04 2019In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained ... More

Learning deep representations by mutual information estimation and maximizationAug 20 2018Feb 22 2019In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of ... More

Learning deep representations by mutual information estimation and maximizationAug 20 2018Jan 26 2019In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of ... More

Boundary-Seeking Generative Adversarial NetworksFeb 27 2017Feb 21 2018Generative adversarial networks (GANs) are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions. GANs, as normally formulated, rely on the generated samples being completely ... More

Prediction of Progression to Alzheimer`s disease with Deep InfoMaxApr 24 2019Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a promising tool for exploring brain structure in a flexible non-linear ... More

ACtuAL: Actor-Critic Under Adversarial LearningNov 13 2017Generative Adversarial Networks (GANs) are a powerful framework for deep generative modeling. Posed as a two-player minimax problem, GANs are typically trained end-to-end on real-valued data and can be used to train a generator of high-dimensional and ... More

Deep Graph InfomaxSep 27 2018Dec 21 2018We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries ... More

Unsupervised State Representation Learning in AtariJun 19 2019State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is ... More

Iterative Refinement of the Approximate Posterior for Directed Belief NetworksNov 19 2015Feb 20 2018Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods. Recent advances that exploit the capacity and flexibility in this approach have ... More

On-line Adaptative Curriculum Learning for GANsJul 31 2018Mar 11 2019Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on ... More

MINE: Mutual Information Neural EstimationJan 12 2018Jun 07 2018We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality ... More

GibbsNet: Iterative Adversarial Inference for Deep Graphical ModelsDec 12 2017Directed latent variable models that formulate the joint distribution as $p(x,z) = p(z) p(x \mid z)$ have the advantage of fast and exact sampling. However, these models have the weakness of needing to specify $p(z)$, often with a simple fixed prior that ... More

Keep Drawing It: Iterative language-based image generation and editingNov 24 2018Conditional text-to-image generation approaches commonly focus on generating a single image in a single step. One practical extension beyond one-step generation is an interactive system that generates an image iteratively, conditioned on ongoing linguistic ... More

Tell, Draw, and Repeat: Generating and modifying images based on continual linguistic instructionNov 24 2018Apr 01 2019Conditional text-to-image generation is an active area of research, with many possible applications. Existing research has primarily focused on generating a single image from available conditioning information in one step. One practical extension beyond ... More

Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm ConfigurationFeb 14 2019May 29 2019Algorithm configuration methods optimize the performance of a parameterized heuristic algorithm on a given distribution of problem instances. Recent work introduced an algorithm configuration procedure (``Structured Procrastination'') that provably achieves ... More

Human Visual Understanding for Cognition and Manipulation -- A primer for the roboticistMay 13 2019Robotic research is often built on approaches that are motivated by insights from self-examination of how we interface with the world. However, given current theories about human cognition and sensory processing, it is reasonable to assume that the internal ... More

Standards and Intellectual Property Rights in the Age of Global Communication - A Review of the International Standardization of Third-Generation Mobile SystemSep 25 2001Oct 30 2001When the European Telecommunications Standards Institute (ETSI) selected a radio access technology based on Wideband Code-Division Multiple Access (WCDMA), sponsored by European telecommunications equipment manufactures Ericsson and Nokia, for its third-generation ... More

Deep Models of Interactions Across SetsMar 07 2018Jun 08 2018We use deep learning to model interactions across two or more sets of objects, such as user-movie ratings, protein-drug bindings, or ternary user-item-tag interactions. The canonical representation of such interactions is a matrix (or a higher-dimensional ... More

Negative Learning Rates and P-LearningMar 27 2016Jun 17 2018We present a method of training a differentiable function approximator for a regression task using negative examples. We effect this training using negative learning rates. We also show how this method can be used to perform direct policy learning in ... More

The Effect of Adsorbed Liquid and Material Density on Saltation Threshold: Insight from Laboratory and Wind Tunnel ExperimentsJun 30 2017Saltation threshold, the minimum wind speed for sediment transport, is a fundamental parameter in aeolian processes. The presence of liquid, such as water on Earth or methane on Titan, may affect the threshold values to a great extent. Sediment density ... More

Proton Radiation Damage Experiment on a Hybrid CMOS DetectorJul 12 2018We report on the initial results of an experiment to determine the effects of proton radiation damage on an X-ray hybrid CMOS detector (HCD). The device was irradiated at the Edwards Accelerator Lab at Ohio University with 8 MeV protons, up to a total ... More

A Modern Laboratory XAFS CookbookSep 18 2015We have recently demonstrated a very favorable, inexpensive modernization of lab-based x-ray absorption fine structure (XAFS) and high-resolution x-ray emission spectroscopy (XES) using only commercially-available optics and x-ray tube sources. Here, ... More

Benchtop Nonresonant X-ray Emission Spectroscopy: Coming Soon to Laboratories and XAS Beamlines Near You?Sep 18 2015Recently developed instrumentation at the University of Washington has allowed for nonresonant x-ray emission spectra (XES) to be measured in a laboratory-setting with an inexpensive, easily operated system. We present a critical evaluation of this equipment ... More

An exact general remeshing scheme applied to physically conservative voxelizationDec 16 2014Jun 01 2015We present an exact general remeshing scheme to compute analytic integrals of polynomial functions over the intersections between convex polyhedral cells of old and new meshes. In physics applications this allows one to ensure global mass, momentum, and ... More

Deep Models for Relational DatabasesMar 21 2019Due to its extensive use in databases, the relational model is ubiquitous in representing big-data. We propose to apply deep learning to this type of relational data by introducing an Equivariant Relational Layer (ERL), a neural network layer derived ... More

Deep Models for Relational DatabasesMar 21 2019May 29 2019Due to its extensive use in databases, the relational model is ubiquitous in representing big-data. We apply deep learning to relational data(bases) by introducing an Equivariant Relational Layer (ERL), a neural network layer derived from the entity-relationship ... More

A Sequential Design Approach for Calibrating a Dynamic Population Growth ModelOct 31 2018A comprehensive understanding of the population growth of a variety of pests is often crucial for efficient crop management. Our motivating application comes from calibrating a two-delay blowfly (TDB) model which is used to simulate the population growth ... More

Voids in cosmological simulations over cosmic timeFeb 27 2016Mar 30 2016We study evolution of voids in cosmological simulations using a new method for tracing voids over cosmic time. The method is based on tracking watershed basins (contiguous regions around density minima) of well developed voids at low redshift, on a regular ... More

Revisiting Submicron-Gap Thermionic Power Generation Based on Comprehensive Charge and Thermal Transport ModelingJul 14 2019Over the past years, thermionic energy conversion (TEC) with a reduced inter-electrode vacuum gap has been studied as an effective way to mitigate a large potential barrier due to space charge accumulation. However, existing theoretical models do not ... More

Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric LearningJan 30 2019This paper presents an approach for learning invariant features for object affordance understanding. One of the major problems for a robotic agent acquiring a deeper understanding of affordances is finding sensory-grounded semantics. Being able to understand ... More

Local Gaussian Process Model for Large-scale Dynamic Computer ExperimentsNov 29 2016The recent accelerated growth in the computing power has generated popularization of experimentation with dynamic computer models in various physical and engineering applications. Despite the extensive statistical research in computer experiments, most ... More

Global Fitting of the Response Surface via Estimating Multiple Contours of a SimulatorFeb 04 2019Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known as experimental ... More

Local Gaussian Process Model for Large-scale Dynamic Computer ExperimentsNov 29 2016Apr 03 2018The recent accelerated growth in the computing power has generated popularization of experimentation with dynamic computer models in various physical and engineering applications. Despite the extensive statistical research in computer experiments, most ... More

The Distance to the Draco CloudSep 14 1998The understanding of the nature of intermediate and high velocity gas in the Milky Way is hampered by a paucity of distance estimates to individual clouds. A project has been started at the David Dunlap Observatory to address this lack of distance measures ... More

On a method to resolve the nuclear activity in galaxies as applied to the Seyfert 2 galaxy NGC1358Jan 14 2010Aug 29 2010Nuclear regions of galaxies generally host a mixture of components with different exitation, composition, and kinematics. Derivation of emission line ratios and kinematics could then be misleading, if due correction is not made for the limited spatial ... More

An Improved Laboratory-Based XAFS and XES Spectrometer for Analytical Applications in Materials Chemistry ResearchJul 21 2018X-ray absorption fine structure (XAFS) and x-ray emission spectroscopy (XES) are advanced x-ray spectroscopies that impact a wide range of disciplines. However, unlike the majority of other spectroscopic methods, XAFS and XES are accompanied by an unusual ... More

Explicit bounds for small prime nonresiduesFeb 12 2019Let $\chi$ be a Dirichlet character modulo a prime~$p$. We give explicit upper bounds on $q_1<q_2<\dots<q_n$, the $n$ smallest prime nonresidues of $\chi$. More precisely, given $n_0$ and $p_0$ there exists an absolute constant $C=C(n_0,p_0)>0$ such that ... More

Near-Infrared Spectroscopy of Trojan Asteroids: Evidence for Two Compositional GroupsDec 06 2010The Trojan asteroids remain quite poorly understood, yet their physical properties provide unique perspective on chemical and dynamical processes that shaped the Solar System. The current study was undertaken to investigate surface compositions of these ... More

Mining for Geographically Disperse Communities in Social Networks by Leveraging Distance ModularityMay 16 2013Social networks where the actors occupy geospatial locations are prevalent in military, intelligence, and policing operations such as counter-terrorism, counter-insurgency, and combating organized crime. These networks are often derived from a variety ... More

The Doppler effect on indirect detection of dark matter using dark matter only simulationsNov 08 2016Aug 03 2017Indirect detection of dark matter is a major avenue for discovery. However, baryonic backgrounds are diverse enough to mimic many possible signatures of dark matter. In this work, we study the newly proposed technique of dark matter velocity spectroscopy\,\cite{speckhard2016}. ... More

Reaction-Diffusion Degradation Model for Delayed Erosion of Cross-Linked Polyanhydride BiomaterialsOct 28 2015We develop a theoretical model to explain the long induction interval of water intake that precedes the onset of erosion due to degradation caused by hydrolysis in the recently synthesized and studied cross-linked polyanhydrides. Various kinetic mechanisms ... More

Advanced Scenario Creation Strategies for Stochastic Economic Dispatch with RenewablesJun 27 2018Real-time dispatch practices for operating the electric grid in an economic and reliable manner are evolving to accommodate higher levels of renewable energy generation. In particular, stochastic optimization is receiving increased attention as a technique ... More

Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm ConfigurationFeb 14 2019Algorithm configuration methods optimize the performance of a parameterized heuristic algorithm on a given distribution of problem instances. Recent work introduced an algorithm configuration procedure ('Structured Procrastination') that provably achieves ... More

Automatic Algorithm Selection In Multi-agent PathfindingJun 10 2019Jun 15 2019In a multi-agent pathfinding (MAPF) problem, agents need to navigate from their start to their goal locations without colliding into each other. There are various MAPF algorithms, including Windowed Hierarchical Cooperative A*, Flow Annotated Replanning, ... More

DOCSDN: Dynamic and Optimal Configuration of Software-Defined NetworksFeb 15 2019Networks are designed with functionality, security, performance, and cost in mind. Tools exist to check or optimize individual properties of a network. These properties may conflict, so it is not always possible to run these tools in series to find a ... More

On the number of primes for which a polynomial is EisensteinJan 25 2019Previously Heyman and Shparlinski gave an asymptotic formula with error term for the number of Eisenstein polynomials of fixed degree and bounded height. Let $\psi(f)$ denote the number of primes for which a polynomial $f$ is Eisenstein. We give expressions ... More

Shaping Operations to Attack Robust Terror NetworksNov 04 2012Security organizations often attempt to disrupt terror or insurgent networks by targeting "high value targets" (HVT's). However, there have been numerous examples that illustrate how such networks are able to quickly re-generate leadership after such ... More

Chirped-pulse interferometry with finite frequency correlationsSep 04 2009Chirped-pulse interferometry is a new interferometric technique encapsulating the advantages of the quantum Hong-Ou-Mandel interferometer without the drawbacks of using entangled photons. Both interferometers can exhibit even-order dispersion cancellation ... More

The Doppler effect on indirect detection of dark matter using dark matter only simulationsNov 08 2016Indirect detection of dark matter is a major avenue for discovery. However, baryonic backgrounds are diverse enough to mimic many possible signatures of dark matter. In this work, we study the newly proposed technique of dark matter velocity spectroscopy ... More

Reducing Noise in Cosmological N-body Simulations with NeutrinosJan 11 2018We present a new method for generating initial conditions for numerical cosmological simulations in which massive neutrinos are treated as an extra set of N-body (collisionless) particles. It allows us to accurately follow the density field for both Cold ... More

Automatic Algorithm Selection In Multi-agent PathfindingJun 10 2019In a multi-agent pathfinding (MAPF) problem, agents need to navigate from their start to their goal locations without colliding into each other. There are various MAPF algorithms, including Windowed Hierarchical Cooperative A*, Flow Annotated Replanning, ... More

The DDO IVC Distance ProjectNov 18 1998We present the first set of distance limits from the David Dunlap Observatory Intermediate Velocity Cloud (DDO IVC) distance project. Such distance measures are crucial to understanding the origins and dynamics of IVCs, as the distances set most of the ... More

Attacking the Nintendo 3DS Boot ROMsFeb 01 2018Feb 02 2018We demonstrate attacks on the boot ROMs of the Nintendo 3DS in order to exfiltrate secret information from normally protected areas of memory and gain persistent early code execution on devices which have not previously been compromised. The attack utilizes ... More

Mapping our Universe in 3D with MITEoRSep 10 2013Mapping our universe in 3D by imaging the redshifted 21 cm line from neutral hydrogen has the potential to overtake the cosmic microwave background as our most powerful cosmological probe, because it can map a much larger volume of our Universe, shedding ... More

Derivation and experimental test of fidelity benchmarks for remote preparation of arbitrary qubit statesSep 30 2009Remote state preparation (RSP) is the act of preparing a quantum state at a remote location without actually transmitting the state itself. Using at most two classical bits and a single shared maximally entangled state, one can in theory remotely prepare ... More

Joint quantum measurements with minimum uncertaintyAug 26 2013Aug 28 2013Quantum physics constrains the accuracy of joint measurements of incompatible observables. Here we test tight measurement-uncertainty relations using single photons. We implement two independent, idealized uncertainty-estimation methods, the 3-state method ... More

Learning to diagnose from scratch by exploiting dependencies among labelsOct 28 2017Feb 01 2018The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures. Many tasks ... More

Was ordinary matter synthesised from mirror matter? An attempt to explain why $Ω_{Baryon} \approx 0.2Ω_{Dark}$Apr 28 2003Jun 03 2003The cosmological dust has begun to settle. A likely picture is a universe comprised (predominantly) of three components: ordinary baryons ($\Omega_B \approx 0.05$), non-baryonic dark matter ($\Omega_{Dark} \approx 0.22$) and dark energy ($\Omega_{\Lambda} ... More

How neutrino oscillations can induce an effective neutrino number of less than 3 during Big Bang NucleosynthesisJun 04 1997Ordinary-sterile neutrino oscillations can generate significant neutrino asymmetry in the early Universe. In this paper we extend this work by computing the evolution of neutrino asymmetries and light element abundances during the Big Bang Nucleosynthesis ... More

Maximal neutrino oscillation solution to the solar neutrino problemOct 17 1995We discuss a simple predictive solution to the solar neutrino problem based on maximal vacuum neutrino oscillations. The solution can be motivated by the exact parity symmetric model which predicts that the neutrino mass eigenstates are maximal mixtures ... More

A mirror world explanation for the Pioneer spacecraft anomalies?Aug 06 2001We show that the anomalous acceleration of the Pioneer 10/11 spacecraft can be explained if there is some mirror gas or mirror dust in our solar system.

The exact parity symmetric model and big bang nucleosynthesisDec 04 1996The assumption of exact, unbroken parity symmetry leads directly to a simple predictive resolution of the atmospheric and solar neutrino puzzles. This is because the existence of this symmetry implies the existence of a set of mirror neutrinos which must ... More

Spheroidal galactic halos and mirror dark matterJul 26 2004Mirror matter has been proposed as a dark matter candidate. It has several very attractive features, including automatic stability and darkness, the ability to mimic the broad features of cold dark matter while in the linear density perturbation regime, ... More

New and improved quark-lepton symmetric modelsMay 18 1995We show how the use of a see-saw mechanism based on a $3 \times 3$ neutrino mass matrix texture can considerably simplify Higgs sectors for quark-lepton symmetric models (and for Standard Model extensions generally). The main theory we discuss also incorporates ... More

The XMM Cluster Survey: evolution of the velocity dispersion -- temperature relation over half a Hubble timeDec 09 2015Aug 03 2016We measure the evolution of the velocity dispersion--temperature ($\sigma_{\rm v}$--$T_{\rm X}$) relation up to $z = 1$ using a sample of 38 galaxy clusters drawn from the \textit{XMM} Cluster Survey. This work improves upon previous studies by the use ... More

Cluster-state quantum computing enhanced by high-fidelity generalized measurementsSep 15 2009We introduce and implement a technique to extend the quantum computational power of cluster states by replacing some projective measurements with generalized quantum measurements (POVMs). As an experimental demonstration we fully realize an arbitrary ... More

Fast Segmentation of Left Ventricle in CT Images by Explicit Shape Regression using Random Pixel Difference FeaturesJul 27 2015Jul 28 2015Recently, machine learning has been successfully applied to model-based left ventricle (LV) segmentation. The general framework involves two stages, which starts with LV localization and is followed by boundary delineation. Both are driven by supervised ... More

Comment on "Big Bang Nucleosynthesis and Active-Sterile Neutrino Mixing: Evidence for Maximal $ν_μ\leftrightarrow ν_τ$ Mixing in Super Kamiokande?"Nov 04 1998The paper "Big Bang Nucleosynthesis and Active-Sterile Neutrino Mixing: Evidence for Maximal $\nu_\mu \leftrightarrow \nu_\tau$ Mixing in Super Kamiokande?" by X. Shi and G. M. Fuller (astro-ph/9810075) discusses the cosmological implications of the $\nu_\mu ... More

Explaining $Ω_{Baryon} \approx 0.2 Ω_{Dark}$ through the synthesis of ordinary matter from mirror matter: a more general analysisFeb 25 2004Mar 25 2004The emerging cosmological picture is of a spatially flat universe composed predominantly of three components: ordinary baryons ($\Omega_B \approx 0.05$), non-baryonic dark matter ($\Omega_{Dark} \approx 0.22$) and dark energy ($\Omega_{\Lambda} \approx ... More

The neutrino puzzle in the light of SNOApr 23 2002Jul 23 2002SNO's neutral current measurement has added a new piece to the emerging neutrino physics puzzle. Putting together the presently available experimental information, an essentially unique picture emerges: The solar neutrino anomaly is explained by nu_e ... More

Natural electroweak symmetry breaking in generalised mirror matter modelsOct 02 2006Nov 28 2006It has recently been pointed out that the mirror or twin Higgs model is more technically natural than the standard model, thus alleviating the ``little'' hierarchy problem. In this paper we generalise the analysis to models with an arbitrary number of ... More

Studies of neutrino asymmetries generated by ordinary-sterile neutrino oscillations in the early universe and implications for big bang nucleosynthesis boundsOct 03 1996Nov 19 1996Ordinary-sterile neutrino oscillations can generate a significant lepton number asymmetry in the early Universe. We study this phenomenon in detail. We show that the dynamics of ordinary-sterile neutrino oscillations in the early Universe can be approximately ... More

Do the SuperKamiokande atmospheric neutrino results explain electric charge quantisation?Aug 24 1998We show that the SuperKamiokande atmospheric neutrino results explain electric charge quantisation, provided that the oscillation mode is $\nu_{\mu} \to \nu_{\tau}$ and that the neutrino mass is of Majorana type.

Neutrino physics and the mirror world: how exact parity symmetry explains the solar neutrino deficit, the atmospheric neutrino anomaly and the LSND experimentMay 23 1995Nov 08 1995Evidence for $\bar \nu_{\mu} \rightarrow \bar \nu_e$ oscillations has been reported at LAMPF using the LSND detector. Further evidence for neutrino mixing comes from the solar neutrino deficit and the atmospheric neutrino anomaly. All of these anomalies ... More

Reconciling sterile neutrinos with big bang nucleosynthesisAug 10 1995Nov 08 1995We re-examine the big bang nucleosynthesis (BBN) bounds on the mixing of neutrinos with sterile species. These bounds depend on the assumption that the relic neutrino asymmetry $L_{\nu}$ is very small. We show that for $L_{\nu}$ large enough (greater ... More

Implications of mirror neutrinos for early universe cosmologyApr 15 1999The Exact Parity Model (EPM) is, in part, a theory of neutrino mass and mixing that can solve the atmospheric, solar and LSND anomalies. The central feature of the neutrino sector is three pairs of maximally mixed ordinary and mirror neutrinos. It has ... More

Generalised leptonic colourJul 05 2006Dec 06 2006It is conceivable that there is an $SU(N)_{\ell}$ `colour' gauge group for leptons, analogous to the gauged $SU(3)_q$ colour group of the quarks. The standard model emerges as the low energy effective theory when the leptonic colour is spontaneously broken. ... More

Legendrian contact homology and topological entropySep 12 2014Apr 23 2016In this paper we study the growth rate of a version of Legendrian contact homology, which we call strip Legendrian contact homology, in 3-dimensional contact manifolds and its relation to the topological entropy of Reeb flows. We show that: if for a pair ... More

The heat-kernel and the average effective potentialMay 29 1995We discuss the definition of the average effective action in terms of the heat-kernel. As an example we examine a model describing a self-interacting scalar field, both in flat and curved background, and study the renormalization group flow of some of ... More

The renormalization group flow of the dilaton potentialDec 20 1994We consider a scalar-metric gauge theory of gravity with independent metric, connection and dilaton. The role of the dilaton is to provide the scale of all masses, via its vacuum expectation value. In this theory, we study the renormalization group flow ... More

Ordinary-derivative formulation of conformal low-spin fieldsJul 30 2007Nov 14 2011Conformal fields in flat space-time of even dimension greater than or equal to four are studied. Second-derivative formulation for spin 0,1,2 conformal bosonic fields and first-derivative formulation for spin 1/2,3/2 conformal fermionic fields are developed. ... More