- On Measurement Assessment and Division Matrices (On Measurement Assessment and Division Matrices)

by Prof. Hasan Keleş01 Aug 2018 11:58 - Pauli matrices (Pauli matrices)

by Prof. Hasan Keleş16 Feb 2018 07:45 - Almost disjoint family (Almost disjoint family)

by Dr. Jonathan Verner08 Jan 2018 14:22 - SSFM (SSFM)

by Emmanouil Markoulakis19 Dec 2017 15:46 - Ferrolens (Ferrolens)

by Emmanouil Markoulakis19 Dec 2017 15:35 - Fermi surface (Fermi surface)

by Dr. Vadim Cheianov05 Dec 2010 22:11 - Leptogenesis (Leptogenesis)

by Dr. Sacha Davidson08 Dec 2010 13:32 - Universal Conductance Fluctuations (Universal Conductance Fluctuations)

by Prof. Carlo Beenakker08 Dec 2010 13:33 - Fingers of God (Fingers of God)

by Dr. Ganna Ivashchenko18 May 2011 22:42 - Quantum shot noise (Quantum shot noise)

by Prof. Carlo Beenakker04 Feb 2014 08:52

- Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple "truncation trick", allowing fine control over the trade-off between sample fidelity and variety by truncating the latent space. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.3 and Frechet Inception Distance (FID) of 9.6, improving over the previous best IS of 52.52 and FID of 18.65.Generative Adversarial NetRegularizationInstabilityStatisticsEmbeddingArchitectureSingular valueHyperparameterPrecisionOptimization...
- In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.Generative Adversarial NetRegularizationMachine translationHidden layerOptimizationArchitectureImage ProcessingFeature spaceNetworksResolution...
- We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. We achieve a new state of the art on the WikiText-103 benchmark of 20.51 perplexity, improving the next best known result by 8.7 perplexity. On the Billion word benchmark, we achieve a state of the art of 24.14 perplexity.Convolutional neural networkArchitectureRegularizationEmbeddingPrecisionMachine translationNatural languageSchedulingComputational linguisticsLong short term memory...
- The Jackiw-Teitelboim (JT) model arises from the dimensional reduction of charged black holes. Motivated by the holographic complexity conjecture, we calculate the late-time rate of change of action of a Wheeler-DeWitt patch in the JT theory. Surprisingly, the rate vanishes. This is puzzling because it contradicts both holographic expectations for the rate of complexification and also action calculations for charged black holes. We trace the discrepancy to an improper treatment of boundary terms when naively doing the dimensional reduction. Once the boundary term is corrected, we find exact agreement with expectations. We comment on the general lessons that this might hold for holographic complexity and beyond.DilatonBlack holeHorizonSachdev-Ye-Kitaev modelDualityAnti de Sitter spaceCharged black holeDimensional ReductionReissner-Nordström black holesGeodesic...
- For each r, 0 <= r <= m, it is presented the class of quaternary linear codes LRM(r,m) whose images under the Gray map are binary codes with parameters of Reed-Muller RM(r,m) code of order r.Metric spaceSubgroupBit arrayClassificationPermutationRing of integersIsometryVector...
- Seglearn is an open-source python package for machine learning time series or sequences using a sliding window segmentation approach. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. This package is compatible with scikit-learn and is listed under scikit-learn Related Projects. The package depends on numpy, scipy, and scikit-learn. Seglearn is distributed under the BSD 3-Clause License. Documentation includes a detailed API description, user guide, and examples. Unit tests provide a high degree of code coverage.Time SeriesPythonClassificationMachine learningApplication programming interfaceRegressionHuman dynamicsStatistical estimatorActivity recognitionDeep learning...
- The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.Deep learningClassificationText ClassificationConvolutional neural networkDeep Neural NetworksArchitectureRecurrent neural networkMachine learningGround truthAlgorithms...
- The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient health histories in tasks such as understanding disease correlations and predicting health outcomes. However, the heterogeneity, sparsity, noise, and bias in this data present many complex challenges. This complexity makes it difficult to translate potentially relevant information into machine learning algorithms. In this paper, we propose a computational framework, Patient2Vec, to learn an interpretable deep representation of longitudinal EHR data which is personalized for each patient. To evaluate this approach, we apply it to the prediction of future hospitalizations using real EHR data and compare its predictive performance with baseline methods. Patient2Vec produces a vector space with meaningful structure and it achieves an AUC around 0.799 outperforming baseline methods. In the end, the learned feature importance can be visualized and interpreted at both the individual and population levels to bring clinical insights.Recurrent neural networkLong short term memoryHidden layerMachine learningComputational linguisticsDeep learningLogistic regressionPersonalizationNoneTime Series...
- The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations over all elements can be fully parallelized during training and optimization is easier since the number of non-linearities is fixed and independent of the input length. Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module. We outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French translation at an order of magnitude faster speed, both on GPU and CPU.OptimizationConvolutional neural networkArchitectureRecurrent neural networkLong short term memoryOrder of magnitudeUnits...
- Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle various NLP tasks. CNN is supposed to be good at extracting position-invariant features and RNN at modeling units in sequence. The state of the art on many NLP tasks often switches due to the battle between CNNs and RNNs. This work is the first systematic comparison of CNN and RNN on a wide range of representative NLP tasks, aiming to give basic guidance for DNN selection.Convolutional neural networkRecurrent neural networkComputational linguisticsLong short term memoryArchitectureHidden stateClassificationHyperparameterPart-of-speechKeyphrase...
- This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets). We apply ConvNets to various large-scale datasets, including ontology classification, sentiment analysis, and text categorization. We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. Evidence shows that our models can work for both English and Chinese.Convolutional neural networkDeep learningWord vectorsClassificationSentiment analysisFoundation of PhysicsBinary numberImage ProcessingStatisticsLong short term memory...
- Modern commercial organisations are facing pressures which have caused them to lose personnel. When they lose people, they also lose their knowledge. Organisations also have to cope with the internationalisation of business forcing collaboration and knowledge sharing across time and distance. Knowledge Management (KM) claims to tackle these issues. This paper looks at an area where KM does not offer sufficient support, that is, the sharing of knowledge that is not easy to articulate. The focus in this paper is on Communities of Practice in commercial organisations. We do this by exploring knowledge sharing in Lave and Wenger's [1] theory of Communities of Practice and investigating how Communities of Practice may translate to a distributed international environment. The paper reports on two case studies that explore the functioning of Communities of Practice across international boundaries.PressureForce
- The ontological and epistemological positions adopted by information systems design methods are incommensur-able when pushed to their extremes. Information systems research has therefore tended to focus on the similarities between different positions, usually in search of a single, unifying position. However, by focusing on the similari-ties, the clarity of argument provided by any one philoso-phical position is necessarily diminished. Consequently, researchers often treat the philosophical foundations of design methods as being of only minor importance. In this paper, we have deliberately chosen to focus on the differences between various philosophical positions. From this focus, we believe we can offer a clearer under-standing of the empirical behaviour of software as viewed from particular philosophical positions. Since the em-pirical evidence does not favour any single position, we conclude by arguing for the validity of ad hoc approaches to software design which we believe provides a stronger and more theoretically grounded approach to software design.
- Design methods in information systems frequently create software descriptions using formal languages. Nonetheless, most software designers prefer to describe software using natural languages. This distinction is not simply a matter of convenience. Natural languages are not the same as formal languages; in particular, natural languages do not follow the notions of equivalence used by formal languages. In this paper, we show both the existence and coexistence of different notions of equivalence by extending the no-tion of oracles used in formal languages. This allows distinctions to be made between the trustworthy oracles assumed by formal languages and the untrust-worthy oracles used by natural languages. By examin-ing the notion of equivalence, we hope to encourage designers of software to rethink the place of ambiguity in software design.
- This paper explores the links between Knowledge Management and new community-based models of the organization from both a theoretical and an empirical perspective. From a theoretical standpoint, we look at Communities of Practice (CoPs) and Knowledge Management (KM) and explore the links between the two as they relate to the use of information systems to manage knowledge. We begin by reviewing technologically supported approaches to KM and introduce the idea of "Systemes d'Aide a la Gestion des Connaissances" SAGC (Systems to aid the Management of Knowledge). Following this we examine the contribution that communal structures such as CoPs can make to intraorganizational KM and highlight some of 'success factors' for this approach to KM that are found in the literature. From an empirical standpoint, we present the results of a survey involving the Chief Knowledge Officers (CKOs) of twelve large French businesses; the objective of this study was to identify the factors that might influence the success of such approaches. The survey was analysed using thematic content analysis and the results are presented here with some short illustrative quotes from the CKOs. Finally, the paper concludes with some brief reflections on what can be learnt from looking at this problem from these two perspectives.Content analysisSurveysObjective
- The stated aim of this conference is to debate the continuing evolution of IS in businesses and other organisations. This paper seeks to contribute to this debate by exploring the concept of appropriation from a number of different epistemological, cultural and linguistic viewpoints to allow us to explore 'the black box' of appropriation and to gain a fuller understanding of the term. At the conceptual level, it will examine some of the different ways in which people have attempted to explain the relationship between the objective and concrete features of technology and the subjective and shifting nature of the people and organisation within which that technology is deployed. At the cultural and linguistic level the paper will examine the notion as it is found in the Francophone literature, where the term has a long and rich history, and the Anglophone literature where appropriation is seen as a rather more specialist term. The paper will conclude with some observations on the ongoing nature of the debate, the value of reading beyond the literature with which one is familiar and the rewards that come from exploring different historical (and linguistic) viewpoints.Objective
- This paper examines the possibility of discovering 'hidden' (potential) Communities of Practice (CoPs) inside electronic networks, and then using this knowledge to nurture them into a fully developed Virtual Community of Practice (VCoP). Starting from the standpoint of the need to manage knowledge, it discusses several questions related to this subject: the characteristics of 'hidden' communities; the relation between CoPs, Virtual Communities (VCs), Distributed Communities of Practice (DCoPs) and Virtual Communities of Practice (VCoPs); the methods used to search for 'hidden' CoPs; and the possible ways of changing 'hidden' CoPs into fully developed VCoPs. The paper also presents some preliminary findings from a semi-structured interview conducted in The Higher Education Academy Psychology Network (UK). These findings are contrasted against the theory discussed and some additional proposals are suggested at the end.
- Although the gulf between the theory and practice in Information Systems is much lamented, few researchers have offered a way forward except through a number of (failed) attempts to develop a single systematic theory for Information Systems. In this paper, we encourage researchers to re-examine the practical consequences of their theoretical arguments. By examining these arguments we may be able to form a number of more rigorous theories of Information Systems, allowing us to draw theory and practice together without undertaking yet another attempt at the holy grail of a single unified systematic theory of Information Systems.
- This paper looks at what happens when Communities of Practice are used as a tool for Knowledge Management. The original concept of a Community of Practice appears to have very little in common with the knowledge sharing communities found in Knowledge Management, which are based on a revised view of 'cultivated' communities. We examine the risks and benefits of cultivating Communities of Practice rather than leaving them 'in the wild'. The paper presents the findings from two years of research in a small microelectronics firm to provide some insights into the wild vs domesticated dichotomy and discusses the implications of attempting to tame Communities of Practice in this way.Micro-electro-mechanical system
- This article explores some of the challenges faced when managing virtual teams, in particular the role played by trust and identity in virtual teams. It outlines why teams and virtual teams have become a valuable part of the modern organization and presents ten short case studies that illustrate the range of activities in which virtual teams can be found. Following this, the article examines some of the common problems encountered in virtual team working. It discusses two broad classes of solutions. The first are solutions that are essentially technical in nature (i.e., where changes to or improvements in technology would help to solve or ameliorate the problem); the second are more organizationally based (i.e., where the root of the problem is in people and how they are managed). The article concludes that both the technical and the organizational solutions need to be considered in parallel if an attempt to build an effective virtual team is to be successful.Virtual TeamsTeams
- The term knowledge management (KM) first came to prominence in the late 1990s. Although initially dismissed as a fad, KM continues to be featured in articles concerning business productivity and innovation. And yet, clear-cut examples that demonstrate the success of KM are few and far between. A brief examination of the history of KM explores the reasons for this and looks at some of the assumptions about what KM can achieve. A subsequent analysis of the experiences of Infosys with KM shows that for KM to be successful, organizational leaders need to engage in a continuous process of modification and maintenance. Although KM initiatives can be made to yield worthwhile returns over an extended period, there are often substantial ongoing costs associated with them.
- Computer-based information systems feature in almost every aspect of our lives, and yet most of us receive handwritten prescriptions when we visit our doctors and rely on paper-based medical records in our healthcare. Although electronic health record (EHR) systems have long been promoted as a cost-effective and efficient alternative to this situation, clear-cut evidence of their success has not been forthcoming. An examination of some of the underlying problems that prevent EHR systems from delivering the benefits that their proponents tout identifies four broad objectives - reducing cost, reducing errors, improving coordination and improving adherence to standards - and shows that they are not always met. The three possible causes for this failure to deliver involve problems with the codification of knowledge, group and tacit knowledge, and coordination and communication. There is, however, reason to be optimistic that EHR systems can fulfil a healthy part, if not all, of their potential.ElectronCommunicationObjectivePotential...
- An increasingly aging population and spiraling healthcare costs have made the search for financially viable healthcare models an imperative of this century. The careful and creative application of information technology can play a significant role in meeting that challenge. Valuable lessons can be learned from an analysis of ten innovative telemedicine and e-health initiatives. Having proven their effectiveness in addressing a variety of medical needs, they have progressed beyond small-scale implementations to become an established part of healthcare delivery systems around the world.
- Big data is one of the most discussed, and possibly least understood, terms in use in business today. Big data is said to offer not only unprecedented levels of business intelligence concerning the habits of consumers and rivals, but also to herald a revolution in the way in which business are organized and run. However, big data is not as straightforward as it might seem, particularly when it comes to the so-called dark data from social media. It is not simply the quantity of data that has changed, it is also the speed and the variety of formats with which it is delivered. This article sets out to look at big data and debunk some of the myths that surround it. It focuses on the role of data from social media in particular and highlights two common myths about big data. The first is that because a data set contains billions of items, traditional methodological issues no longer matter. The second is the belief that big data is both a complete and unbiased source of data upon which to base decisions.Big dataFormate
- Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function (RBF) neural network, general regression neural network (GRNN), support vector machine regression (SVMR), least squares support vector machine regresssion (LS-SVMR). We apply the five models to make price prediction of three individual stocks, namely, Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and average absolute percentage error as criteria, we find BP neural network consistently and robustly outperforms the other four models.Neural networkRadial basis functionMarketRegressionSupport vector machineHidden layerNetwork modelBackpropagationTime SeriesLeast squares support vector machine...
- The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. Many stock prediction studies focus on using macroeconomic indicators, such as CPI and GDP, to train the prediction model. However, daily data of the macroeconomic indicators are almost impossible to obtain. Thus, those methods are difficult to be employed in practice. In this paper, we propose a method that directly uses prices data to predict market index direction and stock price direction. An extensive empirical study of the proposed method is presented on the Korean Composite Stock Price Index (KOSPI) and Hang Seng Index (HSI), as well as the individual constituents included in the indices. The experimental results show notably high hit ratios in predicting the movements of the individual constituents in the KOSPI and HIS.MarketRecommendation systemStock Market
- We design and analyze solution techniques for a linear-quadratic optimal control problem involving the integral fractional Laplacian. We derive existence and uniqueness results, first order optimality conditions, and regularity estimates for the optimal variables. We propose two strategies to discretize the fractional optimal control problem: a semidiscrete approach where the control is not discretized - the so-called variational discretization approach - and a fully discrete approach where the control variable is discretized with piecewise constant functions. Both schemes rely on the discretization of the state equation with the finite element space of continuous piecewise polynomials of degree one. We derive a priori error estimates for both solution techniques. We illustrate the theory with two-dimensional numerical tests.Fractional LaplacianDiscretizationPartial differential equationOptimizationQuadratureDegree of freedomVariational inequalitySobolev spaceProjection operatorOrder of convergence...
- We study finite element approximations of the nonhomogeneous Dirichlet problem for the fractional Laplacian. Our approach is based on weak imposition of the Dirichlet condition and incorporating a nonlocal analogous of the normal derivative as a Lagrange multiplier in the formulation of the problem. In order to obtain convergence orders for our scheme, regularity estimates are developed, both for the solution and its nonlocal derivative. The method we propose requires that, as meshes are refined, the discrete problems be solved in a family of domains of growing diameter.Fractional LaplacianDirichlet problemOrder of convergenceSobolev spaceDualityTriangle inequalityNumerical methodsExact solutionEigenfunctionCauchy-Schwarz inequality...
- We present BAHAMAS-SIDM, the first large-volume, (400/h Mpc)^3, cosmological simulations including both self-interacting dark matter (SIDM) and baryonic physics. These simulations are important for two primary reasons: 1) they include the effects of baryons on the dark matter distribution 2) the baryon particles can be used to make mock observables that can be compared directly with observations. As is well known, SIDM haloes are systematically less dense in their centres, and rounder, than CDM haloes. Here we find that that these changes are not reflected in the distribution of gas or stars within galaxy clusters, or in their X-ray luminosities. However, gravitational lensing observables can discriminate between DM models, and we present a menu of tests that future surveys could use to measure the SIDM interaction strength. We ray-trace our simulated galaxy clusters to produce strong lensing maps. Including baryons boosts the lensing strength of clusters that produce no critical curves in SIDM-only simulations. Comparing the Einstein radii of our simulated clusters with those observed in the CLASH survey, we find that sigma/m < 1 cm^2/g at velocities around 1000 km/s.Self-interacting dark matterCluster of galaxiesDark matter modelCluster Lensing And Supernova survey with HubbleDark matter haloStrong gravitational lensingDark Matter Density ProfileVirial massStarSimulations of structure formation...
- The Weak Gravity Conjecture (WGC) asserts a powerful consistency condition on gauge theories coupled to gravity, and it is widely believed that its proof will shed light on the quantum origin of gravitational interactions. Holography, and in particular the AdS/CFT correspondence, is a well-suited tool by means of which it is possible to explore the world of gravity. From the holographic perspective, gravity is an emergent statistical phenomenon, and the laws of gravitation can be recast as the laws of thermodynamics. It is interesting to ask whether the WGC can be formulated in terms of the AdS/CFT correspondence. A positive answer is given: The WGC in the bulk is linked to the thermalization properties of the CFT living on the boundary. The latter is related to the Sachdev-Ye-Kitaev model of strange metals. In the thermodynamic picture, the validity of the WGC is verified.Black holeAdS/CFT correspondenceThermalisationConformal field theoryHorizonSachdev-Ye-Kitaev modelQuasi-normal modesRetarded propagatorAnti de Sitter spaceNear-horizon geometry...
- In quantum cryptography, a one-way permutation is a bounded unitary operator $U:\mathcal{H} \to \mathcal{H}$ on a Hilbert space $\mathcal{H}$ that is easy to compute on every input, but hard to invert given the image of a random input. Levin [Probl. Inf. Transm., vol. 39 (1): 92-103 (2003)] has conjectured that the unitary transformation $g(a,x)=(a,f(x)+ax)$, where $f$ is any length-preserving function and $a,x \in GF_{{2}^{\|x\|}}$, is an information-theoretically secure operator within a polynomial factor. Here, we show that Levin's one-way permutation is provably secure because its output values are four maximally entangled two-qubit states, and whose probability of factoring them approaches zero faster than the multiplicative inverse of any positive polynomial $poly(x)$ over the Boolean ring of all subsets of $x$. Our results demonstrate through well-known theorems that existence of classical one-way functions implies existence of a universal quantum one-way permutation that cannot be inverted in subexponential time in the worst caPermutationQubitUnitary operatorQuantum cryptographyBell stateEntropySecurityUnitary transformationBoolean ringMaximally entangled states...
- In this letter we use the Anti-de Sitter/Conformal Field Theory (AdS/CFT) correspondence to establish a set of old conjectures about symmetries in quantum gravity. These are that no global symmetries are possible, that internal gauge symmetries must come with dynamical objects that transform in all irreducible representations, and that internal gauge groups must be compact. These conjectures are not obviously true from a bulk perspective, they are nontrivial consequences of the non-perturbative consistency of the correspondence. More details of and background for these arguments are presented in an accompanying paper.Global symmetryGauge symmetryWilson loopQuantum gravityAdS/CFT correspondenceConformal field theorySymmetry groupIrreducible representationHolographic principleQuantum field theory...
- The four-dimensional $S$-matrix is reconsidered as a correlator on the celestial sphere at null infinity. Asymptotic particle states can be characterized by the point at which they enter or exit the celestial sphere as well as their $SL(2,\mathbb C)$ Lorentz quantum numbers: namely their conformal scaling dimension and spin $h\pm \bar h$ instead of the energy and momentum. This characterization precludes the notion of a soft particle whose energy is taken to zero. We propose it should be replaced by the notion of a {\it conformally soft} particle with $h=0$ or $\bar h=0$. For photons we explicitly construct conformally soft $SL(2,\mathbb C)$ currents with dimensions $(1,0)$ and identify them with the generator of a $U(1)$ Kac-Moody symmetry on the celestial sphere. For gravity the generator of celestial conformal symmetry is constructed from a $(2,0)$ $SL(2,\mathbb C)$ primary wavefunction. Interestingly, BMS supertranslations are generated by a spin-one weight $(\frac{3}{2},\frac{1}{2})$ operator, which nevertheless shares holomorphic characteristics of a conformally soft operator. This is because the right hand side of its OPE with a weight $(h,\bar h)$ operator ${\cal O}_{h,\bar h}$ involves the shifted operator ${\cal O}_{h+\frac{1}{2},\bar h+ \frac{1}{2}}$. This OPE relation looks quite unusual from the celestial CFT$_2$ perspective but is equivalent to the leading soft graviton theorem and may usefully constrain celestial correlators in quantum gravity.WavefunctionGoldstone bosonCelestial SphereScaling dimensionGravitonHelicityDiffeomorphismSoft photonsOperator product expansionZero mode...
- A search is performed for a heavy Majorana neutrino (N), produced by leptonic decay of a W boson propagator and decaying into a W boson and a lepton, with the CMS detector at the LHC. The signature used in this search consists of two same-sign leptons, in any flavor combination of electrons and muons, and at least one jet. The data were collected during 2016 in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb$^{-1}$. The results are found to be consistent with the expected standard model backgrounds. Upper limits are set in the mass range between 20 and 1600 GeV in the context of a Type-I seesaw mechanism, on |$V_\mathrm{eN}$|$^2$, |$V_{\mu\mathrm{N}}$|$^2$, and |$V_{\mathrm{eN}}$$V^*_{\mu\mathrm{N}}$|$^2$/(|$V_\mathrm{eN}$|$^2$+|$V_{\mu\mathrm{N}}$|$^2$), where $V_{\ell\mathrm{N}}$ is the matrix element describing the mixing of N with the standard model neutrino of flavor $\ell =$ e, $\mu$. For N masses between 20 and 1600 GeV, the upper limits on |$V_{\ell\mathrm{N}}$|$^2$ range between 2.3 $\times$ 10$^{-5}$ and unity. These are the most restrictive direct limits for heavy Majorana neutrino masses above 430 GeV.QCD jetSterile neutrinoMuonStandard ModelControl regionHigh massInvariant massSystematic errorCMS experimentElectromagnetic calorimeter...
- We consider a bipartite scenario where two parties hold ensembles of $1/2$-spins which can only be measured collectively. We give numerical arguments supporting the conjecture that in this scenario no Bell inequality can be violated for arbitrary numbers of spins if only first order moment observables are available. We then give a recipe to achieve a significant Bell violation with a split many-body system when this restriction is lifted. This highlights the strong requirements needed to detect bipartite quantum correlations in many-body systems device-independently.Bell's inequalityMany-body systemsAlice and BobCollective spinPolytopeBell operatorQuantum correlationStatisticsExpectation ValueCHSH inequality...
- The Square Kilometre Array (SKA) is a planned large radio interferometer designed to operate over a wide range of frequencies, and with an order of magnitude greater sensitivity and survey speed than any current radio telescope. The SKA will address many important topics in astronomy, ranging from planet formation to distant galaxies. However, in this work, we consider the perspective of the SKA as a facility for studying physics. We review four areas in which the SKA is expected to make major contributions to our understanding of fundamental physics: cosmic dawn and reionisation; gravity and gravitational radiation; cosmology and dark energy; and dark matter and astroparticle physics. These discussions demonstrate that the SKA will be a spectacular physics machine, which will provide many new breakthroughs and novel insights on matter, energy and spacetime.Square Kilometre ArrayHydrogen 21 cm lineReionizationCosmic microwave backgroundBrightness temperatureCosmologyNeutral hydrogen gasParticle astrophysicsDark matterPlanet formation...
- We search the Planck data for a thermal Sunyaev-Zel'dovich (tSZ) signal due to gas filaments between pairs of Luminous Red Galaxies (LRG's) taken from the Sloan Digital Sky Survey Data Release 12 (SDSS/DR12). We identify $\sim$260,000 LRG pairs in the DR12 catalog that lie within 6-10 $h^{-1} \mathrm{Mpc}$ of each other in tangential direction and within 6 $h^{-1} \mathrm{Mpc}$ in radial direction. We stack pairs by rotating and scaling the angular positions of each LRG so they lie on a common reference frame, then we subtract a circularly symmetric halo from each member of the pair to search for a residual signal between the pair members. We find a statistically significant (5.3$\sigma$) signal between LRG pairs in the stacked data with a magnitude $\Delta y = (1.31 \pm 0.25) \times 10^{-8}$. The uncertainty is estimated from two Monte Carlo null tests which also establish the reliability of our analysis. Assuming a simple, isothermal, cylindrical filament model of electron over-density with a radial density profile proportional to $r_c/r$ (as determined from simulations), where $r$ is the perpendicular distance from the cylinder axis and $r_c$ is the core radius of the density profile, we constrain the product of over-density and filament temperature to be $\delta_c \times (T_{\rm e}/10^7 \, {\rm K}) \times (r_c/0.5h^{-1} \, {\rm Mpc}) = 2.7 \pm 0.5$. To our knowledge, this is the first detection of filamentary gas at over-densities typical of cosmological large-scale structure. We compare our result to the BAHAMAS suite of cosmological hydrodynamic simulations (McCarthy et al. 2017) and find a slightly lower, but marginally consistent Comptonization excess, $\Delta y = (0.84 \pm 0.24) \times 10^{-8}$.Luminous Red GalaxySloan Digital Sky SurveyCosmologyHalo modelStellar massVirial massGalaxyCFHTLenS surveyLight conesLarge scale structure...
- We use the observed amount of subhaloes and line-of-sight dark matter haloes in a sample of 11 gravitational lens systems from the Sloan Lens ACS Survey to constrain the free-streaming properties of the dark matter particles. In particular, we combine the detection of a small-mass dark matter halo by Vegetti et al. 2010 with the non-detections by Vegetti et al. 2014 and compare the derived subhalo and halo mass functions with expectations from cold dark matter (CDM) and resonantly produced sterile neutrino models. We constrain the half-mode mass, i.e. the mass scale at which the linear matter power spectrum is reduced by 50 per cent relatively to the CDM model, to be $\log M_{\rm{hm}} \left[M_\odot\right] < 12.0$ (equivalent thermal relic mass $m_{\rm th} > 0.3$ keV) at the 2$\sigma$ level. This excludes sterile neutrino models with neutrino masses $m_{\rm s} < 0.8$ keV at any value of $L_{\rm 6}$. Our constraints are weaker than currently provided by the number of Milky Way satellites, observations of the 3.5 keV X-ray line, and the Lyman $\alpha$ forest. However, they are more robust than the former as they are less affected by baryonic processes. Moreover, unlike the latter, they are not affected by assumptions on the thermal histories for the intergalactic medium. Gravitational lens systems with higher data quality and higher source and lens redshift are required to obtain tighter constraints.Sterile neutrinoLine of sightCold dark matterDark matter subhaloDark matterMass functionLepton asymmetryGravitational lens systemsSubhalo mass functionMilky Way satellite...
- Measurements of the rotation curves of dwarf galaxies are often interpreted as requiring a constant density core at the centre, at odds with the "cuspy" inner profiles predicted by $N$-body simulations of cold dark matter (CDM) haloes. It has been suggested that this conflict could be resolved by fluctuations in the inner gravitational potential caused by the periodic removal of gas following bursts of star formation. Earlier work has suggested that core formation requires a bursty and extended star formation history (SFH). Here we investigate the structure of CDM haloes of dwarf galaxies ($M_{{\rm DM}} \sim 10^9-5\times10^{10}\,{\rm M}_\odot$) formed in the APOSTLE and Auriga cosmological hydrodynamic simulations. Our simulations have comparable or better resolution than others that make cores. Yet, we do not find evidence of core formation at any mass or any correlation between the inner slope of the DM density profile and temporal variations in the SFH. APOSTLE and Auriga dwarfs display a similar diversity in their cumulative SFHs to available data for Local Group dwarfs. Dwarfs in both simulations are DM-dominated on all resolved scales at all times, likely limiting the ability of gas outflows to alter significantly the central density profiles of their haloes. We conclude that recurrent bursts of star formation are not sufficient to cause the formation of cores, and that other conditions must also be met for baryons to be able to modify the central DM cusp.Dwarf galaxyGalaxyStar formationStar formation historiesDark Matter Density ProfileStellar massMilky WayCold dark matterInner slopeOf stars...
- We present new HST/WFC3 observations and re-analyse VLT data to unveil the continuum, variability and rest-frame UV lines of the multiple UV clumps of the most luminous Ly$\alpha$ emitter at z=6.6, CR7. Our re-reduced, flux calibrated X-SHOOTER spectra of CR7 reveal a HeII emission line in observations obtained along the major axis of Lyman-alpha (Lya) emission with the best seeing conditions. HeII is spatially offset by +0.8'' from the peak of Lya emission, and it is found towards clump B. Our WFC3 grism spectra detects the UV continuum of CR7's clump A, yielding a power law with $\beta=-2.5^{+0.6}_{-0.7}$ and $M_{UV}=-21.87^{+0.25}_{-0.20}$. No significant variability is found for any of the UV clumps on their own, but there is tentative (~2.2$\sigma$) brightening of CR7 in F110W as a whole from 2012 to 2017. HST grism data fail to robustly detect rest-frame UV lines in any of the clumps, implying fluxes <2x10$^{-17}$ erg s$^{-1}$ cm$^{-2}$ (3 $\sigma$). We perform CLOUDY modelling to constrain the metallicity and the ionising nature of CR7. CR7 seems to be actively forming stars without any clear AGN activity in clump A, consistent with a metallicity of ~0.05-0.2 Z$_{\odot}$. Component C or an inter-clump component between B and C may host a high ionisation source. Our results highlight the need for spatially resolved information to study the formation and assembly of early galaxies.MetallicityWide Field Camera 3GrismGalaxyLyman alpha emitterStarActive Galactic NucleiDirect Collapse Black HoleStellar populationsPhotometry...
- Cosmic strings are important remnants of early-Universe phase transitions. We show that they may be probed in a new way with LIGO and future gravitational-wave (GW) detectors. When the GW from compact binary mergers passes by a cosmic string, it is gravitationally lensed and left with a characteristic and detectable signal -- the GW fringe. High-frequency detectors such as aLIGO and Einstein Telescope (ET) are favored in order to observe many numbers of fringe periods. But if they are augmented by mid-frequency detectors such as Atom Interferometer (AI) and Big Bang Observatory (BBO), the broadband ($f \simeq 0.1-1000$ Hz) detections can have significantly better fringe resolutions, hence enhanced sensitivities to the unconstrained parameter space ($G\mu \lesssim 10^{-7}$) of cosmic strings.Gravitational waveCosmic stringLaser Interferometer Gravitational-Wave ObservatoryEinstein TelescopeInterferenceBig Bang ObserverSignal to noise ratioGravitational lensingDark matterGamma ray burst...
- Modern out-of-order processors have increased capacity to exploit instruction level parallelism (ILP) and memory level parallelism (MLP), e.g., by using wide superscalar pipelines and vector execution units, as well as deep buffers for in-flight memory requests. These resources, however, often exhibit poor utilization rates on workloads with large working sets, e.g., in-memory databases, key-value stores, and graph analytics, as compilers and hardware struggle to expose ILP and MLP from the instruction stream automatically. In this paper, we introduce the IMLP (Instruction and Memory Level Parallelism) task programming model. IMLP tasks execute as coroutines that yield execution at annotated long-latency operations, e.g., memory accesses, divisions, or unpredictable branches. IMLP tasks are interleaved on a single thread, and integrate well with thread parallelism and vectorization. Our DSL embedded in C++, Cimple, allows exploration of task scheduling and transformations, such as buffering, vectorization, pipelining, and prefetching. We demonstrate state-of-the-art performance on core algorithms used in in-memory databases that operate on arrays, hash tables, trees, and skip lists. Cimple applications reach 2.5x throughput gains over hardware multithreading on a multi-core, and 6.4x single thread speedup.SchedulingSoftwareGraphOptimizationMultidimensional ArrayMicroarchitectureSilicon microstrip trackerArchitectureArithmeticGoogle.com...
- We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU.Linear Sigma modelData structuresMultidimensional ArrayCOmoving Lagrangian Acceleration methodCountingRay tracingProgramming LanguageData scienceNearest-neighbor siteLower and upper...
- NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose performance and memory footprint may vary considerably, depending on the layer dimensions. When an algorithm is automatically selected by cuDNN, the decision is performed on a per-layer basis, and thus it often resorts to slower algorithms that fit the workspace size constraints. We present {\mu}-cuDNN, a transparent wrapper library for cuDNN, which divides layers' mini-batch computation into several micro-batches. Based on Dynamic Programming and Integer Linear Programming, {\mu}-cuDNN enables faster algorithms by decreasing the workspace requirements. At the same time, {\mu}-cuDNN keeps the computational semantics unchanged, so that it decouples statistical efficiency from the hardware efficiency safely. We demonstrate the effectiveness of {\mu}-cuDNN over two frameworks, Caffe and TensorFlow, achieving speedups of 1.63x for AlexNet and 1.21x for ResNet-18 on P100-SXM2 GPU. These results indicate that using micro-batches can seamlessly increase the performance of deep learning, while maintaining the same memory footprint.Deep learningOptimizationConvolutional neural networkLinear optimizationDeep Neural NetworksFast Fourier transformArchitectureInferenceBackpropagationPrecision...
- Black holes drive powerful plasma jets to relativistic velocities. This plasma should be collisionless, and self-consistently supplied by pair creation near the horizon. We present general-relativistic collisionless plasma simulations of Kerr-black-hole magnetospheres which begin from vacuum, inject electron-positron pairs based on local unscreened electric fields, and reach steady states with electromagnetically powered Blandford-Znajek jets and persistent current sheets. Particles with negative energy-at-infinity are a general feature, and can contribute significantly to black-hole rotational-energy extraction in a variant of the Penrose process. The generated plasma distribution depends on the pair-creation environment, and we describe two distinct realizations of the force-free electrodynamic solution. This sensitivity suggests that plasma kinetics will be useful in interpreting future horizon-resolving submillimeter and infrared observations.HorizonBlack holeSteady stateErgospherePositronKerr black holeBlack hole magnetosphereForce-Free ElectrodynamicsCollisionless plasmaPenrose process...
- Feeding and feedback of active galactic nuclei (AGN) are critical for understanding the dynamics and thermodynamics of the intracluster medium (ICM) within the cores of galaxy clusters. While radio bubbles inflated by AGN jets could be dynamically supported by cosmic rays (CRs), the impact of CR-dominated jets are not well understood. In this work, we perform three-dimensional simulations of CR-jet feedback in an isolated cluster atmosphere; we find that CR jets impact the multiphase gas differently than jets dominated by kinetic energy. In particular, CR bubbles can more efficiently uplift the cluster gas and cause an outward expansion of the hot ICM. Due to adiabatic cooling from the expansion and less efficient heating from CR bubbles by direct mixing, the ICM is more prone to local thermal instabilities, which will later enhance chaotic cold accretion onto the AGN. The amount of cold gas formed during the bubble formation and its late-time evolution sensitively depend on whether CR transport processes are included or not. We also find that low-level, subsonic driving of turbulence by AGN jets holds for both kinetic and CR jets; nevertheless, the kinematics is consistent with the Hitomi measurements. Finally, we carefully discuss the key observable signatures of each bubble model, focusing on gamma-ray emission (and related comparison with Fermi), as well as thermal Sunyaev-Zel'dovich constraints.Cosmic rayIntra-cluster mediumActive Galactic NucleiAGN jetsCoolingTurbulenceInstabilityRadiative coolingSupermassive black holeLine of sight...
- We prove that there does not exist any weak coupling limit in the space of superconformal field theories in five and six dimensions, based on an analysis of the representation theory of the corresponding superconformal algebras. Holographically, this implies that superstring theories on AdS$_6$ and AdS$_7$ do not admit tensionless limits. Finally, we discuss the implications of our result on the existence of an action for coincident M5-branes.Scaling dimensionSupersymmetrySuperconformal field theoryHigher spinSuperconformal algebraPath integralM5 braneAnti de Sitter spaceRepresentation theoryFree field...
- We obtain the detailed Feynman rules for perturbative gauge theory on a fixed Yang-Mills plane wave background. Using these rules, the tree-level 4-point gluon amplitude is computed and some 1-loop Feynman diagrams are considered. As an application, we test the extent to which colour-kinematics duality, the relation between the colour and kinematic constituents of the amplitude, holds on the plane wave background. Although the duality is obstructed, the obstruction has an interesting constrained structure. This plane wave version of colour-kinematics duality reduces on a flat background to the well-known identities underpinning the BCJ relations for colour-ordered partial amplitudes, and constrains representations of tree-level amplitudes beyond 4-points.Plane waveKinematicsDualityPropagatorGauge theoryJacobi identityGauge fieldFeynman rulesLight conesYang-Mills theory...
- Quantum spin liquids (QSLs) represent a novel state of matter in which quantum fluctuations prevent conventional magnetic order from being established, and the spins remain disordered even at zero temperature. There have been many theoretical developments proposing various QSL states. On the other hand, experimental movement was relatively slow largely due to limitations on the candidate materials and difficulties in the measurements. In recent years, the experimental progress has been accelerated. In this topical review, we give a brief summary of experiments on the QSL candidates under magnetic fields. We arrange our discussions by two categories: i) Geometrically-frustrated systems, including triangular-lattice compounds YbMgGaO4 and YbZnGaO4, kappa-(BEDT-TTF)2Cu2(CN)3, and EtMe3Sb[Pd(dmit)2]2, and kagome system ZnCu3(OH)6Cl2; ii) the Kitaev material alpha-RuCl3. Among these, we will pay special attention to alpha-RuCl3, which has been intensively studied by our and other groups recently. We will present evidence that both supports and unsupports the QSL ground state for these materials, based on which we give several perspectives to stimulate further research activities.MagnonDisorderMagnetic orderThermal conductivitySpin glassNuclear magnetic resonanceSpin liquidTriangular latticePhononSpinon...
- We report the discovery and spectroscopic confirmation of 22 new gravitationally lensed quasars found using $\textit{Gaia}$ data release 2. The selection was made using several techniques: multiple $\textit{Gaia}$ detections around objects in quasar candidate catalogues, modelling of unWISE coadd pixels using $\textit{Gaia}$ astrometry, and $\textit{Gaia}$ detections offset from photometric and spectroscopic galaxies. Spectra of 33 candidates were obtained with the William Herschel Telescope, 22 of which are lensed quasars, 2 highly probably lensed quasars, 5 nearly identical quasar pairs, 1 inconclusive system, and 3 contaminants. Of the 3 confirmed quadruply imaged systems, J2145+6345 is a 2.1 arcsecond separation quad with 4 bright images ($G$=16.86, 17.26, 18.34, 18.56), making it ideal for time delay monitoring. Analysing this new sample alongside known lenses in the Pan-STARRS footprint, and comparing to expected numbers of lenses, we show that, as expected, we are biased towards systems with bright lensing galaxies and low source redshifts. We discuss possible techniques to remove this bias from future searches. A |b|>20 complete sample of lensed quasars detected by $\textit{Gaia}$ and with image separations above 1 arcsecond will provide a valuable statistical sample of around 350 systems. Currently only 96 known lenses satisfy these criteria, yet promisingly, our unWISE modelling technique is able to recover all of these with simple WISE-$\textit{Gaia}$ colour cuts that remove $\sim$80 per cent of previously followed-up contaminants. Finally, we provide an online database of known lenses, quasar pairs, and contaminant systems.QuasarGravitational lens galaxyPan-STARRSGalaxySloan Digital Sky SurveyWide-field Infrared Survey ExplorerStarProper motionGravitationally lensed quasarsQuasar pair...