Without significant changes to data organization, management, and access (DOMA), HEP experiments will find scientific output limited by how fast data can be accessed and digested by computational resources. In this white paper we discuss challenges in DOMA that HEP experiments, such as the HL-LHC, will face as well as potential ways to address them. A research and development timeline to assess these changes is also proposed.

Dario Berzano Riccardo Maria Bianchi Ian Bird Brian Bockelman Simone Campana Kaushik De Dirk Duellmann Peter Elmer Robert Gardner Vincent Garonne Claudio Grandi Oliver Gutsche Andrew Hanushevsky Burt Holzman Bodhitha Jayatilaka Ivo Jimenez Michel Jouvin Oliver Keeble Alexei Klimentov Valentin Kuznetsov Eric Lancon Mario Lassnig Miron Livny Carlos Maltzahn Shawn McKee Dario Menasce Andrew Norman Jim Pivarski Benedikt Riedel Markus Schulz Horst Severini Michael Sevilla Elizabeth Sexton-Kennedy Eric Vaandering Ilija Vukotic Noah Watkins Torre Wenaus Frank Wuerthwein

We consider solutions to the cosmological equations of motion in 11 dimensions with and without 4-form charges. We show explicitly the correspondence between some of these solutions and known solutions in 10 dimensional string gravity. New solutions involving combinations of 4-form charges are explored. We also speculate on the possibility of removing curvature singularities present in 10D theories by oxidizing to 11D.

Nemanja Kaloper Ian I. Kogan Keith A. Olive

Photoproduction of beauty quarks in events with two jets and an electron associated with one of the jets has been studied with the ZEUS detector at HERA using an integrated luminosity of 120 pb-1. The fractions of events containing b quarks, and also of events containing c quarks, were extracted from a likelihood fit using variables sensitive to electron identification as well as to semileptonic decays. Total and differential cross sections for beauty and charm production were measured and compared with next-to-leading-order QCD calculations and Monte Carlo models.

Markus Jüngst Detlef Bartsch Ian C. Brock Oliver M. Kind

Due to the variability in characteristics of audio scenes, some scenes can naturally be recognized earlier than others. In this work, rather than using equal-length snippets for all scene categories, as is common in the literature, we study to which temporal extent an audio scene can be reliably recognized given state-of-the-art models. Moreover, as model fusion with deep network ensemble is prevalent in audio scene classification, we further study whether, and if so, when model fusion is necessary for this task. To achieve these goals, we employ two single-network systems relying on a convolutional neural network and a recurrent neural network for classification as well as early fusion and late fusion of these networks. Experimental results on the LITIS-Rouen dataset show that some scenes can be reliably recognized with a few seconds while other scenes require significantly longer durations. In addition, model fusion is shown to be the most beneficial when the signal length is short.

Huy Phan Oliver Y. Chén Philipp Koch Lam Pham Ian McLoughlin Alfred Mertins Maarten De Vos

Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that works by guessing low-entropy labels for data-augmented unlabeled examples and mixing labeled and unlabeled data using MixUp. We show that MixMatch obtains state-of-the-art results by a large margin across many datasets and labeled data amounts. For example, on CIFAR-10 with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%) and by a factor of 2 on STL-10. We also demonstrate how MixMatch can help achieve a dramatically better accuracy-privacy trade-off for differential privacy. Finally, we perform an ablation study to tease apart which components of MixMatch are most important for its success.

David Berthelot Nicholas Carlini Ian Goodfellow Nicolas Papernot Avital Oliver Colin Raffel

An incremental version of the ELMVIS+ method is proposed in this paper. It iteratively selects a few best fitting data samples from a large pool, and adds them to the model. The method keeps high speed of ELMVIS+ while allowing for much larger possible sample pools due to lower memory requirements. The extension is useful for reaching a better local optimum with greedy optimization of ELMVIS, and the data structure can be specified in semi-supervised optimization. The major new application of incremental ELMVIS is not to visualization, but to a general dataset processing. The method is capable of learning dependencies from non-organized unsupervised data -- either reconstructing a shuffled dataset, or learning dependencies in complex high-dimensional space. The results are interesting and promising, although there is space for improvements.

Anton Akusok Emil Eirola Yoan Miche Ian Oliver Kaj-Mikael Björk Andrey Gritsenko Stephen Baek Amaury Lendasse

We present a policy and process framework for secure environments for productive data science research projects at scale, by combining prevailing data security threat and risk profiles into five sensitivity tiers, and, at each tier, specifying recommended policies for data classification, data ingress, software ingress, data egress, user access, user device control, and analysis environments. By presenting design patterns for security choices for each tier, and using software defined infrastructure so that a different, independent, secure research environment can be instantiated for each project appropriate to its classification, we hope to maximise researcher productivity and minimise risk, allowing research organisations to operate with confidence.

Diego Arenas Jon Atkins Claire Austin David Beavan Alvaro Cabrejas Egea Steven Carlysle-Davies Ian Carter Rob Clarke James Cunningham Tom Doel Oliver Forrest Evelina Gabasova James Geddes James Hetherington Radka Jersakova Franz Kiraly Catherine Lawrence Jules Manser Martin T. O'Reilly James Robinson Helen Sherwood-Taylor Serena Tierney Catalina A. Vallejos Sebastian Vollmer Kirstie Whitaker

Existing generative adversarial networks (GANs) for speech enhancement solely rely on the convolution operation, which may obscure temporal dependencies across the sequence input. To remedy this issue, we propose a self-attention layer adapted from non-local attention, coupled with the convolutional and deconvolutional layers of a speech enhancement GAN (SEGAN) using raw signal input. Further, we empirically study the effect of placing the self-attention layer at the (de)convolutional layers with varying layer indices as well as at all of them when memory allows. Our experiments show that introducing self-attention to SEGAN leads to consistent improvement across the objective evaluation metrics of enhancement performance. Furthermore, applying at different (de)convolutional layers does not significantly alter performance, suggesting that it can be conveniently applied at the highest-level (de)convolutional layer with the smallest memory overhead.

Huy Phan Huy Le Nguyen Oliver Y. Chén Philipp Koch Ngoc Q. K. Duong Ian McLoughlin Alfred Mertins

Colloidal self-assembly allows rational design of structures on the micrometer and submicrometer scale. One architecture that can generate complete 3D photonic band gaps is the diamond cubic lattice, which has remained difficult to realize at length scales comparable to the wavelength of visible or ultraviolet light. Here, we demonstrate three-dimensional photonic crystals self-assembled from DNA origami that act as precisely programmable patchy colloids. Our DNA-based nanoscale tetrapods crystallize into a rod-connected diamond cubic lattice with a periodicity of 170 nm. This structure serves as a scaffold for atomic layer deposition of high refractive index materials such as TiO$_2$, yielding a tunable photonic band gap in the near-ultraviolet.

Gregor Posnjak Xin Yin Paul Butler Oliver Bienek Mihir Dass Ian D. Sharp Tim Liedl

We analyse the mass distribution in the core of A383 (z=0.188), one of 12 X-ray luminous clusters at z~0.2 selected for a comprehensive and unbiased study of the mass distribution in massive clusters. Deep HST imaging reveals a wide variety of gravitationally lensed features in A383, including a giant arc formed from the strongly-lensed images of 2 background galaxies, 2 radial arcs, several multiply-imaged arcs and numerous arclets. Based upon the constraints from the various lensed features, we construct a detailed mass model for the central regions of the cluster, taking into account both the cluster-scale potential and perturbations from individual cluster galaxies. Keck spectroscopy of one component of the giant arc identifies it as a star-forming galaxy at z=1.01 and provides an accurate measurement of the cluster mass within the radius of the giant arc (65kpc) of (3.5+/-0.1)*10^13 Mo. Using the weak shear measured from our HST observations we extend our mass model to determine a mass of (1.8+/-0.2)*10^14 Mo within a radius of 250kpc. On smaller scales we employ the radial arcs as probes of the shape of the mass distribution in the cluster core (r<20kpc), and find that the mass profile is more peaked than a single NFW profile. The optical and X-ray properties of A383 indicate the presence of a central cooling flow, for which we derive a mass deposition rate of >200 Mo/yr. We also use the X-ray emission from A383 to obtain independent estimates of the total mass within projected radii of 65 and 250kpc: (4.0+/-1.4)*10^13 Mo and (1.2+/-0.5)*10^14 Mo, which are consistent with the lensing measurements. [Abridged]

Graham P. Smith Jean-Paul Kneib Harald Ebeling Oliver Czoske Ian Smail