We study the bounds that can be obtained on four-fermion contact terms from the experimental data for (e+ p -> \bar{nu} X) obtained at HERA and (p \bar{p} -> e \nu), measured at TEVATRON. We compare these bounds with the ones available in the literature. Finally, we apply these results to study the compactification radius in theories with large extra dimensions and we obtain the bound M_c >= 3.3 TeV.

Fernando Cornet Monica Relano Javier Rico

A model for the total photoproduction cross section based on the ansatz that resummation of infrared gluons limits the rise induced by QCD minijets in all the total cross-sections, is used to simulate extended air showers initiated by cosmic rays with the AIRES simulation program. The impact on common shower observables, especially those related with muon production, is analysed and compared with the corresponding results obtained with previous photoproduction models.

Fernando Cornet Carlos Garcia Canal Agnes Grau Giulia Pancheri Sergio Sciutto

We present post-LHC updates of estimates of the total photo-production cross section in a mini-jet model with infrared soft gluon resummation, and apply the model to study Cosmic Ray shower development, comparing the results with those obtained from other existing models.

Fernando Cornet Carlos Garcia-Canal Agnes Grau Giulia Pancheri Sergio J. Sciutto

The aim of this paper is to introduce the notion of cornets, which form a particular subclass of ordered semigroups also equipped with a multiplication by natural numbers. The most important standard examples for cornets are the families of the nonempty subsets and the nonempty fuzzy subsets of a vector space. In a cornet, the convexity, nonnegativity, Archimedean property, boundedness, closedness of an element can be defined naturally. The basic properties related to these notions are established. The main result extends the Cancellation Principle discovered by R{\aa}dstr\"om in 1952.

Gábor M. Molnár Zsolt Páles

Data management and analysis tasks are often carried out using spreadsheet software. A popular feature in most spreadsheet platforms is the ability to define data-dependent formatting rules. These rules can express actions such as "color red all entries in a column that are negative" or "bold all rows not containing error or failure." Unfortunately, users who want to exercise this functionality need to manually write these conditional formatting (CF) rules. We introduce CORNET, a system that automatically learns such conditional formatting rules from user examples. CORNET takes inspiration from inductive program synthesis and combines symbolic rule enumeration, based on semi-supervised clustering and iterative decision tree learning, with a neural ranker to produce accurate conditional formatting rules. In this demonstration, we show CORNET in action as a simple add-in to Microsoft Excel. After the user provides one or two formatted cells as examples, CORNET generates formatting rule suggestions for the user to apply to the spreadsheet.

Mukul Singh Jose Cambronero Sumit Gulwani Vu Le Carina Negreanu Gust Verbruggen

Spreadsheets are widely used for table manipulation and presentation. Stylistic formatting of these tables is an important property for both presentation and analysis. As a result, popular spreadsheet software, such as Excel, supports automatically formatting tables based on rules. Unfortunately, writing such formatting rules can be challenging for users as it requires knowledge of the underlying rule language and data logic. We present CORNET, a system that tackles the novel problem of automatically learning such formatting rules from user examples in the form of formatted cells. CORNET takes inspiration from advances in inductive programming and combines symbolic rule enumeration with a neural ranker to learn conditional formatting rules. To motivate and evaluate our approach, we extracted tables with over 450K unique formatting rules from a corpus of over 1.8M real worksheets. Since we are the first to introduce conditional formatting, we compare CORNET to a wide range of symbolic and neural baselines adapted from related domains. Our results show that CORNET accurately learns rules across varying evaluation setups. Additionally, we show that CORNET finds shorter rules than those that a user has written and discovers rules in spreadsheets that users have manually formatted.

Mukul Singh José Cambronero Sumit Gulwani Vu Le Carina Negreanu Mohammad Raza Gust Verbruggen

A covariant formalism is used in order to examine the status of Maxwell equations and to unify the concept of balances, for all chemical engineering applications in relation with electrodynamics. The resulting formal structure serves as a discussion in studying the determinism of electromagnetic systems, and examining the theoretical foundations for a general classification of chemical engineering applications when non-conservative forces are concerned. A strategy for modeling such applications is then sketched and the balances for the main conserved and non-conserved extensive quantities are then summarized in their covariant and classical forms.

Cornet Jean-François

We introduce Equivariant Neural Diffusion (END), a novel diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Compared to current state-of-the-art equivariant diffusion models, the key innovation in END lies in its learnable forward process for enhanced generative modelling. Rather than pre-specified, the forward process is parameterized through a time- and data-dependent transformation that is equivariant to rigid transformations. Through a series of experiments on standard molecule generation benchmarks, we demonstrate the competitive performance of END compared to several strong baselines for both unconditional and conditional generation.

François Cornet Grigory Bartosh Mikkel N. Schmidt Christian A. Naesseth

Deep convolutional neural networks (DCNNs) have demonstrated excellent performance in object recognition and have been found to share some similarities with brain visual processing. However, the substantial gap between DCNNs and human visual perception still exists. Functional magnetic resonance imaging (fMRI) as a widely used technique in cognitive neuroscience can record neural activation in the human visual cortex during the process of visual perception. Can we teach DCNNs human fMRI signals to achieve a more brain-like model? To answer this question, this study proposed ReAlnet-fMRI, a model based on the SOTA vision model CORnet but optimized using human fMRI data through a multi-layer encoding-based alignment framework. This framework has been shown to effectively enable the model to learn human brain representations. The fMRI-optimized ReAlnet-fMRI exhibited higher similarity to the human brain than both CORnet and the control model in within-and across-subject as well as within- and across-modality model-brain (fMRI and EEG) alignment evaluations. Additionally, we conducted an in-depth analyses to investigate how the internal representations of ReAlnet-fMRI differ from CORnet in encoding various object dimensions. These findings provide the possibility of enhancing the brain-likeness of visual models by integrating human neural data, helping to bridge the gap between computer vision and visual neuroscience.

Zitong Lu Yile Wang

This document collects the proceedings of the PHOTON 2017 conference ("International Conference on the Structure and the Interactions of the Photon", including the 22th "International Workshop on Photon-Photon Collisions", and the "International Workshop on High Energy Photon Colliders") held at CERN (Geneva) in May 2017. The latest experimental and theoretical developments on the topics of the PHOTON conference series are covered: (i) $\gamma\,\gamma$ processes in e$^+$e$^-$, proton-proton (pp) and nucleus-nucleus (AA) collisions at current and future colliders, (ii) $\gamma$-hadron interactions in e$^\pm$p, pp, and AA collisions, (iii) final-state photon production (including Standard Model studies and searches beyond it) in pp and AA collisions, and (iv) high-energy $\gamma$-ray astrophysics. These proceedings are dedicated to the memory of Maria Krawczyk.

David d'Enterria Albert de Roeck Michelangelo Mangano Jaroslav Adam Massimiliano Alvioli Christopher D. Anson Hamed Bakhshiansohi Cristian Baldenegro Valerio Bertone Stanley J. Brodsky Peter J. Bussey Chav Chhiv Chau Weiren Chou Ruchi Chudasama Fernando Cornet David d'Enterria Stefan Dittmaier Babette Dobrich Dipanwita Dutta John Ellis Sylvain Fichet Leonid Frankfurt Carlos Garcia-Canal Rohini M. Godbole Agnes Grau Michel Guidal Qianying Guo Alexey Guskov Vadim Guzey Lucian Harland-Lang Ilkka Helenius Jonathan Hollar Kensuke Homma Piotr Homola Alexander Huss Tom Kaufmann Valery A. Khoze Michael Klasen Simon Knapen Piotr Kotko Dimitrii V. Krasnopevtsev Mieczyslaw W. Krasny Beata Krupa Yoshimasa Kurihara Jean-Philippe Lansberg Tongyan Lin Hou Keong Lou Olga Lukina Heikki Mantysaari Daniel Martins Pere Masjuan Laure Massacrier Nick E. Mavromatos Tom Melia Asmita Mukherjee Norbert Novitzky Risto Orava Davide Pagani Giulia Pancheri Albert Puig Navarro Patricia Rebello Teles Mikhail G. Ryskin Pablo Sanchez-Puertas Ken Sasaki Christian Schwinn Sergio J. Sciutto Chengping Shen Mark Strikman Lech Szymanowski Alessio Tiberio Maciej Trzebinski Tokahiro Ueda Tsuneo Uematsu Werner Vogelsang Jakub Wagner Norihisa Watanabe Samuel Webb Tomasz Wojton Tevong You Leszek Zawiejski Michael Zhalov