Reviewerly/exHarmony huggingface dataset:
Application of machine learning techniques at the CERN Large Hadron Collider

Machine learning techniques have been used extensively in several domains of Science and Engineering for decades.These powerful tools have been applied also to the domain of high-energy physics, in the analysis of the data from particle collisions, for years already.Accelerator physics, however, has not started exploiting machine learning until very recently.Several activities are flourishing in this domain, in view of providing new insights to beam dynamics in circular accelerators, in different laboratories worldwide.This is, for instance, the case for the CERN Large Hadron Collider, where since a few years exploratory studies are being carried out.A broad range of topics have been addressed, such as anomaly detection of beam position monitors, analysis of optimal correction tools for linear optics, optimisation of the collimation system, lifetime and performance optimisation, and detection of hidden correlations in the huge data set of beam dynamics observables collected during the LHC Run 2. Furthermore, very recently, machine learning techniques are being scrutinised for the advanced analysis of numerical simulations data, in view of improving our models of dynamic aperture evolution.
The Non-m-Positive Dimension of a Positive Linear Map

We introduce a property of a matrix-valued linear map $\Phi$ that we call its "non-m-positive dimension" (or "non-mP dimension" for short), which measures how large a subspace can be if every quantum state supported on the subspace is non-positive under the action of $I_m \otimes \Phi$. Equivalently, the non-mP dimension of $\Phi$ tells us the maximal number of negative eigenvalues that the adjoint map $I_m \otimes \Phi^*$ can produce from a positive semidefinite input. We explore the basic properties of this quantity and show that it can be thought of as a measure of how good $\Phi$ is at detecting entanglement in quantum states. We derive non-trivial bounds for this quantity for some well-known positive maps of interest, including the transpose map, reduction map, Choi map, and Breuer--Hall map. We also extend some of our results to the case of higher Schmidt number as well as the multipartite case. In particular, we construct the largest possible multipartite subspace with the property that every state supported on that subspace has non-positive partial transpose across at least one bipartite cut, and we use our results to construct multipartite decomposable entanglement witnesses with the maximum number of negative eigenvalues.
Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting

Echo state networks are computationally lightweight reservoir models inspired by the random projections observed in cortical circuitry. As interest in reservoir computing has grown, networks have become deeper and more intricate. While these networks are increasingly applied to nontrivial forecasting tasks, there is a need for comprehensive performance analysis of deep reservoirs. In this work, we study the influence of partitioning neurons given a budget and the effect of parallel reservoir pathways across different datasets exhibiting multi-scale and nonlinear dynamics.
Coreference Resolution System for Indonesian Text with Mention Pair Method and Singleton Exclusion using Convolutional Neural Network

Neural network has shown promising performance on coreference resolution systems that uses mention pair method. With deep neural network, it can learn hidden and deep relations between two mentions. However, there is no work on coreference resolution for Indonesian text that uses this learning technique. The state-of-the-art system for Indonesian text only states the use of lexical and syntactic features can improve the existing coreference resolution system. In this paper, we propose a new coreference resolution system for Indonesian text with mention pair method that uses deep neural network to learn the relations of the two mentions. In addition to lexical and syntactic features, in order to learn the representation of the mentions words and context, we use word embeddings and feed them to Convolutional Neural Network (CNN). Furthermore, we do singleton exclusion using singleton classifier component to prevent singleton mentions entering any entity clusters at the end. Achieving 67.37% without singleton exclusion, 63.27% with trained singleton classifier, and 75.95% with gold singleton classifier on CoNLL average F1 score, our proposed system outperforms the state-of-the-art system.
Efficient and Effective Query Auto-Completion

Query Auto-Completion (QAC) is an ubiquitous feature of modern textual search systems, suggesting possible ways of completing the query being typed by the user. Efficiency is crucial to make the system have a real-time responsiveness when operating in the million-scale search space. Prior work has extensively advocated the use of a trie data structure for fast prefix-search operations in compact space. However, searching by prefix has little discovery power in that only completions that are prefixed by the query are returned. This may impact negatively the effectiveness of the QAC system, with a consequent monetary loss for real applications like Web Search Engines and eCommerce.
Extension of Projection Area using Head Orientation in Projected Virtual Hand Interface for Wheelchair Users

A projected virtual hand interface, which visually extends arms of users using projection images, enables wheelchair users to reach unreachable objects; however, its projection area is narrower than the reaching area required for the users. To address this problem, we propose a wheelchair system enhanced with a projected virtual hand that allows controlling a projection area using user's head orientation. The proposed system estimates the current orientation of a user's head and controls the pan and tilt of a projector accordingly to move a projection area considering the positional relationship with a projection plane. As users can operate the projection area simply by turning their head, this operation can be executed simultaneously with operations of a virtual hand using their hands. We propose a control model for projector rotation according to the user's head direction. The conducted user experience study has revealed that the proposed method enables users to perform pointing tasks in a shorter time compared with the existing method, and moreover, it has acceptable interface usability.
Uncertainty About Evidence

We develop a logical framework for reasoning about knowledge and evidence in which the agent may be uncertain about how to interpret their evidence. Rather than representing an evidential state as a fixed subset of the state space, our models allow the set of possible worlds that a piece of evidence corresponds to to vary from one possible world to another, and therefore itself be the subject of uncertainty. Such structures can be viewed as (epistemically motivated) generalizations of topological spaces. In this context, there arises a natural distinction between what is actually entailed by the evidence and what the agent knows is entailed by the evidence -- with the latter, in general, being much weaker. We provide a sound and complete axiomatization of the corresponding bi-modal logic of knowledge and evidence entailment, and investigate some natural extensions of this core system, including the addition of a belief modality and its interaction with evidence interpretation and entailment, and the addition of a "knowability" modality interpreted via a (generalized) interior operator.
Convolutional Neural Networks for Off-Line Writer Identification Based on Simple Graphemes

The writer’s identification/verification problem has traditionally been solved by analyzing complex biometric sources (text pages, paragraphs, words, signatures, etc.). This implies the need for pre-processing techniques, feature computation and construction of also complex classifiers. A group of simple graphemes (“ S ”, “ ∩ ”, “ C ”, “ ∼ ” and “ U ”) has been recently introduced in order to reduce the structural complexity of biometric sources. This paper proposes to analyze the images of simple graphemes by means of Convolutional Neural Networks. In particular, the AlexNet, VGG-16, VGG-19 and ResNet-18 models are considered in the learning transfer mode. The proposed approach has the advantage of directly processing the original images, without using an intermediate representation, and without computing specific descriptors. This allows to dramatically reduce the complexity of the simple grapheme processing chain and having a high hit-rate of writer identification performance.
A classical limit of Grover’s algorithm induced by dephasing: Coherence versus entanglement

A new approach to the classical limit of Grover's algorithm is discussed by assuming a very rapid dephasing of a system between consecutive Grover's unitary operations, which drives pure quantum states to decohered mixed states. One can identify a specific element among $N$ unsorted elements by a probability of the order of unity after $k\sim N$ steps of classical amplification, which is realized by a combination of Grover's unitary operation and rapid dephasing, in contrast to $k\sim \pi \sqrt{N}/4$ steps in quantum mechanical amplification. The initial two-state system with enormously unbalanced existence probabilities, which is realized by a chosen specific state and a superposition of all the rest of states among $N$ unsorted states, is crucial in the present analysis of classical amplification. This analysis illustrates Grover's algorithm in extremely noisy circumstances. A similar increase from $k\sim \sqrt{N}$ to $k\sim N$ steps due to the loss of quantum coherence takes place in the {\em analog} model of Farhi and Gutmann where the entanglement does not play an obvious role. This supports a view that entanglement is crucial in quantum computation to describe quantum states by a set of qubits, but the actual speedup of the quantum computation is based on quantum coherence.
Enhancing the Identification of Cyberbullying through Participant Roles

Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide.The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying.This paper proposes a novel approach to enhancing cyberbullying detection through role modeling.We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g.victim, harasser).Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.
May I Ask Who’s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance

We investigate using Named Entity Recognition on a new type of user-generated text: a call center conversation.These conversations combine problems from spontaneous speech with problems novel to conversational Automated Speech Recognition, including incorrect recognition, alongside other common problems from noisy user-generated text.Using our own corpus with new annotations, training custom contextual string embeddings, and applying a BiLSTM-CRF, we match stateof-the-art results on our novel task.
Unconventional quantum annealing methods for difficult trial problems

We consider a range of unconventional modifications to quantum annealing (QA), applied to an artificial trial problem with continuously tunable difficulty. In this problem, inspired by ``transverse field chaos'' in larger systems, classical and quantum methods are steered toward a false local minimum. To go from this local minimum to the global minimum, all $N$ spins must flip, making this problem exponentially difficult to solve. We numerically study this problem by using a variety of new methods from the literature: inhomogeneous driving, adding transverse couplers, and other types of coherent oscillations in the transverse field terms (collectively known as RFQA, which stands for Random Field Quantum Annealing or Radio Frequency Quantum Annealing). We show that all of these methods improve the scaling of the time to solution (relative to the standard uniform sweep evolution) in at least some regimes. Comparison of these methods could help identify promising paths towards a demonstrable quantum speedup over classical algorithms in solving some realistic problems with near-term quantum annealing hardware.
Finite Verification of Infinite Families of Diagram Equations

The ZX, ZW and ZH calculi are all graphical calculi for reasoning about pure state qubit quantum mechanics.All of these languages use certain diagrammatic decorations, called !-boxes and phase variables, to indicate not just one diagram but an infinite family of diagrams.These decorations are powerful enough to allow complete rulesets for these calculi to be expressed in around fifteen rules.Historically rules involving !-boxes have not been verifiable by computer.We present the first algorithm for reducing infinite families of equations involving !-boxes into finite verifying subsets.The only requirement for this method is a mild property on the connectivity of the !-boxes.Previous results had focussed on finite case analysis of phase variables in ZX, a result we also extend for ZW and ZH, as well as providing a general framework for further languages.The results presented here allow proof assistants to reduce infinite families of problems (involving combinations of phase variables and !-boxes) down to undecorated, case-by-case verification, in a way not previously possible.In particular we note the removal of the need to reason directly with !-boxes in verification tasks as something entirely new.This forms part of larger work in automated verification of quantum circuitry, conjecture synthesis, and diagrammatic languages in general.The methods described here extend to any diagrammatic languages that meet certain simple conditions.
Geometrical Jitter and Bolometric Regime in Photon Detection by Straight Superconducting Nanowire

We present a direct observation of the geometrical jitter in single photon detection by a straight superconducting nanowire. Differential measurement technique was applied to the 180-μm long nanowire similar to those commonly used in the technology of superconducting nanowire single photon detectors (SNSPD). A non-Gaussian geometrical jitter appears as a wide almost uniform probability distribution (histogram) of the delay time (latency) of the nanowire response to detected photon. White electrical noise of the readout electronics causes broadened, Gaussian-shaped edges of the histogram. Subtracting noise contribution, we found for the geometrical jitter a standard deviation of ≈8.5 ps and the full width at half maximum (FWHM) of the distribution of ≈29 ps. FWHM corresponds to the propagation speed of the electrical signal along the nanowire of ≈6.2 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> m/s or 0.02 of the speed of light. Alternatively the propagation speed was estimated from the central frequency of the measured first-order self-resonance of the nanowire. Both values agree well with each other and with previously reported values. As the intensity of the incident photon flux increases, the wide probability distribution collapses into a much narrower Gaussian distribution with a standard deviation dominated by the noise of electronics. We associate the collapse of the histogram with the transition from the discrete, single photon detection to the uniform bolometric regime.
Flow-based Intrinsic Curiosity Module

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively. See here for our codes and demo videos.
7T MRI super-resolution with Generative Adversarial Network

The high-resolution magnetic resonance image (MRI) provides detailed anatomical information critical for clinical application diagnosis. However, high-resolution MRI typically comes at the cost of long scan time, small spatial coverage, and low signal-to-noise ratio. The benefits of the convolutional neural network (CNN) can be applied to solve the super-resolution task to recover high-resolution generic images from low-resolution inputs. Additionally, recent studies have shown the potential to use the generative advertising network (GAN) to generate high-quality super-resolution MRIs using learned image priors. Moreover, existing approaches require paired MRI images as training data, which is difficult to obtain with existing datasets when the alignment between high and low-resolution images has to be implemented manually.This paper implements two different GAN-based models to handle the super-resolution: Enhanced super-resolution GAN (ESRGAN) and CycleGAN. Different from the generic model, the architecture of CycleGAN is modified to solve the super-resolution on unpaired MRI data, and the ESRGAN is implemented as a reference to compare GAN-based methods performance. The results of GAN-based models provide generated high-resolution images with rich textures compared to the ground-truth. Moreover, results from experiments are performed on both 3T and 7T MRI images in recovering different scales of resolution.
Strongly perfect lattices sandwiched between Barnes–Wall lattices

Journal of the London Mathematical SocietyVolume 101, Issue 3 p. 1068-1089 Research Article Strongly perfect lattices sandwiched between Barnes–Wall lattices Sihuang Hu, husihuang@gmail.com Key Laboratory of Cryptologic Technology and Information Security of Ministry of Education, School of Cyber Science and Technology, Shandong University, Qingdao, 266237 ChinaSearch for more papers by this authorGabriele Nebe, Corresponding Author nebe@math.rwth-aachen.de Lehrstuhl D für Mathematik, RWTH Aachen University, D-52056 Aachen, Germany nebe@math.rwth-aachen.deSearch for more papers by this author Sihuang Hu, husihuang@gmail.com Key Laboratory of Cryptologic Technology and Information Security of Ministry of Education, School of Cyber Science and Technology, Shandong University, Qingdao, 266237 ChinaSearch for more papers by this authorGabriele Nebe, Corresponding Author nebe@math.rwth-aachen.de Lehrstuhl D für Mathematik, RWTH Aachen University, D-52056 Aachen, Germany nebe@math.rwth-aachen.deSearch for more papers by this author First published: 17 December 2019 https://doi.org/10.1112/jlms.12298Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract New series of 4 m -dimensional universally strongly perfect lattices Λ I and Γ J are constructed with 2 BW 2 m # ⊆ Γ J ⊆ BW 2 m ⊆ Λ I ⊆ BW 2 m # . The lattices are found by restricting the half-spin representations of the automorphism group of the Barnes–Wall lattice to its subgroup U m : = C m ( 4 1 H ) . The group U m is the Clifford–Weil group associated to the Hermitian self-dual codes over F 4 containing 1, so the ring of polynomial invariants of U m is spanned by the genus m complete weight enumerators of such codes. This allows us to show that all the U m -invariant lattices are universally strongly perfect. We introduce a new construction, D ( cyc ) , for chains of (extended) cyclic codes to obtain (bounds on) the minimum of the new lattices. Citing Literature Volume101, Issue3June 2020Pages 1068-1089 RelatedInformation
Possibility to generate any Gaussian cluster state by a multimode squeezing transformation

Gaussian cluster states are ideal infinitely squeezed states. In practice it is possible to construct only approximated version of them with finite squeezing. Here we show how to determine the specific multimode squeezing transformation, which generates a faithful approximation of any given Gaussian cluster state.
The Design Process in the Improvement of The Experience Between a Brand and its Target Audience Through a Digital Product: The Lexus Portugal’s used Car Website Case Study

The study aims to demonstrate how the use of the design process can align a brand's strategy with the interests of its target audience through a digital product based on a case study.Currently, Lexus internal studies show that there is a possibility to meet the needs of new audiences, beyond the traditional ones (men, 50+).In order to achieve this goal, the following objectives have been defined: communicate the brand and its cars' main values and philosophy, namely ecology, economy, safety and comfort; promote its services and products; and design an interface that guarantees users easy, pleasant and attractive navigation.The work process consisted of identifying the brand's strategy and values, identifying users, analyzing the main competing brands in the market, and designing a prototype within the framework of User-Centered Design, for which it had to address crucial issues such as the application of character models, UX and UI design, the creation of wireframes and user flows, interface design and the development of usability tests.The results demonstrate that it is possible to align the strategic interests of brands with the needs, objectives and expectations of users in a context of increasing global concerns of citizens related to reuse and sustainability.In this sense, it is vitally important that brands adopt Design processes in order to converge their own brand interests with people's demands.