Reviewerly/exHarmony huggingface dataset:
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.
TCEC13: The 13th top chess engine championship

TCEC15 was the 15th season of the Top Chess Engine Championship and ran from March 5th to May 28th, 2019. TCEC has become the largest Open Computer Chess Championship. It attracts the best engines in the field and provides an opportunity for absolute and comparative analysis of the participating engines in a computational experiment. Once again, the final offered a contrast of playing styles between neural-network engine LEELA CHESS ZERO and STOCKFISH. This time, the ‘new architecture’ LC0 won, 53½-46½, making their ‘TCEC Sufi’ score 103-97 to LC0. Again, KOMODO was third. The attached files at http://centaur.reading.ac.uk/83156/ provide the 808 games with engine PVs, the detail on them and some summary statistics. The decisive games of Divisions 1 and P, and of the Superfinal have been played out by FRITZ17 at depth 24 to mate to enable and benchmark endgame practice.
One-dimensional quantum walks driven by two-entangled-qubit coins

We study one-dimensional quantum walk with four internal degrees of freedom resulted from two entangled qubits. We will demonstrate that the entanglement between the qubits and its corresponding coin operator enable one to steer the walker's state from a classical to standard quantum-walk behavior, and a novel one. Additionally, we report on self-trapped behavior and perfect transfer with highest velocity for the walker. We also show that symmetry of probability density distribution, the most probable place to find the walker and evolution of the entropy are subject to initial entanglement between the qubits. In fact, we confirm that if the two qubits are separable (zero entanglement), entropy becomes minimum whereas its maximization happens if the two qubits are initially maximally entangled. We will make contrast between cases where the entangled qubits are affected by coin operator identically or else, and show considerably different deviation in walker's behavior and its properties.
Experimental verification of anisotropic invariance for three-qubit states

We experimentally test the recently predicted anisotropic invariance properties of pure three-qubit states, via generation and measurement of polarisation-path entangled three-qubit states. These properties do not require aligned reference frames and can be determined from measurements on any two of the qubits. They have several applications, such as a universal ordering of pairwise quantum correlations, strong monogamy relations for Bell inequalities and quantum steering, and a complementarity relation for Bell nonlocality versus 3-tangle, some of which we also test. The results indicate that anisotropic invariance, together with the three qubit Bloch vector lengths, can provide a robust and complete set of invariants for such states under local unitary transformations.
Hierarchical Continuous Time Hidden Markov Model, with Application in Zero-Inflated Accelerometer Data

Wearable devices including accelerometers are increasingly being used to collect high-frequency human activity data in situ. There is tremendous potential to use such data to inform medical decision making and public health policies. However, modeling such data is challenging as they are high-dimensional, heterogeneous, and subject to informative missingness, e.g., zero readings when the device is removed by the participant. We propose a flexible and extensible continuous-time hidden Markov model to extract meaningful activity patterns from human accelerometer data. To facilitate estimation with massive data we derive an efficient learning algorithm that exploits the hierarchical structure of the parameters indexing the proposed model. We also propose a bootstrap procedure for interval estimation. The proposed methods are illustrated using data from the 2003–2004 and 2005–2006 National Health and Nutrition Examination Survey.
Genuine multipartite indistinguishability and its detection via the generalized Hong-Ou-Mandel effect

The question of whether two indistinguishable particles are bosons or fermions can be answered by observing the Hong-Ou-Mandel effect on a beam splitter. However, already for three particles one can consider symmetries that are neither bosonic nor fermionic. In this work, we propose a notion of a genuine multipartite indistinguishability and describe a simple method of quantifying it experimentally.
Approximate exchange-only entangling gates for the three-spin-1/2decoherence-free subsystem

The three-spin-$1/2$ decoherence-free subsystem defines a logical qubit protected from collective noise and supports exchange-only universal gates. Such logical qubits are well-suited for implementation with electrically-defined quantum dots. Exact exchange-only entangling logical gates exist but are challenging to construct and understand. We use a decoupling strategy to obtain straightforward approximate entangling gates. A benefit of the strategy is that if the physical spins are aligned, then it can implement evolution under entangling Hamiltonians. Hamiltonians expressible as linear combinations of logical Pauli products not involving $\sigma_y$ can be implemented directly. Self-inverse gates that are constructible from these Hamiltonians, such as the CNOT, can be implemented without the assumption on the physical spins. We compare the control complexity of implementing CNOT to previous methods and find that the complexity for fault-tolerant fidelities is competitive.
Enhancing entanglement of the two-mode squeezed vacuum state by arbitrary combinations of photon additions and subtractions

We investigate the problem of enhancing quantum entanglement of the two-mode squeezed vacuum state by asymmetric combinations of photon additions and subtractions. Suppose a combination of N operations is performed on the two-mode squeezed vacuum state, in which each operation may be either photon addition or photon subtraction and may be performed on either mode A or mode B of the state; then there are (N+1)2N possible combinations in all. We calculate in detail the entanglement enhancement by all possible combinations for each given N&#x2208;{1,2,&#x2026;,5,6}, respectively, and find that when the squeezing parameter is small, combinations such as aa&#x2020;aa&#x2020;&#x2026;, a&#x2020;aa&#x2020;a&#x2026;, and their equivalent ones always achieve the greatest enhancement of entanglement, while combinations such as aaaa&#x2026;, a&#x2020;a&#x2020;a&#x2020;a&#x2020;&#x2026;, and their equivalent ones always achieve the lowest enhancement of entanglement; when the squeezing parameter is great enough, entanglement enhancement by each combination is nearly the same. We also compare the maximal and minimal entanglement achieved by such combinations for N=1 to 6, and find that when the squeezing parameter is small, a combination of more operations may generate even less entanglement than a combination of fewer operations.
Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation

In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge.
Shape geodesics for robust sign language recognition

In this study, a novel method of pattern recognition for static gesture recognition is proposed. To deal with this application, the authors should tackle the problem of low variability among shape classes in the gestures databases. The authors’ method is based on robust registration and shape geodesics in shape space with a preliminary step of pose estimation accelerating the processing time. The different gestures are considered individual points in a non-linear shape space. Similarity between any two considered shapes can be measured by a distance metric on the shape space. For shape comparison, three distances with and without robust norm are proposed and evaluated for the target application. To cope with low shape variability among classes, the best scheme is to use robust norm in shape matching but not in shape comparison. Experiments are conducted on reference databases used in the literature to evaluate static gesture recognition. These experiments show the outperformance of the proposed scheme compared to state-of-the art methods.
A Questionnaire Model for Cybersecurity Maturity Assessment of Critical Infrastructures

Critical infrastructures are important assets for everyday life and wellbeing of the people. People can be effected dramatically if critical infrastructures are vulnerable and not protected against various threats. Given the increasing cybersecurity risks and the large impact that these risks may bring to the critical infrastructures, assessing and improving the cybersecurity capabilities of the service providers and the administrators is crucial for sustainability. This research aims to provide a questionnaire model for assessing and improving cybersecurity capabilities based on industry standards. Another aim of this research is to provide service providers and the administrators of the critical infrastructures a personalized guidance and an implementation plan for cybersecurity capability improvement.
A robust underwater navigation method fusing data of gravity anomaly and magnetic anomaly

Navigation using geophysical information is often implemented on underwater vehicles to correct the position errors of inertial navigation system. In this paper, we fuse the information of gravity anomaly and magnetic anomaly by developing an improved elitism based multi-objective artificial bee colony algorithm. In the proposed algorithm, multi-objective matching strategy and sequence matching strategy are adopted for the purpose of reducing the possibility of mismatching. Specifically, the mean absolute difference (MAD) of gravity anomaly and the MAD of magnetic anomaly are selected as two matching objective. Single-point matching is changed to sequence matching by introducing affine transformation. To keep diversity without losing too much convergence speed, two elites chosen by different strategies are used to generate new food source. Finally, in order to avoid premature convergence, a new mechanism of scout bee phase is explored. The maximum capacity of external archive is regarded as the monitoring target. Simulations based on the actual gravity anomaly base map and the actual magnetic anomaly base map have been performed for the validation of the proposed algorithm.
Dimensionally sharp inequalities for the linear entropy

We derive an inequality for the linear entropy, that gives sharp bounds for all finite dimensional systems. The derivation is based on generalised Bloch decompositions and provides a strict improvement for the possible distribution of purities for all finite dimensional quantum states. It thus extends the widely used concept of entropy inequalities from the asymptotic to the finite regime, and should also find applications in entanglement detection and efficient experimental characterisations of quantum states.
Isabelle/UTP : Mechanised Theory Engineering for Unifying Theories of Programming

Isabelle/UTP is a mechanised theory engineering toolkit based on Hoare and He’s Unifying Theories of Programming (UTP). UTP enables the creation of denotational, algebraic, and operational semantics for different programming languages using an alphabetised relational calculus. We provide a semantic embedding of the alphabetised relational calculus in Isabelle/HOL, including new type definitions, relational constructors, automated proof tactics, and accompanying algebraic laws. Isabelle/UTP can be used to both capture laws of programming for different languages, and put these fundamental theorems to work in the creation of associated verification tools, using calculi like Hoare logics. This document describes the relational core of the UTP in Isabelle/HOL.
Zero-Correlation Attacks on Tweakable Block Ciphers with Linear Tweakey Expansion

The design and analysis of dedicated tweakable block ciphers is a quite recent and very active research field that provides an ongoing stream of new insights. For instance, results of Kranz, Leander, and Wiemer from FSE 2017 show that the addition of a tweak using a linear tweak schedule does not introduce new linear characteristics. In this paper, we consider – to the best of our knowledge – for the first time the effect of the tweak on zero-correlation linear cryptanalysis for ciphers that have a linear tweak schedule. It turns out that the tweak can often be used to get zero-correlation linear hulls covering more rounds compared to just searching zero-correlation linear hulls on the data-path of a cipher. Moreover, this also implies the existence of integral distinguishers on the same number of rounds. We have applied our technique on round reduced versions of Qarma, Mantis, and Skinny. As a result, we can present – to the best of our knowledge – the best attack (with respect to number of rounds) on a round-reduced variant of Qarma.
Improvement of the Resilience of a Cyber-Physical Remote Diagnostic Communication System against Cyber Attacks

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In the near future, vehicles will operate autonomously and communicate with their environment. This communication includes Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) communication, and comunication with cloud-based servers (V2C). To improve the resilience of remote diagnostic communication between a vehicle and external test equipment against cyberattacks, it is imperative to understand and analyze the functionality and vulnerability of each communication system component, including the wired and wireless communication channels.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This paper serves as a continuation of the SAE Journal publication on measures to prevent unauthorized access to the in-vehicle E/E system [&lt;span class="xref"&gt;9&lt;/span&gt;], explains the components of a cyber-physical system (CPS) for remote diagnostic communication, analyzes their vulnerability against cyberattacks and explains measures to improve the resiliance.&lt;/div&gt;&lt;/div&gt;
Incorporating query constraints for autoencoder enhanced ranking

Learning to rank has been widely used in information retrieval tasks to construct ranking models for document retrieval. Existing learning to rank methods adopt supervised machine learning methods as core techniques and classical retrieval models as document features. The quality of document features can significantly affect the effectiveness of ranking models. Therefore, it is necessary to generate effective document features in ranking to extend the feature space of learning to rank for better modeling the relevance between queries and their corresponding documents. Recently, deep neural network models have been used to generate effective features for various text mining tasks. Autoencoders, as one type of building blocks of neural networks, capture semantic information as effective features based on an encoder-decoder framework. In this paper, we incorporate autoencoders into constructing ranking models based on learning to rank. In our method, autoencoders are used to generate effective documents features for capturing semantic information of documents. We propose a query-level semi-supervised autoencoder by considering three types of query constraints based on Bregman divergence. We evaluate the effectiveness of our model on datasets from LETOR 3.0 and LETOR 4.0, and show that our model significantly outperforms other competing methods to improve retrieval performance.
Variational approach to unsupervised learning

Deep belief networks are used extensively for unsupervised stochastic learning on large datasets. Compared to other deep learning approaches their layer-by-layer learning makes them highly scalable (Hinton 2017 Encyclopedia of Machine Learning and Data Mining 335–338, 10.1007/978-1-4899-7687-1_67 and Bengio 2009 Foundations and Trends® in Machine Learning 2, 1–127). Unfortunately, the principles by which they achieve efficient learning are not well understood. Numerous attempts have been made to explain their efficiency and applicability to a wide class of learning problems in terms of principles drawn from cognitive psychology, statistics, information theory, and more recently physics (Mehta and Schwab [7] arxiv.org/abs/1410.3831), but quite often these imported principles lack strong scientific foundation. Here we demonstrate how one can arrive at convolutional deep belief networks as potential solution to unsupervised learning problems without making assumptions about the underlying framework. To do this, we exploit the notion of symmetry that is fundamental in machine learning, physics and other fields, utilizing the particular form of the functional renormalization group in physics.
Denoising of degraded face images sequence in PCA domain for recognition

In this paper, we propose an efficient algorithm for denoising the degraded face image sequence in the principal component analysis (PCA) domain for recognizing this face. We first apply a temporal filter that performs motion compensation combined with a weighted average filter, then an adaptative spatial filter realized by PCA transformation that decomposes the image temporally filtered into two sub-images using a threshold calculated according to the variance of noise and intensity retained by the first eigenvectors. The first sub-image containing a great intensity variance expresses the small features, and the other with high noise level and low intensity variance describes the large feature. Therefore, the spatial filter can be adapted according to the amount of information in each area, so that small features are reprojected in the Kernel PCA domain where image details are reconstructed efficiently, and large features are denoised by an anisotropic diffusion filter to recover the homogenous regions. Finally, the restored image is used in recognition process. The experimental results obtained from the Cohen-Kanade facial expression (CKFE) database tested against different noise levels and three types of blur (Gaussian, motion and pillbox) show better restoration and recognition performance of this algorithm compared to other methods.
Broadcasting of quantum correlations in qubit-qudit systems

Quantum mechanical properties like entanglement, discord, and coherence act as fundamental resources in various quantum information processing tasks. Consequently, the technique of generating more resources from a few, typically termed as broadcasting, serves as a promising candidate for the design of quantum networks. One way to broadcast resources could be using a cloning operation. In this article, broadcasting of quantum resources beyond $2\ensuremath{\bigotimes}2$ systems is investigated. In particular, in $2\ensuremath{\bigotimes}3$ dimensions, a class of states not useful for broadcasting of entanglement is characterized considering an optimal universal Heisenberg cloning machine. The broadcasting ranges for maximally entangled mixed states and two-parameter class of states are obtained to exemplify our protocol. A significant derivative of our protocol is that the cloning operation generates a qutrit ($3\ensuremath{\bigotimes}3$) entangled pair with positive partial transpose on one of the local sides, and an absolutely separable qubit ($2\ensuremath{\bigotimes}2$) pair on the other side of the input bipartite $2\ensuremath{\bigotimes}3$-dimensional resource state. Moving beyond entanglement, in $2\ensuremath{\bigotimes}d$ dimensions, the impossibility to optimally broadcast quantum discord and quantum coherence (${l}_{1}$ norm) is established. However, some significant illustrations are provided to highlight that nonoptimal broadcasting of discord and coherence is still possible.
Post-editing and legal translation

At UNINT, the courses dedicated to technologies are inspired by the principles of PBL (project-based learning) and experiential learning. Following this approach, in the courses dedicated to assisted and automatic translation the students perform experiments to test some aspects or address problems that are detected through the observation of the translation industry: i.e., the compatibility of screen readers with CATs for blind users, the testing of Adaptive Machine Translation (AMT) systems being developed, the verification of the usefulness of the output of Machine Translation (MT) not only for translators but also for interpreters. This year, during the automatic translation and post-editing laboratory, thanks to the interdisciplinary nature of the courses dealing with translation technologies, a group of students carried out experiments on materials made available by the teacher of active legal translation module. The aim was to verify how effective the automatic translation integrated with the assisted translation from Italian into English was on a determined type of text, using procedures like pre-editing, the creation of ad hoc translation memories based on legacy material and the automatic verification of terminology through the creation of specific glossaries.