pinecone/core-2020-05-10-deduplication huggingface dataset:
Computational Security of Quantum Encryption

Quantum-mechanical devices have the potential to transform cryptography. Most
research in this area has focused either on the information-theoretic
advantages of quantum protocols or on the security of classical cryptographic
schemes against quantum attacks. In this work, we initiate the study of another
relevant topic: the encryption of quantum data in the computational setting.
  In this direction, we establish quantum versions of several fundamental
classical results. First, we develop natural definitions for private-key and
public-key encryption schemes for quantum data. We then define notions of
semantic security and indistinguishability, and, in analogy with the classical
work of Goldwasser and Micali, show that these notions are equivalent. Finally,
we construct secure quantum encryption schemes from basic primitives. In
particular, we show that quantum-secure one-way functions imply IND-CCA1-secure
symmetric-key quantum encryption, and that quantum-secure trapdoor one-way
permutations imply semantically-secure public-key quantum encryption.Comment: 31 pages, 3 figure
Efficient Simulation for Quantum Message Authentication

Quantum message authentication codes are families of keyed encoding and
decoding maps that enable the detection of tampering on encoded quantum data.
Here, we study a new class of simulators for quantum message authentication
schemes, and show how they are applied in the context of two codes: the
Clifford and the trap code. Our results show for the first time that these
codes admit an efficient simulation (assuming that the adversary is efficient).
Such efficient simulation is typically crucial in order to establish a
composable notion of security.Comment: 16 page
Natural Boundary for a Sum Involving Toeplitz Determinants

In the theory of the two-dimensional Ising model, the diagonal susceptibility
is equal to a sum involving Toeplitz determinants. In terms of a parameter k
the diagonal susceptibility is analytic inside the unit circle, and the authors
proved the conjecture that this function has the unit circle as a natural
boundary. The symbol of the Toepltiz determinants was a k-deformation of one
with a single singularity on the unit circle. Here we extend the result, first,
to deformations of a larger class of symbols with a single singularity on the
unit circle, and then to deformations of (almost) general Fisher-Hartwig
symbols.Comment: 17 pages. arXiv admin note: text overlap with arXiv:1307.591
On the Bohr inequality

The Bohr inequality, first introduced by Harald Bohr in 1914, deals with
finding the largest radius $r$, $0<r<1$, such that $\sum_{n=0}^\infty |a_n|r^n
\leq 1$ holds whenever $|\sum_{n=0}^\infty a_nz^n|\leq 1$ in the unit disk
$\mathbb{D}$ of the complex plane. The exact value of this largest radius,
known as the \emph{Bohr radius}, has been established to be $1/3.$ This paper
surveys recent advances and generalizations on the Bohr inequality. It
discusses the Bohr radius for certain power series in $\mathbb{D},$ as well as
for analytic functions from $\mathbb{D}$ into particular domains. These domains
include the punctured unit disk, the exterior of the closed unit disk, and
concave wedge-domains. The analogous Bohr radius is also studied for harmonic
and starlike logharmonic mappings in $\mathbb{D}.$ The Bohr phenomenon which is
described in terms of the Euclidean distance is further investigated using the
spherical chordal metric and the hyperbolic metric. The exposition concludes
with a discussion on the $n$-dimensional Bohr radius
From Preference-Based to Multiobjective Sequential Decision-Making

In this paper, we present a link between preference-based and multiobjective
sequential decision-making. While transforming a multiobjective problem to a
preference-based one is quite natural, the other direction is a bit less
obvious. We present how this transformation (from preference-based to
multiobjective) can be done under the classic condition that preferences over
histories can be represented by additively decomposable utilities and that the
decision criterion to evaluate policies in a state is based on expectation.
This link yields a new source of multiobjective sequential decision-making
problems (i.e., when reward values are unknown) and justifies the use of
solving methods developed in one setting in the other one.Comment: accepted at MIWAI 201
3D point cloud video segmentation based on interaction analysis

Given the widespread availability of point cloud data from consumer depth sensors, 3D segmentation becomes a promising building block for high level applications such as scene understanding and interaction analysis. It benefits from the richer information contained in actual world 3D data compared to apparent (projected) data in 2D images. This also implies that the classical color segmentation challenges have recently shifted to RGBD data, whereas new emerging challenges are added as 3D information from depth measurements is usually noisy, sparse and unorganized. We present a novel segmentation approach for 3D point cloud video based on low level features and oriented to the analysis of object interactions. A hierarchical representation of the input point cloud is proposed to efficiently segment 3D data at the finer level, and to temporally establish the correspondence between segments, while dynamically managing the object split and merge at the coarser level. Experiments illustrate promising results and its potential application in object interaction analysis.Postprint (author's final draft
Human Pose Estimation in Space and Time using 3D CNN

This paper explores the capabilities of convolutional neural networks to deal
with a task that is easily manageable for humans: perceiving 3D pose of a human
body from varying angles. However, in our approach, we are restricted to using
a monocular vision system. For this purpose, we apply a convolutional neural
network approach on RGB videos and extend it to three dimensional convolutions.
This is done via encoding the time dimension in videos as the 3\ts{rd}
dimension in convolutional space, and directly regressing to human body joint
positions in 3D coordinate space. This research shows the ability of such a
network to achieve state-of-the-art performance on the selected Human3.6M
dataset, thus demonstrating the possibility of successfully representing
temporal data with an additional dimension in the convolutional operation.Comment: Accepted at ECCV 2016 Workshop on: Brave new ideas for motion
  representations in video
Direct-from-Video: Unsupervised NRSfM

In this work we describe a novel approach to online dense non-rigid structure from motion. The problem is reformulated, incorporating ideas from visual object tracking, to provide a more general and unified technique, with feedback between the reconstruction and point-tracking algorithms. The resulting algorithm overcomes the limitations of many conventional techniques, such as the need for a reference image/template or precomputed trajectories. The technique can also be applied in traditionally challenging scenarios, such as modelling objects with strong self-occlusions or from an extreme range of viewpoints. The proposed algorithm needs no offline pre-learning and does not assume the modelled object stays rigid at the beginning of the video sequence. Our experiments show that in traditional scenarios, the proposed method can achieve better accuracy than the current state of the art while using less supervision. Additionally we perform reconstructions in challenging new scenarios where state-of-the-art approaches break down and where our method improves performance by up to an order of magnitude
3D point cloud video segmentation based on interaction analysis

Given the widespread availability of point cloud data from consumer depth sensors, 3D segmentation becomes a promising building block for high level applications such as scene understanding and interaction analysis. It benefits from the richer information contained in actual world 3D data compared to apparent (projected) data in 2D images. This also implies that the classical color segmentation challenges have recently shifted to RGBD data, whereas new emerging challenges are added as 3D information from depth measurements is usually noisy, sparse and unorganized. We present a novel segmentation approach for 3D point cloud video based on low level features and oriented to the analysis of object interactions. A hierarchical representation of the input point cloud is proposed to efficiently segment 3D data at the finer level, and to temporally establish the correspondence between segments, while dynamically managing the object split and merge at the coarser level. Experiments illustrate promising results and its potential application in object interaction analysis.Peer ReviewedPostprint (author's final draft
Learning Markerless Human Pose Estimation from Multiple Viewpoint Video

We present a novel human performance capture technique capable of robustly estimating the pose (articulated joint positions) of a performer observed passively via multiple view-point video (MVV). An affine invariant pose descriptor is learned using a convolutional neural network (CNN) trained over volumetric data extracted from a MVV dataset of diverse human pose and appearance. A manifold embedding is learned via Gaussian Processes for the CNN descriptor and articulated pose spaces enabling regression and so estimation of human pose from MVV input. The learned descriptor and manifold are shown to generalise over a wide range of human poses, providing an efficient performance capture solution that requires no fiducials or other markers to be worn. The system is evaluated against ground truth joint configuration data from a commercial marker-based pose estimation syste
gvnn: neural network library for geometric computer vision

We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of Spatial Transformer Networks, we propose several new layers which are often used as parametric transformations on the data in geometric computer vision. These layers can be inserted within a neural network much in the spirit of the original spatial transformers and allow backpropagation to enable end-to-end learning of a network involving any domain knowledge in geometric computer vision. This opens up applications in learning invariance to 3D geometric transformation for place recognition, end-to-end visual odometry, depth estimation and unsupervised learning through warping with a parametric transformation for image reconstruction error
Inseparability and Conservative Extensions of Description Logic Ontologies: A Survey

The question whether an ontology can safely be replaced by another, possibly simpler, one is fundamental for many ontology engineering and maintenance tasks. It underpins, for example, ontology versioning, ontology modularization, forgetting, and knowledge exchange. What safe replacement means depends on the intended application of the ontology. If, for example, it is used to query data, then the answers to any relevant ontology-mediated query should be the same over any relevant data set; if, in contrast, the ontology is used for conceptual reasoning, then the entailed subsumptions between concept expressions should coincide. This gives rise to different notions of ontology inseparability such as query inseparability and concept inseparability, which generalize corresponding notions of conservative extensions. We survey results on various notions of inseparability in the context of description logic ontologies, discussing their applications, useful model-theoretic characterizations, algorithms for determining whether two ontologies are inseparable (and, sometimes, for computing the difference between them if they are not), and the computational complexity of this problem
Reflected Brownian motions in the KPZ universality class

This book presents a detailed study of a system of interacting Brownian
motions in one dimension. The interaction is point-like such that the $n$-th
Brownian motion is reflected from the Brownian motion with label $n-1$. This
model belongs to the Kardar-Parisi-Zhang (KPZ) universality class. In fact,
because of the singular interaction, many universal properties can be
established with rigor. They depend on the choice of initial conditions.
Discussion addresses packed and periodic initial conditions, stationary initial
conditions, and mixtures thereof. The suitably scaled spatial process will be
proven to converge to an Airy process in the long time limit. A chapter on
determinantal random fields and another one on Airy processes are added to have
the notes self-contained. This book serves as an introduction to the KPZ
universality class, illustrating the main concepts by means of a single model
only. It will be of interest to readers from interacting diffusion processes
and non-equilibrium statistical mechanics.Comment: arXiv admin note: text overlap with arXiv:1502.0146
On nonlocal models of Kulish-Sklyanin type and generalized Fourier
  transforms

A special class of multicomponent NLS equations, generalizing the vector NLS
and related to the {\bf BD.I}-type symmetric are shown to be integrable through
the inverse scattering method (ISM). The corresponding fundamental analytic
solutions are constructing thus reducing the inverse scattering problem to a
Riemann-Hilbert problem. We introduce the minimal sets of scattering data
$\mathfrak{T}$ which determines uniquely the scattering matrix and the
potential $Q$ of the Lax operator. The elements of $\mathfrak{T}$ can be viewed
as the expansion coefficients of $Q$ over the `squared solutions' that are
natural generalizations of the standard exponentials. Thus we demonstrate that
the mapping $\mathfrak{T} \to Q$ is a generalized Fourier transform. Special
attention is paid to two special representatives of this MNLS with
three-component and five components which describe spinor ($F=1$ and $F=2$,
respectively) Bose-Einstein condensates.Comment: 16 page
Scaling DBSCAN-like algorithms for event detection systems in Twitter

The increasing use of mobile social networks has lately transformed news media. Real-world events are nowadays reported in social networks much faster than in traditional channels. As a result, the autonomous detection of events from networks like Twitter has gained lot of interest in both research and media groups. DBSCAN-like algorithms constitute a well-known clustering approach to retrospective event detection. However, scaling such algorithms to geographically large regions and temporarily long periods present two major shortcomings. First, detecting real-world events from the vast amount of tweets cannot be performed anymore in a single machine. Second, the tweeting activity varies a lot within these broad space-time regions limiting the use of global parameters. Against this background, we propose to scale DBSCAN-like event detection techniques by parallelizing and distributing them through a novel density-aware MapReduce scheme. The proposed scheme partitions tweet data as per its spatial and temporal features and tailors local DBSCAN parameters to local tweet densities. We implement the scheme in Apache Spark and evaluate its performance in a dataset composed of geo-located tweets in the Iberian peninsula during the course of several football matches. The results pointed out to the benefits of our proposal against other state-of-the-art techniques in terms of speed-up and detection accuracy.Peer ReviewedPostprint (author's final draft
Modeling performance of Hadoop applications: A journey from queueing networks to stochastic well formed nets

Nowadays, many enterprises commit to the extraction of actionable knowledge from huge datasets as part of their core business activities. Applications belong to very different domains such as fraud detection or one-to-one marketing, and encompass business analytics and support to decision making in both private and public sectors. In these scenarios, a central place is held by the MapReduce framework and in particular its open source implementation, Apache Hadoop. In such environments, new challenges arise in the area of jobs performance prediction, with the needs to provide Service Level Agreement guarantees to the enduser and to avoid waste of computational resources. In this paper we provide performance analysis models to estimate MapReduce job execution times in Hadoop clusters governed by the YARN Capacity Scheduler. We propose models of increasing complexity and accuracy, ranging from queueing networks to stochastic well formed nets, able to estimate job performance under a number of scenarios of interest, including also unreliable resources. The accuracy of our models is evaluated by considering the TPC-DS industry benchmark running experiments on Amazon EC2 and the CINECA Italian supercomputing center. The results have shown that the average accuracy we can achieve is in the range 9–14%
D-SPACE4Cloud: A Design Tool for Big Data Applications

The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools or techniques to support the design of the underlying infrastructure configuration backing such systems. In particular, the focus in this paper is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying Quality of Service constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method
Waste Biomass Management - A Holistic Approach

This book gives an overview on techniques and future perspectives of various aspects of waste biomass management. It also presents the economic and environmental evaluation, and also the monetary value-benefits and sustainability of the different processes. Recycling processes of lignocellulosic biomass from palm oil mill waste are covered, as well as from sugar industry waste and agriculture waste. It also includes thermal and non-thermal technologies for resource recovery from waste biomass. Challenges in the reuse and recycling of waste biomass are discussed, i.e., the hygienic safety in biomass management and bioremediation technologies for conversion into valuable products. The book is aiming at scientists, researchers and students alike, who are working in the research areas pertaining to waste management
Student Choice: Blends of Technology beyond the University to support social interaction and social participation in learning.

M. Doolan and Gilbert, T., 'Student Choice: Blends of Technology beyond the University to support social interaction and social participation in learning' , Chapter in E-Learning, E-Education, and Online Training, Third International Conference, eLEOT 2016, Dublin, Ireland, August 31 ??? September 2, 2016, Revised Selected Papers Publisher: Springer International Publishing, the final publication is available at Springer via doi: http://dx.doi.org/10.1007/978-3-319-49625-2_12 Print ISBN: 978-3-319-49624-5 Electronic ISBN: 978-3-319-49625-2 ?? ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017This paper presents an overview of a blended collaborative learning design driven by assessment and feedback. To extend class based activities students were provided with a private group space on the university managed learning environment. They chose to supplement this using technologies beyond those provided by the University to support their social interactions and participate in their learning. Qualitative data analysis of student???s reflections provides insights into the students own blends of technology including Skype, What???s App, Facebook amongst others and accessed via their hand held mobile devices such as Smartphones and laptops. These were used by the students to connect and collaborate with their peer group to complete the set tasks throughout the module and the final group based assessment
Variance Reduction Result for a Projected Adaptive Biasing Force Method

This paper is committed to investigate an extension of the classical adaptive
biasing force method, which is used to compute the free energy related to the
Boltzmann-Gibbs measure and a reaction coordinate function. The issue of this
technique is that the approximated gradient of the free energy, called biasing
force, is not a gradient. The commitment to this field is to project the
estimated biasing force on a gradient using the Helmholtz decomposition. The
variance of the biasing force is reduced using this technique, which makes the
algorithm more efficient than the standard ABF method. We prove exponential
convergence to equilibrium of the estimated free energy, with a precise rate of
convergence in function of Logarithmic Sobolev inequality constants
From wellness to medical diagnostic apps: the Parkinson's Disease case

This paper presents the design and development of the CloudUPDRS app and supporting system developed as a Class I medical device to assess the severity of motor symptoms for Parkinson’s Disease. We report on lessons learnt towards meeting fidelity and regulatory requirements; effective procedures employed to structure user context and ensure data quality; a robust service provision architecture; a dependable analytics toolkit; and provisions to meet mobility and social needs of people with Parkinson’s
A Transparent home Sensors/Actuators layer for Health & Well-being services

International audienceRisks factor leading to type 2 diabetes and cardiovascular diseases can be reduced by a preventive care infrastructure able to modify the habits toward healthier lifestyles, including more exercise, a better diet and a reduced stress. Accessing the home space is important not only to monitor indoor environment quality (IEQ) but also to provide relevant user's feedback and engage users towards a healthy life style. This paper present a new preventive care infrastructure composed of a distributed infrastructure for heterogeneous devices in the home local area network (named xAAL) in conjunction with a machine to machine protocol (MQTT) to external health & well-being services
Towards longitudinal data analytics in Parkinson's Disease

The CloudUPDRS app has been developed as a Class I med- ical device to assess the severity of motor symptoms for Parkinson’s Disease using a fully automated data capture and signal analysis pro- cess based on the standard Unified Parkinson’s Disease Rating Scale. In this paper we report on the design and development of the signal pro- cessing and longitudinal data analytics microservices developed to carry out these assessments and to forecast the long-term development of the disease. We also report on early findings from the application of these techniques in the wild with a cohort of early adopters