pinecone/core-2020-05-10-deduplication huggingface dataset:
How to combine visual features with tags to improve movie recommendation accuracy?

Previous works have shown the effectiveness of using stylistic visual features, indicative of the movie style, in content-based movie recommendation. However, they have mainly focused on a particular recommendation scenario, i.e., when a new movie is added to the catalogue and no information is available for that movie (New Item scenario). However, the stylistic visual features can be also used when other sources of information is available (Existing Item scenario). In this work, we address the second scenario and propose a hybrid technique that exploits not only the typical content available for the movies (e.g., tags), but also the stylistic visual content extracted form the movie files and fuse them by applying a fusion method called Canonical Correlation Analysis (CCA). Our experiments on a large catalogue of 13K movies have shown very promising results which indicates a considerable improvement of the recommendation quality by using a proper fusion of the stylistic visual features with other type of features
Merging relations: a way to compact Petri nets' behaviors uniformly

Compacting Petri nets behaviors means to develop a more succinct representation of all the possible executions of a net, still giving the capability to reason on properties fulfilled by the computations of the net. To do so suitable equivalences on alternative executions have to be engineered. We introduce a general notion of merging relation covering the existing approaches to compact behaviors and we discuss how to enforce that the more succinct net is an unravel net, namely a net where dependencies can be identified (almost) syntactically
A derivational model of discontinuous parsing

The notion of latent-variable probabilistic context-free derivation of syntactic structures is enhanced to allow heads and unrestricted discontinuities. The chosen formalization covers both constituent parsing and dependency parsing. The derivational model is accompanied by an equivalent probabilistic automaton model. By the new framework, one obtains a probability distribution over the space of all discontinuous parses. This lends itself to intrinsic evaluation in terms of perplexity, as shown in experiments.Postprin
On the computational power of affine automata

We investigate the computational power of affine automata (AfAs) introduced
in [4]. In particular, we present a simpler proof for how to change the
cutpoint for any affine language and a method how to reduce error in bounded
error case. Moreover, we address to the question of [4] by showing that any
affine language can be recognized by an AfA with certain limitation on the
entries of affine states and transition matrices. Lastly, we present the first
languages shown to be not recognized by AfAs with bounded-error.Comment: 12 pages. Accepted to LATA201
Energy Efficient Adaptive Network Coding Schemes for Satellite
  Communications

In this paper, we propose novel energy efficient adaptive network coding and
modulation schemes for time variant channels. We evaluate such schemes under a
realistic channel model for open area environments and Geostationary Earth
Orbit (GEO) satellites. Compared to non-adaptive network coding and adaptive
rate efficient network-coded schemes for time variant channels, we show that
our proposed schemes, through physical layer awareness can be designed to
transmit only if a target quality of service (QoS) is achieved. As a result,
such schemes can provide remarkable energy savings.Comment: Lecture Notes of the Institute for Computer Sciences, Social
  Informatics and Telecommunications Engineering, 24 March 201
Ab initio Description of Optoelectronic Properties at Defective
  Interfaces in Solar Cells

In order to optimize the optoelectronic properties of novel solar cell
architectures, such as the amorphous-crystalline interface in silicon
heterojunction devices, we calculate and analyze the local microscopic
structure at this interface and in bulk a-Si:H, in particular with respect to
the impact of material inhomogeneities. The microscopic information is used to
extract macroscopic material properties, and to identify localized defect
states, which govern the recombination properties encoded in quantities such as
capture cross sections used in the Shockley-Read-Hall theory. To this end,
atomic configurations for a-Si:H and a-Si:H/c-Si interfaces are generated using
molecular dynamics. Density functional theory calculations are then applied to
these configurations in order to obtain the electronic wave functions. These
are analyzed and characterized with respect to their localization and their
contribution to the (local) density of states. GW calculations are performed
for the a-Si:H configuration in order to obtain a quasi-particle corrected
absorption spectrum. The results suggest that the quasi-particle corrections
can be approximated through a scissors shift of the Kohn-Sham energies.Comment: 14 pages, 10 figures, to appear in Springer Lecture Notes in Computer
  Science, Proceedings of the JHPCS16 conferenc
Parallel adaptive integration in high-performance functional Renormalization Group computations

The conceptual framework provided by the functional Renormalization Group (fRG) has become a formidable tool to study correlated electron systems on lattices which, in turn, provided great insights to our understanding of complex many-body phenomena, such as high-temperature superconductivity or topological states of matter. In this work we present one of the latest realizations of fRG which makes use of an adaptive numerical quadrature scheme specifically tailored to the described fRG scheme. The final result is an increase in performance thanks to improved parallelism and scalability
Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods

In this paper we focus on the integration of high-performance numerical libraries in ab initio codes and the portability of performance and scalability. The target of our work is FLEUR, a software for electronic structure calculations developed in the Forschungszentrum J\'ulich over the course of two decades. The presented work follows up on a previous effort to modernize legacy code by re-engineering and rewriting it in terms of highly optimized libraries. We illustrate how this initial effort to get efficient and portable shared-memory code enables fast porting of the code to emerging heterogeneous architectures. More specifically, we port the code to nodes equipped with multiple GPUs. We divide our study in two parts. First, we show considerable speedups attained by minor and relatively straightforward code changes to off-load parts of the computation to the GPUs. Then, we identify further possible improvements to achieve even higher performance and scalability. On a system consisting of 16-cores and 2 GPUs, we observe speedups of up to 5x with respect to our optimized shared-memory code, which in turn means between 7.5x and 12.5x speedup with respect to the original FLEUR code
Budgeting practices in European universities

This chapter aims to provide empirical evidence on budgeting
practices in European universities through a cross-country universities
analysis. We investigate what is the diffusion of given processes, allocation criteria and what actors have more the infuence on budgeting, to
what extent are budgeting practices related to the level of competition
for funding and whether budgeting models with distinct practices can
be identifed. To this aim, we exploit evidence from the TRUE survey,
which allows for the frst time a systematic quantitative comparison of
budgeting in European universities. The analysis reveals the complexity
and multiplicity of budgeting practices, which contrasts with the takenfor-granted assumption that NPM reforms are leading to convergence
towards a managerial model of resource allocation within universities
Recognizing Simple-Triangle Graphs by Restricted 2-Chain Subgraph Cover

A simple-triangle graph (also known as a PI graph) is the intersection graph
of a family of triangles defined by a point on a horizontal line and an
interval on another horizontal line. The recognition problem for
simple-triangle graphs was a longstanding open problem, and recently a
polynomial-time algorithm has been given [G. B. Mertzios, The Recognition of
Simple-Triangle Graphs and of Linear-Interval Orders is Polynomial, SIAM J.
Discrete Math., 29(3):1150--1185, 2015]. Along with the approach of this paper,
we show a simpler recognition algorithm for simple-triangle graphs. To do this,
we provide a polynomial-time algorithm to solve the following problem: Given a
bipartite graph $G$ and a set $F$ of edges of $G$, find a 2-chain subgraph
cover of $G$ such that one of two chain subgraphs has no edges in $F$.Comment: 13 pages, 14 figures, the Author's accepted version of a paper in
  WALCOM 2017, Keywords: Chain cover, Graph sandwich problem, PI graphs,
  Simple-triangle graphs, Threshold dimension 2 graph
The c and a-theorems and the Local Renormalisation Group

The Zamolodchikov c-theorem has led to important new insights in our
understanding of the renormalisation group and the geometry of the space of
QFTs. Here, we review the parallel developments of the search for a
higher-dimensional generalisation of the c-theorem and of the Local
Renormalisation Group.
  The idea of renormalisation with position-dependent couplings, running under
local Weyl scaling, is traced from its early realisations to the elegant modern
formalism of the local renormalisation group. The key role of the associated
Weyl consistency conditions in establishing RG flow equations for the
coefficients of the trace anomaly in curved spacetime, and their relation to
the c-theorem and four-dimensional a-theorem, is explained in detail.
  A number of different derivations of the c-theorem in two dimensions are
presented -- using spectral functions, RG analysis of Green functions of the
energy-momentum tensor T_{mu nu}, and dispersion relations -- and are
generalised to four dimensions. The obstruction to establishing monotonic
C-functions related to the beta_c and beta_b trace anomaly coefficients in four
dimensions is discussed. The possibility of deriving an a-theorem, involving
the coefficient beta_a of the Euler-Gauss-Bonnet density in the trace anomaly,
is explored initially by formulating the QFT on maximally symmetric spaces.
Then the formulation of the weak a-theorem using a dispersion relation for
four-point functions of T^mu_mu is presented.
  Finally, we describe the application of the local renormalisation group to
the issue of limit cycles in theories with a global symmetry and it is shown
how this sheds new light on the geometry of the space of couplings in QFT.Comment: Revised and extended version, published in SpringerBriefs in Physics.
  89 pages, 5 figure
Low-effort place recognition with WiFi fingerprints using deep learning

Using WiFi signals for indoor localization is the main localization modality
of the existing personal indoor localization systems operating on mobile
devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals
are usually available indoors and can provide rough initial position estimate
or can be used together with other positioning systems. Currently, the best
solutions rely on filtering, manual data analysis, and time-consuming parameter
tuning to achieve reliable and accurate localization. In this work, we propose
to use deep neural networks to significantly lower the work-force burden of the
localization system design, while still achieving satisfactory results.
Assuming the state-of-the-art hierarchical approach, we employ the DNN system
for building/floor classification. We show that stacked autoencoders allow to
efficiently reduce the feature space in order to achieve robust and precise
classification. The proposed architecture is verified on the publicly available
UJIIndoorLoc dataset and the results are compared with other solutions
Adopting the FAB-MAP algorithm for indoor localization with WiFi
  fingerprints

Personal indoor localization is usually accomplished by fusing information
from various sensors. A common choice is to use the WiFi adapter that provides
information about Access Points that can be found in the vicinity.
Unfortunately, state-of-the-art approaches to WiFi-based localization often
employ very dense maps of the WiFi signal distribution, and require a
time-consuming process of parameter selection. On the other hand, camera images
are commonly used for visual place recognition, detecting whenever the user
observes a scene similar to the one already recorded in a database. Visual
place recognition algorithms can work with sparse databases of recorded scenes
and are in general simple to parametrize. Therefore, we propose a WiFi-based
global localization method employing the structure of the well-known FAB-MAP
visual place recognition algorithm. Similarly to FAB-MAP our method uses
Chow-Liu trees to estimate a joint probability distribution of re-observation
of a place given a set of features extracted at places visited so far. However,
we are the first who apply this idea to recorded WiFi scans instead of visual
words. The new method is evaluated on the UJIIndoorLoc dataset used in the
EvAAL competition, allowing fair comparison with other solutions
Bayesian inference for continuous time animal movement based on steps
  and turns

Although animal locations gained via GPS, etc. are typically observed on a
discrete time scale, movement models formulated in continuous time are
preferable in order to avoid the struggles experienced in discrete time when
faced with irregular observations or the prospect of comparing analyses on
different time scales. A class of models able to emulate a range of movement
ideas are defined by representing movement as a combination of stochastic
processes describing both speed and bearing. A method for Bayesian inference
for such models is described through the use of a Markov chain Monte Carlo
approach. Such inference relies on an augmentation of the animal's locations in
discrete time that have been observed with error, with a more detailed movement
path gained via simulation techniques. Analysis on real data on an individual
reindeer (Rangifer tarandus) illustrates the presented methods.Comment: 8 pages, 2 figures, BAYSM 201
Weighted stress function method for multiobjective evolutionary algorithm based on decomposition

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a well established state-of-the-art framework. Major concerns that must be addressed when applying MOEA/D are the choice of an appropriate scalarizing function and setting the values of main control parameters. This study suggests a weighted stress function method (WSFM) for fitness assignment in MOEA/D. WSFM establishes analogy between the stress-strain behavior of thermoplastic vulcanizates and scalarization of a multiobjective optimization problem. The experimental results suggest that the proposed approach is able to provide a faster convergence and a better performance of final approximation sets with respect to quality indicators when compared with traditional methods. The validity of the proposed approach is also demonstrated on engineering problems.This work has been supported by FCT - Fundação para a Ciência e Tecnologia in the scope of the project: PEst-OE/EEI/UI0319/2014.info:eu-repo/semantics/publishedVersio
Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results

EMO 2017: 9th International Conference on Evolutionary Multi-Criterion Optimization, 19-22 March 2017, Münster, GermanyThis is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.Multi-objective optimization with more than three objectives has become one of the most active topics in evolutionary multi-objective optimization (EMO). However, most existing studies limit their experiments up to 15 or 20 objectives, although they claimed to be capable of handling as many objectives as possible. To broaden the insights in the behavior of EMO methods when facing a massively large number of objectives, this paper presents some preliminary empirical investigations on several established scalable benchmark problems with 25, 50, 75 and 100 objectives. In particular, this paper focuses on the behavior of the currently pervasive reference point based EMO methods, although other methods can also be used. The experimental results demonstrate that the reference point based EMO method can be viable for problems with a massively large number of objectives, given an appropriate choice of the distance measure. In addition, sufficient population diversity should be given on each weight vector or a local niche, in order to provide enough selection pressure. To the best of our knowledge, this is the first time an EMO methodology has been considered to solve a massively large number of conflicting objectives.This work was partially supported by EPSRC (Grant No. EP/J017515/1
Geodesic Distance Histogram Feature for Video Segmentation

This paper proposes a geodesic-distance-based feature that encodes global
information for improved video segmentation algorithms. The feature is a joint
histogram of intensity and geodesic distances, where the geodesic distances are
computed as the shortest paths between superpixels via their boundaries. We
also incorporate adaptive voting weights and spatial pyramid configurations to
include spatial information into the geodesic histogram feature and show that
this further improves results. The feature is generic and can be used as part
of various algorithms. In experiments, we test the geodesic histogram feature
by incorporating it into two existing video segmentation frameworks. This leads
to significantly better performance in 3D video segmentation benchmarks on two
datasets
Recovering pose and 3D deformable shape from multi-instance image ensembles

The final publication is available at link.springer.comIn recent years, there has been a growing interest on tackling the Non-Rigid Structure from Motion problem (NRSfM), where the shape of a deformable object and the pose of a moving camera are simultaneously estimated from a monocular video sequence. Existing solutions are limited to single objects and continuous, smoothly changing sequences. In this paper we extend NRSfM to a multi-instance domain, in which the images do not need to have temporal consistency, allowing for instance, to jointly reconstruct the face of multiple persons from an unordered list of images. For this purpose, we present a new formulation of the problem based on a dual low-rank shape representation, that simultaneously captures the between- and within-individual deformations. The parameters of this model are learned using a variant of the probabilistic linear discriminant analysis that requires consecutive batches of expectation and maximization steps. The resulting approach estimates 3D deformable shape and pose of multiple instances from only 2D point observations on a collection images, without requiring pre-trained 3D data, and is shown to be robust to noisy measurements and missing points. We provide quantitative and qualitative evaluation on both synthetic and real data, and show consistent benefits compared to current state of the art.Peer ReviewedPostprint (author's final draft
Bilinear discriminant analysis hashing: A supervised hashing approach for high-dimensional data

High-dimensional descriptors have been widely used in object recognition and image classification. How to quickly index high-dimensional data into binary codes is a challenging task which has attracted the attention of many researchers. Most existing hashing solutions for high-dimensional dataests are based on unsupervised schemes. On the other hand, existing supervised hashing methods cannot work well on high-dimensional datasets, as they consume too much time and memory to index high-dimensional data. In this paper, we propose a supervised hashing method Bilinear Discriminant Analysis Hashing (BDAH) to solve this problem. BDAH leverages supervised information according to the idea of Linear Discriminant Analysis (LDA), but adopts bilinear projection method. Bilinear projection needs two small matrices rather than one big matrix to project data so that the coding time and memory consumption are drastically reduced. We validate the proposed method on three datasets, and compare it to several state-of-the-art hashing schemes. The results show that our method can achieve comparable accuracy to the state-of-the-art supervised hashing schemes, while, however, cost much less time and memory. What’s more, our method outperforms unsupervised hashing methods in accuracy while achieving comparable time and memory consumption.Full Tex
Task-driven programming pedagogy in the digital humanities

© Springer International Publishing AG 2017. All rights reserved. In this chapter, we advocate for a task-driven approach to teaching computer programming to students of the digital humanities (DH). Our perspective is grounded first in Birnbaum's (2014) plenary address to the University of Pittsburgh Faculty Senate (Birnbaum 2014), in which he argued that coding, like writing, should be taught across the liberal arts curriculum in domain-appropriate ways. This position argued that (1) coding is not an esoteric specialization to be taught solely by computer scientists, and that (2) coding might be taught most effectively in the context of different disciplines. Here, we present a method for embedding Digital Humanities education, and more specifically programming pedagogy, within the long-standing traditions of the Humanities and argue that this approach works most effectively when new learners have access to context-specific mentorship. Our second point of reference lies with oral-proficiency-oriented (OP) foreign language pedagogy. Within an OP model, the ability to communicate in a foreign language is a skill, and the primary goal for learners who seek to acquire that skill is not an academic understanding of the grammar of a language, but, instead, the ability to function successfully within realistic contextualized human interactions. Seen from this perspective, computer-programming curricula organized around the features of the programming language might be compared to older grammar-and-translation foreign-language pedagogies. What we advocate instead is that the ability to use a programming language (programming proficiency) is best acquired in the context of performing contextualized, discipline-conscious tasks that are meaningful to humanists, an approach that has parallels to OP language learning
A neural network approach to human posture classification and fall detection using RGB-D camera

In this paper, we describe a human posture classification and a falling detector module suitable for smart homes and assisted living solutions. The system uses a neural network that processes the human joints produced by a skeleton tracker using the depth streams of an RGB-D sensor. The neural network is able to recognize standing, sitting and lying postures. Using only the depth maps from the sensor, the system can work in poor light conditions and guarantees the privacy of the person. The neural network is trained with a dataset produced with the Kinect tracker, but it is also tested with a different human tracker (NiTE). In particular, the aim of this work is to analyse the behaviour of the neural network even when the position of the extracted joints is not reliable and the provided skeleton is confused. Real-time tests have been carried out covering the whole operative range of the sensor (up to 3.5 m). Experimental results have shown an overall accuracy of 98.3% using the NiTE tracker for the falling tests, with the worst accuracy of 97.5%