pinecone/core-2020-05-10-deduplication huggingface dataset:
The Odyssey Approach for Optimizing Federated SPARQL Queries

Answering queries over a federation of SPARQL endpoints requires combining
data from more than one data source. Optimizing queries in such scenarios is
particularly challenging not only because of (i) the large variety of possible
query execution plans that correctly answer the query but also because (ii)
there is only limited access to statistics about schema and instance data of
remote sources. To overcome these challenges, most federated query engines rely
on heuristics to reduce the space of possible query execution plans or on
dynamic programming strategies to produce optimal plans. Nevertheless, these
plans may still exhibit a high number of intermediate results or high execution
times because of heuristics and inaccurate cost estimations. In this paper, we
present Odyssey, an approach that uses statistics that allow for a more
accurate cost estimation for federated queries and therefore enables Odyssey to
produce better query execution plans. Our experimental results show that
Odyssey produces query execution plans that are better in terms of data
transfer and execution time than state-of-the-art optimizers. Our experiments
using the FedBench benchmark show execution time gains of at least 25 times on
average.Comment: 16 pages, 10 figure
A Computer Vision System to Localize and Classify Wastes on the Streets

Littering quantification is an important step for improving cleanliness of
cities. When human interpretation is too cumbersome or in some cases
impossible, an objective index of cleanliness could reduce the littering by
awareness actions. In this paper, we present a fully automated computer vision
application for littering quantification based on images taken from the streets
and sidewalks. We have employed a deep learning based framework to localize and
classify different types of wastes. Since there was no waste dataset available,
we built our acquisition system mounted on a vehicle. Collected images
containing different types of wastes. These images are then annotated for
training and benchmarking the developed system. Our results on real case
scenarios show accurate detection of littering on variant backgrounds
Diffeomorphic random sampling using optimal information transport

In this article we explore an algorithm for diffeomorphic random sampling of
nonuniform probability distributions on Riemannian manifolds. The algorithm is
based on optimal information transport (OIT)---an analogue of optimal mass
transport (OMT). Our framework uses the deep geometric connections between the
Fisher-Rao metric on the space of probability densities and the right-invariant
information metric on the group of diffeomorphisms. The resulting sampling
algorithm is a promising alternative to OMT, in particular as our formulation
is semi-explicit, free of the nonlinear Monge--Ampere equation. Compared to
Markov Chain Monte Carlo methods, we expect our algorithm to stand up well when
a large number of samples from a low dimensional nonuniform distribution is
needed.Comment: 8 pages, 3 figure
Amari Functors and Dynamics in Gauge Structures

We deal with finite dimensional differentiable manifolds. All items are
concerned with are differentiable as well. The class of differentiability is
$C^\infty$. A metric structure in a vector bundle $E$ is a constant rank
symmetric bilinear vector bundle homomorphism of $E\times E$ in the trivial
bundle line bundle. We address the question whether a given gauge structure in
$E$ is metric. That is the main concerns. We use generalized Amari functors of
the information geometry for introducing two index functions defined in the
moduli space of gauge structures in $E$. Beside we introduce a differential
equation whose analysis allows to link the new index functions just mentioned
with the main concerns. We sketch applications in the differential geometry
theory of statistics. Reader interested in a former forum on the question
whether a giving connection is metric are referred to appendix.Comment: to appear in Springer LNCS 10589 (GSI2017
Pairwise Ising model analysis of human cortical neuron recordings

During wakefulness and deep sleep brain states, cortical neural networks show
a different behavior, with the second characterized by transients of high
network activity. To investigate their impact on neuronal behavior, we apply a
pairwise Ising model analysis by inferring the maximum entropy model that
reproduces single and pairwise moments of the neuron's spiking activity. In
this work we first review the inference algorithm introduced in Ferrari,Phys.
Rev. E (2016). We then succeed in applying the algorithm to infer the model
from a large ensemble of neurons recorded by multi-electrode array in human
temporal cortex. We compare the Ising model performance in capturing the
statistical properties of the network activity during wakefulness and deep
sleep. For the latter, the pairwise model misses relevant transients of high
network activity, suggesting that additional constraints are necessary to
accurately model the data.Comment: 8 pages, 3 figures, Geometric Science of Information 2017 conferenc
Particle Observers for Contracting Dynamical Systems

In the present paper we consider a class of partially observed dynamical systems. As in the Rao-Blackwellized particle filter (RBPF) paradigm (see e.g., Doucet et al. 2000), we assume the state x can be broken into two sets of variables   x=(z,r)  and has the property that conditionally on z the system’s dynamics possess geometrical contraction properties, or is amenable to such a system by using a nonlinear observer whose dynamics possess contraction properties. Inspired by the RBPF we propose to use particles to approximate the r variable and to use a simple copy of the dynamics (or an observer) to estimate the rest of the state. This has the benefits of 1- reducing the computational burden (a particle filter would sample the variable x also), which is akin to the interest of the RBPF, 2- coming with some indication of stability stemming from contraction (actual proofs of stability seem difficult), and 3- the obtained filter is well suited to systems where the dynamics of x conditionally on z is precisely known and the dynamics governing the evolution of z is quite uncertain
Parallel transport in shape analysis: a scalable numerical scheme

The analysis of manifold-valued data requires efficient tools from Riemannian
geometry to cope with the computational complexity at stake. This complexity
arises from the always-increasing dimension of the data, and the absence of
closed-form expressions to basic operations such as the Riemannian logarithm.
In this paper, we adapt a generic numerical scheme recently introduced for
computing parallel transport along geodesics in a Riemannian manifold to
finite-dimensional manifolds of diffeomorphisms. We provide a qualitative and
quantitative analysis of its behavior on high-dimensional manifolds, and
investigate an application with the prediction of brain structures progression
Hamilton-Jacobi Theory and Information Geometry

Recently, a method to dynamically define a divergence function $D$ for a
given statistical manifold $(\mathcal{M}\,,g\,,T)$ by means of the
Hamilton-Jacobi theory associated with a suitable Lagrangian function
$\mathfrak{L}$ on $T\mathcal{M}$ has been proposed. Here we will review this
construction and lay the basis for an inverse problem where we assume the
divergence function $D$ to be known and we look for a Lagrangian function
$\mathfrak{L}$ for which $D$ is a complete solution of the associated
Hamilton-Jacobi theory. To apply these ideas to quantum systems, we have to
replace probability distributions with probability amplitudes.Comment: 8 page
Bounding the convergence time of local probabilistic evolution

Isoperimetric inequalities form a very intuitive yet powerful
characterization of the connectedness of a state space, that has proven
successful in obtaining convergence bounds. Since the seventies they form an
essential tool in differential geometry, graph theory and Markov chain
analysis. In this paper we use isoperimetric inequalities to construct a bound
on the convergence time of any local probabilistic evolution that leaves its
limit distribution invariant. We illustrate how this general result leads to
new bounds on convergence times beyond the explicit Markovian setting, among
others on quantum dynamics.Comment: 8 pages, 2 figure
Neural Machine Translation by Generating Multiple Linguistic Factors

Factored neural machine translation (FNMT) is founded on the idea of using
the morphological and grammatical decomposition of the words (factors) at the
output side of the neural network. This architecture addresses two well-known
problems occurring in MT, namely the size of target language vocabulary and the
number of unknown tokens produced in the translation. FNMT system is designed
to manage larger vocabulary and reduce the training time (for systems with
equivalent target language vocabulary size). Moreover, we can produce
grammatically correct words that are not part of the vocabulary. FNMT model is
evaluated on IWSLT'15 English to French task and compared to the baseline
word-based and BPE-based NMT systems. Promising qualitative and quantitative
results (in terms of BLEU and METEOR) are reported.Comment: 11 pages, 3 figues, SLSP conferenc
Graph-based Features for Automatic Online Abuse Detection

While online communities have become increasingly important over the years,
the moderation of user-generated content is still performed mostly manually.
Automating this task is an important step in reducing the financial cost
associated with moderation, but the majority of automated approaches strictly
based on message content are highly vulnerable to intentional obfuscation. In
this paper, we discuss methods for extracting conversational networks based on
raw multi-participant chat logs, and we study the contribution of graph
features to a classification system that aims to determine if a given message
is abusive. The conversational graph-based system yields unexpectedly high
performance , with results comparable to those previously obtained with a
content-based approach
High-dimensional simplexes for metric search

In a metric space, triangle inequality implies that, for any three objects, a triangle with edge lengths corresponding to their pair- wise distances can be formed. The n-point property is a generalisation of this where, for any (n + 1) objects in the space, there exists an n- dimensional simplex whose edge lengths correspond to the distances among the objects. In general, metric spaces do not have this prop- erty; however in 1953, Blumenthal showed that any semi-metric space which is isometrically embeddable in a Hilbert space also has the n-point property. We have previously called such spaces supermetric spaces, and have shown that many metric spaces are also supermetric, including Euclidean, Cosine, Jensen-Shannon and Triangular spaces of any dimension. Here we show how such simplexes can be constructed from only their edge lengths, and we show how the geometry of the simplexes can be used to determine lower and upper bounds on unknown distances within the original space. By increasing the number of dimensions, these bounds converge to the true distance. Finally we show that for any Hilbert-embeddable space, it is possible to construct Euclidean spaces of arbitrary dimensions, from which these lower and upper bounds of the original space can be determined. These spaces may be much cheaper to query than the original. For similarity search, the engineering tradeoffs are good: we show significant reductions in data size and metric cost with little loss of accuracy, leading to a significant overall improvement in exact search performance
Food Recognition using Fusion of Classifiers based on CNNs

With the arrival of convolutional neural networks, the complex problem of
food recognition has experienced an important improvement in recent years. The
best results have been obtained using methods based on very deep convolutional
neural networks, which show that the deeper the model,the better the
classification accuracy will be obtain. However, very deep neural networks may
suffer from the overfitting problem. In this paper, we propose a combination of
multiple classifiers based on different convolutional models that complement
each other and thus, achieve an improvement in performance. The evaluation of
our approach is done on two public datasets: Food-101 as a dataset with a wide
variety of fine-grained dishes, and Food-11 as a dataset of high-level food
categories, where our approach outperforms the independent CNN models
Pixel classification methods to detect skin lesions on dermoscopic medical images

In recent years the interest of biomedical and computer vision communities in acquisition and analysis of epidermal images increased because melanoma is one of the deadliest form of skin cancer and its early identification could save lives reducing unnecessary medical treatments. User-friendly automatic tools can be very useful for physicians and dermatologists in fact high-resolution images and their annotated data, combined with analysis pipelines and machine learning techniques, represent the base to develop intelligent and proactive diagnostic systems. In this work we present two skin lesion detection pipelines on dermoscopic medical images, by exploiting standard techniques combined with workarounds that improve results; moreover to highlight the performance we consider a set of metrics combined with pixel labeling and classification. A preliminary but functional evaluation phase has been conducted with a sub-set of hard-to-treat images, in order to check which proposed detection pipeline reaches the best results
A Classification Engine for Image Ballistics of Social Data

Image Forensics has already achieved great results for the source camera
identification task on images. Standard approaches for data coming from Social
Network Platforms cannot be applied due to different processes involved (e.g.,
scaling, compression, etc.). Over 1 billion images are shared each day on the
Internet and obtaining information about their history from the moment they
were acquired could be exploited for investigation purposes. In this paper, a
classification engine for the reconstruction of the history of an image, is
presented. Specifically, exploiting K-NN and decision trees classifiers and
a-priori knowledge acquired through image analysis, we propose an automatic
approach that can understand which Social Network Platform has processed an
image and the software application used to perform the image upload. The engine
makes use of proper alterations introduced by each platform as features.
Results, in terms of global accuracy on a dataset of 2720 images, confirm the
effectiveness of the proposed strategy.Comment: 6 pages, 1 figur
How Far Can You Get By Combining Change Detection Algorithms?

Given the existence of many change detection algorithms, each with its own
peculiarities and strengths, we propose a combination strategy, that we termed
IUTIS (In Unity There Is Strength), based on a genetic Programming framework.
This combination strategy is aimed at leveraging the strengths of the
algorithms and compensate for their weakness. In this paper we show our
findings in applying the proposed strategy in two different scenarios. The
first scenario is purely performance-based. The second scenario performance and
efficiency must be balanced. Results demonstrate that starting from simple
algorithms we can achieve comparable results with respect to more complex
state-of-the-art change detection algorithms, while keeping the computational
complexity affordable for real-time applications
Semi-Supervised Phoneme Recognition with Recurrent Ladder Networks

Ladder networks are a notable new concept in the field of semi-supervised
learning by showing state-of-the-art results in image recognition tasks while
being compatible with many existing neural architectures. We present the
recurrent ladder network, a novel modification of the ladder network, for
semi-supervised learning of recurrent neural networks which we evaluate with a
phoneme recognition task on the TIMIT corpus. Our results show that the model
is able to consistently outperform the baseline and achieve fully-supervised
baseline performance with only 75% of all labels which demonstrates that the
model is capable of using unsupervised data as an effective regulariser
Information Bottleneck in Control Tasks with Recurrent Spiking Neural
  Networks

The nervous system encodes continuous information from the environment in the
form of discrete spikes, and then decodes these to produce smooth motor
actions. Understanding how spikes integrate, represent, and process information
to produce behavior is one of the greatest challenges in neuroscience.
Information theory has the potential to help us address this challenge.
Informational analyses of deep and feed-forward artificial neural networks
solving static input-output tasks, have led to the proposal of the
\emph{Information Bottleneck} principle, which states that deeper layers encode
more relevant yet minimal information about the inputs. Such an analyses on
networks that are recurrent, spiking, and perform control tasks is relatively
unexplored. Here, we present results from a Mutual Information analysis of a
recurrent spiking neural network that was evolved to perform the classic
pole-balancing task. Our results show that these networks deviate from the
\emph{Information Bottleneck} principle prescribed for feed-forward networks.Comment: Accepted at ICANN'17 to appear in Springer Lecture Notes in Computer
  Scienc
Accelerating Training of Deep Neural Networks via Sparse Edge Processing

We propose a reconfigurable hardware architecture for deep neural networks
(DNNs) capable of online training and inference, which uses algorithmically
pre-determined, structured sparsity to significantly lower memory and
computational requirements. This novel architecture introduces the notion of
edge-processing to provide flexibility and combines junction pipelining and
operational parallelization to speed up training. The overall effect is to
reduce network complexity by factors up to 30x and training time by up to 35x
relative to GPUs, while maintaining high fidelity of inference results. This
has the potential to enable extensive parameter searches and development of the
largely unexplored theoretical foundation of DNNs. The architecture
automatically adapts itself to different network sizes given available hardware
resources. As proof of concept, we show results obtained for different bit
widths.Comment: Presented at the 26th International Conference on Artificial Neural
  Networks (ICANN) 2017 in Alghero, Ital
DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural
  Networks

In recent years numerous advanced malware, aka advanced persistent threats
(APT) are allegedly developed by nation-states. The task of attributing an APT
to a specific nation-state is extremely challenging for several reasons. Each
nation-state has usually more than a single cyber unit that develops such
advanced malware, rendering traditional authorship attribution algorithms
useless. Furthermore, those APTs use state-of-the-art evasion techniques,
making feature extraction challenging. Finally, the dataset of such available
APTs is extremely small.
  In this paper we describe how deep neural networks (DNN) could be
successfully employed for nation-state APT attribution. We use sandbox reports
(recording the behavior of the APT when run dynamically) as raw input for the
neural network, allowing the DNN to learn high level feature abstractions of
the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs,
we achieved an accuracy rate of 94.6%
Generalising the Discriminative Restricted Boltzmann Machine

We present a novel theoretical result that generalises the Discriminative Restricted Boltzmann Machine (DRBM). While originally the DRBM was defined assuming the {0, 1}-Bernoulli distribution in each of its hidden units, this result makes it possible to derive cost functions for variants of the DRBM that utilise other distributions, including some that are often encountered in the literature. This is illustrated with the Binomial and {-1, +1}-Bernoulli distributions here. We evaluate these two DRBM variants and compare them with the original one on three benchmark datasets, namely the MNIST and USPS digit classification datasets, and the 20 Newsgroups document classification dataset. Results show that each of the three compared models outperforms the remaining two in one of the three datasets, thus indicating that the proposed theoretical generalisation of the DRBM may be valuable in practice
Convolutional neural network for pixel-wise skyline detection

Outdoor augmented reality applications are an emerging class of software systems that demand the fast identification of natural objects, such as plant species or mountain peaks, in low power mobile devices. Convolutional Neural Networks (CNN) have exhibited superior performance in a variety of computer vision tasks, but their training is a labor intensive task and their execution requires non negligible memory and CPU resources. This paper presents the results of training a CNN for the fast extraction of mountain skylines, which exhibits a good balance between accuracy (94,45% in best conditions and 86,87% in worst conditions), memory consumption (9,36 MB on average) and runtime execution overhead (273 ms on a Nexus 6 mobile phone), and thus has been exploited for implementing a real-world augmented reality applications for mountain peak recognition running on low to mid-end mobile phones
DeepBrain: Functional Representation of Neural In-Situ Hybridization
  Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

This paper presents a novel deep learning-based method for learning a
functional representation of mammalian neural images. The method uses a deep
convolutional denoising autoencoder (CDAE) for generating an invariant, compact
representation of in situ hybridization (ISH) images. While most existing
methods for bio-imaging analysis were not developed to handle images with
highly complex anatomical structures, the results presented in this paper show
that functional representation extracted by CDAE can help learn features of
functional gene ontology categories for their classification in a highly
accurate manner. Using this CDAE representation, our method outperforms the
previous state-of-the-art classification rate, by improving the average AUC
from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates
on input images that were downsampled significantly with respect to the
original ones to make it computationally feasible
Marine safety and data analytics : vessel crash stop maneuvering performance prediction

Crash stop maneuvering performance is one of the key indicators of the vessel safety properties for a shipbuilding company. Many different factors affect these performances, from the vessel design to the environmental conditions, hence it is not trivial to assess them accurately during the preliminary design stages. Several first principal equation methods are available to estimate the crash stop maneuvering performance, but unfortunately, these methods usually are either too costly or not accurate enough. To overcome these limitations, the authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance. Results on real-world data provided by the DAMEN Shipyards show the effectiveness of the proposal