pinecone/core-2020-05-10-deduplication huggingface dataset:
Real-Valued Orthogonal Sequences for\ud
Ultra-Low Overhead Channel Estimation in\ud
MIMO-FBMC Systems

Multiple-input multiple-output filterbank multicarrier communication (MIMO-FBMC) is a promising technique to achieve very\ud
tight spectrum confinement (thus, higher spectral efficiency) as well as\ud
strong robustness against dispersive channels. In this paper, we present\ud
a novel training design for MIMO-FBMC system which enables efficient\ud
estimate of frequency-selective channels (associated to multiple transmit\ud
antennas) with only one non-zero FBMC symbol. Our key idea is to design real-valued orthogonal training sequences (in the frequency domain)\ud
which displaying zero-correlation zone properties in the time-domain.\ud
Compared to our earlier proposed training scheme requiring at least two\ud
non-zero FBMC symbols (separated by several zero guard symbols), the\ud
proposed scheme features ultra-low training overhead yet achieves channel estimation performance comparable to our earlier proposed complex\ud
training sequence decomposition(CTSD). Our simulations validate that\ud
the proposed method is an efficient channel estimation approach for practical preamble-based MIMO-FBMC systems
Efficient handling of SPARQL OPTIONAL for OBDA

OPTIONAL is a key feature in SPARQL for dealing with missing information. While this operator is used extensively, it is also known for its complexity, which can make efficient evaluation of queries with OPTIONAL challenging. We tackle this problem in the Ontology-Based Data Access (OBDA) setting, where the data is stored in a SQL relational database and exposed as a virtual RDF graph by means of an R2RML mapping. We start with a succinct translation of a SPARQL fragment into SQL. It fully respects bag semantics and three-valued logic and relies on the extensive use of the LEFT JOIN operator and COALESCE function. We then propose optimisation techniques for reducing the size and improving the structure of generated SQL queries. Our optimisations capture interactions between JOIN, LEFT JOIN, COALESCE and integrity constraints such as attribute nullability, uniqueness and foreign key constraints. Finally, we empirically verify effectiveness of our techniques on the BSBM OBDA benchmark
Reading between the lines: A computational bibliometric analysis on emotion regulation

7th International Conference on Pervasive Computing Paradigms for Mental Health (MindCare 2018)Emotion regulation (ER) is defined as the processes deployed by an individual or
group of individuals to explicitly or implicitly influence the experienced emotions in order
achieve desirable states or goals. The available literature within this domain has grown
exponentially in the last years. Nevertheless, to the best of our knowledge no scientometric
analysis has been carried out yet. This kind of analysis allows to grasp how the scientific
production within this field is configured, by establishing patterns and connections between the
main authorities of the community. In the current analysis, descriptive data of the citation
counts of main authors, institutions, journals, categories and countries are presented. Overall,
the description shows the prominent role some authors as the principal authorities within the
field, the predominance of United States in the citation counts as well as the high-impact
journals in which the articles of this discipline are published. The most relevant implications of
the findings are discussed in terms of future lines regarding the field of emotion regulation
Avoiding Echo-Responses in a Retrieval-Based Conversation System

Retrieval-based conversation systems generally tend to highly rank responses
that are semantically similar or even identical to the given conversation
context. While the system's goal is to find the most appropriate response,
rather than the most semantically similar one, this tendency results in
low-quality responses. We refer to this challenge as the echoing problem. To
mitigate this problem, we utilize a hard negative mining approach at the
training stage. The evaluation shows that the resulting model reduces echoing
and achieves better results in terms of Average Precision and Recall@N metrics,
compared to the models trained without the proposed approach
Deep Video Generation, Prediction and Completion of Human Action
  Sequences

Current deep learning results on video generation are limited while there are
only a few first results on video prediction and no relevant significant
results on video completion. This is due to the severe ill-posedness inherent
in these three problems. In this paper, we focus on human action videos, and
propose a general, two-stage deep framework to generate human action videos
with no constraints or arbitrary number of constraints, which uniformly address
the three problems: video generation given no input frames, video prediction
given the first few frames, and video completion given the first and last
frames. To make the problem tractable, in the first stage we train a deep
generative model that generates a human pose sequence from random noise. In the
second stage, a skeleton-to-image network is trained, which is used to generate
a human action video given the complete human pose sequence generated in the
first stage. By introducing the two-stage strategy, we sidestep the original
ill-posed problems while producing for the first time high-quality video
generation/prediction/completion results of much longer duration. We present
quantitative and qualitative evaluation to show that our two-stage approach
outperforms state-of-the-art methods in video generation, prediction and video
completion. Our video result demonstration can be viewed at
https://iamacewhite.github.io/supp/index.htmlComment: Under review for CVPR 2018. Haoye and Chunyan have equal contributio
Semi-supervised Deep Learning with Memory

© 2018, Springer Nature Switzerland AG. We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data. To address this problem, existing semi-supervised deep learning methods often rely on the up-to-date “network-in-training” to formulate the semi-supervised learning objective. This ignores both the discriminative feature representation and the model inference uncertainty revealed by the network in the preceding learning iterations, referred to as the memory of model learning. In this work, we propose a novel Memory-Assisted Deep Neural Network (MA-DNN) capable of exploiting the memory of model learning to enable semi-supervised learning. Specifically, we introduce a memory mechanism into the network training process as an assimilation-accommodation interaction between the network and an external memory module. Experiments demonstrate the advantages of the proposed MA-DNN model over the state-of-the-art semi-supervised deep learning methods on three image classification benchmark datasets: SVHN, CIFAR10, and CIFAR100
Exploiting temporal information for 3D pose estimation

In this work, we address the problem of 3D human pose estimation from a
sequence of 2D human poses. Although the recent success of deep networks has
led many state-of-the-art methods for 3D pose estimation to train deep networks
end-to-end to predict from images directly, the top-performing approaches have
shown the effectiveness of dividing the task of 3D pose estimation into two
steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from
images and then mapping them into 3D space. They also showed that a
low-dimensional representation like 2D locations of a set of joints can be
discriminative enough to estimate 3D pose with high accuracy. However,
estimation of 3D pose for individual frames leads to temporally incoherent
estimates due to independent error in each frame causing jitter. Therefore, in
this work we utilize the temporal information across a sequence of 2D joint
locations to estimate a sequence of 3D poses. We designed a
sequence-to-sequence network composed of layer-normalized LSTM units with
shortcut connections connecting the input to the output on the decoder side and
imposed temporal smoothness constraint during training. We found that the
knowledge of temporal consistency improves the best reported result on
Human3.6M dataset by approximately $12.2\%$ and helps our network to recover
temporally consistent 3D poses over a sequence of images even when the 2D pose
detector fails
Eliminating the blind spot : adapting 3D object detection and monocular depth estimation to 360° panoramic imagery.

Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360  ∘ panoramic cameras. We present an approach to adapt contemporary deep network architectures developed on conventional rectilinear imagery to work on equirectangular 360  ∘  panoramic imagery. To address the lack of annotated panoramic automotive datasets availability, we adapt contemporary automotive dataset, via style and projection transformations, to facilitate the cross-domain retraining of contemporary algorithms for panoramic imagery. Following this approach we retrain and adapt existing architectures to recover scene depth and 3D pose of vehicles from monocular panoramic imagery without any panoramic training labels or calibration parameters. Our approach is evaluated qualitatively on crowd-sourced panoramic images and quantitatively using an automotive environment simulator to provide the first benchmark for such techniques within panoramic imagery
Predicting Video Saliency with Object-to-Motion CNN and Two-layer
  Convolutional LSTM

Over the past few years, deep neural networks (DNNs) have exhibited great
success in predicting the saliency of images. However, there are few works that
apply DNNs to predict the saliency of generic videos. In this paper, we propose
a novel DNN-based video saliency prediction method. Specifically, we establish
a large-scale eye-tracking database of videos (LEDOV), which provides
sufficient data to train the DNN models for predicting video saliency. Through
the statistical analysis of our LEDOV database, we find that human attention is
normally attracted by objects, particularly moving objects or the moving parts
of objects. Accordingly, we propose an object-to-motion convolutional neural
network (OM-CNN) to learn spatio-temporal features for predicting the
intra-frame saliency via exploring the information of both objectness and
object motion. We further find from our database that there exists a temporal
correlation of human attention with a smooth saliency transition across video
frames. Therefore, we develop a two-layer convolutional long short-term memory
(2C-LSTM) network in our DNN-based method, using the extracted features of
OM-CNN as the input. Consequently, the inter-frame saliency maps of videos can
be generated, which consider the transition of attention across video frames.
Finally, the experimental results show that our method advances the
state-of-the-art in video saliency prediction.Comment: Jiang, Lai and Xu, Mai and Liu, Tie and Qiao, Minglang and Wang,
  Zulin; DeepVS: A Deep Learning Based Video Saliency Prediction Approach;The
  European Conference on Computer Vision (ECCV); September 201
Overview of (pro-)Lie group structures on Hopf algebra character groups

Character groups of Hopf algebras appear in a variety of mathematical and
physical contexts. To name just a few, they arise in non-commutative geometry,
renormalisation of quantum field theory, and numerical analysis. In the present
article we review recent results on the structure of character groups of Hopf
algebras as infinite-dimensional (pro-)Lie groups. It turns out that under mild
assumptions on the Hopf algebra or the target algebra the character groups
possess strong structural properties. Moreover, these properties are of
interest in applications of these groups outside of Lie theory. We emphasise
this point in the context of two main examples: The Butcher group from
numerical analysis and character groups which arise from the Connes--Kreimer
theory of renormalisation of quantum field theories.Comment: 31 pages, precursor and companion to arXiv:1704.01099, Workshop on
  "New Developments in Discrete Mechanics, Geometric Integration and
  Lie-Butcher Series", May 25-28, 2015, ICMAT, Madrid, Spai
A Model for Detection of Angular Velocity of Image Motion Based on the Temporal Tuning of the Drosophila

We propose a new bio-plausible model based on the visual systems of Drosophila for estimating angular velocity of image motion in insects’ eyes. The model implements both preferred direction motion enhancement and non-preferred direction motion suppression which is discovered in Drosophila’s visual neural circuits recently to give a stronger directional selectivity. In addition, the angular velocity detecting model (AVDM) produces a response largely independent of the spatial frequency in grating experiments which enables insects to estimate the flight speed in cluttered environments. This also coincides with the behaviour experiments of honeybee flying through tunnels with stripes of different spatial frequencies
An exploration of dropout with RNNs for natural language inference.

Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In this paper, we propose a novel RNN model for NLI and empirically evaluate the effect of applying dropout at different layers in the model. We also investigate the impact of varying dropout rates at these layers. Our empirical evaluation on a large (Stanford Natural Language Inference (SNLI)) and a small (SciTail) dataset suggest that dropout at each feed-forward connection severely affects the model accuracy at increasing dropout rates. We also show that regularizing the embedding layer is efficient for SNLI whereas regularizing the recurrent layer improves the accuracy for SciTail. Our model achieved an accuracy   86.14%  on the SNLI dataset and   77.05%  on SciTail
A plurality of viewpoints regarding the uncertainties of the agroecological transition

The concept of agroecological transition revives debates on how to deal with complexity and uncertainty. While the adaptive approach and its “adjust along the way” principle have been adopted as a relevant general framework to deal with partially irreducible uncertainty, the different approaches to the definition and management of uncertainty are rarely explicitated. In this chapter we highlight the diversity of these stances through brief presentations of research work that is related to agroecology and sustainable development, and anchored in various disciplines (modelling, management sciences, economics, ecology). This gives us a first glimpse of the variety of concepts used to describe uncertainty, characterising nature and the different approaches to manage it. It shows also that these definitions of uncertainties, clearly derived from particular disciplines or school of thought, can be applied together in a more or less complementary way. Finally, we discuss how this explicitation of the diversity of approaches to uncertainty contributes to highlighting different ways of defining the agroecological transition itself – especially between determinist or more open-ended approaches–, and identifies interdisciplinary research issues
Searching for a Compressed Polyline with a Minimum Number of Vertices

There are many practical applications that require simplification of
polylines. Some of the goals are to reduce the amount of information necessary
to store, improve processing time, or simplify editing. The simplification is
usually done by removing some of the vertices, making the resultant polyline go
through a subset of the source polyline vertices. However, such approaches do
not necessarily produce a new polyline with the minimum number of vertices. The
approximate solution to find a polyline, within a specified tolerance, with the
minimum number of vertices is described in this paper
STYPES: nonrecursive datalog rewriter for linear TGDs and conjunctive queries

We present STYPES, a system that rewrites ontology-mediated queries with linear tuple-generating dependencies and conjunctive queries to equivalent nonrecursive datalog (NDL) queries. The main feature of STYPES is that it produces polynomial-size rewritings whenever the treewidth of the input conjunctive queries and the size of the chases for the ontology atoms as well as their arity are bounded; moreover, the rewritings can be constructed and executed in LOGCFL, indicating high parallelisability in theory. We show experimentally that Apache Flink on a cluster of machines with 20 virtual CPUs is indeed able to parallelise execution of a series of NDL-rewritings constructed by STYPES, with the time decreasing proportionally to the number of CPUs available
Challenges in value-driven data governance

Data is quite popularly considered to be the new oil since\ud
it has become a valuable commodity. This has resulted in many entities\ud
and businesses that hoard data with the aim of exploiting it. Yet, the\ud
‘simple’ exploitation of data results in entities who are not obtaining\ud
the highest benefits from the data, which as yet is not considered to\ud
be a fully-fledged enterprise asset. Such data can exist in a duplicated,\ud
fragmented, and isolated form, and the sheer volume of available data\ud
further complicates the situation. Issues such as the latter highlight the\ud
need for value-based data governance, where the management of data assets is based on the quantification of the data value. This quantification\ud
will provide an opportunity for evidence-based approaches to data governance. This paper has the purpose of creating awareness and further\ud
understanding of challenges that result in untapped data value. We identify niches in related work, and through our experience with businesses\ud
who use data assets, we here analyse four main context-independent challenges that hinder entities from achieving the full benefits of using their\ud
data. This will aid in the advancement of the field of value-driven data\ud
governance and therefore directly affect data asset exploitation
The blockchain: a new framework for robotic swarm systems

Swarms of robots will revolutionize many industrial applications, from
targeted material delivery to precision farming. However, several of the
heterogeneous characteristics that make them ideal for certain future
applications --- robot autonomy, decentralized control, collective emergent
behavior, etc. --- hinder the evolution of the technology from academic
institutions to real-world problems. Blockchain, an emerging technology
originated in the Bitcoin field, demonstrates that by combining peer-to-peer
networks with cryptographic algorithms a group of agents can reach an agreement
on a particular state of affairs and record that agreement without the need for
a controlling authority. The combination of blockchain with other distributed
systems, such as robotic swarm systems, can provide the necessary capabilities
to make robotic swarm operations more secure, autonomous, flexible and even
profitable. This work explains how blockchain technology can provide innovative
solutions to four emergent issues in the swarm robotics research field. New
security, decision making, behavior differentiation and business models for
swarm robotic systems are described by providing case scenarios and examples.
Finally, limitations and possible future problems that arise from the
combination of these two technologies are described.Comment: 12 pages, 13 figure
A wearable general-purpose solution for Human-Swarm Interaction

Swarms of robots will revolutionize many industrial applications, from
targeted material delivery to precision farming. Controlling the motion and
behavior of these swarms presents unique challenges for human operators, who
cannot yet effectively convey their high-level intentions to a group of robots
in application. This work proposes a new human-swarm interface based on novel
wearable gesture-control and haptic-feedback devices. This work seeks to
combine a wearable gesture recognition device that can detect high-level
intentions, a portable device that can detect Cartesian information and finger
movements, and a wearable advanced haptic device that can provide real-time
feedback. This project is the first to envisage a wearable Human-Swarm
Interaction (HSI) interface that separates the input and feedback components of
the classical control loop (input, output, feedback), as well as being the
first of its kind suitable for both indoor and outdoor environments.Comment: 9 pages, 15 figure
Personal Food Computer: A new device for controlled-environment
  agriculture

Due to their interdisciplinary nature, devices for controlled-environment
agriculture have the possibility to turn into ideal tools not only to conduct
research on plant phenology but also to create curricula in a wide range of
disciplines. Controlled-environment devices are increasing their
functionalities as well as improving their accessibility. Traditionally,
building one of these devices from scratch implies knowledge in fields such as
mechanical engineering, digital electronics, programming, and energy
management. However, the requirements of an effective controlled environment
device for personal use brings new constraints and challenges. This paper
presents the OpenAg Personal Food Computer (PFC); a low cost desktop size
platform, which not only targets plant phenology researchers but also
hobbyists, makers, and teachers from elementary to high-school levels (K-12).
The PFC is completely open-source and it is intended to become a tool that can
be used for collective data sharing and plant growth analysis. Thanks to its
modular design, the PFC can be used in a large spectrum of activities.Comment: 9 pages, 11 figures, Accepted at the 2017 Future Technologies
  Conference (FTC
In-depth exploration of engagement patterns in MOOCs.

With the advent of ‘big data’, various new methods have been proposed, to explore data in several domains. In the domain of learning (and e-learning, in particular), the outcomes lag somewhat behind. This is not unexpected, as e-learning has the additional dimensions of learning and engagement, as well as other psychological aspects, to name but a few, beyond ‘simple’ data crunching. This means that the goals of data exploration for e-learning are somewhat different to the goals for practically all other domains: finding out what students do is not enough, it is the means to the end of supporting student learning and increasing their engagement. This paper focuses specifically on student engagement, a crucial issue especially for MOOCs, by studying in much greater detail than previous work, the engagement of students based on clustering students according to three fundamental (and, arguably, comprehensive) dimensions: learning, social and assessment. The study’s value lies also in the fact that it is among the few studies using real-world longitudinal data (6 runs of a course, over 3 years) from a large number of students
A Multi-modal Urban Traffic Agent-Based Framework to Study Individual Response to Catastrophic Events

Urban traffic is made of a variety of mobility modes that have to be taken into account to explore the impact of catastrophic event. From individual mobility behaviors to macroscopic traffic dynamics, agent-based modeling provides an interesting conceptual framework to study this question. Unfortunately, most proposals in the domain do not provide any simple way to model these multi-modal trajectories, and thus fell short at simulating in a credible way the outcomes of a catastrophic event, like natural or industrial hazards. This paper presents an agent-based framework implemented with the GAMA modeling platform that aims at overcoming this lack. An application of this model for the study of flood crisis in a district of Hanoi (Vietnam) is presented