pinecone/core-2020-05-10-deduplication huggingface dataset:
Joint Recognition and Segmentation of Actions via Probabilistic
  Integration of Spatio-Temporal Fisher Vectors

We propose a hierarchical approach to multi-action recognition that performs
joint classification and segmentation. A given video (containing several
consecutive actions) is processed via a sequence of overlapping temporal
windows. Each frame in a temporal window is represented through selective
low-level spatio-temporal features which efficiently capture relevant local
dynamics. Features from each window are represented as a Fisher vector, which
captures first and second order statistics. Instead of directly classifying
each Fisher vector, it is converted into a vector of class probabilities. The
final classification decision for each frame is then obtained by integrating
the class probabilities at the frame level, which exploits the overlapping of
the temporal windows. Experiments were performed on two datasets: s-KTH (a
stitched version of the KTH dataset to simulate multi-actions), and the
challenging CMU-MMAC dataset. On s-KTH, the proposed approach achieves an
accuracy of 85.0%, significantly outperforming two recent approaches based on
GMMs and HMMs which obtained 78.3% and 71.2%, respectively. On CMU-MMAC, the
proposed approach achieves an accuracy of 40.9%, outperforming the GMM and HMM
approaches which obtained 33.7% and 38.4%, respectively. Furthermore, the
proposed system is on average 40 times faster than the GMM based approach
Comparative Evaluation of Action Recognition Methods via Riemannian
  Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions

We present a comparative evaluation of various techniques for action
recognition while keeping as many variables as possible controlled. We employ
two categories of Riemannian manifolds: symmetric positive definite matrices
and linear subspaces. For both categories we use their corresponding nearest
neighbour classifiers, kernels, and recent kernelised sparse representations.
We compare against traditional action recognition techniques based on Gaussian
mixture models and Fisher vectors (FVs). We evaluate these action recognition
techniques under ideal conditions, as well as their sensitivity in more
challenging conditions (variations in scale and translation). Despite recent
advancements for handling manifolds, manifold based techniques obtain the
lowest performance and their kernel representations are more unstable in the
presence of challenging conditions. The FV approach obtains the highest
accuracy under ideal conditions. Moreover, FV best deals with moderate scale
and translation changes
Geriatric pharmacotherapy : optimisation through integrated approach in the hospital setting

Since older patients are more vulnerable to adverse drug-related events, there is a need to ensure appropriate prescribing in these patients in order to prevent misuse, overuse and underuse of drugs. Different tools and strategies have been developed to reduce inappropriate prescribing; the available measures can be divided into medication assessment tools, and speciﬁc interventions to reduce inappropriate prescribing. Implicit criteria of inappropriate prescribing focus on appropriate dosing, search for drug-drug interactions, and increase adherence. Explicit criteria are consensus-based standards focusing on drugs and diseases and include lists of drugs to avoid in general or lists combining drugs with clinical data. These criteria take into consideration differences between patients, and stand for a medication review, by using a systematic approach. Different types of interventions exist in order to reduce inappropriate prescribing in older patients, such as: educational interventions, computerized decision support systems, pharmacist-based interventions, and geriatric assessment. The effects of these interventions have been studied, sometimes in a multifaceted approach combining different techniques, and all types seem to have positive effects on appropriateness of prescribing. Interdisciplinary teamwork within the integrative pharmaceutical care is important for improving of outcomes and safety of drug therapy. The pharmaceutical care process consists offour steps, which are cyclic for an individual patient. These steps are pharmaceutical anamnesis, medication review, design and follow-up of a pharmaceutical care plan. A standardized approach is necessary for the adequate detection and evaluation of drug-related problems. Furthermore, it is clear that drug therapy should be reviewed in-depth, by having full access to medical records, laboratory values and nursing notes. Although clinical pharmacists perform the pharmaceutical care process to manage the patient’s drug therapy in every day clinical practice, the physician takes the ultimate responsibility for the care of the patient in close collaboration with nurses
AUTO2, a saturation-based heuristic prover for higher-order logic

We introduce a new theorem prover for classical higher-order logic named
auto2. The prover is designed to make use of human-specified heuristics when
searching for proofs. The core algorithm is a best-first search through the
space of propositions derivable from the initial assumptions, where new
propositions are added by user-defined functions called proof steps. We
implemented the prover in Isabelle/HOL, and applied it to several formalization
projects in mathematics and computer science, demonstrating the high level of
automation it can provide in a variety of possible proof tasks.Comment: 16 pages, accepted for ITP 201
POSIX lexing with derivatives of regular expressions (proof pearl)

Brzozowski introduced the notion of derivatives for regular expressions. They can be used for a very simple regular expression matching algorithm. Sulzmann and Lu cleverly extended this algorithm in order to deal with POSIX matching, which is the underlying disambiguation strategy for regular expressions needed in lexers. Sulzmann and Lu have made available on-line what they call a “rigorous proof” of the correctness of their algorithm w.r.t. their specification; regrettably, it appears to us to have unfillable gaps. In the first part of this paper we give our inductive definition of what a POSIX value is and show (i) that such a value is unique (for given regular expression and string being matched) and (ii) that Sulzmann and Lu’s algorithm always generates such a value (provided that the regular expression matches the string). We also prove the correctness of an optimised version of the POSIX matching algorithm. Our definitions and proof are much simpler than those by Sulzmann and Lu and can be easily formalised in Isabelle/HOL. In the second part we analyse the correctness argument by Sulzmann and Lu and explain why the gaps in this argument cannot be filled easily.Postprin
Towards incentive-compatible pricing for bandwidth reservation in community network clouds

Community network clouds provide for applications of local interest deployed within community networks through collaborative efforts to provision cloud infrastructures. They complement the traditional large-scale public cloud providers similar to the model of decentralised edge clouds by bringing both content and computation closer to the users at the edges of the network. Services and applications within community network clouds require connectivity to the Internet and to the resources external to the community network, and here the current besteffort model of volunteers contributing gateway access in the community networks falls short. We model the problem of reserving the bandwidth at such gateways for guaranteeing quality-of-service for the cloud applications, and evaluate different pricing mechanisms for their suitability in ensuring maximal social welfare and eliciting truthful requests from the users. We find second-price auction based mechanisms, including Vickrey and generalised second price auctions, suitable for the bandwidth allocation problem at the gateways in the community networks.Peer ReviewedPostprint (author's final draft
LQG for the Bewildered

We present a pedagogical introduction to the notions underlying the
connection formulation of General Relativity - Loop Quantum Gravity (LQG) -
with an emphasis on the physical aspects of the framework. We begin by
reviewing General Relativity and Quantum Field Theory, to emphasise the
similarities between them which establish a foundation upon which to build a
theory of quantum gravity. We then explain, in a concise and clear manner, the
steps leading from the Einstein-Hilbert action for gravity to the construction
of the quantum states of geometry, known as \emph{spin-networks}, which provide
the basis for the kinematical Hilbert space of quantum general relativity.
Along the way we introduce the various associated concepts of \emph{tetrads},
\emph{spin-connection} and \emph{holonomies} which are a pre-requisite for
understanding the LQG formalism. Having provided a minimal introduction to the
LQG framework, we discuss its applications to the problems of black hole
entropy and of quantum cosmology. A list of the most common criticisms of LQG
is presented, which are then tackled one by one in order to convince the reader
of the physical viability of the theory.
  An extensive set of appendices provide accessible introductions to several
key notions such as the \emph{Peter-Weyl theorem}, \emph{duality} of
differential forms and \emph{Regge calculus}, among others. The presentation is
aimed at graduate students and researchers who have some familiarity with the
tools of quantum mechanics and field theory and/or General Relativity, but are
intimidated by the seeming technical prowess required to browse through the
existing LQG literature. Our hope is to make the formalism appear a little less
bewildering to the un-initiated and to help lower the barrier for entry into
the field.Comment: 87 pages, 15 figures, manuscript submitted for publicatio
Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
  figure
Property-driven State-Space Coarsening for Continuous Time Markov Chains

Dynamical systems with large state-spaces are often expensive to thoroughly
explore experimentally. Coarse-graining methods aim to define simpler systems
which are more amenable to analysis and exploration; most current methods,
however, focus on a priori state aggregation based on similarities in
transition rates, which is not necessarily reflected in similar behaviours at
the level of trajectories. We propose a way to coarsen the state-space of a
system which optimally preserves the satisfaction of a set of logical
specifications about the system's trajectories. Our approach is based on
Gaussian Process emulation and Multi-Dimensional Scaling, a dimensionality
reduction technique which optimally preserves distances in non-Euclidean
spaces. We show how to obtain low-dimensional visualisations of the system's
state-space from the perspective of properties' satisfaction, and how to define
macro-states which behave coherently with respect to the specifications. Our
approach is illustrated on a non-trivial running example, showing promising
performance and high computational efficiency.Comment: 16 pages, 6 figures, 1 tabl
Policy learning for time-bounded reachability in continuous-time Markov decision processes via doubly-stochastic gradient ascent

Continuous-time Markov decision processes are an important class of models in a wide range of applications, ranging from cyber-physical systems to synthetic biology. A central problem is how to devise a policy to control the system in order to maximise the probability of satisfying a set of temporal logic specifications. Here we present a novel approach based on statistical model checking and an unbiased estimation of a functional gradient in the space of possible policies. The statistical approach has several advantages over conventional approaches based on uniformisation, as it can also be applied when the model is replaced by a black box, and does not suffer from state-space explosion. The use of a stochastic gradient to guide our search considerably improves the efficiency of learning policies. We demonstrate the method on a proof-of-principle non-linear population model, showing strong performance in a non-trivial task
Modelling technology transfer in Green IT with Multi Agent System

While there is a tremendous increase in academic research and collaboration between academia, the results of exchange between industry and science are steady. To understand  this  complex  situation  and to propose an improvement for technology transfer between academia and industry, it is necessary to investigate the different partners involved. We present a multi-agent system to model this technology transfer of green IT in order to see the impact on the development of sustainability in our society. We define a sustainability indicator and we study its changes according to the parameters defined in the technology transfer
Simulation of an Optional Strategy in the Prisoner's Dilemma in Spatial
  and Non-spatial Environments

This paper presents research comparing the effects of different environments
on the outcome of an extended Prisoner's Dilemma, in which agents have the
option to abstain from playing the game. We consider three different pure
strategies: cooperation, defection and abstinence. We adopt an evolutionary
game theoretic approach and consider two different environments: the first
which imposes no spatial constraints and the second in which agents are placed
on a lattice grid. We analyse the performance of the three strategies as we
vary the loner's payoff in both structured and unstructured environments.
Furthermore we also present the results of simulations which identify scenarios
in which cooperative clusters of agents emerge and persist in both
environments.Comment: 12 pages, 8 figures. International Conference on the Simulation of
  Adaptive Behavio
Co-exploring Actuator Antagonism and Bio-inspired Control in a Printable Robot Arm

The human arm is capable of performing fast targeted movements with high precision, say in pointing with a mouse cursor, but is inherently ‘soft’ due to the muscles, tendons and other tissues of which it is composed. Robot arms are also becoming softer, to enable robustness when operating in real-world environments, and to make them safer to use around people. But softness comes at a price, typically an increase in the complexity of the control required for a given task speed/accuracy requirement. Here we explore how fast and precise joint movements can be simply and effectively performed in a soft robot arm, by taking inspiration from the human arm. First, viscoelastic actuator-tendon systems in an agonist-antagonist setup provide joints with inherent damping, and stiffness that can be varied in real-time through co-contraction. Second, a light-weight and learnable inverse model for each joint enables a fast ballistic phase that drives the arm close to a desired equilibrium point and co-contraction tuple, while the final adjustment is done by a feedback controller. The approach is embodied in the GummiArm, a robot which can almost entirely be printed on hobby-grade 3D printers. This enables rapid and iterative co-exploration of ‘brain’ and ‘body’, and provides a great platform for developing adaptive and bio-inspired behaviours
A Model of Artificial Genotype and Norm of Reaction in a Robotic System

The genes of living organisms serve as large stores of information for replicating their behavior and morphology over generations. The evolutionary view of genetics that has inspired artificial systems with a Mendelian approach does not take into account the interaction between species and with the environment to generate a particular phenotype. In this paper, a genotype model is suggested to shape the relationship with the phenotype and the environment in an artificial system. A method to obtain a genotype from a population of a particular robotic system is also proposed. Finally, we show that this model presents a similar behavior to that of living organisms in what regards the concept of norm of reaction.This paper describes research done at the UJI Robotic
Intelligence Laboratory. Support for this laboratory is provided in part by Ministerio
de Econom´ıa y Competitividad (DPI2015-69041-R), by Generalitat Valenciana
(PROMETEOII/2014/028) and by Universitat Jaume I (P1-1B2014-52,
PREDOC/ 2013/06)
Motion control of mobile autonomous robots using non-linear dynamical systems approach

This paper presents a solution to the problem of motion control of an
autonomous robot, moving in a dynamical and unstable environment. It is based
on non-linear dynamical systems, modelling the state variables that define the
motion of a robot under an omnidirectional platform, like its direction of navigation
and velocity. The approach used, is based on a set of non-linear differential
equations that model the evolution of state variables along time, based on
the concept of attractors and repellers. In the official RoboCup Middle Size
League field, a target is used to attract the robot to a certain position (could be
the ball or a desired position to receive the ball), while a repeller could move
the robot away from its original path (given by obstacles in the surrounding environment).
The research was firstly carried out in a computational simulation
environment and later on with robots in a real environmentThis work was developed at the Automation and Robotics Laboratory by MINHO R&D TEAM, University of Minho, under the supervision of Professor A. Fernando Ribeiro and A. Gil Lopes. The knowledge exchanging between the RoboCup’s MSL teams and community contributed greatly for the development of this work.  This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
Collaborative portal for supporting manufacturing resources selection in a global market

In the last decades the manufacturing industry has shifted from largely single location centralized manufacturing towards networked manufacturing spread over multiple sites. Companies have either established new production sites in multiple locations or established strategic partnerships to respond to market changes more rapidly as well as for more efficient and effective sharing of resources, through the so-called integrated manufacturing systems environments. In this context, proper collaboration among different locations and/or partners becomes more critical. Thus, development of appropriate integrated tools, to enable clients or network partners to accurately select appropriate manufacturing resource from the widened global market context, becomes emergent. Therefore, this work is aimed at providing a contribution to maximize overall benefit of businesses, in the context of a networked scenario, and subject to a set of configurations and restrictions on part of the manufacturing resources providers and searchers. Thus, the authors propose a collaborative portal to enable to perform a better manufacturing resources selection, based on technologies integration for supporting accurate orders processing.The authors wish to acknowledge the support of the Foundation for Science and Technology – FCT, under the scope of the financed Project with reference: PEst20152020, as well as the FCT Doctoral grant referenced SFRH/BD/62313/2009.info:eu-repo/semantics/publishedVersio
A simulation platform prototype for evaluating alternative scenarios of members integration in virtual organizations

Companies are facing growing challenges motivated by globalization. A globalized market means that the number of companies with which they will need to compete to maintain or enlarge their market share is increased. Additionally, it also brings greater opportunities to conquer new markets and increase existing market shares, since the geographical, political and economical boundaries are gradually being removed. This paper proposes and illustrates the use of a platform prototype for enabling the evaluation and selection of attractive members for being included as members in a virtual organization. An extended simulation study is described and results obtained presented which show advantage of a used dynamic evaluation and selection model, based on a varying set of criteria.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
Model predictive current control of a slow battery charger for electric mobility applications

This paper presents a model predictive current control applied to a
slow electric vehicle (EV) battery charger. Taking into account the similarities
between the power converters inside the EV, it is possible to combine the battery
charger and the motor driver in a single integrated converter, thus reducing the
weight and volume of the proposed solution, and also contributing to reduce the
final price of the EV. Due to the bidirectional power flow capability of the integrated
power converter, when working as a slow EV battery charger it can operate
in grid-to-vehicle (G2V) mode and in vehicle-to-grid (V2G) mode, contributing
to make EVs an important assets in the future smart grids. The integrated
power converter working as battery charger operates with sinusoidal current and
unitary power factor, contributing to improve the power quality of the electrical
grid. This paper provides simulation and experimental results that validate the
model predictive control algorithm applied to the proposed integrated power converter
working as slow EV battery charger.(undefined)info:eu-repo/semantics/publishedVersio
Comprehensive analysis and comparison of digital current control techniques for active rectifiers

This paper presents a comprehensive analysis and comparison of digital current control techniques for active rectifiers. These rectifiers are connect-ed to the power grid and are controlled aiming to obtain sinusoidal grid currents and unitary power factor. In this context this paper presents the principle of operation of a full-bridge full-controlled active rectifier, which is controlled by different digital current control techniques, namely, proportional-integral (PI) in stationary frame, PI in synchronous frame, PI sinewave (PIS), feedforward, sliding mode, and predictive. These digital current control techniques are explained in detail and is established a comparison in terms of their current errors in steady-state, as well as in terms of their digital implementation using the digital signal processor (DSP) TMS320F28335 from Texas Instruments.FCTCOMPETE: POCI-01-0145-FEDER-00704