pinecone/core-2020-05-10-deduplication huggingface dataset:
Optimal online two-way trading with bounded number of transactions

We consider a two-way trading problem, where investors buy and sell a stock whose price moves within a certain range. Naturally they want to maximize their profit. Investors can perform up to k trades, where each trade must involve the full amount. We give optimal algorithms for three different models which differ in the knowledge of how the price fluctuates. In the first model, there are global minimum and maximum bounds m and M. We first show an optimal lower bound of  φφ (where  φ=M/mφ=M/m) on the competitive ratio for one trade, which is the bound achieved by trivial algorithms. Perhaps surprisingly, when we consider more than one trade, we can give a better algorithm that loses only a factor of  φ2/3φ2/3 (rather than  φφ) per additional trade. Specifically, for k trades the algorithm has competitive ratio  φ(2k+1)/3φ(2k+1)/3. Furthermore we show that this ratio is the best possible by giving a matching lower bound. In the second model, m and M are not known in advance, and just  φφ is known. We show that this only costs us an extra factor of  φ1/3φ1/3, i.e., both upper and lower bounds become  φ(2k+2)/3φ(2k+2)/3. Finally, we consider the bounded daily return model where instead of a global limit, the fluctuation from one day to the next is bounded, and again we give optimal algorithms, and interestingly one of them resembles common trading algorithms that involve stop loss limits.Peer-reviewedPost-printCOCOON 2017: Computing and Combinatoric
Polynomials over quaternions and coquaternions: a unified approach

This paper aims to present, in a unified manner, results which are valid on both the algebras of quaternions and coquaternions and, simultaneously, call the attention to the main differences between these two algebras. The rings of one-sided polynomials over each of these algebras are studied and some important differences in what concerns the structure of the set of their zeros are remarked. Examples illustrating this different behavior of the zero-sets of quaternionic and coquaternionic polynomials are also presented.(undefined)info:eu-repo/semantics/publishedVersio
A Neural Network Model for Team Viability

Team effectiveness has been the focus of numerous studies
since teams play an increasingly decisive role in modern organizations.
In the present paper, our attention is centered on team viability, which
is one dimension of team effectiveness. Given the challenges that actual
teams face today, exploring the conditions and processes that enhance
the capacity of teams to adapt and continue to work together is a fundamental
research path to pursue. In this study, team psychological capital
and team learning were considered as antecedents of team viability. The
relationships that team psychological capital and team learning establish
with team viability were explored as accurately as possible. Typically,
these relationships are assumed to be linear as multivariate linear models
are often used. However, these linear models fail to explain possible nonlinear
relations between variables, expected to exist in dynamic systems
as teams. Adopting computational modeling strategies in the context
of organizational psychology has become more common. In this paper,
radial basis function models and neural networks were used to study the
complex relationships between team psychological capital, team learning
and team viability
Household packaging waste management

Household packaging waste (HPW) has an important environmental impact and economic relevance. Thus there are networks of collection points (named “ecopontos” in Portugal) where HPW may be deposited for collection by waste management companies. In order to optimize HPW logistics, accurate estimates of the waste generation rates are needed to calculate the number of collections required for each ecoponto in a given period of time. The most important factors to estimate HPW generation rates are linked to the characteristics of the population and the social and economic activities around each ecoponto location. We developed multiple linear regression models and artificial neural networks models to forecast the number of collections per year required for each location. For operational short term planning purposes, these forecasts need to be adjusted for seasonality in order to determine the required number of collections for the relevant planning period. In this paper we describe the methodology used to obtain these forecasts.This research has been partially supported by COMPETE: POCI-01-0145-FEDER007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
A decomposition algorithm for robust lot sizing problem with remanufacturing option

In this paper, we propose a decomposition procedure for constructing robust optimal production plans for reverse inventory systems. Our method is motivated by the need of overcoming the excessive computational time requirements, as well as the inaccuracies caused by imprecise representations of problem parameters. The method is based on a min-max formulation that avoids the excessive conservatism of the dualization technique employed by Wei et al. (2011). We perform a computational study using our decomposition framework on several classes of computer generated test instances and we report our experience. Bienstock and Özbay (2008) computed optimal base stock levels for the traditional lot sizing problem when the production cost is linear and we extend this work here by considering return inventories and setup costs for production. We use the approach of Bertsimas and Sim (2004) to model the uncertainties in the input
Optimal schedule of home care visits for a health care center

The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurse teams from Health Care Centers. Usually, the visits are manually planned and without computer support. The main goal of this work is to carry out the automatic schedule of home care visits, of one Portuguese Health Care Center, in order to minimize the time spent in all home care visits and, consequently, reduce the costs involved. The developed algorithms were coded in MatLab Software and the problem was efficiently solved, obtaining several schedule solutions of home care visits for the presented data. Solutions found by genetic and particle swarm algorithms lead to significant time reductions for both nurse teams and patients.This work has been supported by COMPETE: POCI-01-0145-
FEDER-007043 and FCT - Fundru;ao para a Ciencia e Tecnologia within the Project
Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
Rethinking feasibility analysis for urban development: a multidimensional decision support tool

Large-scale urban development projects featured over the past thirty years have shown some critical issues related to the implementation phase. Con-sequently, the current practice seems oriented toward minimal and wide-spread interventions meant as urban catalyst. This planning practice might solve the problem of limited reliability of large developments’ feasibility studies, but it rises an evaluation demand related to the selection of coali-tion of projects within a multidimensional and multi-stakeholders deci-sion-making context. This study aims to propose a framework for the generation of coalitions of elementary actions in the context of urban regeneration processes and for their evaluation using a Multi Criteria Decision Analysis approach. The proposed evaluation framework supports decision makers in exploring dif-ferent combinations of actions in the context of urban interventions taking into account synergies, i.e. positive or negative effects on the overall per-formance of an alternative linked to the joint realization of specific pairs of actions. The proposed evaluation framework has been tested on a pilot case study dealing with urban regeneration processes in the city of Milan (Italy)
Continuous relaxation of MINLP problems by penalty functions: a practical comparison

A practical comparison of penalty functions for globally solving mixed-integer nonlinear programming (MINLP) problems is presented. The penalty approach relies on the continuous relaxation of the MINLP problem by adding a specific penalty term to the objective function. A new penalty algorithm that addresses simultaneously the reduction of the error tolerances for optimality and feasibility, as well as the reduction of the penalty parameter, is designed. Several penalty terms are tested and different penalty parameter update schemes are analyzed. The continuous nonlinear optimization problem is solved by the deterministic DIRECT optimizer. The numerical experiments show that the quality of the produced solutions are satisfactory and that the selected penalties have different performances in terms of efficiency and robustness.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia, within the projects UID/CEC/00319/2013 and UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio
Combining filter method and dynamically dimensioned search for constrained global optimization

In this work we present an algorithm that combines the filter technique and the dynamically dimensioned search (DDS) for solving nonlinear and nonconvex constrained global optimization problems. The DDS is a stochastic global algorithm for solving bound constrained problems that in each iteration generates a randomly trial point perturbing some coordinates of the current best point. The filter technique controls the progress related to optimality and feasibility defining a forbidden region of points refused by the algorithm. This region can be given by the flat or slanting filter rule. The proposed algorithm does not compute or approximate any derivatives of the objective and constraint functions. Preliminary experiments show that the proposed algorithm gives competitive results when compared with other methods.The first author thanks a scholarship supported by the International
Cooperation Program CAPES/ COFECUB at the University of Minho.
The second and third authors thanks the support given by FCT (Funda¸c˜ao para
Ciˆencia e Tecnologia, Portugal) in the scope of the projects: UID/MAT/00013/2013
and UID/CEC/00319/2013. The fourth author was partially supported by CNPq-Brazil
grants 308957/2014-8 and 401288/2014-5.info:eu-repo/semantics/publishedVersio
A Low-Cost Solution for the Monitoring of Air Pollution Parameters Through Bicycles

The monitoring of air quality parameters is a fundamental requirement for smart cities development and it is of primary importance for the quality of human life. In fact, the knowledge of air quality parameters along the days and in different areas of the city is essential to monitor its behavior and to take some preventive measurements to limit the concentration. Especially in big cities, it is very hard to have widespread updated data about pollutants and air quality parameters since normally only few air quality monitoring stations are available. In Piedmont (Italy), the reference public body for this kind of information is the ARPA (Regional Agency for the Protection of the Environment) which is responsible for the collection and the disclosure of environmental data. However, the number of monitoring stations along the city is limited and they have fixed positions. A solution through mobile sensors would be preferable to have a more comprehensive description of the phenomenon. In this paper, the authors describe the implementation of a new solution based on a mobile system to house environmental air quality and imaging sensors (since also the knowledge of the shape of the environment is a fundamental aspect). In particular, a bicycle is adopted and the paper describes all the analyses involved in the choice of the more appropriate sensors and their evaluation and behavior in a real environment. Finally, the collection and management of the acquired data is analyzed through the implementation of a dedicated GIS (Geographical Information System)
Over-Fitting in Model Selection with Gaussian Process Regression

Model selection in Gaussian Process Regression (GPR) seeks to determine the optimal values of the hyper-parameters governing the covariance function, which allows flexible customization of the GP to the problem at hand. An oft-overlooked issue that is often encountered in the model process is over-fitting the model selection criterion, typically the marginal likelihood. The over-fitting in machine learning refers to the fitting of random noise present in the model selection criterion in addition to features improving the generalisation performance of the statistical model. In this paper, we construct several Gaussian process regression models for a range of high-dimensional datasets from the UCI machine learning repository. Afterwards, we compare both MSE on the test dataset and the negative log marginal likelihood (nlZ), used as the model selection criteria, to find whether the problem of overfitting in model selection also affects GPR. We found that the squared exponential covariance function with Automatic Relevance Determination (SEard) is better than other kernels including squared exponential covariance function with isotropic distance measure (SEiso) according to the nLZ, but it is clearly not the best according to MSE on the test data, and this is an indication of over-fitting problem in model selection
On the determinacy of the moment problem for symmetric algebras of a
  locally convex space

This note aims to show a uniqueness property for the solution (whenever
exists) to the moment problem for the symmetric algebra $S(V)$ of a locally
convex space $(V, \tau)$. Let $\mu$ be a measure representing a linear
functional $L: S(V)\to\mathbb{R}$. We deduce a sufficient determinacy condition
on $L$ provided that the support of $\mu$ is contained in the union of the
topological duals of $V$ w.r.t. to countably many of the seminorms in the
family inducing $\tau$. We compare this result with some already known in
literature for such a general form of the moment problem and further discuss
how some prior knowledge on the support of the representing measure influences
its determinacy.Comment: 7 pages, note in memory of M. Marshall, minor corrections,
  acknowledgements added, update reference
On the interplay between Babai and Černý’s conjectures

Motivated by the Babai conjecture and the Černý conjecture, we study the reset thresholds of automata with the transition monoid equal to the full monoid of transformations of the state set. For automata with n states in this class, we prove that the reset thresholds are upperbounded by 2n2 -6n + 5 and can attain the value (Formula presented). In addition, we study diameters of the pair digraphs of permutation automata and construct n-state permutation automata with diameter (formula presented). © Springer International Publishing AG 2017
Educational potential of quantum cryptography and its experimental
  modular realization

The fundamental principles of quantum mechanics are considered to be hard for
understanding by unprepared listeners, many attempts of its popularization
turned out to be either difficult to grasp or incorrect. We propose quantum
cryptography as a very effective tool for quantum physics introduction as it
has the desired property set to intrigue students and outline the basic quantum
principles. A modular desktop quantum cryptography setup that can be used for
both educational and research purposes is presented. The carried out laboratory
and field tests demonstrated usability and reliability of the developed system.Comment: 4 pages, 4 figures; presented at the Scientific-Practical Conference
  "Research and Development -- 2016" (Moscow, December 14-15, 2016
OTO : A DIY platform for mobile social robots in education

This paper describes the design of OTO, a do-it-yourself expansion kit for OPSORO (Open Platform for Social Robots), that enables and facilitates the creation of mobile social robots. The expansion kit consists of modular, adaptable building blocks combined with a software toolkit, and is aimed at applications within the maker community, STEM education, and the market for creative inventor kits. Keeping reproducibility and adaptability in mind, the expansion kit can be produced entirely using digital manufacturing technology and low-cost, off-the-shelf components. Using the building blocks offered by this system, users can easily design, build and customize mobile social robots. The software is designed to address a wide range of users by offering different programming options depending on the user’s skill and experience. Inexperienced users are offered a graphical programming environment based on Blockly, whereas more advanced users can program their robot using Lua or Python. The OTO toolkit offers a fun and playful context in which a wide range of STEM-related skills are addressed
Large-scale automatic species identification

The crowd-sourced Naturewatch GBIF dataset is used to obtain a species classification dataset containing approximately 1.2 million photos of nearly 20 thousand different species of biological organisms observed in their natural habitat. We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset. The dataset contains images taken under a wide variety of conditions and is heavily imbalanced, with most species associated with only few images. We apply multi-view classification as a way to lend more influence to high frequency details, hierarchical fine-tuning to help with class imbalance and provide regularisation, and automatic specificity control for optimising classification depth. Our system achieves 55.8% accuracy when identifying individual species and around 90% accuracy at an average taxonomy depth of 5.1—equivalent to the taxonomic rank of “family”—when applying automatic specificity control
Deterministic tournament selection in local search for maximum edge weight clique on large sparse graphs

The maximum edge weight clique (MEWC) problem is important in both theories and applications. During last decades, there has been much interest in finding optimal or near-optimal solutions to this problem. Many existing heuristics focuses on academic benchmarks of relatively small size. However, very little attention has been paid to solving the MEWC problem in large sparse graphs. In this work, we exploit the so-called deterministic tournament selection (DTS) strategy to improve the local search MEWC algorithms. Experiments conducted on a broad range of large sparse graphs show that our algorithm outperforms state-of-the-art local search algorithms in this benchmark. Moreover it finds better solutions on a list of them.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Multidimensional relativistic MHD simulations of Pulsar Wind Nebulae:
  dynamics and emission

Pulsar Wind Nebulae, and the Crab nebula in particular, are the best cosmic
laboratories to investigate the dynamics of magnetized relativistic outflows
and particle acceleration up to PeV energies. Multidimensional MHD modeling by
means of numerical simulations has been very successful at reproducing, to the
very finest details, the innermost structure of these synchrotron emitting
nebulae, as observed in the X-rays. Therefore, the comparison between the
simulated source and observations can be used as a powerful diagnostic tool to
probe the physical conditions in pulsar winds, like their composition,
magnetization, and degree of anisotropy. However, in spite of the wealth of
observations and of the accuracy of current MHD models, the precise mechanisms
for magnetic field dissipation and for the acceleration of the non-thermal
emitting particles are mysteries still puzzling theorists to date. Here we
review the methodologies of the computational approach to the modeling of
Pulsar Wind Nebulae, discussing the most relevant results and the recent
progresses achieved in this fascinating field of high-energy astrophysics.Comment: 29 pages review, preliminary version. To appear in the book
  "Modelling Nebulae" edited by D. Torres for Springer, based on the invited
  contributions to the workshop held in Sant Cugat (Barcelona), June 14-17,
  201
Particle Acceleration in Pulsar Wind Nebulae: PIC modelling

We discuss the role of particle-in-cell (PIC) simulations in unveiling the
origin of the emitting particles in PWNe. After describing the basics of the
PIC technique, we summarize its implications for the quiescent and the flaring
emission of the Crab Nebula, as a prototype of PWNe. A consensus seems to be
emerging that, in addition to the standard scenario of particle acceleration
via the Fermi process at the termination shock of the pulsar wind, magnetic
reconnection in the wind, at the termination shock and in the Nebula plays a
major role in powering the multi-wavelength signatures of PWNe.Comment: 32 pages, 16 figures, to appear in the book "Modelling Nebulae"
  edited by D. Torres for Springer, based on the invited contributions to the
  workshop held in Sant Cugat (Barcelona), June 14-17, 201
Pulsar striped winds

According to magnetohydrodynamic (MHD) models, the rotational energy of a
rapidly spinning neutron star is carried away by a relativistic wind and
deposited at a large distance, in the nebula, downstream of the wind
termination shock. The energy transport in the outflow is mediated by Poynting
flux, but it is not clear how the energy stored in the fields is transferred
into the energized population of emitting particles. The most plausible
dissipation mechanisms are thought to be related to the "striped" structure of
the wind, in particular, to the existence of a current sheet, prone to
reconnection events. In this model the current sheet is a natural place for
internal dissipation and acceleration of particles responsible for pulsed,
high-energy emission. Moreover, reconnection is a promising scenario for
explaining annihilation of fields at the shock and conversion of their energy
into the kinetic energy of particles. The shock structure, however, is likely
to differ in the low-density plasmas, in which non-MHD effects intervene. In
this regime, the striped wind can dissipate its energy via an electromagnetic
precursor of the shock.Comment: invited review at the Workshop on Modelling Nebulae, June 14-17,
  2016, Sant Cugat, Spain; submitted book chapte
Efficient Certified RAT Verification

Clausal proofs have become a popular approach to validate the results of SAT
solvers. However, validating clausal proofs in the most widely supported format
(DRAT) is expensive even in highly optimized implementations. We present a new
format, called LRAT, which extends the DRAT format with hints that facilitate a
simple and fast validation algorithm. Checking validity of LRAT proofs can be
implemented using trusted systems such as the languages supported by theorem
provers. We demonstrate this by implementing two certified LRAT checkers, one
in Coq and one in ACL2