445 research outputs found
A Finite-Time Cutting Plane Algorithm for Distributed Mixed Integer Linear Programming
Many problems of interest for cyber-physical network systems can be
formulated as Mixed Integer Linear Programs in which the constraints are
distributed among the agents. In this paper we propose a distributed algorithm
to solve this class of optimization problems in a peer-to-peer network with no
coordinator and with limited computation and communication capabilities. In the
proposed algorithm, at each communication round, agents solve locally a small
LP, generate suitable cutting planes, namely intersection cuts and cost-based
cuts, and communicate a fixed number of active constraints, i.e., a candidate
optimal basis. We prove that, if the cost is integer, the algorithm converges
to the lexicographically minimal optimal solution in a finite number of
communication rounds. Finally, through numerical computations, we analyze the
algorithm convergence as a function of the network size.Comment: 6 pages, 3 figure
Skills substitution and trust: a new conception of attitude towards AI in a-HRM
Attitude towards new technologies depends on different factors. In case of AI (artificial intelligence), workers may perceive their own skills as easily substitutable and look at their job as likely to be replaced. This perception may have negative impact on their acceptance towards implementation of intelligent machines and automation, if there wouldn’t be a well based trust on the improvements brought by these technologies. Unfolding from such considerations, we have collected data from a diversified sample of 183 workers and requested a bootstrapped estimate from 5,000 samples. As a result, we propose a mediated process between skills substitution and perceived overall job replacement, moderated by trust, which leads to attitude towards AI in a-HRM (automated human resources management). Surprisingly for high substitution perceptions, workers manifested more positive attitude towards AI. This provided big room of discussion and great enrichments in current literature; plus considerable practical implication in understanding workers behaviors face automation investments in companies.A atitude em relação às novas tecnologias depende de diferentes fatores. No caso da IA (inteligência artificial), os trabalhadores podem perceber as próprias competências como facilmente substituíveis e perceber a instabilidade do seu trabalho. Essa perceção pode ter um impacto negativo na aceitação da implementação de máquinas inteligentes e de investimentos em automação, se não houvesse uma confiança bem fundamentada nas melhorias trazidas por essas tecnologias. Começando de tais considerações, coletamos dados de uma amostra diversificada de 183 trabalhadores e solicitamos uma bootstrapped estimate de 5.000 amostras. Como resultado, propomos um modelo mediado entre a substituição de competências e a perceção geral da substituição do trabalho, moderada pela confiança, o que leva a atitude face as IA em a-HRM (automated human resources management). Surpreendentemente, para perceções de alta substituição, os trabalhadores manifestaram uma atitude mais positiva em relação as IA. Isso proporcionou grande espaço de discussão e grandes enriquecimentos na literatura atual, mais implicações práticas fundamentais na compreensão dos comportamentos dos trabalhadores em frente aos investimentos em automação nas empresas
Separating Topological Noise from Features Using Persistent Entropy
Topology is the branch of mathematics that studies shapes
and maps among them. From the algebraic definition of topology a new
set of algorithms have been derived. These algorithms are identified
with “computational topology” or often pointed out as Topological Data
Analysis (TDA) and are used for investigating high-dimensional data in a
quantitative manner. Persistent homology appears as a fundamental tool
in Topological Data Analysis. It studies the evolution of k−dimensional
holes along a sequence of simplicial complexes (i.e. a filtration). The set
of intervals representing birth and death times of k−dimensional holes
along such sequence is called the persistence barcode. k−dimensional
holes with short lifetimes are informally considered to be topological
noise, and those with a long lifetime are considered to be topological
feature associated to the given data (i.e. the filtration). In this paper, we
derive a simple method for separating topological noise from topological
features using a novel measure for comparing persistence barcodes called
persistent entropy.Ministerio de Economía y Competitividad MTM2015-67072-
Distributed Mixed-Integer Linear Programming via Cut Generation and Constraint Exchange
Many problems of interest for cyber-physical network systems can be formulated as mixed-integer linear programs in which the constraints are distributed among the agents. In this paper, we propose a distributed algorithmic framework to solve this class of optimization problems in a peer-to-peer network with no coordinator and with limited computation and communication capabilities. At each communication round, agents locally solve a small linear program, generate suitable cutting planes, and communicate a fixed number of active constraints. Within the distributed framework, we first propose an algorithm that, under the assumption of integer-valued optimal cost, guarantees finite-time convergence to an optimal solution. Second, we propose an algorithm for general problems that provides a suboptimal solution up to a given tolerance in a finite number of communication rounds. Both algorithms work under asynchronous, directed, unreliable networks. Finally, through numerical computations, we analyze the algorithm scalability in terms of the network size. Moreover, for a multi-agent multi-task assignment problem, we show, consistently with the theory, its robustness to packet loss
Using Topological Data Analysis for diagnosis pulmonary embolism
Pulmonary Embolism (PE) is a common and potentially lethal condition. Most
patients die within the first few hours from the event. Despite diagnostic
advances, delays and underdiagnosis in PE are common.To increase the diagnostic
performance in PE, current diagnostic work-up of patients with suspected acute
pulmonary embolism usually starts with the assessment of clinical pretest
probability using plasma d-Dimer measurement and clinical prediction rules. The
most validated and widely used clinical decision rules are the Wells and Geneva
Revised scores. We aimed to develop a new clinical prediction rule (CPR) for PE
based on topological data analysis and artificial neural network. Filter or
wrapper methods for features reduction cannot be applied to our dataset: the
application of these algorithms can only be performed on datasets without
missing data. Instead, we applied Topological data analysis (TDA) to overcome
the hurdle of processing datasets with null values missing data. A topological
network was developed using the Iris software (Ayasdi, Inc., Palo Alto). The PE
patient topology identified two ares in the pathological group and hence two
distinct clusters of PE patient populations. Additionally, the topological
netowrk detected several sub-groups among healthy patients that likely are
affected with non-PE diseases. TDA was further utilized to identify key
features which are best associated as diagnostic factors for PE and used this
information to define the input space for a back-propagation artificial neural
network (BP-ANN). It is shown that the area under curve (AUC) of BP-ANN is
greater than the AUCs of the scores (Wells and revised Geneva) used among
physicians. The results demonstrate topological data analysis and the BP-ANN,
when used in combination, can produce better predictive models than Wells or
revised Geneva scores system for the analyzed cohortComment: 18 pages, 5 figures, 6 tables. arXiv admin note: text overlap with
arXiv:cs/0308031 by other authors without attributio
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning
Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain
tumour, it tends to occur in adults between the ages of 45 and 70 and it
accounts for 52 percent of all primary brain tumours. Usually, GBMs are
detected by magnetic resonance images (MRI). Among MRI, Fluid-attenuated
inversion recovery (FLAIR) sequence produces high quality digital tumour
representation. Fast detection and segmentation techniques are needed for
overcoming subjective medical doctors (MDs) judgment. In the present
investigation, we intend to demonstrate by means of numerical experiments that
topological features combined with textural features can be enrolled for GBM
analysis and morphological characterization on FLAIR. To this extent, we have
performed three numerical experiments. In the first experiment, Topological
Data Analysis (TDA) of a simplified 2D tumour growth mathematical model had
allowed to understand the bio-chemical conditions that facilitate tumour
growth: the higher the concentration of chemical nutrients the more virulent
the process. In the second experiment topological data analysis was used for
evaluating GBM temporal progression on FLAIR recorded within 90 days following
treatment (e.g., chemo-radiation therapy - CRT) completion and at progression.
The experiment had confirmed that persistent entropy is a viable statistics for
monitoring GBM evolution during the follow-up period. In the third experiment
we had developed a novel methodology based on topological and textural features
and automatic interpretable machine learning for automatic GBM classification
on FLAIR. The algorithm reached a classification accuracy up to the 97%.Comment: 22 pages; 16 figure
Decentralized Control of Uncertain Multi-Agent Systems with Connectivity Maintenance and Collision Avoidance
This paper addresses the problem of navigation control of a general class of
uncertain nonlinear multi-agent systems in a bounded workspace of
with static obstacles. In particular, we propose a decentralized
control protocol such that each agent reaches a predefined position at the
workspace, while using only local information based on a limited sensing
radius. The proposed scheme guarantees that the initially connected agents
remain always connected. In addition, by introducing certain distance
constraints, we guarantee inter-agent collision avoidance, as well as,
collision avoidance with the obstacles and the boundary of the workspace. The
proposed controllers employ a class of Decentralized Nonlinear Model Predictive
Controllers (DNMPC) under the presence of disturbances and uncertainties.
Finally, simulation results verify the validity of the proposed framework.Comment: IEEE European Control Conference (ECC), Limassol, Cyprus, June 201
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