Bilevel optimisation with embedded neural networks: Application to scheduling and control integration

Scheduling problems requires to explicitly account for control considerations in their optimisation. The literature proposes two traditional ways to solve this integrated problem: hierarchical and monolithic. The monolithic approach ignores the control level's objective and incorporates it as a constraint into the upper level at the cost of suboptimality. The hierarchical approach requires solving a mathematically complex bilevel problem with the scheduling acting as the leader and control as the follower. The linking variables between both levels belong to a small subset of scheduling and control decision variables. For this subset of variables, data-driven surrogate models have been used to learn follower responses to different leader decisions. In this work, we propose to use ReLU neural networks for the control level. Consequently, the bilevel problem is collapsed into a single-level MILP that is still able to account for the control level's objective. This single-level MILP reformulation is compared with the monolithic approach and benchmarked against embedding a nonlinear expression of the neural networks into the optimisation. Moreover, a neural network is used to predict control level feasibility. The case studies involve batch reactor and sequential batch process scheduling problems. The proposed methodology finds optimal solutions while largely outperforming both approaches in terms of computational time. Additionally, due to well-developed MILP solvers, adding ReLU neural networks in a MILP form marginally impacts the computational time. The solution's error due to prediction accuracy is correlated with the neural network training error. Overall, we expose how - by using an existing big-M reformulation and being careful about integrating machine learning and optimisation pipelines - we can more efficiently solve the bilevel scheduling-control problem with high accuracy.Comment: 18 page

An out-of-core method for GPU image mapping on large 3D scenarios of the real world

[Abstract] Image mapping on 3D huge scenarios of the real world is one of the most fundamental and computational expensive processes for the integration of multi-source sensing data. Recent studies focused on the observation and characterization of Earth have been enhanced by the proliferation of Unmanned Aerial Vehicle (UAV) and sensors able to capture massive datasets with a high spatial resolution. Despite the advances in manufacturing new cameras and versatile platforms, only a few methods have been developed to characterize the study area by fusing heterogeneous data such as thermal, multispectral or hyperspectral images with high-resolution 3D models. The main reason for this lack of solutions is the challenge to integrate multi-scale datasets and high computational efforts required for image mapping on dense and complex geometric models. In this paper, we propose an efficient pipeline for multi-source image mapping on huge 3D scenarios. Our GPU-based solution significantly reduces the run time and allows us to generate enriched 3D models on-site. The proposed method is out-of-core and it uses available resources of the GPU’s machine to perform two main tasks: (i) image mapping and (ii) occlusion testing. We deploy highly-optimized GPU-kernels for image mapping and detection of self-hidden geometry in the 3D model, as well as a GPU-based parallelization to manage the 3D model considering several spatial partitions according to the GPU capabilities. Our method has been tested on 3D scenarios with different point cloud densities (66M, 271M, 542M) and two sets of multispectral images collected by two drone flights. We focus on launching the proposed method on three platforms: (i) System on a Chip (SoC), (ii) a user-grade laptop and (iii) a PC. The results demonstrate the method’s capabilities in terms of performance and versatility to be computed by commodity hardware. Thus, taking advantage of GPUs, this method opens the door for embedded and edge computing devices for 3D image mapping on large-scale scenarios in near real-time.This work has been partially supported through the research projects TIN2017-84968-R, PID2019-104184RB-I00 funded by MCIN/AEI/10.13039/501100011033 and ERDF funds “A way of doing Europe”, as well as by ED431C 2021/30, ED431F 2021/11 funded by Xunta de Galicia and 1381202 by Junta de AndalucíaXunta de Galicia; ED431C 2021/30Xunta de Galicia; ED431F 2021/11Junta de Andalucía; 138120

A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios

Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural networks. Strategies proposed to face with both challenges have been separately applied. In this paper, we introduce a hybrid method for handling both class imbalance and class overlap simultaneously in multi-class learning problems. Experimental results on five remote sensing data show that the combined approach is a promising method

A self-determined exploration of adolescents’ and parents’ experiences derived from a multidimensional school-based physical activity intervention

Purpose: Adolescents’ and parents’ experiences within a multidimensional schoolbased physical activity intervention grounded on self-determination theory were explored. Method: Qualitative data from 29 adolescents (aged 15-17 years) and three parents on behalf of the total students' families were collected via participant observation (research diary), semistructured interviews, and focus groups. Results: Adolescents perceived that the application of motivational strategies, based on selfdetermination theory, satisfied their basic psychological needs for autonomy, competence and relatedness, favored self-determined motivation, and gave rise to adaptive consequences (improved physical activity knowledge, creation of affective bonds, and increased leisure-time physical activity). These results were supported by the information reported by the students' parents. Discussion/Conclusions: The findings support the implementation of self-determination theory-based multidimensional interventions to promote adolescents’ physical activity participation. This study also presents several motivational strategies which could be useful for the design and implementation of future school-based physical activity intervention

Phosphomannosylation and the functional analysis of the extended Candida albicans MNN4-like gene family

We thank Luz A. López-Ramírez (Universidad de Guanajuato) for technical assistance. This work was supported by Consejo Nacional de Ciencia y Tecnología (ref. CB2011/166860; PDCPN2014-247109, and FC 2015-02-834), Universidad de Guanajuato (ref. 000025/11; 0087/13; ref. 1025/2016; Convocatoria Institucional para Fortalecer la Excelencia Académica 2015; CIFOREA 89/2016), Programa de Mejoramiento de Profesorado (ref. UGTO-PTC-261), and Red Temática Glicociencia en Salud (CONACYT-México). NG acknowledges the Wellcome Trust (086827, 075470, 101873, and 200208) and MRC Centre for Medical Mycology for funding (N006364/1). KJ was supported by a research visitor grant to Aberdeen from China Scholarship Council (CSC No. 201406055024). The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2017.02156/full#supplementary-materialPeer reviewedPublisher PD

Neutral molecular markers support common origin of aluminium tolerance in three congeneric grass species growing in acidic soils

Aluminium (Al) toxicity is the main abiotic stress limiting plant productivity in acidic soils that are widely distributed among arable lands. Plant species differ in the level of Al resistance showing intraspecific and interspecific variation in many crop species. However, the origin of Al-tolerance is not well known. Three annual species, difficult to distinguish phenotypically and that were until recently misinterpreted as a single complex species under Brachypodium distachyon, have been recently separated into three distinct species: the diploids B. distachyon (2n = 10) and B. stacei (2n = 20), and B. hybridum (2n = 30), the allotetraploid derived from the two diploid species. The aims of this work were to know the origin of Al-tolerance in acidic soil conditions within these three Brachypodium species and to develop new DNA markers for species discrimination. Two multiplex SSR-PCRs allowed to genotype a group of 94 accessions for 17 pentanucleotide microsatellite (SSRs) loci. The variability for 139 inter-microsatellite (ISSRs) markers was also examined. The genetic relationships obtained using those neutral molecular markers (SSRs and ISSRs) support that all Al-tolerant allotetraploid accessions of B. hybridum have a common origin that is related with both geographic location and acidic soils. The possibility that the adaptation to acidic soils caused the isolation of the tolerant B. hybridum populations from the others is discussed. We finally describe a new, easy, DNA barcoding method based in the upstream-intron 1 region of the ALMT1 gene, a tool that is 100 % effective to distinguish among these three Brachypodium species

Modelling the Dynamics of an Aedes albopictus Population

We present a methodology for modelling population dynamics with formal means of computer science. This allows unambiguous description of systems and application of analysis tools such as simulators and model checkers. In particular, the dynamics of a population of Aedes albopictus (a species of mosquito) and its modelling with the Stochastic Calculus of Looping Sequences (Stochastic CLS) are considered. The use of Stochastic CLS to model population dynamics requires an extension which allows environmental events (such as changes in the temperature and rainfalls) to be taken into account. A simulator for the constructed model is developed via translation into the specification language Maude, and used to compare the dynamics obtained from the model with real data.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314

Unveiling the radiative local density of optical states of a plasmonic nanocavity by STM

Atomically-sharp tips in close proximity of metal surfaces create plasmonic nanocavities supporting both radiative (bright) and non-radiative (dark) localized surface plasmon modes. Disentangling their respective contributions to the total density of optical states remains a challenge. Electroluminescence due to tunnelling through the tip-substrate gap could allow the identification of the radiative component, but this information is inherently convoluted with that of the electronic structure of the system. In this work, we present a fully experimental procedure to eliminate the electronic-structure factors from the scanning tunnelling microscope luminescence spectra by confronting them with spectroscopic information extracted from elastic current measurements. Comparison against electromagnetic calculations demonstrates that this procedure allows the characterization of the meV shifts experienced by the nanocavity plasmonic modes under atomic-scale gap size changes. Therefore, the method gives access to the frequency-dependent radiative Purcell enhancement that a microscopic light emitter would undergo when placed at such nanocavityWe acknowledge financial support from the Spanish Ministry for Economy and Competitiveness (grants FIS2015-72482-EXP, FIS2015-64951-R, FIS2016-78591-C3-1-R, PGC2018-098613—B-C21, PGC2018-096047-B-I00, RTI2018-099737-B-I00 and MAT2014-53432-C5-5-R), the regional government of Comunidad de Madrid (grant S2018/NMT-4321), Universidad Autónoma de Madrid (UAM/48 and UAM/134) and IMDEA Nanoscience. Both IMDEA Nanoscience and IFIMAC acknowledge support from the Severo Ochoa and Maria de Maeztu Programmes for Centres and Units of Excellence in R&D (MINECO, Grants SEV-2016-0686 and MDM-2014-0377). We also acknowledge support by the QuantERA program of the European Union with funding by the Spanish AEI through project PCI2018-09314