'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.

A Distributed Support based on Kubeflow to Ease MLOps of Federated Learning Algorithms

The main objective of my thesis work is to exploit the Google native and open-source platform Kubeflow, specifically using Kubeflow pipelines, to execute a Federated Learning scalable ML process in a 5G-like and simplified test architecture hosting a Kubernetes cluster and apply the largely adopted FedAVG algorithm and FedProx its optimization empowered by the ML platform ‘s abilities to ease the development and production cycle of this specific FL process. FL algorithms are more are and more promising and adopted both in Cloud application development and 5G communication enhancement through data coming from the monitoring of the underlying telco infrastructure and execution of training and data aggregation at edge nodes to optimize the global model of the algorithm ( that could be used for example for resource provisioning to reach an agreed QoS for the underlying network slice) and after a study and a research over the available papers and scientific articles related to FL with the help of the CTTC that suggests me to study and use Kubeflow to bear the algorithm we found out that this approach for the whole FL cycle deployment was not documented and may be interesting to investigate more in depth. This study may lead to prove the efficiency of the Kubeflow platform itself for this need of development of new FL algorithms that will support new Applications and especially test the FedAVG algorithm performances in a simulated client to cloud communication using a MNIST dataset for FL as benchmark.

The Effect Of Pelvic Floor Muscle Training Alone Or In Combination With Electrostimulation In The Treatment Of Sexual Dysfunction In Women With Multiple Sclerosis.

Sexual dysfunction (SD) affects up to 80% of multiple sclerosis (MS) patients and pelvic floor muscles (PFMs) play an important role in the sexual function of these patients. The objective of this paper is to evaluate the impact of a rehabilitation program to treat lower urinary tract symptoms on SD of women with MS. Thirty MS women were randomly allocated to one of three groups: pelvic floor muscle training (PFMT) with electromyographic (EMG) biofeedback and sham neuromuscular electrostimulation (NMES) (Group I), PFMT with EMG biofeedback and intravaginal NMES (Group II), and PFMT with EMG biofeedback and transcutaneous tibial nerve stimulation (TTNS) (Group III). Assessments, before and after the treatment, included: PFM function, PFM tone, flexibility of the vaginal opening and ability to relax the PFMs, and the Female Sexual Function Index (FSFI) questionnaire. After treatment, all groups showed improvements in all domains of the PERFECT scheme. PFM tone and flexibility of the vaginal opening was lower after the intervention only for Group II. All groups improved in arousal, lubrication, satisfaction and total score domains of the FSFI questionnaire. This study indicates that PFMT alone or in combination with intravaginal NMES or TTNS contributes to the improvement of SD.

Treadmill Training Increases Sirt-1 And Pgc-1 α Protein Levels And Ampk Phosphorylation In Quadriceps Of Middle-aged Rats In An Intensity-dependent Manner.

The present study investigated the effects of running at 0.8 or 1.2 km/h on inflammatory proteins (i.e., protein levels of TNF- α , IL-1 β , and NF- κ B) and metabolic proteins (i.e., protein levels of SIRT-1 and PGC-1 α , and AMPK phosphorylation) in quadriceps of rats. Male Wistar rats at 3 (young) and 18 months (middle-aged rats) of age were divided into nonexercised (NE) and exercised at 0.8 or 1.2 km/h. The rats were trained on treadmill, 50 min per day, 5 days per week, during 8 weeks. Forty-eight hours after the last training session, muscles were removed, homogenized, and analyzed using biochemical and western blot techniques. Our results showed that: (a) running at 0.8 km/h decreased the inflammatory proteins and increased the metabolic proteins compared with NE rats; (b) these responses were lower for the inflammatory proteins and higher for the metabolic proteins in young rats compared with middle-aged rats; (c) running at 1.2 km/h decreased the inflammatory proteins and increased the metabolic proteins compared with 0.8 km/h; (d) these responses were similar between young and middle-aged rats when trained at 1.2 km. In summary, the age-related increases in inflammatory proteins, and the age-related declines in metabolic proteins can be reversed and largely improved by treadmill training.

Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

Undulatory Physical Resistance Training Program Increases Maximal Strength In Elderly Type 2 Diabetics.

To investigate the effects of a specific protocol of undulatory physical resistance training on maximal strength gains in elderly type 2 diabetics. The study included 48 subjects, aged between 60 and 85 years, of both genders. They were divided into two groups: Untrained Diabetic Elderly (n=19) with those who were not subjected to physical training and Trained Diabetic Elderly (n=29), with those who were subjected to undulatory physical resistance training. The participants were evaluated with several types of resistance training's equipment before and after training protocol, by test of one maximal repetition. The subjects were trained on undulatory resistance three times per week for a period of 16 weeks. The overload used in undulatory resistance training was equivalent to 50% of one maximal repetition and 70% of one maximal repetition, alternating weekly. Statistical analysis revealed significant differences (p<0.05) between pre-test and post-test over a period of 16 weeks. The average gains in strength were 43.20% (knee extension), 65.00% (knee flexion), 27.80% (supine sitting machine), 31.00% (rowing sitting), 43.90% (biceps pulley), and 21.10% (triceps pulley). Undulatory resistance training used with weekly different overloads was effective to provide significant gains in maximum strength in elderly type 2 diabetic individuals.

Leucine Supplementation Does Not Affect Protein Turnover And Impairs The Beneficial Effects Of Endurance Training On Glucose Homeostasis In Healthy Mice.

Endurance exercise training as well as leucine supplementation modulates glucose homeostasis and protein turnover in mammals. Here, we analyze whether leucine supplementation alters the effects of endurance exercise on these parameters in healthy mice. Mice were distributed into sedentary (C) and exercise (T) groups. The exercise group performed a 12-week swimming protocol. Half of the C and T mice, designated as the CL and TL groups, were supplemented with leucine (1.5 % dissolved in the drinking water) throughout the experiment. As well known, endurance exercise training reduced body weight and the retroperitoneal fat pad, increased soleus mass, increased VO2max, decreased muscle proteolysis, and ameliorated peripheral insulin sensitivity. Leucine supplementation had no effect on any of these parameters and worsened glucose tolerance in both CL and TL mice. In the soleus muscle of the T group, AS-160(Thr-642) (AKT substrate of 160 kDa) and AMPK(Thr-172) (AMP-Activated Protein Kinase) phosphorylation was increased by exercise in both basal and insulin-stimulated conditions, but it was reduced in TL mice with insulin stimulation compared with the T group. Akt phosphorylation was not affected by exercise but was lower in the CL group compared with the other groups. Leucine supplementation increased mTOR phosphorylation at basal conditions, whereas exercise reduced it in the presence of insulin, despite no alterations in protein synthesis. In trained groups, the total FoxO3a protein content and the mRNA for the specific isoforms E2 and E3 ligases were reduced. In conclusion, leucine supplementation did not potentiate the effects of endurance training on protein turnover, and it also reduced its positive effects on glucose homeostasis.

Effect Of Concurrent Training With Blood Flow Restriction In The Elderly.

The aim of this present study was to investigate on the effects of concurrent training with blood flow restriction (BFR-CT) and concurrent training (CT) on the aerobic fitness, muscle mass and muscle strength in a cohort of older individuals. 25 healthy older adults (64.7±4.1 years; 69.33±10.8 kg; 1.6±0.1 m) were randomly assigned to experimental groups: CT (n=8, endurance training (ET), 2 days/week for 30-40 min, 50-80% VO2peak and RT, 2 days/week, leg press with 4 sets of 10 reps at 70-80% of 1-RM with 60 s rest), BFR-CT (n=10, ET, similar to CT, but resistance training with blood flow restriction: 2 days/week, leg press with 1 set of 30 and 3 sets of 15 reps at 20-30% 1-RM with 60 s rest) or control group (n=7). Quadriceps cross-sectional area (CSAq), 1-RM and VO2peak were assessed pre- and post-examination (12 wk). The CT and BFR-CT showed similar increases in CSAq post-test (7.3%, P<0.001; 7.6%, P<0.0001, respectively), 1-RM (38.1%, P<0.001; 35.4%, P=0.001, respectively) and VO2peak (9.5%, P=0.04; 10.3%, P=0.02, respectively). The BFR-CT promotes similar neuromuscular and cardiorespiratory adaptations as CT.

About The Article: Effect Of Combined Aerobic And Resistance Training Versus Aerobic Training On Arterial Stiffness.

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Creatina quinase e dor muscular tardia na musculação : estudo experimental em adultos e jovens com o "circuit weight training" e o "multiple set system"

Universidade Estadual de Campinas . Faculdade de Educação Física

Variabilidade da frequência cardíaca após treinamento concorrente : comparação entre homens e mulheres de meia-idade = Heart rate variability after concurrent training : comparison between middle-aged men and women

Universidade Estadual de Campinas . Faculdade de Educação Física

Testes de desempenho aeróbio relacionados a intensidades de corrida em treinamento e competição para fundistas de alto rendimento = : Aerobic performance tests related to training and competition running intensities for high performance distance runners

Universidade Estadual de Campinas . Faculdade de Educação Física

Effect of gender on training-induced vascular remodeling in SHR

There is accumulating evidence that physical inactivity, associated with the modern sedentary lifestyle, is a major determinant of hypertension. It represents the most important modifiable risk factor for cardiovascular diseases, which are the leading cause of morbidity and mortality for both men and women. In addition to involving sympathetic overactivity that alters hemodynamic parameters, hypertension is accompanied by several abnormalities in the skeletal muscle circulation including vessel rarefaction and increased arteriole wall-to-lumen ratio, which contribute to increased total peripheral resistance. Low-intensity aerobic training is a promising tool for the prevention, treatment and control of high blood pressure, but its efficacy may differ between men and women and between male and female animals. This review focuses on peripheral training-induced adaptations that contribute to a blood pressure-lowering effect, with special attention to differential responses in male and female spontaneously hypertensive rats (SHR). Heart, diaphragm and skeletal muscle arterioles (but not kidney arterioles) undergo eutrophic outward remodeling in trained male SHR, which contributed to a reduction of peripheral resistance and to a pressure fall. In contrast, trained female SHR showed no change in arteriole wall-to-lumen ratio and no pressure fall. On the other hand, training-induced adaptive changes in capillaries and venules (increased density) were similar in male and female SHR, supporting a similar hyperemic response to exercise.

Time course of training-induced microcirculatory changes and of vegf expression in skeletal muscles of spontaneously hypertensive female rats

Exercise-induced vessel changes modulate arterial pressure (AP) in male spontaneously hypertensive rats (SHR). Vascular endothelial growth factor (VEGF) is important for angiogenesis of skeletal muscle. The present study evaluated the time course of VEGF and angiogenesis after short- and long-term exercise training of female SHR and Wistar Kyoto (WKY) rats, 8-9 weeks (200-250 g). Rats were allocated to daily training or remained sedentary for 3 days (N = 23) or 13 weeks (N = 23). After training, the carotid artery was catheterized for AP measurements. Locomotor (tibialis anterior and gracilis) and non-locomotor skeletal muscles (temporalis) were harvested and prepared for histologic and protein expression analyses. Training increased treadmill performance by all groups (SHR = 28%, WKY = 64%, 3 days) and (SHR = 141%, WKY = 122%, 13 weeks). SHR had higher values of AP than WKY (174 ± 4 vs 111 ± 2 mmHg) that were not altered by training. Three days of running increased VEGF expression (SHR = 28%, WKY = 36%) simultaneously with an increase in capillary-to-fiber ratio in gracilis muscle (SHR = 19%, WKY = 15%). In contrast, 13 weeks of training increased gracilis capillary-to-fiber ratio (SHR = 18%, WKY = 19%), without simultaneous changes in VEGF expression. Training did not change VEGF expression and capillarity of temporalis muscle. We conclude that training stimulates time- and tissue-dependent VEGF protein expression, independent of pressure levels. VEGF triggers angiogenesis in locomotor skeletal muscle shortly after the exercise starts, but is not involved in the maintenance of capillarity after long-term exercise in female rats.