Miracle-dz/UniFAR-FTUs huggingface dataset:
Considering risk profiles in design optimization for series-parallel systems

The objective of the redundancy allocation problem is to select the parts to be used and a design configuration to optimize some objective function without violating system level constraints. In this paper, the problem was formulated in a more general framework than has previously been accomplished, allowing for the explicit consideration of the risk-profile of system designers and users. Risk is related to the probability that a randomly selected system will be less reliable than a predicted or specified value. Previous formulations of the problem involved a maximization of the expected value of system reliability or the mean-time-to-failure, which implies that designers are risk-neutral. In this paper, both component reliability and time-to-failure are considered as random variables, and lower-bound limits are used in the optimization problem. The extent of the risk associated with a particular system design project was included in the problem formulations and influenced the final design decisions. These formulations were intended to more faithfully address the actual concerns and considerations of the engineering design community. The problems were then solved using genetic algorithms (GAs). Several illustrative examples are provided which show different design configurations depending on the risk profile.
Fault Diagnosis of Hydraulic Actuator Based on Least Squares Support Vector Machines

According to the fault diagnosis method based on a mathematical model of the control system, a fault diagnosis method is proposed for hydraulic actuator based on least squares support vector machines (LS-SVM). This new fault diagnosis approach leads to solving a linear set of equations instead of solving a quadratic programming problem. The LS-SVM formulation also involves less tuning parameters. By using the measurable parameters, the fault diagnosis model of LS-SVM for hydraulic actuator are established. The simulation results show that this method can effectively detect every fault of servo valve, displacement sensor and servo amplifier.
A bundle method for hydrothermal scheduling

Lagrangian relaxation has been widely used in hydrothermal scheduling. Complicating constraints are relaxed by multipliers which are usually updated by a subgradient method (SGM). The SGM suffers from slow convergence caused by the nondifferentiable characteristics of dual functions. This paper presents an algorithm that utilizes the bundle trust region method (BTRM) to update the multipliers within the Lagrangian relaxation framework. The BTRM is shown to converge faster than the SGM as well as other bundle type methods in optimizing nondifferentiable dual functions. The application of BTRM for solving hydro subproblems results in greatly improved convergence over the SGM. Comparing BTRM with another bundle type method in updating the high level multipliers shows that better solution can be obtained by BTRM.
Kinematic accuracy analysis of serial manipulator based on POE formula

In the kinematic calibration of robots, an essential step is to develop an error model. Recently, the product of exponentials (POE) due to its singularity-free property has been widely used in the calibration. However, the previous error models based on POE are of linear kind. In this paper, to investigate the applicability of the liner model, a second order error model is established. Based on the error model, the corresponding statistical models are developed and applied for the Puma 560. It is found that the linear error model is adequately accurate for most practical manipulations.
Generalized Planning with Procedural Domain Control Knowledge

Generalized planning is the task of generating a single solution that is valid for a set of planning problems. In this paper we show how to represent and compute generalized plans using procedural Domain Control Knowledge (DCK). We define a divide and conquer approach that first generates the procedural DCK solving a set of planning problems representative of certain subtasks and then compile it as callable procedures of the overall generalized planning problem. Our procedure calling mechanism allows nested and recursive procedure calls and is implemented in PDDL so that classical planners can compute and exploit procedural DCK. Experiments show that an off-the-shelf classical planner, using procedural DCK as callable procedures, can compute generalized plans in a wide range of domains including non-trivial ones, such as sorting variable-size lists or DFS traversal of binary trees with variable size.
Applying Parallel Design Patterns on Molecular Dynamics Simulation

Molecular dynamics simulates behavior of atoms and molecules. Molecular dynamics Simulation demonstrates and derives macroscopic properties by atomic interactions. In this experiment we have redesigned Molecular Dynamics (MD) Simulation by applying different parallel design pattern. Redesigned MD Simulation focuses on adaptability to different architectures and scalability to use with large number of atoms and long duration simulation. The paper demonstrates performance of MD on different architectures.
Does physical training increase insulin sensitivity in chronic heart failure patients?

To determine the effect of training on insulin sensitivity (IS) and how this relates to peak V(.)O(2) (peak oxygen uptake) in CHF (chronic heart failure), 77 CHF patients (New York Heart Association class, II/III; men/women, 59/18; age, 60+/-9 years; body mass index, 26.7+/-3.9 kg/m(2); left ventricular ejection fraction, 26.9+/-8.1%; expressed as means+/-S.D. ) participated in the study. Patients were randomly assigned to a training or control group (TrG or CG respectively). Sixty-one patients completed the study. Patients participated in training (combined strength and endurance exercises) four times per week, two times supervised and two times at home. Before and after intervention, anthropometry, IS (euglycaemic hyperinsulinaemic clamp) and peak V(.)O(2) (incremental cycle ergometry) were assessed. Intervention did not affect IS significantly, even though IS increased by 20% in TrG and 11% in CG (not significant). Peak V(.)O(2) increased as a result of training (6% increase in TrG; 2% decrease in CG; P <0.05). In both groups (TrG and CG), the change in IS correlated positively with the change in peak V(.)O(2) ( r =0.30, P <0.05). Training resulted in an increase in peak V(.)O(2), but not in IS. Whether physical training actually increases IS in CHF patients remains unclear.
Near-optimal multiuser detection in synchronous CDMA using probabilistic data association

A probabilistic data association (PDA) method is proposed in this letter for multiuser detection over synchronous code-division multiple-access (CDMA) communication channels. PDA models the undecided user signals as binary random variables. By approximating the inter-user interference (IUI) as Gaussian noise with an appropriately elevated covariance matrix, the probability associated with each user signal is iteratively updated. Computer simulations show that the system usually converges within three to four iterations, and the resulting probability of error is very close to that of the optimal maximum-likelihood (ML) detector. Further modifications are also presented to significantly reduce the computational cost.
Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones

Despite being a major public health problem, falls in the elderly cannot be detected efficiently yet. Many studies have used acceleration as the main input to discriminate between falls and activities of daily living (ADL). In recent years, there has been an increasing interest in using smartphones for fall detection. The most promising results have been obtained by supervised Machine Learning algorithms. However, a drawback of these approaches is that they rely on falls simulated by young or mature people, which might not represent every possible fall situation and might be different from older people's falls. Thus, we propose to tackle the problem of fall detection by applying a kind of novelty detection methods which rely only on true ADL. In this way, a fall is any abnormal movement with respect to ADL. A system based on these methods could easily adapt itself to new situations since new ADL could be recorded continuously and the system could be re-trained on the fly. The goal of this work is to explore the use of such novelty detectors by selecting one of them and by comparing it with a state-of-the-art traditional supervised method under different conditions. The data sets we have collected were recorded with smartphones. Ten volunteers simulated eight type of falls, whereas ADL were recorded while they carried the phone in their real life. Even though we have not collected data from the elderly, the data sets were suitable to check the adaptability of novelty detectors. They have been made publicly available to improve the reproducibility of our results. We have studied several novelty detection methods, selecting the nearest neighbour-based technique (NN) as the most suitable. Then, we have compared NN with the Support Vector Machine (SVM). In most situations a generic SVM outperformed an adapted NN.
NANOPARTICLES-BASED WOOD PRESERVATIVES: THE NEXT GENERATION OF WOOD PROTECTION?

Wood is a natural material that presents a great variation of properties. Many treatments have been developed in order to extend the service life of wood products. Nowadays, products with low toxicity and economic viability are a challenge to researchers. Nanotechnology has been indicated as a solution to this issue, since wood preservatives can be utilized in low concentration, promoting satisfactory results in terms of protection with no color changes. The main goal of this review is to present nanotechnology advances on wood protection. Self-cleaning surfaces, scratch and weathering resistance, and biocides properties have been achieved through nanoparticles (NPs) applications. Studies evaluating the performance of NPs from silver (Ag), boron (B), copper (Cu), zinc (Zn), zinc oxide (ZnO), zinc borate (B2O6Zn3), and titanium dioxide (TiO2) on wood protections have reported promising fi ndings. Tests performed against termites, rot, mold and stain fungi, and UV degradation have demonstrated that some biocides have their properties improved in nanoscale. Controlled release and encapsulation technologies are another important matter once it can increase the effectiveness of wood treatments. NPs risk assessment for human health and the environment are still incipient. Despite of that, some products as nanozinc oxide (nanoZnO) and titanium dioxide (nanoTiO2) present encouraging potential. NanoZnO and NanoTiO2 have been reported as promising antifungals, antibacterials, and antiviral agents; beyond theirs biocides properties, low ecotoxicity impacts to the environment are expected. Based on this review, we consider that wood treatments based on NPs may play an important role in the next generation of wood protection systems. v.24 n.4 2018 NANOPARTICLES-BASED WOOD PRESERVATIVES: THE NEXT GENERATION OF WOOD PROTECTION?
Event-driven Pipeline for Low-latency Low-compute Keyword Spotting and Speaker Verification System

This work presents an event-driven acoustic sensor processing pipeline to power a low-resource voice-activated smart assistant. The pipeline includes four major steps; namely localization, source separation, keyword spotting (KWS) and speaker verification (SV). The pipeline is driven by a front-end binaural spiking silicon cochlea sensor. The timing information carried by the output spikes of the cochlea provide spatial cues for localization and source separation. Spike features are generated with low latencies from the separated source spikes and are used by both KWS and SV which rely on state-of-the-art deep recurrent neural network architectures with a small memory footprint. Evaluation on a self-recorded event dataset based on TIDIGITS shows accuracies of over 93% and 88% on KWS and SV respectively, with minimum system latency of 5 ms on a limited resource device.
Nutritional status, antimicrobial and anti-biofilm activity of Potamogeton nodosus Poir

Macrophytes have been approved as a natural resource for nutritional components as well as antibacterial and anti-biofilms agents. In this context the nutritive status and the antimicrobial activity of the Egyptian macrophytes species Potamogeton nodosus have been studied. P. nodosus have approached a high nutritional value which was reflected in lower ash (11.93%) and considerably high organic nutrient level (88.07%). In addition, this plant contains high protein, carbohydrates, potassium and nitrogen contents with very low concentrations of micro-elements (Iron, Manganese, Zinc and Copper) which make it a valuable source for fishes and animals diets. The extracts also revealed a high phenolic and flavonoids contents (19.313 mg/g gallic acid equivalent and 17.885 mg/g rutin respectively). Both antibacterial, and anti-biofilm activity of three solvents, chloroform, methanol and petroleum ether extracts of P. nodosus Poir were tested against Pseudomonas aeruginosa ATCC 27853, and Escherichia coli ATCC 25922.The methanol extract showed the highest antimicrobial and anti-biofilm activity. The findings of this investigation suggested the potential of Potamogeton nodosus as a new supplementary food resource. In addition to the antimicrobial activity of its extracts especially methanol extract, which would have promising applications in this field.
Copper-zinc superoxide dismutase is activated through a sulfenic acid intermediate at a copper ion entry site

Metallochaperones are a diverse family of trafficking molecules that provide metal ions to protein targets for use as cofactors. The copper chaperone for superoxide dismutase (Ccs1) activates immature copper-zinc superoxide dismutase (Sod1) by delivering copper and facilitating the oxidation of the Sod1 intramolecular disulfide bond. Here, we present structural, spectroscopic, and cell-based data supporting a novel copper-induced mechanism for Sod1 activation. Ccs1 binding exposes an electropositive cavity and proposed “entry site” for copper ion delivery on immature Sod1. Copper-mediated sulfenylation leads to a sulfenic acid intermediate that eventually resolves to form the Sod1 disulfide bond with concomitant release of copper into the Sod1 active site. Sod1 is the predominant disulfide bond-requiring enzyme in the cytoplasm, and this copper-induced mechanism of disulfide bond formation obviates the need for a thiol/disulfide oxidoreductase in that compartment.
Under-relaxed quasi-Newton acceleration for an inverse fixed-point problem coming from Positron Emission Tomography

Abstract Quasi-Newton acceleration is an interesting tool to improve the performance of numerical methods based on the fixed-point paradigm. In this paper, a briefly description of the compartmental model employed for a typical task in perfusion imaging is presented. Next, a quasi-Newton strategy for accelerating the convergence of fixed-point iterations is analyzed. For that, classical secant updates are considered. Finally, the quasi-Newton strategy is applied on the practical problem of represent the kinetic behavior of a PET (Positron Emission Tomography) tracer during cardiac perfusion. The performance of the method when applied to real data problems is illustrated numerically.
Bid Truncation, Bid/Bax Targeting to the Mitochondria, and Caspase Activation Associated with Neutrophil Apoptosis Are Inhibited by Granulocyte Colony-Stimulating Factor

Neutrophil apoptosis constitutes a way of managing neutrophil-mediated reactions. It allows coping with infections, but avoiding overt bystander tissue damage. Using digitonin-based subcellular fractionation and Western blotting, we found that spontaneous apoptosis of human neutrophils (after ∼20 h of culture) was associated with translocation of two proapoptotic Bcl-2 homologues, Bid and Bax, to the mitochondria and truncation of Bid, with subsequent release of Omi/HtrA2 and Smac/DIABLO into the cytosol. These events were accompanied by processing and increased enzymatic activity of caspase-8, -9, and -3. A G-CSF-mediated reduction in apoptosis coincided with inhibition of all these reactions. The G-CSF-induced effects were differentially dependent on newly synthesized mediators. Whereas inhibition of Bax targeting to the mitochondria and inhibition of caspase activation by G-CSF were dependent on protein synthesis, Bid truncation and redistribution were prevented by G-CSF regardless of the presence of the protein synthesis inhibitor cycloheximide. Apparently, the observed Bid changes were dispensable for neutrophil apoptosis. Although the regulators of the inhibitor of apoptosis proteins (IAPs), Omi/HtrA2 and Smac/DIABLO, were released into the cytosol during apoptosis, we did not observe cleavage of X-linked IAP, which suggests that another mechanism of IAP deactivation is involved. Together our results support an integrative role of the mitochondria in induction and/or amplification of caspase activity and show that G-CSF may act by blocking Bid/Bax redistribution and inhibiting caspase activation.
THE EFFECT OF TEMPERATURES ON THE BEHAVIOUR OF CZECH FLECKVIEH COWS

A group of 98 Czech Fleckvieh cows (one section) was observed over the period of one year with the aim to determine the variation in their milk performance and behaviour at cowshed different cowshed temperatures. Behaviour were recorded once a week (on the same day) at 10:00. Periods of 8 weeks with the highest temperature (hot period – H) and of 8 weeks with the lowest temperature (cold period – L) were then compared. The cows were housed in one section (1/4 of the total capacity) of the free-cubicle shed and where the cubicles were distributed into three rows. Row A (32 cubicles) was close to the feeding plateau, row B (33 cubicles) was in the centre and row C (38 cubicles) was peripheral, close to the side wall. The cowshed temperature was monitored on a daily basis and the mean temperature was 23.2 °C in the hot period and -1.7°C in the cold period, relative humidity 60.2 % (H) and 74.6 % (L) and THI 69.4 (H) and 33.4 (L). The behaviour of the cows was recorded 1568 times, showing them mostly lying down (1037) or standing (531). The cows tended to prefer lying down on their left sides (594 observations) as opposed to their right sides (443).
A Rule Based Publish/Subscribe Context Dissemination Middleware

Context information is vital data coming from mobile devices to add a value for the application execution. Such applications are called context-aware or context-sensitive applications. To enable context-aware applications in a distributed environment, first the context raw data is measured from sensors. After interpreting and structuring the data, the context objects have to be disseminated all over the distributed system. This paper introduces a context dissemination middleware based on a mobile Web Service framework. It eases the development of context aware applications by providing an abstraction layer hiding complex protocols. In addition, the protocols are optimized in terms of reducing latencies and network load. Moreover, to provide observation (monitoring) service of arbitrary context information, a rule description (policy) language has been designed, developed and structured. Web services eventing has been implemented for sending event notifications. The architecture and a prototypical implementation of the context middleware is presented in detail.
Learning from Sequences with Point Processes

The guiding principle of this thesis is to show how the arsenal of recent optimization methods can help solving challenging new estimation problems on events models.While the classical framework of supervised learning treat the observations as a collection of independent couples of features and labels, events models focus on arrival timestamps to extract information from the source of data.These timestamped events are chronologically ordered and can't be regarded as independent.This mere statement motivates the use of a particular mathematical object called point process to learn some patterns from events.Two examples of point process are treated in this thesis.The first is the point process behind Cox proportional hazards model:its conditional intensity function allows to define the hazard ratio, a fundamental quantity in survival analysis literature.The Cox regression model relates the duration before an event called failure to some covariates.This model can be reformulated in the framework of point processes.The second is the Hawkes process which models how past events increase the probability of future events.Its multivariate version enables encoding a notion of causality between the different nodes.The thesis is divided into three parts.The first focuses on a new optimization algorithm we developed to estimate the parameter vector of the Cox regression in the large-scale setting.Our algorithm is based on stochastic variance reduced gradient descent (SVRG) and uses Monte Carlo Markov Chain to estimate one costly term in the descent direction.We proved the convergence rates and showed its numerical performance on both simulated and real-world datasets.The second part shows how the Hawkes causality can be retrieved in a nonparametric fashion from the integrated cumulants of the multivariate point process.We designed two methods to estimate the integrals of the Hawkes kernels without any assumption on the shape of the kernel functions. Our methods are faster and more robust towards the shape of the kernels compared to state-of-the-art methods. We proved the statistical consistency of the first method, and designed turned the second into a convex optimization problem.The last part provides new insights from order book data using the first nonparametric method developed in the second part.We used data from the EUREX exchange, designed new order book model (based on the previous works of Bacry et al.) and ran the estimation method on these point processes.The results are very insightful and consistent with an econometric analysis.Such work is a proof of concept that our estimation method can be used on complex data like high-frequency financial data.