Miracle-dz/UniFAR-FTUs huggingface dataset:
Priority-Based Energy Scheduling in a Smart Distributed Network With Multiple Microgrids

This paper proposes a priority-based energy scheduling mechanism for a distributed network with multiple microgrids. Based on energy supply and demand mismatch, a role of buyer or seller is assigned to a microgrid. In the proposed mechanism, the distribution network management system (DNMS) gathers surplus energy from all sellers and distributes it among the buyers based on their optimal strategies. A novel method for calculating priority index (PI) is proposed in which the past contributions made by a microgrid and local load demand are considered as vital parameters. Each buyer decides its optimal strategy for buying energy from DNMS using optimal buyer strategy algorithm which gives Nash equilibrium solution of a noncooperative energy competition game. The DNMS allocates optimal energy to each buyer microgrid using an optimal DNMS decision algorithm. Energy allocated by the DNMS is directly proportional to the PI of the buyer. The proposed system is stable enough for real-time implementation in the electricity market.
Investigating Twitter as a Source for Studying Behavioral Responses to Epidemics

Recent studies have shown an ability to track influenza rates from Twitter since Twitter users tweet illnesses (“i am home sick with the flu”). However, users may also tweet concerned awareness of illness (“don’t want to get sick, need a flu shot”). Identifying these messages can support computational epidemic response models. We present preliminary results for mining concerned awareness of influenza tweets. We describe our data set construction and experiments with binary classification of data into influenza versus general messages and classification into concerned awareness and existing infection.
The Authors' Reply: HbA1c Is Insensitive at Month 3 After Kidney Transplantation.

and glucose metabolic status as determined by OGTT is presented in Figure 1B. Although HbA1c is a validated test for the diagnosis of diabetes in the general population, our data support those of Eide et al in demonstrating that this is not the case during the first 3 months after kidney transplantation. Our patients differed from those of Eide et al by having higher exposures to tacrolimus and prednisolone and, likely consequent to this, a higher incidence of PTDM.However, HbA1c was similarly insensitive to the diagnosis of PTDM in both cohorts. During the early posttransplant period, glycemic exposure is likely to be variable due to altered red cell turnover and the changing metabolic environment caused by medications, stress response, infection, improved renal function, and dietary changes. As suggested by published data, it is likely that the performance of HbA1c in diagnosing PTDM would improve over time after transplantation as the metabolic environment and hemoglobin concentration stabilize. Validation during this period is required.
Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors.
Automated annotation of human faces in family albums

Automatic annotation of photographs is one of the most desirable needs in family photograph management systems. In this paper, we present a learning framework to automate the face annotation in family photograph albums. Firstly, methodologies of content-based image retrieval and face recognition are seamlessly integrated to achieve automated annotation. Secondly, face annotation is formulated in a Bayesian framework, in which the face similarity measure is defined as maximum a posteriori (MAP) estimation. Thirdly, to deal with the missing features, marginal probability is used so that samples which have missing features are compared with those having the full feature set to ensure a non-biased decision. The experimental evaluation has been conducted within a family album of few thousands of photographs and the results show that the proposed approach is effective and efficient in automated face annotation in family albums.
Protecting and Sharing of Semantically-Enabled, User-Orientated Electronic Laboratory Notebook Focusing on a Case Study in the e-Science Domain

We discuss the addition to an existing Electronic Laboratory Notebook (ELN) system, a means to permit the sharing of modelling data. One advantage is that sharing of such data is a means of assisting the publication process. This is done by presenting the modelling data and the reasoning behind its creation. This sharing of data is managed in a user sensitive fashion by restricting the release of data based upon the role someone performs. Further sensitivity is shown by fine-grained access control, which permits only part of the ELN to be shown. The performance of the solution presented is reviewed via quantitative analysis that showed a reasonable degree of end-user acceptance of the proposed approach.
Regulation of indoleamine 2,3-dioxygenase in primary human saphenous vein endothelial cells

Background Indoleamine 2,3-dioxygenase (IDO) is an enzyme associated with the regulation of immune responses. Cytokines such as IFNγ induce its expression in endothelial cells originating from immune-privileged sites. In this study, we investigate regulators of IDO in primary endothelial cells from a non-immune-privileged site and determine whether IDO expression affects immune cell behavior. Methods IDO expression was determined using real-time quantitative polymerase chain reaction and immunoblotting. IDO activity was estimated using an IDO enzyme assay. Primary cells were transfected using microporation, and T-cell migration was determined using a cell transmigration assay. Results IDO is expressed in human saphenous vein endothelial cells after stimulation with IFNγ but not after treatment with TNFα, IL-1β, IL-2, IL-4, IL-6, or IL-10. VEGFβ and heparin negatively regulate IFNγ-driven increases in IDO. Overexpression of IDO in endothelial cells does not affect transmigration of T-cells. Conclusion IDO is expressed in human saphenous vein endothelial cells after stimulation with IFNγ. Heparin and angiogenesis stimulators such as VEGFβ negatively regulate its expression.
Walking and Running Produce Similar Reductions in Cause-Specific Disease Mortality in Hypertensives

To test prospectively in hypertensives whether moderate and vigorous exercise produces equivalent reductions in mortality, Cox-proportional hazard analyses were applied to energy expenditure (metabolic equivalents hours/d [METh/d]) in 6973 walkers and 3907 runners who used hypertensive medications at baseline. A total of 1121 died during 10.2-year follow-up: 695 cardiovascular disease (International Classification of Diseases, Tenth Revision [ICD10] I00–99; 465 underlying cause and 230 contributing cause), 124 cerebrovascular disease, 353 ischemic heart disease (ICD10 I20–25; 257 underlying and 96 contributing), 122 heart failure (ICD10 I50; 24 underlying and 98 contributing), and 260 dysrhythmias (ICD10 I46–49; 24 underlying and 236 contributing). Relative to <1.07 METh/d, running or walking 1.8 to 3.6 METh/d produced significantly lower all-cause (29% reduction; 95% confidence interval [CI], 17%–39%; P=0.0001), cardiovascular disease (34% reduction; 95% CI, 20%–46%; P=0.0001), cerebrovascular disease (55% reduction; 95% CI, 27%–73%; P=0.001), dysrhythmia (47% reduction; 95% CI, 27%–62%; P=0.0001), and heart failure mortality (51% reduction; 95% CI, 21%–70%; P=0.003), as did ≥3.6 METh/d with all-cause (22% reduction; 95% CI, 6%–35%; P=0.005), cardiovascular disease (36% reduction; 95% CI, 19%–50%; P=0.0002), cerebrovascular disease (47% reduction; 95% CI, 6%–71%; P=0.03), and dysrhythmia mortality (43% reduction; 95% CI, 16%–62%; P=0.004). Diabetes mellitus and chronic kidney disease mortality also decreased significantly with METh/d. All results remained significant when body mass index adjusted. Merely meeting guideline levels (1.07–1.8 METh/d) did not significantly reduced mortality. The dose-response was significantly nonlinear for all end points except diabetes mellitus, and cerebrovascular and chronic kidney disease. Results did not differ between running and walking. Thus, walking and running produce similar reductions in mortality in hypertensives.
Adaptive multi-threading for dynamic workloads in embedded multiprocessors

We present a framework for run-time parallelism adaptation of multithreaded applications in multi-core systems. Multi-core systems often execute diverse workloads of multithreaded programs with different system resource utilizations and varying levels of parallelism. As a result, the availability of system resources for individual components of the workloads changes at run-time in an unpredictable manner. Consequently, the level of statically determined parallelism by the system infrastructure, e.g. number of concurrent threads, could be suboptimal and lead to performance degradations. The proposed framework monitors the dynamically changing shared system resources, such as the available processor cores, and adapts the number of threads used by the applications throughout a parallel loop execution so as to match the parallelism level to the changed state of the system resources. The end result is the elimination of sizable overheads due to improper level of parallelism, and the resultant serialization of threads on a single core, that could easily occur in a dynamic system environment.
LTCC Microfluidic System

A simple and inexpensive low-temperature cofired ceramic (LTCC) microfluidic device with integrated optical fibers is designed, manufactured, and tested with positive results. Fluidic channels, mixer, detector, optical fiber, light source, light detector, heater, and temperature sensor are integrated in one LTCC module. The optical system in the LTCC microsystem permits measurements of light transmittance and fluorescence. The design, technology, and results of the module's evaluation are presented.
Choosing Parameters of Kernel Subspace LDA for Recognition of Face Images Under Pose and Illumination Variations

This paper addresses the problem of automatically tuning multiple kernel parameters for the kernel-based linear discriminant analysis (LDA) method. The kernel approach has been proposed to solve face recognition problems under complex distribution by mapping the input space to a high-dimensional feature space. Some recognition algorithms such as the kernel principal components analysis, kernel Fisher discriminant, generalized discriminant analysis, and kernel direct LDA have been developed in the last five years. The experimental results show that the kernel-based method is a good and feasible approach to tackle the pose and illumination variations. One of the crucial factors in the kernel approach is the selection of kernel parameters, which highly affects the generalization capability and stability of the kernel-based learning methods. In view of this, we propose an eigenvalue-stability-bounded margin maximization (ESBMM) algorithm to automatically tune the multiple parameters of the Gaussian radial basis function kernel for the kernel subspace LDA (KSLDA) method, which is developed based on our previously developed subspace LDA method. The ESBMM algorithm improves the generalization capability of the kernel-based LDA method by maximizing the margin maximization criterion while maintaining the eigenvalue stability of the kernel-based LDA method. An in-depth investigation on the generalization performance on pose and illumination dimensions is performed using the YaleB and CMU PIE databases. The FERET database is also used for benchmark evaluation. Compared with the existing PCA-based and LDA-based methods, our proposed KSLDA method, with the ESBMM kernel parameter estimation algorithm, gives superior performance.
Proceedings of the Sixth International Workshop on Web Caching and Content Distribution

Recently, several studies have looked into the problem of replicated server placement on the Internet. Some of those studies have demonstrated that there exists a replica placement algorithm that can perform within a factor of 1.1{1.5 of the optimal solution. However, this particular greedy algorithm requires detailed knowledge about network topology, and knowledge about expected client locations on the topology. One of these previous studies has also looked at topology-informed replica placement. They consider placing replicas at highly connected nodes in the Autonomous System level graph. In this paper we extend their work by investigating the performance of topology-informed placement on Internet router-level topology. In our evaluation, we consider approximated policy-based paths, and examine the sensitivity of our results to di erent client placements. We nd that topologyinformed replica placement methods can achieve average client latencies which are within a factor of 1.1{1.2 of the greedy algorithm, but only if the placement method is designed carefully.
A proposed system for the nomenclature of hepatitis C viral genotypes

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OPTIMAL CONSUMPTION PROBLEM IN THE VASICEK MODEL

We consider the problem of an optimal consumption strategy on the infinite time horizon based on the hyperbolic absolute risk aversion utility when the interest rate is an Ornstein-Uhlenbeck process. Using the method of subsolution and supersolution we obtain the existence of solutions of the dynamic programming equation. We illustrate the paper with a numerical example of the optimal consumption strategy and the value function.
Algorithmic Game Semantics and Component−Based Verification

We present a research programme dedicated to the application of Game Semantics to program analysis and verification. We highlight several recent theoretical results and describe a prototypical software modeling and verification tool. The distinctive novel features of the tool are its ability to handle open programs and the fact that the models it produces are observationally fully abstract. These features are essential in the modeling and verification of software components such as modules. Incidentally, these features also lead to very compact models of programs.
Wavelet method of speech segmentation

In this paper a new method of speech segmentation is suggested. It is based on power fluctuations of the wavelet spectrum for a speech signal. In most approaches to speech recognition, the speech signals are segmented using constant-time segmentation. Constant segmentation needs to use windows to decrease the boundary distortions. A more natural approach is to segment the speech signals on the basis of time-frequency analysis. Boundaries are assigned in places where some energy of a frequency band rapidly changes. Most methods of non-constant segmentation need training for particular data or are realized as a part of modelling. In this paper we apply the discrete wavelet transform (DWT) to analyse speech signals, the resulting power spectrum and its derivatives. This information allows us to locate the boundaries of phonemes. It is the first stage of speech recognition process. Additionally we present an evaluation by comparing our method with hand segmentation. The segmentation method proves effective for finding most phoneme boundaries. Results are more useful for speech recognition than constant segmentation.
Classification of bijections between 321- and 132-avoiding permutations

It is well-known, and was first established by Knuth in 1969, that the number of 321-avoiding permutations is equal to that of 132-avoiding permutations. In the literature one can find many subsequent bijective proofs of this fact. It turns out that some of the published bijections can easily be obtained from others. In this paper we describe all bijections we were able to find in the literature and show how they are related to each other via "trivial" bijections. We classify the bijections according to statistics preserved (from a fixed, but large, set of statistics), obtaining substantial extensions of known results. Thus, we give a comprehensive survey and a systematic analysis of these bijections. We also give a recursive description of the algorithmic bijection given by Richards in 1988 (combined with a bijection by Knuth from 1969). This bijection is equivalent to the celebrated bijection of Simion and Schmidt (1985), as well as to the bijection given by Krattenthaler in 2001, and it respects 11 statistics — the largest number of statistics any of the bijections respects.
3-[(3S)-3-Ethyl-1-methylazepan-3-yl]phenyl N-(4-fluorophenyl)carbamate

The asymmetric unit of the title compound, C22H27FN2O2, a (−)-S-meptazinol derivative, contains two molecules. The azepane ring adopts a similar twist chair form in both molecules, while the dihedral angles between the two benzene rings are 88.17 (14) and 89.93 (14)° in the two molecules. The absolute configuration of the molecule was determined from the synthetic starting material. The crystal structure is stabilized by classical intermolecular N—H⋯O hydrogen bonds.
Cardiac functional improvement in rats with myocardial infarction by up-regulating cardiac myosin light chain kinase with neuregulin.

AIMS Recombinant human neuregulin-1 (rhNRG-1) improves cardiac function in experimental heart failure models, but the underlying mechanism remains largely unknown. In this study, we evaluated whether rhNRG-1 could improve cardiac function via the cardiac myosin light chain kinase/myosin light chain 2 ventricular (cMLCK/MLC-2v) pathway in rats with myocardial infarction (MI). METHODS AND RESULTS Rats with MI were intravenously infused with rhNRG-1 (5 µg/kg/h) for 7 days through osmotic pumps. The mechanism of action of rhNRG-1 was investigated by assaying the non-infarcted myocardium with gene chips. The cMLCK expression, phosphorylated MLC-2v and cardiac function were significantly up-regulated, as assessed by real-time PCR, Western blot and echocardiography, in those animals treated with rhNRG-1. Moreover, the restoration of rhNRG-1-induced sarcomeric organization in serum-free cultured neonatal rat cardiomyocytes with rhNRG-1 was inhibited by cMLCK RNA interference or ML-7, an inhibitor of MLCKs. Adenovirus containing the rat cMLCK coding region was injected into non-infarcted myocardium, and cardiac function was monitored using echocardiography and a haemodynamic machine. The dP/dt and fractional shortening decreasing significantly after MI, and improved by 15.7 and 32.1%, respectively, following local cMLCK application (all P < 0.05). CONCLUSION Our results suggest that cMLCK is a downstream effector of rhNRG-1 involved in rhNRG-1-induced cardiac function improvement, and that myocardial cMLCK up-regulation can improve cardiac function in rats with MI.
Visual servoing control of soft robots based on finite element model

In this paper, we propose a strategy for the control of soft robots with visual tracking and simulation-based predictor. A kinematic model of soft robots is obtained thanks to the Finite Element Method (FEM) computed in real-time. The FEM allows to obtain a prediction of the Jacobian matrix of the robot. This allows a first control of the robot, in the actuator space. Then, a second control strategy based on the feedback of infrared cameras is developed to obtain a correction of the effector position. The robust stability of this closed-loop system is obtained based on Lyapunov stability theory. Otherwise, to deal with the problem of image features (the marker points placed on the end effector of soft robot) loss, a switched control strategy is proposed to combine both the open-loop controller and the closed-loop controller. Finally, experiments on a parallel soft robot driven by four cables are conducted and show the effectiveness of these methods for the real-time control of soft robots.
Improved Multilingual Training of Stacked Neural Network Acoustic Models for Low Resource Languages

This paper proposes several improvements to multilingual training of neural network acoustic models for speech recognition and keyword spotting in the context of low-resource languages. We concentrate on the stacked architecture where the first network is used as a bottleneck feature extractor and the second network as the acoustic model. We propose to improve multilingual training when the amount of data from different languages is very different by applying balancing scalers to the training examples. We also explore how to exploit multilingual data to train the second neural network of the stacked architecture. An ensemble training method that can take advantage of both unsupervised pretraining as well as multilingual training is found to give the best speech recognition performance across a wide variety of languages, while system combination of differently trained multilingual models results in further improvements in keyword search performance.
Stereo Matching with Semi-limited Belief Propagation

In this paper, we present an effective scheme with semi-limited belief propagation to improve the performance of stereo matching. the mean shift segmentation is first introduced to decompose both reference image and target image of stereo pair into regions with similar disparity. We use the regions as matching primitives, and figure out disparity range for each segment by applying a fast local method. then a color-weighted method is applied to calculate matching cost. the unreliable cost is detected by a stability test method. It is helpful in formulating the data term of energy function. in the smoothness term, segment cue and occluded cue was incorporated into the prior assumption to control the way messages passes in the Markov network. then semi-limited belief propagation is used to minimize energy function. Experimental results using the Middlebury stereo test bed show the proposed method works well.