Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intelligent Self-Organizing Power-Saving Architecture: An Agent-Based Approach

Corporate energy usage policy is typically difficult to design and impossible to enforce. The problem stems from the fact that there are several complexities in this enforcement and passive tools such as Energy star are naïve; they do not cater for corporate policies. The result of this is an uncontrolled usage of computers in the corporate culture resulting in significant effects on the environment. This is in addition to an effect on the economy due to an increase in the corporate electricity bills. In this paper, we propose the use of a multiagent-based approach comprising of an intelligent self-organizing system managing the energy usage policy. For validation, using an agent-based model we simulate the proposed intelligent self-organizing architecture for monitoring corporate energy utilization. Extensive simulation experiments demonstrate the effectiveness of the proposed approach.
A denitrifying community associated with a major, marine nitrogen fixer

The diazotrophic cyanobacterium, Trichodesmium, is an integral component of the marine nitrogen cycle and contributes significant amounts of new nitrogen to oligotrophic, tropical/subtropical ocean surface waters. Trichodesmium forms macroscopic, fusiform (tufts), spherical (puffs) and raft-like colonies that provide a pseudobenthic habitat for a host of other organisms including marine invertebrates, microeukaryotes and numerous other microbes. The diversity and activity of denitrifying bacteria found in association with the colonies was interrogated using a series of molecular-based methodologies targeting the gene encoding the terminal step in the denitrification pathway, nitrous oxide reductase (nosZ). Trichodesmium spp. sampled from geographically isolated ocean provinces (the Atlantic Ocean, the Red Sea and the Indian Ocean) were shown to harbor highly similar, taxonomically related communities of denitrifiers whose members are affiliated with the Roseobacter clade within the Rhodobacteraceae (Alphaproteobacteria). These organisms were actively expressing nosZ in samples taken from the mid-Atlantic Ocean and Red Sea implying that Trichodesmium colonies are potential sites of nitrous oxide consumption and perhaps earlier steps in the denitrification pathway also. It is proposed that coupled nitrification of newly fixed N is the most likely source of nitrogen oxides supporting nitrous oxide cycling within Trichodesmium colonies.
The dimerization equilibrium of a ClC Cl−/H+ antiporter in lipid bilayers

Interactions between membrane protein interfaces in lipid bilayers play an important role in membrane protein folding but quantification of the strength of these interactions has been challenging. Studying dimerization of ClC-type transporters offers a new approach to the problem, as individual subunits adopt a stable and functionally verifiable fold that constrains the system to two states – monomer or dimer. Here, we use single-molecule photobleaching analysis to measure the probability of ClC-ec1 subunit capture into liposomes during extrusion of large, multilamellar membranes. The capture statistics describe a monomer to dimer transition that is dependent on the subunit/lipid mole fraction density and follows an equilibrium dimerization isotherm. This allows for the measurement of the free energy of ClC-ec1 dimerization in lipid bilayers, revealing that it is one of the strongest membrane protein complexes measured so far, and introduces it as new type of dimerization model to investigate the physical forces that drive membrane protein association in membranes. DOI: http://dx.doi. org/10.7554/eLife.17438. 001
Deepshape: Deep learned shape descriptor for 3D shape matching and retrieval

Complex geometric structural variations of 3D model usually pose great challenges in 3D shape matching and retrieval. In this paper, we propose a high-level shape feature learning scheme to extract features that are insensitive to deformations via a novel discriminative deep auto-encoder. First, a multiscale shape distribution is developed for use as input to the auto-encoder. Then, by imposing the Fisher discrimination criterion on the neurons in the hidden layer, we developed a novel discriminative deep auto-encoder for shape feature learning. Finally, the neurons in the hidden layers from multiple discriminative auto-encoders are concatenated to form a shape descriptor for 3D shape matching and retrieval. The proposed method is evaluated on the representative datasets that contain 3D models with large geometric variations, i.e., Mcgill and SHREC'10 ShapeGoogle datasets. Experimental results on the benchmark datasets demonstrate the effectiveness of the proposed method for 3D shape matching and retrieval.
Achieving high utilization in guaranteed services networks using early-deadline-first scheduling

Among packet scheduling disciplines for providing end-to-end quality-of-service (QoS) guarantees to different applications, two classes of algorithms have received particular attention: those based on generalized processor sharing (GPS) and those based on earliest-deadline first (EDF) scheduling. The powerful properties of GPS-based schemes translate easily into simple call admission control (CAC) procedures. The intense research on GPS has also resulted in very efficient implementation techniques, which have made the cost of these schedulers very affordable. The EDF discipline, in conjunction with per-node traffic shaping [which we refer to as rate-controlled EDF (RC-EDF)] has also been proposed for end-to-end QoS provisioning. However, an appropriate framework for CAC with RC-EDF has not been developed, nor the possible advantages of using RC-EDF in place of GPS have been properly characterized. Furthermore, the implementation complexity of an RC-EDF server is potentially very high, and no technique to reduce costs has been proposed. In this paper, we first formulate an end-to-end CAC framework for RC-EDF that can be implemented in practice. Then, using this framework, we numerically compare the schedulable regions of RC-EDF and GPS and show that, when the traffic mix in the network consists of connections with both stringent and loose delay requirements, RC-EDF can substantially outperform GPS in the number of admitted connections, and can thus achieve much higher network utilization. Finally, we propose a technique to substantially reduce the implementation complexity of a RC-EDF server.
Unidirectional variation and deep CNN denoiser priors for simultaneously destriping and denoising optical remote sensing images

ABSTRACT Stripe and random noise are two different degradation phenomena commonly co-existing in optical remote sensing images, which are often modelled as inverse problems, respectively. When solving those inverse problems, model-based optimization and discriminative learning methods are fashionably employed but have their respective merits and drawbacks, e.g., model-based optimization methods are flexible but usually time-consuming while discriminative learning methods have fast testing speed but are limited by the specialized task. To improve testing speed and obtain good performance, this paper integrates deep convolutional neural network (DCNN) denoiser prior into unidirectional variation (UV) model, named as UV-DCNN, to simultaneously destripe and denoise optical remote sensing images. The proposed UV-DCNN method can be efficiently solved by the alternating minimization optimization method. Both quantitative and qualitative experiment results validate that the proposed method is effective and even better than the state-of-the-arts, its satisfactory computation time makes it suitable for extensive application.
Flexible Microscope Calibration using Virtual Pattern for 3-D Telemicromanipulation

In the context of virtualized reality based telemicromanipulation, we present in this paper a visual calibration technique for optical microscope coupled with a CCD camera. In contrast to previous approaches, a virtual calibration pattern is constructed using the micromanipulator with a sub-pixel localization in the image. We also present a new camera calibration algorithm based on Parallel Single-Plane properties. The proposed procedure leads to a linear system from which the solution gives directly both intrinsic and extrinsic parameters of the geometrical model. Both computer simulation and real data have been used to test the proposed technique, and very good results have been obtained. Compared with classical techniques, our method provides an alternative technical solution, easy to use and flexible in the context of micromanipulation and virtual reality.
Observation depuis le sol des nuages et des poussières dans l'atmosphère. Applications à la stratosphère polaire et à l'atmosphère de Mars

Une methode de detection des nuages et des poussieres dans l'atmosphere depuis le sol par spectrometrie uv-visible du ciel au zenith est proposee. Elle repose sur l'etude de la variation de la couleur du ciel au crepuscule. La methode est appliquee a la detection des nuages stratospheriques polaires observes durant la campagne Cheops III (Chemistry of ozone in the polar stratosphere) en 1990 en scandinavie. Un modele de transfert radiatif en diffusion simple a ete developpe pour l'interpretation des observations. la methode est ensuite appliquee a la definition d'un capteur optique destine a la detection des tempetes de poussiere et de nuages dans l'atmosphere de Mars a partir de stations au sol, lors de la mission franco-russe Mars 94
Toward a Descriptive Model Of Local Search Cost in Job-Shop Scheduling

Many search techniques are available for solving scheduling problems. Yet, little is known about what problem features influence the cost of search. We study a descriptive cost model of local search in the job-shop scheduling problem (JSP), borrowing from the local search cost models available for random 3-SAT. We show that several factors known to influence the difficulty of local search in random 3SAT directly carry over to the general JSP, including the number of solutions, backbone size, the distance to the nearest solution, and an analog of backbone robustness. However, in applying the same analysis to JSPs with structured constraints, we find that many of these factors only weakly influence local search cost. While the factors for the SAT cost models provide an accurate description of local search cost in the general JSP, our results for JSPs with structured constraints raise concerns regarding the applicability of cost models derived using random problems to those exhibiting specific structure.
Constant coefficient tests for random coefficient regression

Random coefficient regression models have been applied in different fields and they constitute a unifying setup for many statistical problems. The nonparametric study of this model started with Beran and Hall (1992) and it has become a fruitful framework. In this paper we propose and study statistics for testing a basic hypothesis concerning this model: the constancy of coefficients. The asymptotic behavior of the statistics is investigated and bootstrap approximations are used in order to determine the critical values of the test statistics. A simulation study illustrates the performance of the proposals.
Linear programming based techniques for synthesis of network-on-chip architectures

Network-on-chip (NoC) has been proposed as a solution for the communication challenges of system-on-chip (SoC) design in the nanoscale regime. SoC design offers the opportunity for incorporating custom NoC architectures that are more suitable for a particular application, and do not necessarily conform to regular topologies. This paper presents novel linear programming based techniques for synthesis of custom NoC architectures. In the nanoscale regime, low power consumption would continue to be an important design goal. We first discuss an optimal mixed integer linear programming (MILP) formulation that synthesizes a low power NoC architecture subject to the performance constraints. The MILP formulation is limited by large run times. We next present heuristic techniques that exploit clustering, and 0-1 constraint relaxation to reduce the run times of the formulation. The techniques minimize power as the primary goal, and minimize the number of routers (area) as a secondary goal. We present an analysis of the quality of the results and the solution times of the proposed techniques by extensive experimentation with the realistic benchmarks. The clustering based heuristic generates results whose power consumption is within 11% of the MILP solutions and its average run time is 171.1 seconds. The average run time of the relaxation and rounding based techniques is less than 2 seconds, and the power consumption of their solutions is within 58% of the MILP result.
Glial membrane potentials and their relationship to [K+]o in man and guinea pig. A comparative study of intracellularly marked normal, reactive, and neoplastic glia.

✓ Glial cells were studied in vitro in physiologically viable brain slices prepared from tissues obtained during operations for focal epilepsy and brain tumors. The results from human normal and reactive cerebral cortex and glial tumors were compared with those obtained in guinea pig cortex. The resting membrane potential (RMP) or the relationship of RMP to changes in extracellular potassium concentration ([K+]o) were recorded, and the cell was injected iontophoretically with horseradish peroxidase (HRP) for later visualization and correlation with the physiological data. In normal cortex, the marked cells were protoplasmic astrocytes; in reactive cortex, fibrous astrocytes with abnormal processes predominated. The majority of neoplastic glia resembled the reactive astrocytes, except that they showed a pleomorphism that increased with the degree of malignancy. The mean RMP's of glia from normal human and guinea pig cortex, human reactive cortex, and well differentiated astrocytomas were comparable, about .
T-optimum design for discrimination between two mul-tiresponse dynamic models

The paper is concerned with a problem of finding an optimum experimental design for discriminating between two rival multiresponse models. The criterion of optimality we use is based on the sum of squares of deviations between the models, and picks up the design points for which the divergence is maximum. An important part of our criterion is an additional vector of experimental conditions, which may affect the design. In the paper we give the necessary conditions for the design and the additional parameters of the experiment to be optimum, we present the algorithm for the numerical optimization procedure and show the relevance of these methods to dynamic systems, especially to chemical kinetic models.
Lipocalin-2 Is an Autocrine Mediator of Reactive Astrocytosis

Astrocytes, the most abundant glial cell type in the brain, provide metabolic and trophic support to neurons and modulate synaptic activity. In response to a brain injury, astrocytes proliferate and become hypertrophic with an increased expression of intermediate filament proteins. This process is collectively referred to as reactive astrocytosis. Lipocalin 2 (lcn2) is a member of the lipocalin family that binds to small hydrophobic molecules. We propose that lcn2 is an autocrine mediator of reactive astrocytosis based on the multiple roles of lcn2 in the regulation of cell death, morphology, and migration of astrocytes. lcn2 expression and secretion increased after inflammatory stimulation in cultured astrocytes. Forced expression of lcn2 or treatment with LCN2 protein increased the sensitivity of astrocytes to cytotoxic stimuli. Iron and BIM (Bcl-2-interacting mediator of cell death) proteins were involved in the cytotoxic sensitization process. LCN2 protein induced upregulation of glial fibrillary acidic protein (GFAP), cell migration, and morphological changes similar to characteristic phenotypic changes termed reactive astrocytosis. The lcn2-induced phenotypic changes of astrocytes occurred through a Rho–ROCK (Rho kinase)–GFAP pathway, which was positively regulated by nitric oxide and cGMP. In zebrafishes, forced expression of rat lcn2 gene increased the number and thickness of cellular processes in GFAP-expressing radial glia cells, suggesting that lcn2 expression in glia cells plays an important role in vivo. Our results suggest that lcn2 acts in an autocrine manner to induce cell death sensitization and morphological changes in astrocytes under inflammatory conditions and that these phenotypic changes may be the basis of reactive astrocytosis in vivo.
Detection of electromyographic signals from facial muscles with neural networks

The goal of this research was to investigate neural network-based methods to be applied in the processing of biomedical signals. We developed a neural network-based method for the detection of voluntarily produced changes in facial muscle action potentials. Electromyographic signals were recorded from the corrugator supercilii and zygomaticus major facial muscles. The facial muscle action potentials of thirty subjects were measured while they performed a series of voluntary contractions of these muscles. Wavelet denoising or digital bandpass filtering was applied to the preprocessing of the signals. A neural network was exploited for an offline classification of various phases of these signals. The results show that the neural network-based technique developed functioned very well, producing a reliable recognition accuracy of 96 to 99%. Because of these promising results, we will proceed in the development of this method for real-time applications that benefit from the analysis of electromyographic signals.
Management and diagnosis of fatty liver disease

Nonalcoholic fatty liver disease is a common cause of chronic liver disease and has been an increasingly studied topic of research as the obesity epidemic has been growing. There is a significant morbidity and mortality with uncontrolled steatohepatitis, which can progress to fibrosis, cirrhosis and hepatocellular carcinoma. The prevalence of this disease has been estimated to be roughly one-third of the western population, thought to be largely due to diet and sedentary lifestyle. Several treatments have been studied including vitamin E, insulin-sensitizing agents and ursodeoxycholic acid; however, the only treatment shown to improve the histologic changes of nonalcoholic fatty liver disease is weight loss. Given the proven benefit of weight loss, there may be reason to screen at-risk populations; however, limited availability of other disease-modifying treatments may limit the cost–benefit ratios. A better understanding of the diagnosis and management of this condition is required to alter the course of this modifiable disease.
Interannual variability of the global carbon cycle (1992–2005) inferred by inversion of atmospheric CO2 and δ13CO2 measurements

We present estimates of the surface sources and sinks of CO2 for 1992–2005 deduced from atmospheric inversions. We use atmospheric CO2 records from 67 sites and 10 δ13CO2 records. We use two atmospheric models to increase the robustness of the results. The results suggest that interannual variability is dominated by the tropical land. Statistically significant variability in the tropical Pacific supports recent ocean modeling studies in that region. The northern land also shows significant variability. In particular, there is a large positive anomaly in 2003 in north Asia, which we associate with anomalous biomass burning. Results using δ13CO2 and CO2 are statistically consistent with those using only CO2, suggesting that it is valid to use both types of data together. An objective analysis of residuals suggests that our treatment of uncertainties in CO2 is conservative, while those for δ13CO2 are optimistic, highlighting problems in our simple isotope model. Finally, δ13CO2 measurements offer a good constraint to nearby land regions, suggesting an ongoing value in these measurements for studies of interannual variability.
MoCham: Robust Hierarchical Clustering Based on Multi-objective Optimization

Many clustering evaluation methods are computed as a ratio between two objectives, typically these objectives express the compactness of all clusters while trying to maximize the separation between individual clusters. However, the clustering process itself is typically implemented as a single objective problem: optimizing a linear combination of between-points closeness. We propose MoCham - a hierarchical clustering algorithm that uses a multi-objective optimization for finding the optimal data points to merge. Our results suggest that a careful candidate selection of Pareto dominating pairs leads to more robust clustering results.
Designing Disk Arrays for High Data Reliability

Abstract Redundancy based on a parity encoding has been proposed for ensuring that disk arrays provide highly reliable data. Parity-based redundancy tolerates many independent and dependent disk failures (shared support hardware) without on-line spare disks and many more such failures with on-line spare disks. This paper explores the design of reliable, redundant disk arrays. In the context of a 70-disk strawman array, it presents and applies analytic and simulation models for the time until data is lost. It shows how to balance requirements for high data reliability against the overhead cost of redundant data, on-line spares, and on-site repair personnel in terms of an array′s architecture, its component reliabilities, and its repair policies.
LineSwitch: Efficiently Managing Switch Flow in Software-Defined Networking while Effectively Tackling DoS Attacks

Software Defined Networking (SDN) is a new networking architecture that aims to provide better decoupling between network control (control plane) and data forwarding functionalities (data plane). This separation introduces several benefits, such as a directly programmable and (virtually) centralized network control. However, researchers showed that the required communication channel between the control and data plane of SDN creates a potential bottleneck in the system, introducing new vulnerabilities.Indeed, this behavior could be exploited to mount powerful attacks, such as the control plane saturation attack, that can severely hinder the performance of the whole network. In this paper we present LineSwitch, an efficient and effective solution against control plane saturation attack. LineSwitch combines SYN proxy techniques and probabilistic blacklisting of network traffic. We implemented LineSwitch as an extension of OpenFlow, the current reference implementation of SDN, and evaluate our solution considering different traffic scenarios (with and without attack). The results of our preliminary experiments confirm that, compared to the state-of-the-art, LineSwitch reduces the time overhead up to 30%, while ensuring the same level of protection.
Optimal Distributed Power Control and Routing in Wireless Networks

We present a unified analytical framework within which power control and routing for wireless networks can be optimized on a node-by-node basis. We consider a multicommodity flow model for an interference-limited wireless network in which power control and routing variables are chosen to minimize convex link costs. Distributed scaled gradient projection algorithms are developed to iteratively adjust power control and routing schemes at individual nodes. We specify appropriate scaling matrices with which the algorithms quickly converge to the global optimum from any initial point. These scaling matrices can be computed locally at each node with limited control message overhead