Miracle-dz/UniFAR-FTUs huggingface dataset:
The middlebox manifesto: enabling innovation in middlebox deployment

Most network deployments respond to changing application, workload, and policy requirements via the deployment of specialized network appliances or "middleboxes". Despite the critical role that middleboxes play in introducing new network functionality, they have been surprisingly ignored in recent efforts for designing networks that are amenable to innovation. We make the case that enabling innovation in middleboxes is at least as important, if not more important, as that for traditional switches and routers. To this end, our vision is a world with software-centric middlebox implementations running on general-purpose hardware platforms that are managed via open and extensible management APIs. While these principles have been applied in other contexts, they introduce unique opportunities and challenges in the context of middleboxes that we highlight in this paper.
Modified parallel interference cancellation for multicode CDMA systems with V-BLAST architecture

Multicode CDMA with V-BLAST architecture is an attractive scheme to achieve high data rate transmission. We propose an advanced detection scheme, order partial parallel interference cancellation with decision feedback (DF-OPPIC), to combat co-antenna interference and multiple access interference in the system. Compared with the original reference, the scheme has better performance on the frame error ratio in flat Rayleigh fading channel and multipath Rayleigh fading channel. Moreover, due to the avoidance of calculating pseudo-inverse and optimal detection order in every time of iteration, the scheme has lower arithmetic complexity.
The influence of physical and cognitive factors on reactive agility performance in men basketball players

Abstract This study explored the influence of physical and cognitive measures on reactive agility performance in basketball players. Twelve men basketball players performed multiple sprint, Change of Direction Speed Test, and Reactive Agility Test trials. Pearson’s correlation analyses were used to determine relationships between the predictor variables (stature, mass, body composition, 5-m, 10-m and 20-m sprint times, peak speed, closed-skill agility time, response time and decision-making time) and reactive agility time (response variable). Simple and stepwise regression analyses determined the individual influence of each predictor variable and the best predictor model for reactive agility time. Morphological (r = –0.45 to 0.19), sprint (r = –0.40 to 0.41) and change-of-direction speed measures (r = 0.43) had small to moderate correlations with reactive agility time. Response time (r = 0.76, P = 0.004) and decision-making time (r = 0.58, P = 0.049) had large to very large relationships with reactive agility time. Response time was identified as the sole predictor variable for reactive agility time in the stepwise model (R2 = 0.58, P = 0.004). In conclusion, cognitive measures had the greatest influence on reactive agility performance in men basketball players. These findings suggest reaction and decision-making drills should be incorporated in basketball training programmes.
Sustained Arginase 1 Expression Modulates Pathological Tau Deposits in a Mouse Model of Tauopathy

Tau accumulation remains one of the closest correlates of neuronal loss in Alzheimer's disease. In addition, tau associates with several other neurodegenerative diseases, collectively known as tauopathies, in which clinical phenotypes manifest as cognitive impairment, behavioral disturbances, and motor impairment. Polyamines act as bivalent regulators of cellular function and are involved in numerous biological processes. The regulation of the polyamines system can become dysfunctional during disease states. Arginase 1 (Arg1) and nitric oxide synthases compete for l-arginine to produce either polyamines or nitric oxide, respectively. Herein, we show that overexpression of Arg1 using adeno-associated virus (AAV) in the CNS of rTg4510 tau transgenic mice significantly reduced phospho-tau species and tangle pathology. Sustained Arg1 overexpression decreased several kinases capable of phosphorylating tau, decreased inflammation, and modulated changes in the mammalian target of rapamycin and related proteins, suggesting activation of autophagy. Arg1 overexpression also mitigated hippocampal atrophy in tau transgenic mice. Conversely, conditional deletion of Arg1 in myeloid cells resulted in increased tau accumulation relative to Arg1-sufficient mice after transduction with a recombinant AAV–tau construct. These data suggest that Arg1 and the polyamine pathway may offer novel therapeutic targets for tauopathies.
Optical Sensing of TiO2 Nanofluid for Self Stability

Nanofluids are colloidal suspensions of nanoparticles in a base fluid which are mainly used as modern heat transfer fluids. The applications of nanofluids include electronic cooling systems, direct absorption solar collectors, magnetic smart fluids and in biomedical treatments. The success of these applications depends on stability of nanofluid. In the present work TiO2 nanofluids have been synthesized by two step technique . The nanoparticle of TiO2 are prepared by sol gel and characterized by XRD, SEM and UV. The sedimentation of prepared nanofluid is observed by the gravity sedimentation method and transmitted intensity is recorded by a photo detector. A profile between the transmitted intensity and distance of nanofluid from laser source is obtained which shows the potential application of nanofluid as optical sensors for detecting stability of nanofluid.
Advanced lead acid battery designs for hybrid electric vehicles

In this paper, the authors present a high power, lead acid battery design that has demonstrated long life. The design uses horizontal plates with multiple lug connectors to deliver high power for hybrid electric vehicle applications. The horizontal plate configuration helps improve life by allowing for better thermal management and mechanical compression. They use computer models that were previously developed to estimate the specific energy performance of this battery when different conductive and/or nonconductive additives are mixed into the active material. They investigate both spherical as well as different aspect ratio additives. They show how the use of small, conductive additives could double the specific energy performance of lead acid batteries (i.e. from 30-35 Wh/kg to 60-70 Wh/kg). However, the exact size of the additive required to accomplish this improvement has never been verified. They therefore suggest some possible experiments to determine the particle size and the amount of additive material needed to make this dramatic improvement in specific energy.
Testing the Accuracy of Surface Roughness Measurements Carried out with a Portable Profilometer

Surface roughness is a factor that has a vital influence on overall quality of machine parts. This is the reason why proper measurements of surface roughness are a matter of great importance in modern manufacturing technology. Nowadays portable profilometers are common instruments to be used under industrial conditions. Measurements with such instruments can be affected by numerous factors, for example environmental changes, human errors of an operator, etc. This paper discusses problem of an evaluation of measurement accuracy of portable profilometers. It also describes the evaluation procedure and presents results experimental tests.
Simultaneous retrieval of aerosol and cloud properties during the MILAGRO field campaign

Abstract. Estimation of Direct Climate Forcing (DCF) due to aerosols in cloudy areas has historically been a difficult task, mainly because of a lack of appropriate measurements. Recently, passive remote sensing instruments have been developed that have the potential to retrieve both cloud and aerosol properties using polarimetric, multiple view angle, and multi spectral observations, and therefore determine DCF from aerosols above clouds. One such instrument is the Research Scanning Polarimeter (RSP), an airborne prototype of a sensor on the NASA Glory satellite, which unfortunately failed to reach orbit during its launch in March of 2011. In the spring of 2006, the RSP was deployed on an aircraft based in Veracruz, Mexico, as part of the Megacity Initiative: Local and Global Research Observations (MILAGRO) field campaign. On 13 March, the RSP over flew an aerosol layer lofted above a low altitude marine stratocumulus cloud close to shore in the Gulf of Mexico. We investigate the feasibility of retrieving aerosol properties over clouds using these data. Our approach is to first determine cloud droplet size distribution using the angular location of the cloud bow and other features in the polarized reflectance. The selected cloud was then used in a multiple scattering radiative transfer model optimization to determine the aerosol optical properties and fine tune the cloud size distribution. In this scene, we were able to retrieve aerosol optical depth, the fine mode aerosol size distribution parameters and the cloud droplet size distribution parameters to a degree of accuracy required for climate modeling. This required assumptions about the aerosol vertical distribution and the optical properties of the coarse aerosol size mode. A sensitivity study was also performed to place this study in the context of future systematic scanning polarimeter observations, which found that the aerosol complex refractive index can also be observed accurately if the aerosol optical depth is larger than roughly 0.8 at a wavelength of (0.555 μm).
Inference and Analysis of Population Structure Using Genetic Data and Network Theory

Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition’s modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of prior conditions. The method is implemented in the software NetStruct (available at https://giligreenbaum.wordpress.com/software/).
Extended K-Modes with Probability Measure

— Clustering Categorical data is more complicated process than numerical clustering. In this paper the traditional K-Modes algorithm is extended with the weighted measure based on the probability of respective matching attribute value in the data set. The proposed method is experimented with the data sets obtained from UCI data repository. Results prove that the proposed weighted measure is superior to K-Modes.
Metal nanowires: atomic arrangement and electrical transport properties

We have studied the atomic arrangement and defect formation in metal nanowires (NWs) generated by mechanical elongation using in situ high resolution transmission electron microscopy. It has been observed that the narrowest constriction of gold and platinum NWs is crystalline and defect-free; in particular, gold NWs adopt only three kinds of atomic arrangement. A model correlating these gold structures and the quantum conductance behaviour is proposed, which showed a remarkable agreement with ultrahigh-vacuum mechanically controllable break junction electrical transport measurements.
Using the Support Vector Regression Approach to Model Human Performance

Empirical data modeling can be used to model human performance and explore the relationships between diverse sets of variables. A major challenge of empirical data modeling is how to generalize or extrapolate the findings with a limited amount of observed data to a broader context. In this paper, we introduce an approach from machine learning, known as support vector regression (SVR), which can help address this challenge. To demonstrate the method and the value of modeling human performance with SVR, we apply SVR to a real-world human factors problem of night vision system design for passenger vehicles by modeling the probability of pedestrian detection as a function of image metrics. The results indicate that the SVR-based model of pedestrian detection shows good performance. Some suggestions on modeling human performance by using SVR are discussed.
Modeling Language for Machine Learning

For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem. Keywords—Formal language, statistical inference problem, reduction.
Microneedles for drug delivery and monitoring

Abstract: Microneedles (MN) are micron-sized needles, ranging from 25 to 2000 μm in height, made of a variety of materials and shapes. Application of MNs to the skin can create micron-sized transport pathways that allow enhanced delivery of a wide range of drug molecules. The concept of MNs was first conceived in 1976; however, it was not possible to make them until the first exploitation of microelectromechanical systems (MEMS) in 1998. Therefore, this chapter will focus on the fabrication techniques of MNs using MEMS, the design and material consideration of MNs, and the application of MNs in drug delivery and monitoring biological fluids.
Phase-field modeling of surface diffusion

Abstract In the classical description of surface diffusion, transport on a curved interface is associated with the Laplace – Beltrami operator acting on a chemical potential (difference). An early attempt to model surface diffusion via the phase-field approach goes back to Cahn, Elliott and Novick-Cohen; they use a scalar mobility approaching zero in the bulk. Similar models have been proposed first on the basis of heuristic ideas and then underpinned by asymptotic analysis. As it turns out, most of these analyses suffer from a subtle flaw, due not to a miscalculation but rather to early termination of the calculation. The asymptotic analysis provides all the equations desired for the correct sharp-interface limit. Unfortunately, it provides an additional equation, which is one restriction too many. Consequences for dynamical simulations of this kind of model are explored numerically. We construct two models based on the introduction of a tensorial mobility that approximate known sharp-interface equations without adding undesired constraints. Numerical simulations suggest superior performance of the new models in at least some situations.
A High-Level Strategy for C-net Discovery

Causal nets have been recently proposed as a suitable model for process mining, due to their declarative semantics and compact representation. However, the discovery of causal nets from a log is a complex problem. The current algorithmic support for the discovery of causal nets comprises either fast but inaccurate methods (compromising quality), or accurate algorithms that are computationally demanding, thus limiting the size of the inputs they can process. In this paper a high-level strategy is presented, which uses appropriate clustering techniques to split the log into pieces, and benefits from the additive nature of causal nets. This allows amalgamating structurally the discovered causal net of each piece to derive a valuable model. The claims in this paper are accompanied with experimental results showing the significance of the high-level strategy presented.
Kv7 Channels Can Function without Constitutive Calmodulin Tethering

M-channels are voltage-gated potassium channels composed of Kv7.2-7.5 subunits that serve as important regulators of neuronal excitability. Calmodulin binding is required for Kv7 channel function and mutations in Kv7.2 that disrupt calmodulin binding cause Benign Familial Neonatal Convulsions (BFNC), a dominantly inherited human epilepsy. On the basis that Kv7.2 mutants deficient in calmodulin binding are not functional, calmodulin has been defined as an auxiliary subunit of Kv7 channels. However, we have identified a presumably phosphomimetic mutation S511D that permits calmodulin-independent function. Thus, our data reveal that constitutive tethering of calmodulin is not required for Kv7 channel function.
Anti money laundering (“AML”) management and the importance of employees' work attitude

Recent high-profile incidents with record high penalties on international banking groups for noncompliance of the AML regulations send a strong message to the CEOs of financial institutions: it is high time to review and refine their AML program thoroughly in all of their operations in different jurisdictions to ensure compliance before it is too late. This paper discusses about the major components of an AML program and provides insights on the practical aspects of managing an effective AML program with reference to banks and casinos. The author also argues that the work attitude of the employees towards AML is one of the most vital elements for such program to be successful. This has significant implications to financial institutions, particularly the multinational institutions with operations in different countries where values and attitudes of the employees are varied. Future research opportunities are discussed
Controlled Nanopatterning of a Polymerized Ionic Liquid in a Strong Electric Field

Nanolithography has become a driving force in advancements of the modern day's electronics, allowing for miniaturization of devices and a steady increase of the calculation, power, and storage densities. Among various nanofabrication approaches, scanning probe techniques, including atomic force microscopy (AFM), are versatile tools for creating nanoscale patterns utilizing a range of physical stimuli such as force, heat, or electric field confined to the nanoscale. In this study, the potential of using the electric field localized at the apex of an AFM tip to induce and control changes in the mechanical properties of an ion containing polymer—a polymerized ionic liquid (PolyIL)—on a very localized scale is explored. In particular, it is demonstrated that by means of AFM, one can form topographical features on the surface of PolyIL‐based thin films with a significantly lower electric potential and power consumption as compared to nonconductive polymer materials. Furthermore, by tuning the applied voltage and ambient air humidity, control over dimensions of the formed structures is reproducibly achieved.
A Feasibility Study on Disseminating Spatio-temporal Information via Vehicular Ad-hoc Networks

In this paper we study the feasibility of disseminating reports about resources via vehicular ad-hoc networks. Each disseminated report represents information about a spatial-temporal event, such as the availability of a parking slot at a particular time. The simple flooding algorithm is used for dissemination in a VANET. The feasibility is analyzed by comparing the effectiveness of VANET with that of client-server. The performance measure integrates throughput and response time, the two traditional measures for evaluation of data dissemination algorithms. The comparison is based on realistic simulation of vehicle mobility in a real road network and of the 802.11 protocol. The comparison enables determining the superior architecture (VANET or client-server) for a given environment.
Handbook of Polymer Synthesis, Characterization, and Processing

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Image statistics: from data collection to applications in graphics

Natural images exhibit statistical regularities that differentiate them from random collections of pixels. Moreover, the human visual system appears to have evolved to exploit such statistical regularities. As computer graphics is concerned with producing imagery for observation by humans, it would be prudent to understand which statistical regularities occur in nature, so they can be emulated by image synthesis methods. In this course we introduce all aspects of natural image statistics, ranging from data collection to analysis and finally their applications in computer graphics, computational photography, visualization and image processing.
The subsurface crack under conditions of slip and stick caused by a surface normal force

On donne la solution pour le champ de contraintes elastiques dans un demi-plan contenant une fissure plane parallele a la surface et soumise a une pression normale uniforme et a une charge normale concentree. Le glissement de Coulomb peut exister entre les faces de la fissure
Hierarchical organization of appearance-based parts and relations for object recognition

Previously a new object representation using appearance-based parts and relations to recognize 3D objects from 2D images, in the presence of occlusion and background clutter, was introduced. Appearance-based parts and relations are defined in terms of closed regions and the union of these regions, respectively. The regions are segmented using the MDL principle, and their appearance is obtained from collection of images and compactly represented by parametric manifolds in the eigenspaces spanned by the parts and the relations. In this paper we introduce the discriminatory power of the proposed features and describe how to use it to organize large databases of objects.