Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing rainfall interpolation techniques for small subtropical urban catchments

Rainfall estimation is an integral component of hydrologic modelling for reliable flood forecasting and for assessing the impact of precipitation and runoff on water quality and ecosystem health in urbanised areas. Spatial variability of rainfall adds to the complexity of estimating rainfall at the catchment scale and is a key factor that must be incorporated into estimations of rain fields. Rainfall interpolation from point measurements is one approach that hydrologists use to account for spatial variation in rainfall. This study focuses on evaluating the suitability of three interpolation methods in terms of their accuracy, for small urban catchments. The Bridgewater Creek catchment in south Brisbane is the region of study. Thirteen storm events measured over 24-hours are interpolated using an inverse-distance weighted average method, thin plate smoothing spline, and an ordinary kriging technique. The delete-one validation method is used for comparing the 3 interpolation techniques. The interpolation methods were applied using 3 different software packages. The inverse-distance weighted method (IDW) was implemented using Microsoft Excel. The spline interpolations were carried out with the ANUSPLIN software while the kriging method was completed using Geostatistical Analyst in ArcGIS. Since ANUSPLIN and Geostatistical Analyst both require a minimum number of data points for computation, it was necessary to consider a reduced set of storms for interpolation by the thin plate spline and kriging methods. The thin plate spline and ordinary kriging interpolation techniques were found to have comparable estimation accuracy when individual storm events were considered. When compared to gauge-based rainfall measurements, the estimation error for these methods was approximately 23 mm, which corresponds to 30% of the observed mean rainfall. The IDW method was observed to display more frequent occurrence of extreme estimation error up to 98% for individual storms. However, an analysis of an aggregated set of storm events for which all 3 methods could be applied showed that the IDW had the lowest estimation error and highest model efficiency of the three interpolation techniques for the storms selected.
A semi-automated methodology for rock mass discontinuity trace map construction

The data obtained from rock mass discontinuity traces are used as significant parameters for rock engineering projects. The methodology of documenting discontinuity orientations by photogrammetry has the advantages of non-contact, fast, safe, and high precision measurement. The discontinuity trace detection and recognition is the key technology. In this paper, a new discontinuity trace detection methodology is presented based on line detection and segmentation traces' semi-automated linking. This methodology considers a rock mass exposure digital image as a discrete surface, the elevation of which is given by pixel brightness levels. The discontinuity trace line feature is automatically detected by using a line detection algorithm based on Hessian matrix to extract the curvilinear structure of the digital image. The discontinuity trace map is constructed by selecting the seed pixel in the line segment map and linking the line segments interpretatively. The experimental results show that this paper's methodology can be used in processing digital images of rock exposures from underground caverns and open environments. The trace map constructed by the new methodology is mainly consistent with a map drawn manually. These results demonstrate the effectiveness of the proposed methodology.
One-Legged Locomotion with a Compliant Passive Joint

There is an increasing attention of exploiting compliant materials for the purpose of legged locomotion, because they provide significant advantages in locomotion performance with respect to energy efficiency and stability. Toward establishing a fundamental basis for this line of research, a minimalistic locomotion model of a single legged system is explored in this paper. By analyzing the dynamic behavior of the system in simulation and a physical robotic platform, it is shown that a stable locomotion process can be achieved without the necessity of sensory feedback. In addition, further analysis characterizes the relation between motor control and the natural body dynamics determined by morphological properties such as body mass and spring constant. © 2006 The authors.
A microfluidic method for sensor fabrication on curved surfaces

We present a microfluidic technique for precision patterning thin-film metals (silver and gold) without the need for photolithography. This technique is employed for the fabrication of metal resistors that are subsequently utilized as temperature sensors, strain gauges, and fluid flow sensors. Patterns of thin film metal are fabricated by premixing and flowing electroless silver or gold plating solutions through predefined polydimethylsiloxane (PDMS) microchannels sealed against the surface of interest. The properties exhibited by PDMS, such as its tendency to adhere to clean surfaces and its flexibility, allow this technique to be carried out on both planar and curved surfaces.
Device-circuit interactions in extremely low voltage CMOS designs (invited)

In this paper, energy and minimum operating voltage (VDDmin) are investigated for extremely-low-voltage CMOS logic designs. The dependences of energy and VDDmin on device parameters, such as threshold voltage, subthreshold swing parameter, and DIBL coefficient, are examined based on simulations and measurements.
Myostatin propeptide‐mediated amelioration of dystrophic pathophysiology

Mutations in myostatin (GDF8) cause marked increases in muscle mass, suggesting that this transforming growth factor‐β (TGF‐β) superfamily member negatively regulates muscle growth. Myostatin blockade therefore offers a strategy for reversing muscle wasting in Duchenne's muscular dystrophy (DMD) without resorting to genetic manipulation. Here, we demonstrate that pharmacological blockade using a myostatin propeptide stabilized by fusion to IgG‐Fc improved pathophysiology of the mdx mouse model of DMD. Functional benefits evidenced by specific force improvement, exceeded those reported previously using myostatin antibody‐mediated blockade. More importantly, use of a propeptide blockade strategy obviates possibilities of anti‐idiotypic responses that could potentially limit the effectiveness of antibody‐mediated myostatin blockade strategies over time. This study provides a novel pharmacological strategy for treatment of diseases associated with muscle wasting such as DMD and since it uses an endogenous inhibitor of myostatin should help circumvent technical hurdles and toxicity associated with conventional gene or cell based therapies. FASEB J. 19, 543–549 (2005)
Finite-dimensional nonlinear output feedback dynamic games and bounds for sector nonlinearities

In general, nonlinear output feedback dynamic games are infinite dimensional. The paper treats a class of minimax games when the nonlinearities enter the dynamics of the unobservable states. An information state approach is introduced to recast these games as one of full information in infinite dimensions. Explicit solutions of the first-order partial differential information state equation are derived in terms of a finite-number of sufficient statistics. When the nonlinearities are sector bounded, suboptimal finite-dimensional strategies are derived.
DNA damage-induced ubiquitylation of proteasome controls its proteolytic activity

Stability of proteins is largely controlled by post-translational covalent modifications. Among those, ubiquitylation plays a central role as it marks the proteins for proteasome-dependent degradation. Proteolytic activities of proteasomes are critical for execution of various cellular processes, including DNA damage signaling and repair. However, very little is known about the regulation of proteasomal activity in cells during genotoxic stress. Here we investigated post-translational modifications of the 20S proteasomal subunits upon DNA damage induced by doxorubicin. Using mass-spectrometry, we found novel sites of phosphorylation and ubiquitylation in multiple proteasome subunits upon doxorubicin treatment. Ectopic co-expression of proteasome subunits and tagged ubiquitin confirmed the presence of ubiquitylated forms of PSMA5, PSMA1, PSMA3 and PSMB5 in cells. Moreover, we demonstrated that ubiquitylation in vitro inhibited chymotrypsin-like and caspase-like activities of proteasomes. In vivo, doxorubicin increased the activity of proteasomes, paralleling with attenuation of the overall level of proteasome ubiquitylation. Collectively, our results suggest a novel mechanism whereby the proteolytic activities of proteasomes are dynamically regulated by ubiquitylation upon DNA damage.
A Wavelet-Modified ESPRIT Hybrid Method for Assessment of Spectral Components from 0 to 150 kHz

Waveform distortions are an important issue in distribution systems. In particular, the assessment of very wide spectra, that include also components in the 2–150 kHz range, has recently become an issue of great interest. This is due to the increasing presence of high-spectral emission devices like end-user devices and distributed generation systems. This study proposed a new sliding-window wavelet-modified estimation of signal parameters by rotational invariance technique (ESPRIT) method, particularly suitable for the spectral analysis of waveforms that have very wide spectra. The method is very accurate and requires reduced computational effort. It can be applied successfully to detect spectral components in the range of 0–150 kHz introduced both by distributed power plants, such as wind and photovoltaic generation systems, and by end-user equipment connected to grids through static converters, such as fluorescent lamps.
A LEARNING ALGORITHM FOR NEURAL NETWORK ENSEMBLES H. D. NAVONE, P. F. VERDES, P. M. GRANITTO and H. A. CECCATTO

The performance of a single regressor/classifier can be improved by combining the outputs of many predictors. This is true provided the combined predictors are accurate and diverse enough, which posses the problem of generating suitable aggregate members in order to have optimal generalization capabilities. We propose here a new method for selecting members of regression/classification ensembles. In particular, using artificial neural networks as learners in a regression context, we show that this method leads to small aggregates with few but very diverse individual networks. The algorithm is favorably tested against other methods recently proposed in the literature, producing a remarkable performance improvement on the standard statistical databases used as benchmarks. E-MAILS: navone@ifir.edu. ar verdes@ifir.edu. ar granitto@ifir.edu. ar ceccatto@ifir.edu. ar
Biform game based cognitive radio scheme for smart grid communications

Smart grid is widely considered to be a next generation power grid, which will be integrated with information feedback communications. However, smart grid communication technologies are subject to inefficient spectrum allocation problems. Cognitive radio networks can solve the problem of spectrum scarcity by opening the under-utilized licensed bands to secondary users. In this paper, adaptive cognitive radio spectrum sensing and sharing algorithms are developed for smart grid environments. Simulation results are presented to demonstrate the effectiveness of the proposed scheme in comparison with other existing schemes.
Towards Energy-aware Multiprocessor Hierarchical Scheduling of Real-time Systems

Multiprocessor platforms are becoming increasingly more popular. Providing more computation capacity on a single hardware platform, multiprocessors make it possible to integrate previously federated real-time systems onto a single platform. Multiprocessor hierarchical scheduling techniques provide the ground for composing real-time components, while guaranteeing the timing correctness of the composed system. A considerable deal of compositional real-time systems are embedded systems that operate on battery power. In such systems, reducing the power consumption is of paramount importance to increase the system lifetime. In this paper, we present our idea on reducing the energy consumption when performing hierarchical scheduling on multiprocessors. We formulate the problem, present the model and sketch the outline of the solution. Finally, we present a number of challenges which will be addressed in our work.
Inflationary quantum cosmology: General framework and exact bianchi type I solution

Using the methods of loop quantum gravity, we derive a framework for describing an inflationary, homogeneous universe in a purely quantum theory. The classical model is formulated in terms of the Ashtekar-Sen connection variables for a general subclass of Bianchi class A spacetimes. This formulation then provides a means to develop a corresponding quantum theory. An exact solution is derived for the classical Bianchi type I model and the corresponding semiclassical quantum state is found to also be an exact solution to the quantum theory. The development of a normalizable quantum state from this exact solution is presented and the implications for the normalizability of the Kodama state discussed. We briefly consider some consequences of such a quantum cosmological framework and show that the quantum scale factor of the universe has a continuous spectrum in this model. This result may suggest further modifications that are required to build a more accurate theory. We close this study by suggesting a variety of directions in which to take the results presented.
How Do Grass Species, Season and Ensiling Influence Mycotoxin Content in Forage?

Mycotoxins are secondary metabolites produced by fungal species that have harmful effects on mammals. The aim of this study was to assess the content of mycotoxins in fresh-cut material of selected forage grass species both during and at the end of the growing season. We further assessed mycotoxin content in subsequently produced first-cutting silages with respect to the species used in this study: Lolium perenne (cv. Kentaur), Festulolium pabulare (cv. Felina), Festulolium braunii (cv. Perseus), and mixtures of these species with Festuca rubra (cv. Gondolin) or Poa pratensis (Slezanka). The mycotoxins deoxynivalenol, zearalenone and T-2 toxin were mainly detected in the fresh-cut grass material, while fumonisin and aflatoxin contents were below the detection limits. July and October were the most risky periods for mycotoxins to occur. During the cold temperatures in November and December, the occurrence of mycotoxins in fresh-cut material declined. Although June was a period with low incidence of mycotoxins in green silage, contents of deoxynivalenol and zearalenone in silages from the first cutting exceeded by several times those determined in their biomass collected directly from the field. Moreover, we observed that use of preservatives or inoculants did not prevent mycotoxin production.
Faster Parameterized Algorithms Using Linear Programming

We investigate the parameterized complexity of Vertex Cover parameterized by the difference between the size of the optimal solution and the value of the linear programming (LP) relaxation of the problem. By carefully analyzing the change in the LP value in the branching steps, we argue that combining previously known preprocessing rules with the most straightforward branching algorithm yields an O*(2.618k) algorithm for the problem. Here, k is the excess of the vertex cover size over the LP optimum, and we write O*(f(k)) for a time complexity of the form O(f(k)nO(1)). We proceed to show that a more sophisticated branching algorithm achieves a running time of O*(2.3146k). Following this, using previously known as well as new reductions, we give O*(2.3146k) algorithms for the parameterized versions of Above Guarantee Vertex Cover, Odd Cycle Transversal, Split Vertex Deletion, and Almost 2-SAT, and O*(1.5214k) algorithms for König Vertex Deletion and Vertex Cover parameterized by the size of the smallest odd cycle transversal and König vertex deletion set. These algorithms significantly improve the best known bounds for these problems. The most notable improvement among these is the new bound for Odd Cycle Transversal—this is the first algorithm that improves on the dependence on k of the seminal O*(3k) algorithm of Reed, Smith, and Vetta. Finally, using our algorithm, we obtain a kernel for the standard parameterization of Vertex Cover with at most 2k − clog k vertices. Our kernel is simpler than previously known kernels achieving the same size bound.
Big data signal processing using boosted RLS algorithm

We propose an efficient method for the high dimensional data regression. To this end, we use a least mean squares (LMS) filter followed by a recursive least squares (RLS) filter and combine them via boosting notion extensively used in machine learning literature. Moreover, we provide a novel approach where the RLS filter is updated randomly in order to reduce the computational complexity while not giving up more on the performance. In the proposed algorithm, after the LMS filter produces an estimate, depending on the error made on this step, the algorithm decides whether or not updating the RLS filter. Since we avoid updating the RLS filter for all data sequence, the computational complexity is significantly reduced. Error performance and the computation time of our algorithm is demonstrated for a highly realistic scenario.
Role of nitric oxide in reactive hyperemia in human forearm vessels.

BACKGROUND The role of nitric oxide (NO) in reactive hyperemia (RH) is not well known. We investigated whether NO plays a role in RH in human forearm vessels by examining the effects of NG-monomethyl-L-arginine (L-NMMA), a blocker of NO synthesis, on reactive hyperemic flow. METHODS AND RESULTS Forearm blood flow (FBF) was measured by strain-gauge plethysmography with a venous occlusion technique. The left brachial artery was cannulated for drug infusion and direct measurement of arterial pressure. To produce RH, blood flow to the forearm was prevented by inflation of a cuff on the upper arm to suprasystolic pressure for intervals of 3 and 10 minutes. After the release of arterial occlusion (AO), FBF was measured every 15 seconds for 3 minutes. Resting FBF was 4.3 +/- 0.3 mL.min-1.100 mL-1 before 3 minutes of AO and 4.1 +/- 0.6 mL.min-1.100 mL-1 before 10 minutes of AO. FBF increased to 32.3 +/- 1.9 and 38.2 +/- 3.1 mL.min-1.100 mL-1 immediately after 3 and 10 minutes of AO, respectively, and gradually decayed (n = 13). Intra-arterial infusion of L-NMMA (4 mumol/min for 5 minutes) decreased baseline FBF (P < .01) without changes in arterial pressure. L-NMMA did not affect the peak reactive hyperemic FBF after 3 and 10 minutes of AO. L-NMMA significantly decreased total reactive hyperemic flow (flow debt repayment) by 20% to 30% after 3 and 10 minutes of AO. Simultaneous infusion of L-arginine (a precursor of NO) with L-NMMA reversed the effects of L-NMMA. CONCLUSIONS Our results suggest that NO plays a minimal role in vasodilation at peak RH but plays a modest yet significant role in maintaining vasodilation after peak vasodilation. Our results also suggest that reactive hyperemia in human forearms is caused largely by mechanisms other than NO.
Measure optimized cost-sensitive neural network ensemble for multiclass imbalance data learning

The performance of traditional classification algorithms can be limited on imbalanced datasets. In recent years, the imbalanced data learning problem has drawn significant interest. In this work, we focus on designing modifications to neural network, in order to appropriately tackle the problem of multiclass imbalance. We propose a hybrid method that combines two ideas: diverse random subspace ensemble learning with evolutionary search, to improve the performance of neural network on multiclass imbalanced data. An evolutionary search technique is utilized to optimize the misclassification cost under the guidance of imbalanced data measures. Moreover, the diverse random subspace ensemble employs the minimum overlapping mechanism to provide diversity so as to improve the performance of the learning and optimization of neural network. We have demonstrated experimentally using UCI datasets that our approach can achieve better result than state-of-the-art methods for imbalanced data.
Highfrequency : Toolkit for the analysis of highfrequency financial data in R .

The highfrequency package contains an extensive toolkit for the use of highfrequency financial data in R. It offers functionality to manage, clean and match highfrequency trades and quotes data. Furthermore, it enables users to: calculate easily various liquidity measures, estimate and forecast volatility, and investigate microstructure noise and intraday periodicity.
N^N^N does not satisfy Normann's condition

We prove that the Kleene-Kreisel space $N^N^N$ does not satisfy Normann's condition. A topological space $X$ is said to fulfil Normann's condition, if every functionally closed subset of $X$ is an intersection of clopen sets. The investigation of this property is motivated by its strong relationship to a problem in Computable Analysis. D. Normann has proved that in order to establish non-coincidence of the extensional hierarchy and the intensional hierarchy of functionals over the reals it is enough to show that $N^N^N$ fails the above condition.
Top-k Query Answering in Datalog+/- Ontologies under Subjective Reports (Technical Report)

The use of preferences in query answering, both in traditional databases and in ontology-based data access, has recently received much attention, due to its many real-world applications. In this paper, we tackle the problem of top-k query answering in Datalog+/- ontologies subject to the querying user's preferences and a collection of (subjective) reports of other users. Here, each report consists of scores for a list of features, its author's preferences among the features, as well as other information. Theses pieces of information of every report are then combined, along with the querying user's preferences and his/her trust into each report, to rank the query results. We present two alternative such rankings, along with algorithms for top-k (atomic) query answering under these rankings. We also show that, under suitable assumptions, these algorithms run in polynomial time in the data complexity. We finally present more general reports, which are associated with sets of atoms rather than single atoms.
Research on the Management Framework of Personal Desktop Document DataSpace

With the rapid development of the Internet and Data Storage technology, people intends to store a large number of documents of various types on their PCs, but having no way to manage such heterogeneous data sources in a integrated way, such as data in more structured sources (e.g., spreadsheets), semi-structured sources (e.g., XML documents) and unstructured sources (e.g., text documents). This paper proposes a new desktop document management system called iDSMS (i' Document Space Management System), which through reusing human attention can incrementally integrate data stored in various types of documents on ones PC in a "pay-as-you-go" fashion. Our goal is to support efficient queries over collections of heterogeneous document resources in a more meaningful way, and building a uniform access interface for the user.