nils-herrmann/clean_scirepeval huggingface dataset:
ORSAP: Abstracting routing state on demand
Venue: 2016 IEEE 24th International Conference on Network Protocols (ICNP)

Providing an interface for network applications to access network state, Software Defined Networking (SDN) northbound API protocol is the foundation for the development of programmable networks with adaptive applications. However, with the growing network scale and applications' need for routing state at multi domain level, feeding complete routing states to applications would jeopardize their scalability and network providers' privacy. Thus a good routing state abstraction is needed, which must be on demand so that different applications can receive customized abstract state suiting their needs. Moreover, it must be minimal and equivalent, i.e. containing all the necessary information for applications to make decisions as the complete state does with no redundancy. Current routing state abstractions are not on demand, and adopt extreme aggregation approaches (e.g. the big switch) to provide a minimal abstraction with the price of severe information loss. For instance, bottleneck links shared between flows are concealed, leading applications to make sub optimal decisions. In this paper, we design ORSAP, the first on demand routing state abstraction protocol, through which network applications can describe their demands while Internet service providers can provide the on demand minimal equivalent routing state accordingly. ORSAP ensures applications' scalability, protects network providers' privacy, and significantly reduces the traffic to disseminate the information. Experiments show that with ORSAP and the abstraction engine we introduced in this paper, one can achieve a state abstraction ratio of up to 60% with an extremely low computation time even with large networks and complex application queries.
The importance of albumin infusion rate for plasma volume expansion following major abdominal surgery AIR: study protocol for a randomised controlled trial
Venue: Trials

BackgroundAdministration of fluids to restore normovolaemia is one of the most common therapeutic interventions performed peri operatively and in the critically ill, but no study has evaluated the importance of infusion rate for the plasma volume expanding effect of a resuscitation fluid. The present study is designed to test the hypothesis that a slow infusion of resuscitation fluid results in better plasma volume expansion than a rapid infusion.designThe study is a single centre, assessor blinded, parallel group, randomised prospective study. Patients over 40 years of age admitted to the post operative care unit after a Whipple procedure or major gynaecological surgery and presenting with signs of hypovolaemia are eligible for inclusion. Patients are randomised in a 1:1 fashion with no stratification to either rapid (30 minutes) or slow (180 minutes) infusion of 5% albumin at a dose of 10 ml/kg ideal body weight. Plasma volume is measured using 125I human serum albumin at baseline (prior to albumin infusion) as well as at 30 minutes and 180 minutes after infusion start. The primary endpoint is change in plasma volume from baseline to 180 minutes after the start of 5% albumin infusion. Secondary endpoints include the integral of plasma volume over time from baseline to 180 minutes after the start of the infusion and transcapillary escape rate of albumin /h) from 180 minutes to 240 minutes after the start of albumin infusion. In addition, diuresis, change in central venous oxygen saturation, lactate and blood pressure will be evaluated. A total of 70 patients will be included in the study, and the study has 80% power to detect a difference of 4 ml/kg in plasma volume expansion between the two groups.DiscussionThe present study is the first clinical investigation of the importance of infusion rate for the plasma volume expanding effect of a resuscitation fluid.
VFC Variational Feedback Controller and its application to semi active suspensions

Active and semi active control of oscillating devices and structures is a challenging field and this paper proposes an original investigation based on variational controls that can be successfully applied to mechanical systems. The method produces a general class of new controllers, named VFC Variational Feedback Controllers, that is the main theoretical contribution of the paper. The value of the theory relies on using a reformulation of the Variational Optimal Control Theory, that has in general the limit of producing control program strategies and not directly feedback control methods. The difficulties are in fact related to the intrinsic nature of the variational optimal control, that must solve initial and final boundary conditions. A special definition of the class of the considered objective functions, permits to skip this difficulty, producing a pure feedback control. The presented theory goes beyond with respect to the most acknowledged LQR variational based techniques, in that VFC can be applied to more general nonlinear dynamical systems, even with finite time horizon. To test the effectiveness of the novel approach in real engineering problems, a deep investigation on nonlinear suspension systems treated by VFC is proposed in this paper. To this aim, VFC is systematically compared with the most recent methods available in this field and suitable to deal with nonlinear system control of car suspensions. In particular, the comparative analysis is made in terms of both comfort and handling key performance indexes, that permits to easily and significantly compare different control logics, such as the Sky hook and Ground hook control families, the Acceleration and Power Driven Dampers. The results of this comparison are collected in a performance plane, having comfort and handling indexes as coordinate axes, showing that VFC controllers completely cover the regions reached by the other mentioned control logics in this plane, but reveal to have access to new valuable areas of unexplored different performance combinations.
Inventory models for deteriorating items with maximum lifetime under downstream partial trade credits to credit risk customers by discounted cash flow analysis

Getting loans from banks are almost impossible after 2008 global financial crisis. As a result, about 80% of companies in United Kingdom and United States offer their products on various short term, free interest loans (i.e. trade credit) to customers. Numerous researchers and academicians apply discounted cash flow (DCF) analysis merely to compute the interest earned and charged during the credit period but not to the revenue and other costs which are considerably larger than the interest earned and charged. For a rigorous analysis, the DCF on all relevant costs is applied. In addition, many products deteriorate continuously and cannot be sold after their maximum lifetimes or expiration dates. However, very few researchers and investigators have implemented the product lifetime expectance into their models. In this paper, a supplier retailer customer chain system is developed in which the retailer gets an upstream full trade credit from the supplier whereas offers a downstream partial trade credit to credit risk customers, the deterioration rate is non decreasing over time and near 100% particularly close to its expiration date, and DCF analysis is applied to compute all relevant costs. This paper demonstrates that the retailer's optimal replenishment cycle time not only exists but also is unique. Thus, the search of the global optimal reduces to finding a local solution. Finally, several numerical examples and sensitivity analysis are performed in order to illustrate the problem and obtain managerial insights.
Attribute Based Signatures for Circuits from Bilinear Map
Venue: Public Key Cryptography

In attribute based signatures, each signer receives a signing key from the authority, which is associated with the signer's attribute, and using the signing key, the signer can issue a signature on any message under a predicate, if his attribute satisfies the predicate. One of the ultimate goals in this area is to support a wide class of predicates, such as the class of arbitrary circuits, with practical efficiency from a simple assumption, since these three aspects determine the usefulness of the scheme. We present an attribute based signature scheme which allows us to use an arbitrary circuit as the predicate with practical efficiency from the symmetric external Diffie Hellman assumption. We achieve this by combining the efficiency of Groth Sahai proofs, which allow us to prove algebraic equations efficiently, and the expressiveness of Groth Ostrovsky Sahai proofs, which allow us to prove any NP relation via circuit satisfiability.
Effects of auditory cues on gait initiation and turning in patients with Parkinson's disease.
Venue: Neurologia

To review the available scientific evidence about the effectiveness of auditory cues during gait initiation and turning in patients with Parkinson's disease.  We conducted a literature search in the following databases: Brain, PubMed, Medline, CINAHL, Scopus, Science Direct, Web of Science, Cochrane Database of Systematic Reviews, Cochrane Library Plus, CENTRAL, Trip Database, PEDro, DARE, OTseeker, and Google Scholar. We included all studies published between 2007 and 2016 and evaluating the influence of auditory cues on independent gait initiation and turning in patients with Parkinson's disease. The methodological quality of the studies was assessed with the Jadad scale.  We included 13 studies, all of which had a low methodological quality (Jadad scale score<=2) In these studies, high intensity, high frequency auditory cues had a positive impact on gait initiation and turning. More specifically, they 1) improved spatiotemporal and kinematic parameters; 2) decreased freezing, turning duration, and falls; and 3) increased gait initiation speed, muscle activation, and gait speed and cadence in patients with Parkinson's disease.  We need studies of better methodological quality to establish the Parkinson's disease stage in which auditory cues are most beneficial, as well as to determine the most effective type and frequency of the auditory cue during gait initiation and turning in patients with Parkinson's disease.
Essays in high dimensional nonlinear time series analysis

In this thesis, I study high dimensional nonlinear time series analysis, and its applications in financial forecasting and identifying risk in highly interconnected financial networks. The first chapter is devoted to the testing for nonlinearity in financial time series. I present a tentative classification of the various linearity tests that have been proposed in the literature. Then I investigate nonlinear features of real financial series to determine if the data justify the use of nonlinear techniques, such as those inspired by machine learning theories. In Chapter 3 5, I develop forecasting strategies with a high dimensional panel of predictors while considering nonlinear dynamics. Combining these two elements is a developing area of research. In the third chapter, I propose a nonlinear generalization of the statistical factor models. As a first step, factor estimation, I employ an auto associative neural network to estimate nonlinear factors from predictors. In the second step, forecasting equation, I apply a nonlinear function feedforward neural networkon estimated factors for prediction. I show that these features can go beyond covariance analysis and enhance forecast accuracy. I apply this approach to forecast equity returns, and show that capturing nonlinear dynamics between equities significantly improves the quality of forecasts over current univariate and multivariate factor models. In Chapter 5, I propose a high dimensional learning based on a shrinkage estimation of a backpropagation algorithm for skip layer neural networks. This thesis emphasizes that linear models can be represented as special cases of these two aforementioned models, which basically means that if there is no nonlinearity between series, the proposed models will reduce to a linear model. This thesis also includes a chapter (chapter 4, with Negar Kiyavash and Seyedjalal Etesami) which in this chapter, we propose a new approach for identifying and measuring systemic risk in financial networks by introducing a nonlinearly modified Granger causality network based on directed information graphs. The suggested method allows for nonlinearity and has predictive power over future economic activity through a time varying network of interconnections. We apply the method to the daily returns of U.S. financial Institutions including banks, brokers and insurance companiesto identifythe level of systemic risk inthe financial sector and the contribution of each financial institution.
Evaluating machine learning classification for financial trading: An empirical approach
Venue: Expert Syst. Appl.

Low complexity machine learning models are used trade in the FOREX market.A six year trading simulation in USDJPY, EURGPB and EURUSD are assessed.Periodic retraining, number of attributes and retraining set size are varied and studied.Middle range accuracies are obtained with high financial returns in the long term. Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.
Critical design of heterogeneous catalysts for biomass valorization: current thrust and emerging prospects

Catalysis in the heterogeneous phase plays a crucial role in the valorization of biorenewable substrates with controlled reactivity, efficient mechanical process separation, greater recyclability and minimization of environmental effects. In this minireview, we have critically analyzed the design aspects of catalysts with multifunctional properties in making biomass conversion processes more productive and sustainable. The catalytic systems have been categorized into subgroups such as metal oxides and phosphates, supported metals, functionalized porous materials, acidic and ion exchange resins, zeolites and carbonaceous materials to discuss their structural features and active sites towards intrinsic reactivity and selectivity in the transformation of biomass intermediates via hydrolysis, dehydration, hydrodeoxygenation and oxidation pathways. Recent advances in photocatalytic materials for the oxidation of biomass derived small molecules (such as sugars, alcohols, carboxylic acids) and lignin model compounds and the role of different heteroatoms in improving photo electronic properties are discussed. The synergistic effect of metal nanoparticulate sites and acid base supports of bimetallic catalytic systems in performing multistep cascade processes in one pot are analyzed.
Imaging at the Single Atom Level in Closed Cell In Situ Gas Reactions
Venue: Microscopy and Microanalysis

Single atom catalysis is emerging as a new frontier in the field of heterogeneous catalysis [1 4] Singleatom catalysts (SACs) have recently been successfully synthesized and proved to be effective for many important catalytic reactions with high atom efficiency [1 4] A key issue facing the practical applications of SACs is, however, the stability of the metal single atoms during catalytic reactions, especially at elevated temperatures. To develop robust SAC synthesis protocols to stabilize noble metal single atoms on appropriate metal oxide supports requires a fundamental understanding of the dynamics of the supported single atoms under gas environments and at elevated temperatures. Understanding the dynamical movement and the anchoring of noble metal atoms on support materials becomes critical to developing novel strategies to synthesize SACs with high levels of metal loading. Recently developed technologies for in situ reaction studies utilizing a closed cell MEMS based reactor specimen holder  in an aberration corrected (S)TEM instrument have shown the capability to image atomic columns on metal particles at pressures up to an atmosphere and temperatures up to 1000degC. However, imaging of single heavy metal atoms on oxide supports and tracking their movements during reaction experiments remains problematical, due to the scattering phenomena relative to the elements of the gas cell geometry, and electron beam effects. In the present study, we demonstrate the capabilities and efficacy for observations of single atom reactions in a closed cell system, using a specimen comprising Pt atoms on NiO nanocrystals. High angle annular dark field (HAADF) mages are shown for 4 cases: (a) the ideal geometry of catalyst support particles suspended over a hole in a holey carbon support film; (b) support particles on the carbon film; (c) support particles on the thin (30nm) SiN membrane of a MEMS heater device with the beam first traversing the SiN (see caption for details) and (d) support particles imaged in the full gas cell geometry at elevated temperature and pressure (see caption)
DAC List of ODA Recipients

Afghanistan Democratic People's Republic of Korea Armenia Albania Angola1 Kenya Bolivia Algeria Bangladesh Tajikistan Cabo Verde Antigua and Barbuda3 Benin Zimbabwe Cameroon Argentina Bhutan Congo Azerbaijan Burkina Faso Cote d'Ivoire Belarus Burundi Egypt Belize Cambodia El Salvador Bosnia and Herzegovina Central African Republic Georgia Botswana Chad Ghana Brazil Comoros Guatemala Chile2 Democratic Republic of the Congo Guyana China (People's Republic of)
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Venue: ECCV

We propose a novel unsupervised learning approach to build features suitable for object detection and classification. The features are pre trained on a large dataset without human annotation and later transferred via fine tuning on a different, smaller and labeled dataset. The pre training consists of solving jigsaw puzzles of natural images. To facilitate the transfer of features to other tasks, we introduce the context free network (CFN) a siamese ennead convolutional neural network. The features correspond to the columns of the CFN and they process image tiles independently (i.e. free of context) The later layers of the CFN then use the features to identify their geometric arrangement. Our experimental evaluations show that the learned features capture semantically relevant content. We pre train the CFN on the training set of the ILSVRC2012 dataset and transfer the features on the combined training and validation set of Pascal VOC 2007 for object detection (via fast RCNN) and classification. These features outperform all current unsupervised features with (51.8\ for detection and (68.6\ for classification, and reduce the gap with supervised learning (56.5\ and (78.2\ respectively)
SUMMARY OF LETTERS TO PARTICIPANTS OF THE WORKSHOP ON THE THEORY OF SHINICHI MOCHIZUKI, DECEMBER 7 11 2015, OXFORD

3. Prerequisites for the workshop. They are mentioned or reviewed in sect. 1 of [17] class field theory, especially local class field theory; elliptic curves over finite, local and global fields, some basic things about the set of Diophantine geometry conjectures the theory of Mochizuki deals with, basics of (classical) anabelian geometry. For the latter Ch.4 of [21] is a very easy to read source. A very short paper  elegantly proves a local version which involves higher ramification filtration, the result plays a role in IUT theory.
Phase Transition for Quenched Coupled Replicas in a Plaquette Spin Model of Glasses.
Venue: Physical review letters

We study a three dimensional plaquette spin model whose low temperature dynamics is glassy, due to localized defects and effective kinetic constraints. The thermodynamics of this system is smooth at all temperatures. We show that coupling it to a second system with a fixed (quenched) configuration leads to a phase transition, at finite coupling. The order parameter is the overlap between the copies, and the transition is between phases of low and high overlap. We find critical points whose properties are consistent with random field Ising universality. We analyze the interfacial free energy cost between the high and low overlap states that coexist at (and below) the critical point, and we use this cost as the basis for a finite size scaling analysis. We discuss these results in the context of mean field and dynamical facilitation theories of the glass transition.
An improved hybrid ant local search algorithm for the partition graph coloring problem
Venue: J. Comput. Appl. Math.

In this paper we propose a hybrid Ant Colony Optimization (ACO) algorithm for the Partition Graph Coloring Problem (PGCP) Given an undirected graph G V E whose nodes are partitioned into a given number of the sets, the goal of the PGCP is to find a subset V V that contains exactly one node from each cluster and a coloring for V so that in the graph induced by V two adjacent nodes have different colors and the total number of used colors is minimal. Our hybrid algorithm is obtained by executing a local search procedure after every ACO iteration. The performance of our algorithm is evaluated on a set of instances commonly used as benchmark and the computational results show that compared to state of the art algorithms, our improved hybrid ACO algorithm achieves solid results in very short run times.