Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Agents for Sequential Vickrey Auctions with Homogeneous Goods

In this paper, we aim to study the behaviour of the autonomous adaptive agents in sequential Vickrey auctions with homogeneous goods. We describe how to revise these adaptive agents, including ZI-C, ZIP, GD, and AA. The performance of these agents is evaluated by two sets of experiments. It turns out that truth-telling is not a dominant strategy; when it is easy for all the buyer agents to buy their goods, one should not tell his true value; when it is difficult to buy, one should not submit a random value; the performance of adaptive agents, such as ZIP and GD, is very good but cannot be guaranteed to be the best in various market environments. Through another set of experiments, the results demonstrate that AA agents perform very well compared with the truth-telling agents and agents using other adaptive bidding strategies.
Homoclinic and Heteroclinic Bifurcations Close to a Twisted Heteroclinic Cycle

We study the interaction of a transcritical (or saddle-node) bifurcation with a codimension-0/codimension-2 heteroclinic cycle close to (but away from) the local bifurcation point. The study is motivated by numerical observations on the traveling wave ODE of a reaction–diffusion equation. The manifold organization is such that two branches of homoclinic orbits to each fixed point are created when varying the two parameters controlling the codimension-2 loop. It is shown that the homoclinic orbits may become degenerate in an orbit-flip bifurcation. We establish the occurrence of multi-loop homoclinic and heteroclinic orbits in this system. The double-loop homoclinic orbits are shown to bifurcate in an inclination-flip bifurcation, where a Smale's horseshoe is found.
Adherence to treatment in patients undergoing dialysis

Background: Chronic Kidney Disease (CKD) is a silent disease that is frequently diagnosed in advanced stages. The prevalence and incidence of chronic kidney disease (end-stage renal disease) are continuously increasing, particularly in elderly patients. Poor adherence to complex multimodal therapies is a widely recognized problem in the daily care of dialysis patients, contributing to excess morbidity and mortality of this population. Aims and objectives was to assess the adherence to medications among patients undergoing haemodialysis and to explore the factors affecting non adherence.Methods: After obtaining approval from Institutional Ethics Committee, a prospective observational study was carried out among 150 patients, who were on dialysis in a tertiary care hospital, Hassan. An eight item Morisky Medication adherence questionnaire was used to assess medication adherence. Chi-square test applied to assess statistics.Results: Adherence levels were 28%, 42% and 30% for high, medium and poor adherence respectively. The overall prevalence of non-adherence among respondents was 30%. Among them 77.77% were males, 44.44% belonged to age group of 41-60 years, 40% illiterate, 60% employed, 51.11% of patients with smoking and alcoholic habits were not adherent to Dialysis. Other reasons contributing to non-adherence to treatment were forget fullness (86.66%), inadequate knowledge about side effects (80%), unhappy clinical visits (71.11%) and lack of assistance (48.88%). Conclusions: In this study patient showed moderate adherence. This emphasizes the need for constant motivation and education at frequent intervals to ensure better adherence.
Population Monte Carlo

Importance sampling methods can be iterated like MCMC algorithms, while being more robust against dependence and starting values. The population Monte Carlo principle consists of iterated generations of importance samples, with importance functions depending on the previously generated importance samples. The advantage over MCMC algorithms is that the scheme is unbiased at any iteration and can thus be stopped at any time, while iterations improve the performances of the importance function, thus leading to an adaptive importance sampling. We illustrate this method on a mixture example with multiscale importance functions. A second example reanalyzes the ion channel model using an importance sampling scheme based on a hidden Markov representation, and compares population Monte Carlo with a corresponding MCMC algorithm.
Interpolation and decimation of digital signals&#8212;A tutorial review

The concepts of digital signal processing are playing an increasingly important role in the area of multirate signal processing, i.e. signal processing algorithms that involve more than one sampling rate. In this paper we present a tutorial overview of multirate digital signal processing as applied to systems for decimation and interpolation. We first discuss a theoretical model for such systems (based on the sampling theorem) and then show how various structures can be derived to provide efficient implementations of these systems. Design techniques for the linear-time-invariant components of these systems (the digital filter) are discussed, and finally the ideas behind multistage implementations for increased efficiency are presented.
Event knowledge vs. verb knowledge

Two kinds of knowledge are often confounded: knowledge about events occurring in the physical world, and knowledge about words occurring in language. We present two studies designed to show that these are distinct, that this distinction is important, and that it is difficult to determine which kind of knowledge is responsible for certain behaviors. In Study 1, we show that priming from verbs to nouns is both capable of and perhaps best explained by knowledge from distributions of word use rather than distributions of experiential knowledge of events. In Study 2, we show that differences in the scope of generalizations one might make, based on distributional statistics in language, can allow us to make testable predictions about whether the source of the knowledge is language-based or experience-based. This oftenoverlooked difference between experiential knowledge and word knowledge is important for developing a theory about how all knowledge is structured and used.
Red cell distribution width and mean platelet volume in patients with irritable bowel syndrome

Introduction Possible pathophysiological mechanisms of irritable bowel syndrome (IBS) are interactions between microbial flora of the gut and the mucosal/systemic immune system, post-infectious status and inflammation. Mean platelet volume (MPV) and red cell distribution width (RDW) have been reported as inflammatory markers in patients with inflammatory bowel disease, but they have not been studied in functional gastrointestinal disorders. Aim To investigate whether there was an association between haemogram parameters (RDW and MPV) and IBS. Material and methods Forty patients with IBS and 44 healthy controls were included to this retrospective study. Patients diagnosed with IBS according to Rome III criteria were included as the IBS group. They were all screened for psychiatric or organic bowel diseases for the sake of precise diagnosis. Results Both RDW (p < 0.001) and MPV (p = 0.046) were increased in patients with IBS compared to controls. This increase in RDW and MPV was independent of the type of IBS. Conclusions The RDW and MPV should be laboratory indicators of IBS. More prospective studies with larger cohorts are needed to confirm our results.
The Dynamical and Chemical Evolution of Dwarf Spheroidal Galaxies with GEAR

We present the fully parallel chemo-dynamical Tree/SPH code GEAR, which allows us to perform high resolution simulations with detailed chemical diagnostics. Starting from the public version of Gadget-2, we included the complex treatment of the baryon physics: gas cooling, star formation law, chemical evolution, and supernova feedback. We qualified the performances of GEAR in the case of dwarf spheroidal galaxies (dSphs) galaxies. Our code GEAR conserves the total energy budget of the systems to better than 5% over 14 Gyr and provides an excellent convergence of the results with numerical resolution. We showed that models of dSphs in a static Euclidean space, where the expansion of the universe is neglected are valid. In addition, we tackled some existing open questions in the field, such as the stellar mass fraction of dSphs and its link to the predicted dark matter halo mass function, the effect of supernova feedback, the spatial distribution of the stellar populations, and the origin of the diversity in star formation histories and chemical abundance patterns. Strong supernova-driven winds seem incompatible with the observed metallicities and luminosities. Despite newly formed stars being preferentially found in the galaxy central parts, turbulent motions in the gas can quickly erase any metallicity gradient. The diversity in properties of dSph are related to a range of total masses, as well as a range of dispersion in the central densities, which is also seen in the halos emerging from a Lambda CDM cosmogony.
Image‐to‐Geometry Registration: a Mutual Information Method exploiting Illumination‐related Geometric Properties

This work concerns a novel study in the field of image‐to‐geometry registration. Our approach takes inspiration from medical imaging, in particular from multi‐modal image registration. Most of the algorithms developed in this domain, where the images to register come from different sensors (CT, X‐ray, PET), are based on Mutual Information, a statistical measure of non‐linear correlation between two data sources. The main idea is to use mutual information as a similarity measure between the image to be registered and renderings of the model geometry, in order to drive the registration in an iterative optimization framework. We demonstrate that some illumination‐related geometric properties, such as surface normals, ambient occlusion and reflection directions can be used for this purpose. After a comprehensive analysis of such properties we propose a way to combine these sources of information in order to improve the performance of our automatic registration algorithm. The proposed approach can robustly cover a wide range of real cases and can be easily extended.
On the Changing Role of Enterprise Architecture in Decentralized Environments: State of the Art

The decentralization of organizations and subsequent change to their management and operation style requires major changes in organization processes and heavily involves the IT. This paper demonstrates, however, that EA is primarily aimed at centralized organizational structures and as such has shortcomings when applied to decentralized organizations. Overcoming these deficiencies requires new principles to be introduced and incorporated into EA knowledge. A potential source for these principles are peer-to-peer architectures, which tackles the problem of decentralization in its own way. This paper presents prevalent decentralization principles from peer-to-peer, and how they have the potential to be applied to EA.
An analysis of learned proximity functions

A lot of recent work has shown that the proximity of terms can be exploited to improve the performance of information retrieval systems. We review a recent approach that uses an intuitive framework to incorporate proximity functions into vector based information retrieval systems. More importantly, we present several proximity functions that were learned within this framework and show that they adhere to previously developed constraints regarding the shape of a good proximity function. Finally, we include results of all of the learned functions on unseen test data that shows the consistency of the learning approach used.
An improved collaborative filtering algorithm adapting to user interest changes

In recent years, recommendation systems have been widely applied in different areas. However, typical collaborative filtering algorithms ignored the changes of user interest over time. To solve the problem, we proposed an improved item-based collaborative filtering algorithm to adapt to user interest changes. The proposed algorithm has two contributions: (1) the algorithm designed a time weight function which shows the interest changes to get a more accurate recommendation; (2) the algorithm introduced a new method to calculate the similarity between items. Essentially, this is a linear combination of time similarity and traditional rating similarity. The experiment results show that this method generates a better accuracy than the traditional collaborative filtering algorithm in recommendation predication.
Hybrid Numerical Solvers for Massively Parallel Eigenvalue Computations and Their Benchmark with Electronic Structure Calculations

Optimally hybrid numerical solvers were constructed for massively parallel generalized eigenvalue problem (GEP). The strong scaling benchmark was carried out on the K computer and other supercomputers for electronic structure calculation problems in the matrix sizes of M = 10^4-10^6 with upto 105 cores. The procedure of GEP is decomposed into the two subprocedures of the reducer to the standard eigenvalue problem (SEP) and the solver of SEP. A hybrid solver is constructed, when a routine is chosen for each subprocedure from the three parallel solver libraries of ScaLAPACK, ELPA and EigenExa. The hybrid solvers with the two newer libraries, ELPA and EigenExa, give better benchmark results than the conventional ScaLAPACK library. The detailed analysis on the results implies that the reducer can be a bottleneck in next-generation (exa-scale) supercomputers, which indicates the guidance for future research. The code was developed as a middleware and a mini-application and will appear online.
Anti Inflammatory Effects of Statins in Cardiac Surgery Patients

Objectives: Cardiac surgery with cardiopulmonary bypass provokes systemic inflammatory response, which may cause organ dysfunction. Studies have suggested that pre-operative statin therapy can reduce morbidity and mortality associated with cardiac surgery; the mechanism for this remains unclear. We hypothesise that underlying mechanism of action for these effects of statins is through inhibition of neutrophil transendothelial migration. Methods: We recruited 151 patients undergoing cardiac surgery. Of these 41 patients were on no-statin; 48 patients on low-dose (10 - 30 mg) and 62 patients were on high-dose statin (40 - 80 mg). Ex vivo neutrophil migration was performed on pre-operative blood samples of 90 patients. Of these 90 patients we used 36 patients to assess the levels of TNF-α and sICAM-1. Clinical parameters of total 151 patients were assessed to analyse outcome. Results: Ex vivo neutrophil migration was decreased in high-dose statin group when compared to neutrophils isolated from no-statin or low-dose statin groups (p < 0.001). Serum TNF-α levels were lower in the high-dose statin group (p = 0.002) and sICAM-1 levels were decreased in both low (p = 0.02) and high-dose statin (p = 0.01) groups. Conclusion: Our study demonstrates that high-dose statins reduce ex-vivo transendothelial neutrophil migration, TNF-α and sICAM-1 serum levels suggesting that statins may help in decreasing the post-cardiac surgery morbidity.
An Architecture for Real-Time Job Characterization

Estimating the quality of a given statistical model is of great importance because they are based on a finite dataset from where all the learning comes. This dataset is usually very large, making it infeasible to continuously process huge amounts of data by statistical models. The Bags of Little Bootstraps (BLB) is a novel method for estimating the quality of a statistical model with a more favorable computational profile than the traditional bootstrap, because it estimates a good model from a limited amount of data. Herein, we suggest an architecture that combines BLB with real time sampling of server metrics in distributed applications. By making use of an Insight Engine (IE), this architecture could be used to efficiently allow applications to share resources and to create a statistical model for predicting their resource usage, enabling schedulers to allocate resources optimally.
14 Computational Aspects in Statistical Signal Processing

Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. The received signal can not be predicted deterministically, so statistical methods are needed to describe the signals. The problem of detecting the signal in the midst of noise arises in a wide variety of areas such as communications , radio location of objects, seismic signal processing and computer assisted medical diagnosis. Statistical signal processing is also used in many physical science applications, such as geophysics, acoustics, texture classifications , voice recognition and meteorology among many others. During the past fifteen years, tremendous advances have been achieved in the area of digital technology in general and in statistical signal processing in particular. Several sophisticated statistical tools have been used quite effectively in solving various important signal processing problems. Various problem specific algorithms have evolved in analyzing different type of signals and they are found to be quite effective in practice. The main purpose of this chapter is to familiarize the statistical community with some of the signal processing problems where statisticians can play a very important role to provide satisfactory solutions. We provide examples of the different statistical signal processing models and describe in detail the computational issues involved in estimating the parameters of one of the most important models, namely the undamped exponential models. The undamped exponential model plays a significant role in statistical signal processing and related areas.
Daily Temperature Fluctuations Alter Interactions between Closely Related Species of Marine Nematodes

In addition to an increase in mean temperature, climate change models predict decreasing amplitudes of daily temperature fluctuations. In temperate regions, where daily and seasonal fluctuations are prominent, such decreases in daily temperature fluctuations can have a pronounced effect on the fitness of species and on the outcome of species interactions. In this study, the effect of a temperature regime with daily fluctuations versus a constant temperature on the fitness and interspecific interactions of three cryptic species of the marine nematode species complex of Litoditis marina (Pm I, Pm III and Pm IV) were investigated. In a lab experiment, different combinations of species (monospecific treatment: Pm I and Pm IV and Pm III alone; two-species treatment: Pm I + Pm IV; three-species treatment: Pm I + Pm IV + Pm III) were subjected to two different temperature regimes: one constant and one fluctuating temperature. Our results showed that fluctuating temperature had minor or no effects on the population fitness of the three species in monocultures. In contrast, interspecific interactions clearly influenced the fitness of all three species, both positively and negatively. Temperature regime did have a substantial effect on the interactions between the species. In the two-species treatment, temperature regime altered the interaction from a sort of mutualism to commensalism. In addition, the strength of the interspecific interactions changed depending on the temperature regime in the three-species treatment. This experiment confirms that interactions between the species can change depending on the abiotic environment; these results show that it is important to incorporate the effect of fluctuations on interspecific interactions to predict the effect of climate change on biodiversity.
Ruptures and Reproductions: A Walking Encounter With a Campus Tour and a Confederate Monument

This article explores a moment of intersections that rupture and reproduce the racist and raced space of a university campus. A walk that I take with a student plugs into a campus tour, becomes connected to a confederate monument, is put in relation to a physical building, and plugs back into reproductions of race and racism on campus. Methodologically, this article moves rhizomatically through practices of place, from the walking interview of the data collection, to a visual analysis that illustrates the overlapping and a/synchronous becomings of the event. Through mapping a visual and affective constellation of affects and materialities that leap and jump across time and bodies, this article explores the multiple and layered discourses at work in this moment to explore what they do: How race and racism are (re)produced through everyday practices, as well as how they crack and stutter and become differently. Following the entanglements of this data event, this analysis maps how molar lines of Whiteness are reproduced and embedded in the discourses of place, and opens the possibility to produce place differently.
An anti-counterfeiting method based on VCS for mobile phone's identification

At present, there are many anti-counterfeiting technologies such as bar code, laser security mark, and digital watermarking to be utilized to protect the copyright and patent. Based on the Visual Cryptography Scheme (VCS), an interesting anti-counterfeiting method for identification of authorized edition mobile phone is proposed in this paper. This paper firstly gives a brief overview on VCS, especially on the classical VCS of (2, 2)-threshold, and then the proposed anti-counterfeiting method is described. At the end of this paper, the simulation results are given, which demonstrate that the proposed method based on VCS is quite feasible and promising.
Extending ICT4D Studies: The Value of Critical Research

The purpose of this paper is to demonstrate the value of critical research for information and communications technology for development (ICT4D) studies. Most previous IS research on ICT4D projects is interpretive and has focused on the immediate organizational context, but there are very few critical studies that have engaged in macro sociopolitical analyses regarding institutional change. Hence we extend previous IS research on ICT4D by adopting a critical research perspective on the macro sociopolitical context within which most ICT4D projects take place. We illustrate this with an ethnographic study of a project that was intended to improve the education and social welfare of the aboriginal people in Taiwan. On the surface the project was tremendously successful; it became a showcase on national radio and TV showing how ICT could be used to support underprivileged children. However, our research uncovered a different story altogether--a story of the aboriginal people themselves feeling marginalized and without much of a voice. We use concepts from postcolonial theory to make sense of these two contradictory stories. We found that the interrelationship between the macro sociopolitical context and the local organizational context of the ICT4D project is the key to understanding what went wrong, something which we would not have discovered if we had taken the traditional approach. The postcolonial context is powerful and pervasive, hampering any real progress.