Miracle-dz/UniFAR-FTUs huggingface dataset:
New Results in Importance Sampling

For many detection schemes, an easily calculated detection threshold T as a function of the desired probability of false alarm PF is not possible. A Monte Carlo simulation can be used to estimate PF as a function of T. To attain a relative accuracy of 10%, the number of Monte Carlos should equal 100=PF . For small PF , this number can be excessively large and hence in order to estimate PF , the Monte Carlo method can be computationally intensive and expensive. The importance sampling technique [1—8] has been demonstrated to significantly reduce the necessary number of Monte Carlos to attain a given relative estimation error. For importance sampling, one modifies the probability distribution of the input random variables (RVs) so as to make low probability events occur more often; the estimate of the PF is then obtained by weighting the sample detection output (a 0 or 1) by a weighting function which equals the ratio of the probability density function (pdf) of the original underlying distribution to the pdf of the modified or distorted distribution evaluated at the sample values of the input RVs [1]. Much of the past research on importance sampling has used the distorted variance technique to generate the RVs of the modified distribution. For this technique, an input RV generated by the original distribution is multiplied by some factor 3⁄4 which is greater than one. The resultant RV is the RV associated with the modified or distorted distribution. This technique is easily implemented and often results in a significant decrease in the required number of Monte Carlos. The difficulty with the technique is obtaining a good 3⁄4. Small and large values of 3⁄4 can result in a large variance for the estimate of PF . In some cases this variance can be quite sensitive to changes in 3⁄4. We present a new methodology for generating the modified distribution. The technique is called the Chernoff Importance Sampling Method. We demonstrate that the number of Monte Carlos can be reduced by a factor of a Chernoff-like bound [11, p. 163] on the PF . In many instances, the Chernoff-like bound is within a few orders of magnitude of the PF . Thus the number of Monte Carlos is significantly reduced. The methodology presented here is actually a simple version of the general method used in Large Deviations Theory [13—16]. In this setting, it is desired to estimate a small probability that vanishes with exponential rate as some parameter n " 1. The Large Deviation Theory suggests that good simulation distributions can be obtained from exponential twisting on tilted formulas. It is said that a sequence of simulation distributions is asymptotically efficient if the number of simulations required to obtain a specified estimator precision does not grow
MBE methodology became available allowing for sequences being sampled sequentially through time ( Rambaut 2000 ) , allowing for changing population sizes through time (

Epidemiological processes leave a fingerprint in the pattern of genetic structure of virus populations. Here, we provide a new method to infer epidemiological parameters directly from viral sequence data. The method is based on phylogenetic analysis using a birth-death model (BDM) rather than the commonly used coalescent as the model for the epidemiological transmission of the pathogen. Using the BDM has the advantage that transmission and death rates are estimated independently and therefore enables for the first time the estimation of the basic reproductive number of the pathogen using only sequence data, without further assumptions like the average duration of infection. We apply the method to genetic data of the HIV-1 epidemic in Switzerland. STADLER, Tanja, et al. Estimating the basic reproductive number from viral sequence data. Molecular Biology and Evolution, 2012, vol. 29, no. 1, p. 347-57 DOI : 10.1093/molbev/msr217
Localization for Intelligent Systems Using Unsupervised Learning and Prediction Approaches

This article proposes the manifold alignment algorithm, followed by learning models for prediction in indoor localization within intelligent systems. Processing data to and from multiple data sets can lead to increased time complexity and a greater demand for disk space and memory if the system is not properly scaled. Herein, the proposed manifold alignment algorithm introduces dimensionality reduction in order to account for the inefficiency caused by the inclusion of multiple data sets in such intelligent systems. Furthermore, data obtained from an indoor localization application can include discrepancies due to the dependence on access points (APs) that vary according to their external environment. In this scope, the indoor localization technique is based on the received signal strength indicator (RSSI). This is regarded as a popular method in localization due to its low overhead and computational requirements though its accuracy tends to weaken in larger applications. To account for the abovementioned disadvantages, eight statistical methods and machine learning models [autoregressive integrated moving average (ARIMA), support vector machine (SVM), multilayer perceptron (MLP), decision tree, random forests, eXtreme Gradient Boosting (XGBoost), long short-term memory (LSTM), and gated recurrent unit (GRU)] are evaluated to generate accurate RSSI predictions based on previous RSSI readings and their positions. Having measured the root mean square error (RMSE) and time complexities of each model, the MLP model had outperformed the other models with the best RMSE value and a relatively low execution time.
Electron and phonon interactions and transport in the ultrahigh-temperature ceramic ZrC

We have simulated the ultrahigh-temperature ceramic zirconium carbide (ZrC) in order to predict electron and phonon scattering properties, including lifetimes and transport. Our predictions of heat and charge conductivity, which extend to 3000 K, are relevant to extreme-temperature applications of ZrC. Mechanisms are identified on a first-principles basis that considerably enhance or suppress heat transport at high temperature, including strain, anharmonic phonon renormalization, and four-phonon scattering. The extent to which boundary confinement and isotope scattering effects lower thermal conductivity is predicted.
Team Mindfulness and Employee Moral Efficacy: The Moderating Effect of Team Bottom-Line Mentality

Different from the previous studies that mainly focused on the mindfulness at the individual level, this research explores the effect of team mindfulness on employee moral efficacy, and considers the influence of the interaction between team bottom-line mentality (BLM) and team mindfulness on employees’ moral efficacy. Results from three-wave surveys of 275 employees indicated that: the team BLM will weaken the positive relationship between team mindfulness and employees’ moral effectiveness. This research advances the current understandings of the influencing process between team mindfulness and employees’ moral efficacy by identifying the moderating role of team BLM, and casts the spotlight on the impact mechanism among team mindfulness, team BLM, and employees’ moral efficacy at the team level. Theoretical and practical implications are also discussed.
Modeling flow inside an anaerobic digester by CFD techniques

Anaerobic processes are used to treat high strength organic wastewater as well as for the treatment of primary and secondary sludge from conventional wastewater treatment plants. In these processes, heterotrophic microorganisms convert biodegradable organic matter to methane and carbon dioxide in the absence of dissolved oxygen and nitrate. Some of the most important aspects of the design of anaerobic digesters are related to hydraulic considerations. In spite of its important role in performance, hydraulics of flow inside digesters has not been quantified or adequately characterized. In this contribution a three-dimensional steady-state computational fluid dynamics (CFD) simulation has been performed for a particular anaerobic digester, in order to visualize the flow patterns. Flow and velocities profiles have been represented inside the digester to identify possible dead zones or stratifications. The geometry of a real digester installed in Valencia Waste Water Treatment Plant (located in Quart-Benager, Valencia, Spain) has been used in order to consider the proposed methodology. Copyright © 2011 International Energy and Environment Foundation - All rights reserved.
Non-parametric Models for SELDI Feature Selection STA 345 Course Project

Mass spectrometry is an analytical technique used to measure the mass-to-charge(M/Z) of ionized proteins. Modern techniques like matrix-assisted laser desorption/ionization(MALDI) and surfaceenhanced laser desorption/ionization(SELDI) allow us to explore patterns of protein expression on a large scale without the prior knowledge that which proteins are present in the biology samples[1]; [4]. One of the interesting scientific question is to discover biomarkers, such as proteins which would differentially expressed in samples of the experiment group and control group. We need to extract features, which are represented as peaks, from the high dimensional mass spectra data, and we also need to build hierarchical models to classify the data. In this report, I will first describe some features of this type of data. Then I will build a statistical model to find the peak locations under some assumptions. I will also illustrate the problem by a simulation study, and the report ends with a discussion.
An Innovative Fuzzy TOPSIS Method to Determine the Location of A New Hospital

The optimal facility location choosing is very necessary action for newly established facilities. There are many phases for building the new hospital. Firstly, hospital’s location must be determined for the new one. While choosing the most appropriate location; a number of criteria is taking into account by facility managers who are in the case of deciding and they evaluate alternatives under the criteria. And also location of healthcare buildings are extremely important. In this study, six experts’ opinions help us to decide the best place for new hospital in Eskisehir. The assessments are decision makers’ subjective assessments. Most commonly used methods in this kinds of problem is fuzzy logic. So in this study it is used for determining methods for multi-criteria problems. 5 candidates are evaluated and the fuzzy TOPSIS method is used to sequence the options for new hospital location. Objective of this study is selecting the best location for new hospital in Eskisehir.
Heterogeneous Firms, Agglomeration and Economic Geography: Spatial Selection and Sorting

A Melitz-style model of monopolistic competition with heterogeneous firms is integrated into a simple New Economic Geography model to show that the standard assumption of identical firms is neither necessary nor innocuous. We show that re-locating to the big region is most attractive for the most productive firms; this implies interesting results for empirical work and policy analysis. A 'selection effect' means standard empirical measures overestimate agglomeration economies. A 'sorting effect' means that a regional policy induces the highest productivity firms to move to the core while the lowest productivity firms to move to the periphery. We also show that heterogeneity dampens the home market effect.
Convergence of optimal solutions in control problems for hyperbolic equations

A sequence of optimal control problems for systems governed by linear hyperbolic equations with the nonhomogeneous Neumann boundary conditions is considered. The integral cost functionals and the differential operators in the equations depend on the parameter k. We deal with the limit behaviour, as k →∞, of the sequence of optimal solutions using the notions of Gand Γ -convergences. The conditions under which this sequence converges to an optimal solution for the limit problem are given.
Probabilistic prediction of short term wind power considering temporal and spatial dependence of prediction error

The short-term probabilistic prediction of wind power has the characteristics of spatial dependence and time-series dependence. Considering the two characteristics at the same time can improve the prediction level. In this paper, a probabilistic short-term wind power prediction model considering the temporal and spatial dependence of prediction error is proposed. Considering the coupling relationship between the two properties, the NWP(Numerical Weather Prediction) wind speed point prediction error in the historical period is hierarchical clustering, and the empirical distribution model is used to fit the probability distribution of the error under different wind conditions; the cumulative empirical distribution probability value corresponding to the NWP wind speed at the time to be predicted is bootstrap sampling; under the given confidence level, the possible wind speed at each time point to be predicted in the short term is calculated The power fluctuation range of the generator. The test results show that this method can ensure the statistical significance and fitting stability of the sub sample set at the same time, and improve the classification accuracy of the days to be predicted. Compared with considering a single property, the result of probability prediction performs better on multiple evaluation indexes.
Learning Grasping Interaction with Geometry-aware 3D Representations

Learning to interact with objects in the environment is a fundamental AI problem involving perception, motion planning, and control. However, learning representations of such interactions is very challenging due to a high dimensional state space, difficulty in collecting large-scale data, and many variations of an object's visual appearance (i.e. geometry, material, texture, and illumination). We argue that knowledge of 3D geometry is at the heart of grasping interactions and propose the notion of a geometry-aware learning agent. Our key idea is constraining and regularizing interaction learning through 3D geometry prediction. Specifically, we formulate the learning process of a geometry-aware agent as a two-step procedure: First, the agent learns to construct its geometry-aware representation of the scene from 2D sensory input via generative 3D shape modeling. Finally, it learns to predict grasping outcome with its built-in geometry-aware representation. The geometry-aware representation plays a key role in relating geometry and interaction via a novel learning-free depth projection layer. Our contributions are threefold: (1) we build a grasping dataset from demonstrations in virtual reality (VR) with rich sensory and interaction annotations; (2) we demonstrate that the learned geometry-aware representation results in a more robust grasping outcome prediction compared to a baseline model; and (3) we demonstrate the benefits of the learned geometry-aware representation in grasping planning.
Local Attitudes and Perceptions Toward Large Carnivores in a Human-Dominated Landscape of Northern Tanzania

ABSTRACT We conducted 300 semi-structured interviews with local people adjacent to Tarangire National Park, northern Tanzania, to determine their attitudes and perceptions toward large carnivores. We analyzed the relationships between attitudes and age, gender, education, occupation, years at residence, income, distance from protected area, livestock owned, livestock lost to predators and knowledge of carnivores. Three-quarters of respondents (79%) held negative attitudes toward large carnivores, while 20% were generally positive. Three variables were positively associated with attitudes towards different species: formal education (all carnivore species), years at residence (lions and cheetahs), and knowledge of carnivores (cheetahs). Attitudes toward large carnivores were not significantly related to distance from protected area, livestock owned. or livestock lost to predators. Findings suggested that interventions aimed at fostering positive attitudes toward large carnivores should focus on improving formal education and securing long-term residency for people in the region.
Time-Frequency Analysis of Asymmetric Triaxial Galaxy Model Including Effect of Spherical Dark Halo Component

A method of time-frequency analysis (TFA) based on wavelets is applied to study the phase space structure of three-dimensional asymmetric triaxial galaxy enclosed by spherical dark halo component. The investigation is carried out in the presence and absence of dark halo component. Time-frequency analysis is based on the extraction of instantaneous frequency from the phase of the continuous wavelet transform. This method is comparatively fast and reliable. This method can differentiate periodic from quasi-periodic, chaotic sticky from chaotic non-sticky, ordered from chaotic and also, it can accurately determine the time interval of the resonance trapping and transitions too. Apart from that, the phenomenon of transient chaos can be explained with the help of time-frequency analysis. Comparison with the method of total angular momentum (denoted as Ltot) proposed recently is also presented.
Comparing many-body approaches against the helium atom exact solution

Over time, many different theories and approaches have been developed to tackle the many-body problem in quantum chemistry, condensed-matter physics, and nuclear physics. Here we use the helium atom, a real system rather than a model, and we use the exact solution of its Schrödinger equation as a benchmark for comparison between methods. We present new results beyond the random-phase approximation (RPA) from a renormalized RPA (r-RPA) in the framework of the self-consistent RPA (SCRPA) originally developed in nuclear physics, and compare them with various other approaches like configuration interaction (CI), quantum Monte Carlo (QMC), time-dependent density-functional theory (TDDFT), and the Bethe-Salpeter equation on top of the \boldsymbol{GW}𝐆𝐖 approximation. Most of the calculations are consistently done on the same footing, e.g. using the same basis set, in an effort for a most faithful comparison between methods.
Stability of entanglement-spectrum crossing in quench dynamics of one-dimensional gapped free-fermion systems

In a recent work by Gong and Ueda (arXiv:1710.05289), the classification of (1+1)-dimensional quench dynamics for the ten Altland-Zirnbauer classes is achieved, and entanglement-spectrum crossings of the time-dependent states for the topological classes (AIII, DIII, CII, BDI, and D) are discovered as a consequence of the bulk-edge correspondence. We note that, their classification scheme relies on the limit that the energy spectrum of the post-quench Hamiltonian is flat, because any finite band dispersion leads to the break down of time-reversal and chiral symmetries for the parent Hamiltonian (which are used for the classification). We show that, because of the reduction of symmetry by finite energy dispersion, the gapless entanglement-spectrum crossing in the flat-band limit in classes AIII, DIII, and CII is unstable, and could be gapped without closing the bulk gap. The entanglement-spectrum crossing in classes BDI and D is still stable against energy dispersion. We show that the quench process for classes BDI and D can be understood as a $\mathbb{Z}_2$ fermion parity pump, and the entanglement-spectrum crossing for this case is protected by the conservation of fermion parity.
Literature Review of Data Mining Techniques in Customer Churn Prediction for Telecommunications Industry

Customer churn is one of the most critical issues faced by the telecommunications industry. In the telecommunications industry, it is more expensive to acquire a new customer as compared to retaining the current one. Hence, customer churn prediction is currently the main mechanism employed by the industry in order to prevent customers from churning. The objective of churn prediction is to identify customers that are going to leave the telecommunications service provider in advance. Customer churn prediction would allow the telecommunications service provider to plan their customer retention strategy. The high volume of data generated by the industry, with the help of data mining techniques implementation, becomes the main asset for predicting customer churn. Due to this reason, recent literature of different data mining techniques and most popular data mining algorithms for customer churn prediction are reviewed in this paper. Additionally, recent literature on newly developed algorithms based on the popular algorithms are also reviewed. Index Terms – Data Mining, Big data analytics, Churn prediction
Bioremediation of aluminium from the waste water of a conventional water treatment plant using the freshwater macroalga Oedogonium

Conventional water treatment processes use aluminium sulphate (alum) as a coagulant in the production of potable water. While alum is an inexpensive and reliable means of treating water, the process generates waste water containing dissolved Al. This waste water is primarily dealt with via on-site retention. In this study we investigate the cultivation of the freshwater macroalga Oedogonium as a means to sequester dissolved Al from waste water from a conventional water treatment plant. Furthermore, we examine the use of CO2 to manipulate the pH of cultivation as a means of enhancing the sequestration of Al by either increasing the productivity of Oedogonium or increasing the bioavailability of Al in the waste water. The relative bioavailability of Al under conditions of CO2 and no-CO2 provision was contrasted by comparing Al uptake by Diffusive Gradients in Thin Films (DGTs). Oedogonium was able to grow rapidly in the waste water (12 g dry weight m−2 day−1) while consistently sequestering Al. The Oedogonium-treated waste water had a sufficiently low Al concentration that it could be used in unrestricted irrigation in the surrounding region. When CO2 was added to the waste water containing concentrations of Al up to 8 mg L−1, there was a slight increase (~10%) in the rate of sequestration of Al by Oedogonium relative to waste water not receiving CO2. This was due to two concurrent processes. The provision of CO2 increased the productivity of Oedogonium by 15% and the bioavailability of Al by up to 200%, as measured by the DGTs. Despite this strong effect of CO2 on Al bioavailability, the increase in Al sequestration by Oedogonium when CO2 was provided was modest (~10%). Al was sequestered by Oedogonium to concentrations below permissible limits for discharge without the need for the addition CO2. The cultivation of Oedogonium in waste water from conventional treatments plants can simultaneously treat waste water for re-use and provide a biomass source for value-added applications.
Doubly nonlinear superconducting qubit

We describe a superconducting circuit consisting of a Josephson junction in parallel with a quantum phase slip wire, which implements a Hamiltonian that is periodic in both charge and flux. This Hamiltonian is exactly diagonalisable in a double-Bloch band, and the eigenstates are shown to be code states of the Gottesman-Kitaev-Preskill quantum error correcting code. The eigenspectrum has several critical points, where the linear sensitivity to external charge and flux noise vanishes. The states at these critical points thus hold promise as qubit states that are insensitive to external noise sources.