Miracle-dz/UniFAR-FTUs huggingface dataset:
ML 5.0 Smoothed Aggregation Users's Guide

ML is a multigrid preconditioning package intended to solve linear systems of equations Ax = b where A is a user supplied nxn sparse matrix, b is a user supplied vector of length n and x is a vector of length n to be computed. ML should be used on large sparse linear systems arising from partial differential equation (PDE) discretizations. While technically any linear system can be considered, ML should be used on linear systems that correspond to things that work well with multigrid methods (e.g. elliptic PDEs). ML can be used as a stand-alone package or to generate preconditioners for a traditional iterative solver package (e.g. Krylov methods). We have supplied support for working with the Aztec 2.1 and AztecOO iterative packages [? ]. However, other solvers can be used by supplying a few functions. This document describes one specific algebraic multigrid approach: smoothed aggregation. This approach is used within several specialized multigrid methods: one for the eddy current formulation for Maxwell? s equations, and a multilevel and domain decomposition method for symmetric and nonsymmetric systems of equations (like elliptic equations, or compressible and incompressible fluid dynamics problems). Other methods exist within ML but are not described in this document. Examples are given illustrating the problem definition and exercising multigrid options.
A Fixed Wing UAV Path Planning Algorithm Based On Genetic Algorithm and Dubins Curve Theory

Path planning is an essential problem in the autonomy research of UAVs. This paper proposes a new path planning algorithm for fixed wing UAVs based on genetic algorithm (GA) and Dubins curve theory. Path planning is treated as a global optimization problem under certain constraints, including the velocity vectors of initial and goal points and the minimum turning radius of UAVs. Dubins curve theory is utilized to satisfy the velocity vector constraints. GA is utilized to generate the shortest threats avoidance path in a 2D environment. A new encoding scheme is proposed, taking into account initial circles, goal circles, threats circles and the positional relationship between the path and these circles. The 2D Dubins path is converted to Dubins airplane path by adding a flight-path angle to it. The algorithm was tested in a complex flight environment and the planned path was tracked by a 6DOF Simulink model of a fixed wing UAV. Results show that the algorithm can generate shortest threats avoidance path in a complicated 3D environment and meanwhile satisfy constraints mentioned above.
Dynamically tunable localized surface plasmons using VO2 phase transition

The control of light with plasmonic devices in practical applications require dynamic tunability of localized surface plasmons. Employing phase change materials in plasmonic structure enables it to respond to light dynamically depending the external stimulate. This study investigates the response in presence of vanadium dioxide (VO2) phase transition for numbers of novel and classic problems. To illustrate the significance of optical spectrum tunability by VO2 two important functionalities for the phenomenon have been introduced. In the first application, a compact and ultrathin plasmonic metasurface is suggested for an ultra-short pulse shaping of transmitted pulse based on linear filtering principle of electromagnetic wave. It is demonstrated that the tunable optical filter by VO2 phase transition can compensate realtime input carrier frequency shifts and pulse span variations to stabilize the output pulse. Second application is dedicated to the field of intrachip optical communication which shows how VO2 phase transition can effectively switch a communicating antenna on and off. A substantial directional gain switching is obtained by employing VO2 phase transition to alternate resonances of a Yagi-Uda antenna elements. VO2 scattering functionality in absence of localized surface plasmons is studied to illustrate their promising performance in light reflection. Finally the behavior of localized surface plasmon resonators is studied and chimera stats which are the concurrent combination of synchronous and incoherent oscillations in a set of identical oscillators is shown for the first time in the optical regime. The effect of coupling strength on the phase scape/synchronization of the spaser-based devised oscillators is investigated.
Cost-Effectiveness of Group Transdiagnostic Cognitive Behavioural Therapy for Anxiety Disorders in Primary Care Settings: Economic Evaluation From the Healthcare System Perspective Over a 1-Year Time Horizon

Aim To assess the incremental cost-effectiveness ratio (ICER) of group transdiagnostic cognitive-behavioural therapy (tCBT) added to treatment as usual (TAU) for anxiety disorders compared to TAU only from the healthcare system perspective over a 1-year time horizon. Methods Data from a pragmatic multisite randomized controlled trial where adults (18–65 years) with an anxiety disorder were randomized to tCBT + TAU (n = 117) or TAU (n = 114). Group tCBT is a 12-week (2h weekly sessions) community-based intervention. Health service utilization and related costs were captured from medico-administrative data and included those for the intervention, ambulatory visits, hospitalizations and medications. Effectiveness was based on quality-adjusted life years (QALYs). The study included measures at baseline, 4, 8, and 12 months. Intention-to-treat and complete case analyses were carried out. Missing data were imputed using multiple imputation analyses. Seemingly unrelated regression analyses were used to assess the effect of the intervention on total costs and QALYs while also adjusting for baseline confounders. The probability of cost-effectiveness of the intervention was assessed according to different willingness-to-pay (WTP) thresholds using the net benefit regression method. Results The ICER of tCBT + TAU as compared to TAU in the intention-to-treat analysis was $6,581/QALY. Complete case analyses showed a similar ICER of $6,642/QALY. The probability at a WTP threshold of $20,000 and $40,000 that tCBT + TAU as compared to TAU is cost-effective is 93.0% and 99.9%. Conclusion tCBT added to TAU appears to be cost-effective from the healthcare system perspective for treating adult patients with anxiety disorders. Larger trials including young and older adults as well as a range of anxiety disorders are needed to further investigate the cost-effectiveness of tCBT in different patient populations.
Seebeck voltage measurement in undoped metal oxide semiconductors

The wide-ranging inaccuracies in experimental data on the Seebeck coefficient (S) and its temperature dependence in metal oxides with no deliberate doping is attributed to the high resistivity (∼1MΩm) of these materials and the poor quality of the metallic contacts formed on them. Here, we report measuring Seebeck voltages generated in such samples with a ‘zero-current’ technique that eliminates the high internal resistance-caused errors and removes the need for ohmic quality contacts. After performance demonstrations on standard samples, the method is applied for the measurement of S in undoped zinc oxide ceramic samples at different temperatures. The observed variation trends are consistent with the classic model describing thermoelectricity in semiconductors. The substantial magnitude of the room temperature S values in our ceramic samples underscores the importance of thermoelectric research on oxide ceramic materials.
QUANTUM INFORMATION SYSTEMS: HARNESSING INDIVIDUAL AND GROUP ENERGIES

In this working paper, we propose that an organization is a living organism that generates energy to achieve certain outcomes. We propose that the relationship between the inputs (individual and group use of information systems) and the outputs (strategic alignment and competitive advantage) of a system (an organization) depends on the basic principles of quantum mechanics. Specifically, we connect the neuroscience research that addresses qualia (individual) and quale (group) to the Management Information Systems (MIS) research. In this paper, we proffer our research objective, discuss our constructs, and present our interview process and survey items that we plan to conduct and administer.
64.25 Sequences—some properties by matrix methods

In a recent article, Silvester [ 1 ] derives some interesting Fibonacci properties by matrix methods. The Fibonacci sequence is generated by the recurrence relation w„+1 = un + un_l with u0 = 0, ul = 1. In this note we extend these results to the general second order recurrence relation un+x = aun + bun+1 and then go on to show an interesting connection with second-order linear differential equations. We define our generalised Fibonacci sequence by
Research of correlation dependences meteorological parameters

The paper describes the analysis of correlation dependences between meteorological parameters for a series of observations obtained at station with a synoptic index of 29430. It was shown that the parameters with strong correlation dependences have virtually unchanging correlation coefficient for every time scale of the sample. In the case of meteorological parameters with weak correlation coefficient, there are non-significant jumps in the correlation coefficient values. For meteorological parameters with moderate correlation coefficient, increasing the sample time scale leads to stabilization of correlation coefficient values.
Predicting Free Achilles Tendon Strain From Motion Capture Data Using Artificial Intelligence

The Achilles tendon (AT) is sensitive to mechanical loading, with appropriate strain improving tissue mechanical and material properties. Estimating free AT strain is currently possible through personalized neuromusculoskeletal (NMSK) modeling; however, this approach is time-consuming and requires extensive laboratory data. To enable in-field assessments, we developed an artificial intelligence (AI) workflow to predict free AT strain during running from motion capture data. Ten keypoints commonly used in pose estimation algorithms (e.g., OpenPose) were synthesized from motion capture data and noise was added to represent real-world data obtained using video cameras. Two AI workflows were compared: (1) a Long Short-Term Memory (LSTM) neural network that predicted free AT strain directly (called LSTM only workflow); and (2) an LSTM neural network that predicted AT force which was subsequently converted to free AT strain using a personalized force-strain curve (called LSTM+ workflow). AI models were trained and evaluated using estimates of free AT strain obtained from a validated NMSK model with personalized AT force-strain curve. The effect of using different input features (position, velocity, and acceleration of keypoints, height and mass) on free AT strain predictions was also assessed. The LSTM+ workflow significantly improved the predictions of free AT strain compared to the LSTM only workflow (p < 0.001). The best free AT strain predictions were obtained using positions and velocities of keypoints as well as the height and mass of the participants as input, with average time-series root mean square error (RMSE) of 1.72±0.95% strain and r2 of 0.92±0.10, and peak strain RMSE of 2.20% and r2 of 0.54. In conclusion, we showed feasibility of predicting accurate free AT strain during running using low fidelity pose estimation data.
Improving Compound–Protein Interaction Prediction by Self-Training with Augmenting Negative Samples

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery.
Cross-Flow Instability : Flow diagnostics and control of swept wing boundary layers

The research presented in this booklet focusses on the cross-flow instability. Applying traditional and advanced flow diagnostics, the boundary layer evolution is studied in detail. The topology and evolution of both primary and secondary instability mechanisms is revealed with unprecedented detail for experimental research paving the way for new advanced-diagnostics investigations. Important confirmations of the outcomes of past experimental, numerical and theoretical studies are achieved together with the description of a newly-reported flow phenomenon. The latter consists of a low frequency motion of the "stationary" primary vortices. While this phenomenon is considered not relevant for the transition evolution, it is deemed important for experimental investigations as it encompasses very high levels of turbulent kinetic energy. Advanced flow control experiments based on alternating current dielectric barrier discharge plasma actuators are also performed following different instability control approaches. The primary instability is conditioned by the external forcing either in the wavenumber spectrum (by inducing selected spanwise modes) or in intensity (by weakening or enhancing the cross-flow velocity). The secondary instability modes are conditioned in the frequency spectrum and phase. These efforts achieved the intended scopes. Although, when selected stationary modes were forced, the boundary layer fluctuations were enhanced. These fluctuations can directly cause the turbulent breakdown vanishing the beneficial effect of the performed instability control. The cross-flow forcing, making use of newer actuators reaching higher frequencies, resulted successful yielding transition promotion or delay depending on the forcing direction.
A CRANK-NICOLSON CHARACTERISTIC FINITE ELEMENT METHOD FOR SOBOLEV EQUATIONS

A Crank-Nicolson characteristic finite element method is introduced to construct approximate solutions of a Sobolev equation with a convection term. The higher order of convergences in the temporal direction and in the spatial direction in L2 normed space are verified for the Crank-Nicolson characteristic finite element method.
Implementation of an adjoint thermal solver for inverse problems

This paper contains a derivation of the adjoint equations for transient heat conduction and describes how to include the adjoint solution in a nonlinear conjugate gradient method. The adjoint equations are useful for optimization problems where a scalar valued target function is optimized with respect to a large number of design variables. In this paper the target function is a least squares fit between calculated and measured transient metal temperatures. The design variables consist of heat transfer coefficients on a set of mesh facets for a finite element mesh. Contrary to intuition the sensitivity of the target function with respect to the different design variables can be evaluated by just two transient solutions; the direct problem and the adjoint problem, with roughly similar cost. If a brute force approach is used the transient problem has to be solved for each individual design variable. Since a typical mesh contains thousands of facets the speedup of the adjoint approach is enormous.
Determination of optimum number of components in partial least squares regression from distributions of the root‐mean‐squared error obtained by Monte Carlo resampling

Monte Carlo resampling is utilized to determine the number of components in partial least squares (PLS) regression. The data are randomly and repeatedly divided into calibration and validation samples. For each repetition, the root‐mean‐squared error (RMSE) is determined for the validation samples for a = 1, 2, … , A PLS components to provide a distribution of RMSE values for each number of PLS components. These distributions are used to determine the median RMSE for each number of PLS components. The component (Amin) having the lowest median RMSE is located. The fraction p of the RMSE values of Amin exceeding the median RMSE for the preceding component is determined. This fraction p represents a probability measure that can be used to decide if the RMSE for the Amin PLS component is significantly lower than the RMSE for the preceding component for a preselected threshold (pupper). If so, it defines the optimum number of PLS components. If not, the process is repeated for the previous components until significance is achieved. The pupper = 0.5 implies that the median is used for selecting the optimum number of components. The RMSE is approximately normally distributed on the smallest components. This can be utilized to relate p to a fraction of a standard deviation. For instance, p = 0.308 corresponds to half a standard deviation if RMSE is normally distributed.
Chinese expert consensus on the diagnosis and treatment of malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is a malignant tumor originating from the pleura, and its incidence has been increasing in recent years. Due to the insidious onset and strong local invasiveness of MPM, most patients are diagnosed in the late stage and early screening and treatment for high‐risk populations are crucial. The treatment of MPM mainly includes surgery, chemotherapy, and radiotherapy. Immunotherapy and electric field therapy have also been applied, leading to further improvements in patient survival. The Mesothelioma Group of the Yangtze River Delta Lung Cancer Cooperation Group (East China LUng caNcer Group, ECLUNG; Youth Committee) developed a national consensus on the clinical diagnosis and treatment of MPM based on existing clinical research evidence and the opinions of national experts. This consensus aims to promote the homogenization and standardization of MPM diagnosis and treatment in China, covering epidemiology, diagnosis, treatment, and follow‐up.
I nstitute for S oftware R esearch

Recommender systems increasingly use contextual and demographical data as a basis for recommendations. Users however often feel uncomfortable providing such information. In a privacy-minded design of recommenders, users are free to decide for themselves what data they want to disclose about themselves. However, this decision is often complex and burdensome, because the consequences of disclosing personal information are uncertain or even unknown. Although a number of researchers have tried to analyze and facilitate such information disclosure decisions, their research results are fragmented and often do not hold up well across studies. This paper describes a unified approach to privacy decision research that describes the cognitive processes involved in users’ “privacy calculus” in terms of system-related perceptions and experiences that act as mediating factors to information disclosure. The approach is applied in an online experiment with 493 participants using a mock-up of a context-aware recommender system. Analyzing the results with a structural linear model, we demonstrate that personal privacy concerns and disclosure justification messages affect the perception of and experience with a system, which in turn drive information disclosure decisions. Overall, disclosure justification messages do not increase disclosure. Although they are perceived to be valuable, they decrease users’ trust and satisfaction. Another result is that manipulating the order of the requests increases the disclosure of items requested early, but decreases the disclosure of items requested later.
Research & Development White

The use of mobile devices during television viewing is now commonplace, and broadcasters are increasingly supplying programme-related ‘companion content’. To produce an optimal user experience, it is important to determine how the delivery of companion content aﬀects and is perceived by the viewer — without this we risk distracting the viewer, leading to frustration and disen-gagement. We present a controlled study investigating how attention, cognitive load, and users’ preferences are aﬀected by the provision of two diﬀerent content delivery modes: pushed and pulled . We ﬁnd that delivery mode aﬀected the temporal distribution of gaze to the tablet, with a consistent viewing pattern for pushed updates, which attracted attention within a few seconds, and a more diverse set of viewing patterns when updates were pulled. Cognitive load was similar in both conditions, and there was no consensus as to which mode was preferred, but users showed strong, polarised individual preferences. The advantages of each delivery mode are presented as a set of recommendations for the delivery of companion content.
Towards a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit

Despite the geographically-situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography – distance decay, structured variation in population density, mental maps
SOLUTION OF INCOMPRESSIBLE NAVIER-STOKES EQUATIONS WITH ALTERNATING CELL DIRECTION IM

* Graduate Student in METU, Email: obas@ae.metu. edu.tr † Dr. Researcher, Email: arcete@turbotek.com. tr ‡ Prof. in METU, Email: tuncer@ae.metu. edu.tr ABSTRACT A line implicit method on structured and unstructured quadrilateral grids for solution of incompressible NavierStokes equations is presented. For this purpose, an incompressible flow solver which is based on cellcentered finite volume formulation with artificial compressibility modification is developed and an Alternating Cell Direction Implicit (ACDI) method is implemented. Roe flux difference splitting scheme with U-MUSCL data reconstruction is employed for discretisation of the convective flux terms while second order central discretisation is used for viscous fluxes. Steady flow fields around NACA 0008 single-element and NACA 23012b multi-element airfoil profiles are computed using ACDI method on both structured and quadrilateral unstructured grids. The results obtained with ACDI scheme are compared with the implicit Point Gauss Seidel (PGS) solutions and it is shown that a reduction of 13-20 percent in the total computation time is achieved by the ACDI method. INTRODUCTION