Miracle-dz/UniFAR-FTUs huggingface dataset:
LECTURERS’ CONSCIOUSNESS LEVEL and VIEWS on STRATEGIC PLANNING

This study aimed to search the lecturers’ (employed at the Eskisehir Osmangazi University, Department of Foreign Languages) consciousness level and views on strategic planning. Although the population includes 70 lecturers at the department, 46 lecturers participated in the study. Because the number of people in this research was limited, c onvergent parallel mixed methods design was used in the study. In the quantitative part of the study, the data were collected via an instrument called “ strategic planning consciousness level instrument, ” and in the qualitative part of the study, the data were collected by the “ strategic planning views form . ” In the data analysis, Kruskall-Wallis Test, Mann-Whitney U Test and descriptive analysis techniques were used. The results showed that the lecturers’ consciousness level of strategic planning and strategic planning belief are low. However, they indicated that strategic planning is important for an organization and it would motivate the employees and enhance the organizational belonging. Moreover, they have middle level negative attitude about strategic planning, and they think that the managers must have the active role in strategic planning.
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation

In this paper, we consider the problem of domain generalization in semantic segmentation, which aims to learn a robust model using only labeled synthetic (source) data. The model is expected to perform well on unseen real (target) domains. Our study finds that the image style variation can largely influence the model's performance and the style features can be well represented by the channel-wise mean and standard deviation of images. Inspired by this, we propose a novel adversarial style augmentation (AdvStyle) approach, which can dynamically generate hard stylized images during training and thus can effectively prevent the model from overfitting on the source domain. Specifically, AdvStyle regards the style feature as a learnable parameter and updates it by adversarial training. The learned adversarial style feature is used to construct an adversarial image for robust model training. AdvStyle is easy to implement and can be readily applied to different models. Experiments on two synthetic-to-real semantic segmentation benchmarks demonstrate that AdvStyle can significantly improve the model performance on unseen real domains and show that we can achieve the state of the art. Moreover, AdvStyle can be employed to domain generalized image classification and produces a clear improvement on the considered datasets.
The SPEC-RG Reference Architecture for the Edge Continuum

—Edge computing promises lower processing latencies and better privacy control than cloud computing for task ofﬂoad-ing as edge devices are positioned closer to users. Realizing this promise depends on building strong theoretical and engineering foundations of computing based on an edge continuum connecting edge to other resources. In the SPEC-RG Cloud Group, we conducted a systematic study of computing models for task ofﬂoading and found that these models have many shared characteristics. Despite these commonalities, no systematic model or architecture for task ofﬂoading currently exists. In this paper, we address this need by proposing a reference architecture for task ofﬂoading in the edge continuum and synthesize its components using well-understood abstractions, services, and resources from cloud computing. We provide domain-speciﬁc architectures for deep learning and industrial IoT and show how this uniﬁed computing model opens up application development as developers are no longer limited to the single set of constraints posed by current isolated computing models. Additionally, we demonstrate the utility of the architecture by designing a deployment and benchmarking framework for edge continuum applications and investigate the performance of various edge continuum deployments. The framework allows for ﬁne-grained discovery of the edge continuum deployment space, including the emulation of complex networks, all with minimal user input required. To enhance the performance analysis capabilities of the benchmark, we introduce an analytical ﬁrst-order performance model that can be used to explore multiple application deployment scenarios such as local processing on endpoints or ofﬂoading between cloud or edge.
Frieze matrices and friezes with coefficients

Frieze patterns are combinatorial objects that are deeply related to cluster theory. Determinants of frieze patterns arise from triangular regions of the frieze, and they have been considered in previous works by Broline-Crowe-Isaacs, and by Baur-Marsh. In this article, we introduce a new type of matrix for any infinite frieze pattern. This approach allows us to give a new proof of the frieze determinant result given by Baur-Marsh.
Exploring reactive power limits on wind farm collector networks with convex inner approximations

A wind farm can provide reactive power at sub-transmission and transmission buses in order to support and improve voltage profiles. It is common for the reactive power capability of a wind farm to be evaluated as the sum of the individual turbine ratings. However, such an assessment does not take into account losses over the collector network, nor the voltage constraints imposed by the turbines and network. In contrast, the paper presents a method for determining the range of reactive power support that each turbine can provide whilst guaranteeing satisfaction of voltage constraints. This is achieved by constructing convex inner approximations of the non-convex set of admissible reactive power injections. We present theoretical analysis that supports the constraint satisfaction guarantees. An example illustrates the effectiveness of the algorithm and provides a comparison with a fully decentralized approach to controlling wind farm reactive power. Such approaches have the potential to improve the design and operation of wind farm collector networks, reducing the need for additional costly reactive power resources.
Multi-Fidelity Aeroelastic Analysis of Flexible Membrane Wind Turbine Blades

This thesis studies a new concept for flexible wind turbine blades. Flexible wings and their advantages have been the topic of various research projects. To realize a flexible wing construction, different concepts have been used including morphing wing, telescopic spars or using smart materials in the construction of the wing. The membrane blade concept, which is studied within the scope of this thesis, is originally an example of a passively controlled structure, whereas one could think of actuation in future realizations. With the increase in a wind turbine's rotor diameter, aeroelastic simulation of rotor blades to study their unsteady response to disturbances or control actions has become more and more important. To realize the so-called "smart rotors", both active and passive aeroelastic devices have been employed for load mitigation of wind turbines. Within this contribution, a simulation environment for multi-fidelity aeroelastic analysis of wind turbine blades is presented. The goal of the multi-fidelity analysis is realized by using three different approaches for calculating the aerodynamic loading on a wind turbine, which include: blade element momentum (BEM) method, vortex panel method and computational fluid dynamics. The developed multi-fidelity analysis workflow is used for analysis of a sample membrane blade based on the NASA-Ames phase VI wind turbine. At each level of modeling, the comparison between the performance of the membrane blade and the rigid baseline blade is made. Furthermore, a new combined methodology based on panel-BEM coupling for using the blade element momentum method for the analysis of membrane blades with form varying cross-section is proposed and tested for the studied sample membrane blade. The studied membrane blade demonstrates advantages over the baseline rigid blade in terms of power generation which should be mainly attributed to the increase of profile's camber due to membrane's deflection.
Fields and Equations of Classical Mechanics for Quantum Mechanics

Pairs of pressures and velocities ﬁelds with kinetic energies are deﬁned for quantum mechanical many-body systems, depending on the probability distribution and phase of a wavefunction. These functions satisfy a set of two classical energy-equations that is equivalent to the many-body time-dependent Schr¨odinger equation, and these equations also deﬁne two energy ﬁelds. An Euler equation of ﬂuid dynamics is also derived that is satisﬁed by the ﬁelds. Interpretation of the Bohmian potential and the integrand of the expectation value of the kinetic energy is given, involving both pressure and classical kinetic-energy ﬁelds. The formalism is built upon Bohmian mechanics and a Bernoulli-equation description of certain quantum mechanical systems, and the three ﬁelds from these approaches are incorporation into the formalism. Deﬁnitions, using the momentum operator and the time-side of the Schr¨odinger equations are given for six ﬁelds, yielding three new ﬁelds and alternate, but equivalent, deﬁnitions for the other three. The two energy ﬁelds are shown to be conserved, over all space, for solutions of the time-dependent Schr¨odinger equation, if the Hamiltonian operator is independent of time. Both particle and ﬂuid interpretations of classical mechanics are considered. A compressible electron-body model is considered. A notational system is introduced that removes subscripts and summation signs, yielding equation for many-body systems that, in most cases, are identical in form to the corresponding special case of one-body.
Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality

We analyze a stochastic approximation algorithm for decision-dependent problems, wherein the data distribution used by the algorithm evolves along the iterate sequence. The primary examples of such problems appear in performative prediction and its multiplayer extensions. We show that under mild assumptions, the deviation between the average iterate of the algorithm and the solution is asymptotically normal, with a covariance that clearly decouples the effects of the gradient noise and the distributional shift. Moreover, building on the work of H\'ajek and Le Cam, we show that the asymptotic performance of the algorithm with averaging is locally minimax optimal.
Domain Alignment Meets Fully Test-Time Adaptation

A foundational requirement of a deployed ML model is to generalize to data drawn from a testing distribution that is different from training. A popular solution to this problem is to adapt a pre-trained model to novel domains using only unlabeled data. In this paper, we focus on a challenging variant of this problem, where access to the original source data is restricted. While fully test-time adaptation (FTTA) and unsupervised domain adaptation (UDA) are closely related, the advances in UDA are not readily applicable to TTA, since most UDA methods require access to the source data. Hence, we propose a new approach, CATTAn, that bridges UDA and FTTA, by relaxing the need to access entire source data, through a novel deep subspace alignment strategy. With a minimal overhead of storing the subspace basis set for the source data, CATTAn enables unsupervised alignment between source and target data during adaptation. Through extensive experimental evaluation on multiple 2D and 3D vision benchmarks (ImageNet-C, Office-31, OfficeHome, DomainNet, PointDA-10) and model architectures, we demonstrate significant gains in FTTA performance. Furthermore, we make a number of crucial findings on the utility of the alignment objective even with inherently robust models, pre-trained ViT representations and under low sample availability in the target domain.
Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature Space

We explore a new idea for learning based shape reconstruction from a point cloud, based on the recently popularized implicit neural shape representations. We cast the problem as a few-shot learning of implicit neural signed distance functions in feature space, that we approach using gradient based meta-learning. We use a convolutional encoder to build a feature space given the input point cloud. An implicit decoder learns to predict signed distance values given points represented in this feature space. Setting the input point cloud, i.e. samples from the target shape function's zero level set, as the support (i.e. context) in few-shot learning terms, we train the decoder such that it can adapt its weights to the underlying shape of this context with a few (5) tuning steps. We thus combine two types of implicit neural network conditioning mechanisms simultaneously for the first time, namely feature encoding and meta-learning. Our numerical and qualitative evaluation shows that in the context of implicit reconstruction from a sparse point cloud, our proposed strategy, i.e. meta-learning in feature space, outperforms existing alternatives, namely standard supervised learning in feature space, and meta-learning in euclidean space, while still providing fast inference.
Learning to Register Unbalanced Point Pairs

Recent 3D registration methods can effectively handle large-scale or partially overlapping point pairs. However, despite its practicality, matching the unbalanced pairs in terms of spatial scale and density has been overlooked. We present a novel 3D registration method, called UPPNet, for the unbalanced point pairs. We propose a hierarchical framework to ﬁnd inlier correspondences effectively by gradually reducing search space. Our method predicts the subregions of the target points likely to be overlapped with the query points. The following super-point matching module and ﬁne-grained reﬁnement module estimate accurate inlier correspondences between two point clouds. Furthermore, we apply geometric constraints to reﬁne the correspondences that satisfy spatial compatibility. Correspondence prediction is trained end-to-end, and our approach can predict the proper rigid transformation with a single forward pass given unbalanced point cloud pairs. To validate the efﬁcacy of the proposed method, we create a KITTI-UPP dataset by augmenting the KITTI LiDAR dataset. Experiments on this dataset reveal that the proposed approach signiﬁcantly outperforms state-of-the-art pairwise point cloud registration methods by a large margin, resulting in 78% improvement in Registration Recall when the target point cloud is about 10 × spatially larger and about 10 × times denser than the query point cloud.
BLANT: Basic Local Alignment of Network Topology, Part 2: Topology-only Extension Beyond Graphlet Seeds

BLAST is a standard tool in bioinformatics for creating local sequence alignments using a"seed-and-extend"approach. Here we introduce an analogous seed-and-extend algorithm that produces local network alignments: BLANT (Basic Local Alignment of Network Topology). In Part 1, we introduced BLANT-seed, which generates graphlet-based seeds using only topological information. Here, in Part 2, we describe BLANT-extend, which"grows"seeds to larger local alignments using only topological information. We allow the user to specify bounds on several measures an alignment must satisfy, including the edge density, edge commonality (i.e., aligned edges), and node-pair similarity if such a measure is used; the latter allows the inclusion of sequence-based similarity, if desired, as well as local topological constraints. BLANT-extend is able to enumerate all possible alignments satisfying the bounds that can be grown from each seed, within a specified CPU time or number of generated alignments. While topology-driven local network alignment has a wide variety of potential applications outside bioinformatics, here we focus on the alignment of Protein-Protein Interaction (PPI) networks. We show that BLANT is capable of finding large, high-quality local alignments when the networks are known to have high topological similarity -- for example recovering hundreds of orthologs between networks of the recent Integrated Interaction Database (IID). Predictably, however, it performs less well when true topological similarity is absent, as is the case in most current experimental PPI networks that are noisy and have wide disparity in edge density which results in low common coverage.
A study on the state-contingent capital tax policy

This paper aims to figure out the impacts of capital tax policy on the economy where the government has a certain policy restriction; state-contingent capital tax rate. Simultaneously, households fully understand how the government behaves and make decisions based on the policy expectation. Under the framework used in this paper, I find that the state-contingent capital tax policy results in larger fluctuations of allocations as well as policies without notable differences in utility and capital accumulation compared to the economy under the state-independent capital tax policy. Abstract
Audio-Visual Segmentation

We propose to explore a new problem called audio-visual segmentation (AVS), in which the goal is to output a pixel-level map of the object(s) that produce sound at the time of the image frame. To facilitate this research, we construct the first audio-visual segmentation benchmark (AVSBench), providing pixel-wise annotations for the sounding objects in audible videos. Two settings are studied with this benchmark: 1) semi-supervised audio-visual segmentation with a single sound source and 2) fully-supervised audio-visual segmentation with multiple sound sources. To deal with the AVS problem, we propose a novel method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process. We also design a regularization loss to encourage the audio-visual mapping during training. Quantitative and qualitative experiments on the AVSBench compare our approach to several existing methods from related tasks, demonstrating that the proposed method is promising for building a bridge between the audio and pixel-wise visual semantics. Code is available at https://github.com/OpenNLPLab/AVSBench.
Cosmological perturbation theory using generalized Einstein–de Sitter cosmologies

The separable analytical solution in standard perturbation theory for an Einstein de Sitter (EdS) universe can be generalized to the wider class of such cosmologies (``generalized EdS'', or gEdS) in which a fraction of the pressure-less fluid does not cluster. We derive the corresponding kernels in both Eulerian perturbation theory (EPT) and Lagrangian perturbation theory, generalizing the canonical EdS expressions to a one-parameter family where the parameter can be taken to be the exponent $\alpha$ of the growing mode linear amplification $D(a) \propto a^{\alpha}$. For the power spectrum (PS) at one loop in EPT, the contribution additional to standard EdS is given, for each of the `13' and `22' terms, as a function of two infra-red safe integrals. In the second part of the paper we show that the calculation of cosmology-dependent corrections in perturbation theory in standard (e.g. LCDM-like) models can be simplified, and their magnitude and parameter dependence better understood, by relating them to our analytic results for gEdS models. At second order the time dependent kernels are equivalent to the analytic kernels of the gEdS model with $\alpha$ replaced by a single redshift dependent effective growth rate $\alpha_2(z)$. At third order the time evolution can be conveniently parametrized in terms of two additional such effective growth rates. For the PS calculated at one loop order, the correction to the PS relative to the EdS limit can be expressed in terms of just $\alpha_2(z)$, one additional effective growth rate function and the four infra-red safe integrals of the gEdS limit. This is much simplified compared to expressions in the literature that use six or eight red-shift dependent functions and are not explicitly infra-red safe. Using the analytic gEdS expression for the PS with $\alpha=\alpha_2(z)$ gives a good approximation (to $\sim 25 \%$) for the exact result.
A Forward Propagation Algorithm for Online Optimization of Nonlinear Stochastic Differential Equations

Optimizing over the stationary distribution of stochastic diﬀerential equations (SDEs) is computationally challenging. [26] proposed a new forward propagation algorithm for the online optimization of SDEs. The algorithm solves an SDE, derived using forward diﬀerentiation, which provides a stochastic estimate for the gradient. The algorithm continuously updates the SDE model’s parameters and the gradient estimate simultaneously. This paper studies the convergence of the forward propagation algorithm for nonlinear dissipative SDEs. We leverage the ergodicity of this class of nonlinear SDEs to characterize the convergence rate of the transition semi-group and its derivatives. Then, we prove bounds on the solution of a Poisson partial diﬀerential equation (PDE) for the expected time integral of the algorithm’s stochastic ﬂuctuations around the direction of steepest descent. We then re-write the algorithm using the PDE solution, which allows us to characterize the parameter evolution around the direction of steepest descent. Our main result is a convergence theorem for the forward propagation algorithm for nonlinear dissipative SDEs.
Differential item functioning via robust scaling

This paper proposes a method for assessing differential item functioning (DIF) in item response theory (IRT) models. The method does not require pre-specification of anchor items, which is its main virtue. It is developed in two main steps, first by showing how DIF can be re-formulated as a problem of outlier detection in IRT-based scaling, then tackling the latter using established methods from robust statistics. The proposal is a redescending M-estimator of IRT scaling parameters that is tuned to flag items with DIF at the desired asymptotic Type I Error rate. One way of quantifying the robustness of the estimator is in terms of its finite sample breakdown point, which is shown to equal to 1/2 (i.e., the estimator remains bounded whenever fewer than 1/2 of the items on an assessment exhibit DIF). This theoretical result is complemented by simulation studies that illustrate the performance of the estimator and its associated test of DIF. The simulation studies show that the proposed method compares favorably to currently available approaches, and a real data example illustrates its application in a research context where pre-specification of anchor items is infeasible. The focus of the paper is the two-parameter logistic model in two independent groups, with extensions to other settings considered in the conclusion.
Almost primes in almost all short intervals II

We show that, for almost all $x$, the interval $(x, x+(\log x)^{2.1}]$ contains products of exactly two primes. This improves on a work of the second author that had $3.51$ in place of $2.1$. To obtain this improvement, we prove a new type II estimate. One of the new innovations is to use Heath-Brown's mean value theorem for sparse Dirichlet polynomials.