Anchor Loss: Modulating Loss Scale Based on Prediction Difficulty

We propose a novel loss function that dynamically re-scales the cross entropy based on prediction difficulty regarding a sample. Deep neural network architectures in image classification tasks struggle to disambiguate visually similar objects. Likewise, in human pose estimation symmetric body parts often confuse the network with assigning indiscriminative scores to them. This is due to the output prediction, in which only the highest confidence label is selected without taking into consideration a measure of uncertainty. In this work, we define the prediction difficulty as a relative property coming from the confidence score gap between positive and negative labels. More precisely, the proposed loss function penalizes the network to avoid the score of a false prediction being significant. To demonstrate the efficacy of our loss function, we evaluate it on two different domains: image classification and human pose estimation. We find improvements in both applications by achieving higher accuracy compared to the baseline methods

Comparison of Lift Path Planning Algorithms for Mobile Crane Operations in Heavy Industrial Projects

Heavy industrial projects, especially oil refineries, are constructed by modules prefabricated in factories, transported to sites and installed by mobile cranes. Due to a large number of lifts on the congested and dynamic site layouts in heavy industrial projects, the lift path planning has been attention for not only safe and efficient mobile crane operation but also better project productivity and safety. Although the path planning algorithms have been introduced over the years, they have not been used actively in practice since the comparison of these algorithms has not been examined yet based on the realistic mobility of mobile cranes and real site environment. Therefore, this thesis compares the path planning algorithms including A* search, rapidly exploring random tree (RRT), genetic algorithms (GA) and 3D visualization-based mathematical algorithm (3DVMA) under the same site environment in order to find a competent method using measurement metrics considering collision-free and optimal lift paths with the lower crane operation cost and less computation time. The proposed comparison is implemented in a case study that includes a series of modules lifted by a mobile crane on various site conditions. This comparison shows the advantages and disadvantages of each algorithm for the crane path planning in heavy industrial projects and suggests the direction of further research in this field

Knowledge and power relations: In a migration storytelling, Derviş Zaim's Film Flashdrive

Starting from Gilles Deleuze's (1989, p.59) concepts of "worldization" or/and "world-image" we should consider the intersection of cinema, architecture and storytelling as an act of thinking about "world-building". Because only such action takes us through creative and political stories that will enable us to understand why the cities of the future are migrant camps. Flashdrive doesn't just give us a refugee camp story; also maps the spatio-temporal distinctions of the survival journey. It presents a migration story shaped by media dispositifs and spatial dispositifs in which power and knowledge are articulated

EXAMINING THE MATH ANXIETY LEVELS OF UNIVERSITY STUDENTS DURING COVID-19

Emotional reactions such as stress, anxiety, and fear from social relations of individuals to communication skills; can have negative effects on many situations from learning situations to success in school and business life. Educational processes related to mathematical skills that develop analytical thinking are also affected by these emotional reactions. In the literature part of the study, the subjects of anxiety and fear, which are important in shaping the students' orientation to mathematics, are mentioned. Within the confines of the study, scores on math anxiety among university students were broken down by gender, grade level, and department, and interaction between math anxiety and math proficiency was established.  Article visualizations

Pulse Frequency Fluctuations of Magnetars

Using \emph{RXTE}, \emph{Chandra}, \emph{XMM-Newton} and \emph{Swift} observations, we for the first time construct the power spectra and torque noise strengths of magnetars. For some of the sources, we measure strong red noise on timescales months to years which might be a consequence of their outbursts. We compare noise strengths of magnetars with those of radio pulsars by investigating possible correlations of noise strengths with spin-down rate, magnetic field and age. Using these correlations, we find that magnetar noise strengths are obeying similar trends with radio pulsars. On the contrary, we do not find any correlation between noise strength and X-ray luminosity which was seen in accretion powered pulsars. Our findings suggest that the noise behaviour of magnetars resembles that of radio pulsars but they possess higher noise levels likely due to their stronger magnetic fields.Comment: 18 pages, 1 table, 4 figures, accepted for publication in MNRA

An Exact and Near-Exact Distribution Approach to the Behrens–Fisher Problem

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1A01100526). Publisher Copyright: © 2022 by the authors.The Behrens–Fisher problem occurs when testing the equality of means of two normal distributions without the assumption that the two variances are equal. This paper presents approaches based on the exact and near-exact distributions for the test statistic of the Behrens–Fisher problem, depending on different combinations of even or odd sample sizes. We present the exact distribution when both sample sizes are odd and the near-exact distribution when one or both sample sizes are even. The near-exact distributions are based on a finite mixture of generalized integer gamma (GIG) distributions, used as an approximation to the exact distribution, which consists of an infinite series. The proposed tests, based on the exact and the near-exact distributions, are compared with Welch’s t-test through Monte Carlo simulations, in particular for small and unbalanced sample sizes. The results show that the proposed approaches are competent solutions to the Behrens–Fisher problem, exhibiting precise sizes and better powers than Welch’s approach for those cases. Numerical studies show that the Welch’s t-test tends to be a bit more conservative than the test statistics based on the exact or near-exact distribution, in particular when sample sizes are small and unbalanced, situations in which the proposed exact or near-exact distributions obtain higher powers than Welch’s t-test.publishersversionpublishe