Miracle-dz/UniFAR-FTUs huggingface dataset:
Consistency of students' conceptions of wave propagation: Findings from a conceptual survey in mechanical waves

this paper we attempt to investigate the strength with which the students were committed to these conceptual models, as evidenced by the consistency with which they answered the questions. For this purpose we focus on the patterns of student responses to questions in one particular subtopic, wave propagation. This study has three main purposes: (1) to investigate the consistency of student conceptions, (2) to explore the relative usefulness of different analysis techniques, and (3) to determine what extra information a study of consistency can give about student understanding of basic concepts. We used two techniques: first, categorizing and counting, which is widely used in the science education community, and second, model analysis, recently introduced into physics education research. The manner in which categorizing and counting is used is very diverse while model analysis has been employed only in prescriptive ways. Research studies have reported that students often use their conceptual models inconsistently when solving a series of questions that test the same idea. Our results support their conclusions. Moreover, our findings suggest that students who have had more experiences in physics learning seem to use the scientifically accepted models more consistently. Further, the two analysis techniques have different advantages and disadvantages. Our findings show that model analysis can be used in more diverse ways, provides flexibility in analyzing multiple-choice questions, and provides more information about consistency and inconsistency of student conceptions. An unexpected finding is that studying waves in other contexts (for example, quantum mechanics or electromagnetism) leads to more consistent answers about mechanical waves. The suggestion is that studying more abstract topics may solidify students’ understanding of more concrete waves. While this might be considered to be intuitive, we have not actually found direct empirical studies supporting this conjecture.
Comparing the Effect of Privacy and Non-Privacy Social Media Photo Tools on Factors of Privacy Concern

: Research into privacy tools for social media content has found that although there is a positive attitude towards these tools, there are also privacy concerns related to the user information involved. This privacy concern towards privacy tools can be higher than for non-privacy tools of a similar type, but the reason for this difference is not clear. To address this, we conducted an online experiment to compare the effect of a privacy and a non-privacy tool on antecedent factors of privacy concern (Perceived value of personal information, Social presence, Affect and Trust) which are hypothesized to be affected by the different purpose of the tools. The results show that participants had higher affect towards the privacy tool compared to the non-privacy tool. On the other hand, the results also show that participants in the privacy tool group had a higher level of perception of value of their personal information, that is, that the information provided to and inferred by the tool is valuable. Finally, both factors mediated a signiﬁcant but opposing effect of the type of tool on privacy concern.
Galois structure of de Rham cohomology of tame covers of schemes

a class (ON) in the Grothendieck group Ko(Z[G]) of all finitely generated projective Z[G]-modules. The class group Cl(Z[G]) of Z[G] is defined to be the quotient of Ko(Z[G]) by the subgroup generated by the class of Z[G]. Let (ON)stab be the image of (ON) in Cl(Z[G]). In [34] Taylor proved Fr6hlich's conjecture that (ON)stab is equal to another invariant WN/K in Cl(Z[G]) which Cassou-Nogues had defined by means of the root-numbers of symplectic representations of the Galois group G. The root-number of a representation V of G is the constant which appears in the functional equation of the Artin Lfunction of V. The connection between Galois-structure invariants and Artin L-functions has been a basic theme in research on Galois structure; for further discussion, see [12] and [4].
Scale Effects in Strength-Dominated Collisions of Rocky Asteroids

Abstract The application of laboratory collision experimental results to the larger scales of asteroid impacts is complicated by the fact that the dynamic strength of rock typically decreases as the loading duration increases. Because loading times increase with the size scale of a collision, large bodies are effectively weaker than small ones. While this effect has been postulated for over a decade, it has never been verified in actual collision experiments. This paper summarizes collision tests performed under the conditions required to examine scale effects, i.e., increasing the size scale of the experiment while holding the impact velocity and impact kinetic energy per target mass constant. Granite targets are used, with a diameter variation of a factor of 18. The larger targets experienced significantly more collisional damage than small ones, confirming a decrease in dynamic strength with increasing size scale. The results are compared to a scaling model based on the concept that fragmentation is accomplished through the growth and coalescence of preexisting flaws. Measurements of the actual flaw-size distribution are used to validate the model. Field observations of flaw and fault sizes at scales to 10~km are used to construct a scaling model that is believed to apply to the shattering of a wide range of rock types. The results show that kilometer-sized rocky bodies may be significantly weaker than indicated by previous estimates.
Experiments on gravitational phase separation of binary immiscible fluids

We conduct experiments on gravitational phase separation of binary immiscible fluids using an oil–water mixture and study how the volumetric and viscosity ratios of the two phases control the separation process. First, we change the volumetric fraction of the two phases. We find that the initial phase separation rate depends strongly on the volumetric ratio of the two phases, and can be modelled by a buoyancy-driven permeable flow using the Blake–Kozeny–Carman permeability formula. Next, we change the viscosity ratios of the two fluids, and we find that there are two distinct regimes with different styles of phase separation. Small viscosity ratio (<100) cases are characterized by a sharp lower boundary and a vertically homogeneous mixture layer. On the other hand, high viscosity ratio (>100) cases are characterized by a diffuse lower boundary and a large vertical gradient of porosity. A polyhedral foam structure develops at the top of the mixture layer which is slow to rupture and to transform into a uniform oil layer. These differences can be interpreted to arise from a faster coalescence rate relative to the separation rate at high viscosity ratios. We simultaneously measured electrical resistivity in order to monitor the temporal change of the mean porosity in the mixture layer. The measurements were found to be consistent with the visual observation.
Fixed Tasks for Continuous Authentication via Smartphone

: Mobile devices feature a variety of knowledge-based authentications such as PINs, passwords, and lock sequences. The weakness of these approaches is that once leaked and/or intercepted, the control over the device is lost and no more authentication steps are required. In this paper, the efficiency of a set of ML algorithms in authenticating users is evaluated with the aim of understanding which are the best tasks to use by submitting Fixed Tasks, which simulate the use of a device in daily life, through Touch Behaviour and motion sensors installed in the device itself. Next, a social problem is posed, in which an attempt is made to understand whether a group of subjects at a trial performed the assigned tasks correctly without permitting other people to do them instead.
First description of atrial fibrillation and congenital thrombotic thrombocytopenic purpura treated by left atrial appendage occlusion

Given an increased risk of both thrombosis and bleeding, thrombotic thrombocytopenic purpura (TTP) presents a unique challenge when anticoagulation is required for comorbid disease, particularly in the setting of major bleeding events. We present for the first time a patient with TTP and atrial fibrillation, presenting with recurrent stroke, but unable to tolerate anticoagulation due to prior intra‐cerebral hemorrhage. To address both issues concomitantly, we describe the successful application of a novel management approach to facilitate left atrial appendage occlusion, there by offering a non‐pharmacologic means of stroke prevention without added bleeding risk.
Co-Incrementation: Combining Co-Training and Incremental Learning for Subject-Specific Facial Expression Recognition

: In this work, we propose to adapt a generic emotion recognizer to a set of individuals in order to improve its accuracy. As this adaptation is weakly supervised, we propose a hybrid framework, the so-called co-incremental learning that combines semi-supervised co-training and incremental learning. The classifier we use is a specific random forest whose internal nodes are nearest class mean classifiers. It has the ability to learn incrementally data covariate shift. We use it in a co-training process by combining multiple view of the data to handle unlabeled data and iteratively learn the model. We performed several personalization and provided a comparative study between these models and their influence on the co-incrementation process. Finally, an in-depth study of the behavior of the models before, during and after the co-incrementation process was carried out. The results, presented on a benchmark dataset, show this hybrid process increases the robustness of the model, with only a few labeled data.
t.ex-Graph: Automated Web Tracker Detection Using Centrality Metrics and Data Flow Characteristics

: The practice of Web tracking raised concerns of privacy activists and data protection authorities over the past two decades. Simultaneously, researchers propose multiple solutions based on machine learning to automatically detect Web trackers. These solutions, while proving to be promising, often remained proofs-of-concept. This paper proposes t.ex-Graph, a representation that models data flows between websites to detect Web trackers in a graph. We use a publicly available dataset containing HTTP/S requests from a crawl of the Tranco top 10K websites to extract our graph. In the second step, we feed our graph into multiple machine-learning models to predict nodes that carry out tracking activities. Our results show high accuracy of 88% and even detect yet unknown Web trackers. We publish our artifacts for fellow researchers to replicate, reproduce, and advance our results.
Development of a technique for modelling clay liner desiccation

This paper presents a model for the analysis of clay liner desiccation in a landfill barrier system due to temperature effects. The model incorporates consideration of fully coupled heat‐moisture‐air flow, a non‐linear constitutive relationship, the dependence of void ratio and volumetric water content on stress, capillary pressure and temperature, and the effect of mechanical deformation on all governing equations. Mass conservative numerical schemes are proposed to improve the accuracy of the finite element solution to the governing equations. The application of the model is then demonstrated by examining three test problems, including isothermal infiltration, heat conduction and non‐isothermal water and heat transport. Comparisons are made with results from literature, and good agreement is observed. Copyright © 2003 John Wiley & Sons, Ltd.
Convergence of a global stochastic optimization algorithm with partial step size restarting

This work develops a class of stochastic global optimization algorithms that are Kiefer-Wolfowitz (KW) type procedures with an added perturbing noise and partial step size restarting. The motivation stems from the use of KW-type procedures and Monte Carlo versions of simulated annealing algorithms in a wide range of applications. Using weak convergence approaches, our effort is directed to proving the convergence of the underlying algorithms under general noise processes.
Pseudo-three-dimensional turbulence in magnetized nonuniform plasma

A simple nonlinear equation is derived to describe the pseudo‐three‐dimensional dynamics of a nonuniform magnetized plasma with Te≫Ti by taking into account the three‐dimensional electron, but two‐dimensional ion dynamics in the direction perpendicular to B0. The equation bears a close resemblance to the two‐dimensional Navier–Stokes equation. A stationary spectrum in the frequency range of drift waves is obtained using this equation by assuming a coexisting large amplitude long wavelength mode. The ω‐integrated k spectrum is given by k1.8(1+k2)−2.2, while the width of the frequency spectrum is proportional to k3(1+k2)−1, where k is normalized by cs/ωci. The result compares well with the recently observed spectrum in the ATC tokamak.
Face-Based Gaze Estimation Using Residual Attention Pooling Network

: Gaze estimation reveals a person’s intent and willingness to interact, which is an important cue in human-robot interaction applications to gain a robot’s attention. With tremendous developments in deep learning architectures and easily accessible cameras, human eye gaze estimation has received a lot of attention. Compared to traditional model-based gaze estimation methods, appearance-based methods have shown a substantial improvement in accuracy. In this work, we present an appearance-based gaze estimation architecture that adopts convolutions, residuals, and attention blocks to increase gaze accuracy further. Face and eye images are generally adopted separately or in combination for the estimation of eye gaze. In this work, we rely entirely on facial features, since the gaze can be tracked under extreme head pose variations. With the proposed architecture, we attain better than state-of-the-art accuracy on the MPIIFaceGaze dataset and the ETH-XGaze open-source benchmark.
The Effect of Eurasian Snow Cover on Regional and Global Climate Variations

The sensitivity of the global climate system to interannual variability of he Eurasian snow cover has been investigated with numerical models. It was found that heavier than normal Eurasian snow cover in spring leads to a “poor” monsoon over Southeast Asia thereby verifying an idea over 100 years old. The poor monsoon was characterized by reduced rainfall over India and Burma, reduced wind stress over the Indian Ocean, lower than normal temperatures on the Asian land mass and in the overlying atmospheric column, reduced tropical jet, increased soil moisture, and other features associated with poor monsoons. Lighter than normal snow cover led to a “good” monsoon with atmospheric anomalies like those described above but of opposite sign. Remote responses from the snow field perturbation include readjustment of the Northern Hemispheric mass field in midlatitude, an equatorially symmetric response of the tropical geopotential height and temperature field and weak, but significant, perturbations in the surface wind stress and heat flux in the tropical Pacific. The physics responsible for the regional response involves all elements of both the surface heat budget and heat budget of the full atmospheric column. In essence, the snow, soil and atmospheric moisture all act to keep the land and overlying atmospheric column colder than normal during a heavy snow simulation thus reducing the land–ocean temperature contrast needed to initiate the monsoon. The remote responses are driven by heating anomalies associated with both large scale air-sea interactions and precipitation events. The model winds from the heavy snow experiment were used to drive an ocean model. The SST field in that model developed a weak El Nino in the equatorial Pacific. A coupled ocean-atmosphere model simulation perturbed only by anomalous Eurasian snow cover was also run and it developed a much stranger El Nino in the Pacific. The coupled system clearly amplified the wind stress anomaly associated with the poor monsoon. These results show the important role of an evolving (not specified) sea surface temperature in numerical experiments and the real climate system. Our general results also demonstrate the importance of land processes in global climate dynamics and their possible role as one of the factors that could trigger ENSO events.
Lp boundedness for parabolic Schrödinger type operators with certain nonnegative potentials

Abstract In this paper, we consider the parabolic Schrödinger differential operators ∂ t – Δ + V(x), where the nonnegative potential V belongs to the reverse Hölder class Bq for q ≥ n/2. The Lp estimates for these operators (∂ t – Δ + V) iγ (γ ∈ ℝ) and ∇(∂ t – Δ + V)–1/2 are obtained.
Minimum-error discrimination of quantum states: Bounds and comparisons

We derive a new bound on the minimum-error probability for ambiguous discrimination and compare the new bound with six other bounds in the literature. Specifically, the main technical contributions are as follows. (1) We obtain a general lower bound on the minimum-error probability for ambiguous discrimination among arbitrary $m$ mixed quantum states with given prior probabilities. We further analyze how this lower bound is attainable by presenting a necessary and sufficient condition related to it. (2) We compare this new lower bound with six other bounds in the literature, in detail. We show that this new bound improves the previous one in the literature and in the equiprobable case; the new bound also improves another one. Moreover, we give an example to show that this new bound is the tightest one for some cases.
Towards Explainability in Using Deep Learning for Face Detection in Paintings

: Explainable Artificial Intelligence (XAI) is an active research area to interpret a neural network’s decision by ensuring transparency and trust in the task-specified learned models. In fact, despite the great success of deep learning networks in many fields, their adoption by practitioners presents some limits, one significant of them is the complex nature of these networks which prevents human comprehension of the decision-making process. This is especially the case in artworks analysis. To address this issue, we explore Detector Randomized Input Sampling for Explanation (DRISE), a visualization method for explainable artificial intelligence to comprehend and improve CNN-based face detector on Tenebrism painting images. The results obtained show local explanations for model’s prediction and consequently offer insights into the model’s decision-making. This paper will be of great help to researchers as a future support for explainability of object detection in other domain application.
Factors Affecting the Application of Management Accounting to MSMEs

The success of an enterprise depends on many managerial decisions that are based on accounting information, in particular management accounting (MA). There are several factors that influence the application of MA in individual and group business, entrepreneurship and MSMEs. This study examines and analyzes the factors that influence the application of MA to MSMEs with HR's Skills, business characteristics, business environment and business strategy. The research sample was 34 MSMEs actors, using purposive sampling techniques. Research findings prove that human resource skills, business characteristics, business environment, and business strategy have a significant positive effect on the application of MA both individually and simultaneously. Management accounting seems to continue to be a scientific tool for managers to carry out operations, control and decision-making in a satisfactory way. The effectiveness of implementing MA is increasingly important to improve operating efficiency and management performance.
A Renal Abscess Caused by Nontyphoid Salmonella in an Immunocompetent Adult

Renal abscesses are uncommon in adults and are typically caused by gram-negative bacteria such as Escherichia coli, Proteus mirabilis, or Staphylococcus aureus. Nontyphoid Salmonella abscesses are infrequent. We discuss the case of a 27-year-old previously healthy female who developed a renal abscess due to Salmonella enterica. Abdominal computed tomography (CT) revealed a left renal abscess (size 11.6 cm × 8.2 cm) and 1.9 cm cyst in the right kidney. The urine and abscess aspirate cultures indicated the presence of gram-negative bacilli and lactose-negative Salmonella. A presumptive diagnosis of the left renal abscess was made. A urologist was consulted, and the patient was prepared for a left-sided nephrectomy. The patient's condition improved following treatment with a combination of piperacillin/tazobactam and moxifloxacin. Based on our experience, prompt recognition of nontyphoidal Salmonella as a potential cause of a renal abscess is important to prevent complications such as the extension of the abscess and the spread of the pathogen to adjacent structures.
Object Detection in Floor Plans for Automated VR Environment Generation

: The development of visually compelling Virtual Reality (VR) environments for serious games is a complex task. Most environments are designed using game engines such as Unity or Unreal Engine and require hours if not days of work. However, most important information of indoor environments can be represented by floor plans. Those have been used in architecture for centuries as a fast and reliable way of depicting building configurations. Therefore, the idea of easing the creation of VR ready environments using floor plans is of great interest. In this paper we propose an automated framework to detect and classify objects in floor plans using a neural network trained with a custom floor plan dataset generator. We evaluate our system on three floor plans datasets: ROBIN (labelled), PFG (our own Procedural Floor plan Generation method) and 100 labelled samples from the CubiCasa Dataset
Can Following Paleolithic and Mediterranean Diets Reduce the Risk of Stress, Anxiety, and Depression: A Cross-Sectional Study on Iranian Women

Background Psychiatric disorders have been a challenge for public health and will bring economic problems to individuals and healthcare systems in the future. One of the important factors that could affect these disorders is diet. Objective In the current study with a cross-sectional design, we investigated the association of Paleolithic and Mediterranean diets with psychological disorders in a sample of adult women. Methods Participants were 435 adult women between 20 and 50 years old that refer to healthcare centers in the south of Tehran, Iran. The diet scores were created by the response to a valid and reliable semiquantitative food frequency questionnaire (FFQ), and the psychological profile was determined by response to the Depression, Anxiety, and Stress Scale (DASS-21). The multivariable-adjusted logistic regression was applied to compute the odds ratio (OR) and 95% confidence interval (CI). Results After adjusted for potential confounders, it is evident that participants in the highest Paleolithic diet tertile had lower odds of depression (OR = 0.21; 95% CI: 0.12, 0.37: P < 0.001), anxiety (OR = 0.27; 95% CI: 0.16, 0.45: P < 0.001), and stress (OR = 0.19; 95% CI: 0.11, 0.32; P < 0.001) in comparison to the lowest tertile. Furthermore, those in the third tertile of the Mediterranean diet score were at lower risk of depression (OR = 0.20; 95% CI: 0.11, 0.36; P < 0.001), anxiety (OR = 0.22; 95% CI: 0.13, 0.38; P < 0.001), and stress (OR = 0.23; 95% CI: 0.13, 0.39; P < 0.001) compared with those in the first tertile. Conclusion The result of the current study suggests that greater adherence to Paleolithic and Mediterranean dietary patterns may be related with a decreased risk of psychological disorders such as depression, anxiety, and stress.