Miracle-dz/UniFAR-FTUs huggingface dataset:
An oscillating motion of a red blood cell and a neutrally buoyant particle in Poiseuille flow in a narrow channel

Two motions of oscillation and vacillating breathing (swing) of a red blood cell have been observed in bounded Poiseuille flows (Phys. Rev. E 85, 16307 (2012)). To understand such motions, we have studied the oscillating motion of a neutrally buoyant rigid particle of the same shape in Poiseuille flow in a narrow channel and obtained that the crucial point is to have the particle interacting with Poiseuille flow with its mass center moving up and down in the channel central region. Since the mass center of the cell migrates toward the channel central region, its oscillating motion of the inclination angle is similar to the aforementioned motion as long as the cell keeps the shape of long body. But as the up-and-down oscillation of the cell mass center damps out, the oscillating motion of the inclination angle also damps out and the cell inclination angle approaches to a fixed angle.
Foundations of recurrent neural networks

"Artificial neural networks" provide an appealing model of computation. Such networks consist of an interconnection of a number of parallel agents, or "neurons. " Each of these receives certain signals as inputs, computes some simple function, and produces a signal as output, which is in turn broadcast to the successive neurons involved in a given computation. Some of the signals originate from outside the network, and act as inputs to the whole system, while some of the output signals are communicated back to the environment and are used to encode the end result of computation. In this dissertation we focus on the "recurrent network" model, in which the underlying graph is not subject to any constraints. We investigate the computational power of neural nets, taking a classical computer science point of view. We characterize the language recognition power of networks in terms of the types of numbers (constants) utilized as weights. From a mathematical viewpoint, it is natural to consider integer, rational, and real numbers. From the standpoint of computer science, natural classes of formal languages are regular, recursive, and "all languages. " We establish a precise correspondence between the mathematical and computing choices. Furthermore, when the computation time of the network is constrained to be polynomial in the input size, the classes recognized by the respective networks are: regular, P, and Analog-P, i.e. P/poly. Among other results described in this thesis are a proper hierarchy of networks using Kolmogorov-complexity characterizations, the imposition of space constraints, and a proposed "Church's thesis of analog computing. "
Electronic Supplementary Material (ESM) for: Distinguishing intergroup and long-distance relationships

Participants (n=120; 40% female) were interviewed by ACP and two research assistants in April–June 2017. Participants received monetary compensation for their time. Study protocol were approved by the communities and the Mosetén tribal organization. Given mixed literacy but familiarity with signing forms, participants were read a consent form and provided their written consent via signature. All field protocols were approved by the Max Planck Institute for Evolutionary Anthropology Department of Human Behavior, Ecology, and Culture, and declared exempt from additional IRB oversight. ACP presented participants with pairs of cards representing hypothetical individuals—each described by six categories of characteristics (Figure S1). The levels for these six categories were as follows (translated from Spanish):
Open Research Online Incorporating student opinion into opinion mining: A student-sourced sentiment analysis classiﬁer

Incorporating student opinion into opinion mining: A student-sourced sentiment analysis classifier Book Section How to cite: Hillaire, Garron; Rienties, Bart; Fenton-O’Creevy, Mark; Zdrahal, Zdenek and Tempelaar, Dirk (2022). Incorporating student opinion into opinion mining: A student-sourced sentiment analysis classifier. In: Rienties, Bart; Hampel, Regine; Scanlon, Eileen and Whitelock, Denise eds. Open World Learning: Research, Innovation and the Challenges of High-Quality Education. New York, USA: Routledge, pp. 171–185.
Optical parametric oscillators in isotropic photonic crystals and cavities: 3D time domain analysis

We investigate optical parametric oscillations through four-wave mixing in resonant cavities and photonic crystals. The theoretical analysis underlines the relevant features of the phenomenon and the role of the density of states. Using fully vectorial 3D time-domain simulations, including both dispersion and nonlinear polarization, for the first time we address this process in a face centered cubic lattice and in a photonic crystal slab. The results lead the way to the development of novel parametric sources in isotropic media.
Occult mathematics under the learning process in convolutional neural networks

In the last decade, artificial intelligence has transformed the world. Big Data and large software companies are prompting researchers to create new algorithms that surpass human intelligence more quickly and efficiently. In 2012 convolutional neural networks (CNN) captured the attention of researchers on the subject of image recognition; becoming popular and efficient. However, the lack of information on a meticulous mathematical process and the tendency of authors to describe this method as a black box, implied that a defined architecture, parameters and hyperparameters generate results with an unknown internal process. The present study develops the detailed mathematical model of forward and backward propagation in a CNN for three-dimensional images (in color), providing researchers with solid tools when developing optimizations in the algorithm. In addition, the models were applied to a proposed architecture that allowed recognizing the stages of the process, the number of network learning parameters and complexity focused on computational expenditure. Finally, tables are included with the most used cost, activation and optimization functions that allow the reader to formulate their own model depending on the architecture, functions and hyperparameters chosen.
Tensor Hero: Generating Playable Guitar Hero Charts from Any Song

In this paper, we summarize our progress towards creating a system capable of ingesting raw audio files and outputting playable Guitar Hero charts which are matched to the musical structure of that audio. We refer to this task as abstract transcription . Drawing on work from music information retrieval (MIR), machine learning, and signal processing, we formulate and review candidate models to accomplish this task, including all data preprocessing and model training steps explored. In addition to abstract transcription, we also detail a sys-tem used for difficulty conversion , the task of taking a dense and fast Guitar Hero chart and simplifying it to a more sparse and easy format. We detail our model selection and evaluation processes and summarize progress as of the submission of this report.
Vortex nucleation in Bose-Einstein Condensates subject to light induced effective magnetic fields

We numerically simulate vortex nucleation in a Bose-Einstein Condensate (BEC) subject to an effective magnetic field. The effective magnetic field is generated from the interplay between light with a non-trivial phase structure and the BEC, and can be shaped and controlled by appropriate modifications to the phase and intensity of the light. We demonstrate that the nucleation of vortices is seeded by instabilities in surface excitations which are coupled to by an asymmetric trapping potential (similar to the case of condensates subject to mechanical rotation) and show that this picture also holds when the applied effective magnetic field is not homogeneous. The eventual configuration of vortices in the cloud depends on the geometry of the applied field.
44 GHZ CLASS I METHANOL (CH3OH) MASER SURVEY IN THE GALACTIC CENTER

We report on a large 44 GHz (70–61 A+) methanol (CH3OH) maser survey of the Galactic Center. The Karl G. Jansky Very Large Array was used to search for CH3OH maser emission covering a large fraction of the region around Sgr A. In 25 pointings, over 300 CH3OH maser sources (>10σ) were detected. The majority of the maser sources have a single peak emission spectrum with line of sight velocities that range from about −13 to 72 km s−1. Most maser sources were found to have velocities around 35−55 km s−1, closely following velocities of neighboring interacting molecular clouds (MCs). The full width at half-maximum of each individual spectral feature is very narrow (∼0.85 km s−1 on average). In the north, where Sgr A East is known to be interacting with the 50 km s−1 MC, more than 100 44 GHz CH3OH masers were detected. In addition, three other distinct concentrations of masers were found, which appear to be located closer to the interior of the interacting MCs. It is possible that a subset of masers is associated with star formation, although conclusive evidence is lacking.
Viscous Overstability in Saturn’s Rings: Influence of Collective Self-gravity

We investigate the influence of collective self-gravity forces on the nonlinear, large-scale evolution of the viscous overstability in Saturn’s rings. We numerically solve the axisymmetric nonlinear hydrodynamic equations in the isothermal and non-isothermal approximation, including radial self-gravity and employing transport coefficients derived by Salo et al. We assume optical depths to model Saturn’s dense rings. Furthermore, local N-body simulations, incorporating vertical and radial collective self-gravity, are performed. Vertical self-gravity is mimicked through an increased frequency of vertical oscillations, while radial self-gravity is approximated by solving the Poisson equation for an axisymmetric thin disk with a Fourier method. Direct particle–particle forces are omitted, which prevents small-scale gravitational instabilities (self-gravity wakes) from forming, an approximation that allows us to study long radial scales and to compare directly the hydrodynamic model and the N-body simulations. Our isothermal and non-isothermal hydrodynamic model results with vanishing self-gravity compare very well with results of Latter & Ogilvie and Rein & Latter, respectively. In contrast, for rings with radial self-gravity we find that the wavelengths of saturated overstable waves settle close to the frequency minimum of the nonlinear dispersion relation, i.e., close to a state of vanishing group velocities of the waves. Good agreement is found between non-isothermal hydrodynamics and N-body simulations for moderate and strong radial self-gravity, while the largest deviations occur for weak self-gravity. The resulting saturation wavelengths of viscous overstability for moderate and strong self-gravity ( ) agree reasonably well with the length scales of axisymmetric periodic microstructure in Saturn’s inner A ring and the B ring, as found by Cassini.
A Generalization of the Tetrad Representation Theorem

The tetrad representation theorem, due to Spirtes, Glymour, and Scheines (1993), gives a graphical condition necessary and sufficient for the vanishing of tetrad differences in a linear correlation structure. This note simplifies their proof and generalizes the theorem. In order to make the ideas as accessible as possible to mathematicians who might develop them further, we begin with a thorough exposition of their purely graphtheoretical aspects.
Low-energy properties of a one-dimensional system of interacting bosons with boundaries

The ground-state properties and low-lying excitations of a (quasi) one-dimensional system of longitudinally confined interacting bosons are studied. This is achieved by extending Haldane's harmonic-fluid description to open boundary conditions. The boson density, one-particle density matrix, and momentum distribution are obtained accounting for finite-size and boundary effects. Friedel oscillations are found in the density. Finite-size scaling of the momentum distribution at zero momentum is proposed as a method to obtain from the experiment the exponent that governs phase correlations. The strong correlations between bosons induced by reduced dimensionality and interactions are displayed by a Bijl-Jastrow wave function for the ground state, which is also derived.
Fitting Cox's Regression Model to Survival Data Using Glim

The proportional hazard regression model is reviewed, and its analysis using GLIM is described. Methods of estimating the underlying survivor functions are discussed. The Poisson model which allows the use of GLIM is introduced and interpreted. Two different treatments of tied observations are mentioned, and their properties are compared in the context of a specific example.
S 2 . Fine-tuning phase S 2 . 1 Overall summary

Table 1 shows the encoder, pre-training, and hyper-parameter settings used by the different experiments performed. In this study, a ResNet-101 [1] encoder was adopted as the feature extractor for all models. To prevent overfitting and to reduce the amount of time needed for training, the parameter values obtained through pre-training with ImageNet [2] or MS COCO 2017 [3] were transferred to the encoder and used for fine-tuning. In the fine-tuning stage, the model predicted 10 mask patches at a time and compared those predictions to the corresponding 10 ground truth patches. After that, the model parameters were updated using the average value of the 10 losses. A mini-batch size of 10 was found to be the maximum value for which the available GPU memory capacity (8GB) was not exceeded. The TR-based deep learning models gradually reduce the training loss that occurs in the fine-tuning CVFs by performing multiple epochs. In each epoch, mini-batches were fed to a model until every patch in the fine-tuning CVFs was used once. At the end of every epoch, the validation CVF was used to obtain the validation loss and validation effectiveness of the model under consideration. At the end of an epoch, the parameters of each model were only kept if the validation loss of the current epoch was less than the lowest validation loss found during the previous epochs. For U-Net, TTA U-Net, and Nested U-Net, this process was repeated for 20 epochs; for FCN and DeepLabv3, only 10 epochs were used since the optimization time for these models was more than double that of the others. The stored parameters were then used to calculate the final model performance for the test set. More details about the models used in our experiments can be found in Supporting information S2.2. Furthermore, information about the loss functions and the optimizers used can be found in Supporting information S2.3 and Supporting information S2.4, respectively. Finally, transfer leaning using pre-trained parameters is explained in Supporting information S2.5.
NON-EXISTENCE OF CONJUGATE-SYMPLECTIC MULTI-STEP METHODS OF ODD ORDER

我们证明那是形式 m &summation 的任何线性多步方法 G1τ ；k=0 αkZk=τ m &summation；有奇怪的顺序 u 的 k=0 βkJ-1λ H (Zk)(u ≥ 3 ) 不能是结合到一个 symplectic 方法(顺序 w 的 G2τ(w ≥ u ) 经由形式 m &summation 的任何概括线性多步方法 G3τ ；k=0 αkZk m &summation；k=0 βkJ-1λ H (m &summation；l=0 γklZl) 。 我们也给一个必要条件让这种概括线性多步方法是 conjugate-symplectic。 当 G3τ 是一个更一般的操作符时，我们也证明这些结果调用容易被扩大到盒子。
Some numerical results of the quasi-two-day wave excitation and propagation in the unstable summer middle atmosphere

It is frequently observed that when a real locomotive starts up, the front set of drive wheels commences to turn before the rear set of drive wheels begins to turn. The present invention is a simple device that permits this effect to be simulated in a model locomotive. The electric motor of the model locomotive drives a worm which turns a first gear. A second gear is mounted on the same shaft as the first gear but is not otherwise connected to the first gear. From the side of the first gear that faces the second gear, a pin extends at a particular radius. Similarly, two pins extend at the particular radius from the side of the second gear that faces the first gear. When the motor starts, the first gear turns immediately, but rotation of the second gear is delayed until the pin on the first gear strikes one of the pins on the second gear. In this way a delayed rotary motion is imparted to the second gear.
Hydrogen Storage Prediction in Dibenzyltoluene as Liquid Organic Hydrogen Carrier Empowered with Weighted Federated Machine Learning

: The hydrogen stored in liquid organic hydrogen carriers (LOHCs) has an advantage of safe and convenient hydrogen storage system. Dibenzyltoluene (DBT), due to its low flammability, liquid nature and high hydrogen storage capacity, is an efficient LOHC system. It is imperative to indicate the optimal reaction conditions to achieve the theoretical hydrogen storage density. Hence, a Hydrogen Storage Prediction System empowered with Weighted Federated Machine Learning (HSPS ‐ WFML) is proposed in this study. The dataset were divided into three classes, i.e., low, me ‐ dium and high, and the performance of the proposed HSPS ‐ WFML was investigated. The accuracy of the medium class is higher (99.90%) than other classes. The accuracy of the low and high class is 96.50% and 96.40%, respectively. Moreover, the overall accuracy and miss rate of the proposed HSPS ‐ WFML are 96.40% and 3.60%, respectively. Our proposed model is compared with existing studies related to hydrogen storage prediction, and its accuracy is found in agreement with these studies. Therefore, the proposed HSPS ‐ WFML is an efficient model for hydrogen storage prediction.
Factorization of laser–pulse ionization probabilities in the multiphotonic regime

We present a detailed study of the ionization probability of H and H2+? > induced by a short intense laser pulse. Starting from a Coulomb–Volkov description of the process we derive a multipole-like expansion where each term is factored into two contributions: one that accounts for the effect of the electromagnetic field on the free-electron final state and a second factor that depends only on the target structure. Such a separation may be valuable to solve complex atomic or molecular systems as well as to interpret the dynamics of the process in simpler terms. We show that the series expansion converges rapidly, and thus the inclusion of the first few terms is sufficient to produce accurate results.
in the Proceedings of the Eighteenth Symposium on Usable Privacy and Security (SOUPS 2022)

Windows Hello for Business is Microsoft’s latest attempt to replace passwords in Windows enterprise environments introduced with Windows 10. It addresses some of the common password problems like password leaks or phishing attacks, comes with built-in support for biometric authentication methods like ﬁngerprint or facial recognition, and a new user interface. We conducted a qualitative study with 13 employees accompanying the introduction of Windows Hello in a small business studying its usability and deploy-ability. Over ﬁve weeks, we measured authentication times, let participants rate their user experience, and conducted interviews at the end. In general, participants liked Windows Hello and found it more usable than the traditional Windows sign-in scheme. Windows Hello was faster and perceived as more responsive than the traditional Windows login. However, participants tended to use PINs as a replacement for their (longer) passwords instead of using biometrics. Lack of hardware support (no biometric hardware available), the form factor of device or setup of their workplace (e.g., bio-metric hardware on the other side of the table) were some reasons to not use biometrics but stick with a well-known authentication method like a PIN.
Long-run Eﬀects of Government Debt on Interest Rate: Evidence for Bangladesh

: This paper explores the phenomenon that each year a major portion of the government debt in Bangladesh is expended on interest payment, giving rise to more budgetary deficit in the future. Ever-rising government debt is, however, not the only budgetary item that may be responsible for crowding out private borrowing. The paper empirically investigates the long-run effects of government debt on long-term nominal interest rate and explores the short-run dynamics in the context of capital market in Bangladesh. Using time-series data on Bangladesh and applying vector error correction model (VECM), this study finds a single cointegrating equation depicting long-run stable relationship between long-term nominal interest rate and the explanatory variables in the model. The study also finds convergence of short-run dynamics of government debt towards statistically significant long-run equilibrium and concludes that government debt has a positive impact on the long-term nominal interest rate in the capital market of Bangladesh.
Usable Privacy and Security

Risk-based authentication (RBA) complements standard password-based logins by using knowledge about previously observed user behavior to prevent malicious login attempts. Correctly conﬁgured, RBA holds the opportunity to increase the overall security without burdening the user by limiting unnecessary security prompts to a minimum. Thus, it is crucial to understand how administrators interact with off-the-shelf RBA systems that assign a risk score to a login and require administrators to conﬁgure adequate responses. In this paper, we let n = 28 system administrators conﬁg-ure RBA using a mock-up system modeled after Amazon Cognito. In subsequent semi-structured interviews, we asked them about the intentions behind their conﬁgurations and experiences with the RBA system. We ﬁnd that administrators want to have a thorough understanding of the system they conﬁgure, show the importance of default settings as they are either directly adopted or depict an important orientation, and identify several confusing wordings. Based on our ﬁndings, we give recommendations for service providers who offer risk-based authentication to ensure both usable and secure logins for everyone.
Users' Perceptions of Chrome Compromised Credential Notification

This paper reports the challenges that users experienced and their concerns regarding the Chrome compromised credentials notification. We adopted a two-step approach to uncover the issues of the notification, including qualitatively analyzing users’ online comments and conducting semi-structured inter-views with participants who had received the notification. We found that users’ issues with the notification are associated with five core aspects of the notification: the authenticity of the notification, data breach incidents, Google’s knowledge of users’ compromised credentials, multiple accounts being associated with one notification, and actions recommended by the notification. We also identified the detailed challenges and concerns users had regarding each aspect of the notification. Based on the results, we offer suggestions to improve the de-sign of browser-based compromised credential notifications to support users in better protecting their online accounts.
Price Changes and Welfare Analysis: Measurement under Individual Heterogeneity

Measuring the welfare impact of price changes on consumers is pivotal in economic analyses. Researchers often measure these impacts with cross-sectional data, where every consumer is observed only once. The representative agent (RA) approach, which assumes all observations stem from a single agent, may lead to biased estimates when agents’ preferences are heterogeneous. We show how to use the higher moments of demand to improve these estimates. In fact, the variance alone captures much of the bias in the RA approach. Our approach also enables inference on the distribution of welfare changes. We then leverage our approach to obtain conditions moments of demand must satisfy to arise from a population of utility maximizers and deliver a characterization of rationality for the two-good case. Using the UK Household Budget Survey, we apply our methodology to estimate the welfare impact of a 10% transport price increase and ﬁnd that the RA approach understates the welfare impact by 27.2%