Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender-inequitable discrimination? A survey experiment on the gendered implications of unauthorized immigrant narratives

ABSTRACT The existing framing and narrative studies focus on the influence of framing and narratives targeting one population as a whole but neglect how these can affect the subgroups of the target population differently. Using a survey experiment, this research investigates whether and how implicitly gendered narrative cues affect public acceptance of male or female unauthorized immigrants from Mexico and the Northern Triangle. Based on the experimental results, younger male unauthorized immigrants were less likely to be accepted by participants when public safety cues were presented and more likely to be accepted when economic contribution cues were presented. Conversely, female unauthorized immigrants were less favored by participants when economic contribution cues were presented and more favored when public safety cues were presented. These findings show how the dominant narratives contribute to the publics’ gender biases against unauthorized immigrants and reveal future direction for developing narratives and framing studies.
DESIGN OF A CONTROL SYSTEM FOR A SAFFLOWER PICKING ROBOT AND RESEARCH ON MULTISENSOR FUSION POSITIONING

This paper discusses the design of a safflower picking robot control system and focuses on a navigation control subsystem based on multisensor fusion. A navigation subsystem, an identification and positioning subsystem, a picking subsystem, and a levelling subsystem are designed. The hardware and software of the navigation subsystem are designed in detail, and a multisensor fusion positioning method based on extended Kalman fusion technology is proposed. The accuracy and stability levels of different combined navigation methods are compared. To test the effectiveness and accuracy of the proposed method, an outdoor test is carried out. The test results show that the outdoor fusion positioning accuracy of the robot is less than 8 cm, and when the satellite signal is lost, the navigation control subsystem can still provide high positioning accuracy. The final positioning result obtained using the integrated positioning method of the
Web version of the protocol of the orofacial myofunctional evaluation with scores: usability and learning

ABSTRACT Purpose The Orofacial Myofunctional Evaluation with Scores (OMES) protocol has been validated and used in clinical practice and research. The goals of this study were to develop, analyze and improve a version of OMES for the Web and to investigate the relationship between the usability judgments and the prior experience of the evaluators and whether using the interface promotes learning, as shown by the task completion time (TCT). Methods Study steps: 1) inspection of the prototype by the team; 2) evaluation of usability by three experienced speech-language pathologists (SLPs); and 3) evaluation of its usability by 12 SLPs with varying levels of experience in the use of OMES. Participants answered the Heuristic evaluation (HE), the Computer System Usability Questionnaire (CSUQ), and expressed free comments. The TCT was recorded. Results The OMES-Web reached excellent usability levels, and the participants were highly satisfied. The correlations between the participants’ experience and the HE and CSUQ scores were not significant. The TCT decreased significantly throughout the tasks. Conclusion OMES-Web meets the usability criteria, and participants feel satisfied with the system regardless of their level of experience. The fact that it is easy to learn favors its adoption by professionals.
Gravitational lensing effects of a Reissner–Nordstrom–de Sitter black hole

We investigate the influence of cosmological constant $\mathrm{\ensuremath{\Lambda}}$ on gravitational lensing. By the method of an elliptic integral, we give out the solution to the null geodesic equation in the Reissner\char21{}Nordstrom\char21{}de Sitter spacetime. The deflection angle of light is obtained by studying the intrinsic geometry of the spatial equatorial plane in the Reissner\char21{}Nordstrom\char21{}de Sitter spacetime. By applying the expansion of elliptic integrals, we find the deflection angle in the weak field limit and in the strong field limit, respectively. We find the angular position and magnification of images and discuss its behavior under changed $\mathrm{\ensuremath{\Lambda}}$. At last, we give out the higher-order term in the strong field limit.
A Cylindrical Near-Field Acoustical Holography Method Based on Cylindrical Translation Window Expansion and an Autoencoder Stacked with 3D-CNN Layers

The performance of near-field acoustic holography (NAH) with a sparse sampling rate will be affected by spatial aliasing or inverse ill-posed equations. Through a 3D convolution neural network (CNN) and stacked autoencoder framework (CSA), the data-driven CSA-NAH method can solve this problem by utilizing the information from data in each dimension. In this paper, the cylindrical translation window (CTW) is introduced to truncate and roll out the cylindrical image to compensate for the loss of circumferential features at the truncation edge. Combined with the CSA-NAH method, a cylindrical NAH method based on stacked 3D-CNN layers (CS3C) for sparse sampling is proposed, and its feasibility is verified numerically. In addition, the planar NAH method based on the Paulis–Gerchberg extrapolation interpolation algorithm (PGa) is introduced into the cylindrical coordinate system, and compared with the proposed method. The results show that, under the same conditions, the reconstruction error rate of the CS3C-NAH method is reduced by nearly 50%, and the effect is significant.
Psychological stress detection based on heart rate variability

Aiming at the problems of complex operation and low accuracy in psychological stress detection, we proposed a new method based on heart rate variability. We set the length gradient exploration for the length of the window and the window repeat part. Get the best window settings, and we extracted the time domain, frequency domain and nonlinear features of heart rate variability for model construction. We used the TOPT Automation Machine learning algorithm to obtain an optimal algorithm - extremely randomized tree algorithm, and the AUC on the test set was 0.934, which can be highly credible to detect psychological stress.
Developing Computational Thinking through Mathematics: An Evaluative Scientific Mapping

Computational thinking (CT) has been accepted and embraced by educators and researchers alike, but many questions remain surrounding what concepts and topics have been used in CT, what tools have been used to help teach CT, and the current range of research on CT. In this paper, we address those questions and the state of professional development (PD) used to train teachers and preservice teachers in CT. Using a scientometrics analysis to map data from the scientific literature based on different kinds of published research, we found that most publications were published in education-related sources and that CT in relation to mathematics teaching was mostly about teaching computing skills and teaching computer programming using practice and algorithmic thinking in engineering and in STEM, mostly at the higher education level. Additionally, our results revealed that Scratch was the dominant tool used to teach programming skills at all school levels and in teacher education. Research on PD illustrated a main focus centred on improving computational thinking via programming skills in rural and urban areas of teaching. Lastly, we conclude that high-impact research outputs support the notion of computational thinking as a problem-solving process.
Optimization of Physical Refining Process of Camellia Oil for Reduction of 3-Monochloropropane-1,2-Diol (3-MCPD) Ester Formation Using Response Surface Methodology on a Laboratory Scale

Refined and deodorized camellia oil has been reported to contain a high amount of 3-monochloropropane-1,2-diol esters (3-MCPDE) due to the high-temperature deodorization step. To reduce 3-MCPDE in camellia oil, the physical refining process of camellia oil was simulated on a laboratory scale. Response surface methodology (RSM) was designed to modify and optimize the refining process with five processing parameters (water degumming dosage, degumming temperature, activated clay dosage, deodorization temperature and deodorization time). The optimized new refining approach achieved a 76.9% reduction in 3-MCPDE contents, in which the degumming moisture was 2.97%, the degumming temperature was 50.5 °C, the activated clay dosage was 2.69%, the deodorizing temperature was 230 °C, and the deodorizing time was 90 min. A significance test and analysis of variance results demonstrated that the deodorization temperature and deodorization time contributed significantly to the reduction of 3-MCPD ester. The joint interaction effects of activated clay dosage and deodorization temperature were significant for 3-MCPD ester formation.
Adaptive time-delay control with a supervising switching technique for robot manipulators

This paper presents adaptive time-delay control (TDC) with a supervising switching technique (SST) for controlling robot manipulators. Two adaptive techniques are used to enhance the TDC. The control gain of TDC is adaptively tuned using a class of Nussbaum functions. With Nussbaum functions, compared with conventional TDC using a constant gain, the proposed control using Nussbaum functions can deal with inertia parameter variations due to the movement of a robot manipulator. The SST is used to deal with discontinuous disturbances. The proposed control is model-free, highly accurate, robust and adaptive.
Retinal Image Normalization Based on Background Estimation and Homomorphic Filtering

Aiming at the retinal image normalization problem,this paper proposes an observation model based on background estimation and homomorphic filtering.By subsampling and interpolation techniques,the background estimation of the retinal images is achieved.On this basis,the details of the image are enhanced with singular value processing and homomorphic filtering techniques.Thus non-uniform illumination and contrast are normalization enhanced.Experimental results show that this method has better normalization performance than other methods.
Edge-Aware Image Color Appearance and Difference Modeling

The perception of color is one of the most important aspects of human vision. From an evolutionary perspective, the accurate perception of color is crucial to distinguishing friend from foe, and food from fatal poison. As a result, humans have developed a keen sense of color and are able to detect subtle differences in appearance, while also robustly identifying colors across illumination and viewing conditions. In this paper, we shall briefly review methods for adapting traditional color appearance and difference models to complex image stimuli, and propose mechanisms to improve their performance. In particular, we find that applying contrast sensitivity functions and local adaptation rules in an edge-aware manner improves image difference predictions.
Efficient Parallel Output-Sensitive Edit Distance

Given two strings $A[1.n]$ and $B[1.m]$, and a set of operations allowed to edit the strings, the edit distance between $A$ and $B$ is the minimum number of operations required to transform $A$ into $B$. Sequentially, a standard Dynamic Programming (DP) algorithm solves edit distance with $\Theta(nm)$ cost. In many real-world applications, the strings to be compared are similar and have small edit distances. To achieve highly practical implementations, we focus on output-sensitive parallel edit-distance algorithms, i.e., to achieve asymptotically better cost bounds than the standard $\Theta(nm)$ algorithm when the edit distance is small. We study four algorithms in the paper, including three algorithms based on Breadth-First Search (BFS) and one algorithm based on Divide-and-Conquer (DaC). Our BFS-based solution is based on the Landau-Vishkin algorithm. We implement three different data structures for the longest common prefix (LCP) queries needed in the algorithm: the classic solution using parallel suffix array, and two hash-based solutions proposed in this paper. Our DaC-based solution is inspired by the output-insensitive solution proposed by Apostolico et al., and we propose a non-trivial adaption to make it output-sensitive. All our algorithms have good theoretical guarantees, and they achieve different tradeoffs between work (total number of operations), span (longest dependence chain in the computation), and space. We test and compare our algorithms on both synthetic data and real-world data. Our BFS-based algorithms outperform the existing parallel edit-distance implementation in ParlayLib in all test cases. By comparing our algorithms, we also provide a better understanding of the choice of algorithms for different input patterns. We believe that our paper is the first systematic study in the theory and practice of parallel edit distance.
Nedum: ‘much less’ or ‘much more’?

Abstract Nedum (‘let alone’) is a connective scalar particle which indicates that the second act is entailed by the first one. Usually the first act contains a semantic, lexical or pragmatic negation; it can however also be used after an affirmative first act. In examining in which contexts nedum can be paraphrased ‘much more’ or ‘much less’, I distinguish four uses of nedum, depending on whether the first act has either a semantic or a lexical/pragmatic/no negation and on whether nedum introduces a phrase or a clause. Typically the first act is the more informative, the second or nedum-act the most relevant in the context. The speaker can make his point in the second act, which is all the more convincing as it is entailed by the first act. Nedum is composed of negating ne and the focus or scalar particle dum. Many scholars connect this with the illocutionary use of ne; but there are several problems with this approach. An alternative explanation of the origin of ne in nedum could be a negation, nedum originally meaning ‘surely not’ or ‘not yet’.
Algorithmic Information Forecastability

The outcome of all time series cannot be forecast, e.g. the flipping of a fair coin. Others, like the repeated {01} sequence {010101. } can be forecast exactly. Algorithmic information theory can provide a measure of forecastability that lies between these extremes. The degree of forecastability is a function of only the data. For prediction (or classification) of labeled data, we propose three categories for forecastability: oracle forecastability for predictions that are always exact, precise forecastability for errors up to a bound, and probabilistic forecastability for any other predictions. Examples are given in each case.
Cryptanalysis of quantum permutation pad

Cryptanalysis increases the level of confidence in cryptographic algorithms. We analyze the security of a symmetric cryptographic algorithm - quantum permutation pad (QPP) [8]. We found the instances of ciphertext the same as plaintext even after the action of QPP with the probability 1/N when the entire set of permutation matrices of dimension N is used and with the probability 1/N^m when an incomplete set of m permutation matrices of dimension N are used. We visually show such instances in a cipher image created by QPP of 256 permutation matrices of different dimensions. For any practical usage of QPP, we recommend a set of 256 permutation matrices of a dimension more or equal to 2048.
Self-Correcting Bayesian Optimization through Bayesian Active Learning

Gaussian processes are the model of choice in Bayesian optimization and active learning. Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full potential, and little effort is devoted to finding good hyperparameters in the literature. We demonstrate the impact of selecting good hyperparameters for GPs and present two acquisition functions that explicitly prioritize hyperparameter learning. Statistical distance-based Active Learning (SAL) considers the average disagreement between samples from the posterior, as measured by a statistical distance. SAL outperforms the state-of-the-art in Bayesian active learning on several test functions. We then introduce Self-Correcting Bayesian Optimization (SCoreBO), which extends SAL to perform Bayesian optimization and active learning simultaneously. SCoreBO learns the model hyperparameters at improved rates compared to vanilla BO, while outperforming the latest Bayesian optimization methods on traditional benchmarks. Moreover, we demonstrate the importance of self-correction on atypical Bayesian optimization tasks.
A Comparative Neural Radiance Field (NeRF) 3D Analysis of Camera Poses from HoloLens Trajectories and Structure from Motion

Abstract. Neural Radiance Fields (NeRFs) are trained using a set of camera poses and associated images as input to estimate density and color values for each position. The position-dependent density learning is of particular interest for photogrammetry, enabling 3D reconstruction by querying and filtering the NeRF coordinate system based on the object density. While traditional methods like Structure from Motion are commonly used for camera pose calculation in pre-processing for NeRFs, the HoloLens offers an interesting interface for extracting the required input data directly. We present a workflow for high-resolution 3D reconstructions almost directly from HoloLens data using NeRFs. Thereby, different investigations are considered: Internal camera poses from the HoloLens trajectory via a server application, and external camera poses from Structure from Motion, both with an enhanced variant applied through pose refinement. Results show that the internal camera poses lead to NeRF convergence with a PSNR of 25 dB with a simple rotation around the x-axis and enable a 3D reconstruction. Pose refinement enables comparable quality compared to external camera poses, resulting in improved training process with a PSNR of 27 dB and a better 3D reconstruction. Overall, NeRF reconstructions outperform the conventional photogrammetric dense reconstruction using Multi-View Stereo in terms of completeness and level of detail.
Scaling Transformer to 1M tokens and beyond with RMT

A major limitation for the broader scope of problems solvable by transformers is the quadratic scaling of computational complexity with input size. In this study, we investigate the recurrent memory augmentation of pre-trained transformer models to extend input context length while linearly scaling compute. Our approach demonstrates the capability to store information in memory for sequences of up to an unprecedented two million tokens while maintaining high retrieval accuracy. Experiments with language modeling tasks show perplexity improvement as the number of processed input segments increases. These results underscore the effectiveness of our method, which has significant potential to enhance long-term dependency handling in natural language understanding and generation tasks, as well as enable large-scale context processing for memory-intensive applications.
Constructing Temporal Networks of OSS Programming Language Ecosystems

One of the primary factors that encourage developers to contribute to open source software (OSS) projects is the collaborative nature of OSS development. However, the collaborative structure of these communities largely remains unclear, partly due to the enormous scale of data to be gathered, processed, and analyzed.In this work, we utilize the World Of Code dataset, which contains commit activity data for millions of OSS projects, to build collaboration networks for ten popular programming language ecosystems, containing in total over 290M commits across over 18M projects. We build a collaboration graph representation for each language ecosystem, having authors and projects as nodes, which enables various forms of social network analysis on the scale of language ecosystems. Moreover, we capture the information on the ecosystems’ evolution by slicing each network into 30 historical snapshots. Additionally, we calculate multiple collaboration metrics that characterise the ecosystems’ states.We make the resulting dataset publicly available1, including the constructed graphs and the pipeline enabling the analysis of more ecosystems.
Physics-informed Neural Network Combined with Characteristic-Based Split for Solving Navier-Stokes Equations

In this paper, physics-informed neural network (PINN) based on characteristic-based split (CBS) is proposed, which can be used to solve the time-dependent Navier-Stokes equations (N-S equations). In this method, The output parameters and corresponding losses are separated, so the weights between output parameters are not considered. Not all partial derivatives participate in gradient backpropagation, and the remaining terms will be reused.Therefore, compared with traditional PINN, this method is a rapid version. Here, labeled data, physical constraints and network outputs are regarded as priori information, and the residuals of the N-S equations are regarded as posteriori information. So this method can deal with both data-driven and data-free problems. As a result, it can solve the special form of compressible N-S equations -- -Shallow-Water equations, and incompressible N-S equations. As boundary conditions are known, this method only needs the flow field information at a certain time to restore the past and future flow field information. We solve the progress of a solitary wave onto a shelving beach and the dispersion of the hot water in the flow, which show this method's potential in the marine engineering. We also use incompressible equations with exact solutions to prove this method's correctness and universality. We find that PINN needs more strict boundary conditions to solve the N-S equation, because it has no computational boundary compared with the finite element method.
Model theory in compactly generated (tensor-)triangulated categories

We give an account of model theory in the context of compactly generated triangulated and tensor-triangulated categories ${\cal T}$. We describe pp formulas, pp-types and free realisations in such categories and we prove elimination of quantifiers and elimination of imaginaries. We compare the ways in which definable subcategories of ${\cal T}$ may be specified. Then we link definable subcategories of ${\cal T}$ and finite-type torsion theories on the category of modules over the compact objects of ${\cal T}$. We briefly consider spectra and dualities. If ${\cal T}$ is tensor-triangulated then new features appear, in particular there is an internal duality in rigidly-compactly generated tensor-triangulated categories.
Gravitationally modulated quantum correlations: Discriminating classical and quantum models of ultra-compact objects with Bell nonlocality

We investigate the relation between quantum nonlocality and gravity at the astrophysical scale, both in the classical and quantum regimes. Considering particle pairs orbiting in the strong gravitational field of ultra-compact objects, we find that the violation of Bell inequality acquires an angular modulation factor that strongly depends on the nature of the gravitational source. We show how such gravitationally-induced modulation of quantum nonlocality readily discriminates between black holes (both classical and inclusive of quantum corrections) and string fuzzballs, i.e., the true quantum description of ultra-compact objects according to string theory. These findings promote Bell nonlocality as a potentially key tool in comparing different models of classical and quantum gravity and putting them to the test.