Miracle-dz/UniFAR-FTUs huggingface dataset:
Fear Learning in Genital Pain: Toward a Biopsychosocial, Ecologically Valid Research and Treatment Model

ABSTRACT Although fear learning mechanisms are implicated in the development, maintenance, exacerbation, and reduction of genital pain, systematic research on how fear of genital pain emerges, spreads, persists, and reemerges after treatment is lacking. This paper provides an overview of the literature on pain-related fear, integrates the ideas on learning and sexual arousal responding, and specifies the pathways through which compromised learning may contribute to the development and persistence of genital pain. In order to refine theories of genital pain and optimize treatments, we need to adopt a biopsychosocial framework to pain-related fear learning and uncover potential moderators that shape individual trajectories. This involves examining the role of physiological processes, subjective experiences, as well as partner and relational cues in fear acquisition, excessive generalization and impaired safety learning, extinction of fear, counterconditioning, and return of fear. Recent methodological advances in fear conditioning and sex research are promising to enable more symptom-specific and ecologically valid experimental paradigms.
A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging

Background: The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to quantity abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in children and adolescents. Methods: 474 abdominal Dixon MRI scans of 136 young healthy volunteers (aged 8–18) were included in this study. For each scan, an axial fat-only Dixon image located at the L2–L3 disc space and another image at the L4–L5 disc space were selected for quantification. For each image, an outer and an inner region around the abdomen wall, as well as SAT and VAT pixel masks, were generated by expert readers as reference standards. A standard U-Net CNN architecture was then used to train two models: one for region segmentation and one for fat pixel classification. The performance was evaluated using the dice similarity coefficient (DSC) with fivefold cross-validation, and by Pearson correlation and the Student’s t-test against the reference standards. Results: For the DSC results, means and standard deviations of the outer region, inner region, SAT, and VAT comparisons were 0.974 ± 0.026, 0.997 ± 0.003, 0.981 ± 0.025, and 0.932 ± 0.047, respectively. Pearson coefficients were 1.000 for both outer and inner regions, and 1.000 and 0.982 for SAT and VAT comparisons, respectively (all p = NS). Conclusion: These results show that our method not only provides excellent agreement with the reference SAT and VAT measurements, but also accurate abdominal wall region segmentation. The proposed combined region- and pixel-based CNN approach provides automated abdominal wall segmentation as well as SAT and VAT quantification with Dixon MRI and enables objective longitudinal assessment of adipose tissues in children during adolescence.
Reflecting boundary conditions in numerical relativity as a model for black hole echoes

Recently, there has been much interest in black hole echoes, based on the idea that there may be some mechanism (e.g. from quantum gravity) that waves/fields falling into a black hole could partially reflect off of an interface before reaching the horizon. There does not seem to be a good understanding of how to properly model a reflecting surface in numerical relativity, as the vast majority of the literature avoids the implementation of artificial boundaries, or applies transmitting boundary conditions. Here, we present a framework for reflecting a scalar field in a fully dynamical spherically symmetric spacetime, and implement it numerically. We study the evolution of a wave packet in this situation and its numerical convergence, including when the location of a reflecting boundary is very close to the horizon of a black hole. This opens the door to model exotic near-horizon physics within full numerical relativity.
CP violation induced by neutral meson mixing interference

We propose a new kind of CP violation effect -- the double-mixing CP asymmetry -- in a type of cascade decays that involves at least two mixing neutral mesons in the decay chain. It is induced by the interference between different oscillation paths of the neutral mesons in the decay process. The double-mixing CP asymmetry is of critical importance for phenomenology, providing opportunities for clean determination of CKM phase angles free of uncertainties induced by the strong dynamics. To illustrate this point, we perform a phenomenological analysis on two examples: $B^0_s \to \rho^0 K \to \rho^0 (\pi^-\ell^+{\nu}_\ell)$ and $B^0 \to D^0 {K} \to D^0 (\pi^+\ell^-\bar{\nu}_\ell)$. Our results demonstrate that the double-mixing CP asymmetry can be numerically significant in the absence of strong phases, as shown by the former example. Additionally, the latter example showcases the direct extraction of weak and strong phases from data, without the need for theoretical inputs.
Functional Components of Variation in Handwriting

Abstract Functional data analysis techniques are used to analyze a sample of handwriting in Chinese. The goals are (a) to identify a differential equation that satisfactorily models the data's dynamics, and (b) to use the model to classify handwriting samples taken from differential individuals. After preliminary smoothing and registration steps, a second-order linear differential equation, for which the forcing function is small, is found to provide a good reconstruction of the original script records. The equation is also able to capture a substantial amount of the variation in the scripts across replication. The cross-validated classification process is 100% effective for the samples analyzed.
Life in one dimension: statistics and self-organized criticality

The controversy regarding the occurrence of self-organized criticality in the cellular automaton Game of Life has not yet been resolved, mainly due to its massive computational requirements. We consider a one-dimensional version of Life which shows essentially the same local complexity as its two-dimensional counterpart but allows a more extensive computational implementation. General cluster statistics, geometrical properties and self-organized criticality are investigated. Implications concerning higher-dimensional Life are discussed.
Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks

Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to play key roles in mitigating risks by automatically inferring active IoT devices per household and notifying users of vulnerable ones. Developing a scalable inference method that can perform robustly across thousands ofhome networks is a non-trivial task. Thispaper focuses on the challenges of developing and applying data-driven inference models when labeled data of device behaviors is limited and the distribution of data changes (concept drift) across time and space domains. Our contributions are three-fold: (1) We collect and analyze network traﬃc of 24 types of consumer IoT devices from 12 real homes over six weeks to highlight the challenge of temporal and spatial concept drifts in network behavior of IoT devices; (2) We analyze the performance of two inference strategies, namely “ global inference ” (a model trained on a combined set of all labeled data from training homes) and “ contextualized inference ” (several models each trained on the labeled data from a training home) in the presence of concept drifts; and (3) To manage concept drifts, we develop a method that dynamically applies the “closest” model (from a set) to network traﬃc of unseen homes during the testing phase, yielding better performance in 20% of scenarios.
The Stoner–Wohlfarth model of ferromagnetism

The Stoner–Wohlfarth (SW) model is the simplest model that describes adequately the physics of fine magnetic grains, the magnetization of which can be used in digital magnetic storage (floppies, hard disks and tapes). Magnetic storage density is presently increasing steadily in almost the same way as electronic device size and circuitry are shrinking, and magnetism in general appears as a new contender for many novel computing applications that were considered traditionally beyond its range. Denser storage leads to finer magnetic grains and smaller size leads to magnetic grains so fine that they contain a single magnetic domain, i.e. a region in the material with a well-defined uniform magnetization best described with the mathematics of the SW model.
All-Optical Magnetothermoelastic Skyrmion Motion

It is predicted magnetic skyrmions can be controllably moved on surfaces using a focused laser beam. Here an absorbed power of the order 1 mW, focused to a spot-size of the order 10 $\mu$m, results in a local temperature increase of around 50 K, and a local perpendicular strain of the order 10$^{-3}$ due to the thermo-elastic effect. For positive magneto-elastic coupling this generates a strong attractive force on skyrmions due to the magneto-elastic effect. The resultant motion is dependent on forces due to i) gradients in the local strain-induced magnetic anisotropy, ii) gradients in the effective anisotropy due to local temperature gradients, and magnetic parameters temperature dependences, and iii) Magnus effect acting on objects with non-zero topological number. Using dynamical magneto-thermo-elastic modelling, it is predicted skyrmions can be moved with significant velocities (up to 80 m/s shown), both for ferromagnetic and antiferromagnetic skyrmions, even in the presence of surface roughness. This mechanism of controllably moving single skyrmions in any direction, as well as addressing multiple skyrmions in a lattice, offers a new approach to constructing and studying skyrmionic devices with all-optical control.
UNCERTAINTIES IN PERIOD DETERMINATIONS

An algorithm for the estimation of the uncertainty in the period of a variable star is presented. The algorithm may be used for periods determined by the Lafler-Kinman (1965) technique, Deeming's (1975) discrete Fourier transform method, or improvements on either of these methods. The algorithm is that developed by Kwee and van Woerden (1956) for the estimation of the uncertainty in the time of minimum of an eclipsing binary determined by Hertzsprung's (1928) method.
Bag of States: A Non-sequential Approach to Video-based Engagement Measurement

Automatic measurement of student engagement provides helpful information for instructors to meet learning program objectives and individualize program delivery. Students' behavioral and emotional states need to be analyzed at fine-grained time scales in order to measure their level of engagement. Many existing approaches have developed sequential and spatiotemporal models, such as recurrent neural networks, temporal convolutional networks, and three-dimensional convolutional neural networks, for measuring student engagement from videos. These models are trained to incorporate the order of behavioral and emotional states of students into video analysis and output their level of engagement. In this paper, backed by educational psychology, we question the necessity of modeling the order of behavioral and emotional states of students in measuring their engagement. We develop bag-of-words-based models in which only the occurrence of behavioral and emotional states of students is modeled and analyzed and not the order in which they occur. Behavioral and affective features are extracted from videos and analyzed by the proposed models to determine the level of engagement in an ordinal-output classification setting. Compared to the existing sequential and spatiotemporal approaches for engagement measurement, the proposed non-sequential approach improves the state-of-the-art results. According to experimental results, our method significantly improved engagement level classification accuracy on the IIITB Online SE dataset by 26% compared to sequential models and achieved engagement level classification accuracy as high as 66.58% on the DAiSEE student engagement dataset.
Evaluating the Spectral Bias of Coordinate Based MLPs

In recent years, representations given by fully connected neural networks have shown to represent scenes, objects, and other measurements well in dense low-dimensional settings. For these models, termed coordinate based MLPs, sinusoidal encodings are necessary in allowing for convergence to the high frequency components of the target function. This requirement is a result of their severe spectral bias when using dense, low dimensional coordinate based inputs. Previous work explained this phenomena using Neural Tangent Kernel (NTK) and Fourier analysis. While these methods provide insight towards this large spectral bias and the bene-ﬁts of positional encoding, the properties of ReLU networks that induce this behavior are not fully determined. Analyzing spectral bias directly through the computations of ReLU networks would expose their limitations in dense settings, while providing a clearer explanation as to how this behavior emerges during the learning process. In this paper, we systematically analyze the spectral bias of a coordinate based MLP through its activation regions and gradient descent dynamics. This allows us to relate the network’s expressive capacity to the speed at which gradient descent converges for components of varying frequency, and how the density of the data further restricts the model.
Search for Extremely Metal-poor Stars with Gemini-N/Graces. I. Chemical-abundance Analysis

We present stellar parameters and abundances of 13 elements for 18 very metal-poor (VMP; [Fe/H] < –2.0) stars, selected as extremely metal-poor (EMP; [Fe/H] < –3.0) candidates from the Sloan Digital Sky Survey and Large sky Area Multi-Object Fiber Spectroscopic Telescope survey. High-resolution spectroscopic observations were performed using GEMINI-N/GRACES. We find 10 EMP stars among our candidates, and we newly identify three carbon-enhanced metal-poor stars with [Ba/Fe] < 0. Although chemical abundances of our VMP/EMP stars generally follow the overall trend of other Galactic halo stars, there are a few exceptions. One Na-rich star ([Na/Fe] = +1.14) with low [Mg/Fe] suggests a possible chemical connection with second-generation stars in a globular cluster. The progenitor of an extremely Na-poor star ([Na/Fe] = –1.02) with high K- and Ni-abundance ratios may have undergone a distinct nucleosynthesis episode, associated with core-collapse supernovae (SNe) having a high explosion energy. We have also found a Mg-rich star ([Mg/Fe] = +0.73) with slightly enhanced Na and extremely low [Ba/Fe], indicating that its origin is not associated with neutron-capture events. On the other hand, the origin of the lowest Mg abundance ([Mg/Fe] = –0.61) star could be explained by accretion from a dwarf galaxy, or formation in a gas cloud largely polluted by SNe Ia. We have also explored the progenitor masses of our EMP stars by comparing their chemical-abundance patterns with those predicted by Population III SNe models, and find a mass range of 10–26 M ⊙, suggesting that such stars were primarily responsible for the chemical enrichment of the early Milky Way.
Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes

OBJECTIVE Deep learning has been shown to be useful in detecting breast cancer metastases by analyzing whole slide images (WSI) of sentinel lymph nodes; however, it requires extensive analysis of all the lymph node slides. Our deep learning study attempts to provide a rapid screen for metastasis by analyzing only a small set of image patches to detect changes in tumor environment. METHODS We designed a convolutional neural network to build a diagnostic model for metastasis detection. We obtained WSIs of Hematoxylin and Eosin-stained slides from 34 cases with equal distribution in positive/negative categories. Two WSIs were selected from each case for a total of 69 WSIs. From each WSI, 40 image patches (100x100 pixels) were obtained to yield 2720 image patches, from which 2160 (79%) were used for training, 240 (9%) for validation, and 320 (12%) for testing. Interobserver variation was also examined among 3 users. RESULTS The test results showed excellent diagnostic results: accuracy (91.15%), sensitivity (77.92%), and specificity (92.09%). No significant variation in results was observed among the 3 observers. CONCLUSION This preliminary study provided a proof of concept for conducting a rapid screen for metastasis rather than an exhaustive search for tumors in all fields of all sentinel lymph nodes.
Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection

Accurate bot detection is necessary for the safety and integrity of online platforms. It is also crucial for research on the influence of bots in elections, the spread of misinformation, and financial market manipulation. Platforms deploy infrastructure to flag or remove automated accounts, but their tools and data are not publicly available. Thus, the public must rely on third-party bot detection. These tools employ machine learning and often achieve near-perfect performance for classification on existing datasets, suggesting bot detection is accurate, reliable and fit for use in downstream applications. We provide evidence that this is not the case and show that high performance is attributable to limitations in dataset collection and labeling rather than sophistication of the tools. Specifically, we show that simple decision rules — shallow decision trees trained on a small number of features — achieve near-state-of-the-art performance on most available datasets and that bot detection datasets, even when combined together, do not generalize well to out-of-sample datasets. Our findings reveal that predictions are highly dependent on each dataset’s collection and labeling procedures rather than fundamental differences between bots and humans. These results have important implications for both transparency in sampling and labeling procedures and potential biases in research using existing bot detection tools for pre-processing.
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making

In AI-assisted decision-making, it is critical for human decision-makers to know when to trust AI and when to trust themselves. However, prior studies calibrated human trust only based on AI confidence indicating AI’s correctness likelihood (CL) but ignored humans’ CL, hindering optimal team decision-making. To mitigate this gap, we proposed to promote humans’ appropriate trust based on the CL of both sides at a task-instance level. We first modeled humans’ CL by approximating their decision-making models and computing their potential performance in similar instances. We demonstrated the feasibility and effectiveness of our model via two preliminary studies. Then, we proposed three CL exploitation strategies to calibrate users’ trust explicitly/implicitly in the AI-assisted decision-making process. Results from a between-subjects experiment (N=293) showed that our CL exploitation strategies promoted more appropriate human trust in AI, compared with only using AI confidence. We further provided practical implications for more human-compatible AI-assisted decision-making.
Entropic Model Predictive Optimal Transport over Dynamical Systems

We consider the optimal control problem of steering an agent population to a desired distribution over an infinite horizon. This is an optimal transport problem over dynamical systems, which is challenging due to its high computational cost. In this paper, by using entropy regularization, we propose Sinkhorn MPC, which is a dynamical transport algorithm integrating model predictive control (MPC) and the so-called Sinkhorn algorithm. The notable feature of the proposed method is that it achieves cost-effective transport in real time by performing control and transport planning simultaneously, which is illustrated in numerical examples. Moreover, under some assumption on iterations of the Sinkhorn algorithm integrated in MPC, we reveal the global convergence property for Sinkhorn MPC thanks to the entropy regularization. Furthermore, focusing on a quadratic control cost, without the aforementioned assumption we show the ultimate boundedness and the local asymptotic stability for Sinkhorn MPC.
Miniature Magnetic Nano islands in a Morphotropic Cobaltite Matrix

High-density magnetic memories are key components in spintronics, quantum computing, and energy-efficient electronics. Reduced dimensionality and magnetic domain stability at the nanoscale are essential for the miniaturization of magnetic storage units. Yet, inducing magnetic order, and selectively tuning spin–orbital coupling at specific locations have remained challenging. Here we demonstrate the construction of switchable magnetic nanoislands in a nonmagnetic matrix based on cobaltite homostructures. The magnetic and electronic states are laterally modified by epitaxial strain, which is regionally controlled by freestanding membranes. Atomically sharp grain boundaries isolate the crosstalk between magnetically distinct regions. The minimal size of magnetic nanoislands reaches ~35 nm in diameter, enabling an areal density of ~400 Gbit/in 2 . Besides providing an ideal platform for precisely controlled read/write schemes, this methodology can enable scalable and patterned memories on silicon and flexible substrates for various applications.
Probing Velocity Structures of Protostellar Envelopes: Infalling and Rotating Envelopes within Turbulent Dense Cores

We have observed the three low-mass protostars, IRAS 15398−3359, L1527 IRS, and TMC-1A, with the ALMA 12 m array, the ACA 7 m array, and the IRAM-30 m and APEX telescopes in the C18O J = 2–1 emission. Overall, the C18O emission shows clear velocity gradients at radii of ∼100–1000 au, which likely originate from the rotation of envelopes, while velocity gradients are less clear and velocity structures are more perturbed on scales of ∼1000–10,000 au. IRAS 15398−3359 and L1527 IRS show a break at radii of ∼1200 and ∼1700 au in the radial profile of the peak velocity, respectively. The peak velocity is proportional to r −1.38 or r −1.7 within the break radius, which can be interpreted as indicating the rotational motion of the envelope with a degree of contamination by gas motions on larger spatial scales. The peak velocity follows v peak ∝ r 0.68 or v peak ∝ r 0.46 outside the break radius, which is similar to the J/M–R relation of dense cores. TMC-1A exhibits a radial profile of the peak velocity that is not consistent with the rotational motion of the envelope nor the J/M–R relation. The origin of the relation of v peak ∝ r 0.46–r 0.68 is investigated by examining correlations of the velocity deviation (δ v) and the spatial scale (τ) in the two sources. The obtained spatial correlations, δ v ∝ τ ∼0.6, are consistent with the scaling law predicted by turbulence models, which may suggest that large-scale velocity structures originate from turbulence.