Miracle-dz/UniFAR-FTUs huggingface dataset:
When are children satisfied with life? An initiative qualitative study from vietnamese children sample

In the context of research on children's life satisfaction, considering children as active participants are very scarce, the main purpose of the present study was to examine children's perspectives on their life satisfaction. The study used a convenient sample of 228 students from primary and secondary schools, living in urban and suburban areas in Vietnam. The subjects’ average age is 9.51 (SD = 1.56). Data were collected using a single open-ended question. The data analysis was performed by thematic analysis then processed with SPSS 22.0 for quantitative assessment. The results of data analysis indicate the following criteria to consider a child as being satisfied with life: (1) Quality of social relationship; (2) Quality of family relationship; (3) Being engaged in interesting and fun leisure and activities; (4) Achieving desired goals; (5) Living environment; and (6) Some other external factors. Among these qualities, the most important relationships in their lives (including social and family relations) were chosen with the highest frequency, followed by their engagement in meaningful and fun activities, and pursuit of their desired goals. This study provided a better insight into understanding how children perceive life satisfaction and what meanings they attach to it. The research results might be explained from a socio-cultural perspective and provide a scientific basis for large-scale studies on this topic in Vietnam.
Preserving data privacy when using multi‐site data to estimate individualized treatment rules

Precision medicine is a rapidly expanding area of health research wherein patient level information is used to inform treatment decisions. A statistical framework helps to formalize the individualization of treatment decisions that characterize personalized management plans. Numerous methods have been proposed to estimate individualized treatment rules that optimize expected patient outcomes, many of which have desirable properties such as robustness to model misspecification. However, while individual data are essential in this context, there may be concerns about data confidentiality, particularly in multi‐center studies where data are shared externally. To address this issue, we compared two approaches to privacy preservation: (i) data pooling, which is a covariate microaggregation technique and (ii) distributed regression. These approaches were combined with the doubly robust yet user‐friendly method of dynamic weighted ordinary least squares to estimate individualized treatment rules. In simulations, we extensively evaluated the performance of the methods in estimating the parameters of the decision rule under different assumptions. The results demonstrate that double robustness is not maintained in data pooling setting and that this can result in bias, whereas the distributed regression provides good performance. We illustrate the methods via an analysis of optimal Warfarin dosing using data from the International Warfarin Consortium.
Quantifying gender biases towards politicians on Reddit

Despite attempts to increase gender parity in politics, global efforts have struggled to ensure equal female representation. This is likely tied to implicit gender biases against women in authority. In this work, we present a comprehensive study of gender biases that appear in online political discussion. To this end, we collect 10 million comments on Reddit in conversations about male and female politicians, which enables an exhaustive study of automatic gender bias detection. We address not only misogynistic language, but also other manifestations of bias, like benevolent sexism in the form of seemingly positive sentiment and dominance attributed to female politicians, or differences in descriptor attribution. Finally, we conduct a multi-faceted study of gender bias towards politicians investigating both linguistic and extra-linguistic cues. We assess 5 different types of gender bias, evaluating coverage, combinatorial, nominal, sentimental and lexical biases extant in social media language and discourse. Overall, we find that, contrary to previous research, coverage and sentiment biases suggest equal public interest in female politicians. Rather than overt hostile or benevolent sexism, the results of the nominal and lexical analyses suggest this interest is not as professional or respectful as that expressed about male politicians. Female politicians are often named by their first names and are described in relation to their body, clothing, or family; this is a treatment that is not similarly extended to men. On the now banned far-right subreddits, this disparity is greatest, though differences in gender biases still appear in the right and left-leaning subreddits. We release the curated dataset to the public for future studies.
Theoretical analysis of thermal/electric-filed poling silica glass

Based on the experimental results, three basic conclusions are summarized for the thermal/electric-field poling (TEFP) silica materials, with which the second-order nonlinear optical coefficient in the bulk silica glass after TEFP is calculated, and relationships between the coefficient and the applied voltage V0 in the poling process and the thickness of the nonlinear layer created from the poling are obtained. Theoretical results show that the second-order susceptibility χ(2) and the second-harmonic efficiency η in the poled bulk glass are proportional to √V0 and V02, respectively; χ(2)∼0.2−1.6 pm/V for the bulk glass after the typical TEFP; χ(2) decreases with an increase in the poling time after the TEFP silica glass approximately reaches the steady state. Theoretical results are well consistent with the experimental reports.
Cost Aggregation Is All You Need for Few-Shot Segmentation

We introduce a novel cost aggregation network, dubbed Volumetric Aggregation with Transformers (VAT), to tackle the few-shot segmentation task by using both convolutions and transformers to efficiently handle high dimensional correlation maps between query and support. In specific, we propose our encoder consisting of volume embedding module to not only transform the correlation maps into more tractable size but also inject some convolutional inductive bias and volumetric transformer module for the cost aggregation. Our encoder has a pyramidal structure to let the coarser level aggregation to guide the finer level and enforce to learn complementary matching scores. We then feed the output into our affinity-aware decoder along with the projected feature maps for guiding the segmentation process. Combining these components, we conduct experiments to demonstrate the effectiveness of the proposed method, and our method sets a new state-of-the-art for all the standard benchmarks in few-shot segmentation task. Furthermore, we find that the proposed method attains state-of-the-art performance even for the standard benchmarks in semantic correspondence task although not specifically designed for this task. We also provide an extensive ablation study to validate our architectural choices. The trained weights and codes are available at: https://seokju-cho.github. io/VAT/.
Arithmetic dynamics of random polynomials

We investigate from a statistical perspective the arithmetic properties of the dynamics of polynomials of fixed degree and defined over the field of rational numbers. To start with, ordering their affine conjugacy classes by height, we show that their average number of rational preperiodic points is equal to zero, thereby proving a strong average version of the uniform boundedness conjecture of Morton and Silverman. Next, inspired by the analogy with the successive minima of a lattice we define the dynamical successive minima of a polynomial. Noting that these quantities are invariant under the action by conjugacy of the affine group we study their average behaviour using the aforementioned ordering by height. In particular, we prove an optimal statistical version of the dynamical Lang conjecture on the canonical height of rational non-preperiodic points.
Triton Haze Analogs: The Role of Carbon Monoxide in Haze Formation

Triton is the largest moon of the Neptune system and possesses a thin nitrogen atmosphere with trace amounts of carbon monoxide and methane, making it of similar composition to that of the dwarf planet Pluto. Like Pluto and Saturn's moon Titan, Triton has a haze layer thought to be composed of organics formed through photochemistry. Here, we perform atmospheric chamber experiments of 0.5% CO and 0.2% CH4 in N2 at 90 K and 1 mbar to generate Triton haze analogs. We then characterize the physical and chemical properties of these particles. We measure their production rate, their bulk composition with combustion analysis, their molecular composition with very high resolution mass spectrometry, and their transmission and reflectance from the optical to the near‐infrared with Fourier Transform Infrared (FTIR) Spectroscopy. We compare these properties to existing measurements of Triton's tenuous atmosphere and surface, as well as contextualize these results in view of all the small, hazy, nitrogen‐rich worlds of our solar system. We find that carbon monoxide present at greater mixing ratios than methane in the atmosphere can lead to significantly oxygen‐ and nitrogen‐rich haze materials. These Triton haze analogs have clear observable signatures in their near‐infrared spectra, which may help us differentiate the mechanisms behind haze formation processes across diverse solar system bodies.
Dynamic structure factor of the magnetized one-component plasma: Crossover from weak to strong coupling

Plasmas in strong magnetic fields have been mainly studied in two distinct limiting cases–that of weak and strong nonideality with very different physical properties. While the former is well described by the familiar theory of Braginskii, the latter regime is closer to the behavior of a Coulomb liquid. Here we study in detail the transition between both regimes. We focus on the evolution of the dynamic structure factor of the magnetized one-component plasma from weak to strong coupling, which is studied with first-principle molecular dynamics simulations. The simulations show the vanishing of Bernstein modes and the emergence of higher harmonics of the upper hybrid mode across the magnetic field, a redistribution of spectral power between the two main collective modes under oblique angles, and a suppression of plasmon damping along the magnetic field. Comparison with results from various models, including the random phase approximation, a Mermin-type dielectric function, and the Quasi-Localized Charge Approximation show that none of the theories is capable of reproducing the crossover that occurs when the coupling parameter is on the order of unity. The findings are relevant to the scattering spectra, stopping power, and transport coefficients of correlated magnetized plasmas.
CO(2–1)/CO(1–0) Line Ratio on a ∼100 Parsec Scale in the Nearby Barred Galaxy NGC 1300

CO(2–1) emission is often used as a tracer of giant molecular clouds (GMCs) as an alternative to CO(1–0) emission in recent years. Therefore, understanding the environmental dependence of the line ratio of CO(2–1)/CO(1–0), R 21, on the GMC scale is important to accurately estimate the mass of GMCs. We thus measured R 21 in the strongly barred galaxy NGC 1300, where star formation activity strongly depends on galactic structure, on a ∼100 pc scale. CO images were obtained from the Atacama Large Millimeter/submillimeter Array and the Nobeyama 45 m telescope. The resultant typical R 21 in NGC 1300 is 0.57 ± 0.06. We find environmental variations in R 21: it is the highest in the bar-end region (0.72 ± 0.08), followed by arm (0.60 ± 0.07) and bar regions (0.50 ± 0.06). GMCs with Hα emission show a systematically higher ratio (0.67 ± 0.07) than those without Hα (0.47 ± 0.05). In the bar region, where massive star formation is suppressed, Hα emission is not associated with most GMCs, resulting in the lowest R 21. These results raise a possibility that properties of GMCs derived from CO(2–1) observations with the assumption of a constant R 21 are different from those derived from CO(1–0) observations. Furthermore, we find the R 21 measured on the kiloparsec scale tends to be lower than that of the GMCs, probably due to the presence of an extended diffuse molecular gas in NGC 1300.
MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation

Vision transformers have recently demonstrated great success in various computer vision tasks, motivating a tremendously increased interest in their deployment into many real-world IoT applications. However, powerful ViTs are often too computationally expensive to be fitted onto real-world resource-constrained platforms, due to (1) their quadratically increased complexity with the number of input tokens and (2) their overparameterized self-attention heads and model depth. In parallel, different images are of varied complexity and their different regions can contain various levels of visual information, e.g., a sky background is not as informative as a foreground object in object classification tasks, indicating that treating those regions equally in terms of model complexity is unnecessary while such opportunities for trimming down ViTs' complexity have not been fully exploited. To this end, we propose a Multi-grained Input-Adaptive Vision Transformer framework dubbed MIA-Former that can input-adaptively adjust the structure of ViTs at three coarse-to-fine-grained granularities (i.e., model depth and the number of model heads/tokens). In particular, our MIA-Former adopts a low-cost network trained with a hybrid supervised and reinforcement learning method to skip the unnecessary layers, heads, and tokens in an input adaptive manner, reducing the overall computational cost. Furthermore, an interesting side effect of our MIA-Former is that its resulting ViTs are naturally equipped with improved robustness against adversarial attacks over their static counterparts, because MIA-Former's multi-grained dynamic control improves the model diversity similar to the effect of ensemble and thus increases the difficulty of adversarial attacks against all its sub-models. Extensive experiments and ablation studies validate that the proposed MIA-Former framework can (1) effectively allocate adaptive computation budgets to the difficulty of input images, achieving state-of-the-art (SOTA) accuracy-efficiency trade-offs, e.g., up to 16.5\% computation savings with the same or even a higher accuracy compared with the SOTA dynamic transformer models, and (2) boost ViTs' robustness accuracy under various adversarial attacks over their vanilla counterparts by 2.4\% and 3.0\%, respectively. Our code is available at https://github.com/RICE-EIC/MIA-Former.
An innovative open-loop CDR based on injection-locked oscillator for high-speed data link applications

A new fully integrated clock and data recovery (CDR) architecture based on an injection-locked oscillator is presented in this paper. Two circuits, implemented in CMOS VLSI technology and dedicated to 1 Gbps point-to-point networks, are detailed. This open-loop CDR topology is compared with classical ones (high-Q filter, phase-locking, .) in term of power consumption, silicon area and complexity. Our circuits exhibit some major advantages versus traditional CDRs: no external component needed, low consumption and low locking time. It is a very simple way to achieve clock extraction and data regeneration for high-speed data link applications.
Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information

We develop a learning-based control algorithm for unknown dynamical systems under very severe data limitations. Specifically, the algorithm has access to streaming and noisy data only from a single and ongoing trial. It accomplishes such performance by effectively leveraging various forms of side information on the dynamics to reduce the sample complexity. Such side information typically comes from elementary laws of physics and qualitative properties of the system. More precisely, the algorithm approximately solves an optimal control problem encoding the system's desired behavior. To this end, it constructs and iteratively refines a data-driven differential inclusion that contains the unknown vector field of the dynamics. The differential inclusion, used in an interval Taylor-based method, enables to over-approximate the set of states the system may reach. Theoretically, we establish a bound on the suboptimality of the approximate solution with respect to the optimal control with known dynamics. We show that the longer the trial or the more side information is available, the tighter the bound. Empirically, experiments in a high-fidelity F-16 aircraft simulator and MuJoCo's environments illustrate that, despite the scarcity of data, the algorithm can provide performance comparable to reinforcement learning algorithms trained over millions of environment interactions. Besides, we show that the algorithm outperforms existing techniques combining system identification and model predictive control.
A Framework in Determining Extended Warranty by Using Two Dimensional Delay Time Model

A framework for determining optimal period and cost in extended warranty is proposed in this paper. The optimal period and cost is derived from two-dimensional delay time model. Two-dimensional delay time model is governed by age and mileage. Age and mileage gives effect for the rate of defect arrival λ. The steps for obtaining the model to estimate the optimal period cost of extended warranty is presented. It starts from modeling the defect arrival rate, the impact of preventive maintenance toward the defect arrival rate; determine the delay time distribution, data analyzing and the last optimal cost and period estimation for extended warranty.
Algorithmic encoding of protected characteristics and its implications on performance disparities

It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. A machine learning model may pick up undesirable correlations, for example, between a patient’s racial identity and clinical outcome. Such correlations are often present in (historical) data used for model development. There has been an increase in studies reporting biases in disease detection models. Besides the scarcity of data from underserved populations, very little is known about how these biases are encoded and how one may reduce or even remove disparate performance. There are concerns that an algorithm may recognize patient characteristics such as biological sex or racial identity, and then directly or indirectly use this information when making predictions. But it remains unclear how we can establish whether such information is actually used. This article aims to shed some light on these issues by exploring methodology allowing intuitive inspections of the inner working of machine learning models for image-based detection of disease. We also investigate how to address performance disparities and ﬁnd automatic threshold selection to be an effective yet questionable technique, resulting in models with comparable true and false positive rates across subgroups. Our ﬁndings call for further research to better understand the underlying causes of performance disparities.
A Comparative Study of Stream-Gaging Methods Employed in Nonpoint Source Pollution Studies in Small Streams

The U.S. Geological Survey started measuring stream flow in 1888 as part of a public land irrigation study. The demand for accurate stream flow measurement has increased with the rising concern about nonpoint source (NPS) pollution. NPS pollution studies, such as TMDL development, often involve quantification of flow in small first and second order streams. This application of technology intended for use in larger streams presents special problems that must be addressed by the user. The goal of this study was to conduct a comparative analysis of the current technologies used to measure flow in small streams with respect to accuracy and cost. The analyses involved field investigations, laboratory experiments, and a cost analysis. The specific study objectives were: 1) Compare the accuracy of various methods for estimating stream discharge in small first and second order streams, 2) Compare the accuracy of various methods for estimating stream discharge in a controlled laboratory environment, and 3) Evaluate the costs associated with installation, operation, and maintenance of each of the systems investigated. Ten stream-gaging methods were evaluated for their field performance, laboratory performance, and costs. Analysis of the field investigation data indicated that the Marsh McBirney current meter and the One-orange method were the most accurate among the methods studied. The results of the laboratory experiments imply that the Starflow acoustic Doppler and Valeport BFM001 current meter performed best among the ten methods. The Starflow acoustic Doppler device also proved to be the most cost-effective method. Overall, the Marsh McBirney and Valeport BFM001 current meters exhibited the best performance for both field and laboratory situations among the methods evaluated.
Exotic Elateridae (Coleoptera: Elateroidea) in Italy: An Overview

Abstract The present contribution summarized the available information on the three exotic Elateridae, namely Aeolus melliculus Candèze, 1859, Conoderus posticus (Eschscholtz, 1829) and Heteroderes vagus Candèze, 1893, currently recorded in Italy. New faunistic records and an identification key to the Italian Oophorini (Agrypninae; inclusive of the exotic species) are provided.
Sampled-data control for asynchronously switched linear systems without MDT constraints

In this paper, the sampled-data control problem is studied for asynchronously switched linear systems (SLSs) without minimum dwell time (MDT) constraints. The asynchronous phenomenon exists due to that the information of system mode can be acquired only at the sampling instant. First, a sufficient condition of global asymptotic stability (GAS) is presented for sampled-date switched control systems with a novel class of switching signals, which allows the switching number to be a non-affine function of time, and does not involve any point-wise bound on the switching number. Moreover, unlike the existing literature concerned with sampled-data control problem of switched systems, the MDT constraints are removed. We allow that no sampling happens between two adjacent switching instants, which makes the results more applicable to practice. Then, a sufficient condition checking the existence of sampled-data controllers is presented in terms of linear matrix inequalities (LMIs). Finally, it is shown by a boost converter circuit system that the designed sampled-data controller and switching signals can stabilize the system cooperatively.