Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Band Circularly Polarized RFID Reader Antenna With Combined Dipole and Monopoles

This article presents a new approach to implement shared-aperture dual-band dual-sense circularly polarized (CP) antenna array for dual-band (0.92 and 2.45 GHz) radio frequency identification (RFID) reader applications. In this approach, each antenna element is obtained by combining one asymmetrical half-wavelength dipole and two monopoles. The asymmetrical half-wavelength dipole operates at low frequency (LF) (0.92 GHz) with right-hand CP (RHCP) radiation. For the left-hand CP (LHCP) radiation at high frequency (HF) (2.45 GHz), the asymmetrical quarter-wavelength monopole works with fundamental mode and the higher order mode plays a major role in three-quarter wavelength monopole. An optimized $2\times2$ array is designed to further extend the 3-dB axial ratio (AR) bandwidth and enhance the realized gain by using a simple 1-to-4 sequential-phase feeding network. An artificial magnetic conductor (AMC) with $9\times9$ unit cells is adopted to reduce the profile height of the antenna array. A prototype of the final optimized antenna array is fabricated and measured to validate this new approach. Experimental results show that good CP characteristics are obtained in 0.92-/2.45-GHz frequency bands, which agree with the simulation results well. Due to its excellent dual-band CP performance, this antenna can be well used in some multifrequency applications.
Standardizing the World Income Inequality Database

Cross-national research on the causes and consequences of income inequality has been hindered by the limitations of existing inequality datasets: greater coverage across countries and over time is available from these sources only at the cost of signicantly reduced comparability across observations. This article presents the Standardized World Income Inequality Database (SWIID), which standardizes the United Nations University database (UNU-WIDER 2008) while minimizing reliance on problematic assumptions by using as much information as possible from proximate years within the same country. The resulting series of gross and net income inequality data maximize comparability for the largest possible sample of countries and years and so are better suited to broadly cross-national research than other sources.
AGRONOMY AND SOILS Water Use Efficiency and Irrigation Response of Cotton Cultivars on Subsurface Drip in West Texas

The High Plains Aquifer is the source of nearly all agricultural irrigation water in the Texas High Plains, and its resources are being depleted due to withdrawals that greatly exceed recharge; therefore, expanding the use of deficit irrigation in this region. Some drought-tolerant crops, including cotton (Gossypium hirsutum L.), can adapt well to deficit irrigation, but this adaptation might be cultivar- and environment-specific. The objective of this study was to determine if the response of commercially available cultivars to differential irrigation was influenced by the environment. Seven cotton cultivars (DP0912B2RF, DP0924B2RF, DP0935B2RF, DP1028B2RF, DP1032B2RF, DP1044B2RF, and FM9160B2F) were evaluated in 2010 and 2011 at three irrigation levels, ranging from severe-deficit to full irrigation in West Texas. In-season soil moisture content, yield, and water use efficiency were compared by cultivar and irrigation level. In 2010, which was wet and cool, the earlier-maturing cultivars, particularly DP0912B2RF, performed favorably compared to the later-maturing cultivars at the highest irrigation levels. In 2011, which was hot and dry, later-maturing cultivars, such as DP1044 and DP0935, had the highest yields. There was also a strong interaction between study year, irrigation rate, and cultivar performance. In 2010, later-maturing cultivars such as DP0935 and DP1044 had lower yields at the highest irrigation, but higher yields in the deficit treatments. DP1044, the latest maturing cultivar in the study, was a top yielding cultivar in both seasons, suggesting that a cultivar with later maturity characteristics can be successful in both hot and heat-limited environments on the Texas High Plains.
Graph-Based Semi-Supervised Learning With Tensor Embeddings for Hyperspectral Data Classification

Hyperspectral data classification is one of the fundamental problems in remote sensing. Several algorithms based on supervised machine learning have been proposed to address it. The performance, however, of the proposed algorithms is inherently dependent on the amount and quality of annotated data. Due to recent advances in hyperspectral imaging and autonomous (unmanned) aerial vehicles collecting new hyperspectral data is an easy task. Annotating those data, however, is a tedious, time-consuming and costly task requiring the in-situ presence of human experts. One way to loosen the requirement of a large number of annotated data is the shift to semi-supervised learning combined with highly sample-efficient tensor-based neural networks. This study provides a comprehensive experimental analysis of the performance of a variety of graph-based semi-supervised learning techniques combined with tensor-based neural network embeddings for the problem of hyperspectral data classification. Experimental results suggest that the combination of tensor-based neural network embeddings with graph-based semi-supervised learning can significantly improve the classification results minimizing human annotation effort.
Model of Helix Traveling Wave Tube Including the Slow-Wave Structure Dispersion Characteristics and Space-Charge Effect

An analytical model for the interaction between a linear electron beam and a realistic helix slow-wave structure (SWS) in a traveling wave tube (TWT) is provided. It is a generalization of the Pierce model for the interaction, assuming $a$ one-dimensional electron flow along a dispersive helix SWS, with space-charge effects accounted for. The used phase velocity and characteristic impedance are extracted from full-wave simulations. The calculated gain versus frequency is compared with results from the 1D Lagrangian TWT code, LATTE.
Re-evaluating Retrosynthesis Algorithms with Syntheseus

The planning of how to synthesize molecules, also known as retrosynthesis, has been a growing focus of the machine learning and chemistry communities in recent years. Despite the appearance of steady progress, we argue that imperfect benchmarks and inconsistent comparisons mask systematic shortcomings of existing techniques. To remedy this, we present a benchmarking library called syntheseus which promotes best practice by default, enabling consistent meaningful evaluation of single-step and multi-step retrosynthesis algorithms. We use syntheseus to re-evaluate a number of previous retrosynthesis algorithms, and find that the ranking of state-of-the-art models changes when evaluated carefully. We end with guidance for future works in this area.
Surveys of eleven species of wild and zoo birds and feeding experiments in white-tailed eagles reveal differences in the composition of the avian gut microbiome based on dietary habits between and within species

The composition of the gut microbiome varies due to dietary habits. We investigated influences of diet on the composition of the gut microbiome using the feces of 11 avian species, which consumed grain-, fish- and meat-based diets. We analyzed gut microbiome diversity and composition by next-generation sequencing (NGS) of 16S ribosomal RNA. The grain-diet group had higher gut microbiome diversity than the meat- and fish-diet group. The ratio of Bacteroidetes and Firmicutes phyla was higher in the grain-diet group than in the meat- and fish-diet groups. The grain-diet group had a higher ratio of Veillonellaceae than the meat-diet group and a higher ratio of Eubacteriaceae than the fish-diet habit group. To clarify the influence of diet within the same species, white-tailed eagles (Haliaeetus albicilla, n=6) were divided into two groups, and given only deer meat or fish for approximately one month. The composition of the gut microbiome of individuals in both groups were analyzed by NGS. There were indications of fluctuation in the levels of some bacteria (Lactobacillus, Coriobacteriales, etc.) in each diet group. Moreover, one individual for each group which switched each diet in last week changed to each feature of composition of bacterial flora. The above results show that the composition of the gut microbiome differ depending on diet, even within the same species.
Influence of Below-Cloud Evaporation on Deuterium Excess in Precipitation of Arid Central Asia and Its Meteorological Controls

AbstractThe deuterium excess is a second-order parameter linking water-stable oxygen and hydrogen isotopes and has been widely used in hydrological studies. The deuterium excess in precipitation is greatly influenced by below-cloud evaporation through unsaturated air, especially in an arid climate. Based on an observation network of isotopes in precipitation of arid central Asia, the difference in deuterium excess from cloud base to ground was calculated for each sampling site. The difference on the southern slope of the Tian Shan is generally larger than that on the northern slope, and the difference during the summer months is greater than that during the winter months. Generally, an increase of 1% in evaporation of raindrops causes deuterium excess to decrease by approximately 1‰. Under conditions of low air temperature, high relative humidity, heavy precipitation, and large raindrop diameter, a good linear correlation is exhibited between evaporation proportion and difference in deuterium excess, and .
Problems facing university students during Corona pandemic

University students face many problems and difficulties that hinder their academic and scientific careers. The researchers, in various locations, seek to find solutions to them, especially in light of the global pandemic of Corona, which has exacerbated these problems. Therefore, this study aimed to identify the problems facing university students at Al-Balqa Applied University in Jordan during the Corona pandemic from their viewpoints. Also, this study aimed to identify the relationship between these problems and some variables, such as gender, type of major and academic degree. The researcher used the descriptive approach and developed a questionnaire. The study sample comprised 700 male and female students in the second semester of the academic year (2021/2022). The study concluded that the students’ levels of problems got a high degree in all domains and on the instrument as a whole. Besides, there were statistically significant differences following the gender variable in favor of males. The study concluded that there were statistically significant differences following the type of academic major variable and in favor of students of scientific majors. There were no statistically significant differences due to the student’s academic degree variable (intermediate diploma, bachelor’s degree).
What is the Chance that the Equity Premium Varies over Time ? Evidence from Regressions on the Dividend-Price Ratio ∗

We examine the evidence on excess stock return predictability in a Bayesian setting in which the investor faces uncertainty about both the existence and strength of predictability. When we apply our methods to the dividend-price ratio, we find that even investors who are quite skeptical about the existence of predictability sharply modify their views in favor of predictability when confronted by the historical time series of returns and predictor variables. Correctly taking into account the stochastic properties of the regressor has a dramatic impact on inference, particularly over the 2000-2005 period.
Bilateral Network with Residual U-blocks and Dual-Guided Attention for Real-time Semantic Segmentation

When some application scenarios need to use semantic segmentation technology, like automatic driving, the primary concern comes to real-time performance rather than extremely high segmentation accuracy. To achieve a good trade-off between speed and accuracy, two-branch architecture has been proposed in recent years. It treats spatial information and semantics information separately which allows the module to be composed of two networks both not heavy. However, the process of fusing features with two different scales becomes a performance bottleneck for many nowaday two-branch models. In this research, we design a new fusion mechanism for two-branch architecture which is guided by attention computation. To be precise, we use the Dual-Guided Attention (DGA) module we proposed to replace some multi-scale transformations with the calculation of attention which means we only use several attention layers of near linear complexity to achieve performance comparable to frequently-used multi-layer fusion. To ensure that our module can be effective, we use Residual U-blocks (RSU) to build one of the two branches in our networks which aims to obtain better multi-scale features. Extensive experiments on Cityscapes and CamVid dataset show the effectiveness of our method. On Cityscapes, our light version network without pretrain weight can achieve 71.1% mIoU at 163 FPS on a single Nvidia RTX 3070 using full resolution images(1024×2048pix). And the large version can achieve 77.9% mIoU with a speed of 43 FPS which still reaches the real-time criterion. Our code and module has been open sourced at https://github.com/LikeLidoA/Mymodule.
Generating Continuations in Multilingual Idiomatic Contexts

The ability to process idiomatic or literal multiword expressions is a crucial aspect of understanding and generating any language. The task of generating contextually relevant continuations for narratives containing idiomatic (or literal) expressions can allow us to test the ability of generative language models (LMs) in understanding nuanced language containing non-compositional figurative text. We conduct a series of experiments using datasets in two distinct languages (English and Portuguese) under three different training settings (zero-shot, few-shot, and fine-tuned). Our results suggest that the models are only slightly better at generating continuations for literal contexts than idiomatic contexts, with exceedingly small margins. Furthermore, the models studied in this work perform equally well across both languages, indicating the robustness of generative models in performing this task.
Deep Stochastic Logic Gate Networks

This paper introduces a novel regularization approach aimed at improving generalization performance by perturbing deterministic logical expressions. We incorporate logical inference into deep neural networks using logic gates and propose stochastic sampling to select appropriate logic gates from a predetermined set at each node, resembling sampling from a categorical distribution. While the Gumbel softmax relaxation facilitates effective sampling learning, the independence of perturbation from the maximum index operation ( $\mathop {\mathrm {arg\,max}} $ ) poses challenges in maintaining consistent sampling and preserving the original categorical probability order. To address this issue, we introduce scaled noise in the Gumbel process, followed by normalization to unnormalized probabilities. By leveraging randomness and introducing stochastic learning into deterministic logical transformations, we demonstrate enhanced classification accuracy. Extensive evaluations on publicly available datasets, including UCI (adult and breast cancer), MNIST, and CIFAR-10, establish the superiority of our method over softmax-based logical gate networks. Our contributions significantly advance the training of logic gate-based networks, inspiring further developments in deep logic gate network training.
Ergodic dichotomy for subspace flows in higher rank

In this paper, we obtain an ergodic dichotomy for {\it directional} flows, more generally, subspace flows, for a class of discrete subgroups of a connected semisimple real algebraic group $G$, called transverse subgroups. The class of transverse subgroups of $G$ includes all discrete subgroups of rank one Lie groups, Anosov subgroups and their relative versions. Let $\Gamma$ be a Zariski dense $\theta$-transverse subgroup for a subset $\theta$ of simple roots. Let $L_\theta=A_\theta S_\theta$ be the Levi subgroup associated with $\theta$ where $A_\theta$ is the central maximal real split torus and $S_\theta$ is the product of a semisimple subgroup and a compact torus. There is a canonical $\Gamma$-invariant subspace $\tilde \Omega_{\theta}$ of $ G/S_\theta$ on which $\Gamma$ acts properly discontinuously. Setting $\Omega_\theta=\Gamma \backslash \tilde \Omega_\theta$, we consider the subspace flow given by $A_W=\exp W$ for any linear subspace $W<\frak a_\theta$. Our main theorem is a Hopf-Tsuji-Sullivan type dichotomy for the ergodicity of $(\Omega_\theta, A_W, \mathsf m)$ with respect to a Bowen-Margulis-Sullivan measure $\mathsf m$ satisfying a certain hypothesis. As an application, we obtain the codimension dichotomy for a $\theta$-Anosov subgroup $\Gamma<G$: for any subspace $W<\mathfrak{a}_\theta$ containing a vector $u$ in the interior of the $\theta$-limit cone of $\Gamma$, we have $\operatorname{codim} W \le 2$ if and only if the $A_W$-action on $(\Omega_\theta, \mathsf{m}_u)$ is ergodic where $\mathsf{m}_u$ is the Bowen-Margulis-Sullivan measure associated with $u$.
Fluid‐faulting evolution in high definition: Connecting fault structure and frequency‐magnitude variations during the 2014 Long Valley Caldera, California, earthquake swarm

An extended earthquake swarm occurred beneath southeastern Long Valley Caldera between May and November 2014, culminating in three magnitude 3.5 earthquakes and 1145 cataloged events on 26 September alone. The swarm produced the most prolific seismicity in the caldera since a major unrest episode in 1997–1998. To gain insight into the physics controlling swarm evolution, we used large‐scale cross correlation between waveforms of cataloged earthquakes and continuous data, producing precise locations for 8494 events, more than 2.5 times the routine catalog. We also estimated magnitudes for 18,634 events (~5.5 times the routine catalog), using a principal component fit to measure waveform amplitudes relative to cataloged events. This expanded and relocated catalog reveals multiple episodes of pronounced hypocenter expansion and migration on a collection of neighboring faults. Given the rapid migration and alignment of hypocenters on narrow faults, we infer that activity was initiated and sustained by an evolving fluid pressure transient with a low‐viscosity fluid, likely composed primarily of water and CO2 exsolved from underlying magma. Although both updip and downdip migration were observed within the swarm, downdip activity ceased shortly after activation, while updip activity persisted for weeks at moderate levels. Strongly migrating, single‐fault episodes within the larger swarm exhibited a higher proportion of larger earthquakes (lower Gutenberg‐Richter b value), which may have been facilitated by fluid pressure confined in two dimensions within the fault zone. In contrast, the later swarm activity occurred on an increasingly diffuse collection of smaller faults, with a much higher b value.
Resummation of local and non-local scalar self energies via the Schwinger-Dyson equation in de Sitter spacetime

We consider a massless and minimally coupled self interacting quantum scalar field in the inflationary de Sitter spacetime. The scalar potential is taken to be a hybrid of cubic and quartic self interactions, $V(\phi)= \lambda \phi^4/4! +\beta \phi^3/3! $ ($\lambda>0$). Compared to the earlier well studied $\beta=0$ case, the present potential has a rolling down effect due to the $\phi^3$ term, along with the usual bounding effect due to the $\phi^4$ term. $V(\phi)$ has shapewise qualitative similarity with the standard slow roll single field inflationary potentials. We begin by constructing the Schwinger-Dyson equation for the scalar Feynman propagator up to two loop, at ${\cal O}(\lambda)$, ${\cal O}(\beta^2)$, ${\cal O}(\lambda^2)$ and ${\cal O}(\lambda \beta^2)$. Using this equation, we consider first the local part of the scalar self energy and compute the mass of the scalar field, dynamically generated via the late time non-perturbative secular logarithms, by resumming the daisy-like graphs. We also argue that unlike the quartic case, considering merely the one loop results for the purpose of resummation does not give us any sensible result here. We next construct the non-perturbative two particle irreducible effective action up to three loop and derive from it the Schwinger-Dyson equation once again. This equation is satisfied by the non-perturbative Feynman propagator. By series expanding this propagator, the resummed local part of the self energy is shown to yield the same dynamical mass as that of the above. We next use this equation to resum the effect of the non-local part of the scalar self energy in the Feynman propagator, and show that even though the perturbatively corrected propagator shows secular growth at late times, there exists a resummed solution which is vanishing for large spacelike separations.
Radar-Lidar Fusion for Object Detection by Designing Effective Convolution Networks

Object detection is a core component of perception systems, providing the ego vehicle with information about its surroundings to ensure safe route planning. While cameras and Lidar have significantly advanced perception systems, their performance can be limited in adverse weather conditions. In contrast, millimeter-wave technology enables radars to function effectively in such conditions. However, relying solely on radar for building a perception system doesn't fully capture the environment due to the data's sparse nature. To address this, sensor fusion strategies have been introduced. We propose a dual-branch framework to integrate radar and Lidar data for enhanced object detection. The primary branch focuses on extracting radar features, while the auxiliary branch extracts Lidar features. These are then combined using additive attention. Subsequently, the integrated features are processed through a novel Parallel Forked Structure (PFS) to manage scale variations. A region proposal head is then utilized for object detection. We evaluated the effectiveness of our proposed method on the Radiate dataset using COCO metrics. The results show that it surpasses state-of-the-art methods by 1.89% and 2.61% in favorable and adverse weather conditions, respectively. This underscores the value of radar-Lidar fusion in achieving precise object detection and localization, especially in challenging weather conditions.