Miracle-dz/UniFAR-FTUs huggingface dataset:
Importance of electron distribution profiles to chorus wave driven evolution of Jovian radiation belt electrons

Wave‐particle interactions triggered by whistler‐mode chorus waves are an important contributor to the Jovian radiation belt electron dynamics. While the sensitivity of chorus‐driven electron scattering to the ambient magnetospheric and wave parameters has been investigated, there is rather limited understanding regarding the extent to which the dynamic evolution of Jovian radiation belt electrons, under the impact of chorus wave scattering, depends on the electron distribution profiles. We adopt a group of reasonable initial conditions based upon the available observations and models for quantitative analyses. We find that inclusion of pitch angle variation in initial conditions can result in increased electron losses at lower pitch angles and substantially modify the pitch angle evolution profiles of > ~500 keV electrons, while variations of electron energy spectrum tend to modify the evolution primarily of 1 MeV and 5 MeV electrons. Our results explicitly demonstrate the importance to the radiation belt electron dynamics in the Jovian magnetosphere of the initial shape of the electron phase space density, and indicate the extent to which variations in electron energy spectrum and pitch angle distribution can contribute to the evolution of Jovian radiation belt electrons caused by chorus wave scattering.
On a Li-type criterion for zero-free regions of certain Dirichlet series with real coefficients

The Li coefficients $\unicode[STIX]{x1D706}_{F}(n)$ of a zeta or $L$ -function $F$ provide an equivalent criterion for the (generalized) Riemann hypothesis. In this paper we define these coefficients, and their generalizations, the $\unicode[STIX]{x1D70F}$ -Li coefficients, for a subclass of the extended Selberg class which is known to contain functions violating the Riemann hypothesis such as the Davenport–Heilbronn zeta function. The behavior of the $\unicode[STIX]{x1D70F}$ -Li coefficients varies depending on whether the function in question has any zeros in the half-plane $\text{Re}(z)>\unicode[STIX]{x1D70F}/2. $ We investigate analytically and numerically the behavior of these coefficients for such functions in both the $n$ and $\unicode[STIX]{x1D70F}$ aspects.
High internal differential efficiency mid-infrared quantum cascade lasers

Implementation of the step-taper active-region (STA) design to 8-9 μm-emitting quantum cascade lasers (QCLs) has resulted in both high T0 and T1 values: 220 K and 665 K, and short lower-level lifetimes: 0.12 ps. In turn, the internal differential efficiency ηid, which is the product of the injection efficiency and the differential laser-transition efficiency, reaches values as high as 86 % for both 8.4 μm- and 8.8 μm-emitting QCLs. Such ηid values are 30-50% higher than those obtained from conventional QCLs emitting in the 7-11 μm wavelength range. Achieving both carrier-leakage suppression and miniband-like carrier extraction in mid-infrared (IR) QCLs leads to ηid values close to the fundamental limit of ~ 90 %. In turn, the currently employed fundamental wallplug-efficiency limits over the mid-IR wavelength range have to be increased by ~ 34 % (e.g., the wallplug-efficiency limit at λ= 4.6 μm increases from 29 % to 39 %). Preliminary results from STA-type 4.8-5.0 μm-emitting QCLs include 1.5 W CW operation, and 77 % internal differential efficiency; that is, 30-50% higher than the ηid values obtained from conventional 4.0-6. 5μm-emitting QCLs.
GSMR-CNN: An End-to-End Trainable Architecture for Grasping Target Objects from Multi-Object Scenes

We present an end-to-end trainable multi-task model that locates and retrieves target objects from multi-object scenes. The model is an extension of the Siamese Mask R-CNN, which combines the components of Siamese Neural Networks (SNNs) and Mask R-CNN for performing one-shot instance segmentation. The proposed network, called Grasping Siamese Mask R-CNN (GSMR-CNN), extends Siamese Mask R-CNN by adding an additional branch for grasp detection in parallel to the previous object detection head branches. This allows our model to identify a target object with a suitable grasp simultaneously, as opposed to other approaches that require the training of separate models to achieve the same task. The inherent SNN properties enable the proposed model to generalize and recognize new object categories that were not present during training, which is beyond the capabilities of standard object detectors. Moreover, an end-to-end solution uses shared features entailing less model parameters. The model achieves grasp accuracy scores of 92.1 % and 90.4% on the OCID grasp dataset on image-wise and object-wise splits. Physical experiments show that the model achieves a grasp success rate of 76.4 % when correctly identifying the object. Code and models are available at https://github.com/valerijah/grasping_siamese_mask_rcnn
A High Sensitivity SQI with Peak-Amplitude Difference Variance for Wearable ECG Signals

Wearable devices for continuous ECG recording have become more popular because of their comfort and compactness. However, ECG signals measured by devices can be affected by different types of noise, such as muscle or baseline noise. There is a demand to classify the quality of signals. In this paper, we propose novel Signal Quality Indices (SQI) based on the amplitude difference between adjacent peaks. The results showed that the proposed algorithm has higher sensitivity than older SQI methods such as template matching and high-order statistical methods based on the ECG amplitude for both noise-free and noisy signals. In particular, the proposed method has a sensitivity of 96.73% for noise-free signals and 96.81% for noisy signals. Besides, this method can reduce power consumption when the wearable devices only send good signals to the server. In the server’s opinion, the costs such as power consumption, and memory usage were also reduced.
Event-based Low-Power and Low-Latency Regression Method for Hand Kinematics from Surface EMG

Human-Machine Interfaces (HMIs) are a rapidly progressing field, and gesture recognition is a promising method in industrial, consumer, and health use cases. Surface electromyography (sEMG) is a State-of-the-Art (SoA) pathway for human-to-machine communication. Currently, the research goal is a more intuitive and fluid control, moving from signal classification of discrete positions to continuous control based on regression. The sEMG-based regression is still scarcely explored in research since most approaches have addressed classification. In this work, we propose the first event-based EMG encoding applied to the regression of hand kinematics suitable for working in streaming on a low-power microcontroller (STM32 F401, mounting ARM Cortex-M4). The motivation for event-based encoding is to exploit upcoming neuromorphic hardware to benefit from reduced latency and power consumption. We achieve a Mean Absolute Error of $8.8\pm 2.3$ degrees on 5 degrees of actuation on the public dataset NinaPro DB8, comparable with the SoA Deep Neural Network (DNN). We use $9\times$ less memory and $13\times$ less energy per inference, with $10\times$ shorter latency per inference compared to the SoA deep net, proving suitable for resource-constrained embedded platforms.
Optimized parameters selected on the basis of the development defect model

Abstract. With the continuous shrinking of critical dimension, it may require more time and effort to reduce or remove the lithography defects in the development process. Therefore, defect reduction has become one of the most important technical challenges in device mass production. With the purpose of finding an optimizing recipe, we can simulate group parameters, including nitrogen gas dispensation and wafer-rotation speed. From previous studies, we have established a model based on viscous fluid dynamics and have calculated the removing force distribution across the 300-mm-diameter wafer for the defect residual. In this model, we assumed that the defects mostly are polymer residual; once the removing force reached a certain threshold level (1 × 10 − 14 N), the defect with a “centered-ring-like” signature could be removed. For illustration, several groups of optimal parameter under postdeveloping rinse process conditions are given. The numerical simulations represent several recipes in the development process. We find that we can reproduce a group of the total force curves. From the simulation, we could find that we can get the minimally required strength from the three parameters for defect removal. We have done some experiments to validate the simulation results. The experimental data are almost in agreement with the simulation data. Therefore, the above simulation results have verified the effectiveness and validity of the proposed optimization methodology, and it also has shown that the trend of parameters provided by the optimized method has the potential to be an efficient candidate for reducing or removing lithography defects in the development process.
MOFT: Monocular odometry based on deep depth and careful feature selection and tracking

Autonomous localization in unknown environments is a fundamental problem in many emerging fields and the monocular visual approach offers many advantages, due to being a rich source of information and avoiding comparatively more complicated setups and multisensor calibration. Deep learning opened new venues for monocular odometry yielding not only end-to-end approaches but also hybrid methods combining the well studied geometry with specific deep components. In this paper we propose a monocular odometry that leverages deep depth within a feature based geometrical framework yielding a lightweight frame-to-frame approach with metrically scaled trajectories and state-of-the-art accuracy. The front-end is based on a multihypothesis matcher with perspective correction coupled with deep depth predictions that enables careful feature selection and tracking; especially of ground plane features that are suitable for translation estimation. The back-end is based on point-to-epipolar line minimization for rotation and unit translation estimation, followed by deep depth aided reprojection error minimization for metrically correct translation estimation. Furthermore, we also present a domain shift adaptation approach that allows for generalization over different camera intrinsic and extrinsic setups. The proposed approach is evaluated on the KITTI and KITTI-360 datasets, showing competitive results and in most cases outperforming other state-of-the-art stereo and monocular methods.
Vaccinated kidney transplant recipients are yet not sufficiently protected against COVID-19

distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com O R IG IN A L U N E D IT E D M A N U S C R IP T Vaccinated kidney transplant recipients are yet not sufficiently protected against COVID-19
Foliar Application of NPK Improves Growth, Yield and Fiber Morphological Properties of Some Kenaf (Hibiscus cannabunus L.) Varieties

Different concentrations of NPK fertilizer applied to enhance growth, fiber yield and bast morphological properties of three kenaf varieties. The research was done at Grdarasha Field/ Erbil, Kurdistan in summer season 2021. Randomized Complete Block Design (RCBD) with three replications by two factors (three varieties and two NPK concentrations with a control) which was applied as experimental design. Results showed the significance differences among all growth, quantity and quality properties except of few situations. The highest plant height was noted from FH952 (V1), while the biggest stem diameter recorded from 4202 (V3), and the highest value of the leaves number were by 4383 (V2), which were (3.70 m, 23.77 mm and 214.67 leaf plant -1 ), respectively. Foliar application of NPK caused to increase fresh and dry matter of total stem, bast and core fiber yields when 2000 mg L -1 applied to plants. Interaction between factors was also significantly affected all studied parameters. The biggest total fresh and dry stalk yield were noted of FH952 var . when 4000 mg L -1 NPK was added (V1NPK2), which were by (284.93 and 72.80 t ha -1 ), respectively. While, the longest single fiber length was recorded from interaction treatment of (V3NPK2), 4202 var. with 4000 mg L -1 NPK by almost (6 mm). Despite that, other bast fiber morphology properties changed dramatically in the other states of interaction between factors. Both concentrations of foliar application of NPK 2000 and 4000 mg L -1 were suggested to use as a nutrient supplement to the kenaf plants, since it caused to improve not just growth parameters but also quantity and quality properties of the stalk and fiber yields.
Near-Optimal Adversarial Reinforcement Learning with Switching Costs

Switching costs, which capture the costs for changing policies, are regarded as a critical metric in reinforcement learning (RL), in addition to the standard metric of losses (or rewards). However, existing studies on switching costs (with a coefficient $\beta$ that is strictly positive and is independent of $T$) have mainly focused on static RL, where the loss distribution is assumed to be fixed during the learning process, and thus practical scenarios where the loss distribution could be non-stationary or even adversarial are not considered. While adversarial RL better models this type of practical scenarios, an open problem remains: how to develop a provably efficient algorithm for adversarial RL with switching costs? This paper makes the first effort towards solving this problem. First, we provide a regret lower-bound that shows that the regret of any algorithm must be larger than $\tilde{\Omega}( ( H S A )^{1/3} T^{2/3} )$, where $T$, $S$, $A$ and $H$ are the number of episodes, states, actions and layers in each episode, respectively. Our lower bound indicates that, due to the fundamental challenge of switching costs in adversarial RL, the best achieved regret (whose dependency on $T$ is $\tilde{O}(\sqrt{T})$) in static RL with switching costs (as well as adversarial RL without switching costs) is no longer achievable. Moreover, we propose two novel switching-reduced algorithms with regrets that match our lower bound when the transition function is known, and match our lower bound within a small factor of $\tilde{O}( H^{1/3} )$ when the transition function is unknown. Our regret analysis demonstrates the near-optimal performance of them.
Adversarially Regularized Autoencoders for Generating Discrete Structures

Generative adversarial networks are an effective approach for learning rich latent representations of continuous data, but have proven difficult to apply directly to discrete structured data, such as text sequences or discretized images. Ideally we could encode discrete structures in a continuous code space to avoid this problem, but it is difficult to learn an appropriate general-purpose encoder. In this work, we consider a simple approach for handling these two challenges jointly, employing a discrete structure autoencoder with a code space regularized by generative adversarial training. The model learns a smooth regularized code space while still being able to model the underlying data, and can be used as a discrete GAN with the ability to generate coherent discrete outputs from continuous samples. We demonstrate empirically how key properties of the data are captured in the model's latent space, and evaluate the model itself on the tasks of discrete image generation, text generation, and semi-supervised learning.
Influence of pitch on the morphology and luminescence properties of self-catalyzed GaAsSb nanowire arrays

We report on the influence of hole pattern pitch lengths in the silicon oxide mask and specific nanowire (NW) locations on the morphology and luminescence properties of self-catalyzed GaAsSb NW arrays grown by molecular beam epitaxy. Due to stronger competition for the limited amount of Ga adatoms, the GaAsSb NWs in the center of arrays with short pitch lengths possess a smaller catalyst droplet contact angle than that of the NWs at the array edge. This smaller contact angle leads to a reduction in the collection of group V flux, bringing about shorter NWs in the center. For pitch lengths beyond the diffusion length of Ga adatoms on the mask, the GaAsSb NWs are taller with larger contact angles than in the case with short pitch lengths. Considering that Sb has a longer diffusion length on the side facets of the NWs than that of As, a reduction/increase of the contact angle will bring about an increase/reduction in the Sb/As ratio of the group-V fluxes collected by the catalyst droplets. By performing micr.
Internalized Stigma and its Association with Hope, Self-Esteem, Self-Efficacy, and Treatment Adherence among Outpatients with Severe Mental Illness: A Cross-Sectional Survey

Background: This study investigated the association between internalized stigma and hope, self-esteem, self-efficacy, and treatment adherence and explored the most influential and predictive factor of internalized stigma among patients with severe mental disorders. Materials and Methods: This correlational descriptive study was conducted on 257 outpatients diagnosed with severe mental illness according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) diagnostic criteria. The participants were seeking treatment at outpatient and affiliated clinics of Razi Hospital, Iran, from October 2018 to May 2019. We used a convenient sampling design. Internalized Stigma of Mental Illness scale, Dispositional Hope Scale, Rosenberg's Self-Esteem Scale, General Self-Efficacy Scale, and Drug Attitude Inventory were used to gather data. The data were analyzed using inferential statistics (Pearson correlation, coefficient logistic, and regression analyses) at a 0.05 significance level. Results: The mean (standard deviation) score of internalized stigma was 1.57 (0.49), and 58% of the participants reported moderate to high internalized stigma. A negative significant relationship was found between internalized stigma and hope (r = −0.55, p < 0.05), self-esteem (r = −0.66, p < 0.05), and self-efficacy (r = −0.64, p < 0.05). Treatment adherence was not found to be significantly associated with the internalized stigma. In the final regression model, self-esteem and self-efficacy significantly predicted internalized stigma. Conclusions: Given the crucial role of self-esteem and self-efficacy in predicting internalized stigma, nurses should devote special attention to these factors and use strategies to improve individuals' self-esteem and self-efficacy.
Performance of European Thyroid Imaging Reporting and Data System in Stratifying Malignancy Risk of Thyroid Nodules: A Prospective Study

Background: There is a limited number of studies reporting the performance of European Thyroid Imaging Reporting and Data System (EU-TIRADS) guideline in identifying thyroid nodule malignancy. We aimed to evaluate diagnostic accuracy of EU-TIRADS regardless of thyroid nodule size. Methods: During August 2019-November 2021, subjects with thyroid nodules were prospectively included. Sonographic characteristics were recorded and scored as per EU-TIRADS guideline. Finally, fine-needle aspiration (FNA) was performed, and cytological findings were reported. Results: Totally, 1266 thyroid nodules from 984 subjects were assessed, of which 295 nodules were smaller than 10 mm and 971 nodules were 10 mm or larger. Among nodules <10 mm, prevalence rates of malignancy for EU-TIRADS classes 2–5 were 0.0%, 3.7%, 20.6%, and 40.9%, respectively; these rates among nodules ≥10 mm were 2.3%, 4.0%, 19.3%, and 43.2%, respectively. The accuracy values of EU-TIRADS class 5 and EU-TIRADS class 4 or 5 in diagnosis of malignancy for nodules <10 mm were 86.4% and 79.7%, respectively; these rates for nodules ≥10 mm were 83.8% and 76.3%, respectively. Hypoechogenicity, microcalcification, ill-defined and irregular margins were predictors for malignancy regardless of thyroid nodule size. Conclusion: EU-TIRADS could provide an acceptable malignancy risk stratification that is helpful for better distinguishing benignity from malignancy, as well as preventing unnecessary FNA biopsies, in thyroid nodules irrespective of their size.
Utvärdering av Matematiklyftets resultat : slutrapport

Matematiklyftet ar en fortbildning for alla larare i Sverige som undervisar i matematik. Den genomfordes 2012-2016. Karnan i fortbildningen var det kollegiala larandet. Larare arbetade tillsammans med olika moduler som var och en bestod av didaktiskt material att anvanda vid planering, diskuss-ioner och genomforande av matematikundervisning, samt vid kollegiala reflektioner och diskussioner. Genom modulerna belystes primart fyra olika didaktiska perspektiv: (1) att undervisa matematik utifran formagorna, (2) bedomning for larande och undervisning i matematik, dvs. formativ bedomning, (3) rutiner/interaktioner i klassrummet och (4) klassrumsnormer/sociomatematiska normer.Denna rapport presenterar en utvardering av Matematiklyftets resultat dar det undersokts i vilken utstrackning Matematiklyftet har bidragit till att utveckla en bestaende undervisningskultur och en bestaende fortbildningskultur. Utvarderingen syftar ocksa till att identifiera faktorer som gynnar eller missgynnar Matematiklyftets resultat och ska aven fungera bade formativt och summativt. Det innebar att bade slutsatser om hur Matematiklyftet har uppnatt malen att utveckla undervisnings- och fortbildningskulturen och slutsatser om hur stod till planeringar och genomforande av liknande fort-bildningssatsningar formuleras. Urvalet i utvarderingen bestar av 35 grund-och gymnasieskolor. Pa varje skola har tre larare slumpmassigt valts ut att inga i utvarderingen, dvs. totalt 105 larare. I utvarderingen ingar ocksa varje skolas rektor och representant for skolhuvudman. Skolorna har besokts vid tva tillfallen for att kunna undersoka forandring i undervisnings- och fortbildningskultur. Datamaterialet bestar av observationer av matematiklektioner och av kollegiala samtal, intervjuer och enkater med larare och rektorer, samt intervjuer med representanter for skolhuvudman. I datamaterialet ingar aven insamlade dokument (t.ex. fortbildningsplaner och kopior av undervisningsmaterial) och bakgrundsinformation om de besokta skolorna (fran externa databaser). Denna rapport utgor slutrapporten for utvarderingen av Matematiklyftets resultat. Nedan redovisas utvarderingens huvudsakliga resultat tillsammans med rekommendationer infor framtida fortbildningssatsningar av samma typ som Matematiklyftet.
Placebo Tests for Synthetic Controls

The synthetic control (SC) method has been recently proposed as an alternative to estimate treatment effects in comparative case studies. An important feature of the SC method is the inferential procedures based on placebo studies, suggested in Abadie et al. (2010). In this paper, we evaluate the statistical properties of these inferential techniques. We first show that the graphical analysis with placebos can be misleading, as placebo runs with lower expected squared prediction errors would still be considered in the analysis. Then we show that a test based on the the post/pre-intervention mean squared prediction error, as suggested in Abadie et al. (2010), ameliorates this problem. However, we show that such test can still have some size distortions, even if we consider a case in which the test statistic has the same marginal distribution for all placebo runs. Finally, we show that the fact that the SC weights are estimated can lead to important additional size distortions.