Miracle-dz/UniFAR-FTUs huggingface dataset:
RADIATE: A Radar Dataset for Automotive Perception in Bad Weather

Datasets for autonomous cars are essential for the development and benchmarking of perception systems. However, most existing datasets are captured with camera and LiDAR sensors in good weather conditions. In this paper, we present the RAdar Dataset In Adverse weaThEr (RADIATE), aiming to facilitate research on object detection, tracking and scene understanding using radar sensing for safe autonomous driving. RADIATE includes 3 hours of annotated radar images with more than 200K labelled road actors in total, on average about 4.6 instances per radar image. It covers 8 different categories of actors in a variety of weather conditions (e.g., sun, night, rain, fog and snow) and driving scenarios (e.g., parked, urban, motorway and suburban), representing different levels of challenge. To the best of our knowledge, this is the first public radar dataset which provides high-resolution radar images on public roads with a large amount of road actors labelled. The data collected in adverse weathers, e.g., fog and snowfall, is unique. Some baseline results of radar based object detection and recognition are given to show that the use of radar data is promising for automotive applications in bad weather, where vision and LiDAR fail. RADIATE also has stereo images, 32-channel LiDAR and GPS data, directed at other applications such as sensor fusion, localisation and mapping. The public dataset can be accessed at http://pro.hw. ac.uk/radiate/.
Ablation Modalities for Therapeutic Intervention in Arrhythmia-Related Cardiovascular Disease: Focus on Electroporation

Targeted cellular ablation is being increasingly used in the treatment of arrhythmias and structural heart disease. Catheter-based ablation for atrial fibrillation (AF) is considered a safe and effective approach for patients who are medication refractory. Electroporation (EPo) employs electrical energy to disrupt cell membranes which has a minimally thermal effect. The nanopores that arise from EPo can be temporary or permanent. Reversible electroporation is transitory in nature and cell viability is maintained, whereas irreversible electroporation causes permanent pore formation, leading to loss of cellular homeostasis and cell death. Several studies report that EPo displays a degree of specificity in terms of the lethal threshold required to induce cell death in different tissues. However, significantly more research is required to scope the profile of EPo thresholds for specific cell types within complex tissues. Irreversible electroporation (IRE) as an ablative approach appears to overcome the significant negative effects associated with thermal based techniques, particularly collateral damage to surrounding structures. With further fine-tuning of parameters and longer and larger clinical trials, EPo may lead the way of adapting a safer and efficient ablation modality for the treatment of persistent AF.
Injection of Traditional Hand-Crafted Features into Modern CNN-Based Models for SAR Ship Classification: What, Why, Where, and How

With the rise of artificial intelligence, many advanced Synthetic Aperture Radar (SAR) ship classifiers based on convolutional neural networks (CNNs) have achieved better accuracies than traditional hand-crafted feature ones. However, most existing CNN-based models uncritically abandon traditional hand-crafted features, and rely excessively on abstract ones of deep networks. This may be controversial, potentially creating challenges to improve classification performance further. Therefore, in view of this situation, this paper explores preliminarily the possibility of injection of traditional hand-crafted features into modern CNN-based models to further improve SAR ship classification accuracy. Specifically, we will—(1) illustrate what this injection technique is, (2) explain why it is needed, (3) discuss where it should be applied, and (4) describe how it is implemented. Experimental results on the two open three-category OpenSARShip-1.0 and seven-category FUSAR-Ship datasets indicate that it is effective to perform injection of traditional hand-crafted features into CNN-based models to improve classification accuracy. Notably, the maximum accuracy improvement reaches 6.75%. Hence, we hold the view that it is not advisable to abandon uncritically traditional hand-crafted features, because they can also play an important role in CNN-based models.
High Knudsen Number Flows (Focusing on Experiments for Gaseous Microflows)

マイクロ・ナノデバイスが容易に作成可能となり,それらを対象とした熱流体解析も非常に活発である. しか し,マイクロ・ナノデバイス周りの気体流に関する研究に関しては,現在ヨーロッパでは組織的な研究が始まり つつあるが,我が国ではあまり重要視されないまま現在に至っている. 流れ場の気体分子の平均自由行程に代表 長さが近づくと連続体の近似が成立しなくなり,原子・分子のレベルで流れを扱う必要がある. 従来はこのよう な流れは希薄気体流がその対象であったが,上述のような代表長さの非常に小さなデバイスが実現されると,そ れらの周りの流れも同様に扱うことが必要となった. 大気圧中で扱われるこれらの流れも含めて,分子・原子レ ベルで扱う必要のある流れを,著者らは「高クヌッセン数流れ」と総称した . 高クヌッセン数流れでは, マクロレベルでは問題にならない特異な現象が現れ,それらの解明には,精緻な実験データや界面のモデル化も 含めた数値計算が必要になるが,いまだ発展途上であり,今後の進展が期待される. 本稿では,「高クヌッセン数流れ」に関するここ 10 年ほどの国内外の研究動向を紹介するとともに,「高クヌッ セン数流れ」に関する研究,特にマイクロ気体流れに関する実験を中心とした研究を展望する.
Gender Differences in the Effects of Complementary versus Competitive Gender Stereotypes on System Justification and Tolerance of Sexism

Research has shown that people often support social systems that are not in their best interests (Kay & Jost, 2003). One way that people may justify support for such social systems is by focusing on beneficial characteristics. For example, people exhibit greater system justification when people are described as poor but happy (complementary attributes) as opposed to poor and unhappy (non-complementary attributes) (Kay & Jost, 2003). The present study examined the effects of complementary (i.e., that women and men fulfill different career roles) versus competitive (i.e., that women and men compete for the same career roles) gender stereotypes on women’s and men’s system justification and tolerance of sexism. Participants, who were recruited through Prolific Academic, were randomly assigned to read one of two ostensible news articles modeled after Eagly et. al. (2020), which described gender career roles as competitive or complementary. Participants then completed measures of system justification (Jost & Banaji, 1994) and tolerance of sexism (Folberg et. al., 2021) The results revealed only that men tolerate and justify sexism more than women. Additionally, participants who were assigned to the competitive (vs. complementary) condition scored higher on system justification. No significant interactions between gender and condition were found.
Vehicle Currency and Price Discovery

This paper investigates competition in price discovery between direct JPY/EUR cross-rates and the rates implied indirectly from exchanges of JPY/USD and USD/EUR. Our results highlight the role of the USD as a vehicle currency in the aspect of price discovery. We find the dominant price discovery of implied JPY/EUR rates relates to the lower transaction cost. Upon the release of macroeconomic announcements in Japan and Europe, the trading cost advantage enhances price discovery of implied rates even more. When implied JPY/EUR rates are more efficient, the USD tends to appreciate, as shown by the upward movement in the USD index. JEL classification: F31, G15
Carotenoids in the Management of Glaucoma: A Systematic Review of the Evidence

Primary open-angle glaucoma (POAG) remains a leading cause of irreversible blindness globally. Recent evidence further substantiates sustained oxidative stress, and compromised antioxidant defenses are key drivers in the onset of glaucomatous neurodegeneration. Overwhelming oxidative injury is likely attributed to compounding mitochondrial dysfunction that worsens with age-related processes, causing aberrant formation of free radical species. Thus, a compromised systemic antioxidant capacity exacerbates further oxidative insult in glaucoma, leading to apoptosis, neuroinflammation, and subsequent tissue injury. The purpose of this systematic review is to investigate the neuroprotective benefits of the macular carotenoids lutein, zeaxanthin, and meso-zeaxanthin on glaucomatous neurodegeneration for the purpose of adjunctive nutraceutical treatment in glaucoma. A comprehensive literature search was conducted in three databases (PubMed, Cochrane Library, and Web of Science) and 20 records were identified for screening. Lutein demonstrated enhanced neuroprotection on retinal ganglion cell survival and preserved synaptic activity. In clinical studies, a protective trend was seen with greater dietary consumption of carotenoids and risk of glaucoma, while greater carotenoid levels in macular pigment were largely associated with improved visual performance in glaucomatous eyes. The data suggest that carotenoid vitamin therapy exerts synergic neuroprotective benefits and has the capacity to serve adjunctive therapy in the management of glaucoma.
The Status and Role of Cooperatives and Cooperative Bank of Oromia in the Socio-Economic Development in Ethiopia

Cooperative is a useful instrument to alleviate the socio-economic problems of the people in many developing countries including Ethiopia. Cooperation among people has existed since time immemorial. However, formal cooperatives began in Ethiopia in 1960. Following this, with the initiative of the government to encourage financial institutions, Cooperative Bank of Oromia was emerged in 2004. Since its establishment, Cooperative Bank of Oromia played its share to solve the bottleneck of institutions and individuals in delivering both financial and non-financial services to the unbanked and under banked communities as well as to sustain the cooperative institutions. The objective of this review article is to show the status and role of cooperatives and Cooperative Bank of Oromia in the socio-economic development in Ethiopia. Intensive conceptual and empirical literature reviews were made in the areas of cooperative development, cooperative policy and regulatory framework documents, annual reports of Cooperative Bank of Oromia and the Federal Cooperative Agency’s report. The study indicated that the number of primary cooperatives has increased from 7,366 in 1991 to 82,089 at the beginning of 2019 showing 1,014% rate of growth. The number of members increased from 4.5million to 18 million during the same period demonstrating growth rate of 297%. Their capital was increased from half billion to 20.5 billion birr during the stated periods. Likewise, Cooperative Bank of Oromia has shown dramatic progress. Its capital is increased from 300 million to more than 5 billion; the numbers of branches increased from 7 to 420, number of employees are 8,163. The review confirmed that the bank is contributing multi-directional socio-economic development in the country. It is obvious that if it continues in this manner, the bank will be one of the leading banking industries in the country.
A generative deep learning approach towards decisions in transient gas networks

A decision support system relies on frequent re-solving of similar problems. These problems arise from the cyclical nature of such systems, and as such each problem has similar structure. We propose a generative neural network design for learning integer decision variables of mixed-integer programming (MIP) formulations of these problems. We utilise a deep neural network discriminator and a MIP solver as our oracle to train our generative neural network. As a ﬁnal product, we present the results of our design applied to the transient gas optimisation problem, where we produce a feasible solution in 2.5s, which can be used as a warm-start solution to decrease global optimal solution solve time by 60.5%.
Areas of Interest and Attitudes towards the Pharmacological Treatment of Attention Deficit Hyperactivity Disorder: Thematic and Quantitative Analysis Using Twitter

We focused on tweets containing hashtags related to ADHD pharmacotherapy between 20 September and 31 October 2019. Tweets were classified as to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. Furthermore, we classified any links included within a tweet as either scientific or non-scientific. We created a dataset of 6568 tweets: 4949 (75.4%) related to stimulants, 605 (9.2%) to non-stimulants and 1014 (15.4%) to alpha-2 agonists. Next, we manually analyzed 1810 tweets. In the end, 481 (48%) of the tweets in the stimulant group, 218 (71.9%) in the non-stimulant group and 162 (31.9%) in the alpha agonist group were considered classifiable. Stimulants accumulated the majority of tweets. Notably, the content that generated the highest frequency of tweets was that related to treatment efficacy, with alpha-2 agonist-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to alpha-2 agonists. Stimulant-related tweets obtained the highest proportion of likes and were the most disseminated within the Twitter community. Understanding the public view of these medications is necessary to design promotional strategies aimed at the appropriate population.
Modeling gravitational wave detection with atom interferometry

The atom interferometry is a promising tool for the detection of the gravitational wave (GW) in the mid-frequency band ranging from 0.01 Hz to 10 Hz. Particularly, the multi-arm atom interference detectors can help to extract better data with the removal of fake detection. The existing models for GW detection with atom interferometry, however, are mainly focused on the one-dimensional situation, and cannot be directly applied to the multi-arm frame of the GW detection. In this work, we develop a universal theoretical model for GW detectors based on atom interferometry, in which the eikonal equation under general relativity is adopted to calculate the perturbation of the GW on light propagation, and the dependence of the response function on the azimuth is also discussed. We further analyze the dominant noise sources, which limits the detection capabilities of detector. This work provides a complete model for GW detection with atom interferometry, with the ability to analyze the performance of the multi-arm atom interference detectors.
Numerical Modeling of NOx Formation in Turbulent Flames Using a Kinetic Post-processing Technique

The numerical prediction of formation of nitrogen oxides (NOx) in combustion devices (domestic and industrial burners and furnaces, internal combustion engines, etc.) is gaining rising importance because of strong limitations imposed by the legislation. Such predictions usually require the adoption of detailed kinetic mechanisms, because the numerical modeling of NOx can be accurate only considering all of the relevant chemical paths (thermal, prompt, N2O, fuel NOx, NNH, etc.). Despite the continuous increase of computational resources, the direct coupling of detailed kinetics and complex computational fluid dynamics (CFD) is very expensive in terms of central processing unit (CPU) time, and alternative approaches must be taken into account. In this work, the prediction of NOx in combustion systems is performed using a kinetic post-processing technique, which is based on the assumption that pollutants, such as NOx, only marginally affect the main combustion process (because they are usually present in ver.
Visual SLAM based on instance segmentation in dynamic scenes

There is a problem in that the existing visual simultaneous localization and mapping (SLAM) algorithm mainly applied to static scenes is less likely to be applied to dynamic scenes directly. A dynamic environment SLAM algorithm is illustrated in this paper based on instance segmentation and epipolar geometry to narrow down the interference between moving objects and localization accuracy of SLAM. For the reason of semantic information, the feature points on moving objects are removed. In terms of the dynamic region without prior knowledge, deletion of the dynamic points can be achieved by the epipolar geometry method to reduce the impact of dynamic points on positioning accuracy. On the premise of that, the experimental verification on the TUM public dataset and comparison with other classical algorithms is done on the proposed algorithm. The results show that effective detection can be realized to remove potential and uncertain moving objects. The SLAM system effectively enhances the robustness of SLAM in highly dynamic scenes and significantly improves the localization accuracy.
X-ray absorption study of defects in reactively sputtered GaN films displaying large variation of conductivity

Undoped GaN films were epitaxially grown on c-sapphire by rf magnetron reactive sputtering of GaAs at different N2 percentages in Ar–N2 sputtering atmosphere. These films display a large increase in conductivity from ∼10−6 Ω−1cm−1 to ∼102 Ω−1cm−1 with decrease of N2 in sputtering atmosphere from 100% to 10%. X-ray photoelectron spectroscopy reveals a monotonous decrease of N/Ga ratio, concurrently with increase of uncoordinated/interstitial Ga in the films, with decrease of N2 percentage in sputtering atmosphere. Extended x-ray absorption fine structure measurements and x-ray absorption near edge structure (XANES) simulation at Ga K-edge demonstrate the absence of N vacancies in sputtered GaN films and point towards the presence of Ga vacancies, which increase with decrease of N2 percentage in sputtering atmosphere. XANES measurements at O K-edge reveal that O interstitials are present in most of the films, except for the film grown at low N2 percentages (∼10%), in which case, O substitution on N-site may also take place. A monotonous increase of n-type conductivity is observed with increase of Ga interstitials, which suggests that Ga interstitials are the main source of n-type conductivity in sputtered GaN films. The low conductivity of the films grown at high N2 percentages (≳75%) is attributed to the limited presence of Ga interstitials and a strong compensation, dominated by N and O interstitials. The high conductivity GaN films grown at low N2 percentages may be partly contributed by O substituted on N site, along with Ga vacancies and possibly VGa-ON complexes, as the compensating defects.
Ion Acceleration Efficiency at the Earth’s Bow Shock: Observations and Simulation Results

Collisionless shocks are some of the most efficient particle accelerators in heliospheric and astrophysical plasmas. Here we study and quantify ion acceleration at Earth’s bow shock with observations from NASA’s Magnetospheric Multiscale (MMS) satellites and in a global hybrid-Vlasov simulation. From the MMS observations, we find that quasiparallel shocks are more efficient at accelerating ions. There, up to 15% of the available energy goes into accelerating ions above 10 times their initial energy. Above a shock-normal angle of ∼50°, essentially no energetic ions are observed downstream of the shock. We find that ion acceleration efficiency is significantly lower when the shock has a low Mach number (M A < 6) while there is little Mach number dependence for higher values. We also find that ion acceleration is lower on the flanks of the bow shock than at the subsolar point regardless of the Mach number. The observations show that a higher connection time of an upstream field line leads to somewhat higher acceleration efficiency. To complement the observations, we perform a global hybrid-Vlasov simulation with realistic solar-wind parameters with the shape and size of the bow shock. We find that the ion acceleration efficiency in the simulation shows good quantitative agreement with the MMS observations. With the combined approach of direct spacecraft observations, we quantify ion acceleration in a wide range of shock angles and Mach numbers.
Do more chargers mean more electric cars?

To reverse the trend of rising CO2 emissions in the European Union’s (EU) transportation sector, several European governments have introduced programs that promote electric vehicles (EVs). One frequently cited impediment to their uptake is insufficient charging infrastructure. Drawing on panel data from Germany, this paper estimates the relationship between public charging infrastructure and the uptake of EVs. We specify models with fixed effects and instrumental variables to gauge the robustness of our findings in the face of alternative channels through which endogeneity bias may emerge. We find that charging infrastructure has a statistically significant and positive impact on EV uptake, with the magnitude of the estimate increasing with population density. The evidence further suggests that although the incidence of charging points in Germany far exceeds the EU’s recommended minimum ratio of one point to ten EVs, inadequate infrastructure coverage remains a binding constraint on EV uptake. We use the model estimates to illustrate the relative cost effectiveness of normal and fast chargers by region, which supports a geographically differentiated targeting of subsidies for charging infrastructure.
What techniques can be used to identify bacteria other than traditional milk culture ? ?

Milk samples can be submitted to certified laboratories or veterinary clinics for bacterial culture. Quality control is vital and trained and experienced operators will provide the most accurate results. The major disadvantage of laboratory milk culture is the time that elapses between sample collection, transport to the laboratory and subsequent bacterial growth and analysis. There may be loss of bacterial viability during sample handling and it important that efforts are made to minimise the growth of contaminants. The costs of laboratory culture vary but can be relatively high when small numbers of samples are submitted.
Ridge regression under dense factor augmented models

This paper establishes a comprehensive theory of the optimality, robustness, and cross-validation selection-consistency for the ridge regression under factor-augmented models with possibly dense idiosyncratic information. Using spectral analysis for random matrices, we show that the ridge regression is asymptotically efficient in capturing both factor and idiosyncratic information by minimizing the limiting predictive loss among the entire class of spectral regularized estimators under large-dimensional factor models and mixed-effects hypothesis. We derive an asymptotically optimal ridge penalty in closed form, and prove that a bias-corrected k-fold cross-validation procedure can select the best ridge penalty adaptively in large samples. We extend the theory to the autoregressive models with many exogenous variables and establish a consistent cross-validation procedure using the what-we-called double ridge regression method. Our results allow for non-parametric distributions for, possibly heavy-tailed, martingale difference errors and idiosyncratic random coefficients, and adapt to both the cross-sectional and temporal dependence structures of the large-dimensional predictors. We demonstrate the performance of our ridge estimators in simulated examples as well as an economic dataset. All the proofs are available in the supplement, where we also provide useful lemmas that may be of independent interest and extra simulation results.