Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of Seasonal Effects on Transport Mode Choices based on GPS Trajectories and a Multinomial Logistic Regression Model

To improve the planning of transportation modes, it is necessary to understand how passenger choose the transportation mode depending on factors such as temperature, precipitation, trip distance, trip duration. In this article, we build up on previous work to analyze the effect of seasonality in the choice of transportation modes. To that end, we use GPS trajectories in Beijing from the Geolife Trajectory data set, and analyze them with respect to the four seasons: Summer, Au-tumn, Winter, and Spring. Our analysis reveals how seasonality influences the choice of transportation mode, e.g. people tend to bike less in Summer as temperature increases, whereas rush hour traffic influences the car share in Autumn. We build a multinomial logit model, one per season, to predict the transportation mode based on the following factors: temperature, air quality, traffic during rush hour, time of day, trip distance and trip duration. The results show how changes in the factors effect the choice of transportation mode. For instance, long trips in Autumn decrease the probability of walking by 54% and increase the probability of car, bus and train use by 37.7%, 11.1% and 13.5%, respectively. We conclude that rush hours only influence the choice of transport mode during Autumn and Spring. Also, we have observed some similarities in the choice of transport modes throughout different seasons, e.g. the trip distance is a relevant factor for the choice of transport mode in all four seasons.
BRNet: a Bilateral Lane Detection Framework Based on Recurrent Feature-Shift Aggregator

The lane is an important representation of the normal driving of the intelligent vehicle. Due to the complexity of the detection environment (e.g., extreme illumination, vehicle occlusion, etc.) and the slender nature of lanes, Effective collection and fusion of features from different levels of the lane is the key to the task. But research in this area is not deep enough. In this work, a Bilateral Lane Detection Framework (BRNet) is proposed to acquire spatial features and rich contextual features by designing Spatial Path and Context Path. In addition, we propose a strategy to fuse low-level features with deep-level features and utilize a feature fusion module to achieve information integration for different-level features. Experiments on two different lane detection benchmark sets demonstrate the state-of-the-art performance of our method.
Image Classification Based On Pcanet And Salient Feature Fusion

Aiming at the shortcomings of traditional image classification model in extracting features, we propose an improved color contrast algorithm to extract higher quality saliency map. We first analyze the feature extraction ability of HC saliency algorithm in image classification and improve it by adding the location information, then we propose a novel features fusion module to combine the saliency map with the output features from PCANet to enhance the feature expression, contributing to classification capability of the model. The accuracy on Caltech101 and Pascal VOC2007 can achieve excellent performance by using our method.
CommonOcean: Composable Benchmarks for Motion Planning on Oceans

Autonomous vessels can increase safety and reduce emissions compared to human-operated vessels. One important task for autonomous vessels is motion planning. Currently, there are no benchmarks for autonomous vessels to compare different motion planning methods. Thus, we introduce composable benchmarks for motion planning onoceans (CommonOcean), which is available at commonocean. cps. cit. tum. de. A CommonOcean benchmark consists of three elements: cost function, vessel model, and motion planning scenario. Bench-marks can be conveniently composed using unique identifiers for these elements, which are highly modular. CommonOcean is easy to use, because we provide meaningful parameters for vessel models, various motion planning scenarios, and comprehensive documentation. Furthermore, we developed a scenario generation tool, which allows one to effortlessly create new scenarios from marine traffic data. We believe that CommonOcean will lead to a better reproducibility and comparability of research on motion planning for vessels.
Rumor Stance Classification: A Case Study on the Propagation of Political Rumors on the Algerian Online Social Space

Social media is extensively an important means for spreading and sharing news. Nevertheless, the lack of mechanisms for verifying posted information has given rise to a widespread of fake news and rumors. This paper particularly examined a real case of political rumor spreading that marked the Algerian online space for a few months between the end of the year 2020 and the beginning of 2021. It concerns the rumors that have circulated on the social platform YouTube about the health of the President of the Algerian Republic Abdelmadjid Tebboune. By this contribution, we aimed to detect the stances of the Algerian online public towards these rumors by following a Transfer Learning approach. The experiments on the collected data reported good performance of monolingual transformers compared to multilingual transformers in the task of rumor stance classification.
Research on Train Autonomous Positioning Method Based on Long Baseline RTK Resolution

Combining the one-dimensional properties of the actual railway lines with the characteristics of long-distance and high-speed operation of trains, this paper controls the satellite navigation signal simulation source in the laboratory environment, and designs a long baseline dynamic simulation scene of the Qinghai-Tibet Railway train. With the continuous increase of train moving distance, the traditional real-time kinematic (RTK) algorithm gradually can no longer meet the requirements of high precision positioning. Therefore, this article is based on double-difference dual-frequency observations, taking residual relative tropospheric delay and relative ionospheric delay as parameters to be estimated, fixes and restricts the partial ambiguity by setting the judgment criterion of satellite elevation threshold, which improves the success rate and reliability of the integer ambiguity solution. The results show that this method can greatly improve the ratio of fixed ambiguity in long baseline RTK scenes and effectively advance the positioning performance.
Modulated metasurface antennas and beam-formers for mm-waves and beyond

Modulated metasurfaces (MTSs) can be efficiently used to guide the propagation of surface-wave (SW) wavefronts or to gradually radiate the power carried by a SW. These two complementary mechanisms can be applied, respectively, to design beamformers and antennas capable of addressing some of the needs in emerging millimeter wave wireless and future net-works beyond 5G. More precisely, we will present the use of modulated metasurfaces for the design of high-gain antennas and broadband beamformers. We will also show how the later can be efficiently combined with the former to provide multi-beam operation.
Integrating Optimized Fishbone Warehouse Layout, Storage Location Assignment and Picker Routing

—New market environments require warehouses to shorten order response time and improve operational efficiency. In this study, we combine the problems of layout optimization, storage location assignment and picker routing to optimize order picking systems. First, we propose an optimal detailed three- dimensional (3D) design of a fishbone layout for a brownfield warehouse project and ensure the minimum average distance between all storage locations to the pick-up and deposit (P&D) point. Then, according to the turnover-based and correlation storage policies, we establish storage location assignment models and apply the traveling salesman problem (TSP) to plan picker routing. Third, we design a cooperative optimization algorithm (COA) to solve multiple order picking planning problems, which can improve the average optimal travel distance by 9% with good reliability. In addition, the proposed COA has good adaptability when solving practical issues. Finally, we find that the fishbone layout can shorten the picking distance by 10-15% without considering aisle congestion.
Integrated Decision Making and Planning Framework for Autonomous Vehicle considering Uncertain Prediction of Surrounding Vehicles

Uncertainties in dynamical driving environments are crucial to the decision making and trajectory planning modules for autonomous vehicles. Without proper handling of such uncertainties, the decision result could be discontinuous, which causes jitters in the planned trajectory, and finally impacts driving safety and comfort. This paper proposes a decision-making and planning scheme based on Partially Observable Markov Decision Process (POMDP) to deal with the uncertainty in predicted trajectories, where future poses of surrounding vehicles are modelled using a multivariate Gaussian distribution. To better utilize decision results in the form of fine-grained discrete actions, construction of a feasible region constraint is also proposed to form an integrated decision and planning framework. By extending POMDP-determined actions considering uncertain trajectories of surrounding vehicles, the proposed scheme avoids jittery decisions and plans a smooth trajectory to reduce safety hazards and mitigate potential collisions.
Latvia's Relative Wealth in the 1930s: Measured as GDP per capita in 1935 as Benchmark Year 1

The interwar independent Republic of Latvia was among the first ten pioneering states, where a national statistical office published official estimates of total output (1934-1936). Paradoxically; however, Latvia is the Baltic country with the most disputed interwar economic growth performance. According to the authoritative account of Roses and Wolf in The Cambridge Economic History of Modern History (2010), Latvia‘s GDP per capita growth rate was the highest among European countries - 1929-1938. It impressively ranked number ten next to Sweden, France, and Norway. However, according to Norkus and Markevičiūtė (2021), it only surpassed Southern European countries, and its growth performance was mediocre. Both these contradictory estimates are derived by indirect methods. This paper contributes to the resolution of this controversy, directly estimating Latvia‘s GDP in 1935 within the SNA 2008 framework, providing gross value-added calculation for 20 ISIC industries at basic and at market prices. It provides a more fine-grained analysis of the composition of Latvia‘s total output in comparison with interwar historical national accounts, where only 11 industries were distinguished. This estimate provides the benchmark for future research on Latvia‘s interwar economic growth performance. Converting our estimates into monetary units, used in the Maddison Project Database, we assess Latvia’s position in the international GDPpc ranking, coming to conclusions that dovetail with Norkus and Markevičiūtė (2021) findings.
A Brief History of Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a class of Deep Neural Networks that have the ability to generate realistic synthetic data including but not limited to images, text, or even videos. The ability to learn and generate new data with the same statistics as the training set has made it the de-facto standard for generative networks, making older architectures (like autoencoders) obsolete. This paper investigates GANs from the ground-up, analysing various architectures , their advantages, caveats, and use cases in pursuit of understanding its widespread success.
Prediction of Oocyte Donation Intention using the Theory of Planned behavior among Women in Iran: cross-sectional

Objective. Oocyte donation is a complex and multifaceted behavior in women. TPB (Theory of Planned behavior) is a suitable model for explaining and predicting behavior in many behavioral domains. The aim of this study was to predict oocyte donation intention using the theory of planned behavior. Materials and Methods. This cross-sectional study was performed on 560 women who were covered by the comprehensive health service centers in Iran in 2022. Data were collected online using researcher-made questionnaires included oocyte donation knowledge questionnaire and oocyte donation intention questionnaire (based on the TPB constructs). The validity of the questionnaires was confirmed by face and content validity and its reliability was confirmed by Cronbach's alpha coefficient. Data were analyzed in SPSS software (version 16) using hierarchical linear regression. The statistical significance was considered as P<0.05. Results. The results showed that there was a significant positive correlation between the oocyte donation intention with all constructs of planned behavioral theory (P <0.050). Theory of Planned behavior constructs explained 57.6% of the variance of oocyte donation intention. In addition, constructs of perceived power (β = 0. 5 61), control beliefs (β = 0. 454) and normative beliefs (β = 0. 525) were the strongest predictors of oocyte donation intention, respectively. Conclusions. It seems that creating a favorable condition for oocyte donation, culture building, providing comprehensive support in society, and planning and implementing educational courses according to the constructs of the theory of planned behavior can be a great step toward the elimination of oocyte deficiency and infertility problems. The inconsistency between the results of different studies and the role of in the study may due positive of the subjects toward the oocyte donation method our can make difference in women’s behavior and lead to oocyte donation behaviors is their perceived to behavior and overcome the potential which are considered to be the constructs of perceived behavior control.
671. Optimum contribution selection in a dairy cattle population with different relationship matrices

We compared the effects of using three genomic relationship matrices and a pedigree based relationship matrix for optimum contribution selection (OCS) with a constraint on number of sires selected, on genetic gain, kinship and additive genetic variance. This was done by simulating a genomically selected small dairy cattle population. The cattle population underwent genomic selection for 11 generations. In total, 6,000 females and 2,500 males were genotyped per generation. We compared a pedigree-based matrix as well as VanRaden’s method 1 to construct genomic relationship matrices using either allele frequencies of the base population, of the current generation, or allele frequencies among all genotyped animals. The pedigree-based OCS resulted in higher kinship and more loss of genetic variance than using genomic OCS based on base population frequencies. Thus, genomic OCS with genomic relationship matrices using base population frequencies are preferable compared with pedigree-based OCS.
Influences of Conventional Feeding Regimen on the Productivity of Bali Cattle in Samarinda

: The pattern of Bali cattle farming in Indonesia is dominated by traditional livestock systems which are identical to local feeding (without taking into account the needs of cattle. This study aims to determine the effect of conventional feeding on the productivity of Bali cattle in Lempake Village, North Samarinda Sub-district, Samarinda City. The study targeted male Bali cattle and measured chest circumference for body weight gain (BWG), Average Daily Gain (ADG), and feed weighing to obtain Dry Matter Intake (DMI) and feed efficiency. The data were analyzed using a multiple linear analysis with the help of a computer program SPSS to assess the correlation between productivity and the independent variables, namely X 1 = DMI; X 2 = BWG; X 3 = ADG; and X 4 = feed efficiency. The results showed that the samples had an average DMI, BWG, ADG, and feed efficiency of 16.54, 0.43, 0.43 kg/head/day, and 2.58%, respectively. The regression equation obtained was Y = -1,974+0,005X 1 -0,0559X 2 + 11,685X 3 + 1,1287X 4 and the coefficient of determination revealed that all the variables had a 90.6% effect on Bali cattle productivity. Bali cattle had a productivity value of 1.85, which shows that the fattening business was profitable or economically feasible.
Globular Clusters in 19 Northern Abell Clusters

We study the globular cluster (GC) populations of 21 giant elliptical galaxies in 19 Abell clusters. This method, applied here primarily in the R band, is based on the surface brightness fluctuations technique of extragalactic distance measurement. The sample galaxies range in redshift from 5000 to 10,000 \kms, and were selected primarily from the Lauer & Postman (1994) survey of brightest cluster galaxies (BCGs). We find a tight correlation between the GC specific frequency S_N of the central bright galaxy in the cluster and the cluster velocity dispersion. S_N also correlates well with the cluster X-ray temperature and with the number of bright neighboring galaxies, less well with the galaxy profile, and only marginally with galaxy luminosity and overall cluster richness. It does not correlate with cluster morphology class. Thus, unlike galaxy luminosity, S_N is determined by the cluster mass, or density. To account for this situation, we propose that the GCs formed early and in proportion to the available mass, while the luminosity growth of the galaxy was later halted, yielding the observed correlations of S_N with density. We introduce a quantity called $\eta_{GC}$, the number of GCs per unit local cluster mass. For a simple cluster mass model, $\eta_{GC}$ is found to be constant, indicating a uniform GC production rate per unit available mass. A measurement of the Gaussian width $\sigma$ of the GC luminosity function (GCLF) is one of the byproducts of our analysis. In the cosmic microwave background frame, the mean width for this sample is $ = 1.43$ mag, virtually identical to the HST value for M87, the galaxy used to calibrate the mean of the GCLF in this analysis.
R EDISCOVERING N EWTON ’ S GRAVITY AND S OLAR S YSTEM PROPERTIES USING DEEP LEARNING AND IN - DUCTIVE BIASES

We present an approach for using machine learning to automatically discover a physical law and associated properties of the system from real observations. We trained a neural network-based architecture, whose structure corresponds to classical mechanics, to simulate the dynamics of our Solar System from 30 years of observed trajectory data. We then used symbolic regression to extract a symbolic formula for the force law, which our results show matches Newtonian gravity. We ﬁnd that by scaling the model’s predicted acceleration by a trainable scalar variable, we could infer bodies’ (relative) masses despite that they were not observable in the data itself. Though “Newtonian” gravity has of course been known since Newton, our approach did not require knowledge of this physical law, and so our results serve as a proof of principle that our method can extract unknown laws from observed data. This work takes a step towards using modern machine learning tools beyond data processing and analysis, but automated scientiﬁc theory formation and development.
Movin’ on Up? The Impacts of a Large-Scale Home Ownership Lottery in Uruguay

We report on a large-scale urban resettlement program in Uruguay. Over the course of seven years the program randomly assigned thousands of low-income households to ownership of apartments in new buildings in middle-class areas, including a subsidy averaging $ 44,000 per household. We match applicants to comprehensive administrative data on employment, schooling, health, fertility, and voting over the decade subsequent to the move. We ﬁnd that the program led to a small decline in fertility for women and a two percentage point increase in formal employment, but did not drive school attendance. Likely due to the lack of strong spatial inequality in Uruguay, this relocation program did not result in transformative improvements in the lives of its beneﬁciaries.
Radio continuum emission in the northern Galactic plane: Sources and spectral indices from the THOR survey (Corrigendum)

Context Radio continuum surveys of the Galactic plane can find and characterize H II regions, supernova remnants (SNRs), planetary nebulae (PNe), and extragalactic sources. A number of surveys at high angular resolution (≤25″) at different wavelengths exist to study the interstellar medium (ISM), but no comparable high-resolution and high-sensitivity survey exists at long radio wavelengths around 21 cm. Aims. Our goal is to investigate the 21 cm radio continuum emission in the northern Galactic plane at < 25″ resolution. Methods We observed a large percentage of the Galactic plane in the first quadrant of the Milky Way (l = 14.0−67.4° and |b| ≤ 1.25°) with the Karl G. Jansky Very Large Array (VLA) in the C-configuration covering six continuum spectral windows (SPW). These data provide a detailed view on the compact as well as extended radio emission of our Galaxy and thousands of extragalactic background sources. Results We used the BLOBCAT software and extracted 10 916 sources. After removing spurious source detections caused by the side lobes of the synthesized beam, we classified 10 387 sources as reliable detections. We smoothed the images to a common resolution of 25″ and extracted the peak flux density of each source in each SPW to determine the spectral indices α (assuming I(ν) ∝ να). By cross-matching with catalogs of H II regions, SNRs, PNe, and pulsars, we found radio counterparts for 840 H II regions, 52 SNRs, 164 PNe, and 38 pulsars. We found 79 continuum sources that are associated with X-ray sources. We identified 699 ultra-steep spectral sources (α < −1.3) that could be high-redshift galaxies. About 9000 of the sources we extracted are not classified specifically, but based on their spatial and spectral distribution, a large percentage of these are likely to be extragalactic background sources. More than 7750 sources do not have counterparts in the SIMBAD database and more than 3760 sources do not have counterparts in the NED database. Conclusions Studying the long wavelengths centimeter continuum emission and the associated spectral indices allows us to characaterize a large percentage of Galactic and extragalactic radio sources in the area of the northern inner Milky Way. This database will be extremely useful for future studies of a diverse set of astrophysical objects.