Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast and Robust Lane Detection via Online Re-Parameterization and Hybrid Attention

Lane detection is a vital component of intelligent driving systems, offering indispensable functionality to keep the vehicle within its designated lane, thereby reducing the risk of lane departure. However, the complexity of the traffic environment, coupled with the rapid movement of vehicles, creates many challenges for detection tasks. Current lane detection methods suffer from issues such as low feature extraction capability, poor real-time detection, and inadequate robustness. Addressing these issues, this paper proposes a lane detection algorithm that combines an online re-parameterization ResNet with a hybrid attention mechanism. Firstly, we replaced standard convolution with online re-parameterization convolution, simplifying the convolutional operations during the inference phase and subsequently reducing the detection time. In an effort to enhance the performance of the model, a hybrid attention module is incorporated to enhance the ability to focus on elongated targets. Finally, a row anchor lane detection method is introduced to analyze the existence and location of lane lines row by row in the image and output the predicted lane positions. The experimental outcomes illustrate that the model achieves F1 scores of 96.84% and 75.60% on the publicly available TuSimple and CULane lane datasets, respectively. Moreover, the inference speed reaches a notable 304 frames per second (FPS). The overall performance outperforms other detection models and fulfills the requirements of real-time responsiveness and robustness for lane detection tasks.
Serratula nudicaulis (L.) DC. (Compositae) e Bulbocodium vernum L. (Liliaceae) rinvenuti sul M. Baldo settentrionale (Italia, provincia di Trento)

Riassunto Viene segnalata la presenza di Serratula nudicaulis (L.) DC. e di Bulbocodium vernum L. in una località del M. Baldo (Provincia di Trento). S. nudicaulis è nuova per le Alpi-centro orientali; B. vernum è nuovo per la regione Trentino-Alto Adige ed è la terza stazione delle Alpi centro-orientali. Vengono presi in considerazione aspetti morfologici, ecologici, corologici, conservazionistici e sono discusse ipotesi concernenti l'origine di questa stazione.
Hierarchical Intelligent Radio Access Network Slicing for Differential Service Level Agreement Guaranteeing

Network slicing (NS) can enable diverse communication services for vertical industries. In radio access network slicing, differential service level agreement (SLA) guaranteeing is an essential resource management task. Benefit from powerful data analysis capabilities, deep learning (DL) is suitable for intelligent resource management under the cases of complex constraints and time-varying states. Thus, DL has been used to manage resources for NS recently. However, the training of these DL-assisted methods is time-consuming and it is difficult to keep high SLA satisfaction rates dynamically. To address this problem, we propose a hierarchical intelligent NS resource configuration method via organically integrating NS preconfiguration models based on deep neural networks and NS reconfiguration models using multiarm bandits. A factory automation system is established to evaluate our proposed methods on different industrial services. Simulation and experimental results demonstrate that our proposed methods outperform benchmarks comprehensively.
Origin of ash deposits in the Santiago area, Central Chile

Summary Non-stratified, poorly-sorted rhyolitic ash deposits previously described as morainic and mudflow deposits are reinterpreted as ignimbrites emplaced in the topographically lower parts of the Santiago basin by pyroclastic flows. These flows probably originated from sources high in the Andes, and swept down the deep Maipo valley to spread out over the sediments filling the basin. An ignimbrite with a zone of dense welding but otherwise of similar character to the non-welded ashes of the basin occurs in one of the Andean valleys; and peculiar ‘pipe’ structures in the ashes of the basin support a pyroclastic flow origin. Further study of the nature and origin of these rocks may lead to a better understanding of the structural evolution of the area.
Transcription factor CsTT8 promotes fruit coloration by positively regulating the methylerythritol 4-phosphate pathway and carotenoid biosynthesis pathway in citrus (Citrus spp.)

Abstract Carotenoids directly influence citrus fruit color and nutritional value, which is critical to consumer acceptance. Elucidating the potential molecular mechanism underlying carotenoid metabolism is of great importance for improving fruit quality. Despite the well-established carotenoid biosynthetic pathways, the molecular regulatory mechanism underlying carotenoid metabolism remains poorly understood. Our previous studies have reported that the Myc-type basic helix–loop–helix (bHLH) transcription factor (TF) regulates citrus proanthocyanidin biosynthesis. Transgenic analyses further showed that overexpression of CsTT8 could significantly promote carotenoid accumulation in transgenic citrus calli, but its regulatory mechanism is still unclear. In the present study, we found that overexpression of CsTT8 enhances carotenoid content in citrus fruit and calli by increasing the expression of CsDXR, CsHDS, CsHDR, CsPDS, CsLCYE, CsZEP, and CsNCED2, which was accompanied by changes in the contents of abscisic acid and gibberellin. The in vitro and in vivo assays indicated that CsTT8 directly bound to the promoters of CsDXR, CsHDS, and CsHDR, the key metabolic enzymes of the methylerythritol 4-phosphate (MEP) pathway, thus providing precursors for carotenoid biosynthesis and transcriptionally activating the expression of these three genes. In addition, CsTT8 activated the promoters of four key carotenoid biosynthesis pathway genes, CsPDS, CsLCYE, CsZEP, and CsNCED2, directly promoting carotenoid biosynthesis. This study reveals a novel network of carotenoid metabolism regulated by CsTT8. Our findings will contribute to manipulating carotenoid metabolic engineering to improve the quality of citrus fruit and other crops.
Incessant ovulation: a review of its importance in predicting cancer risk

Estrous cycles are recurring changes in therian mammals induced by estrogen, progesterone, and other hormones culminating in endometrial proliferation, ovulation, and implantation if fertilization occurred. In women, the estrous cycle is the menstrual cycle; but, unlike most mammals, the end of an infertile cycle is marked by endometrial sloughing and the start of another without an anestrous phase. Women stop cycling at menopause, while in most mammals, cycles continue until death. Epidemiologic studies identified menarche, menopause, births, lactation, and oral contraceptive (OC) use as key risk factors for ovarian, breast, and endometrial cancers. A composite variable was created to estimate the number of cycles not interrupted by events that stop ovulation. Captured by the phrase “incessant ovulation”, repetitive cycles were first postulated to affect ovarian cancer risk and later extended to breast and endometrial cancers. These associations could be explained by cumulative effects of repetitive tissue changes within reproductive organs, immune consequences of repetitive ovulation through the glycoprotein mucin 1, and residual effects of past ovulations that enhance ovarian production of testosterone. The latter two pathways could affect the risk for cancers in other organs not considered “reproductive”.
Comparisons of box model calculations and measurements of formaldehyde from the 1997 North Atlantic Regional Experiment

[1] Formaldehyde (CH2O) measurements from two independent instruments are compared with photochemical box model calculations. The measurements were made on the National Oceanic and Atmospheric Administration P-3 aircraft as part of the 1997 North Atlantic Regional Experiment (NARE 97). The data set considered here consists of air masses sampled between 0 and 8 km over the North Atlantic Ocean which do not show recent influence from emissions or transport. These air masses therefore should be in photochemical steady state with respect to CH2O when constrained by the other P-3 measurements, and methane oxidation was expected to be the predominant source of CH2O in these air masses. For this data set both instruments measured identical CH2O concentrations to within 40 parts per trillion by volume (pptv) on average over the 0–800 pptv range, although differences larger than the combined 2σ total uncertainty estimates were observed between the two instruments in 11% of the data. Both instruments produced higher CH2O concentrations than the model in more than 90% of this data set, with a median measured-modeled [CH2O] difference of 0.13 or 0.18 ppbv (depending on the instrument), or about a factor of 2. Such large differences cannot be accounted for by varying model input parameters within their respective uncertainty ranges. After examining the possible reasons for the model-measurement discrepancy, we conclude that there are probably one or more additional unknown sources of CH2O in the North Atlantic troposphere.
Happiness Is ‘Being Yourself’: Psychological Safety and Fun in Hybrid Work

Our long-term research project is concerned with fun and humour at work and the impacts on employee happiness and well-being. In this study, we explore the relationship between workplace fun and psychological safety and their implications for a positive work context. Our research problem investigates two key questions: How do the experiences of psychological safety and fun at work intersect in hybrid work conditions? What are the implications for positive workplace relations? We present qualitative empirical data from our in-depth, ethnographic engagement in two corporate companies. Both companies recently transitioned to hybrid work modes, providing a relevant and contemporary context for our study. Our findings are generated from our structured, thematic analysis, eliciting themes of risk during transitions, unsafe fun, safety to be yourself, and leadership implications. These themes are discussed in relation to the extant literature, and we extend both fun and psychological safety theory by identifying a circular relationship between both constructs specifically related to the modern form of hybrid work. Implications are identified along with the potential for future research.
Behaviour of New Zealand Glaciers and Atmospheric Circulation Changes over the Past 130 years

There is renewed interest as to the impact of future climate change on temperate alpine glaciers because of their role as indicators of past and ongoing changes, and due to their possible involvement in sea-level rise. Substantial changes in termini of New Zealand glaciers since the nineteenth century are compared with variations in atmospheric circulation patterns over the southwest Pacific region. Reconstructed sea-level pressure patterns are used back to 1911. Atmospheric circulation indices obtained from pressure differences between appropriate stations are extended back to the 1860s. Circulation anomalies are examined for winter (related to glacier accumulation), and for summer (related to glacier ablation). Behaviour of glacier termini is found to be strongly linked with circulation changes, especially in summer. Latitudinal shifts in the southern margin of the subtropical high pressure zone are identified as important. An advance of some glaciers since 1980 is consistent with circulation changes over the New Zealand region induced by two large El Niño events.
Constraining the First Year of Ice‐Free Arctic: Importance of Regional Perspective

The ice‐free Arctic in summer not only symbolizes human‐induced climate change but also highlights the need to critically consider climate change adaptation policies. To constrain projections when the Arctic Ocean will first become ice‐free, studies have typically combined the historical observations of the Arctic sea ice area (SIA) with future emission scenario simulations. However, these studies primarily relied on the historical climatology and trend of the Arctic SIA, without considering regional variations. In this study, we analyze September SIA projections using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations, while considering Arctic sub‐regions. Additionally, we assess the impact of incorporating sub‐region September SIA when constraining the first year of ice‐free Arctic in September. CMIP6 models generally overestimate the historical September SIA decreasing trend in the central Arctic, whereas they underestimate this trend in the surrounding shelf seas. The central Arctic, where the region expected to retain sea ice for the longest period in the future, holds particular significance for projecting when the Arctic will first become ice‐free. Consequently, when we employ the historical trend of September SIA in the central Arctic as a constraint, observationally‐constrained projections suggest a delay of 12 years (2056) for the first ice‐free September compared to raw/unconstrained projections (2044 in model average) under a high‐emission scenario. These findings underscore the importance of considering model biases in central Arctic SIA when constraining projections of the first year of ice‐free Arctic, which may occur later than previously projected in many studies.
Dehydration in cold clouds is enhanced by a transition from cubic to hexagonal ice

Cubic ice (Ic) is a metastable form of ice with very similar physical properties but different crystal symmetry than normal, hexagonal ice (Ih). Below 200 K, water preferentially nucleates to Ic, then transforms to the more stable Ih in minutes to days. As a metastable phase, Ic will necessarily have a higher vapor pressure than Ih. A cloud parcel model shows that nucleation to Ic, conversion to Ih, and the vapor pressure differential together produce larger ice crystals that can more effectively dehydrate air. The modelled effect is largest at 180 to 200 K, just the range most important for the tropical tropopause and polar stratospheric clouds. The cloud model also produces a wider size distribution when Ic is included, in agreement with observations.
Hash Function Design Based on Hybrid Five-Neighborhood Cellular Automata and Sponge Functions

In today’s world of pervasive computing, all the devices have become smart. The need for securing these devices becomes a need of the hour. The traditional cryptographic algorithms will not be ideal for small devices, and this opens a new area of cryptography named lightweight cryptography, which focuses on the implementation of cryptographic algorithms in resource-constrained devices without compromise in security. Cryptographic hash functions enable detection of message tampering by adversaries. This paper proposes a lightweight hash function that makes use of sponge functions and higher radii hybrid cellular automata (CAs). The proposed hash function shows good cryptographic properties as well as collision resistance and serves as an ideal hash function for lightweight applications.
Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes

Simultaneous localization and mapping (SLAM) is a fundamental problem in robotics and computer vision. It involves the task of a robot or an autonomous system navigating an unknown environment, simultaneously creating a map of the surroundings, and accurately estimating its position within that map. While significant progress has been made in SLAM over the years, challenges still need to be addressed. One prominent issue is robustness and accuracy in dynamic environments, which can cause uncertainties and errors in the estimation process. Traditional methods using temporal information to differentiate static and dynamic objects have limitations in accuracy and applicability. Nowadays, many research trends have leaned towards utilizing deep learning-based methods which leverage the capabilities to handle dynamic objects, semantic segmentation, and motion estimation, aiming to improve accuracy and adaptability in complex scenes. This article proposed an approach to enhance monocular visual odometry’s robustness and precision in dynamic environments. An enhanced algorithm using the semantic segmentation algorithm DeeplabV3+ is used to identify dynamic objects in the image and then apply the motion consistency check to remove feature points belonging to dynamic objects. The remaining static feature points are then used for feature matching and pose estimation based on ORB-SLAM2 using the Technical University of Munich (TUM) dataset. Experimental results show that our method outperforms traditional visual odometry methods in accuracy and robustness, especially in dynamic environments. By eliminating the influence of moving objects, our method improves the accuracy and robustness of visual odometry in dynamic environments. Compared to the traditional ORB-SLAM2, the results show that the system significantly reduces the absolute trajectory error and the relative pose error in dynamic scenes. Our approach has significantly improved the accuracy and robustness of the SLAM system’s pose estimation.
Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network

Coffee is one of the famous plants’ commodities in the world. There are some coffee powders such as Arabica dan Robusta. This study aimed to identify two various coffee powders, Arabica and Robusta based on the blended aroma profiles, employing the backpropagation Artificial Neural Network (ANN). Four taste sensors were employed, namely TGS 2602, 2610, 2611, and 2620, to capture the diverse coffee aroma. These detectors were combined with the aroma sensors having transducers integrated with signal amplifiers or processors, which featured a load of 10 KΩ resistance. Three aroma types were investigated, namely Arabica coffee, Robusta coffee, and without coffee beans. The neural network architecture consisted of four inputs from all sensors, with one hidden layer housing eight neurons. Two neuron outputs were employed for classification, with 70 samples used for training ANN for each type. During the training phase, the developed neural network showed an impressive accuracy rate of 91.90%. TGS 2602 and 2611 sensors showed the most significant differences among the three aroma types. When analyzing ground Robusta coffee, TGS 2602 and 2611 sensors recorded 2.967 volts and 1.263 volts, with a gas concentration of 17.92 ppm and 2441.8 ppm. Similarly, the sensors for ground Arabica coffee displayed 3.384 volts and 1.582 volts with a gas concentration of 20.445 ppm and 3058.5 ppm in both TGS 2602 and 2611, respectively. The implemented ANN with aroma sensor as input successfully identify the coffee powders.
Efficacy of frequency on detecting targets in foliage using incoherent change detection

The effectiveness of applying incoherent change detection to multipass SAR images and targets in foliage is affected by the operating radar frequency band. Incoherent change detection is achieved by taking the weighted difference of the magnitude of two well registered passes of SAR imagery. Items which change between two passes, such as a target present in the first pass and not present in the second pass, will appear in the weighted difference image. With well-registered wideband SAR imagery, images can be divided into frequency bands and evaluated using incoherent change detection. An Environmental Research Institute of Michigan (ERIM) Rail SAR experiment provides such a data collection. The Rail SAR is characterized by polarimetric, wideband (400 MHz - 1.3 GHz), multipass (with and without targets), well-registered SAR images. The ERIM Rail SAR data is divided into a number of frequency bands which simulate the high- band Stanford Research Institute, International (SRI) ultra- wideband radar (UWBR) (350-550 MHz), the Loral miniature SAR (MSAR) (500-800 MHz), and the Naval Air Warfare Center (NAWC) P-3 upgraded UWBR (200-900 MHz) sensor. This paper shows how these sensors work on targets in foliage using incoherent change detection and provides an experimental measurement of upper-bound performance.
Neural Network Mapping of Industrial Robots' Task Times for Real-Time Process Optimization

The ability to predict the maximal performance of an industrial robot executing non-deterministic tasks can improve process productivity through time-based planning and scheduling strategies. These strategies require the configuration and the comparison of a large number of tasks in real time for making a decision; therefore, an efficient task execution time estimation method is required. In this work, we propose the use of neural network models to approximate the task time function of a generic multi-DOF robot; the models are trained using data obtained from sophisticated motion planning algorithms that optimize the shape of the trajectory and the executed motion law, taking into account the kinematic and dynamic model of the robot. For scheduling purposes, we propose to evaluate only the neural network models, thus confining the online use of the motion planning software to the full definition of the actually scheduled task. The proposed neural network model presents a uniform interface and an implementation procedure that is easily adaptable to generic robots and tasks. The paper’s results show that the models are accurate and more efficient than the full planning pipeline, having evaluation times compatible with real-time process optimization.
Sargassum horneri Extract Attenuates Depressive-like Behaviors in Mice Treated with Stress Hormone

Sargassum horneri, a brown seaweed, is known for its various health benefits; however, there are no reports on its effects on depression. This study aimed to investigate the antidepressant effects of S. horneri ethanol extract (SHE) in mice injected with corticosterone (CORT) and to elucidate the underlying molecular mechanisms. Behavioral tests were conducted, and corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and CORT levels were measured. A fluorometric monoamine oxidase (MAO) enzyme inhibition assay was performed. Neurotransmitters like serotonin, dopamine, and norepinephrine levels were determined. Moreover, the ERK-CREB-BDNF signaling pathway in the prefrontal cortex and hippocampus was evaluated. Behavioral tests revealed that SHE has antidepressant effects by reducing immobility time and increasing time spent in open arms. Serum CRH, ACTH, and CORT levels decreased in the mice treated with SHE, as did the glucocorticoid-receptor expression in their brain tissues. SHE inhibited MAO-A and MAO-B activities. In addition, SHE increased levels of neurotransmitters. Furthermore, SHE activated the ERK-CREB-BDNF pathway in the prefrontal cortex and hippocampus. These findings suggest that SHE has antidepressant effects in CORT-injected mice, via the regulation of the hypothalamic-pituitary-adrenal axis and monoaminergic pathway, and through activation of the ERK-CREB-BDNF signaling pathway. Thus, our study suggests that SHE may act as a natural antidepressant.
Drilling the Eger Rift in Central Europe

T he workshop was aimed at discussing the scientif ic goals that would justif y deep drilling in the western part of the Eger Rift area (Fig. 1). The Eger Rift, located in the northwestern part of the Bohemian Massif, is a key site that has attracted the international geoscience community for many decades. The rift is the result of young, deep-seated geodynamic processes manifested by episodic Cenozoic volcanism (the youngest at 0.2–0.5 Ma), repeated earthquake swarms, numerous mineral springs, CO2 emissions with high 3He content, and abundant mofettes. T he crust– mantle boundary (27 km) and lithosphere–asthenosphere boundary (80–90 km) are shallow compared to the conditions below the Bohemian Massif. Also, surface heat flow (60–80 mW m -2 ) within the rift zone is higher than within