Miracle-dz/UniFAR-FTUs huggingface dataset:
SRNet: Striped Pyramid Pooling and Relational Transformer for Retinal Vessel Segmentation

The morphology of retinal vessels is crucial for diagnosing and screening retinal diseases such as age-related macular degeneration and diabetic retinopathy. Retinal vessels segmentation is an indispensable part of retinal disease screening and diagnosis. However, due to the inherent complex structural features of retinal vessels, it remains a challenging visual task. Based on the type of input, retinal vessels segmentation approaches can be roughly divided into both image-level and patches-level methods, which have their respective benefits and drawbacks. To better leverage both input methods, we de-sign a Relational Transformer Module (RTM) to effectively combine local patches-level information with image-level global contextual information. Furthermore, retinal vessels exhibit varying lengths with tree-like branching patterns, making the classical rectangular pooling inefficient in capturing accurate vessels information because they are better suited for uniformly distributed objects. To better capture contextual information, we further developed a Striped Pyramid Pooling Module (SPPM) to adapt to the tree-like distribution of retinal vessels. Based upon these foundations, we propose a retinal vessels segmentation Net work with the S triped Pyramid Pooling Module and the R elational Transformer Module ( SRNet ). Experimental validation showed that our SRNet outperforms other advanced methods on the DRIVE and CHASE datasets.
Detection of fallen logs from high-resolution UAV images

Background: High-resolution images from unmanned aerial vehicles (UAVs) can be used to describe the state of forests at regular time periods in a cost-effective manner. The purpose of this study was to assess the performance of a line template matching algorithm, the Hough transformation, for detecting fallen logs from UAV-based high-resolution RGB images. The suggested methodology does not aim to replace any known aerial method for log detection, rather it is more oriented to the detection of fallen logs in open forest stands with a high percentage of log visibility and straightness. Methods: This study describes a line template matching algorithm that can be used for the detection of fallen logs in an automated process. The detection technique was based on object-based image analysis, using both pixel-based and shape descriptors. To determine the actual number of fallen logs, and to compare with the ones predicted by the algorithm, manual visual assessment was used based on six high-resolution orthorectified images. To evaluate if a line matched, we used a voting scheme. The total number of detected fallen logs compared with the actual number of fallen logs based on several accuracy metrics. To evaluate predictive models we tested the cross-validation mean error. Finally, to test how close our results were to chance, we used the Cohen`s Kappa coefficient. Results: The detection algorithm found 136 linear objects, of which 92 of them were detected as fallen logs. From the 92 detected fallen logs, 86 were correctly predicted by the algorithm and 24 were falsely detected as fallen logs. The calculated amount of observed agreement was equal to 0.78, whereas the expected agreement by chance was 0.61. Finally, the kappa statistic was 0.44. Conclusions: Our methodology had high reliability for detecting fallen logs based on total user‘s accuracy (94.9%), whereas a Kappa of 0.44 indicated there was good agreement between the observed and predicted values. Also, the cross-validation analysis denoted the efficiency of the proposed method with an average error of 16%.
From velocity and attenuation tomography to rock physical modeling: Inferences on fluid‐driven earthquake processes at the Irpinia fault system in southern Italy

We retrieve 3‐D attenuation images of the crustal volume embedding the fault system associated with the destructive Ms 6.9, 1980 Irpinia earthquake by tomographic inversion of t* measurements. A high QP anomaly is found to be correlated with the 1980 fault geometry, while the QS model shows regional‐scale variations related to the NE edge of the uplifted pre‐Tertiary limestone. An upscaling strategy is used to infer rock properties such as porosity, consolidation, type of fluid mixing, and relative saturation percentage at 8–10 km fault depth. We constrain the porosity and consolidation in the ranges 4–5% and 5–9, respectively, with the possible fluid mixes being both brine‐CO2 and CH4‐CO2. The consolidation parameter range indicates high pore pressures at the same depths. These results support the evidence for a fracture system, highly saturated in gases and a seismicity triggering mechanism at the fault zone, which is strongly controlled by fluid‐induced pore pressure changes.
Tertiary Deep-Marine Reservoirs of the North Sea Region

Discovery of the Arbroath, Montrose and Forties fields initiated intensive exploration of the Tertiary deep-marine play in the North Sea region. Subsequent discoveries demonstrated the success of this play and the geological diversity of the depositional systems. The play is now mature and in many areas the remaining exploration potential is likely to be dominated by small, subtle traps with a major component of stratigraphic trapping. Economically marginal discoveries need an in-depth understanding of subsurface uncertainty to mitigate risk with limited appraisal wells. Mature fields require detailed geological understanding in the search for the remaining oil. This volume focuses on the regional depositional setting of these deep-marine systems, providing a stratigraphic and palaeogeographical context for exploration, and development case histories that outline the challenges of producing from these reservoirs. The fields are arranged around the production life cycle, describing the changing needs of geological models as the flow of static and dynamic data refines geological understanding and defines the nature of new opportunities as fields mature.
An Empirical Analysis of Administrative Scale Economies in Victorian Local Government

A voluminous literature has empirically examined scale economies in local government functions, particularly in local urban transportation, water and wastewater provision. Conversely, little work has focused on the empirical investigation of economies of scale in municipal administration, including in Australian local government. In order to address this gap in the empirical literature, in this paper we examine administrative scale economies and task density in the Victorian (VIC) local government system for the period 2014/15 to 2018/19 using a data panel. Our findings reveal no administrative scale economies in VIC local government. By contrast, task density is a key determinant of administrative intensity. We conclude by canvassing some public policy implications of our findings.
Smart Principles and Smart Principals: Smart Mechanism to Enhance Street-Level Accountability

This article presents a novel theoretical approach that aims to enhance the accountability of street-level bureaucrats. The authors conceptualize changes and reforms in and around the public sector as a smart mechanism that is composed of two key dimensions; a) smart principles (i.e., institutions and technological tools that support citizen participation), and b) smart principals (i.e., citizens who adopt those smart principles in monitoring and evaluating street-level agents’ behaviors). Then the authors suggest a theoretical framework that explains how applying the smart mechanism can limit deviant behaviors of street-level bureaucrats and contribute to enhancing street-level accountability.
ASSESSMENT OF THE EASTERN ATLANTIC SAILFISH STOCK USING JABBA MODEL

We first attempted to apply the JABBA Models for the eastern Atlantic sailfish ( Istiophorus platypterus ) with the best available data through 2021. Results suggest reasonably robust fits to the data as judged by the presented model diagnostic results. The resulting stock status for 2021 was generally consistent and predicted with high probabilities that current fishing levels are sufficiently low to preclude overfishing (F 2021 < F MSY ), whereas biomass is above the sustainable levels that can produce MSY (B 2021 >B MSY ). As such, our models conclusively estimate that stock is not overfished and no subject to overfishing, with probability ranging from 86.4% - 95.5% for the green quadrant of Kobe. Similarly, it was not observed substantial differences in biomass and fishing mortality yearly trends among models, with the S2 model indicating a slightly more productive stock.
Improvement Obtained by Ordering the Tree-Search of an Agent for Draughts which Learns by Reinforcement

VisionDraughts is a good automatic draughts player which uses Temporal Diﬀerence learning to adjust the weights of an artiﬁcial Neural Network whose role is to help an alpha-beta algorithm combined with transposition table and iterative deepening to choose the best move corresponding to the current board states represented in the input of the network. The output of the network (prediction) is a value that indicate to witch extent the input state is favorable to the agent and it will be used in the process of updating the weights. This work improves the general performance of VisionDraughts by using the data stored in the transposition table to order the tree-search generated by the alpha-beta algorithm. As shown in this paper, this strategy will speed up the search process and will substantially reduce the occurrence of endgame loops.
Monocular Relative Pose Estimation Pipeline for Uncooperative Resident Space Objects

This paper aims to present a deep learning-based pipeline for estimating the pose of an uncooperative target spacecraft, from a single grayscale monocular image. The possibility of enabling autonomous vision-based relative navigation in close proximity to a non-cooperative Resident Space Object (RSO) would be especially appealing for mission scenarios such as on-orbit servicing and active debris removal. The Relative Pose Estimation Pipeline (RPEP) proposed in this work leverages state-of-the-art Convolutional Neural Network (CNN) architectures to detect the features of the target spacecraft using monocular vision. Speciﬁcally, the overall pipeline is composed of three main subsystems. The input image is ﬁrst of all processed using an object detection CNN that localizes the bounding box enclosing our target. This is followed by a second CNN that regresses the location of semantic keypoints of the spacecraft. Eventually, a geometric optimization algorithm exploits the detected keypoint locations to solve for the ﬁnal relative pose. The proposed pipeline demonstrated centimeter/degree-level pose accuracy on the Spacecraft PosE Estimation Dataset (SPEED), along with considerable robustness to changes in illumination and background conditions. In addition, the architecture showed to generalize well on real images, despite having exclusively exploited synthetic data from SPEED to train the CNNs.
Optimal Decision Making for Big Data Processing at Edge-Cloud Environment : SDN Perspective

With the evolution of Internet and extensive usage of smart devices for computing and storage, cloud computing has become popular. It provides seamless services such as; ecommerce, e-health, e-banking, etc to the end users. These services are hosted on massive geo-distributed data centers (DCs) which may be managed by different service providers. For faster response time, such a data explosion creates the need to expand DCs. So, to ease the load on DCs, some of the applications may be executed on the edge devices near to the proximity of the end users. However, such a multi edge-cloud environment involves huge data migrations across the underlying network infrastructure which may generate long migration delay and cost. Hence, in this paper, an efficient workload slicing scheme is proposed for handling data-intensive applications in multi edge-cloud environment using software defined networks (SDN). To handle the inter-DC migrations efficiently, a SDN-based control scheme is presented which provides energyaware network traffic flow scheduling. Finally, a multi-leader multifollower Stackelberg game is proposed to provide cost-effective interDC migrations. The efficacy of the proposed scheme is evaluated on Google workload traces using various parameters. The results obtained shows the effectiveness of the proposed scheme.
Factors associated with definitive observation unit care in patients with non-traumatic epistaxis: a retrospective observational study

Epistaxis is one of the common causes of emergency department (ED) visits. However, most cases are not severe and undergo an unnecessary medical evaluation, especially in non-traumatic epistaxis. This study investigated how many patients require a definite observational unit (DOU) and what factors are associated with DOU among non-traumatic epistaxis patients. This retrospective observational study included 1197 non-traumatic epistaxis patients who visited the ED from January 2016 to December 2020. Multiple logistic regression analysis was performed to evaluate the association between risk factors and DOU care. In addition, the receiver operating characteristic (ROC) curve for predicting DOU care was analyzed to estimate the diagnostic ability of risk factors. A total number of 1122 patients with non-traumatic epistaxis were included in the final analysis. Among them, 41 (3.65%) patients needed DOU care. Male sex (odds ratio (OR) = 3.606, p = 0.003), hypertension (OR = 2.362, p = 0.020), inter-hospital transfer (OR = 2.358, p = 0.039), verbal mental status (OR = 29.436, p = 0.035), hemoglobin (Hb) level (OR = 0.724, p < 0.001), revisit after initial discharge for epistaxis (OR = 8.813, p < 0.001), and delayed ED arrival (≥180 min) (OR = 2.451, p = 0.030) were significant factors for DOU care. In addition, the area under the curve of the multiple logistic regression model for predicting DOU care was 0.870. Among the patients who visited ED due to non-traumatic epistaxis, only 3.65% of patients required DOU care. Male sex, mental status, Hb level, ED revisit after initial discharge, inter-hospital transfer and delayed ED arrival (>180 min) were associated with DOU care. These findings will help to triage epistaxis patients before they visit the ED.
UNSUPERVISED FEATURE SELECTION WITH HILBERT-SCHMIDT INDEPENDENCE CRITERION LASSO

In recent years, it has been witnessed that feature selection can be tackled with the Hilbert-Schmidt independence criterion (HSIC) due to its eﬀectiveness and low computational complexity. However, most of the HSIC-based feature selection methods suﬀer from the following limitations. First, these methods are usually just applicable to labeled data, which is not desirable since most of data in real-world applications is unlabeled. Second, existing HSIC-based unsupervised feature selection methods only addressed the general correlation between the selected features and output values that express the underlying cluster structure, while the redundancy between diﬀerent features was neglected. To address these problems, we present an unsupervised feature selection method based on the HSIC least absolute shrinkage and selection operator (HSIC Lasso), which not only has a clear statistical interpretation that minimum redundant features with maximum dependence on output values can be found in terms of the HSIC, but also enables the global optimal solution to be computed eﬃciently by solving a Lasso optimization problem. Based on the proposed method, a uniﬁed view of feature selection based on the HSIC Lasso is also discussed. The proposed method was demonstrated with several benchmark examples.
PERSPECTIVE The macroevolutionary consequences of niche construction in microbial metabolism

Microorganisms display a stunning metabolic diversity. Understanding the origin of this diversity requires understanding how macroevolutionary processes such as innovation and diversiﬁcation play out in the microbial world. Metabolic networks, which govern microbial resource use, can evolve through diﬀerent mechanisms, e.g. horizontal gene transfer or de novo evolution of enzymes and pathways. This process is governed by a combination of environmental factors, determining selective pressures, and the constraints imposed by the complex genetic architecture of metabolic networks. In addition, many independent results hint that the process of niche construction, by which organisms actively modify their own and each other’s niches and selective pressures, could play a major role in microbial innovation and diversiﬁcation. Yet, the general principles by which niche construction shapes microbial macroevolutionary pa�erns remain largely unexplored. Here, we discuss several new hypotheses and directions, and suggest metabolic modeling methods that could allow us to explore large-scale empirical genotype-phenotype-environment spaces in order to study the macroevolutionary eﬀects of niche construction. We hope that this short piece will further stimulate a systematic and quantitative characterization of macroevolutionary pa�erns and processes in microbial metabolism.
A DEMONSTRATION MSE FRAMEWORK FOR WESTERN SKIPJACK TUNA INCLUDING OPERATING MODEL CONDITIONING

This report documents the demonstration of a management strategy evaluation for Western Atlantic skipjack tuna using the MSEtool R package. The stock is defined using catches from the Brazilian baitboat and handline fleets in the Southwest Atlantic Ocean. The output of a Stock Reduction Analysis (SRA) model, fitted to the catches, CPUE, and length compositions up to 2018, was used to set up and condition OMs. Starting from a base OM, additional OMs were generated to incorporate uncertainty in natural mortality, growth, maturity, selectivity, and steepness to create a reference set of OMs. A suite of example management procedures (MPs), including fixed TACs, index-slope MPs, and harvest control rules (HCRs), was tested in closed-loop simulation. This paper discusses the future work, including additional OM scenarios and consultation with managers and stakeholders, to identify candidate management procedures and performance metrics.
Atlantic Tropical Cyclone Frequency and Intensification Long-termForecast: An AnalysisBased on Multi-decadal Analogues and Multicentennial Proxy Records

The occurrences and impacts of Atlantic tropical cyclones (ATC), which in their higher intensities are known as hurricanes, tend to vary from interannual to interdecadal, and longer time-scales [1-2]. In order to reduce damages and loss of lives caused by this variability , frequent estimations ,based on paleoclimate studies, of future trends for frequency and intensification are critical [3-4]. Taking into account the recently detected multidecadal scale patterns in global cli mate [5], this paper presents bothan experimental analogue forecast of ATC frequencies for the next five years, achieved by using a statistical analysis of the ATC record for the period 1851
THEMATIC ARTICLE Advancing in the neuroleadership field: a systematic and integrative review

Scholars and managers have long discussed the best profile for successful leadership, which can influence employee performance and organizational growth. Despite leadership being the focus of many studies and continuous training, what explains leaders’ effectiveness within institutions and companies remains unclear. To better understand this phenomenon, we perform a comprehensive review of empirical studies and theoretical essays (N = 93) exploring the application of neuroscience in an organization’s practices and leaders’ development. We conducted a systematic review using these references and built narrative arguments organizing understudied areas and defining a process to classify and integrate trends. The study is different from previous research in identifying groups of research areas such as the biological, brain, psychological, management, emotional, and cognitive approaches, as well as research topics such as culture, decision-making, engagement, ethics, and human development. Moreover, we suggest a theoretical framework comprising those approaches and the main research topics to stimulate the move of the field forward. The article contributes to the literature by organizing the state of the art of research and presenting neuroleadership as a construct. We conclude with suggestions for future research that we hope will address existing gaps and contribute to building relevant theories and policy recommendations.
The Role of Intermediate Factors in Explaining Precedential Constraint

Formal accounts of precedential constraint have attracted much attention in AI and Law over the past decade. A recent development in this area has been the proposal to give a finer grained account using the intermediate factors taken from factor hierarchies of the sort found in CATO. Canavotto and Horty show, however, that cases constrained in the hierarchical setting are unconstrained when using only base level factors, and vice versa. We argue that the intermediate factors should play no part in constraining outcomes, but they are invaluable in explaining the reasoning applicable to the case.
Evaluation of a multi-marker immunomagnetic enrichment assay Evaluation of a multi-marker immunomagnetic enrichment assay for the quantification of circulating melanoma cells for the quantification of circulating melanoma cells

Background: Circulating melanoma cells (CMCs) are thought to be valuable in improving measures of prognosis in melanoma patients and may be a useful marker of residual disease to identify non-metastatic patients requiring adjuvant therapy. We investigated whether immunomagnetic enrichment targeting multiple markers allows more efficient enrichment of CMCs from patient peripheral blood than targeting a single marker. Furthermore, we aimed to determine whether the number of CMCs in patient blood was associated with disease stage. Methods: We captured CMCs by targeting the melanoma associated markers MCSP and MCAM as well as the melanoma stem cell markers ABCB5 and CD271, both individually and in combination, by immunomagnetic enrichment. CMCs were enriched and quantified from the peripheral blood of 10 non-metastatic and 13 metastatic melanoma patients. Results: Targeting all markers in combination resulted in the enrichment of more CMCs than when any individual marker was targeted (p < 0.001-0.028). Furthermore, when a combination of markers was targeted, a greater number of CMCs were enriched in metastatic patients compared with non-metastatic patients (p = 0.007). Conclusions: Our results demonstrated that a combination of markers should be targeted for optimal isolation of CMCs. In addition, there are significantly more CMCs in metastatic patients compared with non-metastatic patients and therefore quantification of CMCs may prove to be a useful marker of disease progression.