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This paper examined the strategies put in place by Lagos State Public Procurement Agency in the implementation of public procurement policy. The paper utilised primary and secondary sources of data. Primary data were collected solely through the administration of questionnaires on the respondents. The study population 1,020 comprised the staff members from ministries, and agency that monitors the implementation of procurement policy in the state, Lagos State Public Procurement Agency (LSPPA). Proportionate random sampling technique was used in selecting a sample size of 154 respondents representing 15% of the study population. Secondary data were obtained from books, academic journals, official document of LSPPA and the internet. Data collected were analysed using percentage, frequency, and (RII) Relative Importance Index. The results of the study showed that the strategies put in place by Lagos State Procurement Agency; such as establishment of a threshold (52.6%, rank 7th), constitution of contract performance audit committee (63%, rank 9.5th), supervision of the deliveries (69.5%, rank 9.5th), and creation of a procurement officer's desk (57.8%, rank 8th) were not adequately substantial strategies put in place in monitoring the implementation of public procurement policy in Lagos state. Moreover, the paper revealed strategies that were adequately substantial which were put in place by Lagos State Public Procurement Agency in the implementation of public procurement law, such as composition of technical review committee (79.9%, rank 1st), appraisal of procurement plan for procuring entities (73.3%, rank 2nd), setting of fair pricing standards and benchmarks (61%, rank 6th), strategic oversight on the contracts (65.6%, rank 5th), keeping of contractors' database for proper identification (65.6%, rank 4th), publication of details of major contracts in the State (69.5%, rank 3rd) significantly improved accountability, proficiency, efficacy among the ministries selected. The study therefore, conclude that the strategies put in place by LSPPA, Lagos State Public Procurement Agency in the implementation of public procurement law in the state is substantial, and apt which ensures that procurement law of the State is followed. It was further evident from six claims affirmation as against four claims that refuted it respectively. [ABSTRACT FROM AUTHOR]

Background: Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Results: Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods ("GSA-BenchmarKING"). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. Conclusions: The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data. [ABSTRACT FROM AUTHOR]

The prospect of the energy transition is exciting and sure to benefit multiple aspects of daily life. However, various challenges, such as planning, business models, and energy access are still being tackled. Energy Communities have been gaining traction in the energy transition, as they promote increased integration of Renewable Energy Sources (RESs) and more active participation from the consumers. However, optimization becomes crucial to support decision making and the quality of service for the effective functioning of Energy Communities. Optimization in the context of Energy Communities has been explored in the literature, with increasing attention to metaheuristic approaches. This paper contributes to the ongoing body of work by presenting the results of a benchmark between three classical metaheuristic methods—Differential Evolution (DE), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO)—and three more recent approaches—the Mountain Gazelle Optimizer (MGO), the Dandelion Optimizer (DO), and the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF). Our results show that newer methods, especially the Dandelion Optimizer (DO) and the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF), tend to be more competitive in terms of minimizing the objective function. In particular, the Hybrid Adaptive Differential Evolution with Decay Function (HyDE-DF) demonstrated the capacity to obtain extremely competitive results, being on average 3% better than the second-best method while boasting between around 2× and 10× the speed of other methods. These insights become highly valuable in time-sensitive areas, where obtaining results in a shorter amount of time is crucial for maintaining system operational capabilities. [ABSTRACT FROM AUTHOR]

This paper aims to determine whether there is a case for promoting a new benchmark for forecasting practice via the innovative application of generative artificial intelligence (Gen-AI) for predicting the future. Today, forecasts can be generated via Gen-AI models without the need for an in-depth understanding of forecasting theory, practice, or coding. Therefore, using three datasets, we present a comparative analysis of forecasts from Gen-AI models against forecasts from seven univariate and automated models from the forecast package in R, covering both parametric and non-parametric forecasting techniques. In some cases, we find statistically significant evidence to conclude that forecasts from Gen-AI models can outperform forecasts from popular benchmarks like seasonal ARIMA, seasonal naïve, exponential smoothing, and Theta forecasts (to name a few). Our findings also indicate that the accuracy of forecasts from Gen-AI models can vary not only based on the underlying data structure but also on the quality of prompt engineering (thus highlighting the continued importance of forecasting education), with the forecast accuracy appearing to improve at longer horizons. Therefore, we find some evidence towards promoting forecasts from Gen-AI models as benchmarks in future forecasting practice. However, at present, users are cautioned against reliability issues and Gen-AI being a black box in some cases. [ABSTRACT FROM AUTHOR]

WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weather modeling. WeatherBench 2 consists of an open‐source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state‐of‐the‐art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state‐of‐the‐art physical and data‐driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data‐driven weather forecasting. Plain Language Summary: Traditionally, weather forecasts are made by models that attempt to replicate the physical processes of the atmosphere. This has been very successful over the last few decades as better computers, better observations and model upgrades have lead to steadily improving weather forecasts. However, with rapid advances in artificial intelligence (AI), the question can be asked whether one can simply learn a weather model from past observations or reanalyzes. In the last couple of years, we have seen tremendous progress with state‐of‐the‐art AI models rivaling the best "traditional" weather models in skill. WeatherBench 2 is a benchmark data set designed to evaluate and compare the quality of AI and traditional models. By setting a standard for evaluation, alongside providing open‐source data and code, this project aims to accelerate this research direction and lead to better weather prediction. Key Points: WeatherBench 2 is a framework for evaluating and comparing data‐driven and traditional numerical weather forecasting modelsIt provides an evaluation framework, publicly available data sets and a website to assess the state‐of‐the‐art weather modelsThe evaluation protocol has been designed following best practices established in the operational weather forecasting community [ABSTRACT FROM AUTHOR]

This paper addresses errors in mean return estimates in continuous-time asset allocation models. A standard approach postulates that stochastic factors explain expected asset returns. The problem is then to estimate these factors from observed asset prices via filtering. Recent advances have also combined asset prices with expert opinions to improve the estimates. However, these methods have limitations: stocks prices favor momentum strategies, and expert opinions require careful debiasing. To resolve these issues, we propose a jump-diffusion risk-sensitive benchmarked asset management model in which investors estimate the factors from both traditional and alternative data. We show that this model admits a unique C 1 , 2 solution, and we derive the optimal investment policy in quasi-closed form. We find that investors construct their portfolios from a passive core and an active satellite. The passive core adds considerations for jump risk to a simple benchmark replication. The active satellite blends security selection, and factor tilts with event-driven strategies unique to jump-diffusion problems. Thus, our model explains the most popular investment strategies. Furthermore, the improved expert forecast model and the introduction of alternative data provide factor tilters with new tools to sharpen their asset allocation. [ABSTRACT FROM AUTHOR]

Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in the field of robotic systems, especially for interactive and collaborative mobile robots. The growing reliance on robotics has increased complexity in task execution in real-world applications. Consequently, several types of VSLAM methods have been revealed to facilitate and streamline the functions of robots. This work aims to showcase the latest V-SLAM methodologies, offering clear selection criteria for researchers and developers to choose the right approach for their robotic applications. It chronologically presents the evolution of SLAM methods, highlighting key principles and providing comparative analyses between them. The paper focuses on the integration of the robotic ecosystem with a robot operating system (ROS) as Middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method's workflow. [ABSTRACT FROM AUTHOR]

A recent paper by Jiang et al. in BMC Bioinformatics presented guidelines on long-read sequencing settings for structural variation (SV) calling, and benchmarked the performance of various SV calling tools, including NanoVar. In their simulation-based benchmarking, NanoVar was shown to perform poorly compared to other tools, mostly due to low SV recall rates. To investigate the causes for NanoVar's poor performance, we regenerated the simulation datasets (3× to 20×) as specified by Jiang et al. and performed benchmarking for NanoVar and Sniffles. Our results did not reflect the findings described by Jiang et al. In our analysis, NanoVar displayed more than three times the F1 scores and recall rates as reported in Jiang et al. across all sequencing coverages, indicating a previous underestimation of its performance. We also observed that NanoVar outperformed Sniffles in calling SVs with genotype concordance by more than 0.13 in F1 scores, which is contrary to the trend reported by Jiang et al. Besides, we identified multiple detrimental errors encountered during the analysis which were not addressed by Jiang et al. We hope that this commentary clarifies NanoVar's validity as a long-read SV caller and provides assurance to its users and the scientific community. [ABSTRACT FROM AUTHOR]

Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment. Unfortunately, this process is very time-consuming and requires a high level of expertise, hence the need for automatic wound measurement methods. Recently, automatic wound segmentation methods based on deep learning have shown promising performance; yet, they heavily rely on large training datasets. A few wound image datasets were published including the Diabetic Foot Ulcer Challenge dataset, the Medetec wound dataset, and WoundDB. Existing public wound image datasets suffer from small size and a lack of annotation. There is a need to build a fully annotated dataset to benchmark wound segmentation methods. To address these issues, we propose the Foot Ulcer Segmentation Challenge (FUSeg), organized in conjunction with the 2021 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). It contains 1210 pixel-wise annotated foot ulcer images collected over 2 years from 889 patients. The submitted algorithms are reviewed in this paper and the dataset can be accessed through the Foot Ulcer Segmentation Challenge website. [ABSTRACT FROM AUTHOR]

The European Union's increasing focus on sustainable and eco-friendly product design has resulted in significant pressure on original equipment manufacturers to adopt more environmentally conscious practices. As a result, the remanufacturing of end-of-life electric machines is expected to become a promising industrial segment. Identifying the missing parameters of these types of machines will play an essential role in creating feasible and reliable redesigns and remanufacturing processes. A few case studies related to this problem have been published in the literature; however, some novel, openly accessible benchmark problems can facilitate the research and function as a basis for comparing and validating novel numerical methods. This paper presents the identification process of an experimental synchronous machine. It outlines methodologies for identifying material properties, winding schemes, and other critical parameters for the finite element analysis and modelling of electric machines with incomplete information. The machine in question is intended for remanufacturing, with the plan to replace its faulty winding with an aluminium-based alternative. It also serves as an open benchmark problem for researchers, designers, and practitioners. [ABSTRACT FROM AUTHOR]

The particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards benchmarking different optimization algorithms. However, because of the inherent limitations in the velocity update mechanism of the algorithm, PSO often converges to suboptimal solutions. Thus, this paper aims to enhance the convergence rate and accuracy of the PSO algorithm by introducing a modified variant, which is based on a hybrid of the PSO and the smell agent optimization (SAO), termed the PSO-SAO algorithm. Our specific objective involves the incorporation of the trailing mode of the SAO algorithm into the PSO framework, with the goal of effectively regulating the velocity updates of the original PSO, thus improving its overall performance. By using the trailing mode, agents are continuously introduced to track molecules with higher concentrations, thus guiding the PSO's particles towards optimal fitness locations. We evaluated the performance of the PSO-SAO, PSO, and SAO algorithms using a set of 37 benchmark functions categorized into unimodal and non-separable (UN), multimodal and non-separable (MS), and unimodal and separable (US) classes. The PSO-SAO achieved better convergence towards global solutions, performing better than the original PSO in 76% of the assessed functions. Specifically, it achieved a faster convergence rate and achieved a maximum fitness value of −2.02180678324 when tested on the Adjiman test function at a hopping frequency of 9. Consequently, these results underscore the potential of PSO-SAO for solving engineering problems effectively, such as in vehicle routing, network design, and energy system optimization. These findings serve as an initial stride towards the formulation of a robust hyperparameter tuning strategy applicable to supervised machine learning and deep learning models, particularly in the domains of natural language processing and path-loss modeling. [ABSTRACT FROM AUTHOR]

Using multiple moving cameras with different and time-varying views can significantly expand the capability of multiple human tracking in larger areas and with various perspectives. In particular, the use of moving cameras of complementary top and horizontal views can facilitate multi-human detection and tracking from both global and local perspectives. As a new challenging problem that draws more and more attention in recent years, one main issue is the lack of a comprehensive dataset for credible performance evaluation. In this paper, we present such a new dataset consisting of videos synchronously recorded by drone and wearable cameras, with high-quality annotations of the covered subjects and their cross-frame and cross-view associations. We also propose a pertinent baseline algorithm for multi-view multiple human tracking and evaluate it on this new dataset against the annotated ground truths. Experimental results verify the usefulness of the new dataset and the effectiveness of the proposed baseline algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a benchmark analysis of NVIDIA Jetson platforms when operating deep learning-based 3D object detection frameworks. Three-dimensional (3D) object detection could be highly beneficial for the autonomous navigation of robotic platforms, such as autonomous vehicles, robots, and drones. Since the function provides one-shot inference that extracts 3D positions with depth information and the heading direction of neighboring objects, robots can generate a reliable path to navigate without collision. To enable the smooth functioning of 3D object detection, several approaches have been developed to build detectors using deep learning for fast and accurate inference. In this paper, we investigate 3D object detectors and analyze their performance on the NVIDIA Jetson series that contain an onboard graphical processing unit (GPU) for deep learning computation. Since robotic platforms often require real-time control to avoid dynamic obstacles, onboard processing with a built-in computer is an emerging trend. The Jetson series satisfies such requirements with a compact board size and suitable computational performance for autonomous navigation. However, a proper benchmark that analyzes the Jetson for a computationally expensive task, such as point cloud processing, has not yet been extensively studied. In order to examine the Jetson series for such expensive tasks, we tested the performance of all commercially available boards (i.e., Nano, TX2, NX, and AGX) with state-of-the-art 3D object detectors. We also evaluated the effect of the TensorRT library to optimize a deep learning model for faster inference and lower resource utilization on the Jetson platforms. We present benchmark results in terms of three metrics, including detection accuracy, frame per second (FPS), and resource usage with power consumption. From the experiments, we observe that all Jetson boards, on average, consume over 80% of GPU resources. Moreover, TensorRT could remarkably increase inference speed (i.e., four times faster) and reduce the central processing unit (CPU) and memory consumption in half. By analyzing such metrics in detail, we establish research foundations on edge device-based 3D object detection for the efficient operation of various robotic applications. [ABSTRACT FROM AUTHOR]

Molten Salt Reactors (MSRs) have recently gained resurged research and development interest in the advanced reactor community. Several computational tools are being developed to capture the strong neutronics/thermal-hydraulics coupling effect in this special reactor configuration. This paper presents a consistent one-dimensional (1D) multigroup neutron diffusion model for MSR analysis, with the primary aim for fast and accurate calculations for long transients, as well as sensitivity and uncertainty analysis of the reactor. A fictitious radial leakage cross section is introduced in the model to properly account for the radial leakage effects of the reactor. The leakage cross section and other consistent neutronics parameters are generated with the Monte Carlo code Serpent using high-fidelity three-dimensional (3D) models. The accuracy of the 1D consistent model is verified by the reference solution from the Monte Carlo model on the Molten Salt Reactor Experiment (MSRE) configuration. The 1D consistent model successfully reproduced the integrated flux from the 3D model and the reactor multiplication factor keff with the error in the range of 95 to 397 pcm (per cent mille), depending on discretized energy group structures. The developed model is also extended to estimate the reactivity loss due to fuel circulation in MSRE. The estimate of reactivity loss in dynamics analysis is in great agreement with the experimental data. This model functions as the first step in the development of a 1D fully neutronics/thermal-hydraulics coupled model for short- and long-term MSRE transient analysis. [ABSTRACT FROM AUTHOR]

The creep and shrinkage of concrete play important roles for many nuclear power plant (NPP) and engineering structures. This paper benchmarks the standard and micromechanical models using a revamped and appended Northwestern University database of laboratory creep and shrinkage data with 4663 data sets. The benchmarking takes into account relevant concretes and conditions for NPPs using 781 plausible data sets and 1417 problematic data sets, which cover together 47% of the experimental data sets in the database. The B3, B4, and EC2 models were compared using the coefficient of variation of error (CoV) adjusted for the same significance for short-term and long-term measurements. The B4 model shows the lowest variations for autogenous shrinkage and basic and total creep, while the EC2 model performs slightly better for drying and total shrinkage. In addition, confidence levels at 5, 10, 90, and 95% are quantified in every decade. Two micromechanical models, Vi(CA) 2 T and SCK CEN, use continuum micromechanics for the mean field homogenization and thermodynamics of the water–pore structure interaction. Validations are carried out for the 28-day Young's modulus of concrete, basic creep compliance, and drying shrinkage of paste and concrete. The Vi(CA) 2 T model is the second best model for the 28-day Young's modulus and the basic creep problematic data sets. The SCK CEN micromechanical model provides good prediction for drying shrinkage. [ABSTRACT FROM AUTHOR]

Robust object grasping in cluttered scenes is vital to all robotic prehensile manipulation. In this paper, we present the GraspNet-1Billion benchmark that contains rich real-world captured cluttered scenarios and abundant annotations. This benchmark aims at solving two critical problems for the cluttered scenes parallel-finger grasping: the insufficient real-world training data and the lacking of evaluation benchmark. We first contribute a large-scale grasp pose detection dataset. Two different depth cameras based on structured-light and time-of-flight technologies are adopted. Our dataset contains 97,280 RGB-D images with over one billion grasp poses. In total, 190 cluttered scenes are collected, among which 100 are training set and 90 are for testing. Meanwhile, we build an evaluation system that is general and user-friendly. It directly reports a predicted grasp pose's quality by analytic computation, which is able to evaluate any kind of grasp representation without exhaustively labeling the ground-truth. We further divide the test set into three difficulties to better evaluate algorithms' generalization ability. Our dataset, accessing API and evaluation code, are publicly available at www.graspnet.net. [ABSTRACT FROM AUTHOR]

The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has drawbacks, such as being prone to falling into local optima, low search efficiency, and limited optimization accuracy due to limitations in some innate biological conditions. To address the corresponding shortcomings, this paper proposes three improved strategies: a novel opposition-based learning strategy, a novel exploration mechanism, and a biological elimination update mechanism. Based on the original SCSO, a multi-strategy improved sand cat swarm optimization (MSCSO) is proposed. To verify the effectiveness of the proposed algorithm, the MSCSO algorithm is applied to two types of problems: global optimization and feature selection. The global optimization includes twenty non-fixed dimensional functions (Dim = 30, 100, and 500) and ten fixed dimensional functions, while feature selection comprises 24 datasets. By analyzing and comparing the mathematical and statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, the results show that the proposed MSCSO algorithm has good optimization ability and can adapt to a wide range of optimization problems. [ABSTRACT FROM AUTHOR]

Ontology matching is the task of generating a set of correspondences (i.e., an alignment) between the entities of different ontologies. While most efforts on alignment evaluation have been dedicated to the evaluation of simple alignments (i.e., those linking one single entity of a source ontology to one single entity of a target ontology), the emergence of matchers providing complex alignments (i.e., those composed of correspondences involving logical constructors or transformation functions) requires new strategies for addressing the problem of automatically evaluating complex alignments. This paper proposes (i) a benchmark for complex alignment evaluation composed of an automatic evaluation system that relies on queries and instances, and (ii) a dataset about conference organisation. This dataset is composed of populated ontologies and a set of competency questions for alignment as SPARQL queries. State-of-the-art alignments are evaluated and a discussion on the difficulties of the evaluation task is provided. [ABSTRACT FROM AUTHOR]

Many emerging IoT--Edge--Cloud computing systems are not yet implemented or are too confidential share the code or even tricky to replicate its execution environment, and hence their benchmarking is challenging. This paper uses autonomous vehicles as a typical scenario to build the first benchmark for Edge--Cloudsystems.We propose a set of distilling rules for replicating autonomous vehicle scenarios to extract critical tasks with intertwined interactions. The essential system-level and component-level characteristics captured while the system complexity is reduced significantly so that users can quickly evaluate and pinpoint the system and component bottlenecks. Also, we implement a scalable architecture through which users assess the systems with different sizes of workloads. We conduct several experiments to measure the performance. After testing two thousand autonomous vehicle task requests, we identify the bottleneck modules in autonomous vehicle scenarios and analyze hotspot functions. The experiment results show that the lane-keeping task is the slowest execution module, with a tail latency of 77.49 ms for the 99th percentile latency. We hope this scenario benchmark will helpful for Autonomous Vehicles and even IoT--edge--Cloud research. Now the open-source code is available from the official website https://www.benchcouncil.org/scenariobench/edgeaibench.html. [ABSTRACT FROM AUTHOR]

Mobile cloud computing (MCC) integrates mobile computing and cloud computing aiming to extend the capabilities of mobile devices through offloading techniques. In MCC, many controlled experiments have been performed using mobile applications as benchmarks. Usually, these applications are used to validate proposed algorithms, architectures or frameworks. The task of choosing a specific benchmark to evaluate MCC proposals is difficult because there is no standard applications list. This paper presents a systematic mapping study for benchmarks used in MCC research. Taking 5 months of work, we have read 763 papers from MCC field. We catalogued the applications and characterized them considering three facets: category (e.g., games, imaging tools); evaluated resource (e.g., time, energy); and platform (e.g., Android, iPhone). The mapping study evidences research gaps and research trends. Providing a list of downloadable standardized benchmarks, this work can aid better choices to guide more reliable research studies since the same application could be used for different scientific purposes. [ABSTRACT FROM AUTHOR]

Recently, there has been an upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer (NST). However, the state of performance evaluation in this field is poor, especially compared to the norms in the computer vision and machine learning communities. Unfortunately, the task of evaluating image stylisation is thus far not well defined, since it involves subjective, perceptual, and aesthetic aspects. To make progress towards a solution, this paper proposes a new structured, three-level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its construction, and its consistency was validated by user studies. Moreover, a new methodology has been developed for evaluating portrait stylisation algorithms, which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the faces. We perform evaluation for a wide variety of image stylisation methods (both portrait-specific and general purpose, and also both traditional NPR approaches and NST) using the new benchmark dataset. [ABSTRACT FROM AUTHOR]

This paper focuses on the performance of Lithuanian life-cycle second-pillar pension funds. Every such fund first specifies its benchmark and then attempts to follow the benchmark in some way. This is a form of regulation, meaning that every such fund is somehow regulated and controlled by the central bank authorities. The goal of this paper is twofold: (i) to analyse the returns of the pension funds with respect to their benchmarks and (ii) to determine whether less strict regulation leads to a better outperformance of the fund with respect to the benchmark. In order to achieve this, we introduced a new performance measure called the dominance-tracking index, which combines the ideas of almost stochastic dominance relations and tracking errors. While the tracking error and its modifications measure the strength of the regulation, almost stochastic dominance provides information about preferences between the funds and their benchmarks. Therefore, the new index was constructed in such a way as to take into account both approaches. The empirical section of the study then presents the results separately for the considered pension managers and participants' age groups as usual in the life-cycle pension funds analysis. Finally, by taking into account various periods, we studied the effects of the COVID-19 crisis. [ABSTRACT FROM AUTHOR]

In this paper, we make the first benchmark effort to elaborate on the superiority of using RAW images in the low light enhancement and develop a novel alternative route to utilize RAW images in a more flexible and practical way. Inspired by a full consideration on the typical image processing pipeline, we are inspired to develop a new evaluation framework, Factorized Enhancement Model (FEM), which decomposes the properties of RAW images into measurable factors and provides a tool for exploring how properties of RAW images affect the enhancement performance empirically. The empirical benchmark results show that the Linearity of data and Exposure Time recorded in meta-data play the most critical role, which brings distinct performance gains in various measures over the approaches taking the sRGB images as input. With the insights obtained from the benchmark results in mind, a RAW-guiding Exposure Enhancement Network (REENet) is developed, which makes trade-offs between the advantages and inaccessibility of RAW images in real applications in a way of using RAW images only in the training phase. REENet projects sRGB images into linear RAW domains to apply constraints with corresponding RAW images to reduce the difficulty of modeling training. After that, in the testing phase, our REENet does not rely on RAW images. Experimental results demonstrate not only the superiority of REENet to state-of-the-art sRGB-based methods and but also the effectiveness of the RAW guidance and all components. [ABSTRACT FROM AUTHOR]

The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018. SDC'18 features a lower power object detection challenge (LPODC) on designing and implementing novel algorithms based object detection in images taken from unmanned aerial vehicles (UAV). The dataset includes 95 categories and 150k images, and the hardware platforms include Nvidia's TX2 and Xilinx's PYNQ Z1. DAC-SDC'18 attracted more than 110 entries from 12 countries. This paper presents in detail the dataset and evaluation procedure. It further discusses the methods developed by some of the entries as well as representative results. The paper concludes with directions for future improvements. [ABSTRACT FROM AUTHOR]

The paper presents the statement of the problem of dynamical system „crane-load" optimal control. The acceleration period is under consideration and control must meet the minimum duration condition as well as load oscillations elimination. The objective function, which ensures the final condition satisfaction, is developed and analyzed in terms of its topology features. It includes three arguments and their searching is the essence of the benchmark problem. Two variants of the problem are proposed with varied objective function parameters. Twelve agent-based optimization algorithms have been applied to find solutions to a bunch of problems. A brief analysis of the performance of the algorithms reveals their weaknesses and advantages. Thus, the proposed real-world problem may be exploited to estimate the optimization algorithms' search performance. [ABSTRACT FROM AUTHOR]

Various substances that possess liquid states include drinking water, various types of fuel, pharmaceuticals, and chemicals, which are indispensable in our daily lives. There are numerous real-world applications for liquid content detection in transparent containers, for example, service robots, pouring robots, security checks, industrial observation systems, etc. However, the majority of the existing methods either concentrate on transparent container detection or liquid height estimation; the former provides very limited information for more advanced computer vision tasks, whereas the latter is too demanding to generalize to open-world applications. In this paper, we propose a dataset for detecting liquid content in transparent containers (LCDTC), which presents an innovative task involving transparent container detection and liquid content estimation. The primary objective of this task is to obtain more information beyond the location of the container by additionally providing certain liquid content information which is easy to achieve with computer vision methods in various open-world applications. This task has potential applications in service robots, waste classification, security checks, and so on. The presented LCDTC dataset comprises 5916 images that have been extensively annotated through axis-aligned bounding boxes. We develop two baseline detectors, termed LCD-YOLOF and LCD-YOLOX, for the proposed dataset, based on two identity-preserved human posture detectors, i.e., IPH-YOLOF and IPH-YOLOX. By releasing LCDTC, we intend to stimulate more future works into the detection of liquid content in transparent containers and bring more focus to this challenging task. [ABSTRACT FROM AUTHOR]

Rockfall simulations are often performed at various levels of detail depending on the required safety margins of rockfall-hazard-related assessments. As a pseudo benchmark, the simulation results from different models can be put side-by-side and compared with reconstructed rockfall trajectories, and mapped deposited block fragments from real events. This allows for assessing the objectivity, predictability, and sensitivity of the models. For this exercise, mapped data of past events from the Mel de la Niva site are used in this paper for a qualitative comparison with simulation results obtained from early calibration stages of the Flow-R 2.0.9, Rockyfor3D 5.2.15 and RAMMS::ROCKFALL 1.6.70 software. The large block fragments, reaching hundreds of megajoules during their fall, greatly exceed the rockfall energies of the empirical databases used for the development of most rockfall models. The comparison for this challenging site shows that the models could be improved and that combining the use of software programs with different behaviors could be a workaround in the interim. The findings also highlight the inconvenient importance of calibrating the simulations on a per-site basis from onsite observations. To complement this process, a back calculation tool is briefly described and provided. This work also emphasizes the need to better understand rockfall dynamics to help improve rebound models. [ABSTRACT FROM AUTHOR]

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, including day-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camera pose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches on these datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, in the hopes that this will stimulate research on long-term visual localization, learned local image features, and related research areas. Our datasets are available at visuallocalization.net , where we are also hosting a benchmarking server for automatic evaluation of results on the test set. The presented state-of-the-art results are to a large degree based on submissions to our server. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to compare the performance of several commonly known machine-learning (ML) models versus a classic Autoregression with Exogenous inputs (ARX) model in the prediction of blood glucose (BG) levels using time-series data of patients with Type 1 diabetes (T1D). Methods: The ML algorithms include ML-based regression models and deep learning models such as a vanilla Long-Short-Term-Memory (LSTM) Network and a Temporal Convolution Network (TCN). Evaluations have been conducted with respect to different input features, regression model orders, as well as using the recursive method or direct method for multi-step prediction of BG levels. Prediction performance metrics include the average Root Mean Square Error (RMSE), temporal gain (TG) for early prediction, and the normalized energy of the second-order differences (ESOD) of the predicted time series to reflect risk of false alerts on hypo/hyper glycemia events. Results: The ARX model achieved the lowest average RMSE for both recursive and direct methods, the second highest average TG under the direct method, but with a higher average normalized ESOD than some other models. Conclusion: There was no significant advantage observed from the ML models compared to the classic ARX model in predicting BG levels for T1D, except that TCN's performance was more robust with respect to BG trajectories with spurious oscillations, for which ARX tended to over-predict peak BG values and under-predict valley BG values. Significance: Insight learned from this study could help researchers and clinical practitioners to select appropriate models for BG prediction. [ABSTRACT FROM AUTHOR]