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The recent rise in interest in point clouds as an imaging modality has motivated standardization groups such as JPEG and MPEG to launch activities aiming at developing compression standards for point clouds. Lossy compression usually introduces visual artifacts that negatively impact the perceived quality of media, which can only be reliably measured through subjective visual quality assessment experiments. While MPEG standards have been subjectively evaluated in previous studies on multiple occasions, no work has yet assessed the performance of the recent JPEG Pleno standard in comparison to them. In this study, a comprehensive performance evaluation of JPEG and MPEG standards for point cloud compression is conducted. The impact of different configuration parameters on the performance of the codecs is first analyzed with the help of objective quality metrics. The results from this analysis are used to define three rate allocation strategies for each codec, which are employed to compress a set of point clouds at four target rates. The set of distorted point clouds is then subjectively evaluated following two subjective quality assessment protocols. Finally, the obtained results are used to compare the performance of these compression standards and draw insights about best coding practices. [ABSTRACT FROM AUTHOR]

Purpose: [18F]MK-6240, a second-generation tau PET tracer, is increasingly used for the detection and the quantification of in vivo cerebral tauopathy in Alzheimer's disease (AD). Given that neurological symptoms are better explained by the topography rather than by the nature of brain lesions, our study aimed to evaluate whether cognitive impairment would be more closely associated with the spatial extent than with the intensity of tau-PET signal, as measured by the standard uptake value ratio (SUVr). Methods: [18F]MK6240 tau-PET data from 82 participants in the AD spectrum were quantified in three different brain regions (Braak ≤ 2, Braak ≤ 4, and Braak ≤ 6) using SUVr and the extent of tauopathy (EOT, percentage of voxels with SUVr ≥ 1.3). PET data were first compared between diagnostic categories, and ROC curves were computed to evaluate sensitivity and specificity. PET data were then correlated to cognitive performances and cerebrospinal fluid (CSF) tau values. Results: The EOT in the Braak ≤ 2 region provided the highest diagnostic accuracies, distinguishing between amyloid-negative and positive clinically unimpaired individuals (threshold = 9%, sensitivity = 79%, specificity = 82%) as well as between prodromal AD and preclinical AD (threshold = 38%, sensitivity = 81%, specificity = 93%). The EOT better correlated with cognition than SUVr (∆R2 + 0.08–0.09) with the best correlation observed for EOT in the Braak ≤ 4 region (R2 = 0.64). Cognitive performances were more closely associated with PET metrics than with CSF values. Conclusions: Quantifying [18F]MK-6240 tau PET in terms of EOT rather than SUVr significantly increases the correlation with cognitive performances. Quantification in the mesiotemporal lobe is the most useful to diagnose preclinical AD or prodromal AD. [ABSTRACT FROM AUTHOR]

The sequence decoding framework has dominated the field of scene text recognition. In this framework, the RNN-based (recurrent neural network) decoder is one of the main approaches. The attention mechanism is a key module in the RNN-based decoder. In the decoding stage, the character is decoded based on an estimated attention map. The precision of the attention map is extremely important to the accuracy of the final output. In practice, we find the estimated attention map has encountered attention misalignment phenomena. To address this issue, in this paper, we innovatively propose Cascade 2D attentional decoders with context-enhanced encoder for scene text recognition; we name it CASTER. We employ a thin plate spline transformation to rectify original images with oriented or curved texts and a 31-layer ResNet as backbone to extract visual features. Then, we leverage a two-stage decode mechanism: localization and decoding (coarse decoder) and re-localization and re-decoding (refined decoder) to predict the character sequence. We also introduce a novel context-enhanced encoder by a 2D contextual fusion module to capture the context information. The CASTER can localize the attention region of each character more accurately than the one-stage attention method and thus improve the final recognition performance. Extensive experiments show that CASTER achieves state-of-the-art performance on several standard benchmarks. Our method obtains, respectively, 96.1%, 93.3% and 94.4% recognition accuracies on regular (IIIT5K, SVT) and irregular (CUTE) text datasets. [ABSTRACT FROM AUTHOR]

In recent years, the proliferation of vehicles on roadways, accompanied by an escalating demand for augmented safety and refined traffic management, has fueled substantial progress in the domains of computer vision and deep learning methodologies. This technological surge has precipitated ground-breaking applications, with real-time vehicle detection emerging as a linchpin. Capability to accurately detect vehicles in a real-time holds profound implications across diverse domains. In the context of autonomous driving, it underpins essential functions such as path planning, collision avoidance, and decision-making, essential for the safe operation of autonomous vehicles. In the arena of traffic monitoring, it empowers transportation authorities to acquire real-time insights into traffic dynamics, congestion patterns, and incident detection, thereby optimizing traffic flow and resource allocation strategies. Furthermore, within surveillance systems, real-time vehicle detection equips security personnel with the means to swiftly identify potential security threats, enhancing overall situational awareness and safety. This paper contributes to this dynamic landscape by introducing an innovative architecture expressly crafted for vehicle detection. Leveraging state-of-the-art computer vision techniques and deep learning methodologies, this architecture addresses the evolving challenges posed by contemporary traffic scenarios. Through the enhancement of real-time vehicle detection capabilities, our aim is to fortify the foundations of safety, efficiency, and security within vehicular contexts. The paper includes a fusion of AlexNet and Darknet accompanied by a comparative analysis. It is expected to deliver unmatched dependability and accuracy in real-time traffic monitoring, setting a new standard for performance in this application. [ABSTRACT FROM AUTHOR]

We propose a segmentation-based bounding box generation method for omnidirectional pedestrian detection that enables detectors to tightly fit bounding boxes to pedestrians without omnidirectional images for training. Due to the wide angle of view, omnidirectional cameras are more cost-effective than standard cameras and hence, suitable for large-scale monitoring. However, state-of-the-art pedestrian detectors cannot directly be applied to omnidirectional pedestrian detection because pedestrians' appearance in omnidirectional images may be rotated to any angle, which substantially degrades the performance of standard pedestrian detectors. Existing methods mitigate this issue by transforming images during inference, though the methods have limited detection accuracy and slow detection speed. A recently proposed method obviates the transformation at the cost of laborious annotation works and trains detectors with omnidirectional pedestrian detection datasets. We propose instead leveraging an existing large-scale object detection dataset to obviate both the transformation and annotation works. We train a detector with rotated images and tightly fitted bounding box annotations generated from the segmentation annotations in the object detection dataset, which enable detectors to detect pedestrians in omnidirectional images with tightly fitted bounding boxes. We also develop pseudo-fisheye distortion augmentation, which deforms images of perspective view to imitate the fisheye distortion and further enhances the performance. Extensive analysis shows that our detector successfully fits bounding boxes to pedestrians and demonstrates substantial performance improvement over existing methods. [ABSTRACT FROM AUTHOR]

We discuss a nonlinear domain-decomposition preconditioning method for fully implicit simulations of multicomponent porous media flow based on the additive Schwarz preconditioned exact Newton method (ASPEN). The method efficiently accelerates nonlinear convergence by resolving unbalanced nonlinearities in a local stage and long-range interactions in a global stage. ASPEN can improve robustness and significantly reduce the number of global iterations compared with standard Newton, but extra work introduced in the local steps makes each global iteration more expensive. We discuss implementation aspects for the local and global stages. We show how the global-stage Jacobian can be transformed to the same form as the fully implicit system, so that one can use standard linear preconditioners and solvers. We compare the computational performance of ASPEN to standard Newton on a series of test cases, ranging from conceptual cases with simplified geometry or flow physics to cases representative of real assets. Our overall conclusion is that ASPEN is outperformed by Newton when this method works well and converges in a few iterations. On the other hand, ASPEN avoids time-step cuts and has significantly lower runtimes in time steps where Newton struggles. A good approach to computational speedup is therefore to adaptively switch between Newton and ASPEN throughout a simulation. A few examples of switching strategies are outlined. [ABSTRACT FROM AUTHOR]

Present study focused on to investigate the impact of end friction and lateral inertia on the dynamic properties of concrete obtained by performing the dynamic compression test on developed split Hopkinson pressure bar (SHPB) setup. The standard (SC) and high-strength concrete (HSC) exhibiting characteristic compressive strength of 35 and 60 MPa, respectively used in the present investigation. The concrete specimens with 29.5 and 45 mm diameter describing the aspect ratio about of 1 and 0.67, respectively, has been subjected to strain rates ranging from 78 to 336 s−1. The systematic experimental analysis of the influence of strain rate, end friction, and lateral inertia has been performed on the dynamic compressive properties. The influences of these factors on the dynamic compressive strength (DCS) and dynamic increase factor (DIF) have been discussed thoroughly. The results findings confirmed that as the strain rate rises, the dynamic characteristics of concrete also escalate. This is explained by the observed variations in DIF, which fall within the ranges of 1.76–2.65 for standard concrete (SC) and 1.28–1.70 for high-strength concrete (HSC). It was also confirming that the sensitivity of lateral inertia and end friction confinement is influenced by both the specimen diameter and strain rate. The lateral inertia confinement in larger diameter specimens significantly increases the DIF. In addition, the presence of end friction at the specimen bar interface also constrains the lateral deformation and significantly increases the DIF μ ≠ 0 , but there was no visible effect of specimen diameter was observed on the DIF μ = 0 of the concrete. [ABSTRACT FROM AUTHOR]

To enhance the performance of the diffusion robust adaptive filtering algorithm, this paper presents a novel loss function framework named the "Tanh cost function framework." By utilizing this framework, we are able to incorporate standard cost functions, devise novel cost functions, and introduce the associated diffusion algorithm. Moreover, the convergence of the algorithm is thoroughly analyzed. Furthermore, the proposed diffusion Tanh algorithm family is simulated in a system identification model to assess its performance, demonstrating superior performance compared to the standard diffusive algorithm. Notably, the DTHLMS within the diffusion tanh algorithm family exhibits superior performance over the mainstream robust diffusion algorithms. [ABSTRACT FROM AUTHOR]

A wide variety of bridge barriers are used on highways. The bearing capacities of different types of deck slabs are measured in this study by applying the yield line theory to determine the worst-case scenario. An improved configuration for the worst-case barrier base and deck slab is developed, namely the universal double-beam assembled bridge barrier, which can enhance the safety performance of exiting concrete-base bridge barriers. According to the simulated impact test results, the new barrier meets SS-level requirements in terms of containment, redirective, and buffering performance as specified in the Standard for Safety Performance Evaluation of Highway Barriers (JTG B05-01-2013). The barrier structure's compatibility with various bases is also analyzed. SS-level impact tests are conducted on real vehicles, including cars, buss, and trucks. The results show that the safety performance of the new barrier configuration reaches SS-level, and the barrier is universally compatible with concrete bases with a height of 63 cm and above. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has created a public health crisis. This study aimed to evaluate the diagnostic performance of the Panbio and STANDARD Q COVID-19 antigen rapid diagnostic tests (RDTs) against the real-time polymerase chain reaction (RT-PCR) at one of the largest hospitals in southern Ethiopia. Nasopharyngeal samples, which were collected during the pandemic from individuals suspected of COVID-19 and stored at − 70 °C, were analyzed in June and July 2022. The performance of the Panbio COVID-19 antigen tests was evaluated in 200 randomly selected nasopharyngeal samples (100 positives and 100 negatives for severe acute respiratory syndrome 2 by RT-PCR). The STANDARD Q test was evaluated using 100 positive and 50 negative samples. The respective sensitivity, specificity, positive predictive value and negative predictive values were 88%, 99%, 98.9% and 89.2% for the Panbio test and 91%, 98%, 98.9% and 84.5%, for the STANDARD Q test. The kappa values were 0.87 for the Panbio and 0.86 for the STANDARD Q test. Based on the findings presented here, the RDTs could be utilized as an alternative to conventional RT-PCR when it is challenging to diagnose COVID-19 owing to a lack of time, skilled lab personnel, or suitable equipment or electricity. [ABSTRACT FROM AUTHOR]

The stochastic synthesis of extreme, rare climate scenarios is vital for risk and resilience models aware of climate change, directly impacting society in different sectors. However, creating high-quality variations of under-represented samples remains a challenge for several generative models. This paper investigates quantizing reconstruction losses for helping variational autoencoders (VAE) better synthesize extreme weather fields from conventional historical training sets. Building on the classical VAE formulation using reconstruction and latent space regularization losses, we propose various histogram-based penalties to the reconstruction loss that explicitly reinforces the model to synthesize under-represented values better. We evaluate our work using precipitation weather fields, where models usually strive to synthesize well extreme precipitation samples. We demonstrate that bringing histogram awareness to the reconstruction loss improves standard VAE performance substantially, especially for extreme weather events. [ABSTRACT FROM AUTHOR]

Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. [ABSTRACT FROM AUTHOR]

Mechanical testing by sporting governing bodies ensures artificial turf surfaces conform with performance standards. Rotational traction is measured using two equivalent devices: a rotational traction tester (RTT) and a lightweight rotational traction tester (LRTT). The devices differ in target rotational velocity; 72 deg/s for the RTT and 30 deg/s for the LRTT. The purpose of this study was to investigate the influence of rotational velocity on peak torque during rotational traction testing. An automated rotational traction tester examined nine rotational velocities between 10 and 210 deg/s, and ten artificial turf surface systems with a range of performance infill materials, infill depths and carpet specifications. Rotations at 10 deg/s produced the lowest peak torques on nine of the ten surfaces. Infill materials with intrinsic viscoelastic properties produced significantly higher peak torques at higher rotational velocities, whereas less elastic infill materials saw no significant increase in peak torque. A mean difference in peak torque of 2.6 Nm was found between the target velocities of the RTT and LRTT. The results support the synchronisation of target velocities for the RTT and LRTT. During standards testing, trials completed below a particular velocity should be repeated to negate velocity effects on peak torque. [ABSTRACT FROM AUTHOR]

Increasing spectrum efficiency in new-generation communication networks is possible by raising the operating frequencies or increasing the number of serving cells. Both of the solutions indirectly lead to a decrease in the cell size. By shrinking the coverage area of the cells, the mobility management becomes a critical issue in order to provide seamless connectivity. The main tool for mobility management in connected mode is the handover procedure, which conventionally is triggered based on the user equipment's (UE's) measurements of serving and neighboring cells' signal quality. The performance of the methods provided in the 3GPP standard for setting up the handover is based on a comparison between the quality of the received signal from the serving cell and the neighbor cells, which is severely sensitive to the appropriate setting of threshold values and waiting time for setting up the handover. The remarkable methods in the literature to improve the handover performance in the 3GPP standard either utilize positional information or are based on complex algorithms. Positional information parameters such as speed, location, direction of movement, etc., need extra measurement modules. Also, complex algorithms like deep learning are not suitable for a wide range of active devices in 5 G networks with limited resources. In this paper, a novel fuzzy logic-based method is proposed to trigger the handover procedure based on estimated serving cell's and neighbor cells' radio link quality (RLQ) values. The proposed system consists of two stages. A second-order regressor beside a simple fuzzy logic system is introduced to predict the serving cell's and neighbor cells' RLQ. The final handover trigger decision is made with the help of another cascade fuzzy logic system, which is responsible for eliminating too-early, too-late, and ping-pong handovers. Considering the uncertainty handling feature of the interval type II fuzzy logic systems versus type I, we implement both methods and compare their results. Finally, we simulate the proposed algorithm using the ns-3 LTE module with a very tight setting for detecting radio link failure to meet 5 G strict standards. Proposed method succeeded in improving the performance of the handover process at high-speed scenarios by 50%, only with the help of radio link quality information. The main advantage of the proposed method is the proper management of the handover procedure, independent of UE's velocity, as well as its simple structure with few rules, which makes it suitable for use in UAV and IoT devices. [ABSTRACT FROM AUTHOR]

Objectives: Development of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification. Methods: For the training and validation of attention U-Net models, data coming from a single 3.0-T scanner was used. For testing, additional data from 1.5-T scanner and data acquired in a different institution with a 3.0-T scanner was utilized. The developed models were used to quantify the amount of FGT and BPE in 80 DCE-MRI examinations, and a correlation between these volumetric measures and the classes assigned by radiologists was performed. Results: To assess the model performance using application-relevant metrics, the correlation between the volumes of breast, FGT, and BPE calculated from ground truth masks and predicted masks was checked. Pearson correlation coefficients ranging from 0.963 ± 0.004 to 0.999 ± 0.001 were achieved. The Spearman correlation coefficient for the quantitative and qualitative assessment, i.e., classification by radiologist, of FGT amounted to 0.70 (p < 0.0001), whereas BPE amounted to 0.37 (p = 0.0006). Conclusions: Generalizable algorithms for FGT and BPE segmentation were developed and tested. Our results suggest that when assessing FGT, it is sufficient to use volumetric measures alone. However, for the evaluation of BPE, additional models considering voxels' intensity distribution and morphology are required. Critical relevance statement: A standardized assessment of FGT density can rely on volumetric measures, whereas in the case of BPE, the volumetric measures constitute, along with voxels' intensity distribution and morphology, an important factor. Key points: • Our work contributes to the standardization of FGT and BPE assessment. • Attention U-Net can reliably segment intricately shaped FGT and BPE structures. • The developed models were robust to domain shift. [ABSTRACT FROM AUTHOR]

Background: The EANM Research Ltd. (EARL) guidelines give recommendations for harmonization of [18F]FDG PET-CT image acquisition and reconstruction, aiming to ensure reproducibility of quantitative data between PET scanners. Recent technological advancements in PET-CT imaging resulted in an updated version of the EARL guidelines (EARL2). The aim of this study is to compare quantitative [18F]FDG uptake metrics of the primary tumor and lymph nodes in patients with head and neck squamous cell carcinoma (HNSCC) on EARL2 versus EARL1 reconstructed images and to describe clinical implications for nodal staging and treatment. Methods: Forty-nine consecutive patients with HNSCC were included. For all, both EARL1 and EARL2 images were reconstructed from a singular [18F]FDG PET-CT scan. Primary tumors and non-necrotic lymph nodes ≥ 5 mm were delineated on CT-scan. In the quantitative analysis, maximum standardized uptake values (SUVmax) and standardized uptake ratios (SURmax, i.e., SUVmax normalized to cervical spinal cord uptake) were calculated for all lesions on EARL1 and EARL2 reconstructions. Metabolic tumor volume (MTV) and total lesion glycolysis were compared between EARL1 and EARL2 using different segmentation methods (adaptive threshold; SUV2.5/3.5/4.5; SUR2.5/3.5/4.5; MAX40%/50%). In the qualitative analysis, each lymph node was scored independently by two nuclear medicine physicians on both EARL1 and EARL2 images on different occasions using a 4-point scale. Results: There was a significant increase in SUVmax (16.5%) and SURmax (9.6%) of primary tumor and lymph nodes on EARL2 versus EARL1 imaging (p < 0.001). The proportional difference of both SUVmax and SURmax between EARL2 and EARL1 decreased with increasing tumor volume (p < 0.001). Absolute differences in MTVs between both reconstructions were small (< 1.0 cm3), independent of the segmentation method. MTVs decreased on EARL2 using relative threshold methods (adaptive threshold; MAX40%/50%) and increased using static SUV or SUR thresholds. With visual scoring of lymph nodes 38% (11/29) of nodes with score 2 on EARL1 were upstaged to score 3 on EARL2, which resulted in an alteration of nodal stage in 18% (6/33) of the patients. Conclusions: Using the EARL2 method for PET image reconstruction resulted in higher SUVmax and SURmax compared to EARL1, with nodal upstaging in a significant number of patients. [ABSTRACT FROM AUTHOR]

The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To avoid catastrophic forgetting, various continual learning (CL) approaches have been devised. However, these usually require discrete task boundaries. This requirement seems biologically implausible and often limits the application of CL methods in the real world where tasks are not always well defined. Here, we take inspiration from neuroscience, where sparse, non-overlapping neuronal representations have been suggested to prevent catastrophic forgetting. As in the brain, we argue that these sparse representations should be chosen on the basis of feed forward (stimulus-specific) as well as top-down (context-specific) information. To implement such selective sparsity, we use a bio-plausible form of hierarchical credit assignment known as Deep Feedback Control (DFC) and combine it with a winner-take-all sparsity mechanism. In addition to sparsity, we introduce lateral recurrent connections within each layer to further protect previously learned representations. We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation. Our method achieves similar performance to well-known CL methods, such as Elastic Weight Consolidation and Synaptic Intelligence, without requiring information about task boundaries. Overall, we showcase the idea of adopting computational principles from the brain to derive new, task-free learning algorithms for CL. [ABSTRACT FROM AUTHOR]

Parkinson's disease is one of the most common degenerative conditions that affect people aged 60 and older. The illness is normally diagnosed by clinical indicators that develop as a variety of movement symptoms and medical observations. Conventional diagnostic methods, on the other hand, rely on the detection of small motions, which are notoriously difficult to pin down with absolute precision. This makes it possible for these methods to lend themselves to subjective interpretations. This is because traditional diagnostic methods rely on the interpretation of motions. Image categorization is performed using an altered version of the ResNeXt model in the proposed model. The proposed ResNeXt extends version of ResNet architecture by introducing a new block called the "cardinality block." The cardinality block consists of multiple parallel branches, each with its own set of convolutional layers. These branches are then combined by concatenation before being passed to the next layer. The key idea behind the cardinality block is to increase the capacity of the network without significantly increasing the number of parameters. By using parallel branches with different filter sizes, ResNeXt is able to capture a wider range of features in the input image, leading to better performance on image classification tasks. In order to enhance the performance of the standard ResNeXt model, certain extra dense and dropout layers have been included. The size of the final model is reduced by pruning the model in order to improve the efficiency of the network connections and minimize the overall size of the model. The proposed method is compared to a number of deep learning models that are already in use, and it is shown that the acquired results are superior to those of the deep learning models that are already in use. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced, memory-assisted version of the multi-verse optimizer (MVO) is proposed for the shape and size optimization of various types of truss structures. The cosmology-based MVO processes the interaction of the multiple universes through white, black, and wormholes to solve the target optimization problem. However, the MVO has drawbacks such as slow convergence speed, easy to fall into local optimum, and low efficiency in the case of continuous and discrete variables existing in structural optimization. This paper aims to modify the Multi-verse Optimizer and propose an enhanced version, named as memory-assisted adaptive multi-verse optimizer (MAMVO). The enhanced algorithm seeks to reinforce the performance of the standard algorithm using two mechanisms: (I) Using an adaptive pseudorandom transfer strategy in cases with both continuous and discrete variables to strike a better balance between the diversification and the intensification tasks (II) using a multi-elite memory, by which several best so far solutions are saved and exchanged with several worst ones for better convergence. The performance of the proposed MAMVO is compared with those of the standard MVO and some other optimization methods through various types of truss benchmarks (beams, towers, and bridges) under different constraints such as displacement, stress, buckling, or frequency. Results confirm that MAMVO significantly outperforms the standard MVO and has comparable or superior performance to the other methods. [ABSTRACT FROM AUTHOR]

Object detection in adversarial atmospheric attacks, such as fog, rain, low light, and dust conditions, is a challenging task with regards to computer vision. Moreover, the applicability of convolutional neural network-based object detection architectures in various weather-affected night-time thermal scenes has not been extensively reported in recent and past literatures. The extraction of region of interest through anchors from each multi-resolution feature map (FM), either shallow or deep, suffers from several issues in adverse weather-degraded scenarios. Our proposed architecture, namely adverse weather-affected night scene restorator cum detector net (AWRDNet), focuses on the process of recovering such adverse weather-degraded video frames to restored frames through deeper convolutional layers. Further, our network reduces the time-consuming generation of pre-defined anchors in each FM at a deeper de-convolution layer, which combines different scales and aspect ratios for anchor boxes from multiple sets to naturally handle objects of various sizes. Considering the multi-scale anchor boxes at multiple set, an anchor refinement strategy has been applied to reduce memory consumption. The performance of the AWRDNet architecture is evaluated using standard detection performance metrics over the Tripura University Video Dataset at Night Time (TU-VDN) dataset which contains objects with annotated bounding box of the image frame sequences, and the available PASCAL VOC 2007 2012 datasets, and ZUT thermal dataset. [ABSTRACT FROM AUTHOR]

Deep convolutional networks are data hungry learners and, to compensate for the limited amount of available data, various augmentation methods have been proposed. While the initial approaches aimed to fill the space around existing data points, more recent methods use the "mixing" procedure, which is a convex combination between points, being both holistic and local (from a spatial point of view). Although the mixing techniques improved the performance for standard benchmarks, we do notice that they are less effective for problems that exhibit poor class separation. For these scenarios, we propose a soft labeling technique based on distances to class prototypes, as extracted on an intermediate CNN layer. The mixing is performed between relative positions in the clustering space. The method, named SoftClusterMix, is shown to be competitive on standard image classification benchmarks and leads to significantly improved accuracy for problems where there is a poor class separation. We report better performance in three such categories: face expression recognition, aesthetic image classification and painting style classification. Ablation tests allow deep insights of the method. [ABSTRACT FROM AUTHOR]

Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data. To compare the performance of the three models, we propose the simulation of pseudo-documents as a novel evaluation method. In a case study with short and sparse text, the models are evaluated on tweets filtered by keywords relating to the Covid-19 pandemic. We find that standard coherence scores that are often used for the evaluation of topic models perform poorly as an evaluation metric. The results of our simulation-based approach suggest that the GSDMM and GPM topic models may generate better topics than the standard LDA model. [ABSTRACT FROM AUTHOR]

This paper investigates how volunteer managers from Roskilde Festival experience, enact, and respond to organisational paradoxes and how these paradoxes are both formed by, and sustain, the organisational context of the festival. The festival is a professional world-class event with high standards of performance, while being primarily volunteer-driven. This constitutes a context of "professional volunteerism", which serves as a locus of interwoven paradoxical tensions. Paradox is often portrayed as a pair of poles that are connected, but in opposition, this paper illustrates how different paradoxes and the responses to them can be interwoven, thereby describing a more complex understanding of paradox. [ABSTRACT FROM AUTHOR]

Branch-and-bound is the workhorse of all state-of-the-art mixed integer linear programming (MILP) solvers. These implementations of branch-and-bound typically use variable branching, that is, the child nodes are obtained via disjunctions of the form x j ≤ ⌊ x ¯ j ⌋ ∨ x j ≥ ⌈ x ¯ j ⌉ , where x ¯ is an optimal solution to the LP corresponding to the parent node. Even though modern MILP solvers are able to solve very large-scale instances efficiently, relatively little attention has been given to understanding why the underlying branch-and-bound algorithm performs so well. In this paper, our goal is to theoretically analyze the performance of the standard variable branching based branch-and-bound algorithm. In order to avoid the exponential worst-case lower bounds, we follow the common idea of considering random instances. More precisely, we consider random integer programs where the entries of the coefficient matrix and the objective function are randomly sampled. Our main result is that with good probability branch-and-bound with variable branching explores only a polynomial number of nodes to solve these instances, for a fixed number of constraints. To the best of our knowledge this is the first known such result for a standard version of branch-and-bound. We believe that this result provides an indication as to why branch-and-bound with variable branching works so well in practice. [ABSTRACT FROM AUTHOR]

Sustained multisensory integration over long inter-stimulus time delays is typically found in older adults, particularly those with a history of falls. However, the extent to which the temporal precision of audio-visual integration is associated with longitudinal fall or fall risk trajectories is unknown. A large sample of older adults (N = 2319) were grouped into longitudinal trajectories of self-reported fall incidents (i.e., decrease, stable, or increase in number) and, separately, their performance on a standard, objective measure of fall risk, Timed Up and Go (TUG; stable, moderate decline, severe decline). Multisensory integration was measured once as susceptibility to the Sound-Induced Flash Illusion (SIFI) across three stimulus onset asynchronies (SOAs): 70 ms, 150 ms and 230 ms. Older adults with an increasing fall number showed a significantly different pattern of performance on the SIFI than non-fallers, depending on age: For adults with increasing incidents of falls, those aged 53–59 years showed a much smaller difference in illusion susceptibility at 70 ms versus 150 ms than those aged 70 + years. In contrast, non-fallers showed a more comparable difference between these SOA conditions across age groups. There was no association between TUG performance trajectories and SIFI susceptibility. These findings suggests that a fall event is associated with distinct temporal patterns of multisensory integration in ageing and have implications for our understanding of the mechanisms underpinning brain health in older age. [ABSTRACT FROM AUTHOR]

This paper presents a new linear model design including the KC200GT photovoltaic generator GPV panel, DC-DC boost converter, and resistive load. The small-signal principle is indeed applied to the configuration connecting these three devices that are previously developed graphically using the M a t l a b ® / S i m u l i n k software package. This new model design is established through a set of proposed steps, providing the first contribution of this paper. A new voltage proportional-integral-derivative (PID) controller is synthesized based on the previous linear small-signal model. The controller parameters are computed by proposing a reference open-loop system and using the frequency identification approach, providing the second contribution of this paper. The resulting PID controller is combined with the classical perturb and observe (P &O) algorithm where its fill factor (FF) and its reference tracking dynamic are significantly improved. Improving these two performances for a sudden change of both climatic conditions, i.e., absolute temperature and solar irradiance, constitutes another contribution of this paper. Given improving the previous two performances, the maximum power point tracking (MPPT) scheme with oscillation free tracking of the desired MPP is established for conventional and improved P &O algorithms using S i m p o w e r ® system libraries of the M a t l a b ® / S i m u l i n k environment. The given simulation tests confirm the effectiveness of the proposed P &O-MPPT control strategy in terms of reference tracking accuracy, rise and settling times, and solving the ripple issue that occurred in the DC-DC boost converter output current. [ABSTRACT FROM AUTHOR]

Lifting the 2D human pose to the 3D pose is an important yet challenging task. Existing 3D human pose estimation suffers from (1) the inherent ambiguity between the 2D and 3D data, and (2) the lack of well-labeled 2D–3D pose pairs in the wild. Human beings are able to imagine the 3D human pose from a 2D image or a set of 2D body key-points with the least ambiguity, which should be attributed to the prior knowledge of the human body that we have acquired in our mind. Inspired by this, we propose a new framework that leverages the labeled 3D human poses to learn a 3D concept of the human body to reduce ambiguity. To have consensus on the body concept from the 2D pose, our key insight is to treat the 2D human pose and the 3D human pose as two different domains. By adapting the two domains, the body knowledge learned from 3D poses is applied to 2D poses and guides the 2D pose encoder to generate informative 3D "imagination" as an embedding in pose lifting. Benefiting from the domain adaptation perspective, the proposed framework unifies the supervised and semi-supervised 3D pose estimation in a principled framework. Extensive experiments demonstrate that the proposed approach can achieve state-of-the-art performance on standard benchmarks. More importantly, it is validated that the explicitly learned 3D body concept effectively alleviates the 2D–3D ambiguity, improves the generalization, and enables the network to leverage the abundant unlabeled 2D data. [ABSTRACT FROM AUTHOR]

For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model's performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization. [ABSTRACT FROM AUTHOR]

Wireless sensor network found immense uses in the daily life. Also, the random deployment of nodes is a preferable option in many applications such as earthquake observation, military applications, and forest fire detection. It is expected that deployed nodes should be able to monitor the field of interest (FoI) with the optimum capacity. In order to maximize the coverage of area, each node should be repositioned to an optimal position inside the FoI. A modified particle swarm optimization (PSO) algorithm has been proposed to achieve optimize coverage while keeping the number of nodes minimum. It introduces the concept of negative velocity in order to avoid premature convergence of the algorithm. This paper describes a way to tackle the two dimensional obstacles present inside the FoI. The simulated results show a significant improvement in the performance with compared to the standard PSO in presence of obstacles. [ABSTRACT FROM AUTHOR]

Purpose: The current study compared specific positional and temporal running performance profiles of elite and sub-elite hurling players during competitive match-play. Methods: Running performance data were obtained during match-play using GPS technology (GPEXE lt 18 Hz, Exelio, srl, Udine, Italy) from 53 elite and 81 sub-elite hurlers resulting in 524 total data sets (elite n = 200; sub-elite n = 324). Running performance data consisted of total distance [TD], high-speed running [HSR], sprint distance, accelerations [n], maximal speed [km·h− 1], relative total distance (RTD), relative high-speed running (RHSR), relative sprint distance (RSD), and relative accelerations. All data were analysed across level and positions. Additionally, HSR and RHSR were analysed across halves of play. Multiple two-way ANOVAs determined the effect of two fixed factors (level and position) on each of the running performance variables. Furthermore, two three-way ANOVAs determined the effect of level, position and halves on HSR and RHSR. Results: Elite level players covered a greater relative TD (η2 = 0.037; small) and relative HSR (η2 = 0.023; small) across positional lines of half-back (η2 = 0.008, small), midfield (η2 = 0.025, small) and half-forward (η2 = 0.009, small) when compared to their sub-elite counterparts. Temporal decrements in relative HSR of 11.3% at elite level and 5.7% at sub-elite level were observed across halves of match-play. Specifically, across the positional lines of half-back (13.1 v 2.7%), midfield (9.7 v 9.1%) and half-forward (11.9 v 6.1%) elite level players were observed to have a higher decrement in relative HSR when compared to sub-elite players. Conclusion: The current data are the first to show differences in positional running performance between standards of play within hurling. These normative data provide additional considerations for hurling coaches with respect to transitioning players from sub-elite to elite level of play. [ABSTRACT FROM AUTHOR]

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability. [ABSTRACT FROM AUTHOR]

Knowledge Graphs represent real-world facts and are used in several applications; however, they are often incomplete and have many missing facts. Link prediction is the task of completing these missing facts from existing ones. Embedding models based on Tensor Factorization attain state-of-the-art results in link prediction. Nevertheless, the embeddings they produce can not be easily interpreted. Inspired by previous work on word embeddings, we propose inducing sparsity in the bilinear tensor factorization model, RESCAL, to build interpretable Knowledge Graph embeddings. To overcome the difficulties that stochastic gradient descent has when producing sparse solutions, we add l 1 regularization to the learning objective by using the generalized Regularized Dual Averaging online optimization algorithm. The proposed method substantially improves the interpretability of the learned embeddings while maintaining competitive performance in the standard metrics. [ABSTRACT FROM AUTHOR]

Objective: The aim of this double-blind, randomized clinical trial was to evaluate the 6- and 18-month clinical performances of a new universal adhesive applied in the "no-waiting" (NW) technique to non-carious cervical lesions (NCCLs) using two evaluation criteria. Materials and methods: One hundred and seventy-six restorations were assigned to four groups according to the adhesive system, adhesive strategy, and application mode: Prime&Bond Active (PB) applied using the etch-and-rinse (ER) and self-etch (SE) strategies with 20 s applications and Clearfil Universal Bond Quick (CQ) applied using the ER and SE strategies with the NW technique. The composite resin restorations were evaluated at baseline and after 6 and 18 months using the World Dental Federation (FDI) and US Public Health Service (USPHS) criteria. The Friedman repeated measures analysis of variance and Wilcoxon test were used for statistical analyses (α = 0.05). Results: No significant differences were observed among any of the groups or criteria after 6 months (p > 0.05). After 18 months, 10 restorations were lost (p > 0.05) (2 with PB-ER [95.5%; 95%CI: 92–100%], 4 with PB-SE [90.9%; 95%CI: 82–98%], 0 with CQ-ER [100%; 95%CI: 92–100%], and 4 with CQ-SE [90.9%; 82–98%]). The restorations performed with the SE strategy showed more marginal discrepancies than those performed with the ER strategy, mainly when the FDI criteria were used (p < 0.05). Those that used the PB-SE showed fewer marginal discrepancies than those that used the CQ-SE (FDI; p < 0.05). A few restorations showed marginal discrepancies after the USPHS analysis (p > 0.05). Conclusions: The results when using the CQ-SE and -ER strategies with the NW technique were similar to those when using the PB-SE and -ER strategies in standard applications to non-carious cervical lesions after 6 and 18 months of clinical evaluation. Clinical relevance: After 6 and 18 months, the application of Clearfil Universal Bond Quick with the "no-waiting" technique showed similar clinical performance compared to the standard application of Prime & Bond Active applied using the standard application time (20 s). Trial registration: ClinicalTrials.gov identifier RBR-5f9gps. [ABSTRACT FROM AUTHOR]

Motivation is key for performance in domains such as work, sport, and learning. Research has established that motivation and the willingness to invest effort generally increase as a function of reward. However, this view struggles to explain some empirical observations—for example, in the domain of sport, athletes sometimes appear to lose motivation when playing against weak opponents—this despite objective rewards being high. This and similar evidence highlight the role of subjective value in motivation and effort allocation. To capture this, here, we advance a novel theory and computational model where motivation and effort allocation arise from reference-based evaluation processes. Our proposal argues that motivation (and the ensuing willingness to exert effort) stems from subjective value, which in turns depends on one's standards about performance and on the confidence about these standards. In a series of simulations, we show that the model explains puzzling motivational dynamics and associated feelings. Crucially, the model identifies realistic standards (i.e., those matching one's own actual ability) as those more beneficial for motivation and performance. On this basis, the model establishes a normative solution to the problem of optimal allocation of effort, analogous to the optimal allocation of neural and computational resources as in efficient coding. [ABSTRACT FROM AUTHOR]

Radiomic approaches in precision medicine are promising, but variation associated with image acquisition factors can result in severe biases and low generalizability. Multicenter datasets used in these studies are often heterogeneous in multiple imaging parameters and/or have missing information, resulting in multimodal radiomic feature distributions. ComBat is a promising harmonization tool, but it only harmonizes by single/known variables and assumes standardized input data are normally distributed. We propose a procedure that sequentially harmonizes for multiple batch effects in an optimized order, called OPNested ComBat. Furthermore, we propose to address bimodality by employing a Gaussian Mixture Model (GMM) grouping considered as either a batch variable (OPNested + GMM) or as a protected clinical covariate (OPNested − GMM). Methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography (CT) datasets. We found that OPNested ComBat improved harmonization performance over standard ComBat. OPNested + GMM ComBat exhibited the best harmonization performance but the lowest predictive performance, while OPNested − GMM ComBat showed poorer harmonization performance, but the highest predictive performance. Our findings emphasize that improved harmonization performance is no guarantee of improved predictive performance, and that these methods show promise for superior standardization of datasets heterogeneous in multiple or unknown imaging parameters and greater generalizability. [ABSTRACT FROM AUTHOR]

Quantum annealing has been actively researched since D-Wave Systems produced the first commercial machine in 2011. Controlling a large fleet of automated guided vehicles is one of the real-world applications utilizing quantum annealing. In this study, we propose a formulation to control the traveling routes to minimize the travel time. We validate our formulation through simulation in a virtual plant and authenticate the effectiveness for faster distribution compared to a greedy algorithm that does not consider the overall detour distance. Furthermore, we utilize reverse annealing to maximize the advantage of the D-Wave's quantum annealer. Starting from relatively good solutions obtained by a fast greedy algorithm, reverse annealing searches for better solutions around them. Our reverse annealing method improves the performance compared to standard quantum annealing alone and performs up to 10 times faster than a commercial classical solver, Gurobi. This study extends a use of optimization with general problem solvers in the application of multi-AGV systems and reveals the potential of reverse annealing as an optimizer. [ABSTRACT FROM AUTHOR]

Rainwater may penetrate through the cladding, enter the wall assembly, and cause damage to the façade components and interior finishes, which are moisture sensitive, most of the time. In the current paper, the performance assessment of a skylight with a full-scale mock-up has been evaluated. The mock-up was erected for an actual project to verify the acceptance criteria for the skylight's technical, functional, and architectural details. The test was conducted at the Aluminum Technology Auxiliary Ind. (ALUTEC) facility in the Industrial Zone, Doha, Qatar. The fabricated full-scale mock-up has a rectangular plane configuration of 6.625 m (length) by 3.315 m (width) that belongs to a skylight measuring 36 m by 18 m. The ASTM E283, ASTM E331, and ASTM E330 standards were followed during the experiment. Air infiltration/exfiltration, static water penetration, structural performance, post-structural static water penetration, and structural proof loading tests were performed in an airtight chamber. After recording the residual displacements, the test findings for all the make-up components, namely steel tubes, glass, and aluminum sandwich panels, were determined and found within acceptable limits. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an emerging paradigm that decreases the computational burden of mobiles by task offloading. MEC is regarded as an effective method to offer computing capacities in close proximities to mobile users. The major issue in MEC is how to offload the heterogeneous task of mobile apps effectively from the user equipment to the MEC host. Some of the existing techniques contain feeble adaptability to new circumstance due to minimum sample effectiveness and require complete retraining. The purpose of MEC is to efficiently solve offloading issues such as network load and latency. In this paper, a deep belief network is proposed for solving the offloading issue in the clusters of numerous service node and several dependencies for a mobile task in huge-scale heterogeneous mobile edge computing. The deep belief network's weight is tuned optimally using Adaptive Harris Hawks optimization (AHHO) algorithm for solving the offloading issue optimally in the MEC environment. An AHHO algorithm is employed to improve the search performances of the standard algorithm. The limitations of the standard algorithm are poor stability among exploitation as well as exploration. Hence, two schemes such as the Gaussian mutation scheme and the cuckoo search are combined so as to form an adaptive Harris hawks optimization to enhance this stability. The task offloading issues are implemented using Google cluster trace and iFogSim. Furthermore, the simulation results depict that the offloading scheme based on the deep belief network-based adaptive Harris Hawks optimization approach has better results with respect to measures such as load balancing, energy consumption, average execution time, and latency than any other approach. [ABSTRACT FROM AUTHOR]

Circuit quantum electrodynamics (cQED) experiments on superconducting qubit systems typically employ radiation shields coated in photon absorbing materials to achieve high qubit coherence and low microwave resonator losses. In this work, we present preliminary results on the performance of Vantablack as a novel infrared (IR) shielding material for cQED systems. We compare the coherence properties and residual excited state population (or effective qubit temperature) of a single-junction transmon qubit housed in a shield coated with a standard epoxy-based IR absorbing material, i.e. Berkeley Black, to the coherence and effective temperature of the same qubit in a shield coated in Vantablack. Based on a statistical analysis of multiple qubit coherence measurements, we find that the performance of the radiation shield coated with Vantablack is comparable in performance to the standard coating. However, we find that in the Vantablack coated shield the qubit has a higher effective temperature. These results indicate that improvements are likely required to optimize the performance of Vantablack as an IR shielding material for superconducting qubit experiments and we discuss possible routes for such improvements. Finally, we describe possible future experiments to more precisely quantify the performance of Vantablack to improve the coherences of more complex cQED systems. [ABSTRACT FROM AUTHOR]

Algorithms provide recommendations to human decision makers across a variety of task domains. For many problems, humans will rely on algorithmic advice to make their choices and at times will even show complacency. In other cases, humans are mistrustful of algorithmic advice, or will hold algorithms to higher standards of performance. Given the increasing use of algorithms to support creative work such as text generation and brainstorming, it is important to understand how humans will respond to algorithms in those scenarios—will they show appreciation or aversion? This study tests the effects of algorithmic advice for a word association task, the remote associates test (RAT). The RAT task is an established instrument for testing critical and creative thinking with respect to multiple word association. We conducted a preregistered online experiment (154 participants, 2772 observations) to investigate whether humans had stronger reactions to algorithmic or crowd advice when completing multiple instances of the RAT. We used an experimental format in which subjects see a question, answer the question, then receive advice and answer the question a second time. Advice was provided in multiple formats, with advice varying in quality and questions varying in difficulty. We found that individuals receiving algorithmic advice changed their responses 13 % more frequently ( χ 2 = 59.06 , p < 0.001 ) and reported greater confidence in their final solutions. However, individuals receiving algorithmic advice also were 13 % less likely to identify the correct solution ( χ 2 = 58.79 , p < 0.001 ). This study highlights both the promises and pitfalls of leveraging algorithms to support creative work. [ABSTRACT FROM AUTHOR]

In Text Classification, modern neural networks have achieved great performance, but simultaneously, it is sensitive to adversarial examples. Existing studies usually use synonym replacement or token insertion strategies to generate adversarial examples. These strategies focus on obtaining semantically similar adversarial examples, but they ignore the richness of generating adversarial examples. To expand the richness of adversarial samples. Here, we propose a simple Rule-based Adversarial sample Generator (RAG) to generate adversarial samples by controlling the size of the perturbation added to the sentence matrix representation. Concretely, we introduce two methods to control the size of the added perturbation, i) Control the number of word replacements in sentences (RAG(R)); ii) Control the size of the offset value added to the sentence matrix representation (RAG(A)). Based on RAG, we will obtain numerous adversarial samples to make the model more robust to adversarial noise, and thereby improving the model's generalization ability. Compared with the BERT and BiLSTM model baseline, experiments show that our method reduces the error rate by an average of 18% on four standard training datasets. Especially in low-training data scenarios, the overall average accuracy is increased by 12%. Extensive experimental results demonstrate that our method not only achieves excellent classification performance on the standard training datasets, but it still gets prominent performance on few-shot text classification. [ABSTRACT FROM AUTHOR]

A displacement aggregation strategy is proposed for the curvature pairs stored in a limited-memory BFGS (a.k.a. L-BFGS) method such that the resulting (inverse) Hessian approximations are equal to those that would be derived from a full-memory BFGS method. This means that, if a sufficiently large number of pairs are stored, then an optimization algorithm employing the limited-memory method can achieve the same theoretical convergence properties as when full-memory (inverse) Hessian approximations are stored and employed, such as a local superlinear rate of convergence under assumptions that are common for attaining such guarantees. To the best of our knowledge, this is the first work in which a local superlinear convergence rate guarantee is offered by a quasi-Newton scheme that does not either store all curvature pairs throughout the entire run of the optimization algorithm or store an explicit (inverse) Hessian approximation. Numerical results are presented to show that displacement aggregation within an adaptive L-BFGS scheme can lead to better performance than standard L-BFGS. [ABSTRACT FROM AUTHOR]

Performance analysis is a key process in finance to evaluate or compare investment opportunities, allocations, or management. The classical method is to compute the market or sub-market returns and volatilities, and then calculate the standard performance measure, namely, the Sharpe ratio. This measure is based on the first two moments of a return distribution. Therefore, a significant weakness of this method is that it implicitly assumes that the distribution is Gaussian (if it is not Gaussian, the approach may lead to a bad fit). In fact, risk comes from not only volatility, but also from higher moments of distribution such as skewness and kurtosis. The standard method to resolve this issue is to use the modified Sharpe ratio; this method replaces the classical Sharpe ratio volatility with the value at risk. The latter is computed using the Cornish Fisher expansion, a tool based on the first four moments of return distribution. This methodology, however, may present a major pitfall: in some cases, quantile functions do not stay monotone. In this paper, we show how this tool can be used effectively through a specific procedure, rearrangement. We compare various metrics using rank correlation, and demonstrate how and in which cases the proposed procedure delivers ranking different from the standard Sharpe ratio ranking. Furthermore, we show how our technique offers better distribution approximations and is therefore a more useful performance metric. Institutional investors may find the technique proposed here useful in that it allows for considering non-normality in performance analysis. [ABSTRACT FROM AUTHOR]

Recent progress in the runtime analysis of evolutionary algorithms (EAs) has allowed the derivation of upper bounds on the expected runtime of standard steady-state genetic algorithms (GAs). These upper bounds have shown speed-ups of the GAs using crossover and mutation over the same algorithms that only use mutation operators (i.e., steady-state EAs) both for standard unimodal (i.e., OneMax) and multimodal (i.e., Jump) benchmark functions. The bounds suggest that populations are beneficial to the GA as well as higher mutation rates than the default 1/n rate. However, making rigorous claims was not possible because matching lower bounds were not available. Proving lower bounds on crossover-based EAs is a notoriously difficult task as it is hard to capture the progress that a diverse population can make. We use a potential function approach to prove a tight lower bound on the expected runtime of the (2+1) GA for OneMax for all mutation rates c/n with c < 1.422 . This provides the last piece of the puzzle that completes the proof that larger population sizes improve the performance of the standard steady-state GA for OneMax for various mutation rates, and it proves that the optimal mutation rate for the (2+1) GA on OneMax is (97 - 5) / (4 n) ≈ 1.2122 / n . [ABSTRACT FROM AUTHOR]

Three-phase unbalanced system can be represented mathematically in terms of positive, zero, and negative sequence components. These components are really useful to provide information regarding power quality disturbances in an unbalanced system. Thus effective estimation of sequence components using appropriate signal processing models helps to detect short-duration disturbances like sag, swell, etc. The use of fractional-order calculus-based signal processing models has given a new dimension to estimate parameters and track non-stationary power quality events. The input matrix and weight vector are generated using trigonometric expansion considering the voltages across all the three phases. The magnitude and phase of the sequence components can be estimated from the optimal weight vector which is recursively updated using the proposed algorithm. In this study, the strength of fractional-order calculus is exploited using least mean square/fourth (LMS/F) to estimate sequence components in an unbalanced three-phase power system. The proposed fractional LMS/F (FLMS/F)-based model is tested using signals generated from MATLAB 2020 and IEEE 1159 PQE database. All the comparisons of results are made using standard performance measures, and also the convergence analysis of the algorithm is presented. [ABSTRACT FROM AUTHOR]

Community detection and hierarchy extraction are usually thought of as separate inference tasks on networks. Considering only one of the two when studying real-world data can be an oversimplification. In this work, we present a generative model based on an interplay between community and hierarchical structures. It assumes that each node has a preference in the interaction mechanism and nodes with the same preference are more likely to interact, while heterogeneous interactions are still allowed. The sparsity of the network is exploited for implementing a more efficient algorithm. We demonstrate our method on synthetic and real-world data and compare performance with two standard approaches for community detection and ranking extraction. We find that the algorithm accurately retrieves the overall node's preference in different scenarios, and we show that it can distinguish small subsets of nodes that behave differently than the majority. As a consequence, the model can recognize whether a network has an overall preferred interaction mechanism. This is relevant in situations where there is no clear "a priori" information about what structure explains the observed network datasets well. Our model allows practitioners to learn this automatically from the data. [ABSTRACT FROM AUTHOR]

Radiomic features have a wide range of clinical applications, but variability due to image acquisition factors can affect their performance. The harmonization tool ComBat is a promising solution but is limited by inability to harmonize multimodal distributions, unknown imaging parameters, and multiple imaging parameters. In this study, we propose two methods for addressing these limitations. We propose a sequential method that allows for harmonization of radiomic features by multiple imaging parameters (Nested ComBat). We also employ a Gaussian Mixture Model (GMM)-based method (GMM ComBat) where scans are split into groupings based on the shape of the distribution used for harmonization as a batch effect and subsequent harmonization by a known imaging parameter. These two methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography datasets. We found that Nested ComBat exhibited similar performance to standard ComBat in reducing the percentage of features with statistically significant differences in distribution attributable to imaging parameters. GMM ComBat improved harmonization performance over standard ComBat (− 11%, − 10% for Lung3/CAPTK, Lung3/PyRadiomics harmonizing by kernel resolution). Features harmonized with a variant of the Nested method and the GMM split method demonstrated similar c-statistics and Kaplan–Meier curves when used in survival analyses. [ABSTRACT FROM AUTHOR]

Early diagnosis of hepatitis C virus (HCV) infection is essential for prompt initiation of treatment and prevention of transmission, yet several logistical barriers continue to limit access to HCV testing. Dried blood spot (DBS) technology involves a simple fingerstick that eliminates the need for trained personnel, and DBS can be stored and transported at room temperature. We evaluated the use of DBS whole blood samples in the modified Abbott ARCHITECT anti-HCV assay, comparing assay performance against the standard assay run using DBS and venous plasma samples. 144 HCV positive and 104 HCV negative matched venous plasma and whole blood specimens were selected from a retrospective study with convenience sampling in Cameroon. Results obtained using a modified volume DBS assay were highly correlated to the results of the standard assay run with plasma on clinical samples and dilution series (R2 = 0.71 and 0.99 respectively). The ARCHITECT Anti-HCV assay with input volume modification more accurately detects HCV antibodies in DBS whole blood samples with 100% sensitivity and specificity, while the standard assay had 90.97% sensitivity. The use of DBS has the potential to expand access to HCV testing to underserved or marginalized populations with limited access to direct HCV care. [ABSTRACT FROM AUTHOR]

Gold nanoparticle–catalyzed chemiluminescence (CL) of luminol is an attractive alternative to strategies relying on enzymes, as their aggregation leads to significantly enhanced CL signals. Consequently, analytes disturbing such aggregation will lead to an easy-to-quantify weakening of the signal. Based on this concept, a homogeneous aptamer-based assay for the detection of sulfadimethoxine (SDM) has been developed as a microfluidic CL flow-injection platform. Here, the efficient mixing of gold nanoparticles, aptamers, and analyte in short channel distances is of utmost importance, and two-dimensional (2D) and three-dimensional (3D) mixer designs made via Xurography were investigated. In the end, since 2D designs could not provide sufficient mixing, a laminated 3D 5-layer microfluidic mixer was developed and optimized with respect to mixing capability and observation by the charge-coupled device (CCD) camera. Furthermore, the performance of standard luminol and its more hydrophilic derivative m-carboxy luminol was studied identifying the hydrophilic derivative to provide tenfold more signal enhancement and reliable results. Finally, the novel detection platform was used for the specific detection of SDM via its aptamer and yielded a stunning dynamic range over 5 orders of magnitude (0.01–1000 ng/ml) and a limit of detection of 4 pg/ml. This new detection concept not only outperforms other methods for SDM detection, but can be suggested as a new flow-injection strategy for aptamer-based rapid and cost-efficient analysis in environmental monitoring and food safety. [ABSTRACT FROM AUTHOR]

This study explores the contributions of different dimensions of the intellectual capital of higher education institutions (ICHEIs) to their performance, incorporating the previously unexplored dimension of quality of life (QoL) as a performance measure, and correspondingly taking into account the connection between the Theory of Stakeholders and the Theory of Legitimacy. Applying a Structural Equation Model and the Partial Least Squares method, we present new empirical findings from a sample of 738 students and 587 lecturers/researchers from seven Portuguese public HEIs. The ICHEIs positively influence the institution's performance, through relational capital and structural capital; and QoL emerges as an important dimension of HEIs' performance standards, especially regarding students' perception of academic QoL. When HEIs understand and measure their IC, they will gain a better understanding of their core competencies, enabling a better allocation of resources and implementation of more effective strategic and operational actions. [ABSTRACT FROM AUTHOR]