Miracle-dz/UniFAR-FTUs huggingface dataset:
CFD modeling of tidal bores: development and validation challenges

ABSTRACT A tidal bore is a natural estuarine phenomenon forming a positive surge in a funnel-shaped river mouth during the early flood tide under spring tide conditions and low freshwater levels. The bore passage may induce some enhanced turbulent mixing, with upstream advection of suspended material. Herein, the flow field and turbulence characteristics of tidal bores were measured using both numerical computational fluid dynamics (CFD) and physical modeling. This joint modeling approach, combined with some theoretical knowledge, led to some new understanding of turbulent velocity field, turbulent mixing process, Reynolds stress tensor, and tidal bore hydrodynamics. Thétis is a CFD model using the volume of fluid technique to model the free-surface and Large Eddy Simulation (LES) technique for the turbulence modeling. Physical data sets were used to map the velocity and pressure field and resolve some unusual feature of the unsteady flow motion. A discussion will be provided to explain why a detailed validation process, involving a physical knowledge of the flow, is crucial. Comparison of the numerical model results and experimental data over broad ranges of conditions for the same flow is mandatory. The validation process from two-dimensional to three-dimensional will be commented and difficulties will be highlighted.
Adaptive Crossover Selection for Differential Evolution to Solve Global Optimization Problems

In response to the escalating demand for optimal solutions to intricate optimization problems in real world applications, differential evolution (DE) algorithms have shown very promising performance. However, most of existing DE studies leverage only one crossover strategy to generate the offspring. Since different crossover strategies preserve different advantages in generating the offspring with different qualities, this paper develops four hybridization ways to assemble two popular crossover schemes, namely the binomial crossover and the exponential crossover. In this way, it is expected that the advantages of the two crossover schemes are integrated to generate more promising offspring and thus help DE achieve more promising performance. Experiments have been extensively conducted on the CEC2017 benchmark functions by embedding the four hybridizations of the two crossover mechanisms into DE with totally five different mutation schemes. Experimental results have demonstrated the effectiveness and efficacy of the proposed four hybridizations of the two crossover strategies.
RESEARCH ARTICLE Factors Associated with Health-Related Quality of Life Among Breast Cancer Survivors in Saudi Arabia: Cross-Sectional Study

Background: Assessment of health-related quality of life (HRQOL) is a major concern among breast cancer survivors. Little is known about HRQO and its determinants in Saudi women with breast cancer (BC). This study aimed to describe the HRQOL of Saudi women with breast cancer and the association between HRQO and sociodemographic and clinical variables. Methods: This cross-sectional study involved 147 BC participants who completed the 36-item Short Form Health Survey (SF-36). Multivariable linear logistic regression analysis was conducted to determine factors associated with HRQOL. Results: For the physical component scale, general health scored the highest (55.41±17.99) while, role physical function scored the lowest (48.61±22.51). The mean scores in the mental component were higher for social function (56.91±20.84), while the lowest score was observed for energy and vitality (49.62±20.84). Significant differences were observed in many SF-36 domains across the sociodemographic and clinical variables. Older age, having had a mastectomy as well as comorbid conditions, and receiving chemotherapy and hormonal therapy were associated with decreased HRQOL. However, being physically active, employed, and having more education were associated with higher HRQOL. Conclusion: HRQOL is generally poor among breast cancer survivors in Saudi Arabia, depending on a variety of factors. These findings highlight the need for routine QOL assessment. Furthermore, recognizing these predictors may play a key role in maximizing HRQOL for breast cancer participants.
Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm’s execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks.
A 5G n77 Filter Using Shear Bulk Mode Resonator With Crystalline X-cut Lithium Niobate Films

This paper presents the use of a shear bulk acoustic mode resonator made of a membrane of X-cut lithium niobate (LiNbO3) for ultra-wide band filter applications. Aluminum interdigitated (IDT) electrodes are deposited on top of the LiNbO3 thin film as well as a floating electrode made of aluminum deposited on the backside of the membrane. Through vertical field excitation, shear waves are excited with very good performances. The resonator displays more than 40% of electromechanical coupling at 3.3 GHz and 4.2 GHz. We used simulation and characterization results to synthesize and simulate filters and show the potential of this resonator for wideband filtering applications like the n77 band.
Secure and dynamic public audit scheme based on blockchain and red-black tree

In the Cyber-Physical-Social System (CPSS), mobile service applications share and exchange data in the cyberspace and physical world. With the explosive growth of data, cloud storage service is introduced as it has broken through the limitations of local storage space. However, since cloud storage servers may remove some data to deceive users to save space, it is important to check the integrity of data in cloud storage servers. Most existing verification schemes support users to delegate a semi-trusted entity third-party auditor (TPA) to audit the stored data publicly, but it may result in user data security risks for its interests. The security risk is one aspect, what is worse, the high overhead of auditing also limits the the practicality of auditing. To tackle the issues, we put forward a secure dynamic public audit scheme based on blockchain and a red-black tree structure. Unlike relying on any single TPA, every user on the blockchain can act as an auditor to achieve public auditing. Secondly, based on the red-black tree, a new storage structure is proposed to store data signatures, which achieves efficient data storage and auditing. Based on the raised prototype system, we prove the security of our scheme and implement it on Hyperledger Fabric. Many experiments indicate that the scheme achieves the expected efficacy.
Fusion of Dynamic Hypergraph and Clinical Event for Sequential Diagnosis Prediction

Sequential diagnosis prediction (SDP) is a challenging task, aiming to predict patients’ future diagnoses based on their historical medical records. While methods based on graph neural networks (GNNs) have proven successful for this task, they typically focus on modeling pairwise diseases using a global disease combination graph. However, these approaches neglect the fine-grained higher-order relations among persistent and emerging diseases within a single visit, which may contain crucial clues to predict the next diagnosis. Additionally, they fail to fully leverage patient-related clinical information present in electronic health records (EHRs). To address these challenges, this paper proposes a novel approach called the fusion of Dynamic Hypergraph and Clinical Event (DHCE) for sequential diagnosis prediction. The proposed method aims to exploit the fine-grained higher-order relations among diagnoses within a visit and leverage clinical event information from EHRs to improve the accuracy of predicting the next diagnosis. Specifically, DHCE categorizes diagnoses within a single visit in a fine-grained granularity into persistent and emerging categories based on a patient’s historical diagnoses. It then constructs dynamic hypergraphs to capture higher-order disease relations within each visit. Next, we design a transition function to extract the transitional context from previous visits in order to generate the visit representation. Furthermore, to fully leverage patient-related clinical events in a visit, we utilize Bio-Clinical BERT to encode them and generate the clinical event representation for each visit. Finally, we combine the visit representation and event representation to generate a comprehensive patient representation, which is then used to predict the patient’s next diagnosis. Experimental results on two benchmark datasets consistently demonstrate that DHCE outperforms state-of-the-art methods1.
Two-phase Switching Optimization for Generative Adversarial Network in Pose Transfer

Optimizer is crucial for training Generative Adversarial Networks (GAN) to generate images with enhanced image quality and achieve better evaluation metrics. The most widely used optimizers in GAN are the first-order derivative optimization methods due to their simplicity and efficient computation. However, these methods contain flaws such as fixed learning rate, slow convergence speed, and sensitivity to hyperparameter selection. In this paper, a two-phase switching optimization strategy known as Stochastic Diagonal Approximate Greatest Descent (SDAGD) is proposed in optimizing a pose transfer network named as XingGAN. To evaluate the optimization performance of SDAGD, the quantitative and qualitative results are compared with Adam. In terms of quantitative results, the XingGAN with SDAGD applied to the appearance-guided discriminator achieved 4.583% and 3.733% better PCKh and FID than Adam for 20% of the training dataset while 18.039% and 18.111% better PCKh and FID than Adam for 10% of the training dataset. Additionally, the qualitative results for XingGAN with SDAGD applied to the appearance-guided discriminator also achieved the best appearance and shape consistency.
Interacting with communication appliances: an evaluation of two computer vision-based selection techniques

Communication appliances, intended for home settings, require intuitive forms of interaction. Computer vision offers a potential solution, but is not yet sufficiently accurate.As interaction designers, we need to know more than the absolute accuracy of such techniques: we must also be able to compare how they will work in our design settings, especially if we allow users to collaborate in the interpretation of their actions. We conducted a 2x4 within-subjects experiment to compare two interaction techniques based on computer vision: motion sensing, with EyeToy®-like feedback, and object tracking. Both techniques were 100% accurate with 2 or 5 choices. With 21 choices, object-tracking had significantly fewer errors and took less time for an accurate selection. Participants' subjective preferences were divided equally between the two techniques. This study compares these techniques as they would be used in real-world applications, with integrated user feedback, allowing interface designers to choose the one that best suits the specific user requirements for their particular application.
Injecting Revenue-awareness into Cold-start Recommendation: The Case of Online Insurance

In online insurance, one of the central challenges is the cold-starting of new insurance products, which means there are no previous samples to refer to. Previous studies have mainly focused on improving the prediction accuracy of new items, but they have failed to consider the revenue generated by existing items. As a result, the total revenue may suffer a loss even if new items get conversions. To address this issue, in this paper we propose RACRec, a Revenue-Aware Cold-start Recommendation framework for online insurance. Unlike previous works, RACRec uses a revenue-based objective function to ensure profitability when cold-starting new items. With this dedicated objective, RACRec orchestrates the cold-start and warm-start model by predicting their expected revenue from each user, thereby preserving the total profit. In order to accurately predict revenue, a double-ranking scheme is designed for the warm-start model to mitigate position bias, while an item embedding alignment algorithm is proposed for the cold-start model to learn the revenue of new items from similar old items. Furthermore, a reinforced orchestrate update scheme is designed to eliminate the impact of sparse conversions and continuously update revenue estimation. Extensive offline and online experiments have been conducted to validate the effectiveness of RACRec.
Effect of Green function model for the ultrasonic time reversal based super resolution imaging algorithm

In this paper, the effect of Green function model on the ultrasonic time reversal (TR) based super resolution (SR) imaging algorithm has been investigated. Time reversal with multiple signal classification (TR-MUSIC) and phase-coherent multiple signal classification (PC-MUSIC) have been introduced as the typical TR based SR imaging algorithms. Point spread function modelling has been implemented to compare the performance of TR-MUSIC and PC-MUSIC when different Green function models were applied in the imaging process. An experimental ultrasonic array imaging system has been built to capture the real ultrasonic array data from the steel sample with a EDM line, which can be considered as the extended target. It is shown that PC-MUSIC is sensitive to the Green function model when compared with TR-MUSIC. When proper Green function model is chosen, the shape of extended target in solid can be distinguished and its size can be evaluated based on PC-MUSIC image.
FLASH: Low-Latency Serverless Model Inference with Multi-Core Parallelism in Edge

Low response latency holds a pivotal role in the landscape of edge deep learning model inference, yet the constrained resources within edge computing environments often limit its full potential. Within the scope of this paper, we substantiate that even amid the resource limitations inherent to edge computing environments, it remains feasible to curtail model response latency by elevating multi-core parallel efficiency. Our research encompasses a comprehensive analysis of the parallel acceleration effects observed across models featuring diverse parameter magnitudes during the inference process. This analysis culminates in the development of FLASH, an online deep model inference system tailored for Serverless edge inference, strategically optimized through the utilization of multi-core parallelism. FLASH exhibits dynamic adaptability by modulating the number of CPU cores within computational instances in accordance with traffic request loads. It also employs a dynamic scaling mechanism to finely adjust model placement, ultimately facilitating inference acceleration and mitigating the concomitant cold start overhead. Empirical experimentation conducted across a spectrum of burst-level workloads serves to underscore FLASH’s capacity, resulting in an average reduction in response latency by 33% and a maximum reduction of 75%, while concurrently realizing a throughput enhancement of 2.94x.
Optimal Factored LT codes for Distributed Matrix Multiplication

Due to the presence of slow or failed worker computers (called stragglers), distributed matrix multiplication over large clusters may encounter delays. To tackle this issue, Factored Luby Transform (FLT) codes have been proposed for edge computing scenarios involving distributed matrix multiplication of the form C = A * B. However, the decoding time of FLT codes becomes a bottleneck in coded distributed matrix multiplication, primarily due to the high computational complexity associated with peeling decoding. To overcome this limitation, this paper introduces the modified generalized degree distribution algorithm (MGDDA) for designing an optimal degree distribution in FLT codes with BP decoding. The MGDDA algorithm minimizes overhead and maximizes performance efficiency in coded distributed matrix multiplication scenarios. Additionally, we propose an optimal encoding algorithm that achieves a balanced probability of connecting input symbols from matrices A and B to encoded symbols. This algorithm further enhances the performance of FLT codes. Simulation results consistently demonstrate that our proposed Optimal FLT (OFTL) method outperforms other existing approaches in terms of average overhead, block error rate (BER) and computational delay.
Citation for Published Item: Use Policy the Role of the Superior Temporal Lobe in Auditory False Perceptions: a Transcranial Direct Current Stimulation Study

(2014) 'The role of the superior temporal lobe in auditory false perceptions : a transcranial direct current stimulation study. ', Neuropsychologia. , 62. pp. 202-208. The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that: • a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders. Please consult the full DRO policy for further details. Keywords: Auditory verbal hallucinations Signal detection Superior temporal gyrus tDCS a b s t r a c t Neuroimaging has shown that a network of cortical areas, which includes the superior temporal gyrus, is active during auditory verbal hallucinations (AVHs). In the present study, healthy, non-hallucinating participants (N¼30) completed an auditory signal detection task, in which participants were required to detect a voice in short bursts of white noise, with the variable of interest being the rate of false auditory verbal perceptions. This paradigm was coupled with transcranial direct current stimulation, a non-invasive brain stimulation technique, to test the involvement of the left posterior superior temporal gyrus in the creation of auditory false perceptions. The results showed that increasing the levels of excitability in this region led to a higher rate of 'false alarm' responses than when levels of excitability were decreased, with false alarm responses under a sham stimulation condition lying at a mid-point between anodal and cathodal stimulation conditions. There were also corresponding changes in signal detection parameters. These results are discussed in terms of prominent cognitive neuroscientific theories of AVHs, and potential future directions for research are outlined. Auditory verbal hallucinations (AVHs) are the experience of hearing a voice in the absence of any speaker. Although experienced by between 60% and 80% of people with a diagnosis of schizophrenia (Sartorius et al., 1986), the experience is also reported by approximately 1.5–3% of the general population (Beavan, Read & Cartwright, 2011; Tien, 1991). Neuroimaging findings relating to AVHs have been variable, but tend to show that AVHs coincide with activation in areas of the temporal lobe such as the superior temporal gyrus (STG), and frontal lobe areas such as the inferior frontal gyrus …
Cocaine Increases Dopamine Release by Mobilization of a Synapsin-Dependent Reserve Pool

Cocaine primarily exerts its behavioral effects by enhancing dopaminergic neurotransmission, amplifying dopamine-encoded sensorimotor integration. The presumed mechanism for this effect is inhibition of the dopamine transporter, which blocks dopamine uptake and prolongs the duration of dopamine in the extracellular space. However, there is growing evidence that cocaine can also augment dopamine release. Here, we directly monitored the actions of cocaine on dopamine release by using electrochemical detection to measure extracellular dopamine in the striatum of anesthetized mice. Cocaine enhanced the levels of striatal dopamine produced by electrical stimulation of dopaminergic neurons. Even after pretreatment with α-methyl-p-tyrosine, which depletes the readily releasable pool of dopamine, cocaine was still capable of elevating dopamine levels. This suggests that cocaine enhances dopamine release by mobilizing a reserve pool of dopamine-containing synaptic vesicles. To test this hypothesis, we examined electrically evoked dopamine release in synapsin I/II/III triple knock-out mice, which have impaired synaptic vesicle reserve pools. Knock-out of synapsins greatly reduced the ability of cocaine to enhance dopamine release with long stimulus trains or after depletion of the newly synthesized pool. We therefore conclude that cocaine enhances dopamine release and does so by mobilizing a synapsin-dependent reserve pool of dopamine-containing synaptic vesicles. This capacity to enhance exocytotic release of dopamine may be important for the psychostimulant actions of cocaine.
Cost-Efficient Cloud-Edge Video Analytics with Hybird IaaS and FaaS Resources

Video analytics services are extensively employed in various real-time applications, including crime monitoring, business intelligence, and traffic flow control. These applications typically depend on cloud data centers to aggregate video stream tasks using Infrastructure as a Service (IaaS). However, the conventional approach of employing limited-term leased virtual machines (VMs) often leads to leads to high costs and idle time. Serverless computing or Function as a Service (FaaS) offers a more adaptable, pay-as-you-go solution but at a higher price, introducing latency challenges. Edge servers can reduce latency and costs, but limited physical resources affect accuracy. Opting for high-quality analytic services with better frame rates and resolutions can maintain accuracy but may not control costs. Therefore, the key challenge in video analytics is choosing the right configuration and computing methods for high accuracy, low cost, and low latency in edge, IaaS and FaaS environments. To address the challenge, we consider a scenario where the video analytics task is offloaded by combining the above three placements (i.e., VM, serverless, and edge node) to optimize the cost and latency while keeping the accuracy. The optimization problem can be formulated as an Integer Programming (IP) problem of which the NP-hardness is proved. To further deal with it, we propose an efficient online algorithm, which can optimize cost and latency using the Lyapunov optimization analysis while ensuring accuracy over long periods. Finally, the multi-angle simulation experiment results show that OCPA can effectively reduce the cost and delay compared with the benchmarks. At the same time, the achieved accuracy is close to the set long-term time-averaged value.