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We present a method to estimate the detection expectations of host galaxies of long gamma-ray bursts (LGRBs) in the {\it grizJHKL} bands. It is found that given the same limiting magnitude $m_{grizJHKL,\rm lim}$ in each band, the {\it z} band produces the largest number of overall LGRB hosts and low-mass hosts ($M_\ast\leq10^8$ M$_\odot$) at $m_{grizJHKL,\rm lim}\gtrsim 26$ mag. For the detection of high-redshift LGRB hosts (redshift $\geq5$), it is recommended to prioritize the {\it L} band due to its good performance at both low and high limiting magnitudes. We specifically estimate the expectation of LGRB-host detection with $m_{grizJHKL,\rm lim}=28$ mag, which the James Webb Space Telescope can partially attain. We find that there may exist 116, 259, 277, 439, 266, 294, 274, and 316 LGRB hosts, including 0.54, 31, 28, 143, 12, 20, 14, and 35 low-mass ones in the {\it grizJHKL} bands and 13, 14, 15, 14, and 15 high-redshift ones in the {\it zJHKL} bands, for 15 yr {\it Swift} LGRBs with $S\geq10^{-6}$ erg cm$^{-2}$. The results show that the study of LGRB hosts under next-generation observational conditions holds significant potential, especially for low-mass host studies. However, it appears that deeper sensitivities of galaxy telescopes may not significantly enhance statistical studies of high-redshift hosts. Strategies aimed at increasing the number of distant LGRB hosts may require the expansion of high-redshift LGRB detection., Comment: Updating published version

Jiashu Zhu,1 Ye Li,1 Rui Wang,1 Jinfang Zhang,2 Can Liu,1 Hongyu Li,1 Deguang Yang,1 Shuqin Ma,2 Suzhen Guan1 1School of Public Health, Ningxia Medical University, Yinchuan City, Ningxia, People’s Republic of China; 2Obstetric Clinic, General Hospital of Ningxia Medical University, Yinchuan City, Ningxia, People’s Republic of ChinaCorrespondence: Suzhen Guan, School of Public Health, Ningxia Medical University, Yinchuan City, Ningxia, 750004, People’s Republic of China, Tel +86 13389511821, Email guansz_nx2017@sina.com Shuqin Ma, General Hospital of Ningxia Medical University, Yinchuan City, Ningxia, 750004, People’s Republic of China, Tel +86 13895493649, Email 3117957376@qq.comObjective: To examine the relationship between pregnancy-related anxiety, family functions, and sleep quality, and to determine whether family functions mediate the relationship between pregnancy-related anxiety and sleep quality.Methods: A cross-sectional survey was conducted on pregnant women between April to August in 2022 in the obstetrics outpatient clinic of a tertiary care hospital in the Ningxia Hui Autonomous Region of China. A total of 1014 pregnant women aged 18 years and older were surveyed. They completed questionnaires, including: general demographic characteristics, the Pregnancy-related anxiety scale (PAQ), the Family Adaptation, Partnership, Growth, Affection, and Resolve (APGAR), and the Pittsburgh Sleep Quality Index Questionnaire (PSQI). Model 4 in PROCESS was used to analyze the relationships among pregnancy-related anxiety, family functions, and sleep quality, with family functions as a mediator.Results: Among the 1014 pregnant women, the pregnancy-related anxiety scale score was (21.84 ± 5.64). The total score of the family functions scale was (8.10± 2.26), and the overall sleep quality scale score was (7.89± 2.99). When participants were grouped according to different socio-demographic characteristics, the study showed that all variables differed from anxie

The robustness of driving perception systems under unprecedented conditions is crucial for safety-critical usages. Latest advancements have prompted increasing interests towards multi-LiDAR perception. However, prevailing driving datasets predominantly utilize single-LiDAR systems and collect data devoid of adverse conditions, failing to capture the complexities of real-world environments accurately. Addressing these gaps, we proposed Place3D, a full-cycle pipeline that encompasses LiDAR placement optimization, data generation, and downstream evaluations. Our framework makes three appealing contributions. 1) To identify the most effective configurations for multi-LiDAR systems, we introduce a Surrogate Metric of the Semantic Occupancy Grids (M-SOG) to evaluate LiDAR placement quality. 2) Leveraging the M-SOG metric, we propose a novel optimization strategy to refine multi-LiDAR placements. 3) Centered around the theme of multi-condition multi-LiDAR perception, we collect a 364,000-frame dataset from both clean and adverse conditions. Extensive experiments demonstrate that LiDAR placements optimized using our approach outperform various baselines. We showcase exceptional robustness in both 3D object detection and LiDAR semantic segmentation tasks, under diverse adverse weather and sensor failure conditions. Code and benchmark toolkit are publicly available., Comment: Preprint; 40 pages, 11 figures, 15 tables; Code at https://github.com/ywyeli/Place3D

In the context of changing travel behaviors and the expanding user base of Geographic Information System (GIS) services, conventional centralized architectures responsible for handling shortest distance queries are facing increasing challenges, such as heightened load pressure and longer response times. To mitigate these concerns, this study is the first to develop an edge computing framework specially tailored for processing distance queries. In conjunction with this innovative system, we have developed a straightforward, yet effective, labeling technique termed Border Labeling. Furthermore, we have devised and implemented a range of query strategies intended to capitalize on the capabilities of the edge computing infrastructure. Our experiments demonstrate that our solution surpasses other methods in terms of both indexing time and query speed across various road network datasets. The empirical evidence from our experiments supports the claim that our edge computing architecture significantly reduces the latency encountered by end-users, thus markedly decreasing their waiting times., Comment: 15pages, 18(sub)pages

Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs perform in specific domains (e.g, the intellectual property (IP) domain). In this paper, we contribute a new benchmark, the first Multilingual-oriented quiZ on Intellectual Property (MoZIP), for the evaluation of LLMs in the IP domain. The MoZIP benchmark includes three challenging tasks: IP multiple-choice quiz (IPQuiz), IP question answering (IPQA), and patent matching (PatentMatch). In addition, we also develop a new IP-oriented multilingual large language model (called MoZi), which is a BLOOMZ-based model that has been supervised fine-tuned with multilingual IP-related text data. We evaluate our proposed MoZi model and four well-known LLMs (i.e., BLOOMZ, BELLE, ChatGLM and ChatGPT) on the MoZIP benchmark. Experimental results demonstrate that MoZi outperforms BLOOMZ, BELLE and ChatGLM by a noticeable margin, while it had lower scores compared with ChatGPT. Notably, the performance of current LLMs on the MoZIP benchmark has much room for improvement, and even the most powerful ChatGPT does not reach the passing level. Our source code, data, and models are available at \url{https://github.com/AI-for-Science/MoZi}.

Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Simulation-based benchmarks have emerged as a highly scalable approach for robustness evaluation compared to real-world data collection. However, crafting a challenging and controllable noisy world with diverse perturbations remains relatively under-explored. To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations. This pipeline incorporates customizable hardware setups, software components, and perturbed environments. In particular, we introduce comprehensive perturbation taxonomy along with a perturbation composition toolbox, allowing the transformation of clean simulations into challenging noisy environments. Utilizing the pipeline, we instantiate the Robust-SLAM benchmark, which includes diverse perturbation types, to evaluate the risk tolerance of existing advanced multi-modal SLAM models. Our extensive analysis uncovers the susceptibilities of existing SLAM models to real-world disturbance, despite their demonstrated accuracy in standard benchmarks. Our perturbation synthesis toolbox, SLAM robustness evaluation pipeline, and Robust-SLAM benchmark will be made publicly available at https://github.com/Xiaohao-Xu/SLAM-under-Perturbation/., Comment: 40 pages

This paper presents Graph-of-Thought (GoT), a new model for workflow automation that enhances the flexibility and efficiency of Large Language Models (LLMs) in complex task execution. GoT advances beyond traditional linear and tree-like cognitive models with a graph structure that enables dynamic path selection. The open-source engine GoTFlow demonstrates the practical application of GoT, facilitating automated, data-driven decision-making across various domains. Despite challenges in complexity and transparency, GoTFlow's potential for improving business processes is significant, promising advancements in both efficiency and decision quality with continuous development., Comment: Keywords: Graph-of-Thought (GoT), Workflow Automation, Large Language Models (LLMs), Task Execution, Data-Driven Decision Making, Complexity Management

Recent studies showed that the generalization of neural networks is correlated with the sharpness of the loss landscape, and flat minima suggests a better generalization ability than sharp minima. In this paper, we propose a novel method called \emph{optimum shifting}, which changes the parameters of a neural network from a sharp minimum to a flatter one while maintaining the same training loss value. Our method is based on the observation that when the input and output of a neural network are fixed, the matrix multiplications within the network can be treated as systems of under-determined linear equations, enabling adjustment of parameters in the solution space, which can be simply accomplished by solving a constrained optimization problem. Furthermore, we introduce a practical stochastic optimum shifting technique utilizing the Neural Collapse theory to reduce computational costs and provide more degrees of freedom for optimum shifting. Extensive experiments (including classification and detection) with various deep neural network architectures on benchmark datasets demonstrate the effectiveness of our method.

Complementary RGB and TIR modalities enable RGB-T tracking to achieve competitive performance in challenging scenarios. Therefore, how to better fuse cross-modal features is the core issue of RGB-T tracking. Some previous methods either insufficiently fuse RGB and TIR features, or depend on intermediaries containing information from both modalities to achieve cross-modal information interaction. The former does not fully exploit the potential of using only RGB and TIR information of the template or search region for channel and spatial feature fusion, and the latter lacks direct interaction between the template and search area, which limits the model's ability to fully exploit the original semantic information of both modalities. To alleviate these limitations, we explore how to improve the performance of a visual Transformer by using direct fusion of cross-modal channels and spatial features, and propose CSTNet. CSTNet uses ViT as a backbone and inserts cross-modal channel feature fusion modules (CFM) and cross-modal spatial feature fusion modules (SFM) for direct interaction between RGB and TIR features. The CFM performs parallel joint channel enhancement and joint multilevel spatial feature modeling of RGB and TIR features and sums the features, and then globally integrates the sum feature with the original features. The SFM uses cross-attention to model the spatial relationship of cross-modal features and then introduces a convolutional feedforward network for joint spatial and channel integration of multimodal features. Comprehensive experiments show that CSTNet achieves state-of-the-art performance on three public RGB-T tracking benchmarks. Code is available at https://github.com/LiYunfengLYF/CSTNet., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

ObjectivesMultidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB.MethodsWe retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (n = 295, 102), nodules (n = 302, 97), and their combination (n = 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves.ResultsSixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877, p > 0.05) and testing cohort (0.820 versus 0.786, p < 0.05) and similar to the performance of the nodules model in the combined training cohort (0.911 versus 0.933, p > 0.05) and testing cohort (0.820 versus 0.855, p > 0.05).ConclusionsThe CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB.Clinical relevance statementOur study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients.Key points• This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB. • The radiomics model showed a favorable performance for the identification of MDR-TB. • The com

IntroductionAlthough severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA has been frequently detected in sewage from many university dormitories to inform public health decisions during the COVID-19 pandemic, a clear understanding of SARS-CoV-2 RNA persistence in site-specific raw sewage is still lacking. To investigate the SARS-CoV-2 RNA persistence, a field trial was conducted in the University of Tennessee dormitories raw sewage, similar to municipal wastewater.MethodsThe decay of enveloped SARS-CoV-2 RNA and non-enveloped Pepper mild mottle virus (PMMoV) RNA was investigated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in raw sewage at 4°C and 20°C.ResultsTemperature, followed by the concentration level of SARS-CoV-2 RNA, was the most significant factors that influenced the first-order decay rate constants (k) of SARS-CoV-2 RNA. The mean k values of SARS-CoV-2 RNA were 0.094 day-1 at 4°C and 0.261 day-1 at 20°C. At high-, medium-, and low-concentration levels of SARS-CoV-2 RNA, the mean k values were 0.367, 0.169, and 0.091 day-1, respectively. Furthermore, there was a statistical difference between the decay of enveloped SARS-CoV-2 and non-enveloped PMMoV RNA at different temperature conditions.DiscussionThe first decay rates for both temperatures were statistically comparable for SARS-CoV-2 RNA, which showed sensitivity to elevated temperatures but not for PMMoV RNA. This study provides evidence for the persistence of viral RNA in site-specific raw sewage at different temperature conditions and concentration levels.

In this work, we systematically study the OZI-allowed two-body strong decay properties of $1P$- and $1D$-wave $\Sigma_c$ baryons within the $j $-$j$ coupling scheme in the framework of the quark pair creation model. For a comparison, we also give the predictions of the chiral quark model. Some model dependencies can be found in the predictions of two models. The calculations indicate that: (i) The $1P$-wave $\lambda$-mode $\Sigma_c$ states most likely to be relatively narrow states with a width of $\Gamma<80$ MeV. Their main decay channels are $\Lambda_c\pi$, or $\Sigma_c\pi$, or $\Sigma_c^*\pi$. The $1P$-wave $\rho$-mode states most might be broad states with a width of $\Gamma\sim 100-200$ MeV. They dominantly decay into $\Sigma_c\pi$ and $\Sigma_c^*\pi$ channels. Some evidences of these $1P$-wave states are most likely to be observed in the $\Lambda_c\pi$ and $\Lambda_c\pi\pi$ invariant mass spectra around the energy range of $2.75-2.95$ GeV. (ii) The $1D$-wave $\lambda$-mode $\Sigma_c$ excitations may be moderate states with a width of about dozens of MeV. The $1D$-wave $\lambda$-mode states mainly decay into the $1P$-wave charmed baryon via the pionic decay processes. Meanwhile, several $1D$-wave $\lambda$-mode states have significant decay rates into $DN$ or $D^*N$. Hence, the $DN$ and $D^*N$ are likely to be interesting channels for experimental exploration. (iii) Furthermore, the two $1D$-wave $\rho$-mode excitations $\Sigma_c|J^P=5/2^+,3\rangle_{\rho\rho}$ and $|J^P=7/2^+,3\rangle_{\rho\rho}$ are most likely to be fairly narrow state with a width of dozens of MeV, and they mainly decay into $\Lambda_c\pi$. Some evidences of them might be observed in the $\Lambda_c\pi$ invariant mass spectra around the energy range of $3.1-3.2$ GeV., Comment: 12 pages,5 figures, 4 tables. arXiv admin note: text overlap with arXiv:2208.10116

A large literature implicates time preference (i.e., how much an outcome retains value as it is delayed) as a predictor of a wide range of behaviors, because most behaviors involve sooner and delayed consequences. We aimed to provide the most comprehensive examination to date of how well laboratory-derived estimates of time preference relate to self-reports of 36 behaviors, ranging from retirement savings to flossing, in a test-rest design using a large sample (N = 1,308) and two waves of data collection separated by 4.5 months. Time preference is significantly-albeit modestly-associated with about half of the behaviors; this is true even when controlling for 15 other demographic variables and psychologically relevant scales. There is substantial variance in the strengths of associations that is not easily explained. Time preference's predictive validity falls in the middle of these 16 possible predictors. Finally, we ask time preference researchers (N = 55) to predict the variation in the relationship between time preference and behaviors, and although they are reasonably well-calibrated, these experts tend to overestimate the predictive power of time preference estimates. We discuss implications of invoking time preference as a predictor and/or determinant of behaviors with delayed consequences in light of our findings. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Jiaxing Liu,1 Ye Li,2 Ting Liu,2 Yuru Shi,2 Yun Wang,2 Jing Wu,2 Yingjie Qi2 1Department of Clinical Laboratory, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu Province, 210008, People’s Republic of China; 2Department of Laboratory Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230000, People’s Republic of ChinaCorrespondence: Yingjie Qi, Department of Laboratory Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230000, People’s Republic of China, Email 7yingjie@sina.comBackground: Mycobacterium tuberculosis (Mtb) survives inside a human host for a long time in the form of latent tuberculosis infection (LTBI). Latent infection of tuberculosis has the opportunity of developing into active tuberculosis (ATB), which has greatly endangered human health. The existing diagnostic methods cannot effectively distinguish LTBI from ATB. Therefore, more effective diagnostic biomarkers and methods are urgently needed.Methods: Here, we screened the GEO data set, conducted joint differential analysis and target gene enrichment analysis, after filtering the disease-related database, we screened the differential miRNA related to TB. The qPCR was used to verify the miRNAs in 84 serum samples. Different combinations of biomarkers were evaluated by logistic regression to obtain a biomarker panel with good performance for diagnosing LTBI.Results: A panel with two miRNAs (hsa-let-7d-5p, hsa-miR-140-5p) was established to differentiate LTBI from ATB. Receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) are 0.930 (sensitivity = 100%, specificity = 88.5%) and 0.923 (sensitivity = 100%, specificity = 92.3%) with the biomarker panel for the training set and test set respectively.Conclusion: T

Xinna Lv,1,&ast; Ye Li,1,&ast; Botao Cai,2 Wei He,1 Ren Wang,1 Minghui Chen,1 Junhua Pan,1 Dailun Hou1 1Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, People’s Republic of China; 2Department of Radiology, Harbin Chest Hospital, Harbin, 150000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Junhua Pan; Dailun Hou, Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, People’s Republic of China, Tel +8613901097074 ; +8618001286699, Email 13901097074@139.com; hou.dl@mail.ccmu.edu.cnBackground: Sputum culture result at the sixth month is essential for predicting therapeutic response to longer multidrug-resistant tuberculosis (MDR-TB) regimens. This study aimed to construct a predictive model using cavity-based radiomics to predict sputum status at the sixth month for MDR-TB patients treated with longer regimens.Methods: This retrospective study recruited 315 MDR-TB patients treated with longer regimens from two centers (250 patients from center 1 and 65 patients from center 2), who were divided into persistently positive and conversion to negative sputum culture groups according to sputum results. Radiomics features were extracted based on the cavity, and a radiomics model was selected and established using a random forest classifier. The clinical characteristics and primary CT signs with significant differences were integrated to build a clinical model. A combined model was generated using the radiomics and clinical model. ROC curves, F1-score and DCA curves were used to assess the predictive performance of the models.Results: Twenty-eight radiomics features were selected to build a radiomics model for predicting the sputum status. The radiomics model achieved good performance, with AUCs of 0.892 and 0.839 in the training and testing cohort, respectively, which was similar to the performance of the combined model (0.913

In the digital media age, video advertisements are ubiquitous, including on plat- forms such as YouTube and TikTok. People generally do not like advertisements, especially non-skippable ones, and may prefer to do something else while an ad plays. Following recent research suggesting that faster speeds may increase engage- ment with videos, we examine if speeding up video advertisements will increase people’s attention when watching non-skippable ads as well as improve ad expe- rience and memory. In two experiments, college participants were randomly as- signed to watch an ad (Vrbo in Study 1, Five Star Notebooks in Study 2) at either normal or 25% faster (1.25x) speed. We did not find significant effects of play- back speed in Study 1, perhaps because the ad did not have much informational content. However, in Study 2, we found that participants who watched the ad at 1.25x speed enjoyed it less, remembered it less well, and had lower brand sentiment than those who watched it at normal speed. While these findings need replication and further research to test robustness across a range of ad types and lengths, our results suggest that increase the speed of ads may not be beneficial to advertisers.

This study reports the results of the second round of analyses of the Offshore Code Comparison, Collaboration, Continued, with Correlation and unCertainty (OC6) project Phase III. While the first round investigated rotor aerodynamic loading, here, focus is given to the wake behavior of a floating wind turbine under large motion. Wind tunnel experimental data from the UNsteady Aerodynamics for FLOating Wind (UNAFLOW) project are compared with the results of simulations provided by participants with methods and codes of different levels of fidelity. The effect of platform motion on both the near and the far wake is investigated. More specifically, the behavior of tip vortices in the near wake is evaluated through multiple metrics, such as streamwise position, core radius, convection velocity, and circulation. Additionally, the onset of velocity oscillations in the far wake is analyzed because this can have a negative effect on stability and loading of downstream rotors. Results in the near wake for unsteady cases confirm that simulations and experiments tend to diverge from the expected linearized quasi-steady behavior when the rotor reduced frequency increases over 0.5. Additionally, differences across the simulations become significant, suggesting that further efforts are required to tune the currently available methodologies in order to correctly evaluate the aerodynamic response of a floating wind turbine in unsteady conditions. Regarding the far wake, it is seen that, in some conditions, numerical methods overpredict the impact of platform motion on the velocity fluctuations. Moreover, results suggest that the effect of platform motion on the far wake, differently from original expectations about a faster wake recovery in a floating wind turbine, seems to be limited or even oriented to the generation of a wake less prone to dissipation.

Extensive rare earth element (REE) mining activities pose threats to agricultural soils surrounding the mining areas. Here, low and high REE-contaminated soils from farmlands around mine tailings were remediated with hydroxyapatite. A toxicokinetic approach was applied to assess whether the use of hydroxyapatite reduced the bioavailability of REEs and thus inhibited their accumulation in the terrestrial organism Enchytraeus crypticus. Our results showed that addition of hydroxyapatite increased soil pH, DOC and anion contents. CaCl2-extractable REE concentrations significantly decreased, indicating the stabilization by hydroxyapatite. The influence of hydroxyapatite on the REE accumulation in enchytraeids was quantified by fitting a toxicokinetic model to dynamic REE body concentrations. The estimated uptake (Ku) and elimination rate constants (Ke), and bioaccumulation factor (BAF) for REEs were in the range of 0.000821 – 0.122 kgsoil/kgworm day−1, 0.0224 – 0.136 day−1, and 0.00135 – 1.96, respectively. Both Ku and BAF were significantly reduced by over 80% by hydroxyapatite addition, confirming the decreased REE bioavailability. Low atomic number REEs had higher BAFs in slightly contaminated soil, suggesting a higher bioaccumulation potential of light REEs in soil organisms. Overall, chemical stabilization with amendments can attenuate the bioavailability of REEs and reduce the potential ecological risk of contaminated agricultural soils near REE mining areas.

With the increase of anthropogenic oceanic activities, a great number of innovative marine vehicles have been proposed and developed in the past few decades. Among these new vehicles, the amphibious marine vehicle is considered one of the most unique as it operates in air and water. This unique mode of operation necessitates simultaneous and coupled analyses of the aerodynamic and hydrodynamic performance. However, most of the existing methods are still not able to separate and resolve the hydrodynamic behavior of the hull from the coupled system, which hinders in-depth research and development of such vehicles. To advance the understanding of the amphibious vehicle and support a more comprehensive design process, in this article, we propose an improved experimental framework that can decouple the hydrodynamic and aerodynamic effects. As a result, the hydrodynamic performance data retrieved under this framework can be scaled to a prototype by Froude's similarity law. Using a scaled model, a comparative analysis is performed to demonstrate the advantages of the new framework. Results show that the new framework not only can isolate aerodynamics and hydrodynamics loads from each other, but also provides a more scalable and reliable resistance coefficient compared with those obtained from the existing frameworks. Furthermore, this framework can also reveal more detailed hydroplaning lift characteristics, which constitute a crucial feature in amphibious vehicle analysis. Moving forward, this new framework is expected to serve as a more accurate platform to derive a data-enhanced explanatory model, which will facilitate multidisciplinary design and optimization on an amphibious marine vehicle.

This paper provides a summary of the work done within Phase III of the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) project, under the International Energy Agency Wind Technology Collaboration Programme Task 30. This phase focused on validating the aerodynamic loading on a wind turbine rotor undergoing large motion caused by a floating support structure. Numerical models of the Technical University of Denmark 10 MW reference wind turbine were validated using measurement data from a 1:75 scale test performed during the UNsteady Aerodynamics for FLOating Wind (UNAFLOW) project and a follow-on experimental campaign, both performed at the Politecnico di Milano wind tunnel. Validation of the models was performed by comparing the loads for steady (fixed platform) and unsteady (harmonic motion of the platform) wind conditions. For the unsteady wind conditions, the platform was forced to oscillate in the surge and pitch directions under several frequencies and amplitudes. These oscillations result in a wind variation that impacts the rotor loads (e.g., thrust and torque). For the conditions studied in these tests, the system aerodynamic response was almost steady. Only a small hysteresis in airfoil performance undergoing angle of attack variations in attached flow was observed. During the experiments, the rotor speed and blade pitch angle were held constant. However, in real wind turbine operating conditions, the surge and pitch variations would result in rotor speed variations and/or blade pitch actuations, depending on the wind turbine controller region that the system is operating. Additional simulations with these control parameters were conducted to verify the fidelity of different models. Participant results showed, in general, a good agreement with the experimental measurements and the need to account for dynamic inflow when there are changes in the flow conditions due to the rotor speed variations or blade pitch actuation

Wave breaking is the main mechanism that dissipates energy from ocean waves by wind. Its effects on the frequency spectrum cause a downshift of the spectral peak and dissipation of the total energy of the spectrum. Various reduced-form wave breaking models have been developed to capture wave breaking in envelope-based wave evolution equations for perturbed plane-wave systems, but their applicability to waves with a continuous spectrum has not been examined. In this paper we perform modified nonlinear Schrödinger equation simulations to study four existing wave breaking models and compare the results with new experimental data for breaking unidirectional wave groups. We first compare the different wave breaking models for perturbed plane-wave simulations and then examine their potential extension to waves with a continuous spectrum. We find that most existing models are able to model breaking in perturbed plane waves, but none produce the correct spectral dissipation for focused wave groups. We propose a modification to the breaking model by Kato and Oikawa [J. Phys. Soc. Jpn. 64, 4660 (1995)0031-901510.1143/JPSJ.64.4660] in order to model breaking in focused wave groups. The modified model incorporates both a breaking criterion, which activates and deactivates the dissipation term proposed by Kato and Oikawa, and a heuristic spectral weighting function that is obtained by fitting to experimental data. The modified model also predicts breaking in perturbed plane waves well., Environmental Fluid Mechanics

Co-phase and co-focus detection is one of the key technologies for large-aperture segmented mirror telescopes. In this paper, a new edge sensor based on fringes of equal thickness is developed, which can detect each segment's relative piston, tilt, and tip errors from the interferograms. Based on the co-focus demand for many ground-based seeing limited segmented mirror telescopes, an edge sensor prototype based on such a principle is built and applied in the indoor segmented mirror experiment system in the lab. According to the co-focus requirement of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, many simulations and experiments are carried out for co-focus error detection of the segmented mirror system. Experiment results show that the co-focus accuracy is better than 0."02 rms, which can meet the co-focus requirements of most large or extremely large segmented mirror astronomical telescopes.

The Wide Field Survey Telescope (WFST) is a dedicated photometric surveying facility being built jointly by the University of Science and Technology of China and the Purple Mountain Observatory. It is equipped with a 2.5-meter diameter primary mirror, an active optics system, and a mosaic CCD camera with 0.73 gigapixels on the primary focal plane for high-quality image capture over an FOV of 6.5-square-degree. It is anticipated that WFST will be set up at the Lenghu site in the summer of 2023 and begin to observe the northern sky in four optical bands (u, g, r, and i) with a range of cadences, from hourly/daily in the Deep High-Cadence Survey (DHS) program to semiweekly in the Wide-Field Survey (WFS) program, three months later. During a photometric night, a nominal 30 s exposure in the WFS program will reach a depth of 22.27, 23.32, 22.84, and 22.31 (AB magnitudes) in these four bands, respectively, allowing for the detection of a tremendous amount of transients in the low-z universe and a systematic investigation of the variability of Galactic and extragalactic objects. In the DHS program, intranight 90 s exposures as deep as 23 (u) and 24 mag (g), in combination with target of opportunity follow-ups, will provide a unique opportunity to explore energetic transients in demand for high sensitivities, including the electromagnetic counterparts of gravitational wave events, supernovae within a few hours of their explosions, tidal disruption events and fast, luminous optical transients even beyond a redshift of unity. In addition, the final 6-year co-added images, anticipated to reach g=25.8 mag in WFS or 1.5 mags deeper in DHS, will be of fundamental importance to general Galactic and extragalactic science. The highly uniform legacy surveys of WFST will serve as an indispensable complement to those of LSST that monitor the southern sky., Comment: 48 pages

Hua-Man Wu,1,2,&ast; Zhi-Jun Wang,1,&ast; Chao-Hong Cheng,1 Tong Su,1 Jing Wang,1 Ye-Zhou Li,3 Qiao-Jun Wang,4 Fei Han,4 Rui Chen1,4 1Department of Respiratory, the Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, Zigong First People’s Hospital, Zigong, Sichuan, People’s Republic of China; 3School of Medicine, the University of Manchester, Manchester, UK; 4Department of Sleeping Center, the Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Rui Chen, Department of Respiratory/Department of Sleeping Center, the Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou, 215004, People’s Republic of China, Tel +86(0512)67784857, Email chenruigood@126.comPurpose: Obstructive sleep apnea hypopnea syndrome (OSAHS) can lead to cognitive impairment, though few studies have so far examined hypercapnia as its causal mechanism, due to the invasive nature of conventional arterial CO2 measurement. The study aims to investigate the effects of daytime hypercapnia on working memory in young and middle-aged patients with OSAHS.Patients and Methods: This prospective study screened 218 patients and eventually recruited 131 patients (aged 25– 60 years) with polysomnography (PSG)-diagnosed OSAHS. Using a cut-off of 45mmHg daytime transcutaneous partial pressure of carbon dioxide (PtcCO2), 86 patients were assigned into the normocapnic group and 45 patients into the hypercapnic group. Working memory was evaluated using the Digit Span Backward Test (DSB) and the Cambridge Neuropsychological Test Automated Battery.Results: Compared with the normocapnic group, the hypercapnic group performed worse in verbal, visual, and spatial working memory tasks. PtcCO2≥ 45mmHg was an independent predictor for lower DSB scores (OR=4.057), lower accuracy

Naked eye recognition of age is usually based on comparison with the age of others. However, this idea is ignored by computer tasks because it is difficult to obtain representative contrast images of each age. Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature difference with each age, which obtains the style map of each age through the learned values representing the mean and standard deviation. We let the input of transfer learning as the binary code of age natural number to obtain continuous age feature information. The learned two groups of values in Binary code mapping are corresponding to the mean and standard deviation of the comparison ages. In summary, our method consists of four parts: FaceEncoder, DAA operation, Binary code mapping, and AgeDecoder modules. After getting the delta age via AgeDecoder, we take the average value of all comparison ages and delta ages as the predicted age. Compared with state-of-the-art methods, our method achieves better performance with fewer parameters on multiple facial age datasets., Comment: Accepted by CVPR2023; 8 pages, 3 figures

Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features. In this paper, we propose to employ implicit stochastic gradient descent (ISGD) method to train PINNs for improving the stability of training process. We heuristically analyze how ISGD overcome stiffness in the gradient flow dynamics of PINNs, especially for problems with multi-scale solutions. We theoretically prove that for two-layer fully connected neural networks with large hidden nodes, randomly initialized ISGD converges to a globally optimal solution for the quadratic loss function. Empirical results demonstrate that ISGD works well in practice and compares favorably to other gradient-based optimization methods such as SGD and Adam, while can also effectively address the numerical stiffness in training dynamics via gradient descent., Comment: 17 pages, published as a conference paper at AAAI23

Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications. This paper develops a human-in-the-loop template mining framework to support interactive log analysis, which is highly desirable in real-world diagnosis or troubleshooting of Web applications but yet previous template mining algorithms fails to support it. We formulate three types of light-weight user feedbacks and based on them we design three atomic human-in-the-loop template mining algorithms. We derive mild conditions under which the outputs of our proposed algorithms are provably correct. We also derive upper bounds on the computational complexity and query complexity of each algorithm. We demonstrate the versatility of our proposed algorithms by combining them to improve the template mining accuracy of five representative algorithms over sixteen widely used benchmark datasets.

With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we analyze the development trend of the new generation machine learning framework and introduce the design principles of VQNet 2.0 in detail: unity, practicality, efficiency, and compatibility, as well as full particulars of implementation. We illustrate the functions of VQNet 2.0 through several basic applications, including classical convolutional neural networks, quantum autoencoders, hybrid classical-quantum networks, etc. After that, through extensive experiments, we demonstrate that the operation speed of VQNet 2.0 is higher than the comparison method. Finally, through extensive experiments, we demonstrate that VQNet 2.0 can deploy on different hardware platforms, the overall calculation speed is faster than the comparison method. It also can be mixed and optimized with quantum circuits composed of multiple quantum computing libraries.

A visual single-object tracker is an indispensable component of underwater vehicles (UVs) in marine organism grasping tasks. Its accuracy and stability are imperative to guide the UVs to perform grasping behavior. Although single-object trackers show competitive performance in the challenge of underwater image degradation, there are still issues with sample imbalance and exclusion of similar objects that need to be addressed for application in marine organism grasping. This paper proposes Underwater OSTrack (UOSTrack), which consists of underwater image and open-air sequence hybrid training (UOHT), and motion-based post-processing (MBPP). The UOHT training paradigm is designed to train the sample-imbalanced underwater tracker so that the tracker is exposed to a great number of underwater domain training samples and learns the feature expressions. The MBPP paradigm is proposed to exclude similar objects. It uses the estimation box predicted with a Kalman filter and the candidate boxes in the response map to relocate the lost tracked object in the candidate area. UOSTrack achieves an average performance improvement of 4.41% and 7.98% maximum compared to state-of-the-art methods on various benchmarks, respectively. Field experiments have verified the accuracy and stability of our proposed UOSTrack for UVs in marine organism grasping tasks. More details can be found at https://github.com/LiYunfengLYF/UOSTrack.

As a highly specialized shock-absorbing connective tissue, articular cartilage (AC) has very limited self-repair capacity after traumatic injuries, posing a heavy socioeconomic burden. Common clinical therapies for small- to medium-size focal AC defects are well-developed endogenous repair and cell-based strategies, including microfracture, mosaicplasty, autologous chondrocyte implantation (ACI), and matrix-induced ACI (MACI). However, these treatments frequently result in mechanically inferior fibrocartilage, low cost-effectiveness, donor site morbidity, and short-term durability. It prompts an urgent need for innovative approaches to pattern a pro-regenerative microenvironment and yield hyaline-like cartilage with similar biomechanical and biochemical properties as healthy native AC. Acellular regenerative biomaterials can create a favorable local environment for AC repair without causing relevant regulatory and scientific concerns from cell-based treatments. A deeper understanding of the mechanism of endogenous cartilage healing is furthering the (bio)design and application of these scaffolds. Currently, the utilization of regenerative biomaterials to magnify the repairing effect of joint-resident endogenous stem/progenitor cells (ESPCs) presents an evolving improvement for cartilage repair. This review starts by briefly summarizing the current understanding of endogenous AC repair and the vital roles of ESPCs and chemoattractants for cartilage regeneration. Then several intrinsic hurdles for regenerative biomaterials-based AC repair are discussed. The recent advances in novel (bio)design and application regarding regenerative biomaterials with favorable biochemical cues to provide an instructive extracellular microenvironment and to guide the ESPCs (e.g. adhesion, migration, proliferation, differentiation, matrix production, and remodeling) for cartilage repair are summarized. Finally, this review outlines the future directions of engineering the next-generat

Tao Sun,1,&ast; Xian-Hong Huang,1,&ast; Shu-E Zhang,2,&ast; Hong-Yan Yin,3 Qing-Lin Li,2 Lei Gao,2 Ye Li,2 Li Li,4 Bing Cao,5 Jin-Hong Yang,6 Bei Liu7 1Department of Health Policy and Management, School of Public Health, Hangzhou Normal University, Hangzhou, 311121, People’s Republic of China; 2Department of Health Management, School of Health Management, Harbin Medical University, Harbin, 150081, People’s Republic of China; 3Department of Humanities and Social Sciences, Harbin Medical University (Daqing), Daqing, 163319, People’s Republic of China; 4Department of Administration, School of Law, Zhejiang University City College, Hangzhou, 310015, People’s Republic of China; 5Department of Oncology, Weifang People’s Hospital, Weifang, 261000, People’s Republic of China; 6Key Laboratory of Cognition and Personality, Faculty of Psychology, Ministry of Education, Southwest University, Chongqing, 400715, People’s Republic of China; 7Global Center for Infectious Disease and Policy Research, Peking University, Beijing, 100191, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bei Liu, Global Center for Infectious Disease and Policy Research, Peking University, Beijing, 100191, People’s Republic of China, Email 18827380717@163.comAim: To clarify the mediating role of burnout and the moderating role of turnover intention in the association between fatigue and job satisfaction among Chinese nurses in intensive care units (ICU) during the COVID-19 pandemic.Methods: A cross-sectional survey of fifteen provinces in China was conducted, using an online questionnaire, from December 2020 to January 2021, during the COVID-19 pandemic. A total of 374 ICU nurses (effective response rate: 71.37%) provided sufficient responses. Sociodemographic factors, job demographic factors, fatigue, burnout, job satisfaction, and turnover intention were assessed using questionnaires. General linear modeling (GLM), hierarchic

Seasonal-trend decomposition is one of the most fundamental concepts in time series analysis that supports various downstream tasks, including time series anomaly detection and forecasting. However, existing decomposition methods rely on batch processing with a time complexity of O(W), where W is the number of data points within a time window. Therefore, they cannot always efficiently support real-time analysis that demands low processing delay. To address this challenge, we propose OneShotSTL, an efficient and accurate algorithm that can decompose time series online with an update time complexity of O(1). OneShotSTL is more than $1,000$ times faster than the batch methods, with accuracy comparable to the best counterparts. Extensive experiments on real-world benchmark datasets for downstream time series anomaly detection and forecasting tasks demonstrate that OneShotSTL is from 10 to over 1,000 times faster than the state-of-the-art methods, while still providing comparable or even better accuracy., Comment: PVLDB 2023

Fast radio bursts (FRBs) are highly dispersed millisecond-duration radio bursts, of which the physical origin is still not fully understood. FRB 20201124A is one of the most actively repeating FRBs. In this paper, we present the collection of 1863 burst dynamic spectra of FRB 20201124A measured with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The current collection, taken from the observation during the FRB active phase from April to June 2021, is the largest burst sample detected in any FRB so far. The standard PSRFITs format is adopted, including dynamic spectra of the burst, and the time information of the dynamic spectra, in addition, mask files help readers to identify the pulse positions are also provided.

Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning. However, PINNs face training difficulties for evolutionary PDEs, particularly for dynamical systems whose solutions exhibit multi-scale or turbulent behavior over time. The reason is that PINNs may violate the temporal causality property since all the temporal features in the PINNs loss are trained simultaneously. This paper proposes to use implicit time differencing schemes to enforce temporal causality, and use transfer learning to sequentially update the PINNs in space as surrogates for PDE solutions in different time frames. The evolving PINNs are better able to capture the varying complexities of the evolutionary equations, while only requiring minor updates between adjacent time frames. Our method is theoretically proven to be convergent if the time step is small and each PINN in different time frames is well-trained. In addition, we provide state-of-the-art (SOTA) numerical results for a variety of benchmarks for which existing PINNs formulations may fail or be inefficient. We demonstrate that the proposed method improves the accuracy of PINNs approximation for evolutionary PDEs and improves efficiency by a factor of 4-40x., Comment: 23 pages, 20 figures

Multimodal semantic segmentation is developing rapidly, but the modality of RGB-Polarization remains underexplored. To delve into this problem, we construct a UPLight RGB-P segmentation benchmark with 12 typical underwater semantic classes. In this work, we design the ShareCMP, an RGB-P semantic segmentation framework with a shared dual-branch architecture, which reduces the number of parameters by about 26-33% compared to previous dual-branch models. It encompasses a Polarization Generate Attention (PGA) module designed to generate polarization modal images with richer polarization properties for the encoder. In addition, we introduce the Class Polarization-Aware Loss (CPALoss) to improve the learning and understanding of the encoder for polarization modal information and to optimize the PGA module. With extensive experiments on a total of three RGB-P benchmarks, our ShareCMP achieves state-of-the-art performance in mIoU with fewer parameters on the UPLight (92.45(+0.32)%), ZJU (92.7(+0.1)%), and MCubeS (50.99(+1.51)%) datasets compared to the previous best methods. The code is available at https://github.com/LEFTeyex/ShareCMP., Comment: 10 pages, 5 figures

Survival prediction plays a crucial role in assisting clinicians with the development of cancer treatment protocols. Recent evidence shows that multimodal data can help in the diagnosis of cancer disease and improve survival prediction. Currently, deep learning-based approaches have experienced increasing success in survival prediction by integrating pathological images and gene expression data. However, most existing approaches overlook the intra-modality latent information and the complex inter-modality correlations. Furthermore, existing modalities do not fully exploit the immense representational capabilities of neural networks for feature aggregation and disregard the importance of relationships between features. Therefore, it is highly recommended to address these issues in order to enhance the prediction performance by proposing a novel deep learning-based method. We propose a novel framework named Two-stream Transformer-based Multimodal Fusion Network for survival prediction (TTMFN), which integrates pathological images and gene expression data. In TTMFN, we present a two-stream multimodal co-attention transformer module to take full advantage of the complex relationships between different modalities and the potential connections within the modalities. Additionally, we develop a multi-head attention pooling approach to effectively aggregate the feature representations of the two modalities. The experiment results on four datasets from The Cancer Genome Atlas demonstrate that TTMFN can achieve the best performance or competitive results compared to the state-of-the-art methods in predicting the overall survival of patients.

Cameras and LiDARs are both important sensors for autonomous driving, playing critical roles in 3D object detection. Camera-LiDAR Fusion has been a prevalent solution for robust and accurate driving perception. In contrast to the vast majority of existing arts that focus on how to improve the performance of 3D target detection through cross-modal schemes, deep learning algorithms, and training tricks, we devote attention to the impact of sensor configurations on the performance of learning-based methods. To achieve this, we propose a unified information-theoretic surrogate metric for camera and LiDAR evaluation based on the proposed sensor perception model. We also design an accelerated high-quality framework for data acquisition, model training, and performance evaluation that functions with the CARLA simulator. To show the correlation between detection performance and our surrogate metrics, We conduct experiments using several camera-LiDAR placements and parameters inspired by self-driving companies and research institutions. Extensive experimental results of representative algorithms on nuScenes dataset validate the effectiveness of our surrogate metric, demonstrating that sensor configurations significantly impact point-cloud-image fusion based detection models, which contribute up to 30% discrepancy in terms of the average precision.

Although single object trackers have achieved advanced performance, their large-scale models hinder their application on limited resources platforms. Moreover, existing lightweight trackers only achieve a balance between 2-3 points in terms of parameters, performance, Flops and FPS. To achieve the optimal balance among these points, this paper proposes a lightweight full-convolutional Siamese tracker called LightFC. LightFC employs a novel efficient cross-correlation module (ECM) and a novel efficient rep-center head (ERH) to improve the feature representation of the convolutional tracking pipeline. The ECM uses an attention-like module design, which conducts spatial and channel linear fusion of fused features and enhances the nonlinearity of the fused features. Additionally, it refers to successful factors of current lightweight trackers and introduces skip-connections and reuse of search area features. The ERH reparameterizes the feature dimensional stage in the standard center-head and introduces channel attention to optimize the bottleneck of key feature flows. Comprehensive experiments show that LightFC achieves the optimal balance between performance, parameters, Flops and FPS. The precision score of LightFC outperforms MixFormerV2-S on LaSOT and TNL2K by 3.7 % and 6.5 %, respectively, while using 5x fewer parameters and 4.6x fewer Flops. Besides, LightFC runs 2x faster than MixFormerV2-S on CPUs. In addition, a higher-performance version named LightFC-vit is proposed by replacing a more powerful backbone network. The code and raw results can be found at https://github.com/LiYunfengLYF/LightFC.

Automated Valet Parking (AVP) requires precise localization in challenging garage conditions, including poor lighting, sparse textures, repetitive structures, dynamic scenes, and the absence of Global Positioning System (GPS) signals, which often pose problems for conventional localization methods. To address these adversities, we present AVM-SLAM, a semantic visual SLAM framework with multi-sensor fusion in a Bird's Eye View (BEV). Our framework integrates four fisheye cameras, four wheel encoders, and an Inertial Measurement Unit (IMU). The fisheye cameras form an Around View Monitor (AVM) subsystem, generating BEV images. Convolutional Neural Networks (CNNs) extract semantic features from these images, aiding in mapping and localization tasks. These semantic features provide long-term stability and perspective invariance, effectively mitigating environmental challenges. Additionally, data fusion from wheel encoders and IMU enhances system robustness by improving motion estimation and reducing drift. To validate AVM-SLAM's efficacy and robustness, we provide a large-scale, high-resolution underground garage dataset, available at https://github.com/yale-cv/avm-slam. This dataset enables researchers to further explore and assess AVM-SLAM in similar environments., Comment: Submitted to ICRA2024 for review

Underwater object detection faces the problem of underwater image degradation, which affects the performance of the detector. Underwater object detection methods based on noise reduction and image enhancement usually do not provide images preferred by the detector or require additional datasets. In this paper, we propose a plug-and-play Underwater joint image enhancement Module (UnitModule) that provides the input image preferred by the detector. We design an unsupervised learning loss for the joint training of UnitModule with the detector without additional datasets to improve the interaction between UnitModule and the detector. Furthermore, a color cast predictor with the assisting color cast loss and a data augmentation called Underwater Color Random Transfer (UCRT) are designed to improve the performance of UnitModule on underwater images with different color casts. Extensive experiments are conducted on DUO for different object detection models, where UnitModule achieves the highest performance improvement of 2.6 AP for YOLOv5-S and gains the improvement of 3.3 AP on the brand-new test set (URPCtest). And UnitModule significantly improves the performance of all object detection models we test, especially for models with a small number of parameters. In addition, UnitModule with a small number of parameters of 31K has little effect on the inference speed of the original object detection model. Our quantitative and visual analysis also demonstrates the effectiveness of UnitModule in enhancing the input image and improving the perception ability of the detector for object features.

Singularly perturbed problems present inherent difficulty due to the presence of a thin boundary layer in its solution. To overcome this difficulty, we propose using deep operator networks (DeepONets), a method previously shown to be effective in approximating nonlinear operators between infinite-dimensional Banach spaces. In this paper, we demonstrate for the first time the application of DeepONets to one-dimensional singularly perturbed problems, achieving promising results that suggest their potential as a robust tool for solving this class of problems. We consider the convergence rate of the approximation error incurred by the operator networks in approximating the solution operator, and examine the generalization gap and empirical risk, all of which are shown to converge uniformly with respect to the perturbation parameter. By utilizing Shishkin mesh points as locations of the loss function, we conduct several numerical experiments that provide further support for the effectiveness of operator networks in capturing the singular boundary layer behavior.

Researchers and practitioners in marketing, economics, and public policy often use preference elicitation tasks to forecast real-world behaviors. These tasks typically ask a series of similarly structured questions. The authors posit that every time a respondent answers an additional elicitation question, two things happen: (1) they provide information about some parameter(s) of interest, such as their time preference or the partworth for a product attribute, and (2) the respondent increasingly “adapts” to the task—that is, using task-specific decision processes specialized for this task that may or may not apply to other tasks. Importantly, adaptation comes at the cost of potential mismatch between the task-specific decision process and real-world processes that generate the target behaviors, such that asking more questions can reduce external validity. The authors used mouse and eye tracking to trace decision processes in time preference measurement and conjoint choice tasks. Respondents increasingly relied on task-specific decision processes as more questions were asked, leading to reduced external validity for both related tasks and real-world behaviors. Importantly, the external validity of measured preferences peaked after as few as seven questions in both types of tasks. When measuring preferences, less can be more.

Chinese horror films have special oriental cultural elements as well as rich traditional culture, but in the last decade, Chinese horror films have declined sharply in the mainland as well as in the international market. The inferiority of the cinematic effect of horror elements, the receding passion of the market, and the cultural regulation under CCP rule are all factors that have led to the gradual disappearance of Chinese horror films. This thesis compares Chinese and Western horror films to further understand the horror genre, the characteristics of Chinese horror films, and the prospects and possibilities of market development.