Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance evaluation of shortest multipath source routing scheme

Multipath routing in mobile ad-hoc networks allows the establishment of multiple paths for routing between a source-destination pair. It exploits the resource redundancy and diversity in the underlying network to provide benefits such as fault tolerance, load balancing, bandwidth aggregation and the improvement in quality-of-service metrics such as delay. Previous work shows that on-demand multipath routing schemes achieve better performance under certain scenarios with respect to a number of key performance metrics when compared with traditional single-path routing mechanisms. A multipath routing scheme, referred to as shortest multipath source (SMS) routing based on dynamic source routing (DSR) is proposed here. The mechanism has two novel aspects compared with other on-demand multipath routing schemes: it achieves shorter multiple partial-disjoint paths and allows more rapid recovery from route breaks. The performance differentials are investigated using NS-2 under conditions of varying mobility, offered load and network size. Results reveal that SMS provides a better solution than existing source-based approaches in a truly mobile ad-hoc environment.
A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

In this paper we propose and analyze a stochastic collocation method to solve elliptic partial differential equations with random coefficients and forcing terms (input data of the model). The input data are assumed to depend on a finite number of random variables. The method consists in a Galerkin approximation in space and a collocation in the zeros of suitable tensor product orthogonal polynomials (Gauss points) in the probability space and naturally leads to the solution of uncoupled deterministic problems as in the Monte Carlo approach. It can be seen as a generalization of the stochastic Galerkin method proposed in [I. Babuscka, R. Tempone, and G. E. Zouraris, SIAM J. Numer. Anal. , 42 (2004), pp. 800-825] and allows one to treat easily a wider range of situations, such as input data that depend nonlinearly on the random variables, diffusivity coefficients with unbounded second moments, and random variables that are correlated or even unbounded. We provide a rigorous convergence analysis and demonstrate exponential convergence of the “probability error” with respect to the number of Gauss points in each direction in the probability space, under some regularity assumptions on the random input data. Numerical examples show the effectiveness of the method.
MFTN: Multi-Level Feature Transfer Network Based on MRI-Transformer for MR Image Super-resolution

Due to the unique environment and inherent properties of magnetic resonance imaging (MRI) instruments, MR images typically have lower resolution. Therefore, improving the resolution of MR images is beneficial for assisting doctors in diagnosing the condition. Currently, the existing MR image super-resolution (SR) methods still have the problem of insufficient detail reconstruction. To overcome this issue, this paper proposes a multi-level feature transfer network (MFTN) based on MRI-Transformer to realize SR of low-resolution MRI data. MFTN consists of a multi-scale feature reconstruction network (MFRN) and a multi-level feature extraction branch (MFEB). MFRN is constructed as a pyramid structure to gradually reconstruct image features at different scales by integrating the features obtained from MFEB, and MFEB is constructed to provide detail information at different scales for low resolution MR image SR reconstruction by constructing multiple MRI-Transformer modules. Each MRI-Transformer module is designed to learn the transfer features from the reference image by establishing feature correlations between the reference image and low-resolution MR image. In addition, a contrast learning constraint item is added to the loss function to enhance the texture details of the SR image. A large number of experiments show that our network can effectively reconstruct high-quality MR Images and achieves better performance compared to some state-of-the-art methods. The source code of this work will be released on GitHub.
Convex Design of a Robust Antiwindup Controller for an LFT Model

In the context of a closed-loop LFT model, an LMI technique is proposed for the synthesis of a static or dynamic antiwindup controller, which can be seen as a nontrivial extension of existing LMI methods for designing a robust filter or feedforward controller. A missile example is given to illustrate the feasibility of the technique.
The Impact of Chocolate Goat's and Cow's Milk on Postresistance Exercise Endocrine Responses and Isometric Mid-Thigh Pull Performance

ABSTRACT The present investigation examined the effects of chocolate cow's and goat's milk on endocrine responses and isometric mid-thigh pull performance post back squat exercise. Twelve college-aged males volunteered to participate and reported to the lab on four occasions. The first visit included anthropometric measurement, one-repetition back squat (1RM), and familiarization with the isometric mid-thigh pull assessment (IMTP). During the subsequent three visits, five sets of eight repetitions of the back squat exercise at 80% of 1RM were performed. For these trials, the participants performed an IMTP and gave a saliva sample prior to, immediately after, 1 hr and 2 hr post exercise. After exercise, a treatment of low-fat chocolate goat's milk (355 ml, 225 kcal), low-fat chocolate cow's milk (355 ml, 225 kcal), or control (water 355 ml, 0 kcal) was given in a counterbalanced order. Saliva samples were analyzed for testosterone, cortisol, and dehydroepiandrosterone (DHEA). Cortisol and DHEA hormone were unaffected by exercise; however, testosterone values did increase significantly post exercise. For IMTP, there was a significant main effect for time (F = 8.41, p = .007) but no treatment or interactions effects. N changes were noted post supplementation for cortisol or DHEA, but testosterone was found to be significantly reduced in both diary treatments compared to control (F = 4.27, p = .022). Based upon these data, it appears that a single treatment of chocolate goat's or cow's milk results in similar endocrine alterations but both fail to enhance postexercise isometric strength following resistance exercise.
Interpretable3D: An Ad-Hoc Interpretable Classifier for 3D Point Clouds

3D decision-critical tasks urgently require research on explanations to ensure system reliability and transparency. Extensive explanatory research has been conducted on 2D images, but there is a lack in the 3D field. Furthermore, the existing explanations for 3D models are post-hoc and can be misleading, as they separate explanations from the original model. To address these issues, we propose an ad-hoc interpretable classifier for 3D point clouds (i.e., Interpretable3D). As an intuitive case-based classifier, Interpretable3D can provide reliable ad-hoc explanations without any embarrassing nuances. It allows users to understand how queries are embedded within past observations in prototype sets. Interpretable3D has two iterative training steps: 1) updating one prototype with the mean of the embeddings within the same sub-class in Prototype Estimation, and 2) penalizing or rewarding the estimated prototypes in Prototype Optimization. The mean of embeddings has a clear statistical meaning, i.e., class sub-centers. Moreover, we update prototypes with their most similar observations in the last few epochs. Finally, Interpretable3D classifies new samples according to prototypes. We evaluate the performance of Interpretable3D on four popular point cloud models: DGCNN, PointNet2, PointMLP, and PointNeXt. Our Interpretable3D demonstrates comparable or superior performance compared to softmax-based black-box models in the tasks of 3D shape classification and part segmentation. Our code is released at: github.com/FengZicai/Interpretable3D.
Factors influencing surface ozone variability over continental South Africa and implications for air quality and agriculture

vii ABSTRACT Extensive scientific research has been conducted on surface ozone (O3) concentrations over several decades in North America and Europe. Through these efforts, significant information is available on the role of chemistry, meteorology and transport in the formation and local accumulation of O3, as well as the adverse effects of O3 on human health and vegetation in these regions. However, only a limited number of studies on surface O3 measurements are available for southern Africa. The Highveld, located in the high-lying plateau in the interior of South Africa, is considered to be exposed to the highest O3 concentrations. This region is the most densely populated part of South Africa, and the industrial and economic heartland of the country. High O3 concentrations are observed in many urban and rural areas within the interior of South Africa, where concentrations exceed the South African National Ambient Air Quality Standard for O3. Continuous, long-term measurements of surface O3 concentrations are valuable indicators of possible health and environmental impacts, which can be used to inform efficient and effective regulatory standards. Within the southern African region, the only continuous, long-term record of surface O3 concentrations is from the Cape Point Global Atmosphere Watch station, which is far removed from the Highveld region of South Africa and the meteorological patterns that dominate the interior of South Africa. Together with understanding the factors affecting surface O3 concentrations, more research is needed on the impacts of O3 pollution on ecosystems. In terms of the effects on vegetation, although local O3 concentrations in southern Africa are cumulatively above the European critical levels for crop damages, no vegetation damage has been reported. Scepticism remains that the reason that damages are not identified is due to a lack of local research attention on this topic. The impact of O3 on agriculture in southern Africa is important from an economic and food security perspective. An atmospheric measurement-based study using continuous, long-term O3 measurements from four sites representing regional background and anthropogenically polluted regions in the northeastern interior of South Africa, covering different time periods between 2006 and 2015, was conducted to explore regional and seasonal O3 pollution variability over continental South Africa, as well as the most important sources contributing to O3 concentrations in this region. Previous studies have suggested that the formation of surface O3 over southern Africa is attributed to the combined contribution of precursors from anthropogenic and biogenic sources. The four different environments, i.e. clean savannah at Botsalano, polluted savannah at Marikana, semi-clean grassland at Welgegund, and polluted grassland at Elandsfontein showed
Decomposition of an alignment problem of two 3D images by a multi-agent approach

In this work, we present an iterative technique for 3D images alignment based on multi-agent approach. This technique tries to reduce the dependency to the initial transformation from which suffers most of the traditional iterative methods. In deed, they require a good estimate of the transformation to guaranty the convergence to the correct solution. The proposed multi-agent approach considers the alignment problem of two 3D images not as a single problem, but as a set of alignment problems of the image regions. Then, a preliminary step of segmentation, followed by a region matching, is needed. The best solution is obtained by confrontation of the solutions given by each couple of homologous regions. This confrontation consists in applying two consecutive voting schemas. The first voting schema gives the best class of transformations, and the second gives the target solution.
Satellitelab: adding heterogeneity to planetary-scale network testbeds

Planetary-scale network testbeds like PlanetLab and RON have become indispensable for evaluating prototypes of distributed systems under realistic Internet conditions. However, current testbeds lack the heterogeneity that characterizes the commercial Internet. For example, most testbed nodes are connected to well-provisioned research networks, whereas most Internet nodes are in edge networks. In this paper, we present the design, implementation, and evaluation of SatelliteLab, a testbed that includes nodes from a diverse set of Internet edge networks. SatelliteLab has a two-tier architecture, in which well-provisioned nodes called planets form the core, and lightweight nodes called satellites connect to the planets from the periphery. The application code of an experiment runs on the planets, whereas the satellites only forward network traffic. Thus, the traffic is subjected to the network conditions of the satellites, which greatly improves the testbed's network heterogeneity. The separation of code execution and traffic forwarding enables satellites to remain lightweight, which lowers the barrier to entry for Internet edge nodes. Our prototype of SatelliteLab uses PlanetLab nodes as planets and a set of 32 volunteered satellites with diverse network characteristics. These satellites consist of desktops, laptops, and handhelds connected to the Internet via cable, DSL, ISDN, Wi-Fi, Bluetooth, and cellular links. We evaluate SatelliteLab's design, and we demonstrate the benefits of evaluating applications on SatelliteLab.
Bayesian Hierarchical Scale Mixtures of Log-Normal Models for Inference in Reliability with Stochastic Constraint

This paper develops Bayesian inference in reliability of a class of scale mixtures of log-normal failure time (SMLNFT) models with stochastic (or uncertain) constraint in their reliability measures. The class is comprehensive and includes existing failure time (FT) models (such as log-normal, log-Cauchy, and log-logistic FT models) as well as new models that are robust in terms of heavy-tailed FT observations. Since classical frequency approaches to reliability analysis based on the SMLNFT model with stochastic constraint are intractable, the Bayesian method is pursued utilizing a Markov chain Monte Carlo (MCMC) sampling based approach. This paper introduces a two-stage maximum entropy (MaxEnt) prior, which elicits a priori uncertain constraint and develops Bayesian hierarchical SMLNFT model by using the prior. The paper also proposes an MCMC method for Bayesian inference in the SMLNFT model reliability and calls attention to properties of the MaxEnt prior that are useful for method development. Finally, two data sets are used to illustrate how the proposed methodology works.
Biotechnology and Bioengineering

State Key Laboratory of Medicinal Chemical Biology, Key Laboratory of Molecular Microbiology and Technology of the Ministry of Education, College of Life Sciences, Nankai University, Tianjin, China Key Laboratory of Regenerative Biology, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida
The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales

We demonstrate how data from search engines such as Google provide an accurate but simple way to predict future business activities. Applying our methodology to predict housing market trends, we find that a housing search index is strongly predictive of future housing market sales and prices. For state-level predictions in the US, the use of search data produces out-of-sample predictions with a smaller mean absolute error than the baseline model that uses conventional data but lacks search data. Furthermore, we find that our simple model of using search frequencies beat the predictions made by experts from the National Association of Realtors by 23.6% for future US home sales. We also demonstrate how these data can be used in other markets, such as home appliance sales. In the near future, this type of “nanoeconomic” data can transform prediction in numerous markets, and thus business and consumer decision-making.
The Performance Effect of Environmental Innovations

While recent literature has focused on explaining the determinants of green innovations, it is not well understood how such innovations affect performance. To analyse the relationship between green innovation and performance, new industry-level panel data were exploited: these include 12 OECD countries, the whole manufacturing sector and a period of 30 years. The results show that green inventions are U-shape related to performance. However, the turning point is quite high and hence only relevant for a few industries. This indicates that - given the current level of green promotion - market incentives alone are not sufficient to allow the green invention activities of industries to rise considerably.
State Anxiety Is Associated with Cardiovascular Reactivity in Young, Healthy African Americans

Although several studies have shown that enhanced cardiovascular reactivity can predict hypertension development in African Americans, these findings have not been consistent among all studies examining reactivity and hypertension susceptibility. This inconsistency may be explained by the influence of anxiety (state and trait) on the blood pressure response to stress. Therefore, this study sought to determine whether anxiety is associated with blood pressure response to cold pressor (CP) and anger recall (AR) stress tests in young healthy African Americans. Modeling using state and trait anxiety revealed that state anxiety predicts systolic (SBP) and diastolic blood pressure DBP response to CP and AR (P ≤ 0.02). Interestingly, state anxiety predicted heart rate changes only to CP (P < 0.01; P = 0.3 for AR). Although trait anxiety was associated with SBP response to AR and not CP, it was not a significant predictor of reactivity in our models. We conclude that anxiety levels may contribute to the variable blood pressure response to acute stressors and, therefore, should be assessed when performing cardiovascular reactivity measures.
Geochronology, geochemistry, and tectonic implications of Jishou Cretaceous diabase, western Xuefengshan tectonic zone in South China

From the east of the Xuefengshan tectonic zone (XTZ) to the Pacific coast of the South China Block, there exist widespread Mesozoic magmatic rocks, which attract a great deal of attention for their forming mechanism and evolutional history. Among them, the Mesozoic Jishou diabase located at the west of the XTZ is reported in this study. Based on our geochronologic analysis, the diabase has a U–Pb age of 134 ± 2.3 Ma. The diabase belongs to calc‐alkaline series in a SiO2‐K2O diagram and illustrates significant enrichment in light rare earth elements and flat heavy rare earth elements without obvious Eu anomalies. Meanwhile, the diabase has negative εHf(t) and εNd(t) values and higher radiogenic 87Sr/86Sr(t) ratios, suggestive of EM2‐like Sr‐Nd isotopic compositions. The diabase shows high variations in 207Pb/204Pb(t; 15.609–15.671), 206Pb/204Pb(t; 17.943–18.742), and 208Pb/204Pb(t; 38.268–39.302). It is suggested that the Jishou diabase may be generated from the lithospheric mantle in response to the decompression melting accompanied by lithospheric extension during Pacific subduction process. During the Early Cretaceous (145–120 Ma), the upwelling and melting of the mantle occurred under the XTZ, causing intraplate Jishou diabase magma. Subsequently, the significantly descending of the subducted Paleo‐Pacific slab led progressively eastward generation and migration of subduction‐related magmas, resulting in a widespread distribution of igneous rocks in the Cathaysia Block.
Simultaneous pose, correspondence and non-rigid shape

Recent works have shown that 3D shape of non-rigid surfaces can be accurately retrieved from a single image given a set of 3D-to-2D correspondences between that image and another one for which the shape is known. However, existing approaches assume that such correspondences can be readily established, which is not necessarily true when large deformations produce significant appearance changes between the input and the reference images. Furthermore, it is either assumed that the pose of the camera is known, or the estimated solution is pose-ambiguous. In this paper we relax all these assumptions and, given a set of 3D and 2D unmatched points, we present an approach to simultaneously solve their correspondences, compute the camera pose and retrieve the shape of the surface in the input image. This is achieved by introducing weak priors on the pose and shape that we model as Gaussian Mixtures. By combining them into a Kalman filter we can progressively reduce the number of 2D candidates that can be potentially matched to each 3D point, while pose and shape are refined. This lets us to perform a complete and efficient exploration of the solution space and retain the best solution.
Real-Time Weakly Supervised Video Anomaly Detection

Weakly supervised video anomaly detection is an important problem in many real-world applications where during training there are some anomalous videos, in addition to nominal videos, without labelled frames to indicate when the anomaly happens. State-of-the-art methods in this domain typically focus on offline anomaly detection without any concern for real-time detection. Most of these methods rely on ad hoc feature aggregation techniques and the use of metric learning losses, which limit the ability of the models to detect anomalies in real-time. In line with the premise of deep neural networks, there also has been a growing interest in developing end-to-end approaches that can automatically learn effective features directly from the raw data. We propose the first real-time and end-to-end trained algorithm for weakly supervised video anomaly detection. Our training procedure builds upon recent action recognition literature and trains a large video model to learn visual features. This is in contrast to existing approaches which largely depend on pre-trained feature extractors. The proposed method significantly improves the anomaly detection speed and AUC performance compared to the existing meth-ods. Specifically, on the UCF-Crime dataset, our method achieves 86.94% AUC with a decision period of 6.4 seconds while the competing methods achieve at most 85.92% AUC with a decision period of 273 seconds.
web-based interventions for ulcerative colitis and Crohn’s disease: systematic review and future directions

: Behavioral intervention technologies (BITs), the application of psychological and behavioral interventions through the use of technology, provide the opportunity for clinicians to deliver care through a means that overcomes a number of treatment barriers. Web-based interventions are a subset of BITs developing as promising alternatives to face-to-face delivery of treatments and monitoring for patients with ulcerative colitis (UC) and Crohn’s disease (CD). A systematic review of literature resulted in five empirical studies of web-based interventions for UC and CD. Additionally, an informal search of a popular search engine yielded limited, currently available, web-based interventions for patients with UC and CD. Despite being an ideal population for the development and dissemination of online interventions, patients with UC and CD have far less treatment options compared to other behavioral health concerns. However, given the growing body of research involving web-based interventions for other conditions, researchers and clinicians targeting UC and CD management and treatment have the benefit of being able to utilize the BIT model, an existing conceptual framework for the development of web-based interventions for both conditions. The BIT model is presented and applied to the treatment of UC and CD, as well as a technology development program, Purple, and usability guidelines to guide clinical researchers in the future development, evaluation, and dissemination of BITs for patients with UC and CD.