Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Transferable Visual Models From Natural Language Supervision

We use the 12 datasets from the well-studied evaluation suite introduced by (Kornblith et al., 2019) and add 15 additional datasets in order to assess the performance of models on a wider variety of distributions and tasks. These datasets include MNIST, the Facial Expression Recognition 2013 dataset (Goodfellow et al., 2015), STL-10 (Coates et al., 2011), EuroSAT (Helber et al., 2019), the NWPURESISC45 dataset (Cheng et al., 2017), the German Traffic Sign Recognition Benchmark (GTSRB) dataset (Stallkamp et al., 2011), the KITTI dataset (Geiger et al., 2012), PatchCamelyon (Veeling et al., 2018), the UCF101 action recognition dataset (Soomro et al., 2012), Kinetics 700 (Carreira et al., 2019), 2,500 random samples of the CLEVR dataset (Johnson et al., 2017), the Hateful Memes dataset (Kiela et al., 2020), and the ImageNet-1k dataset (Deng et al., 2012). For the two video datasets (UCF101 and Kinetics700), we use the middle frame of each video clip as the input image. STL-10 and UCF101 have multiple predefined train/validation/test splits, 10 and 3 respectively, and we report the average over all splits. Details on each dataset and the corresponding evaluation metrics are provided in Table 2.
Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2

Abstract. In light of the dramatic increase in economic impacts due to wildfires over recent years, the need for globally consistent impact modelling of wildfire damages is ever increasing. Insurance companies, individual households, humanitarian organisations and governmental authorities, as well as investors and portfolio owners, are increasingly required to account for climate-related physical risks. In this study we present a globally consistent and spatially explicit approach to modelling wildfire impacts using the open-source and open-access risk modelling platform CLIMADA (CLImate ADAptation). All input data is free, public and globally available, ensuring applicability in data-scarce regions of the Global South. The model was calibrated at resolutions of 1, 4 and 10 kilometers using information on past wildfire damage reported by the disaster database EM-DAT. Despite the large remaining uncertainties, the model yields sound damage estimates with a model performance well in line with the results of other natural catastrophe impact models, such as for tropical cyclones. To complement the global 10 perspective of this study, we conducted two case studies on the recent mega fires in Chile (2017) and Australia (2020). The model is made available online as part of a Python package, ready for application in practical contexts such as disaster risk assessment or physical climate risk disclosure.
Effect of rice husk Biochar (RHB) on some of chemical properties of an acidic soil and the absorption of some nutrients

The purpose of this study is to evaluate the biochemical effects of rice husk biochar (RHB) obtained from pyrolysis of rice husk under limited oxygen conditions for three hours at temperature of 500 °C. Then, the effect of addition of 2 and 4% biochar to acidic soil was studied. The samples were stored in greenhouse conditions for three months, and then soil characteristics including pH, electrical conductivity, cation exchange capacity and organic carbon content, potassium forms and total nitrogen content of soil were measured. The results showed that RHB caused a significant increase in soil pH. RHB also increased significantly the organic carbon content and electrical conductivity. Soluble, exchangeable and non-exchangable potassium were affected by the addition of RHB. RHB caused 5-fold increased soluble potassium. The exchange rate of potassium increased from 76 mg / kg, with a RHB of 2% to 112 mg / kg with a 4% RHB application. Non- exchangable potassium also increased from 290 in control to 532 mg / kg in 4% RHB. The usability of nitrogen and phosphorus was not affected by biochar. Considering the significant increase in the amounts of different forms of potassium and considering the lack of potassium in some acidic soils of Mazandaran province, we can consider the use of biochar as a source of potassium in soil fertility management.
Online vs. Face-to-Face Lecture Courses: Factors Impacting the Effectiveness of Online Learning

During the COVID-19 outbreak, most university courses have been offered on online platforms. A sudden shift from face-to-face classroom learning to online formats could influence the learning effectiveness of classes. This study aims to investigate differences in the learning effectiveness of online and face-to-face lecture courses. It also explores factors that impact the effectiveness of online instruction. These factors include interactions among learners, interactions between learners and the instructor, the quality of online platforms, learners’ ability to use devices and follow instructions, and learners’ situational challenges. The study participants were 261 university students at King Mongkut’s University of Technology Thonburi in Bangkok, Thailand. All participants were enrolled in at least one lecture course, such as history, humans and the environment, the environment and development, or general philosophy, during the 2019 academic year. A questionnaire was distributed to participants after they completed these courses in May 2020. Paired simple t-test analyses were used to compare the effectiveness of online and face-to-face classes, and a multiple regression analysis was used to identify factors that impact the learning effectiveness of online classes. The results show that online classes are less effective than face-to-face courses. The multiple regression analysis also revealed that the effectiveness of online learning was significantly impacted by learners’ ability to interact with classmates during class, their ability to interact with instructors after the class, the quality of online platforms, and disturbances or distractions in learners’ environments.
Models of the Nucleus: Incompatible Things, Compatible Processes

Nuclear models are incompatible in their thing-terms: terms that refer to static entities like objects, structures, and substances. Specifically, the two most prevalent models—the liquid drop and shell models—treat the nucleus, its internal structure, and the component nucleons as entities that contradict each other’s properties. These differences allow these two models to describe and explain different nuclear experiments: fission and scattering in the liquid drop model, and single-nucleon excitation and nuclear decay in the shell model. However, prima facie, these differences also suggest that these models are incompatible in their ontology. However, by taking seriously the experimental methods by which these models are constructed and the calculational tools these models provide for interpreting experimental outcomes, I construct a new form of realism about these models that renders them ontologically compatible. Namely, I argue that nuclear models are consistent in the dynamic entities to which they refer. I therefore advocate a pure process realism with respect to nuclear models. Critical to this process realism is the recognition that the processes referred to within nuclear models are essential parts of the observation acts that form nuclear experiments. In particular, because the dynamics within the nucleus must always be a continuous intermediary of our experimental interventions and the receptions of signals from the system, these dynamics are essential dynamic parts of nuclear experiments. We are therefore licensed in inferring these dynamic parts on the basis of experimental practice alone. In contrast, the thing terms reified by the thing realist in these models require additional inferences, the premises of which cannot be supported on the basis of experiment alone. Thus, inferences to processes are experimentally supportable, whereas inferences to things are dubious at best.
A-ULMPM: An Arbitrary Updated Lagrangian Material Point Method for Efficient Simulation of Solids and Fluids

We present an arbitrary updated Lagrangian Material Point Method (A-ULMPM) to alleviate issues, such as the cell-crossing instability and numerical fracture, that plague state of the art Eulerian formulations of MPM, while still allowing for large deformations that arise in fluid simulations. Our proposed framework spans MPM discretizations from total Lagrangian formulations to Eulerian formulations. We design an easy-to-implement physics-based criterion that allows A-ULMPM to update the reference configuration adaptively for measuring physical states including stress, strain, interpolation kernels and their derivatives. For better efficiency and conservation of angular momentum, we further integrate the APIC[Jiang et al. 2015] and MLS-MPM[Hu et al. 2018] formulations in A-ULMPM by augmenting the accuracy of velocity rasterization using both the local velocity and its first-order derivatives. Our theoretical derivations use a nodal discretized Lagrangian, instead of the weak form discretization in MLS-MPM[Hu et al. 2018], and naturally lead to a"modified"MLS-MPM in A-ULMPM, which can recover MLS-MPM using a completely Eulerian formulation. A-ULMPM does not require significant changes to traditional Eulerian formulations of MPM, and is computationally more efficient since it only updates interpolation kernels and their derivatives when large topology changes occur. We present end-to-end 3D simulations of stretching and twisting hyperelastic solids, splashing liquids, and multi-material interactions with large deformations to demonstrate the efficacy of our novel A-ULMPM framework.
PREVENTING PAY PHONE DAMAGE by Cressida Bridgeman U . K . Home Office Police Research Group

In the 1992-93 financial year, there were 55,000 attacks on the cash compartments of pay phones in Britain, costing British Telecom (BT) some £20 million. By 1994-95 this had been cut to 17,000 attacks, costing £5 million. A major explanation for this significant reduction was the change in approach following the reorganization of pay phone management in the late 1980s. A brief overview of earlier studies provides the context for a description ofBT's strategy from the 1960s to date. Data on the effects of its latest strategy, the Security Enhancement Programme, are then presented. A comparison is drawn with Telecom Australia's approach to pay phone damage. Both strategies are related to Clarke's situational crime prevention framework (1992), and general lessons and principles for effective pay phone management are outlined.
Paying Attention to Video Generation

Video generation is a challenging research topic which has been tackled by a variety of methods including Generative Adversarial Networks (GANs), Variational Autoencoders (VAE), optical ﬂow and autoregressive models. However, most of the existing works model the task as image manipulation and learn pixel-level transforms. In contrast, we propose a latent vector manipulation approach using sequential models, particularly the Generative Pre-trained Transformer (GPT). Further, we propose a novel Attention-based Discretized Autoencoder (ADAE) which learns a ﬁnite-sized codebook that serves as a basis for latent space representations of frames, to be modelled by the sequential model. To tackle the reduced resolution or the diversity bottleneck caused by the ﬁnite codebook, we propose attention-based soft-alignment instead of a hard distance-based choice for sampling the latent vectors. We extensively evaluate the proposed approach on the BAIR Robot Pushing, Sky Time-lapse and Dinosaur Game datasets and compare with state-of-the-art (SOTA) approaches. Upon experimentation, we ﬁnd that our model suﬀers mode collapse owing to a single vector latent space learned by the ADAE. The cause for this mode collapse is traced back to the peaky attention scores resulting from the codebook (Keys and Values) and the encoder’s output (Query). Through our ﬁndings, we highlight the importance of reliable latent space frame representations for successful sequential modelling.
Metal pollution status of Tajan River – Northern Iran

ABSTRACT The monitoring of surface water quality of rivers is crucial to protect aquatic life and receiving water bodies of economic importance. The aim of the current study was to examine selected physicochemical parameters of River Tajan due to its ecological importance in Iran. Water samples from nine points covering the entire Tajan River and Caspian Sea estuary were collected and analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), carbonates, chlorides and selected heavy metals including zinc, cadmium, lead and chromium. The pH and levels of EC, TDS, chlorides, carbonates and all metals were within the USEPA reference guidelines for surface water quality standards. Although these water quality parameters are considered safe, continuous monitoring and assessment is recommended in order to protect the coastal receiving waters of Caspian Sea which serves as a tourist attraction in northern Iran.
Archives of Depression and Anxiety

For a long time researchers and clinicians have been trying to clarify the possible existing relationships between Alzheimer’s disease and the depression in the elderly. This article tries to take stock of these complex links. It seems that often depression is clinically confused with apathy. Review Article The complexity of the relationship between “dementia” and depression Michel Bourin* Neurobiology of anxiety and depression, University of Nantes, 98, rue Joseph Blanchart 44100 Nantes, France Received: 20 January, 2018 Accepted: 31 January, 2018 Published: 02 February, 2018 *Corresponding author: Michel Bourin, Neurobiology of Anxiety and Depression, University of Nantes, 98, rue Joseph Blanchart 44100 Nantes, France, E-mail:
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling

According to recent studies, commonly used computer vision datasets contain about 4% of label errors. For example, the COCO dataset is known for its high level of noise in data labels, which limits its use for training robust neural deep architectures in a real-world scenario. To model such a noise, in this paper we have proposed the homoscedastic aleatoric uncertainty estimation, and present a series of novel loss functions to address the problem of image object detection at scale. Specifically, the proposed functions are based on Bayesian inference and we have incorporated them into the common community-adopted object detection deep learning architecture RetinaNet. We have also shown that modeling of homoscedastic aleatoric uncertainty using our novel functions allows to increase the model interpretability and to improve the object detection performance being evaluated on the COCO dataset.
Evaluating Deep Graph Neural Networks

Graph Neural Networks (GNNs) have already been widely applied in various graph mining tasks. However, they suffer from the shallow architecture issue, which is the key impediment that hinders the model performance improvement. Although several relevant approaches have been proposed, none of the existing studies provides an in-depth understanding of the root causes of performance degradation in deep GNNs. In this paper, we conduct the first systematic experimental evaluation to present the fundamental limitations of shallow architectures. Based on the experimental results, we answer the following two essential questions: (1) what actually leads to the compromised performance of deep GNNs; (2) when we need and how to build deep GNNs. The answers to the above questions provide empirical insights and guidelines for researchers to design deep and well-performed GNNs. To show the effectiveness of our proposed guidelines, we present Deep Graph Multi-Layer Perceptron (DGMLP), a powerful approach (a paradigm in its own right) that helps guide deep GNN designs. Experimental results demonstrate three advantages of DGMLP: 1) high accuracy -- it achieves state-of-the-art node classification performance on various datasets; 2) high flexibility -- it can flexibly choose different propagation and transformation depths according to graph size and sparsity; 3) high scalability and efficiency -- it supports fast training on large-scale graphs. Our code is available in https://github.com/zwt233/DGMLP.
Comparing Object Recognition in Humans and Deep Convolutional Neural Networks—An Eye Tracking Study

Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast architectural and functional similarities in visual challenges such as object recognition. Recent insights have demonstrated that both hierarchical cascades can be compared in terms of both exerted behavior and underlying activation. However, these approaches ignore key differences in spatial priorities of information processing. In this proof-of-concept study, we demonstrate a comparison of human observers (N = 45) and three feedforward DCNNs through eye tracking and saliency maps. The results reveal fundamentally different resolutions in both visualization methods that need to be considered for an insightful comparison. Moreover, we provide evidence that a DCNN with biologically plausible receptive field sizes called vNet reveals higher agreement with human viewing behavior as contrasted with a standard ResNet architecture. We find that image-specific factors such as category, animacy, arousal, and valence have a direct link to the agreement of spatial object recognition priorities in humans and DCNNs, while other measures such as difficulty and general image properties do not. With this approach, we try to open up new perspectives at the intersection of biological and computer vision research.
Debonding Failure Analysis of Reinforced Concrete Beams Strengthened with CFRP Plates

In this study, experimental work was carried out on reinforced concrete (RC) beams strengthened with carbon fiber reinforced polymers (CFRP) plates. This study aims to examine the effect of the reinforcement ratio on the flexural behavior of these beams and propose a new model for predicting the debonding moment. Six RC beams consisting of three control beams and three beams strengthened with CFRP plates were tested. The beams were simply supported and loaded with four-point bending. The test variable was the tensile reinforcement ratio (1%, 1.5%, and 2.5%). Analytical prediction using the fiber element method was also carried out to obtain the complete theoretical response of the beam due to flexural loads. The test results show that the reinforcement ratio affected the bending performance of RC beams with CFRP plates. Following this, the experimental data from 60 beam test results from published literature and this study were analyzed. From these data, it was found that the ratio of tensile reinforcement, the ratio of modulus of elasticity of concrete, the modulus of elasticity of the plate, and plate thickness all affect the value of debonding moment. A parametric study using fiber elements and the two-dimensional finite element method was also carried out to confirm the effect of these variables on debonding failure. These variables were then used to develop an equation to predict the debonding moment of RC beams strengthened with CFRP plates, using simple statistical analysis. This analysis resulted in a simple model for predicting the debonding moment. Then the model is entered into a computer program, and the complete response of the cross-section due to debonding failure can be obtained.
The forkhead transcription factor Foxo1 links insulin signaling to Pdx1 regulation of pancreatic β cell growth

Diabetes is caused by an absolute (type 1) or relative (type 2) deficiency of insulin-producing β cells. The mechanisms governing replication of terminally differentiated β cells and neogenesis from progenitor cells are unclear. Mice lacking insulin receptor substrate-2 (Irs2) develop β cell failure, suggesting that insulin signaling is required to maintain an adequate β cell mass. We report that haploinsufficiency for the forkhead transcription factor Foxo1 reverses β cell failure in Irs2–/– mice through partial restoration of β cell proliferation and increased expression of the pancreatic transcription factor pancreas/duodenum homeobox gene-1 (Pdx1). Foxo1 and Pdx1 exhibit mutually exclusive patterns of nuclear localization in β cells, and constitutive nuclear expression of a mutant Foxo1 is associated with lack of Pdx1 expression. We show that Foxo1 acts as a repressor of Foxa2-dependent (Hnf-3β–dependent) expression from the Pdx1 promoter. We propose that insulin/IGFs regulate β cell proliferation by relieving Foxo1 inhibition of Pdx1 expression in a subset of cells embedded within pancreatic ducts. Article Metabolism
Revisiting the Short and Long-Run Impacts of Trade Openness: Evidence from the African Growth Experience

It is well established that international trade can impact economic growth directly through economies of scale and diversification, or indirectly through technological diffusion. There can, however, be differences in the impacts based on the various components of trade openness and the period under consideration. Most of the existing literature on the impact of trade on the African economic growth experience has primarily focused on the aggregate impact of trade, ignoring the differences in the components and the direction of trade. Using a panel dataset of 28 African countries over the 1980-2016 period, this study investigates the short and long-run impacts of the various trade openness components by employing the recently developed dynamic commoncorrelated effects panel model with heterogeneous coefficients. We find that services import only have a short-run positive impact on economic growth, while merchandise imports and service exports exhibit long-term positive impacts on growth. We also find that overall trade openness is beneficial for economic growth, but its impact is short lived. JEL Classification: F13, F14, F14, 040
Capacity and flow assignment of packet-switched networks with a concave line cost function

The packet-switched network design problem can be formulated as a capacity and flow assignment (CFA) problem, which esser.t,ially has a non-convex structure. Efficient methods for obtaining the global optimum or a good local optimum are introduced by confining attention to the class of hierarchical networks which consist of elementa.ry networks. A basic iterative procedure obtains the global optimum of the elementary networks within finite iterations.