Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychology needs to get tired of winning

Psychological science is on an extraordinary winning streak. A review of the published literature shows that nearly all study hypotheses are supported. This means that either all the theories are correct, or the literature is biased towards positive findings. Results from large-scale replication projects and the prevalence of questionable research practices indicate the latter. This is a problem because science progresses from being wrong. For decades, there have been calls for better theories and the adoption of a strong inference approach to science. However, there is little reason to believe that psychological science is ready to change. Although recent developments like the open science movement have improved transparency and replicability, they have not addressed psychological science's method-oriented (rather than problem-oriented) mindset. Psychological science still does not embrace the scientific method of developing theories, conducting critical tests of those theories, detecting contradictory results, and revising (or disposing of) the theories accordingly. In this article, I review why psychologists must embrace being wrong and how the Registered Report format might be one strategy for stopping psychology's winning streak.
Diversity and homogeneity in Spanish-language picture books with non-human characters

ABSTRACT We analyse a corpus of 99 recent Spanish-language picturebooks, based on converging recommendations of prestigious reading promotion institutions in different countries. Focusing on the books that feature non-human characters, we inquire into how animals and other creatures may open possibilities to present diverse and intercultural worlds narrating difference differently. Using critical content analysis with an intersectional lens, we give an overview of the predominant narratives about groups and societal organisations in recommended children’s books. Most of the books in this sample (re)produce a desire for a social homogeneity, depicting communities of similar creatures threatened by those of a different kind and/or by the destabilisation of hierarchical orders. We highlight a few books that open up possibilities to narrate shifting identities and social positionings, as well as dynamics of exclusion and propose to look at the potential of non-representational stories for intercultural education.
Remote sensing based yield estimation of wheat using support vector machine (SVM) in semi-arid environment

The increasing demand for food and necessary decision-making on management and security of food crops require prior knowledge of the upcoming yield. The accurate prediction of wheat yield is a hard process that requires information such as location and climatic conditions. In this paper, the accurate prediction of wheat yield is facilitated by integrating both the current and past data of the soil, and climate, along with the spatial features obtained from satellite images. Initially, the normalization of data is carried out to balance the values of different ranges. Then the measurement of current readings of soil characteristics such as soil moisture, air temperature, humidity, and precipitations along with the climatic conditions is performed. These measurements along with the previous historical measurements were considered in order to perform an effective prediction of wheat yield. The multi-kernel-based Support Vector Machine (SVM) is implemented for this purpose. The effectiveness of the proposed approach is validated in terms of performance metrics such as accuracy, precision, recall, and F score. The proposed approach outperforms the existing approaches in predicting the wheat yield with increased accuracy.
Double functionalization of Mo2C and NiMn‐LDH assembling g‐C3N4 as efficient bifunctional electrocatalysts for selective electrocatalytic reactions and overall water splitting

Recently, the development of durable electrocatalysts with bifunctional and high‐efficiency applications in overall water splitting has become a hot spot in the energy field. Herein, Mo2C@g‐C3N4@NiMn‐LDH, a bifunctional electrocatalyst, is obtained through self‐assembly strategies of heat treatment, g‐C3N4 coating, and hydrothermal growth of NiMn‐LDH nanosheets using homogeneous Mo2C precursor nanowire as template. Specifically, Mo2C and NiMn‐LDH are located on the inner wall and outer wall of g‐C3N4, respectively, realizing separate‐sided different functions of g‐C3N4 by electronic control of the interface. The Mo2C@g‐C3N4@NiMn‐LDH composite exhibits excellent bifunctional electrocatalytic performance, in which the inner Mo2C@g‐C3N4 are the dominant catalytic active center for hydrogen evolution reaction (HER) and the outer NiMn‐LDH acts as co‐catalyst, whereas the active center transfer to g‐C3N4@NiMn‐LDH for oxygen evolution reaction (OER) and the inner Mo2C turns into the co‐catalyst. The performance of Mo2C@g‐C3N4@NiMn‐LDH electrocatalyst is reflected by the overpotential of 116 mV (for HER) and 290 mV (for OER) at the current density of 10 mA cm−2 in alkaline medium, respectively. In addition, the voltage required for overall water splitting to reach 10 mA cm−2 is only 1.587 V, and the sample has excellent stability under constant applied voltage. This work provides a strategy for the development of high‐performance electrocatalysts with controllable active sites through interface engineering, so as to achieve competitive equilibrium of different reaction processes.
New track shift safety limits for high-speed rail applications

The analytic development of the fundamental mechanics of track lateral shift due to vehicle and thermally induced loads for the determination of "allowable limit" loads on the track to prevent progressive lateral shift was presented in an earlier work [1]. This paper presents the results of a follow-on study on improving and validating the model, and applying the analytic and experimental results to the development of new track shift limits for high-speed rail applications. Specifically, this paper reviews current European and US standards for track lateral shift, provides a description of the TREDA analysis model to predict residual track lateral deflections under multiple axle passes, discusses results of recent model verification studies based on field tests at the US DOTs Transportation Technology Center, and applies the model to develop elastic limit based (Prudhomme type) allowable lateral loads to prevent excessive lateral shift. It is shown that for a set of fixed track parameters these allowable loads are dependent on curvature, thermal load, vertical axle loads, and on the nature of the lateral forces generated (i.e. constant versus variable). Parametric studies show that the classical Prudhomme limit tends to be conservative. Additionally, the paper presents a prototype system for monitoring in-service net axle lateral forces through wayside and vehicle borne instrumentation. The work presented here is part of the US DOT/Federal Railroad Administrations research efforts to develop the technical information required to establish safe operating practices for high-speed rail service.
Siamese Prototypical Contrastive Learning

Contrastive Self-supervised Learning (CSL) is a practical solution that learns meaningful visual representations from massive data in an unsupervised approach. The ordinary CSL embeds the features extracted from neural networks onto specific topological structures. During the training progress, the contrastive loss draws the different views of the same input together while pushing the embeddings from different inputs apart. One of the drawbacks of CSL is that the loss term requires a large number of negative samples to provide better mutual information bound ideally. However, increasing the number of negative samples by larger running batch size also enhances the effects of false negatives: semantically similar samples are pushed apart from the anchor, hence downgrading downstream performance. In this paper, we tackle this problem by introducing a simple but effective contrastive learning framework. The key insight is to employ siamese-style metric loss to match intra-prototype features, while increasing the distance between inter-prototype features. We conduct extensive experiments on various benchmarks where the results demonstrate the effectiveness of our method on improving the quality of visual representations. Specifically, our unsupervised pre-trained ResNet-50 with a linear probe, out-performs the fully-supervised trained version on the ImageNet-1K dataset.
Risk variance: Towards a definition of varying outcomes of IT security risk assessment

: Assessing IT-security risks in order to achieve adequate and efficient protection measures has become the core idea of various industry practices and regulatory frameworks in the last five years. Some research however suggests that the practice of assessing IT security risks may be subject to varying outcomes depending on personal, situational and contextual factors. In this contribution we first provide a definition of risk variance as the variation of risk assessment outcomes due to individual traits, the processual environment, the domain of the assessor, and possibly the target of the assessed risk. We then present the outcome of an interview series with 9 decision makers from different companies that aimed at discussing whether risk variance is an issue in their risk assessment procedures. Finally, we elaborate on the generalizability of the concept of risk variance, despite the low sample size in light of varying risk assessment procedures discussed in the interviews. We find that risk variance could be a general problem of current risk assessment procedures.
The Dynamics of Exploration on Spotify

Digital media platforms give users access to enormous amounts of content that they must explore to avoid boredom and satisfy their needs for heterogeneity. Existing strands of work across psychology, marketing, computer science, and music underscore the importance of the lifecycle to understanding exploratory behavior, but they are also often inconsistent with each other. In this study, we examine how users explore online content on Spotify over time, whether by discovering entirely novel music or by refreshing their listening habits from one time frame to the next. We find clear differences between users at different points of their off-platform lifecycles, with younger listeners consistently exploring unknown content less and exploiting known content more. Across their on-platform histories, users also explore in bursts by following seasonal cycles and exploratory phases. We also find that these patterns of exploration do not translate to other notions of heterogeneity like diversity; notably, younger listeners are more diverse in their consumption despite exploring less. Exploration and diversity thus capture different ways in which people find variety, potentially accounting for the inconsistencies in existing work. Together, these nuanced dynamics of exploration suggest that online platforms may be better poised to support users by incorporating different measures of heterogeneous consumption.
The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse

We introduce the Reddit Politosphere, a large-scale resource of online political discourse covering more than 600 political discussion groups over a period of 12 years. It is to the best of our knowledge the largest and ideologically most comprehensive dataset of its type now available. One key feature of the Reddit Politosphere is that it consists of both text and network data, allowing for methodologically-diverse analyses. We describe in detail how we create the Reddit Politosphere, present descriptive statistics, and sketch potential directions for future research based on the resource.
Move to See Better: Self-Improving Embodied Object Detection

Passive methods for object detection and segmentation treat images of the same scene as individual samples, and do not exploit object permanence across multiple views. Generalization to novel or difﬁcult viewpoints thus requires additional training with lots of annotations. In contrast, humans often recognize objects by simply moving around, to get more informative viewpoints. In this paper, we propose a method for improving object detection in testing environments, assuming nothing but an embodied agent with a pre-trained 2D object detector. Our agent collects multi-view data, generates 2D and 3D pseudo-labels, and ﬁne-tunes its detector in a self-supervised manner. Experiments on both indoor and outdoor datasets show that (1) our method obtains high quality 2D and 3D pseudo-labels from multi-view RGB-D data; (2) ﬁne-tuning with these pseudo-labels improves the 2D detector signiﬁcantly in the test environment; (3) training a 3D detector with our pseudo-labels out-performs a prior self-supervised method by a large margin; (4) given weak supervision, our method can generate better pseudo-labels for novel objects.
Mitigating Reverse Engineering Attacks on Local Feature Descriptors

As autonomous driving and augmented reality evolve a practical concern is data privacy, notably when these applications rely on user image-based localization. The widely adopted technology uses local feature descriptors derived from the images. While it was long thought that they could not be reverted back, recent work has demonstrated that under certain conditions reverse engineering attacks are possible and allow an adversary to reconstruct RGB user images. This poses a potential risk to user privacy. We take this further and model potential adversaries using a privacy threat model. We show a reverse engineering attack on sparse feature maps under controlled conditions and analyze the vulnerability of popular descriptors including FREAK, SIFT and SOSNet. Finally, we evaluate potential mitigation techniques that select a subset of descriptors to
Influence of impeller staggered arrangement on radial force and pressure fluctuation for a double-suction centrifugal pump

Influence of impeller arrangement on performance, radial force, and pressure fluctuation for a double-suction centrifugal pump is investigated. In comparison of the original impeller with staggered angle α of 0°, the maximum improvement of pump efficiency is 2.19% for impeller with α of 30°, and the maximum improvement of pump head is 1.76% for impeller with α of 20°. The radial force vector on impeller is related to the staggered angle, and the phase angle of radial force vector is equal to the staggered angle. The staggered arrangement can effectively reduce the amplitude of radial force fluctuation. In comparison of case 0, the maximum amplitude of radial force fluctuation for case 30 reduces by 15.8%. The dominant frequencies of the radial force fluctuation for α = 0°, 10°, and 20° are fBP, while the dominant frequency for α = 30° is 2fBP. An empirical formula is proposed to predict the radial fluctuation amplitude at specific staggered angle. For a point of the volute far from the exit volute, in comparison of case 0, the maximum amplitudes of pressure fluctuation for case 10, case 20, and case 30 are reduced by 13.1%, 49.8%, and 93.3%.
Identifying Hurricane Evacuation Intent on Twitter

Evacuations have a significant impact on saving human lives during hurricanes. However, as a complex dynamic process, it is typically difficult to know individual evacuation decisions in real-time. Since a large amount of information is continuously posted through social media platforms, we can use them to understand individual evacuation behavior. In this paper, we collect tweets during Hurricane Irma in 2017 and train a text classifier in an active learning way to distinguish tweets expressing positive evacuation decisions from both negative and irrelevant ones. Additionally, we perform a demographic analysis and content clustering to investigate the potential causes and correlates of evacuation decisions. The results can be used to help inform planning strategies of emergency response agencies.
Exploring Data Literacy Levels in the Crowd - the Case of COVID-19

During global health crises, the use of data becomes critical to control the spread of infections, to inform the general public and to foster safe behaviors. The ability of people to read and understand data (i.e., data literacy) has the potential to affect human behaviors. In this paper, we study non-expert human subjects' ability to make accurate interpretations of complex pandemic data visualizations designed for general public consumption. We present them with popular plots and graphs that have been shown by traditional and social media, and ask them to answer questions to assess their data literacy at three levels: extracting information, finding relationships among data, and expanding or predicting information. Our results show the presence of variance in interpretations and reveal insights into how messages communicated through data may be perceived differently by different people. We also highlight the importance of designing communication strategies that ensure the spread of the right message through data.
Investigation on the Seismic Damage of Recycled Aggregate Concrete Frame Structure

Abstract The current research trends focus on the popularization and application of recycled aggregate concrete (RAC) structures in civil engineering. So far, the damage evolution mechanism of RAC structures has been investigated and reviewed by few engineering researchers and practitioners. In this study, a classification criterion for earthquake damage for the RAC frame structure is put forward. The damage index-based limit state criterion is proposed, and the corresponding seismic performance level is established. The refined models based on the stiffness degradation and the cumulative dissipated energy, and the modified Park and Ang model are developed and compared with the experimental results.
A UVM simulation environment for the study, optimization and verification of HL-LHC digital pixel readout chips

The operating conditions of the High Luminosity upgrade of the Large Hadron Collider are very demanding for the design of next generation hybrid pixel readout chips in terms of particle rate, radiation level and data bandwidth. To this purpose, the RD53 Collaboration has developed for the ATLAS and CMS experiments a dedicated simulation and verification environment using industry-consolidated tools and methodologies, such as SystemVerilog and the Universal Verification Methodology (UVM). This paper presents how the so-called VEPIX53 environment has first guided the design of digital architectures, optimized for processing and buffering very high particle rates, and secondly how it has been reused for the functional verification of the first large scale demonstrator chip designed by the collaboration, which has recently been submitted.
HAT-Net: A Hierarchical Transformer Graph Neural Network for Grading of Colorectal Cancer Histology Images

Graph-based learning methods have gained more attention in colorectal adenocarcinoma cancer (CRA) grading tasks for encoding the tissue structure information, which patch-wise CNN based methods fail to. Graph-based methods usually involve extracting nuclei features in the histology images as cell-graph node features and modeling the connections between nodes to construct cell-graphs. However, it is infeasible to directly train a classiﬁcation model to extract nuclei features as we normally do in nature images since different types of nuclei often cluster together. We propose a Masked Nuclei Patch (MNP) approach to train a ResNet-50 as a strong feature encoder to extract more representative nuclei feature for enhancing the overall performance. Graph Neural Networks (GNNs) are often used to train cell-graphs for different tasks. But GNN may struggle to capture the long-range dependency due to its underlying recurrent structure. Therefore, we propose a new network architecture named H ier A rchical T ransformer Graph Neural Net work (HAT-Net), which merits both GNN and Transformer, as a strong competitor for CRA grading tasks. We have achieved the state-of-the-art results on two publicly available CRA grading datasets: the colorectal cancer (CRC) dataset (98.55%) and the extended colorectal cancer (Extended CRC) dataset (95.33%).
PS-Transformer: Learning Sparse Photometric Stereo Network using Self-Attention Mechanism

Existing deep calibrated photometric stereo networks basically aggregate observations under different lights based on the pre-defined operations such as linear projection and max pooling. While they are effective with the dense capture, simple first-order operations often fail to capture the high-order interactions among observations under small number of different lights. To tackle this issue, this paper presents a deep sparse calibrated photometric stereo network named {\it PS-Transformer} which leverages the learnable self-attention mechanism to properly capture the complex inter-image interactions. PS-Transformer builds upon the dual-branch design to explore both pixel-wise and image-wise features and individual feature is trained with the intermediate surface normal supervision to maximize geometric feasibility. A new synthetic dataset named CyclesPS+ is also presented with the comprehensive analysis to successfully train the photometric stereo networks. Extensive results on the publicly available benchmark datasets demonstrate that the surface normal prediction accuracy of the proposed method significantly outperforms other state-of-the-art algorithms with the same number of input images and is even comparable to that of dense algorithms which input 10$\times$ larger number of images.
AttNAS: Searching Attentions for Lightweight Semantic Segmentation

To use multiple attentions in semantic segmentation task, attentions are generally arranged in parallel and then combined by concatenation or summation. In this work, we use Neural Architecture Search (NAS) to explore attentions and their combination patterns (e.g., parallel, hybrid parallel+sequential) in semantic segmentation. We propose AttNAS, which searches both backbone and attention for a lightweight semantic segmentation model. In particular, we deﬁne a new attention search space with a two-layer structure and propose a uniﬁed differentiable formulation to support both attention type search and attention combination search. Our experimental results show that the model searched with AttNAS achieves state-of-the-art compared to existing lightweight meth-ods on Cityscapes, CamVid and Pascal VOC 2012. Moreover, the attention patterns obtained by AttNAS have robust generalization capability and can be used in combination with existing backbones, such as MobileNetV2 and ResNet18, to improve segmentation performance.