Reviewerly/exHarmony huggingface dataset:
Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

Oil is an important resource for the development of modern society. Accurate detection of oil wells is of great significance to the investigation of oil exploitation status and the formulation of an exploitation plan. However, detecting small objects in large-scale and high-resolution remote sensing images, such as oil wells, is a challenging task due to the problems of large number, limited pixels, and complex background. In order to overcome this problem, first, we create our own oil well dataset to conduct experiments given the lack of a public dataset. Second, we provide a comparative assessment of two state-of-the-art object detection algorithms, SSD and YOLO v4, for oil well detection in our image dataset. The results show that both of them have good performance, but YOLO v4 has better accuracy in oil well detection because of its better feature extraction capability for small objects. In view of the fact that small objects are currently difficult to be detected in large-scale and high-resolution remote sensing images, this article proposes an improved algorithm based on YOLO v4 with sliding slices and discarding edges. The algorithm effectively solves the problems of repeated detection and inaccurate positioning of oil well detection in large-scale and high-resolution remote sensing images, and the accuracy of detection result increases considerably. In summary, this study investigates an appropriate algorithm for oil well detection, improves the algorithm, and achieves an excellent effect on a large-scale and high-resolution satellite image. It provides a new idea for small objects detection in large-scale and high-resolution remote sensing images.
Developmental changes in natural scene viewing in infancy.

We extend decades of research on infants' visual processing by examining their eye gaze during viewing of natural scenes. We examined the eye movements of a racially diverse group of 4- to 12-month-old infants (N = 54; 27 boys; 24 infants were White and not Hispanic, 30 infants were African American, Asian American, mixed race and/or Hispanic) as they viewed images selected from the MIT Saliency Benchmark Project. In general, across this age range infants' fixation distributions became more consistent and more adult-like, suggesting that infants' fixations in natural scenes become increasingly more systematic. Evaluation of infants' fixation patterns with saliency maps generated by different models of physical salience revealed that although over this age range there was an increase in the correlations between infants' fixations and saliency, the amount of variance accounted for by salience actually decreased. At the youngest age, the amount of variance accounted for by salience was very similar to the consistency between infants' fixations, suggesting that the systematicity in these youngest infants' fixations was explained by their attention to physically salient regions. By 12 months, in contrast, the consistency between infants was greater than the variance accounted for by salience, suggesting that the systematicity in older infants' fixations reflected more than their attention to physically salient regions. Together these results show that infants' fixations when viewing natural scenes becomes more systematic and predictable, and that predictability is due to their attention to features other than physical salience. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Reasoning about properties: A computational theory.

We present a theory of how people reason about properties. Such inferences have been studied since Aristotle's invention of Western logic. But, no previous psychological theory gives an adequate account of them, and most theories do not go beyond syllogistic inferences, such as: All the bankers are architects; Some of the chefs are bankers; What follows? The present theory postulates that such assertions establish relations between properties, which mental models represent in corresponding relations between sets of entities. The theory combines the construction of models with innovative heuristics that scan them to draw conclusions. It explains the processes that can generate a conclusion from premises, decide if a given conclusion is necessary or possible, assess its probability, and evaluate the consistency of a set of assertions. A computer program implementing the theory embodies an intuitive system 1 and a deliberative system 2, and it copes with quantifiers such as more than half the architects. It fit data from over 200 different sorts of inference, including those about the properties of individuals, the properties of a set of individuals, and the properties of several such sets in syllogisms. Another innovation is that the program accounts for differences in reasoning from one individual to another, and from one group of individuals to another: Some tend to reason intuitively but some go beyond intuitions to search for alternative models. The theory extends to inferences about disjunctions of properties, about relations rather than properties, and about the properties of properties. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
A MLIR Dialect for Quantum Assembly Languages

We demonstrate the utility of the Multi-Level Intermediate Representation (MLIR) for quantum computing. Specifically, we extend MLIR with a new quantum dialect that enables the expression and compilation of common quantum assembly languages. The true utility of this dialect is in its ability to be lowered to the LLVM intermediate representation (IR) in a manner that is adherent to the quantum intermediate representation (QIR) specification recently proposed by Microsoft. We leverage a qcor-enabled implementation of the QIR quantum runtime API to enable a retargetable (quantum hardware agnostic) compiler workflow mapping quantum languages to hybrid quantum-classical binary executables and object code. We evaluate and demonstrate this novel compiler workflow with quantum programs written in OpenQASM 2.0. We provide concrete examples detailing the generation of MLIR from OpenQASM source files, the lowering process from MLIR to LLVM IR, and ultimately the generation of executable binaries targeting available quantum processors.
Towards efficient and photorealistic 3D human reconstruction: A brief survey

Reconstructing 3D digital models of humans from sensory data is a long-standing problem in computer vision and graphics with a variety of applications in VR/AR, film production, and human–computer interaction, etc. While a huge amount of effort has been devoted to developing various capture hardware and reconstruction algorithms, traditional reconstruction pipelines may still suffer from high-cost capture systems and tedious capture processes, which prevent them from being easily accessible. Moreover, the dedicatedly hand-crafted pipelines are prone to reconstruction artifacts, resulting in limited visual quality. To solve these challenges, the recent trend in this area is to use deep neural networks to improve reconstruction efficiency and robustness by learning human priors from existing data. Neural network-based implicit functions have been also shown to be a favorable 3D representation compared to traditional forms like meshes and voxels. Furthermore, neural rendering has emerged as a powerful tool to achieve highly photorealistic modeling and re-rendering of humans by end-to-end optimizing the visual quality of output images. In this article, we will briefly review these advances in this fast-developing field, discuss the advantages and limitations of different approaches, and finally, share some thoughts on future research directions.
Bounded agency1

Just as all real-world rationality is bounded rationality, all real-world agency is bounded agency: resource constraints make performance tradeoffs inevitable, and consequently any agential architecture will perform poorly in some environments. Ideal agency could be exhibited only by an agent that shifted its architecture to match its current environment whatever that was. Success concepts for agency in general, and the determination that agents exhibit in particular, must be formulated with a concrete agential architecture in view. Consequently, we are unable to formulate success concepts for ideal agency; thus, theory of agency should not take the ideal agent as its reference point.
Why Bitcoin Will Fail to Scale?

Bitcoin falls dramatically short of the scale provided by banks for payments. Currently, its ledger grows by the addition of blocks of ∼2,000 transactions every 10 minutes. Intuitively, one would expect that increasing the block capacity would solve this scaling problem. However, we show that increasing the block capacity would be futile. We analyze strategic interactions of miners, who are heterogeneous in their power over block addition, and users, who are heterogeneous in the value of their transactions, using a game-theoretic model. We show that a capacity increase can facilitate large miners to tacitly collude—artificially reversing back the capacity via strategically adding partially filled blocks in order to extract economic rents. This strategic partial filling crowds out low-value payments. Collusion is sustained if the smallest colluding miner has a share of block addition power above a lower bound. We provide empirical evidence of such strategic partial filling of blocks by large miners of Bitcoin. We show that a protocol design intervention can breach the lower bound and eliminate collusion. However, this also makes the system less secure. On the one hand, collusion crowds out low-value payments; on the other hand, if collusion is suppressed, security threatens high-value payments. As a result, it is untenable to include a range of payments with vastly different outside options, willingness to bear security risk, and delay onto a single chain. Thus, we show economic limits to the scalability of Bitcoin. Under these economic limits, collusive rent extraction acts as an effective mechanism to invest in platform security and build responsiveness to demand shocks. These traits are otherwise hard to attain in a disintermediated setting owing to the high cost of consensus. This paper was accepted by Kartik Hosanagar, information systems.
Multiscale Representation Learning for Image Classification: A Survey

Feature representation has been widely used and developed recently. Multiscale features have led to remarkable breakthroughs for representation learning process in many computer vision tasks. This paper aims to provide a comprehensive survey of the recent multiscale representation learning achievements in classification tasks. Multiscale representation learning methods can be divided into two broad categories (multiscale geometric analysis and multiscale networks). Eleven kinds of multiscale geometric tools and seven kinds of multiscale networks are introduced. Some corresponding fundamental subproblems of these two broad categories are also described, including some concepts in representation process, specific representation methods with multiscale geometric analysis, and multiscale representation design strategies for networks. Then, the correlation between these two broad categories is illustrated, including respective characteristics, combination strategies, and characteristics of optimal representation. Some datasets and evaluation results are included to verify the effectiveness of the multiscale representation learning. Eventually, conclusion and future work are given, covering four directions [a) Choice and fusion; b) Self-adaption; c) Structure; and d) Generalization and proof].
Photoelectric Logic and <i>In Situ</i> Memory Transistors with Stepped Floating Gates of Perovskite Quantum Dots

Electronic-Photonic integrated systems have attracted intensive attention in addressing the explosively increasing data-processing issue in the post-Moore era. However, the tremendous size difference between basic electronic and photonic units poses challenges for the further deep convergence of optoelectronic microprocessors. Here, we report a floating-gate transistor fabricated with complementary metal-oxide-semiconductor compatible technologies, which can realize multilevel photoelectric logic computing and in situ memory simultaneously. The transistor presents stepped floating gates of perovskite quantum dots with different bandgaps and exhibits nonvolatile multilevel memory states written/erased by electrical and high-bandwidth optical signals. Meanwhile, the device can also realize logic functions such as an optoelectronic AND gate by separably programming the states of the stepped floating gates with bias and optical wavelength. A convergence of multilevel logic computing and storage is further achieved on the transistor. By demonstrating such multifunctionality in a single device, the photoelectric transistors, even with a rather large size to match photonic cells, can provide the optoelectronic microprocessors with substantially improved performances.
Normal cloud model theory-based comprehensive fuzzy assessment of wooden pagoda safety

Currently the safety assessment of wood structure ancient buildings is mainly qualitative description, ignoring accurate quantitative calculation, and there are uncertain factors such as fuzziness and randomness that affect the results in the assessment process. Therefore, based on the cloud model theory of transformation between qualitative and quantitative uncertainties, this paper proposes a normal cloud model-based safety assessment approach for the protection research of wooden pagoda. A multi-level assessment system combining qualitative and quantitative indexes is constructed, and the comprehensive weight of the evaluation indexes is determined by combination weighting method. And combining with the scoring of evaluation indexes through quantitative analysis of point cloud, qualitative evaluation of experts, and published high-level papers, the comprehensive evaluation cloud model is constructed. According to the standard grade division, the criteria cloud model is constructed. And safety performance grade is determined by comparing the cloud similarity between the two clouds. Then this paper sets Yingxian wooden pagoda in China as an example, safety performance is quantified under this approach to determine its safety performance grade. The results show that the pagoda is the closest to third grade, in a certain dangerous state and consistent with the actual situation. The method takes into account the randomness and fuzziness factors, which makes assessment process more comprehensive and objective, provides a research paradigm for the safety assessment of ancient wooden buildings.
MSAFF-Net: Multiscale Attention Feature Fusion Networks for Single Image Dehazing and Beyond

Single image dehazing is a critical problem in computer vision. However, most recently proposed learning-based dehazing methods achieve unsatisfactory quality with dehazed images due to inaccurate parametric estimation. The size of these models is also large to be applied with mobile devices limited resources. Last, most models are tailored to image dehazing, achieving poor migration. Thus, we propose a compact multiscale attention feature fusion network with a model size of 2 MB called MSAFF-Net to perform end-to-end single image dehazing. In the proposed model, we design a simple and powerful feature extraction module to extract complex features from hazy images. We use a channel attention module and a multiscale spatial attention module to consider the regions with haze-relevant features. To our knowledge, this study is the first to directly apply the attention mechanism rather than to embed it into certain modules for single image dehazing. We compare MSAFF-Net with other approaches on the NTIRE18, RESIDE, and Middlebury Stereo datasets. We show that MSAFF-Net achieves comparable or better performance than other models. We also extend MSAFF-Net to single image deraining, and various experiments demonstrate its effectiveness. Results suggest that MSAFF-Net can directly restore clear images using channels with the most useful haze- or rain-relevant features and spatial locations.
Image Encryption Through Rössler System, PRNG S-Box and Recamán's Sequence

This paper proposes a lightweight image encryption scheme that is based on 3 stages. The first stage incorporates the use of the Rössler attractor for the Rössler system, the second stage incorporates the use of a PRNG S-Box, while the third stage makes use of the Recamán's sequence. Performance of the proposed encryption scheme is evaluated using a number of metrics. The computed values of the metrics indicate a comparable performance to counterpart schemes from the literature, at a very low cost of processing time. Such a trait indicates that the proposed image encryption scheme possesses potential for real-time image security applications.
Pruning Networks With Cross-Layer Ranking &amp; <i>k</i>-Reciprocal Nearest Filters

This article focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a “long-tail” pruning problem in magnitude-based weight pruning methods and then propose a computation-aware measurement for individual weight importance, followed by a cross-layer ranking (CLR) of weights to identify and remove the bottom-ranked weights. Consequently, the per-layer sparsity makes up the pruned network structure in our filter pruning. Then, we introduce a recommendation-based filter selection scheme where each filter recommends a group of its closest filters. To pick the preserved filters from these recommended groups, we further devise a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -reciprocal nearest filter (RNF) selection scheme where the selected filters fall into the intersection of these recommended groups. Both our pruned network structure and the filter selection are nonlearning processes, which, thus, significantly reduces the pruning complexity and differentiates our method from existing works. We conduct image classification on CIFAR-10 and ImageNet to demonstrate the superiority of our CLR-RNF over the state-of-the-arts. For example, on CIFAR-10, CLR-RNF removes 74.1% FLOPs and 95.0% parameters from VGGNet-16 with even 0.3% accuracy improvements. On ImageNet, it removes 70.2% FLOPs and 64.8% parameters from ResNet-50 with only 1.7% top-five accuracy drops. Our project is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lmbxmu/CLR-RNF</uri> .
A scoping review of publicly available language tasks in clinical natural language processing

To provide a scoping review of papers on clinical natural language processing (NLP) shared tasks that use publicly available electronic health record data from a cohort of patients.We searched 6 databases, including biomedical research and computer science literature databases. A round of title/abstract screening and full-text screening were conducted by 2 reviewers. Our method followed the PRISMA-ScR guidelines.A total of 35 papers with 48 clinical NLP tasks met inclusion criteria between 2007 and 2021. We categorized the tasks by the type of NLP problems, including named entity recognition, summarization, and other NLP tasks. Some tasks were introduced as potential clinical decision support applications, such as substance abuse detection, and phenotyping. We summarized the tasks by publication venue and dataset type.The breadth of clinical NLP tasks continues to grow as the field of NLP evolves with advancements in language systems. However, gaps exist with divergent interests between the general domain NLP community and the clinical informatics community for task motivation and design, and in generalizability of the data sources. We also identified issues in data preparation.The existing clinical NLP tasks cover a wide range of topics and the field is expected to grow and attract more attention from both general domain NLP and clinical informatics community. We encourage future work to incorporate multidisciplinary collaboration, reporting transparency, and standardization in data preparation. We provide a listing of all the shared task papers and datasets from this review in a GitLab repository.
Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

With the rapid progress of autonomous driving and advanced driver assistance systems, there are growing efforts to promote their safety in natural driving scenarios, especially for the detection of the traffic accidents. However, because of the dynamic camera motion and complex scene in driving situations, traffic accident detection is still challenging. In this work, we aim to give the ability of Traffic Accident Detection for driving systems by proposing a Self-Supervised Consistency learning framework, termed as SSC-TAD, that involves the appearance, motion, and context consistency learning. The key formulation is to find the inconsistency of video frames, object locations and the spatial relation structure of scene temporally between different frames captured by the dashcam videos. Within this field, different from the previous works which concentrate on predicting the future object locations or frames, we further focus on predicting the visual scene context in driving scenarios and detecting the traffic accident by considering the temporal frame consistency, temporal object location consistency, and the spatial-temporal relation consistency of road participants. In this work, this formulation is fulfilled by a collaborative multi-task consistency learning network and the visual scene context feature is represented by a graph convolution network. The superiority to the state-of-the-art is verified by exhaustive evaluations on two large scale datasets, i.e., the AnAn Accident Detection (A3D) dataset and DADA-2000 dataset collected recently.
Linear Regression

This chapter is about linear regression, a very simple approach for supervised learning. In particular, linear regression is a useful tool for predicting a quantitative response. It has been around for a long time and is the topic of innumerable textbooks. Though it may seem somewhat dull compared to some of the more modern statistical learning approaches described in later chapters of this book, linear regression is still a useful and widely used statistical learning method. Moreover, it serves as a good jumping-off point for newer approaches: as we will see in later chapters, many fancy statistical learning approaches can be seen as generalizations or extensions of linear regression. Consequently, the importance of having a good understanding of linear regression before studying more complex learning methods cannot be overstated. In this chapter, we review some of the key ideas underlying the linear regression model, as well as the least squares approach that is most commonly used to fit this model.