Showing papers on "Point (geometry) published in 2022"

Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends

Abstract: This paper discusses two important limitations of the common practice of testing for preexisting differences in trends (“ pre-trends”) when using difference-in-differences and related methods. First, conventional pre-trends tests may have low power. Second, conditioning the analysis on the result of a pretest can distort estimation and inference, potentially exacerbating the bias of point estimates and under-coverage of confidence intervals. I analyze these issues both in theory and in simulations calibrated to a survey of recent papers in leading economics journals, which suggest that these limitations are important in practice. I conclude with practical recommendations for mitigating these issues. (JEL A14, C23, C51)

Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text

Abstract: Evaluation practices in natural language generation (NLG) have many known flaws, but improved evaluation approaches are rarely widely adopted. This issue has become more urgent, since neural generation models have improved to the point where their outputs can often no longer be distinguished based on the surface-level features that older metrics rely on. This paper surveys the issues with human and automatic model evaluations and with commonly used datasets in NLG that have been pointed out over the past 20 years. We summarize, categorize, and discuss how researchers have been addressing these issues and what their findings mean for the current state of model evaluations. Building on those insights, we lay out a long-term vision for evaluation research and propose concrete steps for researchers to improve their evaluation processes. Finally, we analyze 66 generation papers from recent NLP conferences in how well they already follow these suggestions and identify which areas require more drastic changes to the status quo.

Productivity assessment of neural code completion

Abstract: Neural code synthesis has reached a point where snippet generation is accurate enough to be considered for integration into human software development workflows. Commercial products aim to increase programmers’ productivity, without being able to measure it directly. In this case study, we asked users of GitHub Copilot about its impact on their productivity, and sought to find a reflection of their perception in directly measurable user data. We find that the rate with which shown suggestions are accepted, rather than more specific metrics regarding the persistence of completions in the code over time, drives developers’ perception of productivity.

The DAO to MetaControl for MetaSystems in Metaverses: The System of Parallel Control Systems for Knowledge Automation and Control Intelligence in CPSS

Abstract: Briefing: An investigation and outline of MetaControl and DeControl in Metaverses for control intelligence and knowledge automation are presented. Prescriptive control with prescriptive knowledge and parallel philosophy is proposed as the starting point for the new control philosophy and technology, especially for computational control of metasystems in cyber-physical-social systems. We argue that circular causality, the generalized feedback mechanism for complex and purposive systems, should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in metaverses. Particularly, an interdisciplinary approach is suggested for MetaControl and DeControl as a new form of intelligent control based on five control metaverses: Meta Verses, MultiVerses, InterVerses, TransVerse, and Deep Verses.

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

Abstract: Perception techniques for autonomous driving should be adaptive to various environments. In essential perception modules for traffic line detection, many conditions should be considered, such as a number of traffic lines and computing power of the target system. To address these problems, in this paper, we propose a traffic line detection method called Point Instance Network (PINet); the method is based on the key points estimation and instance segmentation approach. The PINet includes several hourglass models that are trained simultaneously with the same loss function. Therefore, the size of the trained models can be chosen according to the target environment’s computing power. We cast a clustering problem of the predicted key points as an instance segmentation problem; the PINet can be trained regardless of the number of the traffic lines. The PINet achieves competitive accuracy and false positive on CULane and TuSimple datasets, popular public datasets for lane detection. Our code is available at https://github.com/koyeongmin/PINet_new

O'Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy . Crown.

Abstract: The book Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy provides a very important point of view on the mathematical models and algorithms used for decisionmaking in our society. As the title suggests the author sees the possible dangerous influences of algorithms used for the specific groups in the society as huge and disastrous. The book describes the mechanism and functioning of mathematical models and algorithms in a comprehensible manner. It uses practical examples of various mechanisms commonly used in today's society. The main aim of the book is to break the myths about the mathematical models and their results, mainly the widespread beliefs about the algorithms as being fair, objective, and unbiased.

Review—Lab-in-a-Mouth and Advanced Point-of-Care Sensing Systems: Detecting Bioinformation from the Oral Cavity and Saliva

Abstract: Cavitas sensors and point-of-need sensors capable of providing physical and biochemical information from the oral cavity and saliva have attracted great attention because they offer remarkable advantages for noninvasive sensing systems. Herein, we introduce the basic anatomy and physiology of important body cavities to understand their characteristics as it is a pivotal foundation for the successful development of in-mouth devices. Next, the advanced development in lab-in-a-mouth sensors and point-of-need sensors for analyzing saliva are explained. In addition, we discuss the integrations of artificial intelligence and electronic technologies in smart sensing networks for healthcare systems. This review ends with a discussion of the challenges, future research trends, and opportunities in relevant disciplines. Mouthguard-based sensors and conventional salivary sensing devices will continue to be significant for the progress in the next-generation sensing technologies and smart healthcare systems.

Low-code development and model-driven engineering: Two sides of the same coin?

Abstract: Abstract The last few years have witnessed a significant growth of so-called low-code development platforms (LCDPs) both in gaining traction on the market and attracting interest from academia. LCDPs are advertised as visual development platforms, typically running on the cloud, reducing the need for manual coding and also targeting non-professional programmers. Since LCDPs share many of the goals and features of model-driven engineering approaches, it is a common point of debate whether low-code is just a new buzzword for model-driven technologies, or whether the two terms refer to genuinely distinct approaches. To contribute to this discussion, in this expert-voice paper, we compare and contrast low-code and model-driven approaches, identifying their differences and commonalities, analysing their strong and weak points, and proposing directions for cross-pollination.

SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds

Abstract: With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations. Fine-grained understanding of urban-scale 3D scenes is still in its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km $$^2$$ . Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset. In addition to the more commonly encountered categories such as road and vegetation, urban-level categories including rail, bridge, and river are also included in our dataset. Based on this dataset, we further build a benchmark to evaluate the performance of state-of-the-art segmentation algorithms. In particular, we provide a comprehensive analysis and identify several key challenges limiting urban-scale point cloud understanding. The dataset is available at http://point-cloud-analysis.cs.ox.ac.uk/ .

What Is Ergonomics?

Abstract: Ergonomics is derived from the Greek word ergon (work) and nomos (laws) to indicate the science of work. Ergonomics is an applied science that draws on many interrelated disciplines, such as engineering, psychology, occupational hygiene and physiology. It is the study of humans as components of complex systems, focusing on the interaction between people and technology. Ergonomicsis about focusing on understanding the relationship between people in their work environment (both the physical and the organisation) and aims to ensure compatibility between the operator's capabilities and limitations with the demands of the task, job and/or work environment.

Cross self-attention network for 3D point cloud

Abstract: It is a challenge to design a deep neural network for raw point cloud, which is disordered and unstructured data. In this paper, we introduce a cross self-attention network (CSANet) to solve raw point cloud classification and segmentation tasks. It has permutation invariance and can learn the coordinates and features of point cloud at the same time. To better capture features of different scales, a multi-scale fusion (MF) module is proposed, which can adaptively consider the information of different scales and establish a fast descent branch to bring richer gradient information. Extensive experiments on ModelNet40, ShapeNetPart, and S3DIS demonstrate that the proposed method can achieve competitive results.

Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian

Abstract: Recent evolutions in the e-commerce market have led to an increasing importance attributed by consumers to product reviews made by third parties before proceeding to purchase. The industry, in order to improve the offer intercepting the discontent of consumers, has placed increasing attention towards systems able to identify the sentiment expressed by buyers, whether positive or negative. From a technological point of view, the literature in recent years has seen the development of two types of methodologies: those based on lexicons and those based on machine and deep learning techniques. This study proposes a comparison between these technologies in the Italian market, one of the largest in the world, exploiting an ad hoc dataset: scientific evidence generally shows the superiority of language models such as BERT built on deep neural networks, but it opens several considerations on the effectiveness and improvement of these solutions when compared to those based on lexicons in the presence of datasets of reduced size such as the one under study, a common condition for languages other than English or Chinese.

Seepage Characteristics in Loess Strata Subjected to Single Point Water Supply

Abstract: • A Conical-like cavity was formed near the water source, and the preferential passage of water flow appeared below the cavity. • The wetting body shape gradually transformed from oblate ellipsoid to long ellipsoid after the formation of the passage. • Seepage process can be defined as four periods: rapid infiltration, stable infiltration, slow infiltration, and preferential infiltration. • A two-dimensional seepage diffusion calculation model was established and preliminarily verified by example calculations.

An Algorithm for Painting Large Objects Based on a Nine-Axis UR5 Robotic Manipulator

Abstract: An algorithm for automatically planning trajectories designed for painting large objects is proposed in this paper to eliminate the difficulty of painting large objects and ensure their surface quality. The algorithm was divided into three phases, comprising the target point acquisition phase, the trajectory planning phase, and the UR5 robot inverse solution acquisition phase. In the target point acquisition phase, the standard triangle language (STL) file, algorithm of principal component analyses (PCA), and k-dimensional tree (k-d tree) were employed to obtain the point cloud model of the car roof to be painted. Simultaneously, the point cloud data were compressed as per the requirements of the painting process. In the trajectory planning phase, combined with the maximum operating space of the UR5 robot, the painting trajectory of the target points was converted into multiple traveling salesman problem (TSP) models, and each TSP model was created with a genetic algorithm (GA). In the last phase, in conformity with the singularities of the UR5 robot’s motion space, the painting trajectory was divided into a recommended area trajectory and a non-recommended area trajectory and created by the analytical method and sequential quadratic programming (SQP). Finally, the proposed algorithm for painting large objects was deployed in a simulation experiment. Simulation results showed that the accuracy of the algorithm could meet the requirements of painting technology, and it has promising engineering practicability.

Review and Classification of MTPA Control Algorithms for Synchronous Motors

Abstract: This article discusses the maximum torque per ampere (MTPA) control of synchronous motors, which have become an indispensable part of highly efficient motor drives. It explains the nature of torque produced by synchronous motors, ways to find its maximum and algorithms to operate at this point, despite changes of loads and motor parameter variations. The authors propose a classification of the MTPA methods, based on the features of each algorithm or group of similar methods. They demonstrate the conventional control scheme and discuss the modifications necessary for the implementation of each method. This article reviews existing MTPA control algorithms, discusses their pros and cons, and suggests possible areas of usage for each group of methods. The authors of the article share their vast experience in the industry and research aspects, which were obtained by developing industrial, commercial, traction, and military drives, and report on their views on the perspective of each method taken into consideration.

How time-inconsistent preferences influence venture capital exit decisions? A new perspective for grandstanding

Abstract: Abstract Considering that the assumption of time consistency does not adequately reveal the mechanisms of exit decisions of venture capital (VC), this study proposes two kinds of time-inconsistent preferences (i.e., time-flow inconsistency and time-point inconsistency) to advance research in this field. Time-flow inconsistency is in line with the previous time inconsistency literature, while time-point inconsistency is rooted in the VC fund’s finite lifespan. Based on the assumption about the strategies guiding future behaviors, we consider four types of venture capitalists: time-consistent, time-point-inconsistent, naïve, and sophisticated venture capitalists, of which the latter three are time-inconsistent. We derive and compare the exit thresholds of these four types of venture capitalists. The main results include: (1) time-inconsistent preferences accelerate the exits of venture capitalists; (2) the closer the VC funds expiry dates are, the more likely time-inconsistent venture capitalists are to accelerate their exits; and (3) future selves caused by time-flow inconsistency weaken the effect of time-point inconsistency. Our study provides a behavioral explanation for the empirical fact of young VCs’ grandstanding.

Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling

Abstract: We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT [8] to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically, we first divide a point cloud into several local point patches, and a point cloud Tokenizer with a discrete Variational AutoEncoder (dVAE) is designed to generate discrete point tokens containing meaningful local information. Then, we randomly mask out some patches of input point clouds and feed them into the backbone Transformers. The pre-training objective is to recover the original point tokens at the masked locations under the supervision of point tokens obtained by the Tokenizer. Extensive experiments demonstrate that the proposed BERT-style pre-training strategy significantly improves the performance of standard point cloud Transformers. Equipped with our pre-training strategy, we show that a pure Transformer architecture attains 93.8% accuracy on ModelNet40 and 83.1% accuracy on the hardest setting of ScanObjectNN, surpassing carefully designed point cloud models with much fewer hand-made designs. We also demonstrate that the representations learned by Point-BERT transfer well to new tasks and domains, where our models largely advance the state-of-the-art of few-shot point cloud classification task. The code and pre-trained models are available at https://github.com/lulutang0608/Point-BERT.