Reviewerly/exHarmony huggingface dataset:
A Community of Practice Model Suggests Practical Implications for Teaching Geologic Mapping in the Field

Earth and Space Science Open Archive posterOpen AccessYou are viewing the latest version by default [v1]A Community of Practice Model Suggests Practical Implications for Teaching Geologic Mapping in the FieldAuthors Heather Petcovic iD Peggy McNeal Samuel Nyarko iDSee all authors Heather PetcoviciDCorresponding AuthorWestern Michigan UniversityiDhttps://orcid.org/0000-0002-1669-6802view email addressThe email was not providedcopy email addressPeggy McNealTowson Universityview email addressThe email was not providedcopy email addressSamuel NyarkoiDWestern Michigan UniversityiDhttps://orcid.org/0000-0002-2434-5949view email addressThe email was not providedcopy email address
The Concept of Metamorphism

This second edition is fully updated to include new developments in the study of metamorphism as well as enhanced features to facilitate course teaching. It integrates a systematic account of the mineralogical changes accompanying metamorphism of the major rock types with discussion of the conditions and settings in which they formed. The use of textures to understand metamorphic history and links to rock deformation are also explored. Specific chapters are devoted to rates and timescales of metamorphism and to the tectonic settings in which metamorphic belts develop. These provide a strong connection to other parts of the geology curriculum. Key thermodynamic and chemical concepts are introduced through examples which demonstrate their application and relevance. Richly illustrated in colour and featuring end-of-chapter and online exercises, this textbook is a comprehensive introduction to metamorphic rocks and processes for undergraduate students of petrology, and provides a solid basis for advanced study and research.
Immunex Corp. v. Sanofi-Aventis U.S. LLC United States Court of Appeals of the Federal Circuit, 202977 F.3d 1212

Biotechnology Law ReportVol. 40, No. 1 As presented by Chris HolmanImmunex Corp. v. Sanofi-Aventis U.S. LLC United States Court of Appeals of the Federal Circuit, 202977 F.3d 1212Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:3 Feb 2021https://doi.org/10.1089/blr.2020.29215.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 40Issue 1Feb 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Immunex Corp. v. Sanofi-Aventis U.S. LLC United States Court of Appeals of the Federal Circuit, 202977 F.3d 1212.Biotechnology Law Report.Feb 2021.54-60.http://doi.org/10.1089/blr.2020.29215.cmhPublished in Volume: 40 Issue 1: February 3, 2021Online Ahead of Print:January 15, 2021PDF download
Introduction

In recent times, one can observe the increasing development of multimedia technologies and their rising dominance in life and business. Society is becoming more eager to use new solutions as they facilitate life, primarily by simplifying contact and accelerating the exchange of experience with others, what was not encountered on such a large scale many years ago.
Nonseparability Without Monotonicity: The Couterfactual Distribution Estimator for Causal Inference

Nonparametric identification strategy is employed to capture causal relationships without imposing any variant of monotonicity existing in the nonseparable nonlinear error model literature. Monotonicity may fail to exist for fundamental reasons related e.g., to the strategic behavior of economic agents or to a multidimensional utility which is neither monotonic nor scalar. Thus, the monotonicity-based estimators might be severely biased as shown here in comparative Monte Carlo simulations. We offer a two-step M-Estimator in which causality is preserved by uncovering the latent counterfactual distribution of the dependent variable, reflecting random assignment of the treatment, imitating a "natural experiment". The offered estimator is based on a resolution dependent reproducing kernel rather than on the bandwidth-dependent classical kernel, attesting to very high accuracy. Further, this estimator is induced by a biorthogonal wavelet and thus, less sensitive to bandwidth choice. Asymptotic properties are established.
Chapter 4: Concepts from Graph Theory

One of the important developments in compressed sensing in recent times is the use of sparse binary measurement matrices. There is a natural connection between binary matrices and graphs. The objective of the present chapter is to present enough background material so that these connections can be explored. The actual construction of sparse binary measurement matrices for compressed sensing is studied in Section 5.4.
Small Target Detection Algorithm Based on LBP Model

Target recognition is the ultimate goal of image processing and analysis. Target detection and target tracking are usually the pre-processing of target recognition, which distinguishes and recognizes targets and background clutter, decoys and even other targets, and determines target type and even model information. In different applications or under different conditions in the same application, people have different requirements for target recognition. This paper introduces the Local Binary Pattern (LBP) model that describes the image texture characteristics, combines the LBP model to carry out a small target detection algorithm analysis and research, and analyzes the experimental results.
Persistent Execution of Continuous Time Multi-Robot Pathfinding

Many one-shot Multi-Robot Path Finding (MRPF) algorithms have been able to find collision-free paths for hundreds or even thousands of robots moving to their respective goal positions in dense scenarios, and some persistent execution frameworks are able to drive MRPF persistently. However, these frameworks have great limitations in practical applications, which are based on discrete time and grids. In this paper, we present a persistent execution framework independent of MRPF solvers, which can not only operate in discrete time and grids, but also solve the MRPF problem based on roadmap in continuous time. Our presented prediction replanning method is at the core of the framework driving the operation of the MRPF algorithm in the case of persistent tasks, and it also deals with the impact of algorithm computation time and message transmission delay of the multi-robot system. We also presented a dynamic map addition method to solve the problem that replanning cannot be performed when robot is not on map vertex during the runtime. We evaluate our framework using Continuous-time conflict-based search (CCBS) as the MRPF solver and the results show that it can efficiently drive the MRPF algorithm to run under continuous-time persistent task situations.
Feedback-based Coarse Time-Granularity POI Recommendation and Itinerary Planning

For tourists, planning their own travel itinerary to a strange city is really challenging. Although there are many researches on Point of Interests (POIs) recommendation and itinerary planning, two problems occur in currently proposed recommendation algorithms. First, the time granularity of the algorithms is too fine. POI recommendation should be considered within a more coarse-grained time factor. For example, a visitor wouldn’t like to visit a water park in winter, although he appears to love such places. However, seldom researches have taken this into consideration. Second, most of the algorithms build their models based on the tourists’ trajectory, which means, they did not consider users’ feedback. In fact, quite a number of tourists do not like their travel experience and give negative feedbacks after their visits. In this situation, we shouldn’t recommend similar POIs to this user because he may not like such kind of scenic spots at all. To address these two problems, this paper proposes an algorithm called Feedback-based Coarse Time-Granularity POI Recommendation, which can extract information from comments and distinguish tourists’ attitudes towards the POIs they have visited. At the same time, the coarse-grained time factor is considered while making recommendations. In addition, an itinerary planning algorithm is proposed. Experiments show that our algorithm can reach SOTA.
A hybrid intelligent path planning method based on improved Dyna-H architecture for unmanned surface vessel

Path planning is one of the key technologies to support the autonomous and safe navigation of unmanned surface vessel (USV). In order to solve the problems of slow convergence speed, many iterations, and unstable convergence results when the traditional reinforcement learning algorithm is applied to the path planning of the USV. In this paper, an improved hybrid path planning method based on Dyna architecture(IDH) is proposed. The Dyna architecture constructed based on model learning and direct reinforcement learning can give full play to the advantages of both and has good robustness in dealing with uncertain existing problems. The path searching method combining Q-learning and heuristic algorithm can learn quickly according to different environmental conditions and search for safe paths that meet the planning objectives (such as shortest path and lowest energy consumption, etc.). In this paper, the kinematic characteristics of target distance, distance to closest point of approach(DCPA), time to closest point of approach(TCPA), and steering angle are comprehensively considered when constructing the reward and punishment strategy and action selection rules of Q-Learning. Then the post smoother was developed to improve the performance of the generated route, reducing unnecessary ’jags’. Taking the actual environment maps as examples, the proposed planning method is simulated and verified. The Q-Learning, Dyna-Q and Rapid-exploration Random Tree(RRT) algorithm are chosen as the comparison method. Experiments show that the proposed improved path planning method has the best adaptability and robustness under the influence of actual environment maps. The performance of the proposed approach in term of total distance is superior over the other algorithm. In addition, the search speed is faster than the other two reinforcement learning algorithms.
Semantic Approach to Opening Museum Collections of Everyday Life History for Services in Internet of Things Environments

Technologies of the Internet of Things (IoT) and of smart spaces support creating smart museums based on digitized infrastructures and information systems already deployed in modern museums. Cultural heritage knowledge in such a museum is used by interested visitors as well as by personnel. This work continues the authors' research on the smart museum concept and its case study of everyday life history in the History Museum of Petrozavodsk State University (PetrSU). The authors develop an ontological model for the needs of studying the everyday life history. The ontology supports integrating descriptions of collected exhibits into a semantic network, where the links reflect meaningful relations between exhibits and other historical objects. They apply the wiki technology within the smart spaces-based architecture of a smart museum. The wiki implements an ontology-enabled system that experts use to extract and represent knowledge hidden in the museum collection. The authors discuss possible semantic algorithms for data mining in the museum semantic network.
RETRACTED ARTICLE: The change of rainfall and atmosphere characteristics based on target tracking algorithm and real-time monitoring of college students’ physical training

This paper studies the real-time monitoring of rainfall and atmosphere characteristics and college students’ physical training based on target tracking algorithm. In this paper, we use the target tracking algorithm, combined with the GNSS observation stations of BJFS, LHAZ, URUM, CUSV, and PIMO, to calculate the change of rainfall atmosphere characteristics of ULUM station, and then get the PWV above the ULUM station, and compare the PWV obtained by the radiosonde station with the ground-based GNSS inversion results to verify the change of rainfall atmosphere characteristics based on the target tracking algorithm. In order to solve the problem of inaccurate tracking and positioning when the background is cluttered, a target tracking algorithm based on channel reliable local rank transform is proposed. The local rank transform feature combined with lab three-channel image is used to form 16-dimensional feature channel, from which the effective feature channel is selected for fusion to enhance the expression ability of the algorithm for target features. The experimental results show that, compared with the feature channel selected by reliability coefficient, the local rank transform feature channel selected by experience has better tracking effect in specific scene and more accurate position estimation of target. At the same time, with the transformation of China from a sports power to a sports power, sports training is scientific, science and technology help sports, and the guarantee system of sports science integration is gradually improved. The state attaches great importance to the role of science and technology in the training of college athletes, and has purchased a set of Firstbeat sports monitoring system to monitor the athletes’ heart rate in real time. A scientific research group is set up to provide scientific and technological services, record the heart rate of athletes during training and competition, so as to determine the “load characteristics” of athletes, and explore the monitoring of athletes’ training with heart rate as the object, so as to improve the training quality of athletes.
Sound and Fourier Series

A major part of the information we receive and perceive every day is in the form of audio. Most sounds are transferred directly from the source to our ears, like when we have a face to face conversation with someone or listen to the sounds in a forest or a street. However, a considerable part of the sounds are generated by loudspeakers in various kinds of audio machines like cell phones, digital audio players, home cinemas, radios, television sets and so on. The sounds produced by these machines are either generated from information stored inside, or electromagnetic waves are picked up by an antenna, processed, and then converted to sound.
Smart Mobile Learning Environment

Digital technology is promoting a vision for self-directed, motivated, adaptive, resource-enriched, and technology-embedded smart higher education, as in this chapter about a smart mobile learning environment. A smart learning environment shall encourage mobile and blended prerequisites and activities across various dimensions, including the notion that students can learn anywhere and anytime and easily switch learning contexts to another or a variety of scenarios. Methodically, the qualitative data illustrates a smart mobile learning hub, MLH, for student-centered learning, both in and out of the university and during practical training in the profession by using smart devices such as mobile laptop, tablet, or smart mobile. Theoretically, the analysis joins the research tradition of computer-supported collaborative learning, CSCL, complemented with computer self-efficacy, CSE. The result show that it is important in the design of smart learning environment to motivate a variety of students so they can be self-directed and adaptive in joining the resource-enriched and technology-embedded education.
E-Journal Management and Access Methods

SUMMARY. Libraries are struggling to determine how to invest their e-resource dollars to meet the needs of their patrons. During this session, Melissa Holmberg and Bobby Bothmann presented their analysis of two e-journal management tools: EBSCO’s EJS and OCLC’s ECO. Their study compared the usage of print-plus-free-online titles at Minnesota State University, Mankato which are accessed through a fee-based and free e-journal management tool. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;https://www.HaworthPress.com&gt;]
OCR finds fault with inaccessible web content on university's homepage

Case name : Letter to: University of Nevada‐Reno , No. 10172016 (OCR 01/19/17).
Discriminative-Transformer for Emotion Classification

The development of social media encourages people to freely express their attitudes on many political issues, movies, products, etc., on the Internet. Emotion classification helps to understand the emotions contained in people’s natural language descriptions, and mining the emotions in people’s language helps organizations understand people’s responses to their actions. In recent years, more and more researches on emotion classification have been published. Although many methods in these works have achieved good results, most of these methods ignore the discrimination in the classification process. Therefore, we propose a Transformer model that imposes constraints on the margin size between categories to improve the discriminativeness of the model in emotion classification tasks. The experimental results show that the model in this paper can learn the latent text representation and make the model discriminative. We prove that improving the model’s discriminativeness helps improve the model’s performance in emotion classification tasks.
Usability Testing

In this chapter, we’ll focus on testing products with people with disabilities. We’ll start by reviewing what makes a good product and see how to recruit people with disabilities. We’ll then examine usability testing best practices. Most of this will be covered in a BBC iPlayer Case Study.
Two to Trust: AutoML for Safe Modelling and Interpretable Deep Learning for Robustness

With great power comes great responsibility. The success of machine learning, especially deep learning, in research and practice has attracted a great deal of interest, which in turn necessitates increased trust. Sources of mistrust include matters of model genesis (“Is this really the appropriate model?”) and interpretability (“Why did the model come to this conclusion?”, “Is the model safe from being easily fooled by adversaries?”). In this paper, two partners for the trustworthiness tango are presented: recent advances and ideas, as well as practical applications in industry in (a) Automated machine learning (AutoML), a powerful tool to optimize deep neural network architectures and fine-tune hyperparameters, which promises to build models in a safer and more comprehensive way; (b) Interpretability of neural network outputs, which addresses the vital question regarding the reasoning behind model predictions and provides insights to improve robustness against adversarial attacks.
The Geometric Relationship Between Object and Image

In this chapter I discuss the geometrical relationship between the object and the image when light does not travel in a straight line, that is, when the light is reflected by a plane, concave, or convex mirror, or refracted by a converging or diverging lens. I discuss how to predict the nature of the image using ray tracing methods, algebraically using the lens maker's equation, and finally by using the concept of optical path length and Fermat's principle of least time. I also discuss the problems of spherical and chromatic aberrations and the ways to mitigate them in order to get the optimal geometrical image.
Touch enabled wall using LiDAR

Currently, the touch screen is the default choice for creating large-size interactive screens. Such large touch screens are very expensive and require special hardware to use. Touchscreens are also used in the majority of smart education tools. The high cost of such tools prevents a lot of schools and colleges from buying them. In this paper, we are presenting a low-cost method to turn any flat surface into a touch-interactive surface using a LiDAR sensor. Our system requires a laptop/PC screen to be projected onto a flat surface. The LiDAR sensor is then placed below the surface in such a manner that the surface is in the plane of the sensor. The four corners of the screen have to be calibrated into the system by placing a finger on each of them. Once the system is calibrated the mouse of the laptop/PC can be controlled by the movement of a finger on the projected screen. Mouse clicks can also be performed through our system. The response time of our system is 10 ms and the calculated cursor position was within $\pm$ 5 pixels of the actual cursor position. The cost of our system only consists of the price of the LiDAR sensor since laptop/PC and projectors are already available at schools, colleges, and offices where large interactive surfaces would be required. Through our system, we wish to make smart education tools affordable for a majority of schools and colleges.
Problem-Solving

Abstract Human and artificial agents are both intelligent problem solvers. Therefore, problem-solving will be central to their collaboration. Among notable developments in this domain is the capability for artificial agents to sample and search in a very farsighted fashion, or to be hyperopic, which is the technical term for farsighted vision, the opposite of myopia. This inverts the dominant concern of prior theory, which focuses on limited, bounded capabilities in problem-solving and decision-making. This shift poses significant opportunities and risks for augmented agents. Human processing will likely remain naturally myopic and limited, while artificial processing is increasingly hyperopic and powerful. Given these differences, digitally augmented problem-solving could be extremely divergent and dysfunctional, for example, by sampling and searching in a hyperopic fashion, while guided by persistent human myopia. Alternatively, one agent might dominate the other, leading to extreme convergence and possibly the digital domination of problem-solving.
Light Field Visual Attention Prediction Using Fourier Disparity Layers

In this paper, we present a novel layered saliency model for Light Fields (LF) called Fourier Disparity Layer Saliency Estimation (FDLSE). The layers are constructed from the existing Fourier Disparity Layer (FDL) LF representation. Our FDLSE model can be used to predict the visual attention (VA) of any LF rendering with arbitrary viewpoint, aperture and depth-of-focus, without the need to generate the rendered image itself. The proposed model surpasses previous work in the following areas. Our method requires the estimation of the saliency map of only one sub-aperture image instead of the full view array. Furthermore, this model does not require pre-estimated disparity maps, but instead relies on the FDL model whose computation fully takes advantage of GPU parallelisation. Finally, FDLSE shows visual improvements and performs quantitatively on par with our previous FGSE model when evaluated on VA prediction of refocus renderings. To our knowledge these are the only two models which can be used to predict LF VA.