Computer Science Student Wins Quip Diversity Technology Scholarship

Blake Lewis visited organization\u27s San Francisco offices in Augus

Intelligent Voice Augmented Reality Interactive

Voice enables people to transmit information better and more quickly, and people can control all kinds of machines to communicate and work by intelligent voice. This paper intends to use intelligent voice to achieve new cloud classroom teaching. The effect that the teacher can move the picture in real time through voice control and reply accordingly can be achieved by speech synthesis, speech recognition and voice interaction technology. The efficiency of the classroom is improved while the interest of the classroom has been enhanced

The Application Of Insurance As A Risk Management Tool For Alternative Dispute Resolution (ADR) Implementation In Construction Disputes

In modern days, construction projects have become more and more complex and intriguing. One source of the complexity arises from the large number of parties involved. This is especially the case for large-scale construction projects. Because of such complexity, disputes are almost inevitable and implementation costs associated with dispute resolution have become increasingly expensive. Because most projects operate on tight budgets, cost effective dispute resolution plays an important role in the success of a construction project. For this purpose, Alternative Dispute Resolution (ADR) techniques such as negotiation, mediation, and arbitration are being widely adopted in large-scale construction projects to resolve disputes in more effective and cost-saving ways. However, the risk of incurring dispute-related cost overruns always exists because of the uncertainty in the distribution of dispute occurrence and the effectiveness of contractually-predetermined ADR techniques. As a result, the traditional self-insured structure which simply retains all dispute resolution costs to the project through contingency fees is no longer considered economical. While many insurance policies cover the settlement of a dispute, such as professional liability insurance, no specific insurance policy is dedicated to cover the ADR implementation costs such as fees and expenses that are paid to the owner/contractor's employees, lawyers, claims consultants, third party neutrals, and other experts involved in the resolution process. To fill the gap, this dissertation proposes an insurance model to reduce the potential variations in the dispute resolution budget by pricing ADR techniques as an insurance product. It is designed to transfer the risk of dispute-related cost overruns from the project to a third-party insurance company. To achieve this goal, this dissertation focuses on three major tasks: 1) investigate the role of ADR implementation insurance in construction risk management, 2) construct a mathematical model to represent the risk attitudes of project participants using utility theory and derive the basic premium of ADR implementation insurance using insurance pricing theory, and 3) develops a comprehensive framework to determine the optimal insurance premium by considering two additional insurance limits a Deductible Limit (DL) and a Maximum Payment Limit (MPL). The objective of this dissertation is to provide project participants with an advantageous insurance policy that minimizes their total expected subjective loss. The model can serve as a decision-making support system to help project participants determine whether an ADR implementation insurance policy is attractive for a certain project. To illustrate the benefits of the proposed model, numerical examples are provided for simulation purpose. The results show that ADR implementation insurance, although not a tool to eliminate dispute resolution costs, is a powerful alternative in risk management to transfer the financial implications of ADR implementation risk to a third party

Critical role of vertical radiative cooling contrast in triggering episodic deluges in small-domain hothouse climates

Seeley and Wordsworth (2021) showed that in small-domain cloud-resolving simulations the pattern of precipitation transforms in extremely hot climates ($\ge$ 320 K) from quasi-steady to organized episodic deluges, with outbursts of heavy rain alternating with several dry days. They proposed a mechanism for this transition involving increased water vapor absorption of solar radiation leading to net lower-tropospheric radiative heating. This heating inhibits lower-tropospheric convection and decouples the boundary layer from the upper troposphere during the dry phase, allowing lower-tropospheric moist static energy to build until it discharges, resulting in a deluge. We perform cloud-resolving simulations in polar night and show that the same transition occurs, implying that some revision of their mechanism is necessary. We show that episodic deluges can occur even if the lower-tropospheric radiative heating rate is negative, as long as the magnitude of the upper-tropospheric radiative cooling is about twice as large. We find that in the episodic deluge regime the mean precipitation can be inferred from the atmospheric column energy budget and the period can be predicted from the time for radiation and reevaporation to cool the lower atmosphere

Critical Role of Vertical Radiative Cooling Contrast in Triggering Episodic Deluges in Small-Domain Hothouse Climates

Seeley and Wordsworth (2021, https://doi.org/10.1038/s41586-021-03919-z) showed that in small-domain cloud-resolving simulations the temporal pattern of precipitation transforms in extremely hot climates (≥320&nbsp;K) from quasi-steady to organized episodic deluges, with outbursts of heavy rain alternating with several dry days. They proposed a mechanism for this transition involving increased water vapor greenhouse effect and solar radiation absorption leading to net lower-tropospheric radiative heating. This heating inhibits lower-tropospheric convection and decouples the boundary layer from the upper troposphere during the dry phase, allowing lower-tropospheric moist static energy to build until it discharges, resulting in a deluge. We perform cloud-resolving simulations in polar night and show that the same transition occurs, implying that some revision of their mechanism is necessary. We perform further tests to show that episodic deluges can occur even if the lower-tropospheric radiative heating rate is negative, as long as the magnitude of the upper-tropospheric radiative cooling is about twice as large. We find that in the episodic deluge regime the period can be predicted from the time for radiation and reevaporation to cool the lower atmosphere.</p

Cross-domain Few-shot Segmentation with Transductive Fine-tuning

Few-shot segmentation (FSS) expects models trained on base classes to work on novel classes with the help of a few support images. However, when there exists a domain gap between the base and novel classes, the state-of-the-art FSS methods may even fail to segment simple objects. To improve their performance on unseen domains, we propose to transductively fine-tune the base model on a set of query images under the few-shot setting, where the core idea is to implicitly guide the segmentation of query images using support labels. Although different images are not directly comparable, their class-wise prototypes are desired to be aligned in the feature space. By aligning query and support prototypes with an uncertainty-aware contrastive loss, and using a supervised cross-entropy loss and an unsupervised boundary loss as regularizations, our method could generalize the base model to the target domain without additional labels. We conduct extensive experiments under various cross-domain settings of natural, remote sensing, and medical images. The results show that our method could consistently and significantly improve the performance of prototypical FSS models in all cross-domain tasks.Comment: 12 pages, 8 figure

Text Classification Based on Knowledge Graphs and Improved Attention Mechanism

To resolve the semantic ambiguity in texts, we propose a model, which innovatively combines a knowledge graph with an improved attention mechanism. An existing knowledge base is utilized to enrich the text with relevant contextual concepts. The model operates at both character and word levels to deepen its understanding by integrating the concepts. We first adopt information gain to select import words. Then an encoder-decoder framework is used to encode the text along with the related concepts. The local attention mechanism adjusts the weight of each concept, reducing the influence of irrelevant or noisy concepts during classification. We improve the calculation formula for attention scores in the local self-attention mechanism, ensuring that words with different frequencies of occurrence in the text receive higher attention scores. Finally, the model employs a Bi-directional Gated Recurrent Unit (Bi-GRU), which is effective in feature extraction from texts for improved classification accuracy. Its performance is demonstrated on datasets such as AGNews, Ohsumed, and TagMyNews, achieving accuracy of 75.1%, 58.7%, and 68.5% respectively, showing its effectiveness in classifying tasks

Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network

3D point cloud semantic segmentation aims to group all points into different semantic categories, which benefits important applications such as point cloud scene reconstruction and understanding. Existing supervised point cloud semantic segmentation methods usually require large-scale annotated point clouds for training and cannot handle new categories. While a few-shot learning method was proposed recently to address these two problems, it suffers from high computational complexity caused by graph construction and inability to learn fine-grained relationships among points due to the use of pooling operations. In this paper, we further address these problems by developing a new multi-layer transformer network for few-shot point cloud semantic segmentation. In the proposed network, the query point cloud features are aggregated based on the class-specific support features in different scales. Without using pooling operations, our method makes full use of all pixel-level features from the support samples. By better leveraging the support features for few-shot learning, the proposed method achieves the new state-of-the-art performance, with 15\% less inference time, over existing few-shot 3D point cloud segmentation models on the S3DIS dataset and the ScanNet dataset

ATLANTIS: A Benchmark for Semantic Segmentation of Waterbody Images

Vision-based semantic segmentation of waterbodies and nearby related objects provides important information for managing water resources and handling flooding emergency. However, the lack of large-scale labeled training and testing datasets for water-related categories prevents researchers from studying water-related issues in the computer vision field. To tackle this problem, we present ATLANTIS, a new benchmark for semantic segmentation of waterbodies and related objects. ATLANTIS consists of 5,195 images of waterbodies, as well as high quality pixel-level manual annotations of 56 classes of objects, including 17 classes of man-made objects, 18 classes of natural objects and 21 general classes. We analyze ATLANTIS in detail and evaluate several state-of-the-art semantic segmentation networks on our benchmark. In addition, a novel deep neural network, AQUANet, is developed for waterbody semantic segmentation by processing the aquatic and non-aquatic regions in two different paths. AQUANet also incorporates low-level feature modulation and cross-path modulation for enhancing feature representation. Experimental results show that the proposed AQUANet outperforms other state-of-the-art semantic segmentation networks on ATLANTIS. We claim that ATLANTIS is the largest waterbody image dataset for semantic segmentation providing a wide range of water and water-related classes and it will benefit researchers of both computer vision and water resources engineering