Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18

Provably Convergent Federated Trilevel Learning

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach; 2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes $\mu$-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed $\mu$-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the $\mu$-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain $\epsilon$-stationary point is upper bounded by $\mathcal{O}(\frac{1}{\epsilon^2})$. Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80$\%$.Comment: Accepted at AAAI 202

Vertical Federated Learning Hybrid Local Pre-training

Vertical Federated Learning (VFL), which has a broad range of real-world applications, has received much attention in both academia and industry. Enterprises aspire to exploit more valuable features of the same users from diverse departments to boost their model prediction skills. VFL addresses this demand and concurrently secures individual parties from exposing their raw data. However, conventional VFL encounters a bottleneck as it only leverages aligned samples, whose size shrinks with more parties involved, resulting in data scarcity and the waste of unaligned data. To address this problem, we propose a novel VFL Hybrid Local Pre-training (VFLHLP) approach. VFLHLP first pre-trains local networks on the local data of participating parties. Then it utilizes these pre-trained networks to adjust the sub-model for the labeled party or enhance representation learning for other parties during downstream federated learning on aligned data, boosting the performance of federated models. The experimental results on real-world advertising datasets, demonstrate that our approach achieves the best performance over baseline methods by large margins. The ablation study further illustrates the contribution of each technique in VFLHLP to its overall performance

Microbial communities associated with epilithic algal matrix with different morphological characters in Luhuitou fringing reef

The microbiota is an important component of the epilithic algal matrix (EAM) and plays a central role in the biogeochemical cycling of important nutrients in coral reef ecosystems. Insufficient studies on EAM microbiota diversity have led to a limited understanding of the ecological functions of EAMs in different states. To explore the microbial community of EAMs in the Luhuitou fringing reef in Sanya, China, which has undergone the incessant expansion and domination of algae over the past several decades, investigations were conducted in the reef’s intertidal zone. Five types of substrate habitats (dead branching coral, dead massive coral, dead flat coral, granite block, and concrete block) were selected, and their microbial communities were analyzed by high-throughput sequencing of EAM holobionts using the 16S rDNA V4 region. Proteobacteria was the most abundant group, accounting for more than 70% of reads of the microbial composition across all sites, followed by Cyanobacteria (15.89%) and Bacteroidetes (5.93%), respectively. Cluster analysis divided all microbial communities into three groups, namely short, medium, and long EAMs. Algal length was the most important morphological factor impacting the differences in the composition of the EAM microbiota. The three EAM groups had 52 common OTUs and 78.52% common sequences, among which the most abundant were Vibrio spp. and Photobacterium spp. The three types of EAM also had unique OTUs. The short EAMs had 238 unique OTUs and 48.61% unique sequences, mainly in the genera Shewanella and Cyanobacterium. The medium EAMs contained 130 unique OTUs and 4.36% unique sequences, mainly in the genera Pseudomonas and Bacillus. The long EAMs only had 27 unique OTUs and 4.13% unique sequences, mainly in the genus Marinobacter. Compared with short EAM, medium and long EAM had a lower proportion of autotrophic bacteria and higher proportion of potential pathogenic bacteria. It is suggested that EAMs with different phenotypes have different microbial compositions, and the ecological function of the EAM microbiota changes from autotrophic to pathogenic with an increase in algal length. As EAMs have expanded on coastal coral reefs worldwide, it is essential to comprehensively explore the community structure and ecological role of their microbial communities

Robotic-assisted burring in total hip replacement:A new surgical technique to optimise acetabular preparation

Background: In Total Hip replacement (THR) surgery, a critical step is to cut an accurate hemisphere into the acetabulum so that the component can be fitted accurately and obtain early stability. This study aims to determine whether burring rather than reaming the acetabulum can achieve greater accuracy in the creation of this hemisphere.Methods: A preliminary robotic system was developed to demonstrate the feasibility of burring the acetabulum using the Universal Robot (UR10). The study will describe mechanical design, robot trajectory optimisation, control algorithm development, and results from phantom experiments compared with both robotic reaming and conventional reaming. The system was also tested in a cadaver experiment.Results: The proposed robotic burring system can produce a surface in 2 min with an average error of 0.1 and 0.18 mm, when cutting polyurethane bone block #15 and #30, respectively. The performance was better than robotic reaming and conventional hand reaming.Conclusion: The proposed robotic burring system outperformed robotic and conventional reaming methods to produce an accurate acetabular cavity. The findings show the potential usage of a robotic-assisted burring in THR for acetabular preparation