Due to recent advances in large language models and robotics, social robots will potentially play an important role in people’s daily lives soon, and are expected to improve dynamic multi-party group discussions in social scenarios. In this paper, we developed a system to assist dynamic group discussion with our social robot Haru. Our system is composed of three modules: a Dialogue Assistance module via integrating Haru with large language models which facilitates Haru to be an embodied chatbot; a Balancing and Welcoming Behavior module to improve users’ engagement and welcome new users to join the discussion with verbal behaviors; an Autonomous Eye Gazing module to show politeness during group discussion, e.g., gazing to the talking user or the less-engaging user to encourage her, looking to the new comer when she joins the discussion, gazing via eyeball movement when the current speaking user is close to the previous one. The autonomous eye gazing behavior was first trained via deep reinforcement learning in simulation and transferred to physical Haru in the real world. Results of our user study with 50 subjects show the significant performance of our system in assisting dynamic group discussion.

To improve the performance of Chinese new words detection, this paper enhances the traditional method through two aspects. Firstly, a new and more effective metric for branch entropy is chosen through an extensive empirical study of different metrics; besides, punctuation are classified to two categories and assigned with different weights according to their contribution to differentiating new words. After the first step, a set of candidate new words are generated. Secondly, a multi-criteria Transformer-CRF segmentation probability model is introduced to filter the noisy candidates. The new method is tested on SIGHAN 2005 and 2008 Bakeoff data sets, and the performance demonstrates that for the resulted top 1000 ~100 words, our new metric contributes to 2.8% ~9.05% improvement compared with the commonly used minimum entropy metric; while punctuation processing leads to 3.26% ~3.77% improvement. Experiments on the legal corpus show that the Transformer-CRF model's top 1000 MAP results are further improved by 2.34% comparing with the unsupervised method.

Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particular, AlphaFold, a ground-breaking AI system, could potentially reshape structural biology. However, the utility of the PLM module in AlphaFold, Evoformer, has not been explored beyond structure prediction. In this paper, we investigate the representation ability of three popular PLMs: ESM-1b (single sequence), MSA-Transformer (multiple sequence alignment), and Evoformer (structural), with a special focus on Evoformer. Specifically, we aim to answer the following key questions: (1) Does the Evoformer trained as part of AlphaFold produce representations amenable to predicting protein function? (2) If yes, can Evoformer replace ESM-1b and MSA-Transformer? (3) How much do these PLMs rely on evolution-related protein data? In this regard, are they complementary to each other? We compare these models by empirical study along with new insights and conclusions. All code and datasets for reproducibility are available at https://github.com/elttaes/Revisiting-PLMs .

Tremendous historical and recent building vector maps are available from private surveying and mapping departments or open-source platforms such as Open Street Map (OSM). However, there invariably exist offsets between the building vectors and new remote sensing images, caused by geometric registration errors and parallax from hypsography. Consequently, these vector maps cannot be directly used as labels for the up-to-date GIS productions, as well as popular supervised machine learning. As the first work of this kind, this paper proposes an offset correction method, based on deep learning, to predict the bias between a vector building map and a new remote sensing image. The method is based on a dense regression model to predict the offsets between the image and the vector map at the pixel level, which are then processed to retrieve the offsets of each polygon. Two datasets consist of remote sensing images and biased building polygons, the WHU change detection and London datasets, are prepared to test the effectiveness of our approach. In the WHU change detection dataset, approaching 80% polygons can be correctly registered within the tolerance of three pixels, in the London dataset, more than 60% polygons can be corrected by a pre-trained model on an available open-source dataset, both of which demonstrated that our method can contribute to the GIS map updating and more efficient preparation of training samples for a deep learning model.

We evaluate the impact of probabilistically-constructed digital identity data collected between Sep. 2017 and Dec. 2017, approximately, in the context of Lookalike-targeted campaigns. The backbone of this study is a large set of probabilistically-constructed "identities", represented as small bags of cookies and mobile ad identifiers with associated metadata, that are likely all owned by the same underlying user. The identity data allows to generate "identity-based", rather than "identifier-based", user models, giving a fuller picture of the interests of the users underlying the identifiers. We employ off-policy evaluation techniques to evaluate the potential of identity-powered lookalike models without incurring the risk of allowing untested models to direct large amounts of ad spend or the large cost of performing A/B tests. We add to historical work on off-policy evaluation by noting a significant type of "finite-sample bias" that occurs for studies combining modestly-sized datasets and evaluation metrics based on ratios involving rare events (e.g., conversions). We illustrate this bias using a simulation study that later informs the handling of inverse propensity weights in our analyses on real data. We demonstrate significant lift in identity-powered lookalikes versus an identity-ignorant baseline: on average ~70% lift in conversion rate, CVR, with a concordant drop in cost-per-acquisition, CPA. This rises to factors of ~(4-32)x for identifiers having little data themselves, but that can be inferred to belong to users with substantial data to aggregate across identifiers. This implies that identity-powered user modeling is especially important in the context of identifiers having very short lifespans (i.e., frequently churned cookies). Our work motivates and informs the use of probabilistically-constructed digital identities in the marketing context. It also deepens the canon of examples in which off-policy learning has been employed to evaluate the complex systems of the internet economy.