Co-designing a Child-Robot Relational Norm Intervention to Regulate Children's Handwriting Posture

Persuasive social robots employ their social influence to modulate children's behaviours in child-robot interaction. In this work, we introduce the Child-Robot Relational Norm Intervention (CRNI) model, leveraging the passive role of social robots and children's reluctance to inconvenience others to influence children's behaviours. Unlike traditional persuasive strategies that employ robots in active roles, CRNI utilizes an indirect approach by generating a disturbance for the robot in response to improper child behaviours, thereby motivating behaviour change through the avoidance of norm violations. The feasibility of CRNI is explored with a focus on improving children's handwriting posture. To this end, as a preliminary work, we conducted two participatory design workshops with 12 children and 1 teacher to identify effective disturbances that can promote posture correction.

Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning

Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks, where old data from experienced tasks is unavailable when learning from a new task. To mitigate the problem, a line of methods propose to replay the data of experienced tasks when learning new tasks. These methods usually adopt an extra memory to store the data for replay. However, it is not expected in practice considering the memory constraint or data privacy issue. As a replacement, data-free data replay methods are proposed by inverting samples from the classification model. Though achieving good results, these methods still suffer from the inconsistency of the inverted and real training data, which is neglected in the inversion stage in recent works. To that effect, we propose to measure the data consistency quantitatively by some simplification and assumptions. Using the measurement, we analyze existing techniques for inverting samples and get some insightful information that inspires a novel loss function to reduce the inconsistency. Specifically, the loss minimizes the KL divergence of the distributions of inverted and real data under the tied multivariate Gaussian assumption, which is easy to implement in continual learning. In addition, we observe that the norms of old class weights turn to decrease continually as learning progresses. We thus analyze the underlying reasons and propose a simple regularization term to balance the class weights so that the samples of old classes are more distinguishable. To conclude, we propose the Consistency enhanced data replay with debiased classifier for Class Incremental Learning (CCIL). Extensive experiments on CIFAR-100, Tiny-ImageNet, and ImageNet100 show consistently improved performance of CCIL compared to previous approaches.

Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation

Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation. Traditional sequential recommendation models usually utilize ID embeddings to represent items, which are not shared across different domains and lack the transferable ability. Recent studies use pre-trained language models (PLM) for item text embeddings (text-based IRL) that are universally applicable across domains. However, the existing text-based IRL is unaware of the important collaborative filtering (CF) information. In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation. To effectively incorporate CF information into text-based IRL, we convert the item-level interaction data to a word graph containing word-level collaborations. Subsequently, we design a novel pre-training task to align the word-level semantic- and CF-related item representation. Extensive experimental results on multiple public datasets demonstrate that compared to state-of-the-art transferable sequential recommenders, CoWPiRec achieves significantly better performances in both fine-tuning and zero-shot settings for cross-scenario recommendation and effectively alleviates the cold-start issue. The code is available at: https://github.com/ysh-1998/CoWPiRec.

Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control

In this paper, we present a Deep Reinforcement Learning (RL)-driven Adaptive Stochastic Nonlinear Model Predictive Control (SNMPC) to optimize uncertainty handling, constraints robustification, feasibility, and closed-loop performance. To this end, we conceive an RL agent to proactively anticipate upcoming control tasks and to dynamically determine the most suitable combination of key SNMPC parameters - foremost the robustification factor $\kappa$ and the Uncertainty Propagation Horizon (UPH) $T_u$. We analyze the trained RL agent's decision-making process and highlight its ability to learn context-dependent optimal parameters. One key finding is that adapting the constraints robustification factor with the learned policy reduces conservatism and improves closed-loop performance while adapting UPH renders previously infeasible SNMPC problems feasible when faced with severe disturbances. We showcase the enhanced robustness and feasibility of our Adaptive SNMPC (aSNMPC) through the real-time motion control task of an autonomous passenger vehicle to follow an optimal race line when confronted with significant time-variant disturbances. Experimental findings demonstrate that our look-ahead RL-driven aSNMPC outperforms its Static SNMPC (sSNMPC) counterpart in minimizing the lateral deviation both with accurate and inaccurate disturbance assumptions and even when driving in previously unexplored environments.

A Stochastic Nonlinear Model Predictive Control with an Uncertainty Propagation Horizon for Autonomous Vehicle Motion Control

Employing Stochastic Nonlinear Model Predictive Control (SNMPC) for real-time applications is challenging due to the complex task of propagating uncertainties through nonlinear systems. This difficulty becomes more pronounced in high-dimensional systems with extended prediction horizons, such as autonomous vehicles. To enhance closed-loop performance in and feasibility in SNMPCs, we introduce the concept of the Uncertainty Propagation Horizon (UPH). The UPH limits the time for uncertainty propagation through system dynamics, preventing trajectory divergence, optimizing feedback loop advantages, and reducing computational overhead. Our SNMPC approach utilizes Polynomial Chaos Expansion (PCE) to propagate uncertainties and incorporates nonlinear hard constraints on state expectations and nonlinear probabilistic constraints. We transform the probabilistic constraints into deterministic constraints by estimating the nonlinear constraints' expectation and variance. We then showcase our algorithm's effectiveness in real-time control of a high-dimensional, highly nonlinear system-the trajectory following of an autonomous passenger vehicle, modeled with a dynamic nonlinear single-track model. Experimental results demonstrate our approach's robust capability to follow an optimal racetrack trajectory at speeds of up to 37.5m/s while dealing with state estimation disturbances, achieving a minimum solving frequency of 97Hz. Additionally, our experiments illustrate that limiting the UPH renders previously infeasible SNMPC problems feasible, even when incorrect uncertainty assumptions or strong disturbances are present.

Discrete Conditional Diffusion for Reranking in Recommendation

Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list to model interplay between items. Considering the inherent challenges of reranking such as combinatorial searching space, some previous studies have adopted the evaluator-generator paradigm, with a generator producing feasible sequences and a evaluator selecting the best one based on estimated listwise utility. Inspired by the remarkable success of diffusion generative models, this paper explores the potential of diffusion models for generating high-quality sequences in reranking. However, we argue that it is nontrivial to take diffusion models as the generator in the context of recommendation. Firstly, diffusion models primarily operate in continuous data space, differing from the discrete data space of item permutations. Secondly, the recommendation task is different from conventional generation tasks as the purpose of recommender systems is to fulfill user interests. Lastly, real-life recommender systems require efficiency, posing challenges for the inference of diffusion models. To overcome these challenges, we propose a novel Discrete Conditional Diffusion Reranking (DCDR) framework for recommendation. DCDR extends traditional diffusion models by introducing a discrete forward process with tractable posteriors, which adds noise to item sequences through step-wise discrete operations (e.g., swapping). Additionally, DCDR incorporates a conditional reverse process that generates item sequences conditioned on expected user responses. Extensive offline experiments conducted on public datasets demonstrate that DCDR outperforms state-of-the-art reranking methods. Furthermore, DCDR has been deployed in a real-world video app with over 300 million daily active users, significantly enhancing online recommendation quality.

Super-Resolving Face Image by Facial Parsing Information

Face super-resolution is a technology that transforms a low-resolution face image into the corresponding high-resolution one. In this paper, we build a novel parsing map guided face super-resolution network which extracts the face prior (i.e., parsing map) directly from low-resolution face image for the following utilization. To exploit the extracted prior fully, a parsing map attention fusion block is carefully designed, which can not only effectively explore the information of parsing map, but also combines powerful attention mechanism. Moreover, in light of that high-resolution features contain more precise spatial information while low-resolution features provide strong contextual information, we hope to maintain and utilize these complementary information. To achieve this goal, we develop a multi-scale refine block to maintain spatial and contextual information and take advantage of multi-scale features to refine the feature representations. Experimental results demonstrate that our method outperforms the state-of-the-arts in terms of quantitative metrics and visual quality. The source codes will be available at https://github.com/wcy-cs/FishFSRNet.

A Self-Correcting Sequential Recommender

Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user's historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations. To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items. It then uses the corrected item sequence to train a recommender and make the next item prediction.We design an item-wise corrector that can adaptively select one type of operation for each item in the sequence. The operation types are 'keep', 'delete' and 'insert.' In order to train the item-wise corrector without requiring additional labeling, we design two self-supervised learning mechanisms: (i) deletion correction (i.e., deleting randomly inserted items), and (ii) insertion correction (i.e., predicting randomly deleted items). We integrate the corrector with the recommender by sharing the encoder and by training them jointly. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate that STEAM outperforms state-of-the-art sequential recommendation baselines. Our in-depth analyses confirm that STEAM benefits from learning to correct the raw item sequences.

Modeling Sequential Recommendation as Missing Information Imputation

Side information is being used extensively to improve the effectiveness of sequential recommendation models. It is said to help capture the transition patterns among items. Most previous work on sequential recommendation that uses side information models item IDs and side information separately. This can only model part of relations between items and their side information. Moreover, in real-world systems, not all values of item feature fields are available. This hurts the performance of models that rely on side information. Existing methods tend to neglect the context of missing item feature fields, and fill them with generic or special values, e.g., unknown, which might lead to sub-optimal performance. To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them. By considering the next item as a missing feature field, sequential recommendation can be formulated as a special case of MII. We propose a sequential recommendation model, called missing information imputation recommender (MIIR), that builds on the idea of MII and simultaneously imputes missing item feature values and predicts the next item. We devise a dense fusion self-attention (DFSA) for MIIR to capture all pairwise relations between items and their side information. Empirical studies on three benchmark datasets demonstrate that MIIR, supervised by MII, achieves a significantly better sequential recommendation performance than state-of-the-art baselines.

Towards Representation Alignment and Uniformity in Collaborative Filtering

Collaborative filtering (CF) plays a critical role in the development of recommender systems. Most CF methods utilize an encoder to embed users and items into the same representation space, and the Bayesian personalized ranking (BPR) loss is usually adopted as the objective function to learn informative encoders. Existing studies mainly focus on designing more powerful encoders (e.g., graph neural network) to learn better representations. However, few efforts have been devoted to investigating the desired properties of representations in CF, which is important to understand the rationale of existing CF methods and design new learning objectives. In this paper, we measure the representation quality in CF from the perspective of alignment and uniformity on the hypersphere. We first theoretically reveal the connection between the BPR loss and these two properties. Then, we empirically analyze the learning dynamics of typical CF methods in terms of quantified alignment and uniformity, which shows that better alignment or uniformity both contribute to higher recommendation performance. Based on the analyses results, a learning objective that directly optimizes these two properties is proposed, named DirectAU. We conduct extensive experiments on three public datasets, and the proposed learning framework with a simple matrix factorization model leads to significant performance improvements compared to state-of-the-art CF methods. Our implementations are publicly available at https://github.com/THUwangcy/DirectAU.