Small talk can foster rapport building in human-human teamwork; yet how non-anthropomorphic robots, such as collaborative manipulators commonly used in industry, may capitalize on these social communications remains unclear. This work investigates how robot-initiated small talk influences task performance, rapport, and interaction dynamics in human-robot collaboration. We developed an autonomous robot system that assists a human in an assembly task while initiating and engaging in small talk. A user study ($N = 58$) was conducted in which participants worked with either a functional robot, which engaged in only task-oriented speech, or a social robot, which also initiated small talk. Our study found that participants in the social condition reported significantly higher levels of rapport with the robot. Moreover, all participants in the social condition responded to the robot's small talk attempts; 59% initiated questions to the robot, and 73% engaged in lingering conversations after requesting the final task item. Although active working times were similar across conditions, participants in the social condition recorded longer task durations than those in the functional condition. We discuss the design and implications of robot small talk in shaping human-robot collaboration.

This paper proposes a robot action planning scheme that provides an efficient and probabilistically safe plan for a robot interacting with an unconcerned human -- someone who is either unaware of the robot's presence or unwilling to engage in ensuring safety. The proposed scheme is predictive, meaning that the robot is required to predict human actions over a finite future horizon; such predictions are often inaccurate in real-world scenarios. One possible approach to reduce the uncertainties is to provide the robot with the capability of reasoning about the human's awareness of potential dangers. This paper discusses that by using a binary variable, so-called danger awareness coefficient, it is possible to differentiate between concerned and unconcerned humans, and provides a learning algorithm to determine this coefficient by observing human actions. Moreover, this paper argues how humans rely on predictions of other agents' future actions (including those of robots in human-robot interaction) in their decision-making. It also shows that ignoring this aspect in predicting human's future actions can significantly degrade the efficiency of the interaction, causing agents to deviate from their optimal paths. The proposed robot action planning scheme is verified and validated via extensive simulation and experimental studies on a LoCoBot WidowX-250.

The deployment of IoT (Internet of Things) sensor-based machine learning models in industrial systems for anomaly classification tasks poses significant challenges due to distribution shifts, as the training data acquired in controlled laboratory settings may significantly differ from real-time data in production environments. Furthermore, many real-world applications cannot provide a substantial number of labeled examples for each anomalous class in every new environment. It is therefore crucial to develop adaptable machine learning models that can be effectively transferred from one environment to another, enabling rapid adaptation using normal operational data. We extended this problem setting to an arbitrary classification task and formulated the one-class domain adaptation (OC-DA) problem setting. We took a meta-learning approach to tackle the challenge of OC-DA, and proposed a task sampling strategy to adapt any bi-level meta-learning algorithm to OC-DA. We modified the well-established model-agnostic meta-learning (MAML) algorithm and introduced the OC-DA MAML algorithm. We provided a theoretical analysis showing that OC-DA MAML optimizes for meta-parameters that enable rapid one-class adaptation across domains. The OC-DA MAML algorithm is evaluated on the Rainbow-MNIST meta-learning benchmark and on a real-world dataset of vibration-based sensor readings. The results show that OC-DA MAML significantly improves the performance on the target domains and outperforms MAML using the standard task sampling strategy.

Identifying individual animals within large wildlife populations is essential for effective wildlife monitoring and conservation efforts. Recent advancements in computer vision have shown promise in animal re-identification (Animal ReID) by leveraging data from camera traps. However, existing methods rely exclusively on visual data, neglecting environmental metadata that ecologists have identified as highly correlated with animal behavior and identity, such as temperature and circadian rhythms. To bridge this gap, we propose the Meta-Feature Adapter (MFA), a lightweight module designed to integrate environmental metadata into vision-language foundation models, such as CLIP, to enhance Animal ReID performance. Our approach translates environmental metadata into natural language descriptions, encodes them into metadata-aware text embeddings, and incorporates these embeddings into image features through a cross-attention mechanism. Furthermore, we introduce a Gated Cross-Attention mechanism that dynamically adjusts the weights of metadata contributions, further improving performance. To validate our approach, we constructed the Metadata Augmented Animal Re-identification (MAAR) dataset, encompassing six species from New Zealand and featuring paired image data and environmental metadata. Extensive experiments demonstrate that MFA consistently improves Animal ReID performance across multiple baseline models.

This paper describes Meta's ACH system for mutation-guided LLM-based test generation. ACH generates relatively few mutants (aka simulated faults), compared to traditional mutation testing. Instead, it focuses on generating currently undetected faults that are specific to an issue of concern. From these currently uncaught faults, ACH generates tests that can catch them, thereby `killing' the mutants and consequently hardening the platform against regressions. We use privacy concerns to illustrate our approach, but ACH can harden code against {\em any} type of regression. In total, ACH was applied to 10,795 Android Kotlin classes in 7 software platforms deployed by Meta, from which it generated 9,095 mutants and 571 privacy-hardening test cases. ACH also deploys an LLM-based equivalent mutant detection agent that achieves a precision of 0.79 and a recall of 0.47 (rising to 0.95 and 0.96 with simple pre-processing). ACH was used by Messenger and WhatsApp test-a-thons where engineers accepted 73% of its tests, judging 36% to privacy relevant. We conclude that ACH hardens code against specific concerns and that, even when its tests do not directly tackle the specific concern, engineers find them useful for their other benefits.

Sharpness-Aware Minimization (SAM) has attracted significant attention for its effectiveness in improving generalization across various tasks. However, its underlying principles remain poorly understood. In this work, we analyze SAM's training dynamics using the maximum eigenvalue of the Hessian as a measure of sharpness, and propose a third-order stochastic differential equation (SDE), which reveals that the dynamics are driven by a complex mixture of second- and third-order terms. We show that alignment between the perturbation vector and the top eigenvector is crucial for SAM's effectiveness in regularizing sharpness, but find that this alignment is often inadequate in practice, limiting SAM's efficiency. Building on these insights, we introduce Eigen-SAM, an algorithm that explicitly aims to regularize the top Hessian eigenvalue by aligning the perturbation vector with the leading eigenvector. We validate the effectiveness of our theory and the practical advantages of our proposed approach through comprehensive experiments. Code is available at https://github.com/RitianLuo/EigenSAM.

Current time series forecasting methods can be broadly classified into two categories: Channel Independent (CI) and Channel Dependent (CD) strategies, both aiming to capture the complex dependencies within time series data. However, the CI strategy fails to exploit highly correlated covariate information, while the CD strategy integrates all dependencies, including irrelevant or noisy ones, thus compromising generalization. To mitigate these issues, recent works have introduced the Channel Clustering (CC) strategy by grouping channels with similar characteristics and applying different modeling techniques to each cluster. However, coarse-grained clustering cannot flexibly capture complex, time-varying interactions. Addressing the above challenges, we propose TimeFilter, a graph-based framework for adaptive and fine-grained dependency modeling. Specifically, after constructing the graph with the input sequence, TimeFilter filters out irrelevant correlations and preserves the most critical ones through patch-specific filtering. Extensive experiments on 13 real-world datasets from various application domains demonstrate the state-of-the-art performance of TimeFilter. The code is available at https://github.com/TROUBADOUR000/TimeFilter.

We introduce Toyteller, an AI-powered storytelling system where users generate a mix of story text and visuals by directly manipulating character symbols like they are toy-playing. Anthropomorphized symbol motions can convey rich and nuanced social interactions; Toyteller leverages these motions (1) to let users steer story text generation and (2) as a visual output format that accompanies story text. We enabled motion-steered text generation and text-steered motion generation by mapping motions and text onto a shared semantic space so that large language models and motion generation models can use it as a translational layer. Technical evaluations showed that Toyteller outperforms a competitive baseline, GPT-4o. Our user study identified that toy-playing helps express intentions difficult to verbalize. However, only motions could not express all user intentions, suggesting combining it with other modalities like language. We discuss the design space of toy-playing interactions and implications for technical HCI research on human-AI interaction.

Multivariate time series anomaly detection is essential for failure management in web application operations, as it directly influences the effectiveness and timeliness of implementing remedial or preventive measures. This task is often framed as a semi-supervised learning problem, where only normal data are available for model training, primarily due to the labor-intensive nature of data labeling and the scarcity of anomalous data. Existing semi-supervised methods often detect anomalies by capturing intra-variate temporal dependencies and/or inter-variate relationships to learn normal patterns, flagging timestamps that deviate from these patterns as anomalies. However, these approaches often fail to capture salient intra-variate temporal and inter-variate dependencies in time series due to their focus on excessively fine granularity, leading to suboptimal performance. In this study, we introduce MtsCID, a novel semi-supervised multivariate time series anomaly detection method. MtsCID employs a dual network architecture: one network operates on the attention maps of multi-scale intra-variate patches for coarse-grained temporal dependency learning, while the other works on variates to capture coarse-grained inter-variate relationships through convolution and interaction with sinusoidal prototypes. This design enhances the ability to capture the patterns from both intra-variate temporal dependencies and inter-variate relationships, resulting in improved performance. Extensive experiments across seven widely used datasets demonstrate that MtsCID achieves performance comparable or superior to state-of-the-art benchmark methods.

Accurate healthcare prediction is essential for improving patient outcomes. Existing work primarily leverages advanced frameworks like attention or graph networks to capture the intricate collaborative (CO) signals in electronic health records. However, prediction for rare diseases remains challenging due to limited co-occurrence and inadequately tailored approaches. To address this issue, this paper proposes UDC, a novel method that unveils discrete clues to bridge consistent textual knowledge and CO signals within a unified semantic space, thereby enriching the representation semantics of rare diseases. Specifically, we focus on addressing two key sub-problems: (1) acquiring distinguishable discrete encodings for precise disease representation and (2) achieving semantic alignment between textual knowledge and the CO signals at the code level. For the first sub-problem, we refine the standard vector quantized process to include condition awareness. Additionally, we develop an advanced contrastive approach in the decoding stage, leveraging synthetic and mixed-domain targets as hard negatives to enrich the perceptibility of the reconstructed representation for downstream tasks. For the second sub-problem, we introduce a novel codebook update strategy using co-teacher distillation. This approach facilitates bidirectional supervision between textual knowledge and CO signals, thereby aligning semantically equivalent information in a shared discrete latent space. Extensive experiments on three datasets demonstrate our superiority.