Shared-account usage is common on short-video platforms, especially on mobile and tablet devices, where a single device is accessed by multiple users. While existing industrial solutions generally focus on behavior sequence purification to disentangle mixed user preferences, such approaches inherently depend on behavior accumulation and therefore lack the capability for real-time user identification. To adapt to online recommendation, utilizing gesture interaction features is a natural and promising option, as they (1) are instantaneous without behavior collection and (2) naturally encode fine-grained user operation habits. Nevertheless, we empirically observe that directly incorporating raw gesture features into recommendation models yields limited gains. Identity-discriminative patterns embedded in gesture signals are largely entangled during the main model training, preventing them from being leveraged as explicit and reliable identity cues. As a result, efficiently utilizing gesture information to provide more distinct identity signals for recommendation models remains a critical challenge. To address this issue, we propose G-CORE (Gesture Clustering for Real-time REcommendation), an unsupervised framework that disentangles gesture representations via clustering before integrating them into the main recommendation model. By providing clearer and more identity-aware signals, G-CORE enables the main model with faster user switching without relying on a volume of behavior accumulation. Through extensive offline experiments and online A/B tests on Kuaishou platform, G-CORE demonstrates its effectiveness in various shared-account scenarios, and has been successfully deployed in the Mobile and Tablet system of the platform.

Understanding and reasoning about objects' physical properties in the natural world is a fundamental challenge in artificial intelligence. While some properties like colors and shapes can be directly observed, others, such as mass and electric charge, are hidden from the objects' visual appearance. This paper addresses the unique challenge of inferring these hidden physical properties from objects' motion and interactions and predicting corresponding dynamics based on the inferred physical properties. We first introduce the Compositional Physical Reasoning (ComPhy) dataset. For a given set of objects, ComPhy includes limited videos of them moving and interacting under different initial conditions. The model is evaluated based on its capability to unravel the compositional hidden properties, such as mass and charge, and use this knowledge to answer a set of questions. Besides the synthetic videos from simulators, we also collect a real-world dataset to show further test physical reasoning abilities of different models. We evaluate state-of-the-art video reasoning models on ComPhy and reveal their limited ability to capture these hidden properties, which leads to inferior performance. We also propose a novel neuro-symbolic framework, Physical Concept Reasoner (PCR), that learns and reasons about both visible and hidden physical properties from question answering. Leveraging an object-centric representation, PCR utilizes videos and the associated natural language to infer objects' physical properties without dense object annotations. Furthermore, It incorporates property-aware graph networks to approximate the dynamic interactions among objects. PCR also employs a semantic parser to convert questions into semantic programs, and a program executor to execute the programs based on the learned physical properties and dynamics. After training, PCR demonstrates remarkable capabilities. It can detect and associate objects across frames, ground visible and hidden physical properties, make future and counterfactual predictions, and utilize these extracted representations to answer challenging questions. We hope the proposed ComPhy dataset and the PCR model present a promising step towards more comprehensive physical reasoning in AI systems.

Humans intuitively recognize objects’ physical properties and predict their motion, even when the objects are engaged in complicated interactions. The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans, remain challenging to state-of-the-art computational models. In this work, we present a neural model that simultaneously reasons about physics and makes future predictions based on visual and dynamics priors. The visual prior predicts a particle-based representation of the system from visual observations. An inference module operates on those particles, predicting and refining estimates of particle locations, object states, and physical parameters, subject to the constraints imposed by the dynamics prior, which we refer to as visual grounding. We demonstrate the effectiveness of our method in environments involving rigid objects, deformable materials, and fluids. Experiments show that our model can infer the physical properties within a few observations, which allows the model to quickly adapt to unseen scenarios and make accurate predictions into the future.

The goal of this paper is to automatically generate synthetic data to enable data analyzers to cope with the problem of insufficient data. Taking the most typical machine learning tasks, classification and regression, as an example, limited and insufficient samples cause low generalization of machine learning models, which cannot provide reasonable predictions. Data are insufficient either because of sample rarity or because data are impeded to be accessed for privacy concerns or confidential protection. To overcome this, we present a Synthetic Data Approach for Classification and Regression, adopting probability distribution and k-nearest neighbor model to generate synthetic data. We first estimate the probability distribution of each feature and construct a k-nearest neighbor model for all original data samples. Then we generate random samples based on probability distributions, adopt the k-nearest neighbor model to validate these random samples, and output the synthetic samples. We use proposed synthetic approaches to generate synthetic data of five publicly available datasets for classification and regression, respectively, and evaluate the performance of machine learning models to evaluate the resemblance between synthetic data and original data. The experimental results show that the synthetic data can resemble the original data, which indicates it is an effective approach for data analyzer to overcome the problem of insufficient data.

We marry two powerful ideas: deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our neural-symbolic visual question answering (NS-VQA) system first recovers a structural scene representation from the image and a program trace from the question. It then executes the program on the scene representation to obtain an answer. Incorporating symbolic structure as prior knowledge offers three unique advantages. First, executing programs on a symbolic space is more robust to long program traces; our model can solve complex reasoning tasks better, achieving an accuracy of 99.8% on the CLEVR dataset. Second, the model is more data- and memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering. Third, symbolic program execution offers full transparency to the reasoning process; we are thus able to interpret and diagnose each execution step.