Dynamic RF Beam Codebook Reduction for Cost-Efficient mmWave Full-Duplex Systems

The recent attempts to realize full-duplex (FD) communications in millimeter wave (mmWave) systems have garnered a significant amount of interest for its potential. In this paper, we present a cost-efficient design of mmWave FD systems, where the system dynamically reduces the RF beam codebook in a computationally efficient manner, so that it is comprised of the RF beams that will prevent the Rx receive chain from saturating due to the self-interference (SI). The analog beamformer will suppress the SI to the level that the residual SI can be completely removed with digital SI cancellation, allowing the digital beamformer to concentrate on the desired channel, free of the SI. To reduce the computation required for the proposed method, we propose two sufficient conditions that prevent the Rx side saturations, which are practically tight. Through performance valuations conducted in realistically modeled mmWave FD scenarios, we demonstrate that the proposed design achieves comparable performance with the ideal FD and other benchmarks with significantly lower costs.

FontNet: Closing the gap to font designer performance in font synthesis

Font synthesis has been a very active topic in recent years because manual font design requires domain expertise and is a labor-intensive and time-consuming job. While remarkably successful, existing methods for font synthesis have major shortcomings; they require finetuning for unobserved font style with large reference images, the recent few-shot font synthesis methods are either designed for specific language systems or they operate on low-resolution images which limits their use. In this paper, we tackle this font synthesis problem by learning the font style in the embedding space. To this end, we propose a model, called FontNet, that simultaneously learns to separate font styles in the embedding space where distances directly correspond to a measure of font similarity, and translates input images into the given observed or unobserved font style. Additionally, we design the network architecture and training procedure that can be adopted for any language system and can produce high-resolution font images. Thanks to this approach, our proposed method outperforms the existing state-of-the-art font generation methods on both qualitative and quantitative experiments.

Sequential Movie Genre Prediction using Average Transition Probability with Clustering

In recent movie recommendations, predicting the user's sequential behavior and suggesting the next movie to watch is one of the most important issues. However, capturing such sequential behavior is not easy because each user's short-term or long-term behavior must be taken into account. For this reason, many research results show that the performance of recommending a specific movie is not very high in a sequential recommendation. In this paper, we propose a cluster-based method for classifying users with similar movie purchase patterns and a movie genre prediction algorithm rather than the movie itself considering their short-term and long-term behaviors. The movie genre prediction does not recommend a specific movie, but it predicts the genre for the next movie to watch in consideration of each user's preference for the movie genre based on the genre included in the movie. Through this, it is possible to provide appropriate guidelines for recommending movies including the genre to users who tend to prefer a specific genre. In particular, in this paper, users with similar genre preferences are organized into clusters to recommend genres, and in clusters that do not have relatively specific tendencies, genre prediction is performed by appropriately trimming genres that are not necessary for recommendation in order to improve performance. We evaluate our method on well-known movie datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.

DIME: An Online Tool for the Visual Comparison of Cross-Modal Retrieval Models

Cross-modal retrieval relies on accurate models to retrieve relevant results for queries across modalities such as image, text, and video. In this paper, we build upon previous work by tackling the difficulty of evaluating models both quantitatively and qualitatively quickly. We present DIME (Dataset, Index, Model, Embedding), a modality-agnostic tool that handles multimodal datasets, trained models, and data preprocessors to support straightforward model comparison with a web browser graphical user interface. DIME inherently supports building modality-agnostic queryable indexes and extraction of relevant feature embeddings, and thus effectively doubles as an efficient cross-modal tool to explore and search through datasets.