With recent advancements in Large Language Models, task planning methods that interpret human commands have garnered significant attention. However, as home robots become more common, specifying every daily task could become impractical. This paper introduces a novel semantic map called the Task-Aware Semantic Map (TASMap), which enables robots to autonomously assign and propose necessary tasks in a scene without explicit human commands. The core innovation of this approach is the ability of TASMap to comprehend the context of objects within a scene and autonomously generate task proposals. This capability significantly advances autonomous robotic assistance, reducing the dependency on specific commands and enhancing interaction with environments. We present two key applications of TASMap: contextual task proposal and spatial task proposal. Our results, verified across 35 diverse and realistically disordered scenes, underscore the effectiveness of TASMap in both simulation and real-world environments.

Despite its practical importance across a wide range of modalities, recent advances in self-supervised learning (SSL) have been primarily focused on a few well-curated domains, e.g., vision and language, often relying on their domain-specific knowledge. For example, Masked Auto-Encoder (MAE) has become one of the popular architectures in these domains, but less has explored its potential in other modalities. In this paper, we develop MAE as a unified, modality-agnostic SSL framework. In turn, we argue meta-learning as a key to interpreting MAE as a modality-agnostic learner, and propose enhancements to MAE from the motivation to jointly improve its SSL across diverse modalities, coined MetaMAE as a result. Our key idea is to view the mask reconstruction of MAE as a meta-learning task: masked tokens are predicted by adapting the Transformer meta-learner through the amortization of unmasked tokens. Based on this novel interpretation, we propose to integrate two advanced meta-learning techniques. First, we adapt the amortized latent of the Transformer encoder using gradient-based meta-learning to enhance the reconstruction. Then, we maximize the alignment between amortized and adapted latents through task contrastive learning which guides the Transformer encoder to better encode the task-specific knowledge. Our experiment demonstrates the superiority of MetaMAE in the modality-agnostic SSL benchmark (called DABS), significantly outperforming prior baselines.

With the recent advances in NeRF-based 3D aware GANs quality, projecting an image into the latent space of these 3D-aware GANs has a natural advantage over 2D GAN inversion: not only does it allow multi-view consistent editing of the projected image, but it also enables 3D reconstruction and novel view synthesis when given only a single image. However, the explicit viewpoint control acts as a main hindrance in the 3D GAN inversion process, as both camera pose and latent code have to be optimized simultaneously to reconstruct the given image. Most works that explore the latent space of the 3D-aware GANs rely on ground-truth camera viewpoint or deformable 3D model, thus limiting their applicability. In this work, we introduce a generalizable 3D GAN inversion method that infers camera viewpoint and latent code simultaneously to enable multi-view consistent semantic image editing. The key to our approach is to leverage pre-trained estimators for better initialization and utilize the pixel-wise depth calculated from NeRF parameters to better reconstruct the given image. We conduct extensive experiments on image reconstruction and editing both quantitatively and qualitatively, and further compare our results with 2D GAN-based editing to demonstrate the advantages of utilizing the latent space of 3D GANs. Additional results and visualizations are available at https://3dgan-inversion.github.io/.

Recently, a so called orthogonal spatial multiplexing (OSM) scheme was presented which allows simple maximum likelihood decoding at the receiver with single phase feedback. In this paper, by serially concatenating this scheme by a linear precoder, a new closed-loop SM scheme is proposed for two transmit and two receive antennas. By computer simulation results, we show that the proposed scheme outperforms the conventional SM and OSM.

For energy-aware embedded and mobile processors, this paper proposes a new energy-effective design of the instruction fetch unit that exploits the fact that the per-access energy consumption decreases as the cache size decreases.

In this paper, we propose and analyze an adaptive modulation system with optimal turbo coded V-BLAST (vertical-bell-lab layered space-time) technique that adopts the extrinsic information from MAP (maximum a posteriori) decoder with iterative decoding as a priori probability in two decoding procedures of V-BLAST scheme; the ordering and the slicing. The result indicates that the proposed systems achieve a better throughput performance than the conventional systems in the whole SNR range. In addition, the simulation result shows that the maximum throughput improvement in each MIMO scheme is respectively about 421 kbps, 545 kbps, and 880 kbps. It is suggested that the effect of the proposed decoding algorithm accordingly gets higher as the number of system antenna increases.