Despite recent advances in diffusion models, top-tier text-to-image (T2I) models still struggle to achieve precise spatial layout control, *i.e.* accurately generating entities with specified attributes and locations. Segmentation-mask-to-image (S2I) generation has emerged as a promising solution by incorporating pixel-level spatial guidance and regional text prompts. However, existing S2I methods fail to simultaneously ensure semantic consistency and shape consistency. To address these challenges, we propose Seg2Any, a novel S2I framework built upon advanced multimodal diffusion transformers (*e.g.* FLUX). First, to achieve both semantic and shape consistency, we decouple segmentation mask conditions into regional semantic and high-frequency shape components. The regional semantic condition is introduced by a Semantic Alignment Attention Mask, ensuring that generated entities adhere to their assigned text prompts. The high-frequency shape condition, representing entity boundaries, is encoded as an Entity Contour Map and then introduced as an additional modality via multi-modal attention to guide image spatial structure. Second, to prevent attribute leakage across entities in multi-entity scenarios, we introduce an Attribute Isolation Attention Mask mechanism, which constrains each entity’s image tokens to attend exclusively to themselves during image self-attention. To support open-set S2I generation, we construct SACap-1M, a large-scale dataset containing 1 million images with 5.9 million segmented entities and detailed regional captions, along with a SACap-Eval benchmark for comprehensive S2I evaluation. Extensive experiments demonstrate that Seg2Any achieves state-of-the-art performance on both open-set and closed-set S2I benchmarks, particularly in fine-grained spatial and attribute control of entities.

A Bayes-rule based hierarchical binaural sound source localization system is proposed. By combining three localization cues: interaural time differences (ITDs), interaural intensity differences (IIDs), and spectral cues, and a hierarchical decision making structure, this system enables a sound source to be located in a 3D space by using only the binaural inputs. Preliminary simulations have shown the effectiveness of this system. It can be used in studying binaural localization mechanism and applications such as in hearing aids and robotics.

There is no such thing as a disembodied mind. We posit that cognitive development can only occur through interaction with the physical world. To this end, we are developing a robotic platform for the purpose of studying cognition. We suggest that the central component of cognition is a memory which is primarily associative, one where learning occurs as the correlation of events from diverse inputs. We also posit that human-like cognition requires a well-integrated sensory-motor system, to provide these diverse inputs. As implemented in our robot, this system includes binaural hearing, stereo vision, tactile sense, and basic proprioceptive control. On top of these abilities, we are implementing and studying various models of processing, learning and decision making. Our goal is to produce a robot that will learn to carry out simple tasks in response to natural language requests. The robot's understanding of language will be learned concurrently with its other cognitive abilities. We have already developed a robust system and conducted a number or experiments on the way to this goal, some details of which appear in this paper. This is a first progress report of what we believe will be a long term project with significant implications.

A robust linear phase unwrapping method is proposed to solve the 2/spl pi/ discontinuities in the phase of the cross power spectrum from the binaural inputs using two omnidirectional microphones. The relative incident angle of the interested sound is then estimated according to the time difference of arrival (TDOA) which is obtained from the unwrapped phase of the cross power spectrum. The frequency components associated with the high power are clustered into groups by the phase and frequency distance, and the dominant group is then used to obtain the initial slope estimation. The phase is unwrapped by checking the difference between the actual and the predicted phase by the estimated slope. The re-estimation is then performed by the unwrapped phase. The algorithm is tested under different incident angles and signal to noise ratio (SNR) using real speech signal and white Gaussian noise. The simulation results show the high accuracy and the robustness. This method is also Implemented to control a robot to adaptively adjust itself to the position facing the sound source directly. The satisfactory result was achieved in an open house demonstration.

This paper addresses the problem of hidden Markov model (HMM) topology estimation in the context of on-line handwriting recognition. HMM have been widely used in applications related to speech and handwriting recognition with great success. One major drawback with these approaches, however, is that the techniques that they use for estimating the topology of the models (number of states, connectivity between the states and the number of Gaussians), are usually heuristically derived, without optimal certainty. This paper addresses this problem, by comparing a couple of commonly used heuristically derived methods to an approach that uses the Bayesian information criterion (BIC) for computing the optimal topology. Experimental results on discretely written letters show that using BIC gives comparable results to heuristic approaches with a model that has nearly 10% fewer parameters.