In complex multi-speaker scenarios with significant speaker overlap and background noise, extracting the target speaker's speech remains a major challenge. This capability is crucial for dialogue-based applications such as AI speech assistants, where downstream tasks such as speech recognition depend on clean speech. A potential solution to address these challenges is Target Speaker Extraction (TSE), which leverages auxiliary information to extract target speech from mixed and noisy speech, thus overcoming the limitations of Speech Separation (SS) and Speech Enhancement (SE). In particular, we propose a multi-modal TSE network, namely Text Prompt Extractor with echo cue block (TPEech), which uses historical dialogue text as cues for extraction and incorporates the echo cue block (ECB) to further exploit this cue and enhance TSE performance. The experiments show the excellent extraction and denoising capabilities of our proposed network. TPEech achieves an SI-SDRi of 9.632 dB, an SDR of 13.045 dB, a PESQ of 2.814, and a STOI of 0.885, outperforming competitive baselines. Additionally, we experimentally verify that TPEech is robust against semantically incomplete textual prompts. Dataset and source code will be publicly available.

Photonic tensor cores (PTCs) are essential building blocks for optical artificial intelligence (AI) accelerators based on programmable photonic integrated circuits. Most PTC designs today are manually constructed, with low design efficiency and unsatisfying solution quality. This makes it challenging to meet various hardware specifications and keep up with rapidly evolving AI applications. Prior work has explored gradient-based methods to learn a good PTC structure differentiably. However, it suffers from slow training speed and optimization difficulty when handling multiple non-differentiable objectives and constraints. Therefore, in this work, we propose a more flexible and efficient zero-shot multi-objective evolutionary topology search framework ADEPT-Z that explores Pareto-optimal PTC designs with advanced devices in a larger search space. Multiple objectives can be co-optimized while honoring complicated hardware constraints. With only <3 hours of search, we can obtain tens of diverse Pareto-optimal solutions, 100× faster than the prior gradient-based method, outperforming prior manual designs with 2× higher accuracy weighted area-energy efficiency. The code of ADEPT-Z is available at link.

Multi-treatment multi-outcome treatment effect estimation plays a vital role in today's industry-level applications. For example, in social media ads, practitioners simultaneously deploy multiple interventions to users' experience and track multi-faceted metrics (e.g., ad performance, engagement, churn). However, existing methods for estimating treatment effects struggle to simultaneously address the complex interplays and ensure robust counterfactual balancing across treatment-outcome pairs.

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks.