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Millimeter-wave (mmWave) sensing has emerged as a promising technology for non-contact health monitoring, offering high spatial resolution, material sensitivity, and integration potential with wireless platforms. While prior work has focused on specific applications or signal processing methods, a unified understanding of how mmWave signals map to clinically relevant biomarkers remains lacking. This survey presents a full-stack review of mmWave-based medical sensing systems, encompassing signal acquisition, physical feature extraction, modeling strategies, and potential medical and healthcare uses. We introduce a taxonomy that decouples low-level mmWave signal features—such as motion, material property, and structure—from high-level biomedical biomarkers, including respiration pattern, heart rate, tissue hydration, and gait. We then classify and contrast the modeling approaches—ranging from physics-driven analytical models to machine learning techniques—that enable this mapping. Furthermore, we analyze representative studies across vital signs monitoring, cardiovascular assessment, wound evaluation, and neuro-motor disorders. By bridging wireless sensing and medical interpretation, this work offers a structured reference for designing next-generation mmWave health monitoring systems. We conclude by discussing open challenges, including model interpretability, clinical validation, and multimodal integration. 

Real-time, all-electronic control of non-Newtonian fluid flow through a microscale channel is crucial for various applications in manufacturing and healthcare. However, existing methods lack the sensitivity required for accurate measurement and the real-time responsiveness necessary for effective adjustment. Here, we demonstrate an all-electronic system that enables closed-loop, real-time, high-sensitivity control of various waveforms of non-Newtonian fluid flow (0.76 μl min−1) through a micro-sized outlet. Our approach combines a contactless, cuff-like flow sensor with a neural-network control program. This system offers a simple, miniaturized, versatile, yet high-performance solution for non-Newtonian fluid flow control, easily integrated into existing setups. 

A common failure mode for policies trained with imitation is compounding execution errors at test time. When the learned policy encounters states that are not present in the expert demonstrations, the policy fails, leading to degenerate behavior. The Dataset Aggregation, or DAgger approach to this problem simply collects more data to cover these failure states. However, in practice, this is often prohibitively expensive. In this work, we propose Diffusion Meets DAgger (DMD), a method that reaps the benefits of DAgger but without the cost, for eye-in-hand imitation learning problems. Instead of collecting new samples to cover out-of-distribution states, DMD uses recent advances in diffusion models to synthesize these samples. This leads to robust performance from few demonstrations. We compare DMD against behavior cloning baseline across four tasks: pushing, stacking, pouring, and hanging a shirt. In pushing, DMD achieves 80% success rate with as few as 8 expert demonstrations, where naive behavior cloning reaches only 20%. In stacking, DMD succeeds on average 92% of the time across 5 cups, versus 40% for BC. When pouring coffee beans, DMD transfers to another cup successfully 80% of the time. Finally, DMD attains 90% success rate for hanging shirt on a clothing rack. 

Large language models have gained significant popularity and are often provided as a service (i.e., LLMaaS). Companies like OpenAI and Google provide online APIs of LLMs to allow downstream users to create innovative applications. Despite its popularity, LLM safety and quality assurance is a well-recognized concern in the real world, requiring extra efforts for testing these LLMs. Unfortunately, while end-to-end services like ChatGPT have garnered rising attention in terms of testing, the LLMaaS embeddings have comparatively received less scrutiny. We state the importance of testing and uncovering problematic individual embeddings without considering downstream applications. The abstraction and non-interpretability of embedded vectors, combined with the black-box inaccessibility of LLMaaS, make testing a challenging puzzle. This paper proposes COSTELLO, a black-box approach to reveal potential defects in abstract embedding vectors from LLMaaS bycontrastive testing. Our intuition is that high-quality LLMs can adequately capture the semantic relationships of the input texts and properly represent their relationships in the high-dimensional space. For the given interface of LLMaaS and seed inputs, COSTELLO can automatically generate test suites and output words with potential problematic embeddings. The idea is to synthesize contrastive samples with guidance, including positive and negative samples, by mutating seed inputs. Our synthesis guide will leverage task-specific properties to control the mutation procedure and generate samples with known partial relationships in the high-dimensional space. Thus, we can compare the expected relationship (oracle) and embedding distance (output of LLMs) to locate potential buggy cases. We evaluate COSTELLO on 42 open-source (encoder-based) language models and two real-world commercial LLMaaS. Experimental results show that COSTELLO can effectively detect semantic violations, where more than 62% of violations on average result in erroneous behaviors (e.g., unfairness) of downstream applications.