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We demonstrate efficient spin transfer across a disordered interfacial layer that forms in low damping ferrimagnetic insulator lithium aluminum ferrite (LAFO) and tantalum bilayers. Despite the interfacial disorder, confirmed by transmission electron microscopy, we find a room temperature interfacial spin mixing conductance on the order of 1014 Ω−1m−2 similar to other LAFO-based bilayers with epitaxial interfaces. Broadband ferromagnetic resonance measurements confirm a linewidth broadening in LAFO following the addition of a Ta layer, consistent with the effects of spin pumping. Furthermore, the presence of spin current generated in the Ta layer by spin pumping is confirmed with inverse spin Hall effect measurements. Measurements of the Ta thickness dependence of the spin Hall magnetoresistance and the Gilbert damping enhancement indicate that the Ta spin diffusion length is on the order of 1 nm. This work not only provides a surprising example of efficient spin transport across a disordered interface but also demonstrates the potential for low damping spinel ferrites as a robust system for efficient spin wave spintronics. 

Modality fusion is a cornerstone of multimodal learning, enabling information integration from diverse data sources. However, vanilla fusion methods are limited by (1) inability to account for heterogeneous interactions between modalities and (2) lack of interpretability in uncovering the multimodal interactions inherent in the data. To this end, we propose I2MoE (Interpretable Multimodal Interaction-aware Mixture of Experts), an end-to-end MoE framework designed to enhance modality fusion by explicitly modeling diverse multimodal interactions, as well as providing interpretation on a local and global level. First, I2MoE utilizes different interaction experts with weakly supervised interaction losses to learn multimodal interactions in a data-driven way. Second, I2MoE deploys a reweighting model that assigns importance scores for the output of each interaction expert, which offers sample-level and dataset-level interpretation. Extensive evaluation of medical and general multimodal datasets shows that I2MoE is flexible enough to be combined with different fusion techniques, consistently improves task performance, and provides interpretation across various real-world scenarios. 

Abstract Machine learning methods are well established in the classification of quasars (QSOs). However, the advent of light-curve observations adds a great amount of complexity to the problem. Our goal is to use the Zwicky Transient Facility (ZTF) to create a catalog of QSOs. We process the ZTF DR20 light curves with a transformer artificial neural network and combine different surveys with extreme gradient boosting. Based on ZTFg-band and Wide-field Infrared Survey Explorer (WISE) observations, we find 4,849,574 objects classified as QSOs with confidence higher than 90% (QZO). We robustly classify objects fainter than the 5σsignal-to-noise ratio (SNR) limit atg= 20.8 by requiringg < n_obs/80 + 20.375. For 33% of QZO objects, with available WISE data, we publish redshifts with estimated error Δz/(1 + z) = 0.14. We find that ZTF classification is superior to the Pan-STARRS static bands, and on par with WISE and Gaia measurements, but the light curves provide the most important features for QSO classification in the ZTF data set. Using ZTFg-band data with at least 100 observational epochs per light curve, we obtain a 97% F1 score for QSOs. We find that with 3 day median cadence, a survey time span of at least 900 days is required to achieve a 90% QSO F1 score. However, one can obtain the same score with a survey time span of 1800 days and the median cadence prolonged to 12 days. 

Efficient spectrum use represents an important objective given the rapid growth in mobile data and emergence of Beyond-5G networks. ● NOAA passive radiometer receivers operating at the same millimeter-wave (mmWave) frequency used by COSMOS and 5G at 28 GHz and have experienced interference, particularly from a nearby bridge. ● We manually create interference using programmable 28 GHz COSMOS mobile phased array antenna modules (PAAMs) for the creation of Spectrum Consumption Models (SCMs). 

Massive MIMO has the potential to support demands of next generation networks and emerging applications such as V2V/V2X communication and augmented reality. ● Millimeter-Wave (mmWave) frequencies allow for larger bandwidth as well as compact form factor of antenna arrays with many elements. ● The COSMOS testbed has deployed indoor and outdoor 28GHz phased array antenna modules (PAAMs) to support experimentation with these emerging technologies. ● Mobile PAAMs have been developed to enable experimentation anywhere and with mobility. 

Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations-where LLMs direct the discourse and steer the conversation's objectives-remains under-explored. In this study, we first characterize LLM-guided conversation into three fundamental components: (i) Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and propose GuideLLM as an installation. We then implement an interviewing environment for the evaluation of LLM-guided conversation. Specifically, various topics are involved in this environment for comprehensive interviewing evaluation, resulting in around 1.4k turns of utterances, 184k tokens, and over 200 events mentioned during the interviewing for each chatbot evaluation. We compare GuideLLM with 6 state-of-the-art LLMs such as GPT-4o and Llama-3-70b-Instruct, from the perspective of interviewing quality, and autobiography generation quality. For automatic evaluation, we derive user proxies from multiple autobiographies and employ LLM-as-a-judge to score LLM behaviors. We further conduct a human-involved experiment by employing 45 human participants to chat with GuideLLM and baselines. We then collect human feedback, preferences, and ratings regarding the qualities of conversation and autobiography. Experimental results indicate that GuideLLM significantly outperforms baseline LLMs in automatic evaluation and achieves consistent leading performances in human ratings.