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We present two spatial-multiplexed switch-and-select (S&S) 8x8 optical switches incorporating a tri-layer SiN-on-Si platform, one equipped with thermo-optic (T-O) and the other electro-optic (E-O) switching elements. To the best of our knowledge, the electro-optic switch fabric is the first-of-its-kind device assembled in such a multi-layer platform. The shuffle between the multiplexer and demultiplexer array is established via a tri-layer Si-SiN-SiN structure, creating a three-dimensional crossing-free photonic shuffle network. At the same time, the implementation of the S&S topology can effectively suppress the first-order crosstalk. The measured on-chip losses for the T-O switch range from 2.1 to 11.5 dB, with a 5.2 dB average, while the E-O device exhibits losses between 8.7 to 19.6 dB, with a 15.1 dB average. Both switches demonstrate ultra-low crosstalk, with measured ranges of 38.9 to 50.8 dB and 42.8 to 51.9 dB, for the T-O and E-O devices respectively. The switching times are 17.6 us for the T-O switch and 5.9 ns with the E-O actuated one. These performance metrics highlight the potential of these switches for next-generation data center applications.

Optical spectrometers are fundamental across numerous disciplines in science and technology. However, miniaturized versions, while essential for in situ measurements, are often restricted to coarse identification of signature peaks and inadequate for metrological purposes. Here, we introduce a new class of spectrometer, leveraging the convolution theorem as its mathematical foundation. Our convolutional spectrometer offers unmatched performance for miniaturized systems and distinct structural and computational simplicity, featuring a centimeter-scale footprint for the fully packaged unit, low cost (~$10) and a 2400 cm-1 (approximately 500 nm) bandwidth. We achieve excellent precision in resolving complex spectra with sub-second sampling and processing time, demonstrating a wide range of applications from industrial and agricultural analysis to healthcare monitoring. Specifically, our spectrometer system classifies diverse solid samples, including plastics, pharmaceuticals, coffee, flour and tea, with 100% success rate, and quantifies concentrations of aqueous and organic solutions with detection accuracy surpassing commercial benchtop spectrometers. We also realize the non-invasive sensing of human biomarkers, such as skin moisture (mean absolute error; MAE = 2.49%), blood alcohol (1.70 mg/dL), blood lactate (0.81 mmol/L), and blood glucose (0.36 mmol/L), highlighting the potential of this new class of spectrometers for low-cost, high-precision, portable/wearable spectral metrology.

Vision-Language Models (VLMs) have demonstrated remarkable capabilities in understanding multimodal inputs and have been widely integrated into Retrieval-Augmented Generation (RAG) based conversational systems. While current VLM-powered chatbots can provide textual source references in their responses, they exhibit significant limitations in referencing contextually relevant images during conversations. In this paper, we introduce Contextual Image Reference -- the ability to appropriately reference relevant images from retrieval documents based on conversation context -- and systematically investigate VLMs' capability in this aspect. We conduct the first evaluation for contextual image referencing, comprising a dedicated testing dataset and evaluation metrics. Furthermore, we propose ImageRef-VL, a method that significantly enhances open-source VLMs' image referencing capabilities through instruction fine-tuning on a large-scale, manually curated multimodal conversation dataset. Experimental results demonstrate that ImageRef-VL not only outperforms proprietary models but also achieves an 88% performance improvement over state-of-the-art open-source VLMs in contextual image referencing tasks. Our code is available at https://github.com/bytedance/ImageRef-VL.

Silicon photonic switches are widely considered as a cost-effective solution for addressing the ever-growing data traffic in datacenter networks, as they offer unique advantages such as low power consumption, low latency, small footprint and high bandwidth. Despite extensive research efforts, crosstalk in large-scale photonic circuits still poses a threat to the signal integrity. In this paper, we present two designs of silicon Mach-Zehnder Interferometer (MZI) switches achieving ultra-low-crosstalk, driven thermally and electrically. Each switch fabric is optimized at both the device and circuit level to suppress crosstalk and reduce system complexity. Notably, for the first time to the best of our knowledge, we harness the inherent self-heating effect in a carrier-injection-based MZI switch to create a pair of phase shifters that offer arbitrary phase differences. Such a pair of phase shifters induces matched insertion loss at each arm, thus minimizing crosstalk. Experimentally, an ultra-low crosstalk ratio below -40 dB is demonstrated for both thermo-optic (T-O) and electro-optic (E-O) switches. The T-O switch exhibits an on-chip loss of less than 5 dB with a switching time of 500 microseconds, whereas the E-O switch achieves an on-chip loss as low as 8.5 dB with a switching time of under 100 ns. In addition, data transmission of a 50 Gb/s on-off keying signal is demonstrated with high fidelity on the E-O switch, showing the great potential of the proposed switch designs., Comment: 12 pages, 5 figures

Miniaturized spectrometers hold great promise for in situ, in vitro, and even in vivo sensing applications. However, their size reduction imposes vital performance constraints in meeting the rigorous demands of spectroscopy, including fine resolution, high accuracy, and ultra-wide observation window. The prevailing view in the community holds that miniaturized spectrometers are most suitable for the coarse identification of signature peaks. In this paper, we present an integrated reconstructive spectrometer that enables near-infrared (NIR) spectroscopic metrology, and demonstrate a fully packaged sensor with auxiliary electronics. Such a sensor operates over a 520 nm bandwidth together with a resolution of less than 8 pm, which translates into a record-breaking bandwidth-to-resolution ratio of over 65,000. The classification of different types of solid substances and the concentration measurement of aqueous and organic solutions are performed, all achieving approximately 100% accuracy. Notably, the detection limit of our sensor matches that of the commercial benchtop counterparts, which is as low as 0.1% (i.e. 100 mg/dL) for identifying the concentration of glucose solution.

Recent years have seen the rapid development of miniaturized reconstructive spectrometers (RSs), yet they still confront a range of technical challenges, such as bandwidth/resolution ratio, sensing speed, and/or power efficiency. Reported RS designs often suffer from insufficient decorrelation between sampling channels, which results in limited compressive sampling efficiency, in essence, due to inadequate engineering of sampling responses. This in turn leads to poor spectral-pixel-to-channel ratios (SPCRs), typically restricted at single digits. So far, there lacks a general guideline for manipulating RS sampling responses for the effectiveness of spectral information acquisition. In this study, we shed light on a fundamental parameter from the compressive sensing theory - the average mutual correlation coefficient v - and provide insight into how it serves as a critical benchmark in RS design with regards to the SPCR and reconstruction accuracy. To this end, we propose a novel RS design with multi-resonant cavities, consisting of a series of partial reflective interfaces. Such multi-cavity configuration offers an expansive parameter space, facilitating the superlative optimization of sampling matrices with minimized v. As a proof-of-concept demonstration, a single-shot, dual-band RS is implemented on a SiN platform, tailored for capturing signature spectral shapes across different wavelength regions, with customized photonic crystal nanobeam mirrors. Experimentally, the device demonstrates an overall operation bandwidth of 270 nm and a <0.5 nm resolution with only 15 sampling channels per band, leading to a record high SPCR of 18.0. Moreover, the proposed multi-cavity design can be readily adapted to various photonic platforms. For instance, we showcase that by employing multi-layer coatings, an ultra-broadband RS can be optimized to exhibit a 700 nm bandwidth with an SPCR of over 100.

With the rapid development of artificial intelligence, large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning. This has sparked significant interest in applying LLMs to enhance various aspects of healthcare, ranging from medical education to clinical decision support. However, medicine involves multifaceted data modalities and nuanced reasoning skills, presenting challenges for integrating LLMs. This paper provides a comprehensive review on the applications and implications of LLMs in medicine. It begins by examining the fundamental applications of general-purpose and specialized LLMs, demonstrating their utilities in knowledge retrieval, research support, clinical workflow automation, and diagnostic assistance. Recognizing the inherent multimodality of medicine, the review then focuses on multimodal LLMs, investigating their ability to process diverse data types like medical imaging and EHRs to augment diagnostic accuracy. To address LLMs' limitations regarding personalization and complex clinical reasoning, the paper explores the emerging development of LLM-powered autonomous agents for healthcare. Furthermore, it summarizes the evaluation methodologies for assessing LLMs' reliability and safety in medical contexts. Overall, this review offers an extensive analysis on the transformative potential of LLMs in modern medicine. It also highlights the pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice. Visit https://github.com/mingze-yuan/Awesome-LLM-Healthcare for an accompanying GitHub repository containing latest papers., Comment: 24 pages, 1 figure, 3 tables

Singlet fission (SF) is a very significant photophysical phenomenon and possesses potential applications. In this work, we try to give the rather detailed theoretical investigation of the SF process in the stacked polyacene dimer by combining the high-level quantum chemistry calculations, and the quantum dynamics simulations based on the tensor train decomposition method. Starting from the construction of the linear vibronic coupling model, we explore the pure electronic dynamics and the vibronic dynamics in the SF processes. The role of vibrational modes in nonadiabatic dynamics is addressed. The results show that the super-exchange mechanism mediated by the charge-transfer state is found in both pure electronic dynamics and the nonadiabatic dynamics. Particularly, the vibrational modes with the frequency resonance with the adiabatic energy gap play very import roles in the SF dynamics. This work not only provides a deep and detailed understanding of the SF process, but also verifies the efficiency of the tensor train decomposition method that can serve as the reference dynamics method to explore the dynamics behaviors of complex systems.

Community search is a personalized community discovery problem aimed at finding densely-connected subgraphs containing the query vertex. In particular, the search for communities with high-importance vertices has recently received a great deal of attention. However, existing works mainly focus on conventional homogeneous networks where vertices are of the same type, but are not applicable to heterogeneous information networks (HINs) composed of multi-typed vertices and different semantic relations, such as bibliographic networks. In this paper, we study the problem of high-importance community search in HINs. A novel community model is introduced, named heterogeneous significant community (HSC), to unravel the closely connected vertices of the same type with high attribute values through multiple semantic relationships. An HSC not only maximizes the exploration of indirect relationships across entities of the anchor-type but incorporates their significance. To search the HSCs, we first develop online algorithms by exploiting both segmented-based meta-path expansion and significance increment. Specially, a solution space reuse strategy based on structural nesting is designed to boost the efficiency. In addition, we further devise a two-level index to support searching HSCs in optimal time, based on which a space-efficient compact index is proposed. Extensive experiments on real-world large-scale HINs demonstrate that our solutions are effective and efficient for searching HSCs, and the index-based algorithms are 2-4 orders of magnitude faster than online algorithms., Comment: 14 pages, 11figures

Objective: Intensity-modulated radiation therapy (IMRT) beam angle optimization (BAO) is a challenging combinatorial optimization problem that is NP-hard. In this study, we aim to develop a personalized BAO algorithm for IMRT that improves the quality of the final treatment. Methods: To improve the quality of IMRT treatment planning, we propose a deep reinforcement learning (DRL)-based approach for IMRT BAO. We consider the task as a sequential decision-making problem and formulate it as a Markov Decision Process. To facilitate the training process, a 3D-Unet is designed to predict the dose distribution for the different number of beam angles, ranging from 1 to 9, to simulate the IMRT environment. By leveraging the simulation model, double deep-Q network (DDQN) and proximal policy optimization (PPO) are used to train agents to select the personalized beam angle sequentially within a few seconds. Results: The treatment plans with beam angles selected by DRL outperform those with clinically used evenly distributed beam angles. For DDQN, the overall average improvement of the CIs is 0.027, 0.032, and 0.03 for 5, 7, and 9 beam angles respectively. For PPO, the overall average improvement of CIs is 0.045, 0.051, and 0.025 for 5, 7, and 9 beam angles respectively. Conclusion: The proposed DRL-based beam angle selection strategy can generate personalized beam angles within a few seconds, and the resulting treatment plan is superior to that obtained using evenly distributed angles. Significance: A fast and automated personalized beam angle selection approach is been proposed for IMRT BAO.

Accurate trajectory prediction of vehicles is essential for reliable autonomous driving. To maintain consistent performance as a vehicle driving around different cities, it is crucial to adapt to changing traffic circumstances and achieve lifelong trajectory prediction model. To realize it, catastrophic forgetting is a main problem to be addressed. In this paper, a divergence measurement method based on conditional Kullback-Leibler divergence is proposed first to evaluate spatiotemporal dependency difference among varied driving circumstances. Then based on generative replay, a novel lifelong vehicle trajectory prediction framework is developed. The framework consists of a conditional generation model and a vehicle trajectory prediction model. The conditional generation model is a generative adversarial network conditioned on position configuration of vehicles. After learning and merging trajectory distribution of vehicles across different cities, the generation model replays trajectories with prior samplings as inputs, which alleviates catastrophic forgetting. The vehicle trajectory prediction model is trained by the replayed trajectories and achieves consistent prediction performance on visited cities. A lifelong experiment setup is established on four open datasets including five tasks. Spatiotemporal dependency divergence is calculated for different tasks. Even though these divergence, the proposed framework exhibits lifelong learning ability and achieves consistent performance on all tasks., Comment: 12pages,7 figures