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Abstract We present a method for capturing the BSSRDF (bidirectional scattering‐surface reflectance distribution function) of arbitrary geometry with a neural network. We demonstrate how a compact neural network can represent the full 8‐dimensional light transport within an object including heterogeneous scattering. We develop an efficient rendering method using importance sampling that is able to render complex translucent objects under arbitrary lighting. Our method can also leverage the common planar half‐space assumption, which allows it to represent one BSSRDF model that can be used across a variety of geometries. Our results demonstrate that we can render heterogeneous translucent objects under arbitrary lighting and obtain results that match the reference rendered using volumetric path tracing. 

Recent advancements in large language models (LLMs) have achieved promising performances across various applications. Nonetheless, the ongoing challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains. In this work, we introduce DALK, a.k.a. Dynamic Co-Augmentation of LLMs and KG, to address this limitation and demonstrate its ability on studying Alzheimer's Disease (AD), a specialized sub-field in biomedicine and a global health priority. With a synergized framework of LLM and KG mutually enhancing each other, we first leverage LLM to construct an evolving AD-specific knowledge graph (KG) sourced from AD-related scientific literature, and then we utilize a coarse-to-fine sampling method with a novel self-aware knowledge retrieval approach to select appropriate knowledge from the KG to augment LLM inference capabilities. The experimental results, conducted on our constructed AD question answering (ADQA) benchmark, underscore the efficacy of DALK. Additionally, we perform a series of detailed analyses that can offer valuable insights and guidelines for the emerging topic of mutually enhancing KG and LLM. 

A new growth approach, based on the hot-wall metalorganic chemical vapor deposition concept, is developed for high-quality homoepitaxial growth of Si-doped single-crystalline β-Ga2O3 layers on (010)-oriented native substrates. Substrate annealing in argon atmosphere for 1 min at temperatures below 600 °C is proposed for the formation of epi-ready surfaces as a cost-effective alternative to the traditionally employed annealing process in oxygen-containing atmosphere with a time duration of 1 h at about 1000 °C. It is shown that the on-axis rocking curve widths exhibit anisotropic dependence on the azimuth angle with minima for in-plane direction parallel to the [001] and maximum for the [100] for both substrate and layer. The homoepitaxial layers are demonstrated to have excellent structural properties with a β-Ga2O3(020) rocking curve full-widths at half-maximum as low as 11 arc sec, which is lower than the corresponding one for the substrates (19 arc sec), even for highly Si-doped (low 1019 cm−3 range) layers. Furthermore, the structural anisotropy in the layer is substantially reduced with respect to the substrate. Very smooth surface morphology of the epilayers with a root mean square roughness value of 0.6 nm over a 5 × 5 μm2 area is achieved along with a high electron mobility of 69 cm2 V−1 s−1 at a free carrier concentration n=1.9×1019 cm−3. These values compare well with state-of-the-art parameters reported in the literature for β-Ga2O3(010) homoepitaxial layers with respective Si doping levels. Thermal conductivity of 17.4 Wm−1K−1 is determined along the [010] direction for the homoepitaxial layers at 300 K, which approaches the respective value of bulk crystal (20.6 Wm−1K−1). This result is explained by a weak boundary effect and a low dislocation density in the homoepitaxial layers. 

Ultra-miniaturized microendoscopes are vital for numerous biomedical applications. Such minimally invasive imagers allow for navigation into hard-to-reach regions and observation of deep brain activity in freely moving animals. Conventional solutions use distal microlenses. However, as lenses become smaller and less invasive, they develop greater aberrations and restricted fields of view. In addition, most of the imagers capable of variable focusing require mechanical actuation of the lens, increasing the distal complexity and weight. Here, we demonstrate a distal lens-free approach to microendoscopy enabled by computational image recovery. Our approach is entirely actuation free and uses a single pseudorandom spatial mask at the distal end of a multicore fiber. Experimentally, this lensless approach increases the space-bandwidth product, i.e., field of view divided by resolution, by threefold over a best-case lens- based system. In addition, the microendoscope demonstrates color resolved imaging and refocusing to 11 distinct depth planes from a single camera frame without any actuated parts.