Nonlinear Photon Pair Generation in a Highly Dispersive Medium

Photon pair generation in silicon photonic integrated circuits relies on four wave mixing via the third order nonlinearity. Due to phase matching requirements and group velocity dispersion, this method has typically required TE polarized light. Here, we demonstrate TM polarized photon pair production in linearly uncoupled silicon resonators with more than an order of magnitude more dispersion than previous work. We achieve measured rates above 2.8 kHz and a heralded second order correlation of . This method enables phase matching in dispersive media and paves the way for novel entanglement generation in silicon photonic device

Use of ceragenins to create novel biofouling resistant water-treatment membranes.

Scoping studies have demonstrated that ceragenins, when linked to water-treatment membranes have the potential to create biofouling resistant water-treatment membranes. Ceragenins are synthetically produced molecules that mimic antimicrobial peptides. Evidence includes measurements of CSA-13 prohibiting the growth of and killing planktonic Pseudomonas fluorescens. In addition, imaging of biofilms that were in contact of a ceragenin showed more dead cells relative to live cells than in a biofilm that had not been treated with a ceragenin. This work has demonstrated that ceragenins can be attached to polyamide reverse osmosis (RO) membranes, though work needs to improve the uniformity of the attachment. Finally, methods have been developed to use hyperspectral imaging with multivariate curve resolution to view ceragenins attached to the RO membrane. Future work will be conducted to better attach the ceragenin to the RO membranes and more completely test the biocidal effectiveness of the ceragenins on the membranes

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report