Lightmatter (United States)

The rising computational demands of Artificial Intelligence (AI) are driving a rapid surge in carbon emissions from the Information and Communications Technology (ICT) sector. Traditional CMOS-based computing is reaching its scaling limits and struggling to meet these immense demands in a carbon-sustainable manner—underscoring the need for alternative hardware solutions for AI. Photonic computing has emerged as a promising alternative to CMOS through its energy-efficient computing capabilities in the optical domain. This perspective explores the potential of photonics as a carbon-sustainable solution for the next-generation AI hardware. The growing computational demands of AI are significantly increasing carbon emissions from the ICT sector, while traditional CMOS-based computing faces scaling and sustainability limits. This perspective explores photonic computing as a promising, energy-efficient alternative for enabling carbon-sustainable next-generation AI hardware.

This integrative literature review analyzes the corpus of biology education research published in the main biology education journals of major professional societies. The goal of this analysis is to determine which approaches (including groups of focus, research methods, and settings/perspectives) from social science fields (i.e., psychology, sociology, and anthropology) are utilized in published peer-reviewed biology education research relating to diversity, equity, and inclusion (DEI). Scoping how social science approaches are used in this area is important to understanding whether biology education research could benefit from complementary approaches that might advance praxis. This analysis found that research informing the biology education community draws heavily from psychological perspectives that are overwhelmingly not disaggregated (78% of articles identifying a group lumped the participant together), are by far more quantitative (58% used survey, 26% grades, 20% school data) than qualitative (17% used interview, 10% observation), and did not adopt structural approaches (72%). The addition of missing contributions from social science is critical to advancing interventions to broaden STEM participation, given that merging paradigms can offer more robust, multi-level explanations for observed phenomena. This has important implications for education, biology education, biology education research, social science, and research in related STEM fields.

Ultra-large ‘building block’ catalogs provide inexpensive access to billions of synthesis-on-demand molecules, but the combinatorial scale renders conventional virtual screening impractical. We present Vector Virtual Screen (VVS), a score-function-agnostic machine learning framework for efficient navigation of combinatorial libraries and rapid identification of promising molecules for experimental validation. VVS comprises four key innovations: (i) the Embedding Decomposer, which factors molecules into building blocks in latent space; (ii) ChemRank, a correlation-based loss that improves retrieval precision; (iii) BBKNN, an algorithm for nearest-neighbor search directly in building block space; and (iv) a multi-scale hill-climbing algorithm for gradient-based navigation of molecular embedding vector databases. Across diverse scoring functions, VVS consistently outperforms existing methods in retrieving high-scoring molecules while evaluating only a fraction of the library, achieving orders-of-magnitude runtime improvements. By turning ultra-large libraries into tractable search spaces, VVS enables virtual screening to keep pace with the rapid expansion of chemical space and adapt seamlessly to future advances in scoring functions.

We report the recent progress of waveguide-coupled O-band GeSi quantum confined stark effect electroabsorption modulators, monolithically integrated in a Si photonics platform on 300 mm Silicon-on-insulator wafers with 220 nm thick Si top layer. A wafer-scale analysis of static insertion loss (IL) and extinction ratio (ER) is presented, showing IL down to 7.5 dB with ER of 5 dB for a 36.8 µm long device, at drive voltages of 2 V peak-to-peak. Modulation bandwidths beyond 50 GHz are demonstrated, with an extracted junction capacitance of 57 fF and series resistance of 8.3 Ω. Finally, open eye diagrams are demonstrated for non-return-to-zero on-off keying (NRZ-OOK) modulation for data rates from 40 Gb/s up to 64 Gb/s, with dynamic extinction ratio of 2.5 dB, at 1320 nm wavelength.

This study aims to assess the in vitro durability of Nitinol retainers, manufactured using computer-aided methods with hydrophilic or superhydrophilic surfaces to reduce debonding, alongside a commonly used composite adhesive. The 112 lower incisor teeth were embedded in blocks in pairs. Retainer wires were made up of 0.018 × 0.018 inch Nickel Titanium alloy(G4™ Nickel Titanium G&H Orthodontics, USA) by bending a robot arm. A total of 16 teeth(8 blocks) were used for each of the mentioned 7 groups Ni-Ti Retainer; Laser Textured Ni-Ti Retainer; Laser Texturing and Atmospheric Plasma Applicated Ni-Ti Retainer; Atmospheric Plasma Applicated Ni-Ti Retainer; Laser Texturing and Atmospheric Plasma Applicated Ni-Ti Retainer*2; Laser Texturing and Atmospheric Plasma Applicated Ni-Ti Retainer*3; SS-0.0018“(Morelli, Brazil). Transbond LR(3 M Unitek, California) was used. The shear bond strength tests were conducted. The Kruskal-Wallis H test was employed, pairwise comparisons followed by Dunn’s test with Bonferroni correction as a post-hoc analysis. There was no statistically significant difference between the groups for maximum force and maximum stress(p > 0.05). However, a significant difference was found in maximum elongation (p:0.0023). Pairwise comparisons highlighted significantly higher elongation values in the SS-0.0018” group. The stainless-steel wire demonstrated higher elongation values, which may offer clinical advantages in cases with higher occlusal forces and periodontal problems due to its material flexibility. Laser Texturing and Atmospheric Plasma Applied Ni-Ti Retainers exhibited higher test performance. Surface treatments applied to CAD/CAM retainers can provide an advantage by enhancing bond strength, potentially reducing the risk of debonding. These findings underline the importance of material selection and surface treatments in optimizing fixed retention strategies for long-term clinical success.

We report silicon microring transmitter and receiver with monolithically integrated driver and analog front end at up to 64Gbps NRZ. The total die area of the circuits is 0.006 mm 2, 10x smaller than prior reports.

Guangyi Liu, Zeyu Feng, Yuan Gao, Zichao Yang, Xiaodan Liang, Junwei Bao, Xiaodong He, Shuguang Cui, Zhen Li, Zhiting Hu. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

In contrast to efforts focusing on improving inclusion in STEM classrooms from kindergarten through undergraduate (K–16), efforts to improve inclusion in scientific meetings and conferences, important hubs of STEM culture, are more recent. Markers of inclusion that are sometimes overlooked at these events can include the composition of panels, how workshops are run, the affordability of conferences, and various other mechanisms that maintain pre-existing hierarchies and norms that limit the participation of early-career researchers and individuals of minoritized cultural, linguistic, and economic backgrounds. The Inclusive Environments and Metrics in Biology Education and Research (iEMBER) network coordinates efforts of researchers from many fields interested in diversity and inclusion in biology education. Given the concerns regarding inclusion at professional meetings, iEMBER has developed and implemented several practices in planning and executing our meetings to make them more inclusive. In this report, we share our experiences developing inclusive meetings on biology education research and discuss the outcomes of such efforts. Specifically, we present our approach to planning and executing the iEMBER 2019 conference and the National Association of Biology Teachers iEMBER 2019 workshop. This report adds to the growing body of resources on inclusive meetings, provides readers with an account of how such an attempt at implementation might unfold, and complements existing theories and work relating to the importance and functioning of such meetings in terms of representation in STEM.

Since its invention by McEliece in 1978, cryptography based on Error Correcting Codes (ECC) has suffered from the reputation of not being suitable for constrained devices. Indeed, McEliece's scheme and its variants have large public keys and relatively long ciphertexts. Recent works on these downsides explored the possible use of ECC based on rank metric instead of Hamming metric. These codes were introduced in the late 80's to eliminate errors with repeating patterns, regardless of their Hamming weight. Numerous proposals for the NIST Post-Quantum Cryptography (PQC) competition rely on these codes. It has been proven that lattice-based cryptography and even hash-based signatures can run on lightweight devices, but the question remains for code-based cryptography. In this work, we demonstrate that this is actually possible for rank metric: we have implemented the encryption operation of 5 schemes based on ECC in rank metric and made them run on an Arm Cortex-M0 processor, the smallest Arm processor available. We describe the technical difficulties of porting rank-based cryptography to a resource-constrained device while maintaining decent performance and a suitable level of security against side-channel attacks, especially timing attacks.

Current neural networks for 3D object recognition are vulnerable to 3D rotation. Existing works mostly rely on massive amounts of rotation-augmented data to alleviate the problem, which lacks solid guarantee of the 3D rotation invariance. In this paper, we address the issue by introducing a novel point cloud representation that can be mathematically proved rigorously rotation-invariant, i.e., identical point clouds in different orientations are unified as a unique and consistent representation. Moreover, the proposed representation is conditional information-lossless, because it retains all necessary information of point cloud except for orientation information. In addition, the proposed representation is complementary with existing network architectures for point cloud and fundamentally improves their robustness against rotation transformation. Finally, we propose a deep hierarchical cluster network called ClusterNet to better adapt to the proposed representation. We employ hierarchical clustering to explore and exploit the geometric structure of point cloud, which is embedded in a hierarchical structure tree. Extensive experimental results have shown that our proposed method greatly outperforms the state-of-the-arts in rotation robustness on rotation-augmented 3D object classification benchmarks.