ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training

We propose ProtLLM, a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. ProtLLM features a unique dynamic protein mounting mechanism, enabling it to handle complex inputs where the natural language text is interspersed with an arbitrary number of proteins. Besides, we propose the protein-as-word language modeling approach to train ProtLLM. By developing a specialized protein vocabulary, we equip the model with the capability to predict not just natural language but also proteins from a vast pool of candidates. Additionally, we construct a large-scale interleaved protein-text dataset, named InterPT, for pre-training. This dataset comprehensively encompasses both (1) structured data sources like protein annotations and (2) unstructured data sources like biological research papers, thereby endowing ProtLLM with crucial knowledge for understanding proteins. We evaluate ProtLLM on classic supervised protein-centric tasks and explore its novel protein-language applications. Experimental results demonstrate that ProtLLM not only achieves superior performance against protein-specialized baselines on protein-centric tasks but also induces zero-shot and in-context learning capabilities on protein-language tasks.Comment: https://protllm.github.io/project

Effect of rubber particles and fibers on the dynamic compressive behavior of novel ultra-lightweight cement composites:Numerical simulations and metamodeling

This paper presents, first, a finite element (FE) model for a rubberized ultra-lightweight cement composite (RULCC), which uses a modified Holmquist-Johnson-Concrete (H-J-C) constitutive law that is calibrated and validated by new Split Hopkinson pressure bar (SHPB) tests on the material. The validated FE model is used then as the core of a cloud computing platform using a multi node cloud simulation framework to carry out the parametric simulations, which generate required data to develop a meta-model to predict the dynamic impact strength of the RULCC. Design of experiment (DoE) and Generic Programming techniques are the main instruments in developing meta-models with reduced size of data. Finally, a meta-model of explicit expression, which is the first of its kind and considers the effect of rubber ratio, fiber ratio and dynamic impact strain rate, is proposed to predict the dynamic impact strength of the RULCC

A Highly-efficient Lattice-based Post-Quantum Cryptography Processor for IoT Applications

Lattice-Based Cryptography (LBC) schemes, like CRYSTALS-Kyber and CRYSTALS-Dilithium, have been selected to be standardized in the NIST Post-Quantum Cryptography standard. However, implementing these schemes in resourceconstrained Internet-of-Things (IoT) devices is challenging, considering efficiency, power consumption, area overhead, and flexibility to support various operations and parameter settings. Some existing ASIC designs that prioritize lower power and area can not achieve optimal performance efficiency, which are not practical for battery-powered devices. Custom hardware accelerators in prior co-processor and processor designs have limited applications and flexibility, incurring significant area and power overheads for IoT devices. To address these challenges, this paper presents an efficient lattice-based cryptography processor with customized Single-Instruction-Multiple-Data (SIMD) instruction. First, our proposed SIMD architecture supports efficient parallel execution of various polynomial operations in 256-bit mode and acceleration of Keccak in 320-bit mode, both utilizing efficiently reused resources. Additionally, we introduce data shuffling hardware units to resolve data dependencies within SIMD data. To further enhance performance, we design a dual-issue path for memory accesses and corresponding software design methodologies to reduce the impact of data load/store blocking. Through a hardware/software co-design approach, our proposed processor achieves high efficiency in supporting all operations in lattice-based cryptography schemes. Evaluations of Kyber and Dilithium show our proposed processor achieves over 10x speedup compared with the baseline RISC-V processor and over 5x speedup versus ARM Cortex M4 implementations, making it a promising solution for securing IoT communications and storage. Moreover, Silicon synthesis results show our design can run at 200 MHz with 2.01 mW for Kyber KEM 512 and 2.13 mW for Dilithium 2, which outperforms state-of-the-art works in terms of PPAP (Performance x Power x Area)