Sound Absorption by Acoustic Microlattice with Optimized Pore Configuration

Sound absorption or dissipation principally involves joint interactions between sound waves, material morphology and the air medium. How these elements work most efficiently for sound absorption remains elusive to date. In this paper, we suggest a fundamental relation concisely cross-linking the three elements, which reveals that optimal sound absorption efficiency occurs when the pore size of the material is twice the thickness of the viscous boundary layer of the acoustic air medium. The study is validated by microlattice materials comprising of well-controlled regular structures that absorb sound in a tunable manner. Optimized material morphology in terms of pore size and porosity is determined to provide a robust guidance for optimizing sound absorbing materials.Comment: 14 pages, 7 figure

Multiphysics modeling and simulation for large-scale integrated circuits

This dissertation is a process of seeking solutions to two important and challenging problems related to the design of modern integrated circuits (ICs): the ever increasing couplings among the multiphysics and the large problem size arising from the escalating complexity of the designs. A multiphysics-based computer-aided design methodology is proposed and realized to address multiple aspects of a design simultaneously, which include electromagnetics, heat transfer, fluid dynamics, and structure mechanics. The multiphysics simulation is based on the finite element method for its unmatched capabilities in handling complicate geometries and material properties. The capability of the multiphysics simulation is demonstrated through its applications in a variety of important problems, including the static and dynamic IR-drop analyses of power distribution networks, the thermal-ware high-frequency characterization of through-silicon-via structures, the full-wave electromagnetic analysis of high-power RF/microwave circuits, the modeling and analysis of three-dimensional ICs with integrated microchannel cooling, the characterization of micro- and nanoscale electrical-mechanical systems, and the modeling of decoupling capacitor derating in the power integrity simulations. To perform the large-scale analysis in a highly efficient manner, a domain decomposition scheme, parallel computing, and an adaptive time-stepping scheme are incorporated into the proposed multiphysics simulation. Significant reduction in computation time is achieved through the two numerical schemes and the parallel computing with multiple processors

Large-Scale Discrete Fourier Transform on TPUs

In this work, we present two parallel algorithms for the large-scale discrete Fourier transform (DFT) on Tensor Processing Unit (TPU) clusters. The two parallel algorithms are associated with two formulations of DFT: one is based on the Kronecker product, to be specific, dense matrix multiplications between the input data and the Vandermonde matrix, denoted as KDFT in this work; the other is based on the famous Cooley-Tukey algorithm and phase adjustment, denoted as FFT in this work. Both KDFT and FFT formulations take full advantage of TPU's strength in matrix multiplications. The KDFT formulation allows direct use of nonuniform inputs without additional step. In the two parallel algorithms, the same strategy of data decomposition is applied to the input data. Through the data decomposition, the dense matrix multiplications in KDFT and FFT are kept local within TPU cores, which can be performed completely in parallel. The communication among TPU cores is achieved through the one-shuffle scheme in both parallel algorithms, with which sending and receiving data takes place simultaneously between two neighboring cores and along the same direction on the interconnect network. The one-shuffle scheme is designed for the interconnect topology of TPU clusters, minimizing the time required by the communication among TPU cores. Both KDFT and FFT are implemented in TensorFlow. The three-dimensional complex DFT is performed on an example of dimension $8192 \times 8192 \times 8192$ with a full TPU Pod: the run time of KDFT is 12.66 seconds and that of FFT is 8.3 seconds. Scaling analysis is provided to demonstrate the high parallel efficiency of the two DFT implementations on TPUs

Enable Language Models to Implicitly Learn Self-Improvement From Data

Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that there is always room for improvement in the quality of model responses. To address this challenge, various approaches have been proposed to enhance the performance of LLMs. There has been a growing focus on enabling LLMs to self-improve their response quality, thereby reducing the reliance on extensive human annotation efforts for collecting diverse and high-quality training data. Recently, prompting-based methods have been widely explored among self-improvement methods owing to their effectiveness, efficiency, and convenience. However, those methods usually require explicitly and thoroughly written rubrics as inputs to LLMs. It is expensive and challenging to manually derive and provide all necessary rubrics with a real-world complex goal for improvement (e.g., being more helpful and less harmful). To this end, we propose an ImPlicit Self-ImprovemenT (PIT) framework that implicitly learns the improvement goal from human preference data. PIT only requires preference data that are used to train reward models without extra human efforts. Specifically, we reformulate the training objective of reinforcement learning from human feedback (RLHF) -- instead of maximizing response quality for a given input, we maximize the quality gap of the response conditioned on a reference response. In this way, PIT is implicitly trained with the improvement goal of better aligning with human preferences. Experiments on two real-world datasets and one synthetic dataset show that our method significantly outperforms prompting-based methods.Comment: 28 pages, 5 figures, 4 table

Instruction-Following Evaluation for Large Language Models

One core capability of Large Language Models (LLMs) is to follow natural language instructions. However, the evaluation of such abilities is not standardized: Human evaluations are expensive, slow, and not objectively reproducible, while LLM-based auto-evaluation is potentially biased or limited by the ability of the evaluator LLM. To overcome these issues, we introduce Instruction-Following Eval (IFEval) for large language models. IFEval is a straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set of "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times". We identified 25 types of those verifiable instructions and constructed around 500 prompts, with each prompt containing one or more verifiable instructions. We show evaluation results of two widely available LLMs on the market. Our code and data can be found at https://github.com/google-research/google-research/tree/master/instruction_following_eva

Nonconvex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances

The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem that is also studied in the context of zero-sum games. Given a class of objective functions, the goal is to find a value for the argument that leads to a small objective value even for the worst-case function in the given class. Min-max optimization problems have recently become very popular in a wide range of signal and data processing applications, such as fair beamforming, training generative adversarial networks (GANs), and robust machine learning (ML), to just name a few. © 1991-2012 IEEE

Potential antidepressant effects of Traditional Chinese botanical drug formula Chaihu-Shugan-San and its active ingredients

Background: Depression is a severe mental disorder that poses a significant threat to both the physical and mental wellbeing of individuals. Currently, there are various methods for treating depression, including traditional Chinese herbal formulations like Chaihu-Shugan-San (CSS), which have shown effective antidepressant effects in both clinical and animal research.Objective: This review aims to provide a comprehensive synthesis of evidence related to CSS, considering both preclinical and clinical studies, to uncover its potential multi-level, multi-pathway, and multi-target mechanisms for treating depression and identify its active ingredients.Methods: A thorough search was conducted in electronic databases, including PubMed, MEDLINE, Web of Science, Google Scholar, CNKI, and Wanfang, using keywords such as “Chaihu Shugan” and “depression” to retrieve relevant literature on CSS and its active ingredients. The review process adhered to the PRISMA guidelines.Results: This review consolidates the mechanisms underlying antidepressant effects of CSS and its active ingredients. It emphasizes its involvement in the regulation of monoaminergic neurotransmitter systems, synaptic plasticity, and the hypothalamic-pituitary-adrenal axis, among other aspects.Conclusion: CSS exerts a pivotal role in treating depression through various pathways, including the monoaminergic neurotransmitter system, the hypothalamic-pituitary-adrenal axis, synaptic plasticity, inflammation, brain-derived neurotrophic factor levels, and the brain-gut axis. This review facilitates a comprehensive understanding of the current state of CSS research, fostering an in-depth exploration of the etiological mechanisms of depression and the potential discovery of novel antidepressant drugs

Mutant p53 Gains Its Function via c-Myc Activation upon CDK4 Phosphorylation at Serine 249 and Consequent PIN1 Binding

TP53 missense mutations significantly influence the development and progression of various human cancers via their gain of new functions (GOF) through different mechanisms. Here we report a unique mechanism underlying the GOF of p53-R249S (p53-RS), a p53 mutant frequently detected in human hepatocellular carcinoma (HCC) that is highly related to hepatitis B infection and aflatoxin B1. A CDK inhibitor blocks p53-RS\u2019s nuclear translocation in HCC, whereas CDK4 interacts with p53-RS in the G1/S phase of the cells, phosphorylates it, and enhances its nuclear localization. This is coupled with binding of a peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1) to p53-RS, but not the p53 form with mutations of four serines/threonines previously shown to be crucial for PIN1 binding. As a result, p53-RS interacts with c-Myc and enhances c-Myc-dependent rDNA transcription key for ribosomal biogenesis. These results unveil a CDK4-PIN1-p53-RS-c-Myc pathway as a novel mechanism for the GOF of p53-RS in HCC