The research of the drilling pipe's small-scale mode used in acoustic telemetry while drilling

The acoustic telemetry technology while drilling has a greater advantage in the transmission rate and the application of media than the commercial mud pulse mode and electromagnetic wave mode. There are limits to the existence of the periodic drill pipe acoustic transmission transmission model, which can only calculate the acoustic transmission characteristics of two kinds of periodic structures and the acoustic transmission characteristics of the composite structures with arbitrary cross section, but the variable cross-section or various special-shaped drill strings. According to the characteristics of the small scale structure of drill string, a small scale model of the drill string in the data transmission is set up by using the theory of longitudinal vibration of structures, in which the small scale vibration transfer function of cylindrical rods with different variable cross sections is analyzed. According to certain boundary conditions, the vibration transfer characteristics of drill string are obtained, and the simulation research is completed.Показано, что технология акустической телеметрии в процессе бурения композитных структур имеет большое преимущество в скорости и способах передачи данных по сравнению с режимом электромагнитной волны и обычным методом измерения колебаний бура. Рассмотрены возможности метода акустической передачи данных бурильных труб, которые позволяют рассчитать акустические характеристики периодических структур и композитных структур при произвольном поперечном сечении и для различной специальной формы сверла. В соответствии с характеристиками мелкоструктурной бурильной скважины модель бурильных стержней рассматривается с помощью теории продольных колебаний конструкций, в которой анализируются малые передаточные вибрации цилиндрических стержней различных сечений

DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

Recent advances in natural language processing, primarily propelled by Large Language Models (LLMs), have showcased their remarkable capabilities grounded in in-context learning. A promising avenue for guiding LLMs in intricate reasoning tasks involves the utilization of intermediate reasoning steps within the Chain-of-Thought (CoT) paradigm. Nevertheless, the central challenge lies in the effective selection of exemplars for facilitating in-context learning. In this study, we introduce a framework that leverages Dual Queries and Low-rank approximation Re-ranking (DQ-LoRe) to automatically select exemplars for in-context learning. Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge. Moreover, for the second query, LoRe employs dimensionality reduction techniques to refine exemplar selection, ensuring close alignment with the input question's knowledge. Through extensive experiments, we demonstrate that DQ-LoRe significantly outperforms prior state-of-the-art methods in the automatic selection of exemplars for GPT-4, enhancing performance from 92.5% to 94.2%. Our comprehensive analysis further reveals that DQ-LoRe consistently outperforms retrieval-based approaches in terms of both performance and adaptability, especially in scenarios characterized by distribution shifts. DQ-LoRe pushes the boundary of in-context learning and opens up new avenues for addressing complex reasoning challenges. Our code is released at https://github.com/AI4fun/DQ-LoRe}{https://github.com/AI4fun/DQ-LoRe.Comment: Accepted in ICLR 202

LEGO-Prover: Neural Theorem Proving with Growing Libraries

Despite the success of large language models (LLMs), the task of theorem proving still remains one of the hardest reasoning tasks that is far from being fully solved. Prior methods using language models have demonstrated promising results, but they still struggle to prove even middle school level theorems. One common limitation of these methods is that they assume a fixed theorem library during the whole theorem proving process. However, as we all know, creating new useful theorems or even new theories is not only helpful but crucial and necessary for advancing mathematics and proving harder and deeper results. In this work, we present LEGO-Prover, which employs a growing skill library containing verified lemmas as skills to augment the capability of LLMs used in theorem proving. By constructing the proof modularly, LEGO-Prover enables LLMs to utilize existing skills retrieved from the library and to create new skills during the proving process. These skills are further evolved (by prompting an LLM) to enrich the library on another scale. Modular and reusable skills are constantly added to the library to enable tackling increasingly intricate mathematical problems. Moreover, the learned library further bridges the gap between human proofs and formal proofs by making it easier to impute missing steps. LEGO-Prover advances the state-of-the-art pass rate on miniF2F-valid (48.0% to 57.0%) and miniF2F-test (45.5% to 47.1%). During the proving process, LEGO-Prover also manages to generate over 20,000 skills (theorems/lemmas) and adds them to the growing library. Our ablation study indicates that these newly added skills are indeed helpful for proving theorems, resulting in an improvement from a success rate of 47.1% to 50.4%. We also release our code and all the generated skills

TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models

Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models. However, current ATP benchmarks mainly focus on symbolic inference, but rarely involve the understanding of complex number combination reasoning. In this work, we propose TRIGO, an ATP benchmark that not only requires a model to reduce a trigonometric expression with step-by-step proofs but also evaluates a generative LM's reasoning ability on formulas and its capability to manipulate, group, and factor number terms. We gather trigonometric expressions and their reduced forms from the web, annotate the simplification process manually, and translate it into the Lean formal language system. We then automatically generate additional examples from the annotated samples to expand the dataset. Furthermore, we develop an automatic generator based on Lean-Gym to create dataset splits of varying difficulties and distributions in order to thoroughly analyze the model's generalization ability. Our extensive experiments show our proposed TRIGO poses a new challenge for advanced generative LM's including GPT-4 which is pre-trained on a considerable amount of open-source formal theorem-proving language data, and provide a new tool to study the generative LM's ability on both formal and mathematical reasoning.Comment: Accepted by EMNLP 2023. Code is available at https://github.com/menik1126/TRIG

Broad-Wavevector Spin Pumping of Flat-Band Magnons

We report the experimental observation of large spin pumping signals in YIG/Pt system driven by broad-wavevector spin-wave spin current. 280 nm-wide microwave inductive antennas offer broad-wavevector excitation which, in combination with quasi-flatband of YIG, allows a large number of magnons to participate in spin pumping at a given frequency. Through comparison with ferromagnetic resonance spin pumping, we attribute the enhancement of the spin current to the multichromatic magnons. The high efficiency of spin current generation enables us to uncover nontrivial propagating properties in ultra-low power regions. Additionally, our study achieves the spatially separated detection of magnons, allowing the direct extraction of the decay length. The synergistic combination of the capability of broad-wavevector excitation, enhanced voltage signals, and nonlocal detection provides a new avenue for the electrical exploration of spin waves dynamics

Label-free visualization of carbapenemase activity in living bacteria

Evaluating enzyme activity intracellularly on natural substrates is a significant experimental challenge in biomedical research. We report a label‐free method for real‐time monitoring of the catalytic behavior of class A, B, and D carbapenemases in live bacteria based on measurement of heat changes. By this means, novel biphasic kinetics for class D OXA‐48 with imipenem as substrate is revealed, providing a new approach to detect OXA‐48‐like producers. This in‐cell calorimetry approach offers major advantages in the rapid screening (10 min) of carbapenemase‐producing Enterobacteriaceae from 142 clinical bacterial isolates, with superior sensitivity (97 %) and excellent specificity (100 %) compared to conventional methods. As a general, label‐free method for the study of living cells, this protocol has potential for application to a wider range and variety of cellular components and physiological processes