High-Dimensional Isotope Relationships

High-dimensional isotope relationships describes the relationships of two or more element or position-specific (PS) elements in the same molecule or ion. It provides us more powerful tools to study reaction mechanisms and dynamics. Chapter 1 is about dual or multiple stable isotope relationship on δ-δ (or δ\u27-δ\u27) space. While temporal data sampled from a closed-system can be treated by a Rayleigh Distillation Model (RDM), spatial data should be treated by a Reaction-Transport Model (RTM). Here we compare the results of a closed-system RDM to a RTM for systems with diffusional mass transfer by simulating the trajectories on nitrate\u27s δ\u2718O-δ\u2715N space. Our results highlight the importance of linking the underlying physical model to the plotted data points before interpreting their high-dimensional isotope relationships. Chapter 2 proposed a rigorous approach that can describe isotope distribution among biomolecules and their apparent deviation from equilibrium state. Applying the concept of distance matrix in graph theory, we propose that apparent local isotope equilibrium among a subset of biomolecules can be assessed using an apparent fractionation difference (|Δα|) matrix. The application of |Δα| matrix can help us to locate potential reversible reactions or reaction networks in a complex system like a metabolic system. Chapter 3 calculated the equilibrium PS isotope composition for large organic molecules. A prevailing idea is that each of the positions can reach equilibrium with each other, if a reaction is fully reversible. However, such an equilibrium intramolecular isotope distribution (Intra-ID) can only be achieved when every carbon atom of different positions exchange with each other within a molecule. Equilibrium Intra-IDs (reduced partition function ratios, β) can serve as a fixed reference for measured Intra-ID. The analysis of calculated PS 13β factors of acetate and C16 fatty acid showed that equilibrium isotope effect can produce fatty acid with alternating Intra-ID from disequilibrium precursors

Vibrational properties of the phononic crystal structural cavity

This paper discusses the construction of a model of phononic crystals and the calculation of the band gap by the finite element method. The physical parameters on the band structure are studied in order to find the proper material suitable for a low frequency vibration. We investigate modal analysis, forbidden band gap characteristics, and the resonance mechanism of the crystal’s cavity. We compare the results of the experiments with those obtained for the phononic crystal cavity, such as the use of crystals on the roof or the floor. This study intends to make phononic crystal cavity applicable for engineers, especially in vehicles

Vibrational properties of the phononic crystal structural cavity

This paper discusses the construction of a model of phononic crystals and the calculation of the band gap by the finite element method. The physical parameters on the band structure are studied in order to find the proper material suitable for a low frequency vibration. We investigate modal analysis, forbidden band gap characteristics, and the resonance mechanism of the crystal’s cavity. We compare the results of the experiments with those obtained for the phononic crystal cavity, such as the use of crystals on the roof or the floor. This study intends to make phononic crystal cavity applicable for engineers, especially in vehicles

Understanding Programs by Exploiting (Fuzzing) Test Cases

Semantic understanding of programs has attracted great attention in the community. Inspired by recent successes of large language models (LLMs) in natural language understanding, tremendous progress has been made by treating programming language as another sort of natural language and training LLMs on corpora of program code. However, programs are essentially different from texts after all, in a sense that they are normally heavily structured and syntax-strict. In particular, programs and their basic units (i.e., functions and subroutines) are designed to demonstrate a variety of behaviors and/or provide possible outputs, given different inputs. The relationship between inputs and possible outputs/behaviors represents the functions/subroutines and profiles the program as a whole. Therefore, we propose to incorporate such a relationship into learning, for achieving a deeper semantic understanding of programs. To obtain inputs that are representative enough to trigger the execution of most part of the code, we resort to fuzz testing and propose fuzz tuning to boost the performance of program understanding and code representation learning, given a pre-trained LLM. The effectiveness of the proposed method is verified on two program understanding tasks including code clone detection and code classification, and it outperforms current state-of-the-arts by large margins. Code is available at https://github.com/rabbitjy/FuzzTuning.Comment: Findings of the Association for Computational Linguistics: ACL 202

PYATB: An Efficient Python Package for Electronic Structure Calculations Using Ab Initio Tight-Binding Model

We present PYATB, a Python package designed for computing band structures and related properties of materials using the ab initio tight-binding Hamiltonian. The Hamiltonian is directly obtained after conducting self-consistent calculations with first-principles packages using numerical atomic orbital (NAO) bases, such as ABACUS. The package comprises three modules: Bands, Geometric, and Optical. In the Bands module, one can calculate essential properties of band structures, including the partial density of states (PDOS), fat bands, Fermi surfaces, and Weyl/Dirac points. The band unfolding method is utilized to obtain the energy band spectra of a supercell by projecting the electronic structure of the supercell onto the Brillouin zone of the primitive cell. With the Geometric module, one can compute the Berry phase and Berry curvature-related quantities, such as electric polarization, Wilson loops, Chern numbers, and anomalous Hall conductivities. The Optical module offers a range of optical property calculations, including optical conductivity and nonlinear optical responses, such as shift current and Berry curvature dipole

A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks

Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing clients' data privacy. It has gained substantial popularity in recent years, especially since the enforcement of data protection laws and regulations in many countries. To foster the application of FL, a variety of FL frameworks have been proposed, allowing non-experts to easily train ML models. As a result, understanding bugs in FL frameworks is critical for facilitating the development of better FL frameworks and potentially encouraging the development of bug detection, localization and repair tools. Thus, we conduct the first empirical study to comprehensively collect, taxonomize, and characterize bugs in FL frameworks. Specifically, we manually collect and classify 1,119 bugs from all the 676 closed issues and 514 merged pull requests in 17 popular and representative open-source FL frameworks on GitHub. We propose a classification of those bugs into 12 bug symptoms, 12 root causes, and 18 fix patterns. We also study their correlations and distributions on 23 functionalities. We identify nine major findings from our study, discuss their implications and future research directions based on our findings

P-Glycoprotein/MDR1 Regulates Pokemon Gene Transcription Through p53 Expression in Human Breast Cancer Cells

P-glycoprotein (Pgp), encoded by the multidrug resistance 1 (MDR1) gene, is an efflux transporter and plays an important role in pharmacokinetics. In this study, we demonstrated that the pokemon promoter activity, the pokemon mRNA and protein expression can be significantly inhibited by Pgp. Chromatin immunoprecipitation assay showed that Pgp can bind the pokemon prompter to repress pokemon transcription activity. Furthermore, Pgp regulated pokemon transcription activity through expression of p53 as seen by use of p53 siRNA transfected MCF-7 cells or p53 mutated MDA-MB-231 cells. Moreover, p53 was detected to bind with Pgp in vivo using immunoprecipitation assay. Taken together, we conclude that Pgp can regulate the expression of pokemon through the presence of p53, suggesting that Pgp is a potent regulator and may offer an effective novel target for cancer therapy

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%