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Computational biology and chemistry
The use of computers and software tools in biochemistry (biology) has led to a deep revolution in basic sciences and medicine. Bioinformatics and systems biology are the direct results of this revolution. With the involvement of computers, software tools, and internet services in scientific disciplines comprising biology and chemistry, new terms, technologies, and methodologies appeared and established. Bioinformatic software tools, versatile databases, and easy internet access resulted in the occurrence of computational biology and chemistry. Today, we have new types of surveys and laboratories including 'in silico studies' and 'dry labs' in which bioinformaticians conduct their investigations to gain invaluable outcomes. These features have led to 3-dimensioned illustrations of different molecules and complexes to get a better understanding of nature.
Book
Large language models, social demography, and hegemony: comparing authorship in human and synthetic text
Large language models have become popular over a short period of time because they can generate text that resembles human writing across various domains and tasks. The popularity and breadth of use also put this technology in the position to fundamentally reshape how written language is perceived and evaluated. It is also the case that spoken language has long played a role in maintaining power and hegemony in society, especially through ideas of social identity and “correct” forms of language. But as human communication becomes even more reliant on text and writing, it is important to understand how these processes might shift and who is more likely to see their writing styles reflected back at them through modern AI. We therefore ask the following question: who does generative AI write like? To answer this, we compare writing style features in over 150,000 college admissions essays submitted to a large public university system and an engineering program at an elite private university with a corpus of over 25,000 essays generated with GPT-3.5 and GPT-4 to the same writing prompts. We find that human-authored essays exhibit more variability across various individual writing style features (e.g., verb usage) than AI-generated essays. Overall, we find that the AI-generated essays are most similar to essays authored by students who are males with higher levels of social privilege. These findings demonstrate critical misalignments between human and AI authorship characteristics, which may affect the evaluation of writing and calls for research on control strategies to improve alignment.
Journal Article
DenseHillNet: a lightweight CNN for accurate classification of natural images
The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fruits, land sliding, and medical images, there is a need for further research on the detection of natural images, particularly glaciers and mountains. To address the limitations of traditional CNNs, such as vanishing gradients and the need for many layers, the proposed work introduces a novel model called DenseHillNet. The model utilizes a DenseHillNet architecture, a type of CNN with densely connected layers, to accurately classify images as glaciers or mountains. The model contributes to the development of automation technologies in transportation and outdoor activities. The dataset used in this study comprises 3,096 images of each of the “glacier” and “mountain” categories. Rigorous methodology was employed for dataset preparation and model training, ensuring the validity of the results. A comparison with a previous work revealed that the proposed DenseHillNet model, trained on both glacier and mountain images, achieved higher accuracy (86%) compared to a CNN model that only utilized glacier images (72%). Researchers and graduate students are the audience of our article.
Journal Article
Unified Polynomial Dynamic Programming Algorithms for P-Center Variants in a 2D Pareto Front
With many efficient solutions for a multi-objective optimization problem, this paper aims to cluster the Pareto Front in a given number of clusters K and to detect isolated points. K-center problems and variants are investigated with a unified formulation considering the discrete and continuous versions, partial K-center problems, and their min-sum-K-radii variants. In dimension three (or upper), this induces NP-hard complexities. In the planar case, common optimality property is proven: non-nested optimal solutions exist. This induces a common dynamic programming algorithm running in polynomial time. Specific improvements hold for some variants, such as K-center problems and min-sum K-radii on a line. When applied to N points and allowing to uncover M
Journal Article
Planar 3-SAT with a Clause/Variable Cycle
In the Planar 3-SAT problem, we are given a 3-SAT formula together with its incidence graph, which is planar, and are asked whether this formula is satisfiable. Since Lichtenstein's proof that this problem is NP-complete, it has been used as a starting point for a large number of reductions. In the course of this research, different restrictions on the incidence graph of the formula have been devised, for which the problem also remains hard. In this paper, we investigate the restriction in which we require that the incidence graph can be augmented by the edges of a Hamiltonian cycle that first passes through all variables and then through all clauses, in a way that the resulting graph is still planar. We show that the problem of deciding satisfiability of a 3-SAT formula remains NP-complete even if the incidence graph is restricted in that way and the Hamiltonian cycle is given. This complements previous results demanding cycles only through either the variables or clauses. The problem remains hard for monotone formulas, as well as for instances with exactly three distinct variables per clause. In the course of this investigation, we show that monotone instances of Planar 3-SAT with exactly three distinct variables per clause are always satisfiable, thus settling the question by Darmann, Döcker, and Dorn on the complexity of this problem variant in a surprising way. Comment: Implementing style of DMTCS journal
Journal Article
iTabNet: an improved neural network for tabular data and its application to predict socioeconomic and environmental attributes
There is a growing application of machine learning methods to predict socioeconomic and environmental attributes in computational social science, where big data are usually presented in tabular format. However, it is still a challenge to develop novel deep learning models to deal with tabular data, fill missing value, improve prediction accuracy, and enhance interpretability. In this study, we for the first time apply a tabular deep learning methodology (TabNet) to predict socioeconomic and environmental attributes (number of population and companies, volume of consumption, poker players’ behaviors, forest cover, etc.). Furthermore, we develop a new network architecture, referred to as improved TabNet (iTabNet), that can simultaneously learn local and global features in the tabular data to improve prediction accuracy. We also introduce a difference loss to constrain the feature selection process in iTabNet so that the model can use different features at different steps to enhance interpretability. To deal with missing values, we introduce a fusion strategy based on data mean and Auto-Encoder network to efficiently complete a more reasonable value filling. Experimental results demonstrate that the proposed iTabNet achieves competitive performances in the application to predict socioeconomic and environmental attributes based on tabular data, iTabNet using the proposed fusion strategy significantly outperforms other machine learning models when tabular data have missing values.
Journal Article