With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously.

Social dilemmas (SDs, e.g., prisoner's dilemma), spanning various domains including environmental pollution, public health crises, and resource management, present a fundamental conflict between personal interests and the common good [Nowak, 2006]. While cooperation is beneficial for the collective, individuals are tempted to exploit or free-ride others' efforts, potentially leading to a tragedy of the commons. Historically rooted in the study of biological altruism [Smith, 1982], the traditional research on cooperation in SDs has unveiled the pivotal roles of reciprocity and social preferences in fostering cooperative behaviors in human societies [Fehr et al., 2002; Rand and Nowak, 2013]. Recently, propelled by advances in artificial intelligence (AI), this field has been undergoing a profound transformation--as AI agents now increasingly represent and engage with humans, our understanding of how cooperation emerges, evolves, and sustains in SDs is being significantly reshaped. This is particularly evident in two lines of research: multi-agent cooperation, where AI agents interact with each other in SDs, and human-agent cooperation, which examines the intricacies of human interactions with AI agents in SDs.

This paper embarks on an exploration into the Large Language Model (LLM) datasets, which play a crucial role in the remarkable advancements of LLMs. The datasets serve as the foundational infrastructure analogous to a root system that sustains and nurtures the development of LLMs. Consequently, examination of these datasets emerges as a critical topic in research. In order to address the current lack of a comprehensive overview and thorough analysis of LLM datasets, and to gain insights into their current status and future trends, this survey consolidates and categorizes the fundamental aspects of LLM datasets from five perspectives: (1) Pre-training Corpora; (2) Instruction Fine-tuning Datasets; (3) Preference Datasets; (4) Evaluation Datasets; (5) Traditional Natural Language Processing (NLP) Datasets. The survey sheds light on the prevailing challenges and points out potential avenues for future investigation. Additionally, a comprehensive review of the existing available dataset resources is also provided, including statistics from 444 datasets, covering 8 language categories and spanning 32 domains. Information from 20 dimensions is incorporated into the dataset statistics. The total data size surveyed surpasses 774.5 TB for pre-training corpora and 700M instances for other datasets. We aim to present the entire landscape of LLM text datasets, serving as a comprehensive reference for researchers in this field and contributing to future studies. Related resources are available at: https://github.com/lmmlzn/Awesome-LLMs-Datasets.

Due to the excellent capacities of large language models (LLMs), it becomes feasible to develop LLM-based agents for reliable user simulation. Considering the scarcity and limit (e.g., privacy issues) of real user data, in this paper, we conduct large-scale user simulation for web search, to improve the analysis and modeling of user search behavior. Specially, we propose BASES, a novel user simulation framework with LLM-based agents, designed to facilitate comprehensive simulations of web search user behaviors. Our simulation framework can generate unique user profiles at scale, which subsequently leads to diverse search behaviors. To demonstrate the effectiveness of BASES, we conduct evaluation experiments based on two human benchmarks in both Chinese and English, demonstrating that BASES can effectively simulate large-scale human-like search behaviors. To further accommodate the research on web search, we develop WARRIORS, a new large-scale dataset encompassing web search user behaviors, including both Chinese and English versions, which can greatly bolster research in the field of information retrieval. Our code and data will be publicly released soon.

Deep Learning (DL) frameworks such as PyTorch and TensorFlow include runtime infrastructures responsible for executing trained models on target hardware, managing memory, data transfers, and multi-accelerator execution, if applicable. Additionally, it is a common practice to deploy pre-trained models on environments distinct from their native development settings. This led to the introduction of interchange formats such as ONNX, which includes its runtime infrastructure, and ONNX Runtime, which work as standard formats that can be used across diverse DL frameworks and languages. Even though these runtime infrastructures have a great impact on inference performance, no previous paper has investigated their energy efficiency. In this study, we monitor the energy consumption and inference time in the runtime infrastructures of three well-known DL frameworks as well as ONNX, using three various DL models. To have nuance in our investigation, we also examine the impact of using different execution providers. We find out that the performance and energy efficiency of DL are difficult to predict. One framework, MXNet, outperforms both PyTorch and TensorFlow for the computer vision models using batch size 1, due to efficient GPU usage and thus low CPU usage. However, batch size 64 makes PyTorch and MXNet practically indistinguishable, while TensorFlow is outperformed consistently. For BERT, PyTorch exhibits the best performance. Converting the models to ONNX yields significant performance improvements in the majority of cases. Finally, in our preliminary investigation of execution providers, we observe that TensorRT always outperforms CUDA.

Machine learning (ML) is set to accelerate innovations in clinical microbiomics, such as in disease diagnostics and prognostics. This will require high-quality, reproducible, interpretable workflows whose predictive capabilities meet or exceed the high thresholds set for clinical tools by regulatory agencies. Here, we capture a snapshot of current practices in the application of supervised ML to microbiomics data, through an in-depth analysis of 100 peer-reviewed journal articles published in 2021-2022. We apply a data-driven approach to steer discussion of the merits of varied approaches to experimental design, including key considerations such as how to mitigate the effects of small dataset size while avoiding data leakage. We further provide guidance on how to avoid common experimental design pitfalls that can hurt model performance, trustworthiness, and reproducibility. Discussion is accompanied by an interactive online tutorial that demonstrates foundational principles of ML experimental design, tailored to the microbiomics community. Formalizing community best practices for supervised ML in microbiomics is an important step towards improving the success and efficiency of clinical research, to the benefit of patients and other stakeholders.

Transport-based density estimation methods are receiving growing interest because of their ability to efficiently generate samples from the approximated density. We further invertigate the sequential transport maps framework proposed from arXiv:2106.04170 arXiv:2303.02554, which builds on a sequence of composed Knothe-Rosenblatt (KR) maps. Each of those maps are built by first estimating an intermediate density of moderate complexity, and then by computing the exact KR map from a reference density to the precomputed approximate density. In our work, we explore the use of Sum-of-Squares (SoS) densities and $\alpha$-divergences for approximating the intermediate densities. Combining SoS densities with $\alpha$-divergence interestingly yields convex optimization problems which can be efficiently solved using semidefinite programming. The main advantage of $\alpha$-divergences is to enable working with unnormalized densities, which provides benefits both numerically and theoretically. In particular, we provide two new convergence analyses of the sequential transport maps: one based on a triangle-like inequality and the second on information geometric properties of $\alpha$-divergences for unnormalizied densities. The choice of intermediate densities is also crucial for the efficiency of the method. While tempered (or annealed) densities are the state-of-the-art, we introduce diffusion-based intermediate densities which permits to approximate densities known from samples only. Such intermediate densities are well-established in machine learning for generative modeling. Finally we propose and try different low-dimensional maps (or lazy maps) for dealing with high-dimensional problems and numerically demonstrate our methods on several benchmarks, including Bayesian inference problems and unsupervised learning task.

Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive and black-box experimental design processes. While Bayesian optimization is a principled data-driven approach to experimental optimization, it learns everything from scratch and could greatly benefit from the expertise of its human (domain) experts who often reason about systems at different abstraction levels using physical properties that are not necessarily directly measured (or measurable). In this paper, we propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into the surrogate modeling to further boost the performance of BO. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments. We discuss the convergence behavior of our proposed framework. Our experimental results involving synthetic functions and real-world datasets show the superiority of our method against the baselines.

A combination of AI, a wild 1970s plan to build underwater cities, and a designer creating furniture on the seabed around the Bahamas might be the solution to the widespread destruction of coral reefs. It could even save the world from coastal erosion. Industrial designer Tom Dixon and technologist Suhair Khan, founder of AI incubator Open-Ended Design, are collaborating on regenerating the ocean floor. "Coral reefs are endangered by climate change, shipping, development, and construction--but they're vital," Khan explains. "They cover 1 percent of the ocean floor, but they're home to more than 25 percent of marine life."

U.S. Central Command said Sunday that Houthis launched an anti-ballistic missile toward a tanker ship that carries oil and chemicals in the Gulf of Aiden on Saturday, though it struck the water and did not cause damage to the ship or injuries to those on board. In a post on X, U.S. Central Command said the Iranian-backed Houthis were likely targeting the M/V Torm Thor, which is flagged and owned by a U.S. company. The ship was sailing in the Gulf of Aden at the time of the incident, which was reportedly at 11:45 p.m. local time. Central Command said a third UAV was also heading toward the area and crashed from what appeared to be an in-flight failure. A protestor holds a model of a Houthi missile during a protest held against the U.S.-led airstrikes and sanctions against the Houthi group in Sanaa, Yemen, Feb. 16, 2024.