Investigating Geochemical Processes of Fluid-Rock Interactions on Materials Related to Energy and Environment

Understanding the potential processes of radionuclides released from nuclear waste forms is essential to the safe disposal and containment of nuclear waste. Iodoapatite, a potential waste form for radioiodine, was chosen as a model system to examine the impact by common aqueous anions on iodine release processes. Four semi-dynamic leaching tests were performed using 0.1 mol/L NaCl, Na2CO3, Na3PO4, and Na2SO4 solutions respectively under 90 °C, 1 bar, fixed S/V ratio 5/m (sample surface area to solution volume), and with 24-hour replacement of the leaching solutions. Solution analysis and surface characterization show that these ion-rich solutions accelerated the iodine release processes due to the increased ionic strength, reduced concentration coefficients of dissolved species, and elevated solution pH. Secondary phases produced by the experiments were observed at the leached surfaces. These produces were induced by ion-exchange, dissolution, and re-precipitation. This research suggests that maintaining neutral pH and low ion content in aqueous environments is imperative to ensure the safe disposal of radioactive iodine when contained by this apatite waste form. Characterizing the behavior of petroleum-bearing fluids in natural reservoirs is challenging due to the heterogeneous composition of hydrocarbon systems. However, the fluid– rock interactions are important for recovering oil from the natural reservoirs. Molecular dynamics simulations were used to investigate the interactions of octane and octanethiol with kerogen and with calcite, respectively. To quantify their interactions, free energy surfaces were computed by umbrella sampling to obtain the minimum energy required to recover oil molecules from kerogen and from calcite surfaces. The effects of surface composition, oil molecular polarity, surface water, and size of the oil molecular cluster were examined through the calculations. The results suggest that (1) polar oil compounds require more energy to be recovered from the reservoir rocks than non-polar molecules, (2) isolated oil molecules or oil clusters of a smaller size are more difficult to be displaced than a larger size of molecular clusters, and (3) the presence of surface water reduces the energy required for oil recovery. This study provides an energetic perspective on the interfacial interactions for oil recovery in natural reservoirs

Thermodynamical properties of dark energy with the equation of state ω=ω0+ω1z% \omega =\omega_{0}+\omega_{1}z

The thermodynamical properties of dark energy are usually investigated with the equation of state $\omega =\omega_{0}+\omega_{1}z$. Recent observations show that our universe is accelerating, and the apparent horizon and the event horizon vary with redshift $z$. When definitions of the temperature and entropy of a black hole are used to the two horizons of the universe, we examine the thermodynamical properties of the universe which is enveloped by the apparent horizon and the event horizon respectively. We show that the first and the second laws of thermodynamics inside the apparent horizon in any redshift are satisfied, while they are broken down inside the event horizon in some redshift. Therefore, the apparent horizon for the universe may be the boundary of thermodynamical equilibrium for the universe like the event horizon for a black hole.Comment: 6 pages, 5 figures, Accepted for publication in Physical Review

From Kepler to Newton: Explainable AI for Science Discovery

The Observation--Hypothesis--Prediction--Experimentation loop paradigm for scientific research has been practiced by researchers for years towards scientific discoveries. However, with data explosion in both mega-scale and milli-scale scientific research, it has been sometimes very difficult to manually analyze the data and propose new hypotheses to drive the cycle for scientific discovery. In this paper, we discuss the role of Explainable AI in scientific discovery process by demonstrating an Explainable AI-based paradigm for science discovery. The key is to use Explainable AI to help derive data or model interpretations, hypotheses, as well as scientific discoveries or insights. We show how computational and data-intensive methodology -- together with experimental and theoretical methodology -- can be seamlessly integrated for scientific research. To demonstrate the AI-based science discovery process, and to pay our respect to some of the greatest minds in human history, we show how Kepler's laws of planetary motion and Newton's law of universal gravitation can be rediscovered by (Explainable) AI based on Tycho Brahe's astronomical observation data, whose works were leading the scientific revolution in the 16-17th century. This work also highlights the important role of Explainable AI (as compared to Blackbox AI) in science discovery to help humans prevent or better prepare for the possible technological singularity that may happen in the future, since science is not only about the know how, but also the know why.Comment: Presented at ICML-AI4Science 202

Positive 2-bridge knots and chirally cosmetic surgeries

In this paper we verify that with the exception of the $(2, 2n+1)$ torus knots, positive 2-bridge knots up to 31 crossings do not admit chirally cosmetic surgeries. A knot $K$ admits chirally cosmetic surgeries if there exist surgeries $S^3_r$ and $S^3_{r'}$ with distinct slopes $r$ and $r'$ such that $S^3_r(K) \cong -S^3_{r'}(K)$, where the negative represents an orientation reversal. To verify this, we use the obstruction formula from arXiv:2112.03144 which relates classical knot invariants to the existence of chirally cosmetic surgeries. To check the formula, we develop a Python program that computes the classical knot invariants $a_2$, $a_4$, $v_3$, $\det$, and $g$ of a positive 2-bridge knot.Comment: 25 pages, 22 figures, code developed can be found at https://github.com/zl830/chiral_cosmetic_surgery_for_pos_2_bridge_knot

A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection

Salient object detection (SOD) in panoramic video is still in the initial exploration stage. The indirect application of 2D video SOD method to the detection of salient objects in panoramic video has many unmet challenges, such as low detection accuracy, high model complexity, and poor generalization performance. To overcome these hurdles, we design an Inter-Layer Attention (ILA) module, an Inter-Layer weight (ILW) module, and a Bi-Modal Attention (BMA) module. Based on these modules, we propose a Spatial-Temporal Dual-Mode Mixed Flow Network (STDMMF-Net) that exploits the spatial flow of panoramic video and the corresponding optical flow for SOD. First, the ILA module calculates the attention between adjacent level features of consecutive frames of panoramic video to improve the accuracy of extracting salient object features from the spatial flow. Then, the ILW module quantifies the salient object information contained in the features of each level to improve the fusion efficiency of the features of each level in the mixed flow. Finally, the BMA module improves the detection accuracy of STDMMF-Net. A large number of subjective and objective experimental results testify that the proposed method demonstrates better detection accuracy than the state-of-the-art (SOTA) methods. Moreover, the comprehensive performance of the proposed method is better in terms of memory required for model inference, testing time, complexity, and generalization performance

GenRec: Large Language Model for Generative Recommendation

In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively unexplored. This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. In this paper, we present a novel LLM for generative recommendation (GenRec) that utilized the expressive power of LLM to directly generate the target item to recommend, rather than calculating ranking score for each candidate item one by one as in traditional discriminative recommendation. GenRec uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our proposed approach leverages the vast knowledge encoded in large language models to accomplish recommendation tasks. We first we formulate specialized prompts to enhance the ability of LLM to comprehend recommendation tasks. Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics. Our research underscores the potential of LLM-based generative recommendation in revolutionizing the domain of recommendation systems and offers a foundational framework for future explorations in this field. We conduct extensive experiments on benchmark datasets, and the experiments shows that our GenRec has significant better results on large dataset

Label Mask for Multi-Label Text Classification

One of the key problems in multi-label text classification is how to take advantage of the correlation among labels. However, it is very challenging to directly model the correlations among labels in a complex and unknown label space. In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model. LM-MTC is able to capture implicit relationships among labels through the powerful ability of pre-train language models. On the basis, we assign a different token to each potential label, and randomly mask the token with a certain probability to build a label based Masked Language Model (MLM). We train the MTC and MLM together, further improving the generalization ability of the model. A large number of experiments on multiple datasets demonstrate the effectiveness of our method

Counterfactual Collaborative Reasoning

Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models. More specifically, by integrating two important types of reasoning ability -- counterfactual reasoning and (neural) logical reasoning -- we propose Counterfactual Collaborative Reasoning (CCR), which conducts counterfactual logic reasoning to improve the performance. In particular, we use recommender system as an example to show how CCR alleviate data scarcity, improve accuracy and enhance transparency. Technically, we leverage counterfactual reasoning to generate "difficult" counterfactual training examples for data augmentation, which -- together with the original training examples -- can enhance the model performance. Since the augmented data is model irrelevant, they can be used to enhance any model, enabling the wide applicability of the technique. Besides, most of the existing data augmentation methods focus on "implicit data augmentation" over users' implicit feedback, while our framework conducts "explicit data augmentation" over users explicit feedback based on counterfactual logic reasoning. Experiments on three real-world datasets show that CCR achieves better performance than non-augmented models and implicitly augmented models, and also improves model transparency by generating counterfactual explanations