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We present an algorithm that combines quantum scattering calculations with probabilistic machine-learning models to predict quantum dynamics rate coefficients for a large number of state-to-state transitions in molecule–molecule collisions much faster than with direct solutions of the Schrödinger equation. By utilizing the predictive power of Gaussian process regression with kernels, optimized to make accurate predictions outside of the input parameter space, the present strategy reduces the computational cost by about 75%, with an accuracy within 5%. Our method uses temperature dependences of rate coefficients for transitions from the isolated states of initial rotational angular momentum j, determined via explicit calculations, to predict the temperature dependences of rate coefficients for other values of j. The approach, demonstrated here for rovibrational transitions of SiO due to thermal collisions with H2, uses different prediction models and is thus adaptive to various time and accuracy requirements. The procedure outlined in this work can be used to extend multiple inelastic molecular collision databases without exponentially large computational resources required for conventional rigorous quantum dynamics calculations. 

The long-standing one-to-many problem of gold standard responses in open-domain dialogue systems presents challenges for automatic evaluation metrics. Though prior works have demonstrated some success by applying powerful Large Language Models (LLMs), existing approaches still struggle with the one-to-many problem, and exhibit subpar performance in domain-specific scenarios. We assume the commonsense reasoning biases within LLMs may hinder their performance in domain-specific evaluations. To address both issues, we propose a novel framework SLIDE (Small and Large Integrated for Dialogue Evaluation), that leverages both a small, specialized model (SLM), and LLMs for the evaluation of open-domain dialogues. Our approach introduces several techniques: (1) Contrastive learning to differentiate between robust and non-robust response embeddings; (2) A novel metric for semantic sensitivity that combines embedding cosine distances with similarity learned through neural networks, and (3) A strategy for incorporating the evaluation results from both the SLM and LLMs. Our empirical results demonstrate that our approach achieves state-of-the-art performance in both the classification and evaluation tasks, and additionally, the SLIDE evaluator exhibits a better correlation with human judgments. 

As one of the most primitive operators in graph algorithms, such as the triangle counting, maximal clique enumeration, and subgraph listing, a set intersection operator returns common vertices between any two given sets of vertices in data graphs. It is therefore very important to accelerate the set intersection, which will benefit a bunch of tasks that take it as a built-in block. Existing works on the set intersection usually followed the merge intersection or galloping-search framework, and most optimization research focused on how to leverage the SIMD hardware instructions. In this paper, we propose a novel multi-level set intersection framework, namely hierarchical set partitioning and join (HERO), by using our well-designed set intersection bitmap tree (SIB-tree) index, which is independent of SIMD instructions and completely orthogonal to the merge intersection framework. We recursively decompose the set intersection task into small-sized subtasks and solve each subtask using bitmap and boolean AND operations. To sufficiently achieve the acceleration brought by our proposed intersection approach, we formulate a graph reordering problem, prove its NP-hardness, and then develop a heuristic algorithm to tackle this problem. Extensive experiments on real-world graphs have been conducted to confirm the efficiency and effectiveness of our HERO approach. The speedup over classic merge intersection achieves up to 188x and 176x for triangle counting and maximal clique enumeration, respectively. 

Abstract Hydrogen chloride (HCl) is a key repository of chlorine in the interstellar medium. Accurate determinations of its abundance are critical to assessing the chlorine elemental abundance and constraining stellar nucleosynthesis models. To aid in modeling recent and future observations of HCl rovibrational spectra, we present cross sections and rate coefficients for collisions between HCl and molecular hydrogen. Transitions between rovibrational states of HCl are considered for temperatures ranging from 10 to 3000 K. Cross sections are computed using a full dimensional quantum close-coupling (CC) method and a reduced dimensionality coupled-states (CS) approach. The CS results, benchmarked against the CC results, are used with a recoupling approach to calculate hyperfine-resolved rate coefficients for rovibrational transitions of HCl induced by H₂. The rate coefficients will allow for a better determination of the HCl abundance in the interstellar medium and an improved understanding of interstellar chlorine chemistry. We demonstrate the utility of the new rate coefficients in a nonthermodynamic equilibrium radiative transfer model applied to observations of HCl rovibrational transitions in a circumstellar shell. 

CHEESEHub is a web-accessible, public science gateway that hosts containerized, hands-on demonstrations of cybersecurity concepts. There are now a plethora of services and tools designed to simplify modern gateway deployment and configuration such as commercial and academic composable cloud, the Terraform infrastructure as service tool, Kubernetes and Helm for container orchestration, as well as CILogon for simplified user authentication. Despite leveraging these tools, our day-to-day experience with deploying, upgrading, scaling, and extending CHEESEHub has not been entirely straightforward. We describe here some of the major challenges we have encountered in managing CHEESEHub and developing web-accessible demonstrations for the last five years. We hope this will help both new and seasoned gateway developers to effectively leverage these modern tools while avoiding these same pitfalls, while also providing starting points for discussions about gateway development and deployment best-practices. 

Women make up only 28% of the workforce in STEM fields. It’s important to engage more girls in learning STEM; however, girls’ interests in STEM careers keep declining. It is well studied that the lack of sense of belonging underlies gender differences in STEM differentiation and achievement. Researchers have found that secondary girls’ sense of belonging declines as they age. To enhance secondary female students’ interests and self-concept in computing and engineering fields, the UNLV ITEST project sets the focus on engaging Girls in Ubiquitous Intelligence and Computing (GUIC) through a constructivist learning environment. In the GUIC Summer Camp, 40 secondary female students will take three-week training courses in Arduino & Internet of Things and Robotics Design and conduct two-week engineering project development in tiered teams co-mentored by STEM teachers and college student mentors. Based on the active learning method, the training courses are designed with interactive lectures and hands-on labs/activities. The engineering projects in ubiquitous intelligent systems are designed to connect computing & engineering concepts with real-world problems. Project demo results and students’ feedbacks have confirmed the effectiveness of the project activities in enhancing female students’ interests and self-efficacy in learning engineering and STEM. The unique constructivist learning environment is helpful in improving female students’ sense of belonging in STEM.