The chimp optimization algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Although ChOA has shown promising results on optimization functions, it suffers from a slow convergence rate and low exploration capability. Therefore, in this paper, a modified ChOA is proposed to improve the exploration and exploitation capabilities of the ChOA. This improvement is performed using greedy search and opposition-based learning (OBL), respectively. In order to investigate the efficiency of the OBLChOA, the OBLChOA's performance is evaluated by twenty-three standard benchmark functions, ten suit tests of IEEE CEC06-2019, randomly generated landscape, and twelve real-world Constrained Optimization Problems (IEEE COPs-2020) from a variety of engineering fields, including industrial chemical producer, power system, process design and synthesis, mechanical design, power-electronic, and livestock feed ration.

In South Korea, these sleepless nights happen in many places because of the noise from upstairs neighbors. Living in the apartment units means dealing with a level of noise from the neighborhood on a daily basis. The Korea Institute of Civil Engineering and Building Technology has announced a new approach for predicting the footstep sounds of upstairs residents using a convolutional neural network (CNN) model based on vibration signals. The CNN models are widely applied in computer vision tasks. The vibration sensors are designed to be installed on the wall and floor slab of a residential building to monitor footstep-induced vibration in real–time.

The accurate prediction of physicochemical properties of chemical compounds in mixtures (such as the activity coefficient at infinite dilution $\gamma_{ij}^\infty$) is essential for developing novel and more sustainable chemical processes. In this work, we analyze the performance of previously-proposed GNN-based models for the prediction of $\gamma_{ij}^\infty$, and compare them with several mechanistic models in a series of 9 isothermal studies. Moreover, we develop the Gibbs-Helmholtz Graph Neural Network (GH-GNN) model for predicting $\ln \gamma_{ij}^\infty$ of molecular systems at different temperatures. Our method combines the simplicity of a Gibbs-Helmholtz-derived expression with a series of graph neural networks that incorporate explicit molecular and intermolecular descriptors for capturing dispersion and hydrogen bonding effects. We have trained this model using experimentally determined $\ln \gamma_{ij}^\infty$ data of 40,219 binary-systems involving 1032 solutes and 866 solvents, overall showing superior performance compared to the popular UNIFAC-Dortmund model. We analyze the performance of GH-GNN for continuous and discrete inter/extrapolation and give indications for the model's applicability domain and expected accuracy. In general, GH-GNN is able to produce accurate predictions for extrapolated binary-systems if at least 25 systems with the same combination of solute-solvent chemical classes are contained in the training set and a similarity indicator above 0.35 is also present. This model and its applicability domain recommendations have been made open-source at https://github.com/edgarsmdn/GH-GNN.

Data-driven models are central to scientific discovery. In efforts to achieve state-of-the-art model accuracy, researchers are employing increasingly complex machine learning algorithms that often outperform simple regressions in interpolative settings (e.g. random k-fold cross-validation) but suffer from poor extrapolation performance, portability, and human interpretability, which limits their potential for facilitating novel scientific insight. Here we examine the trade-off between model performance and interpretability across a broad range of science and engineering problems with an emphasis on materials science datasets. We compare the performance of black box random forest and neural network machine learning algorithms to that of single-feature linear regressions which are fitted using interpretable input features discovered by a simple random search algorithm. For interpolation problems, the average prediction errors of linear regressions were twice as high as those of black box models. Remarkably, when prediction tasks required extrapolation, linear models yielded average error only 5% higher than that of black box models, and outperformed black box models in roughly 40% of the tested prediction tasks, which suggests that they may be desirable over complex algorithms in many extrapolation problems because of their superior interpretability, computational overhead, and ease of use. The results challenge the common assumption that extrapolative models for scientific machine learning are constrained by an inherent trade-off between performance and interpretability.

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datasets produce impressive results and top many of today's benchmarks. A notable omission within this family of large-scale datasets is 3D data. Despite considerable interest and potential applications in 3D vision, datasets of high-fidelity 3D models continue to be mid-sized with limited diversity of object categories. Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. We demonstrate the large potential of Objaverse via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models. Objaverse can open new directions for research and enable new applications across the field of AI.

Though they may look like they are made from rubber, human brains are softer and squishier. Their ability to resist pressure is much less than the polystyrene foam used for packaging, more comparable to that of Jell-O. Nicholas Bennion at Cardiff University in the UK and his colleagues set out to develop a method for obtaining more accurate measurements of the brain's physical properties inside living humans. Most of what we know about how brain tissue reacts to instruments touching it during neurosurgery comes from organs that have been cut into or removed and preserved in chemicals, which can affect tissue stiffness and resilience. The researchers performed MRI scans of people lying face down and then face up to shift the location of the brain in the skull.

Though they may look like they're made from rubber, human brains are softer and squishier. Their ability to resist pressure is like that of a slab of gelatine, and they break apart more easily than polystyrene foam used for packaging. Nicholas Bennion at Cardiff University in the UK and his colleagues set out to develop a method for obtaining more accurate measurements of the brain's physical properties inside living humans. Most of what we know about how brain tissue reacts to instruments touching it during neurosurgery comes from organs that have been cut into or removed and preserved in chemicals, which can affect tissue stiffness and resiliency. Combining a machine learning algorithm with MRI scans of people lying face down and then face up to shift the location of the brain in the skull, the researchers were able to work out different material characteristics of the brain and tissues that connect it to the skull.

Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas \textit{outside} such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To this end, we develop an exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains. Furthermore, end-users can `zoom in' to specific domain clusters to retrieve more papers from them and understand nuanced differences within the clusters. Our case studies with scientists uncover opportunities and design implications for systems aimed at facilitating cross-domain exploration and inspiration.

Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found. Consequently, the goal is to provide a valid synthesis route for a molecule. As more single-step models develop, we see increasing accuracy in the prediction of molecular disconnections, potentially improving the creation of synthetic paths. Multi-step approaches repeatedly apply the chemical information stored in single-step retrosynthesis models. However, this connection is not reflected in contemporary research, fixing either the single-step model or the multi-step algorithm in the process. In this work, we establish a bridge between both tasks by benchmarking the performance and transfer of different single-step retrosynthesis models to the multi-step domain by leveraging two common search algorithms, Monte Carlo Tree Search and Retro*. We show that models designed for single-step retrosynthesis, when extended to multi-step, can have a tremendous impact on the route finding capabilities of current multi-step methods, improving performance by up to +30% compared to the most widely used model. Furthermore, we observe no clear link between contemporary single-step and multi-step evaluation metrics, showing that single-step models need to be developed and tested for the multi-step domain and not as an isolated task to find synthesis routes for molecules of interest.

Tool flank wear monitoring can minimize machining downtime costs while increasing productivity and product quality. In some industrial applications, only a limited level of tool wear is allowed to attain necessary tolerances. It may become challenging to monitor a limited level of tool wear in the data collected from the machine due to the other components, such as the flexible vibrations of the machine, dominating the measurement signals. In this study, a tool wear monitoring technique to predict limited levels of tool wear from the spindle motor current and dynamometer measurements is presented. High-frequency spindle motor current data is collected with an industrial edge device while the cutting forces and torque are measured with a rotary dynamometer in drilling tests for a selected number of holes. Feature engineering is conducted to identify the statistical features of the measurement signals that are most sensitive to small changes in tool wear. A neural network based on the long short-term memory (LSTM) architecture is developed to predict tool flank wear from the measured spindle motor current and dynamometer signals. It is demonstrated that the proposed technique predicts tool flank wear with good accuracy and high computational efficiency. The proposed technique can easily be implemented in an industrial edge device as a real-time predictive maintenance application to minimize the costs due to manufacturing downtime and tool underuse or overuse.