User-generated content from social media is produced in many languages, making it technically challenging to compare the discussed themes from one domain across different cultures and regions. It is relevant for domains in a globalized world, such as market research, where people from two nations and markets might have different requirements for a product. We propose a simple, modern, and effective method for building a single topic model with sentiment analysis capable of covering multiple languages simultanteously, based on a pre-trained state-of-the-art deep neural network for natural language understanding. To demonstrate its feasibility, we apply the model to newspaper articles and user comments of a specific domain, i.e., organic food products and related consumption behavior. The themes match across languages. Additionally, we obtain an high proportion of stable and domain-relevant topics, a meaningful relation between topics and their respective textual contents, and an interpretable representation for social media documents. Marketing can potentially benefit from our method, since it provides an easy-to-use means of addressing specific customer interests from different market regions around the globe. For reproducibility, we provide the code, data, and results of our study.

However, despite its potential, metal AM has not yet reached its expected level of usage in industries, in part due to a lack of accurate prediction of the properties of printed components. For example, in laser powder bed fusion (LPBF), the layer of metal powder is scanned by a laser heat source which converts the metal powder to liquid, which eventually solidifies and converts to the final product. Accurate thermal history prediction is crucial for LPBF, as all other phenomena, including thermal residual stress and microstructure, depend on it. The melt pool dynamics play a very important role in the development of the thermal map for LPBF. Many factors influence the melt pool dynamics in LPBF such as the unique thermal cycle of rapid heating and solidification, steep temperature gradient and high cooling rate, evaporation, surface tension, natural convection, Marangoni convection, vapor recoil pressure, and Argon flow over the melt pool. Several researchers have developed computational models to better understand melt pool dynamics, incorporating these complex phenomena [1-5]. Physics-based simulation such as computational fluid dynamics (CFD) is the key method to model melt pool dynamics (Figure 1). Li et al. [6] utilized a 2D model to examine the melting and

Soil near-Infrared (NIR) spectral absorbance/reflectance libraries are utilized towards improving agricultural production and analysis of soil properties which are key prerequisite for agro-ecological balance and environmental sustainability. Carbonates in particular, represent a soil property which is mostly affected even by mild, let alone extreme, changes of environmental conditions during climate change. In this study we propose a rapid and efficient way to predict carbonates content in soil by means of Fourier Transform Near-Infrared (FT-NIR) reflectance spectroscopy and by use of deep learning methods. We exploited multiple machine learning methods, such as: 1) a Multi-Layered Perceptron Regressor (MLP) and 2) a Convolutional Neural Network (CNN) and compare their performance with other traditional machine learning algorithms such as Partial Least Squares Regression (PLSR), Cubist and Support Vector Machines (SVM) on the combined dataset of two NIR spectral libraries: Kellogg Soil Survey Laboratory (KSSL) of the United States Department of Agriculture (USDA), a dataset of soil samples reflectance spectra collected nationwide, and Land Use and Coverage Area Frame Survey (LUCAS) TopSoil (European Soil Library) which contains soil sample absorbance spectra from all over the European Union, and use them to predict carbonate content on never-before-seen soil samples. Soil samples in KSSL and in TopSoil spectral libraries were acquired in the spectral region of visible-near infrared (Vis-NIR) (350-2500 nm), however in this study, only the NIR spectral region (1150-2500 nm) was utilized. Quantification of carbonates by means of X-ray-Diffraction is in good agreement with the volumetric method and the MLP prediction. Our work contributes to rapid carbonates content prediction in soil samples in cases where: 1) no volumetric method is available and 2) only NIR spectra absorbance data are available. Up till now and to the best of our knowledge, there exists no other study, that presents a prediction model trained on such an extensive dataset with such promising results on unseen data, undoubtedly supporting the notion that deep learning models present excellent prediction tools for soil carbonates content.

Plastic waste entering the riverine harms local ecosystems leading to negative ecological and economic impacts. Large parcels of plastic waste are transported from inland to oceans leading to a global scale problem of floating debris fields. In this context, efficient and automatized monitoring of mismanaged plastic waste is paramount. To address this problem, we analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios. We enable near-real-time tracking of partially submerged plastics by using snapshot Visible-Shortwave Infrared hyperspectral imaging. Our experiments indicate that imaging strategies associated with machine learning classification approaches can lead to high detection accuracy even in challenging scenarios, especially when leveraging hyperspectral data and nonlinear classifiers. All code, data, and models are available online: https://github.com/RIVeR-Lab/hyperspectral_macro_plastic_detection.

Molecular knowledge resides within three different modalities of information sources: molecular structures, biomedical documents, and knowledge bases. Effective incorporation of molecular knowledge from these modalities holds paramount significance in facilitating biomedical research. However, existing multimodal molecular foundation models exhibit limitations in capturing intricate connections between molecular structures and texts, and more importantly, none of them attempt to leverage a wealth of molecular expertise derived from knowledge graphs. In this study, we introduce MolFM, a multimodal molecular foundation model designed to facilitate joint representation learning from molecular structures, biomedical texts, and knowledge graphs. We propose cross-modal attention between atoms of molecular structures, neighbors of molecule entities and semantically related texts to facilitate cross-modal comprehension. We provide theoretical analysis that our cross-modal pre-training captures local and global molecular knowledge by minimizing the distance in the feature space between different modalities of the same molecule, as well as molecules sharing similar structures or functions. MolFM achieves state-of-the-art performance on various downstream tasks. On cross-modal retrieval, MolFM outperforms existing models with 12.13% and 5.04% absolute gains under the zero-shot and fine-tuning settings, respectively. Furthermore, qualitative analysis showcases MolFM's implicit ability to provide grounding from molecular substructures and knowledge graphs.

Two dates from 2022 are destined to echo in geopolitical history. The first, Russia's invasion of Ukraine on February 24, hardly needs further elaboration. The second is October 7, 2022, when the United States enacted a new set of export controls designed to cripple China's future progress in AI technology. Rather than target AI software, the export controls choke off China's access to the advanced (and almost exclusively American-designed) computer chip hardware that powers AI. More than a decade of breakthrough after breakthrough in AI technology has convinced policymakers in both Beijing and Washington that leadership in AI technology is foundational to the future of economic and military power.

This letter addresses the problem of trajectory planning in a marsupial robotic system consisting of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) through a non-taut tether with controllable length. To the best of our knowledge, this is the first method that addresses the trajectory planning of a marsupial UGV-UAV with a non-taut tether. The objective is to determine a synchronized collision-free trajectory for the three marsupial system agents: UAV, UGV, and tether. First, we present a path planning solution based on optimal Rapidly-exploring Random Trees (RRT*) with novel sampling and steering techniques to speed-up the computation. This algorithm is able to obtain collision-free paths for the UAV and the UGV, taking into account the 3D environment and the tether. Then, the letter presents a trajectory planner based on non-linear least squares. The optimizer takes into account aspects not considered in the path planning, like temporal constraints of the motion imposed by limits on the velocities and accelerations of the robots, or raising the tether's clearance. Simulated and field test results demonstrate that the approach generates obstacle-free, smooth, and feasible trajectories for the marsupial system.

Due to complexity and dynamics of construction work, resource, and cash flows, poor management of them usually leads to time and cost overruns, bankruptcy, even project failure. Existing approaches in construction failed to achieve optimal control of resource flow in a dynamic environment with uncertainty. Therefore, this paper introducess a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects. First, a mathematical model based on a partially observable Markov decision process is established to formulate the complex interactions of construction work, resource, and cash flows as well as uncertainty and variability of diverse influence factors. Meanwhile, to efficiently find the optimal solutions, a deep reinforcement learning (DRL) based method is introduced to realize the continuous adaptive optimal control of labor and material flows, thereby optimizing the work and cash flows. To assist the training process of DRL, a simulator based on discrete event simulation is also developed to mimic the dynamic features and external environments of a project. Experiments in simulated scenarios illustrate that our method outperforms the vanilla empirical method and genetic algorithm, possesses remarkable capability in diverse projects and external environments, and a hybrid agent of DRL and empirical method leads to the best result. This paper contributes to adaptive control and optimization of coupled work, resource, and cash flows, and may serve as a step stone for adopting DRL technology in construction project management.

Recently, molecular relational learning, whose goal is to predict the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. In this work, we propose CMRL that is robust to the distributional shift in molecular relational learning by detecting the core substructure that is causally related to chemical reactions. To do so, we first assume a causal relationship based on the domain knowledge of molecular sciences and construct a structural causal model (SCM) that reveals the relationship between variables. Based on the SCM, we introduce a novel conditional intervention framework whose intervention is conditioned on the paired molecule. With the conditional intervention framework, our model successfully learns from the causal substructure and alleviates the confounding effect of shortcut substructures that are spuriously correlated to chemical reactions. Extensive experiments on various tasks with real-world and synthetic datasets demonstrate the superiority of CMRL over state-of-the-art baseline models. Our code is available at https://github.com/Namkyeong/CMRL.

Miniature robots provide unprecedented access to confined environments and show promising potential for novel applications such as search-and-rescue and high-value asset inspection. The capability of body deformation further enhances the reachability of these small robots in complex cluttered terrains similar to those of insects and soft arthropods. Motivated by this concept, we present CLARI, an insect-scale 2.59g quadrupedal robot capable of body deformation with tethered electrical connections for power and control and manufactured using laminate fabrication and assembled using origami pop-up techniques. In order to enable locomotion in multiple shape configurations, we designed a novel body architecture comprising of modular, actuated leg mechanisms. Overall, CLARI has eight independently actuated degrees of freedom (two per modular leg unit) driven by custom piezoelectric actuators, making it mechanically dextrous. We characterize open-loop robot locomotion at multiple stride frequencies (1-10Hz) using multiple gaits (trot, walk, etc.) in three different fixed body shapes (long, symmetric, wide) and illustrate the robot's capabilities. Finally, we demonstrate preliminary results of CLARI locomoting with a compliant body in open terrain and through a laterally constrained gap, a novel capability for legged robots. Our results represent the first step towards achieving effective cluttered terrain navigation with adaptable compliant robots in real-world environments.