Asset-specific factors are commonly used to forecast financial returns and quantify asset-specific risk premia. Using various machine learning models, we demonstrate that the information contained in these factors leads to even larger economic gains in terms of forecasts of sector returns and the measurement of sector-specific risk premia. To capitalize on the strong predictive results of individual models for the performance of different sectors, we develop a novel online ensemble algorithm that learns to optimize predictive performance. The algorithm continuously adapts over time to determine the optimal combination of individual models by solely analyzing their most recent prediction performance. This makes it particularly suited for time series problems, rolling window backtesting procedures, and systems of potentially black-box models. We derive the optimal gain function, express the corresponding regret bounds in terms of the out-of-sample R-squared measure, and derive optimal learning rate for the algorithm. Empirically, the new ensemble outperforms both individual machine learning models and their simple averages in providing better measurements of sector risk premia. Moreover, it allows for performance attribution of different factors across various sectors, without conditioning on a specific model. Finally, by utilizing monthly predictions from our ensemble, we develop a sector rotation strategy that significantly outperforms the market. The strategy remains robust against various financial factors, periods of financial distress, and conservative transaction costs. Notably, the strategy's efficacy persists over time, exhibiting consistent improvement throughout an extended backtesting period and yielding substantial profits during the economic turbulence of the COVID-19 pandemic.

Classical methods, such as ab initio calculations and molecular dynamics simulations, compute atomic and electronic states based on fundamental principles of quantum and classical mechanics (Figure 1A.). Although accurate, these methods are restricted to materials with simple structures, like molecules and crystals, and struggle with complex materials at larger scales. Advances in computational materials science have significantly broadened the range of materials that can be addressed, not only through improvements in classical approaches but also through new data-driven methods, including machine learning, deep learning, and generative deep learning. However, even the most advanced techniques still face challenges in predicting multiple physical properties of conventional composites like plastics, metal alloys, and rubbers, commonly used in everyday life. One example of advanced classical simulations is high-throughput simulations, which accelerate ab initio calculations using efficient algorithms and advanced computational resources to calculate electronic states for billions of atoms and various physical properties of polymers.

Rapid discovery of new reactions and molecules in recent years has been facilitated by the advancements in high throughput screening, accessibility to a much more complex chemical design space, and the development of accurate molecular modeling frameworks. A holistic study of the growing chemistry literature is, therefore, required that focuses on understanding the recent trends and extrapolating them into possible future trajectories. To this end, several network theory-based studies have been reported that use a directed graph representation of chemical reactions. Here, we perform a study based on representing chemical reactions as hypergraphs where the hyperedges represent chemical reactions and nodes represent the participating molecules. We use a standard reactions dataset to construct a hypernetwork and report its statistics such as degree distributions, average path length, assortativity or degree correlations, PageRank centrality, and graph-based clusters (or communities). We also compute each statistic for an equivalent directed graph representation of reactions to draw parallels and highlight differences between the two. To demonstrate the AI applicability of hypergraph reaction representation, we generate dense hypergraph embeddings and use them in the reaction classification problem. We conclude that the hypernetwork representation is flexible, preserves reaction context, and uncovers hidden insights that are otherwise not apparent in a traditional directed graph representation of chemical reactions.

Energetic materials (EM) cover a wide spectrum of propellants, pyrotechnics, and explosives and are key components in military applications for propulsion and munition systems and in civilian applications such as construction and mining [1]. Heterogenous/composite EMs have complex microstructures which significantly influence--along with chemistry--the property and performance of these materials [2-8]. There is increasing research interest in controlling the microstructure of EM, to engineer their properties and performance for targeted functional specificity [9-10]. EMs are typically solid-solid composites of organic energetic crystals (commonly CHNO compounds), inclusions (i.e., metals, nanoparticles), and plastic binders. The CHNO materials are commonly categorized based on how sensitive they are to an external load/mechanical insult. They can range f rom'insensitive' (such as TATB - based EMs [11]) to'highly sensitive' (PETN-based EMs [12-13]) with others such as HMX, CL-20, and RDX ranging in between [14]. The sensitivity is closely connected with the molecular structure of these species of EMs within the CHNO family. However, when they are formed into propellants and explosives, the sensitivity is also impacted by the physical structure, composition, and formulation of the material mixtures, as reviewed by Handley et al. [1]. In other words, the design of a mixture and its microstructure can define the overall properties and performance characteristics of formed EM, thus opening the possibility of systematic methods to engineer materials by their design.

The synthesis of tactile sensing with compliance is essential to many fields, from agricultural usages like fruit picking, to sustainability practices such as sorting recycling, to the creation of safe home-care robots for the elderly to age with dignity. From tactile sensing, we can discern material properties, recognize textures, and determine softness, while with compliance, we are able to securely and safely interact with the objects and the environment around us. These two abilities can culminate into a useful soft robotic gripper, such as the original GelSight Fin Ray, which is able to grasp a large variety of different objects and also perform a simple household manipulation task: wine glass reorientation. Although the original GelSight Fin Ray solves the problem of interfacing a generally rigid, high-resolution sensor with a soft, compliant structure, we can improve the robustness of the sensor and implement techniques that make such camera-based tactile sensors applicable to a wider variety of soft robot designs. We first integrate flexible mirrors and incorporate the rigid electronic components into the base of the gripper, which greatly improves the compliance of the Fin Ray structure. Then, we synthesize a flexible and high-elongation silicone adhesive-based fluorescent paint, which can provide good quality 2D tactile localization results for our sensor. Finally, we incorporate all of these techniques into a new design: the Baby Fin Ray, which we use to dig through clutter, and perform successful classification of nuts in their shells. The supplementary video can be found here: https://youtu.be/_oD_QFtYTPM

Chemists need to perform many laborious and time-consuming experiments in the lab to discover and understand the properties of new materials. To support and accelerate this process, we propose a robot framework for manipulation that autonomously performs chemistry experiments. Our framework receives high-level abstract descriptions of chemistry experiments, perceives the lab workspace, and autonomously plans multi-step actions and motions. The robot interacts with a wide range of lab equipment and executes the generated plans. A key component of our method is constrained task and motion planning using PDDLStream solvers. Preventing collisions and spillage is done by introducing a constrained motion planner. Our planning framework can conduct different experiments employing implemented actions and lab tools. We demonstrate the utility of our framework on pouring skills for various materials and two fundamental chemical experiments for materials synthesis: solubility and recrystallization.

Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating information within scientific and other literature published between 1790 and 1970 documenting their theft, donation, sale, or exchange between institutions. This article reports on collaborative research by data scientists and social science researchers in the Research, Reconcile, Renew Network (RRR) to develop and apply text mining techniques to identify this vital information. We describe our work to date on developing a machine learning-based solution to automate the process of finding and semantically analysing relevant texts. Classification models, particularly deep learning-based models, are known to have low accuracy when trained with small amounts of labelled (i.e. relevant/non-relevant) documents. To improve the accuracy of our detection model, we explore the use of an Informed Neural Network (INN) model that describes documentary content using expert-informed contextual knowledge. Only a few labelled documents are used to provide specificity to the model, using conceptually related keywords identified by RRR experts in provenance research. The results confirm the value of using an INN network model for identifying relevant documents related to the investigation of the global commercial trade in Indigenous human remains. Empirical analysis suggests that this INN model can be generalized for use by other researchers in the social sciences and humanities who want to extract relevant information from large textual corpora.

Graphene's 2D nature, single molecule sensitivity, low noise, and high carrier concentration have generated a lot of interest in its application in gas sensors. However, due to its inherent non-selectivity, and huge p-doping in atmospheric air, its applications in gas sensing are often limited to controlled environments such as nitrogen, dry air, or synthetic humid air. While humidity conditions in synthetic air could be used to achieve controlled hole doping of the graphene channel, this does not adequately mirror the situation in atmospheric air. Moreover, atmospheric air contains several gases with concentrations similar to or larger than the analytic gas. Such shortcomings of graphene-based sensors hinder selective gas detection and molecular species identification in atmospheric air, which is required for applications in environmental monitoring, and non-invasive medical diagnosis of ailments.

Everyday activities like using a GPS to map out the best driving route or translating a document consume energy, water and mineral resources -- lots of it. These applications run in the cloud, a nebulous term for the millions of powerful computers in vast data centers worldwide. Mobile applications depend on legions of computers to store trillions of data and perform split-second operations (e.g. Estimates of the energy consumption of data centers range between 1-2% of total global consumption. All signs indicate that data center energy consumption is about to skyrocket.

SensOre Ltd (ASX:S3N) is pleased to announce that SensOre and Gold Road (ASX: GOR) have reached agreement to restructure arrangements surrounding the Yilgarn Exploration Ventures (YEV) portfolio. SensOre has agreed to acquire Gold Road Resources' 40% minority interest in YEV for 800,000 SensOre shares. Yilgarn Exploration Ventures holds a portfolio of prospective gold assets in the Eastern Goldfields of Western Australia. SensOre aims to become the top performing minerals targeting company in the world through the deployment of AI and machine learning (ML) technologies, specifically its Discriminant Predictive Targeting (DPT) workflow. SensOre collects all available geological information in a terrane and places it in a multidimensional hypercube or data cube.