Understanding the nature of glass transition, as well as precise estimation of the glass transition temperature for polymeric materials, remain open questions in both experimental and theoretical polymer sciences. We propose a data-driven approach, which utilizes the high-resolution details accessible through the molecular dynamics simulation and considers the structural information of individual chains. It clearly identifies the glass transition temperature of polymer melts of weakly semiflexible chains. By combining principal component analysis and clustering, we identify the glass transition temperature in the asymptotic limit even from relatively short-time trajectories, which just reach into the Rouse-like monomer displacement regime. We demonstrate that fluctuations captured by the principal component analysis reflect the change in a chain's behaviour: from conformational rearrangement above to small rearrangements below the glass transition temperature. Our approach is straightforward to apply, and should be applicable to other polymeric glass-forming liquids.

The growing utilization of artificial intelligence (AI) is apparent across all facets of society, from the models used to enable semi-autonomous cars, to models that serve up recommendations on streaming or e-commerce sites, and in the language models used to create more natural, intuitive human-machine interaction. However, these technological achievements come with costs, namely the massive amounts of electrical power required to train AI algorithms, build and operate the hardware on which these algorithms are run, and to run and maintain that hardware throughout its life cycle. The cost of the electricity is not the only impact; traditional power plants that use fossil fuels (as well as some geothermal processes) to create power emit relatively high amounts of carbon dioxide (CO2) as they generate electricity, compared with renewable energy sources such as solar, wind, or nuclear plants, which do not. That emitted CO2 has a direct impact on the environment. While all software has a carbon footprint--the amount of CO2 directly related to its use--large and complex AI models have a significant environmental cost and are increasingly coming under scrutiny.

At the beginning era of large language model, it is quite critical to generate a high-quality financial dataset to fine-tune a large language model for financial related tasks. Thus, this paper presents a carefully designed data creation pipeline for this purpose. Particularly, we initiate a dialogue between an AI investor and financial expert using ChatGPT and incorporate the feedback of human financial experts, leading to the refinement of the dataset. This pipeline yielded a robust instruction tuning dataset comprised of 103k multi-turn chats. Extensive experiments have been conducted on this dataset to evaluate the model's performance by adopting an external GPT-4 as the judge. The promising experimental results verify that our approach led to significant advancements in generating accurate, relevant, and financial-style responses from AI models, and thus providing a powerful tool for applications within the financial sector.

Consistency and reliability are crucial for conducting AI research. Many famous research fields, such as object detection, have been compared and validated with solid benchmark frameworks. After AlphaFold2, the protein folding task has entered a new phase, and many methods are proposed based on the component of AlphaFold2. The importance of a unified research framework in protein folding contains implementations and benchmarks to consistently and fairly compare various approaches. To achieve this, we present Solvent, a protein folding framework that supports significant components of state-of-the-art models in the manner of an off-the-shelf interface Solvent contains different models implemented in a unified codebase and supports training and evaluation for defined models on the same dataset. We benchmark well-known algorithms and their components and provide experiments that give helpful insights into the protein structure modeling field. We hope that Solvent will increase the reliability and consistency of proposed models and give efficiency in both speed and costs, resulting in acceleration on protein folding modeling research.

CMU's Autonomous Exploration Research Team has developed a suite of robotic systems and planners enabling robots to explore more quickly, probe the darkest corners of unknown environments, and create more accurate and detailed maps -- all without human help. A research group in Carnegie Mellon University's Robotics Institute is creating the next generation of explorers -- robots. The Autonomous Exploration Research Team has developed a suite of robotic systems and planners enabling robots to explore more quickly, probe the darkest corners of unknown environments, and create more accurate and detailed maps. The systems allow robots to do all this autonomously, finding their way and creating a map without human intervention. "You can set it in any environment, like a department store or a residential building after a disaster, and off it goes," said Ji Zhang, a systems scientist in the Robotics Institute.

The continuous thriving of the Blockchain society motivates research in novel designs of schemes supporting cryptocurrencies. Previously multiple Proof-of-Deep-Learning(PoDL) consensuses have been proposed to replace hashing with useful work such as deep learning model training tasks. The energy will be more efficiently used while maintaining the ledger. However deep learning models are problem-specific and can be extremely complex. Current PoDL consensuses still require much work to realize in the real world. In this paper, we proposed a novel consensus named Proof-of-Federated-Learning-Subchain(PoFLSC) to fill the gap. We applied a subchain to record the training, challenging, and auditing activities and emphasized the importance of valuable datasets in partner selection. We simulated 20 miners in the subchain to demonstrate the effectiveness of PoFLSC. When we reduce the pool size concerning the reservation priority order, the drop rate difference in the performance in different scenarios further exhibits that the miner with a higher Shapley Value (SV) will gain a better opportunity to be selected when the size of the subchain pool is limited. In the conducted experiments, the PoFLSC consensus supported the subchain manager to be aware of reservation priority and the core partition of contributors to establish and maintain a competitive subchain.

In this research, the application of the Physics-Informed Neural Network (PINN) model is explored to solve transport equation-based Partial Differential Equations (PDEs). The primary objective is to analyze the impact of different activation functions incorporated within the PINN model on its predictive performance, specifically assessing the Mean Squared Error (MSE) and Mean Absolute Error (MAE). The dataset used in the study consists of a varied set of input parameters related to strut diameter, unit cell size, and the corresponding yield stress values. Through this investigation the aim is to understand the effectiveness of the PINN model and the significance of choosing appropriate activation functions for solving complex PDEs in real-world applications. The outcomes suggest that the choice of activation function may have minimal influence on the model's predictive accuracy for this particular problem. The PINN model showcases exceptional generalization capabilities, indicating its capacity to avoid overfitting with the provided dataset. The research underscores the importance of striking a balance between performance and computational efficiency while selecting an activation function for specific real-world applications. These valuable findings contribute to advancing the understanding and potential adoption of PINN as an effective tool for solving challenging PDEs in diverse scientific and engineering domains.

This paper presents two self-contained tutorials on stance detection in Twitter data using BERT fine-tuning and prompting large language models (LLMs). The first tutorial explains BERT architecture and tokenization, guiding users through training, tuning, and evaluating standard and domain-specific BERT models with HuggingFace transformers. The second focuses on constructing prompts and few-shot examples to elicit stances from ChatGPT and open-source FLAN-T5 without fine-tuning. Various prompting strategies are implemented and evaluated using confusion matrices and macro F1 scores. The tutorials provide code, visualizations, and insights revealing the strengths of few-shot ChatGPT and FLAN-T5 which outperform fine-tuned BERTs. By covering both model fine-tuning and prompting-based techniques in an accessible, hands-on manner, these tutorials enable learners to gain applied experience with cutting-edge methods for stance detection.

Fine assembly tasks such as electrical connector insertion have tight tolerances and sensitive components, requiring compensation of alignment errors while applying sufficient force in the insertion direction, ideally at high speeds and while grasping a range of components. Vision, tactile, or force sensors can compensate alignment errors, but have limited bandwidth, limiting the safe assembly speed. Passive compliance such as silicone-based fingers can reduce collision forces and grasp a range of components, but often cannot provide the accuracy or assembly forces required. To support high-speed mechanical search and self-aligning insertion, this paper proposes monolithic additively manufactured fingers which realize a moderate, structured compliance directly proximal to the gripped object. The geometry of finray-effect fingers are adapted to add form-closure features and realize a directionally-dependent stiffness at the fingertip, with a high stiffness to apply insertion forces and lower transverse stiffness to support alignment. Design parameters and mechanical properties of the fingers are investigated with FEM and empirical studies, analyzing the stiffness, maximum load, and viscoelastic effects. The fingers realize a remote center of compliance, which is shown to depend on the rib angle, and a directional stiffness ratio of $14-36$. The fingers are applied to a plug insertion task, realizing a tolerance window of $7.5$ mm and approach speeds of $1.3$ m/s.

The hydrometallurgical method of zinc production involves leaching zinc from ore and then separating the solid residue from the liquid solution by pressure filtration. This separation process is very important since the solid residue contains some moisture that can reduce the amount of zinc recovered. This study modeled the pressure filtration process through Random Forest (RF) and Support Vector Machine (SVM). The models take continuous variables (extracted features) from the lab samples as inputs. Thus, regression models namely Random Forest Regression (RFR) and Support Vector Regression (SVR) were chosen. A total dataset was obtained during the pressure filtration process in two conditions: 1) Polypropylene (S1) and 2) Polyester fabrics (S2). To predict the cake moisture, solids concentration (0.2 and 0.38), temperature (35 and 65 centigrade), pH (2, 3.5, and 5), pressure, cake thickness (14, 20, 26, and 34 mm), air-blow time (2, 10 and 15 min) and filtration time were applied as input variables. The models' predictive accuracy was evaluated by the coefficient of determination (R2) parameter. The results revealed that the RFR model is superior to the SVR model for cake moisture prediction.