Numerous industrial sectors necessitate models capable of providing robust forecasts across various horizons. Despite the recent strides in crafting specific architectures for time-series forecasting and developing pre-trained universal models, a comprehensive examination of their capability in accommodating varied-horizon forecasting during inference is still lacking. This paper bridges this gap through the design and evaluation of the Elastic Time-Series Transformer (ElasTST). The ElasTST model incorporates a non-autoregressive design with placeholders and structured self-attention masks, warranting future outputs that are invariant to adjustments in inference horizons. A tunable version of rotary position embedding is also integrated into ElasTST to capture time-series-specific periods and enhance adaptability to different horizons. Additionally, ElasTST employs a multi-scale patch design, effectively integrating both fine-grained and coarse-grained information. During the training phase, ElasTST uses a horizon reweighting strategy that approximates the effect of random sampling across multiple horizons with a single fixed horizon setting. Through comprehensive experiments and comparisons with state-of-the-art time-series architectures and contemporary foundation models, we demonstrate the efficacy of ElasTST's unique design elements. Our findings position ElasTST as a robust solution for the practical necessity of varied-horizon forecasting.

High-speed video (HSV) segmentation is essential for analyzing dynamic physical processes in scientific and industrial applications, such as boiling heat transfer. Existing models like U-Net struggle with generalization and accurately segmenting complex bubble formations. We present VideoSAM, a specialized adaptation of the Segment Anything Model (SAM), fine-tuned on a diverse HSV dataset for phase detection. Through diverse experiments, VideoSAM demonstrates superior performance across four fluid environments -- Water, FC-72, Nitrogen, and Argon -- significantly outperforming U-Net in complex segmentation tasks. In addition to introducing VideoSAM, we contribute an open-source HSV segmentation dataset designed for phase detection, enabling future research in this domain. Our findings underscore VideoSAM's potential to set new standards in robust and accurate HSV segmentation. The code and dataset used in this study are available online at https://github.com/chikap421/videosam .

Large language models (LLMs) have achieved high accuracy, i.e., more than 90% pass@1, in solving Python coding problems in HumanEval and MBPP. Thus, a natural question is, whether LLMs achieve comparable code completion performance compared to human developers? Unfortunately, one cannot answer this question using existing manual crafted or simple (e.g., single-line) code generation benchmarks, since such tasks fail to represent real-world software development tasks. In addition, existing benchmarks often use poor code correctness metrics, providing misleading conclusions. To address these challenges, we create REPOCOD, a code generation benchmark with 980 problems collected from 11 popular real-world projects, with more than 58% of them requiring file-level or repository-level context information. In addition, REPOCOD has the longest average canonical solution length (331.6 tokens) and the highest average cyclomatic complexity (9.00) compared to existing benchmarks. Each task in REPOCOD includes 313.5 developer-written test cases on average for better correctness evaluation. In our evaluations of ten LLMs, none of the models achieve more than 30% pass@1 on REPOCOD, indicating the necessity of building stronger LLMs that can help developers in real-world software development. REPOCOD is available at https://github.com/lt-asset/REPOCOD

With the rapid development of robotics swarm technology, there are more tasks that require the swarm to pass through complicated environments safely and efficiently. Virtual tube technology is a novel way to achieve this goal. Virtual tubes are free spaces connecting two places that provide safety boundaries and direction of motion for swarm robotics. How to determine the design quality of a virtual tube is a fundamental problem. For such a purpose, this paper presents a degree of flowability (DOF) for two-dimensional virtual tubes according to a minimum energy principle. After that, methods to calculate DOF are proposed with a feasibility analysis. Simulations of swarm robotics in different kinds of two-dimensional virtual tubes are performed to demonstrate the effectiveness of the proposed method of calculating DOF.

We propose an entropy-stable conservative flux form neural network (CFN) that integrates classical numerical conservation laws into a data-driven framework using the entropy-stable, second-order, and non-oscillatory Kurganov-Tadmor (KT) scheme. The proposed entropy-stable CFN uses slope limiting as a denoising mechanism, ensuring accurate predictions in both noisy and sparse observation environments, as well as in both smooth and discontinuous regions. Numerical experiments demonstrate that the entropy-stable CFN achieves both stability and conservation while maintaining accuracy over extended time domains. Furthermore, it successfully predicts shock propagation speeds in long-term simulations, {\it without} oracle knowledge of later-time profiles in the training data.

Large language models (LLMs) have exhibited remarkable reasoning and planning capabilities. Most prior work in this area has used LLMs to reason through steps from an initial to a goal state or criterion, thereby effectively reasoning in a forward direction. Nonetheless, many planning problems exhibit an inherent asymmetry such that planning backward from the goal is significantly easier -- for example, if there are bottlenecks close to the goal. We take inspiration from this observation and demonstrate that this bias holds for LLM planning as well: planning performance in one direction correlates with the planning complexity of the problem in that direction. However, our experiments also reveal systematic biases which lead to poor planning in the backward direction. With this knowledge, we propose a backward planning algorithm for LLMs that first flips the problem and then plans forward in the flipped problem. This helps avoid the backward bias, generate more diverse candidate plans, and exploit asymmetries between the forward and backward directions in planning problems -- we find that combining planning in both directions with self-verification improves the overall planning success rates by 4-24% in three planning domains.

Federated Learning (FL) enables deep learning model training across edge devices and protects user privacy by retaining raw data locally. Data heterogeneity in client distributions slows model convergence and leads to plateauing with reduced precision. Clustered FL solutions address this by grouping clients with statistically similar data and training models for each cluster. However, maintaining consistent client similarity within each group becomes challenging when data drifts occur, significantly impacting model accuracy. In this paper, we introduce Fielding, a clustered FL framework that handles data drifts promptly with low overheads. Fielding detects drifts on all clients and performs selective label distribution-based re-clustering to balance cluster optimality and model performance, remaining robust to malicious clients and varied heterogeneity degrees. Our evaluations show that Fielding improves model final accuracy by 1.9%-5.9% and reaches target accuracies 1.16x-2.61x faster.

Mathematical optimization is fundamental to decision-making across diverse domains, from operations research to healthcare. Yet, translating real-world problems into optimization models remains a formidable challenge, often demanding specialized expertise. This paper formally introduces the concept of $\textbf{autoformulation}$ -- an automated approach to creating optimization models from natural language descriptions for commercial solvers. We identify the three core challenges of autoformulation: (1) defining the vast, problem-dependent hypothesis space, (2) efficiently searching this space under uncertainty, and (3) evaluating formulation correctness (ensuring a formulation accurately represents the problem). To address these challenges, we introduce a novel method leveraging $\textit{Large Language Models}$ (LLMs) within a $\textit{Monte-Carlo Tree Search}$ framework. This approach systematically explores the space of possible formulations by exploiting the hierarchical nature of optimization modeling. LLMs serve two key roles: as dynamic formulation hypothesis generators and as evaluators of formulation correctness. To enhance search efficiency, we introduce a pruning technique to remove trivially equivalent formulations. Empirical evaluations across benchmarks containing linear and mixed-integer programming problems demonstrate our method's superior performance. Additionally, we observe significant efficiency gains from employing LLMs for correctness evaluation and from our pruning techniques.

Tiny Machine Learning (TinyML) has become a growing field in on-device processing for Internet of Things (IoT) applications, capitalizing on AI algorithms that are optimized for their low complexity and energy efficiency. These algorithms are designed to minimize power and memory footprints, making them ideal for the constraints of IoT devices. Within this domain, Spiking Neural Networks (SNNs) stand out as a cutting-edge solution for TinyML, owning to their event-driven processing paradigm which offers an efficient method of handling dataflow. This paper presents a novel SNN architecture based on the 1st Order Leaky Integrate-and-Fire (LIF) neuron model to efficiently deploy vision-based ML algorithms on TinyML systems. A hardware-friendly LIF design is also proposed, and implemented on a Xilinx Artix-7 FPGA. To evaluate the proposed model, a collision avoidance dataset is considered as a case study. The proposed SNN model is compared to the state-of-the-art works and Binarized Convolutional Neural Network (BCNN) as a baseline. The results show the proposed approach is 86% more energy efficient than the baseline.

The spatiotemporally continuous data of normalized difference snow index (NDSI) are key to understanding the mechanisms of snow occurrence and development as well as the patterns of snow distribution changes. However, the presence of clouds, particularly prevalent in polar regions such as the Greenland Ice Sheet (GrIS), introduces a significant number of missing pixels in the MODIS NDSI daily data. To address this issue, this study proposes the utilization of a spatiotemporal extreme gradient boosting (STXGBoost) model generate a comprehensive NDSI dataset. In the proposed model, various input variables are carefully selected, encompassing terrain features, geometry-related parameters, and surface property variables. Moreover, the model incorporates spatiotemporal variation information, enhancing its capacity for reconstructing the NDSI dataset. Verification results demonstrate the efficacy of the STXGBoost model, with a coefficient of determination of 0.962, root mean square error of 0.030, mean absolute error of 0.011, and negligible bias (0.0001). Furthermore, simulation comparisons involving missing data and cross-validation with Landsat NDSI data illustrate the model's capability to accurately reconstruct the spatial distribution of NDSI data. Notably, the proposed model surpasses the performance of traditional machine learning models, showcasing superior NDSI predictive capabilities. This study highlights the potential of leveraging auxiliary data to reconstruct NDSI in GrIS, with implications for broader applications in other regions. The findings offer valuable insights for the reconstruction of NDSI remote sensing data, contributing to the further understanding of spatiotemporal dynamics in snow-covered regions.