Building a predictive model that rapidly adapts to real-time condition monitoring (CM) signals is critical for engineering systems/units. Unfortunately, many current methods suffer from a trade-off between representation power and agility in online settings. For instance, parametric methods that assume an underlying functional form for CM signals facilitate efficient online prediction updates. However, this simplification leads to vulnerability to model specifications and an inability to capture complex signals. On the other hand, approaches based on over-parameterized or non-parametric models can excel at explaining complex nonlinear signals, but real-time updates for such models pose a challenging task. In this paper, we propose a neural process-based approach that addresses this trade-off. It encodes available observations within a CM signal into a representation space and then reconstructs the signal's history and evolution for prediction. Once trained, the model can encode an arbitrary number of observations without requiring retraining, enabling on-the-spot real-time predictions along with quantified uncertainty and can be readily updated as more online data is gathered. Furthermore, our model is designed to incorporate qualitative information (i.e., labels) from individual units. This integration not only enhances individualized predictions for each unit but also enables joint inference for both signals and their associated labels. Numerical studies on both synthetic and real-world data in reliability engineering highlight the advantageous features of our model in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.

Olena Rozumovska is at the end of her rope. Her two-bedroom apartment in an Soviet-era concrete building has no electricity or water supply, and the central heating is off after Russian drones and missiles struck Kharkiv, Ukraine's second largest city, on Friday. I want to howl with despair," the 33-year-old, whose husband, Mykhailo, is fighting against Russian forces in southeastern Ukraine, told Al Jazeera over the phone. The outdoor temperatures in Kharkiv barely rose above freezing on Friday, a cold drizzle was falling, and her apartment building "is losing warmth", she said. Early in the morning, she jumped out of bed on hearing the thud of a powerful explosion. More than a dozen heavy, blood-curdling blasts followed as she hid in the frigid basement with her two children, Bohdan, who is seven, and four-year-old Roxana. The children were "hysterical" because they had to leave their Siamese cat behind. Their pet, named Monya, wouldn't come out from under the sofa. What roiled her and millions of Ukrainians was the scope of the bombardment, which became the largest strike on their nation's energy infrastructure since the war began in 2022. "The aim is not just to destroy but to try yet again, like last year, to cause a massive disruption of the energy infrastructure," Energy Minister Herman Halushchenko wrote on Facebook. In the winter of 2022-2023, Moscow switched to massive shelling that targeted energy infrastructure and civilian sites after realising that its blitzkrieg to take over all of Ukraine had failed. Friday's attacks with about 60 drones and 90 missiles killed at least two people, wounded scores, struck Ukraine's largest dam and severed the power supply to the Russia-occupied Zaporizhzhia nuclear plant, officials said. Ukrainian President Volodymyr Zelenskyy rebuked the West for months-long delays in military aid. "Russian missiles have no delays, unlike aid packages for Ukraine.

Web user data plays a central role in the ecosystem of pre-trained large language models (LLMs) and their fine-tuned variants. Billions of data are crawled from the web and fed to LLMs. How can \textit{\textbf{everyday web users}} confirm if LLMs misuse their data without permission? In this work, we suggest that users repeatedly insert personal passphrases into their documents, enabling LLMs to memorize them. These concealed passphrases in user documents, referred to as \textit{ghost sentences}, once they are identified in the generated content of LLMs, users can be sure that their data is used for training. To explore the effectiveness and usage of this copyrighting tool, we define the \textit{user training data identification} task with ghost sentences. Multiple datasets from various sources at different scales are created and tested with LLMs of different sizes. For evaluation, we introduce a last $k$ words verification manner along with two metrics: document and user identification accuracy. In the specific case of instruction tuning of a 3B LLaMA model, 11 out of 16 users with ghost sentences identify their data within the generation content. These 16 users contribute 383 examples to $\sim$1.8M training documents. For continuing pre-training of a 1.1B TinyLlama model, 61 out of 64 users with ghost sentences identify their data within the LLM output. These 64 users contribute 1156 examples to $\sim$10M training documents.

Recent work shows that path gradient estimators for normalizing flows have lower variance compared to standard estimators for variational inference, resulting in improved training. However, they are often prohibitively more expensive from a computational point of view and cannot be applied to maximum likelihood training in a scalable manner, which severely hinders their widespread adoption. In this work, we overcome these crucial limitations. Specifically, we propose a fast path gradient estimator which improves computational efficiency significantly and works for all normalizing flow architectures of practical relevance. We then show that this estimator can also be applied to maximum likelihood training for which it has a regularizing effect as it can take the form of a given target energy function into account. We empirically establish its superior performance and reduced variance for several natural sciences applications.

Inspired by the behavior of birds, we present AirCrab, a hybrid aerial ground manipulator (HAGM) with a single active wheel and a 3-degree of freedom (3-DoF) manipulator. AirCrab leverages a single point of contact with the ground to reduce position drift and improve manipulation accuracy. The single active wheel enables locomotion on narrow surfaces without adding significant weight to the robot. To realize accurate attitude maintenance using propellers on the ground, we design a control allocation method for AirCrab that prioritizes attitude control and dynamically adjusts the thrust input to reduce energy consumption. Experiments verify the effectiveness of the proposed control method and the gain in manipulation accuracy with ground contact. A series of operations to complete the letters 'NTU' demonstrates the capability of the robot to perform challenging hybrid aerial-ground manipulation missions.

Stance detection aims to determine the attitude expressed in text towards a given target. Zero-shot stance detection (ZSSD) has emerged to classify stances towards unseen targets during inference. Recent data augmentation techniques for ZSSD increase transferable knowledge between targets through text or target augmentation. However, these methods exhibit limitations. Target augmentation lacks logical connections between generated targets and source text, while text augmentation relies solely on training data, resulting in insufficient generalization. To address these issues, we propose an encoder-decoder data augmentation (EDDA) framework. The encoder leverages large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships. The decoder generates new samples based on these expressions using a semantic correlation word replacement strategy to increase syntactic diversity. We also analyze the generated expressions to develop a rationale-enhanced network that fully utilizes the augmented data. Experiments on benchmark datasets demonstrate our approach substantially improves over state-of-the-art ZSSD techniques. The proposed EDDA framework increases semantic relevance and syntactic variety in augmented texts while enabling interpretable rationale-based learning.

This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification. Our findings indicate that the feature representation capacity of LAMPER is influenced by the maximum input token threshold imposed by PLMs. The exploration of time series (TS)-based tasks constitutes a research-intensive domain with significant implications with wide-ranging implications in diverse professional fields, including healthcare, finance, and energy (Zhang et al., 2022; Zheng et al., 2023; Santoro et al., 2023). Within the realms of natural language processing (NLP), the dynamic landscape witnesses the rapid evolution of pre-trained language models (PLMs) and prompt engineering (Min et al., 2023; Wei et al., 2022). These advancements underscore their commendable capacity to adeptly execute an extensive array of tasks, particularly under few-shot or even zero-shot conditions (Brown et al., 2020; Webson & Pavlick, 2022).

Placing applications in mobile edge computing servers presents a complex challenge involving many servers, users, and their requests. Existing algorithms take a long time to solve high-dimensional problems with significant uncertainty scenarios. Therefore, an efficient approach is required to maximize the quality of service while considering all technical constraints. One of these approaches is machine learning, which emulates optimal solutions for application placement in edge servers. Machine learning models are expected to learn how to allocate user requests to servers based on the spatial positions of users and servers. In this study, the problem is formulated as a two-stage stochastic programming. A sufficient amount of training records is generated by varying parameters such as user locations, their request rates, and solving the optimization model. Then, based on the distance features of each user from the available servers and their request rates, machine learning models generate decision variables for the first stage of the stochastic optimization model, which is the user-to-server request allocation, and are employed as independent decision agents that reliably mimic the optimization model. Support Vector Machines (SVM) and Multi-layer Perceptron (MLP) are used in this research to achieve practical decisions from the stochastic optimization models. The performance of each model has shown an execution effectiveness of over 80%. This research aims to provide a more efficient approach for tackling high-dimensional problems and scenarios with uncertainties in mobile edge computing by leveraging machine learning models for optimal decision-making in request allocation to edge servers. These results suggest that machine-learning models can significantly improve solution times compared to conventional approaches.

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations.

In this article we address the colony maintenance problem, where a team of robots are tasked with continuously maintaining the energy supply of an autonomous colony. We model this as a global game, where robots measure the energy level of a central nest to determine whether or not to forage for energy sources. We design a mechanism that avoids the trivial equilibrium where all robots always forage. Furthermore, we demonstrate that when the game is played iteratively a negative feedback term stabilizes the number of foraging robots at a non-trivial Nash equilibrium. We compare our approach qualitatively to existing global games, where a positive positive feedback term admits threshold-based decision making, and encourages many robots to forage simultaneously. We discuss how positive feedback can lead to a cascading failure in the presence of a human who recruits robots for external tasks, and we demonstrate the performance of our approach in simulation.