The intermittent nature of photovoltaic (PV) solar energy, driven by variable weather, leads to power losses of 10-70% and an average energy production decrease of 25%. Accurate loss characterization and fault detection are crucial for reliable PV system performance and efficiency, integrating this data into control signal monitoring systems. Computational modeling of PV systems supports technological, economic, and performance analyses, but current models are often rigid, limiting advanced performance optimization and innovation. Conventional fault detection strategies are costly and often yield unreliable results due to complex data signal profiles. Artificial intelligence (AI), especially machine learning algorithms, offers improved fault detection by analyzing relationships between input parameters (e.g., meteorological and electrical) and output metrics (e.g., production). Once trained, these models can effectively identify faults by detecting deviations from expected performance. This research presents a computational model using the PVlib library in Python, incorporating a dynamic loss quantification algorithm that processes meteorological, operational, and technical data. An artificial neural network (ANN) trained on synthetic datasets with a five-minute resolution simulates real-world PV system faults. A dynamic threshold definition for fault detection is based on historical data from a PV system at Universidad de los Andes. Key contributions include: (i) a PV system model with a mean absolute error of 6.0% in daily energy estimation; (ii) dynamic loss quantification without specialized equipment; (iii) an AI-based algorithm for technical parameter estimation, avoiding special monitoring devices; and (iv) a fault detection model achieving 82.2% mean accuracy and 92.6% maximum accuracy.

There has been a growing interest in multi-agent simulators in the domain of economic modeling. However, contemporary research often involves developing reinforcement learning (RL) based models that focus solely on a single type of agents, such as households, firms, or the government. Such an approach overlooks the adaptation of interacting agents thereby failing to capture the complexity of real-world economic systems. In this work, we consider a multi-agent simulator comprised of RL agents of numerous types, including heterogeneous households, firm, central bank and government. In particular, we focus on the crucial role of the government in distributing tax credits to households. We conduct two broad categories of comprehensive experiments dealing with the impact of tax credits on 1) households with varied degrees of myopia (short-sightedness in spending and saving decisions), and 2) households with diverse liquidity profiles. The first category of experiments examines the impact of the frequency of tax credits (e.g. annual vs quarterly) on consumption patterns of myopic households. The second category of experiments focuses on the impact of varying tax credit distribution strategies on households with differing liquidities. We validate our simulation model by reproducing trends observed in real households upon receipt of unforeseen, uniform tax credits, as documented in a JPMorgan Chase report. Based on the results of the latter, we propose an innovative tax credit distribution strategy for the government to reduce inequality among households. We demonstrate the efficacy of this strategy in improving social welfare in our simulation results.

With the increasing integration of cyber-physical systems (CPS) into critical applications, ensuring their resilience against cyberattacks is paramount. A particularly concerning threat is the vulnerability of CPS to deceptive attacks that degrade system performance while remaining undetected. This paper investigates perfectly undetectable false data injection attacks (FDIAs) targeting the trajectory tracking control of a non-holonomic mobile robot. The proposed attack method utilizes affine transformations of intercepted signals, exploiting weaknesses inherent in the partially linear dynamic properties and symmetry of the nonlinear plant. The feasibility and potential impact of these attacks are validated through experiments using a Turtlebot 3 platform, highlighting the urgent need for sophisticated detection mechanisms and resilient control strategies to safeguard CPS against such threats. Furthermore, a novel approach for detection of these attacks called the state monitoring signature function (SMSF) is introduced. An example SMSF, a carefully designed function resilient to FDIA, is shown to be able to detect the presence of a FDIA through signatures based on systems states.

As machine learning (ML) models and datasets increase in complexity, the demand for methods that enhance explainability and interpretability becomes paramount. Prototypes, by encapsulating essential characteristics within data, offer insights that enable tactical decision-making and enhance transparency. Traditional prototype methods often rely on sub-symbolic raw data and opaque latent spaces, reducing explainability and increasing the risk of misinterpretations. This paper presents a novel framework that utilizes semantic descriptions to define prototypes and provide clear explanations, effectively addressing the shortcomings of conventional methods. Our approach leverages concept-based descriptions to cluster data on the semantic level, ensuring that prototypes not only represent underlying properties intuitively but are also straightforward to interpret. Our method simplifies the interpretative process and effectively bridges the gap between complex data structures and human cognitive processes, thereby enhancing transparency and fostering trust. Our approach outperforms existing widely-used prototype methods in facilitating human understanding and informativeness, as validated through a user survey.

Aligning large language models (LLMs) to value systems has emerged as a significant area of research within the fields of AI and NLP. Currently, this alignment process relies on the availability of high-quality supervised and preference data, which can be both time-consuming and expensive to curate or annotate. In this paper, we introduce a systematic end-to-end methodology for aligning LLMs to the implicit and explicit values represented in unstructured text data. Our proposed approach leverages the use of scalable synthetic data generation techniques to effectively align the model to the values present in the unstructured data. Through two distinct use-cases, we demonstrate the efficiency of our methodology on the Mistral-7B-Instruct model. Our approach credibly aligns LLMs to the values embedded within documents, and shows improved performance against other approaches, as quantified through the use of automatic metrics and win rates.

Belarusian President Alexander Lukashenko says Kyiv has stationed more than 120,000 soldiers along its border with Belarus, the country's state news agency reported, as fighting continues amid Ukraine's incursion into Russia's Kursk region. Lukashenko, a staunch ally of Russian President Vladimir Putin, said on Sunday that Minsk had deployed nearly a third of its armed forces along the entire border in response to the Ukrainian deployment, BelTA reported. Kyiv did not immediately respond to the claims. "Seeing their aggressive policy, we have introduced there and placed in certain points – in case of war, they would be defence – our military along the entire border," BelTA quoted Lukashenko as saying in an interview with Russian state television. The president made it clear that should Ukraine try to enter Belarusian soil, they will be on the offensive, Jabari added.

Pre-trained large language models (PLLMs) like OpenAI ChatGPT and Google Gemini face challenges such as inaccurate factual recall, hallucinations, biases, and future data leakage for temporal Knowledge Graph (tKG) forecasting. To address these issues, we introduce sLA-tKGF (small-scale language assistant for tKG forecasting), which utilizes Retrieval-Augmented Generation (RAG) aided, custom-trained small-scale language models through a tabula rasa approach from scratch for effective tKG forecasting. Our framework constructs knowledge-infused prompts with relevant historical data from tKGs, web search results, and PLLMs-generated textual descriptions to understand historical entity relationships prior to the target time. It leverages these external knowledge-infused prompts for deeper understanding and reasoning of context-specific semantic and temporal information to zero-shot prompt small-scale language models for more accurate predictions of future events within tKGs. It reduces hallucinations and mitigates distributional shift challenges through comprehending changing trends over time. As a result, it enables more accurate and contextually grounded forecasts of future events while minimizing computational demands. Rigorous empirical studies demonstrate our framework robustness, scalability, and state-of-the-art (SOTA) performance on benchmark datasets with interpretable and trustworthy tKG forecasting.

Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities across many challenging real-world tasks. As the extension of RL in the multi-agent system domain, multi-agent RL (MARL) not only need to learn the control policy but also requires consideration regarding interactions with all other agents in the environment, mutual influences among different system components, and the distribution of computational resources. This augments the complexity of algorithmic design and poses higher requirements on computational resources. Simultaneously, simulators are crucial to obtain realistic data, which is the fundamentals of RL. In this paper, we first propose a series of metrics of simulators and summarize the features of existing benchmarks. Second, to ease comprehension, we recall the foundational knowledge and then synthesize the recently advanced studies of MARL-related autonomous driving and intelligent transportation systems. Specifically, we examine their environmental modeling, state representation, perception units, and algorithm design. Conclusively, we discuss open challenges as well as prospects and opportunities. We hope this paper can help the researchers integrate MARL technologies and trigger more insightful ideas toward the intelligent and autonomous driving.

In recent years, computational improvements have allowed for more nuanced, data-driven and geographically explicit agent-based simulations. So far, simulations have struggled to adequately represent the attributes that motivate the actions of the agents. In fact, existing population synthesis frameworks generate agent profiles limited to socio-demographic attributes. In this paper, we introduce a novel value-enriched population synthesis framework that integrates a motivational layer with the traditional individual and household socio-demographic layers. Our research highlights the significance of extending the profile of agents in synthetic populations by incorporating data on values, ideologies, opinions and vital priorities, which motivate the agents' behaviour. This motivational layer can help us develop a more nuanced decision-making mechanism for the agents in social simulation settings. Our methodology integrates microdata and macrodata within different Bayesian network structures. This contribution allows to generate synthetic populations with integrated value systems that preserve the inherent socio-demographic distributions of the real population in any specific region.

It can be difficult to interpret a coefficient of an uncertain model. A slope coefficient of a regression model may change as covariates are added or removed from the model. In the context of high-dimensional data, there are too many model extensions to check. However, as we show here, it is possible to efficiently search, with a branch and bound algorithm, for maximum and minimum values of that adjusted slope coefficient over a discrete space of regularized regression models. Here we introduce our algorithm, along with supporting mathematical results, an example application, and a link to our computer code, to help researchers summarize high-dimensional data and assess the stability of regression coefficients in uncertain models.