Boolean satisfiability (SA T) is a fundamental problem in computer science.

AOC hit by shockingly crude sex insult by White House after she mocked'TINY' Stephen Miller Biden ordered CIA cover-up of his'corrupt' business ties to Ukraine, astonishing secret files show NYC girls aged 12 and 13 meet tragic end after going subway surfing across Williamsburg Bridge at 3.10am ERIC TRUMP: The darkest day in my dad's marriage to Melania... before the ugly truth was exposed More girls are starting their periods younger than ever before - scientists think they've finally found what's causing it Taylor Swift reveals truth behind raunchy song about Travis Kelce's manhood Meghan is accused of'giggling as model stumbles on the catwalk': More Paris Fashion Week disasters emerge, including awkward moment with Kristin Scott Thomas The TRUTH to the doting mother who slaughtered her children and husband told by those she'd been quietly tormenting for years The troubled background of delivery man stabbed by Mark Sanchez... as he launches million-dollar lawsuit and sparks civil war at Fox Revealed: Which slimming jab REALLY works best. The doctors' ultimate expert guide on which to pick, how to save money, beat every side effect... and what you need to know about the'golden dose' I haven't heard that name in so long' Ominous warning for humanity as birds suddenly adopt'unsettling' behavior And a humiliating lifeline: Backroom secrets of Taylor Swift and Blake Lively... after hit new song Bottled water contains dangerous levels of microplastics that lodge in vital organs and raise cancer risk', scientists warn Sea level rise could plunge 100 MILLION buildings underwater, warn scientists - so, is your home at risk? Rising sea levels could plunge more than 100 million buildings underwater by 2100, scientists have warned. The experts in Canada estimated how many buildings in Africa, Southeast Asia and Central and South America would be flooded by different sea level changes. Their assessment found that sea level rises of just 1.6 feet (0.5 metres) would flood three million buildings in the global south alone.

Quantified Integer Programming (QIP) bridges multiple domains by extending Quantified Boolean Formulas (QBF) to incorporate general integer variables and linear constraints while also generalizing Integer Programming through variable quantification. As a special case of Quantified Constraint Satisfaction Problems (QCSP), QIP provides a versatile framework for addressing complex decision-making scenarios. Additionally, the inclusion of a linear objective function enables QIP to effectively model multistage robust discrete linear optimization problems, making it a powerful tool for tackling uncertainty in optimization. While two primary solution paradigms exist for QBF -- search-based and expansion-based approaches -- only search-based methods have been explored for QIP and QCSP. We introduce an expansion-based approach for QIP using Counterexample-Guided Abstraction Refinement (CEGAR), adapting techniques from QBF. We extend this methodology to tackle multistage robust discrete optimization problems with linear constraints and further embed it in an optimization framework, enhancing its applicability. Our experimental results highlight the advantages of this approach, demonstrating superior performance over existing search-based solvers for QIP in specific instances. Furthermore, the ability to model problems using linear constraints enables notable performance gains over state-of-the-art expansion-based solvers for QBF.

This paper presents Phoeni6, a systematic approach for assessing the energy consumption of neural networks while upholding the principles of fair comparison and reproducibility. The methodology automates energy evaluations through containerized tools, robust database management, and versatile data models. In the first case study, the energy consumption of AlexNet and MobileNet was compared using raw and resized images. Results showed that MobileNet is up to 6.25% more energy-e fficient for raw images and 2.32% for resized datasets, while maintaining competitive accuracy levels. In the second study, the impact of image file formats on energy consumption was evaluated. BMP images reduced energy usage by up to 30% compared to PNG, highlighting the influence of file formats on energy e fficiency. These findings emphasize the importance of Phoeni6 in optimizing energy consumption for diverse neural network applications and establishing sustainable artificial intelligence practices. Introduction Deep Neural Networks (DNN) are being used with relative success in fields such as computer vision and natural language processing) [1, 2]. A growing number of initiatives have been promoting the development of these networks to solve everyday problems, including optimizing resource allocation in energy-constrained environments like wireless sensor networks [3]. There are repositories [4, 5] with hundreds of networks created and made available in lists ordered by accuracy, which is the primary metric used to assess the quality of each network. Their results emphasize that the search for energy efficiency can significantly benefit mobile devices' autonomy and positively a ff ect the financial costs and carbon footprints of large data centers distributed worldwide. These works measure energy to evaluate their technique. There is an evident global concern for the energy consumption of software products that a ffect people's daily lives--neural networks are becoming one of them. This fact has important implications on the criteria used to choose these products. It is reasonable to say that energy consumption is becoming part of the criteria for selecting neural networks, just as accuracy is. However, unlike the accuracy calculation, which fundamentally depends on the dataset and the procedures used during the training phase, the energy calculation depends on the devices involved. This aspect adds extra challenges to reproducing the results (RR) and making fair comparisons (FC) between di ff er-ent networks [24]. Evaluating the energy consumption of neural networks while adhering to the principles of Fair Comparison (FC) and Result Reproducibility (RR) presents significant challenges.

Continuous Integration (CI) is a cornerstone of modern software development. However, while widely adopted in traditional software projects, applying CI practices to Machine Learning (ML) projects presents distinctive characteristics. For example, our previous work revealed that ML projects often experience longer build durations and lower test coverage rates compared to their non-ML counterparts. Building on these quantitative findings, this study surveys 155 practitioners from 47 ML projects to investigate the underlying reasons for these distinctive characteristics through a qualitative perspective. Practitioners highlighted eight key differences, including test complexity, infrastructure requirements, and build duration and stability. Common challenges mentioned by practitioners include higher project complexity, model training demands, extensive data handling, increased computational resource needs, and dependency management, all contributing to extended build durations. Furthermore, ML systems' non-deterministic nature, data dependencies, and computational constraints were identified as significant barriers to effective testing. The key takeaway from this study is that while foundational CI principles remain valuable, ML projects require tailored approaches to address their unique challenges. To bridge this gap, we propose a set of ML-specific CI practices, including tracking model performance metrics and prioritizing test execution within CI pipelines. Additionally, our findings highlight the importance of fostering interdisciplinary collaboration to strengthen the testing culture in ML projects. By bridging quantitative findings with practitioners' insights, this study provides a deeper understanding of the interplay between CI practices and the unique demands of ML projects, laying the groundwork for more efficient and robust CI strategies in this domain.

Selecting an appropriate book is crucial for fostering reading habits in children. While children exhibit varying levels of complexity when generating oral narratives, the question arises: do children's books also differ in narrative complexity? This study explores the narrative dynamics of literary texts used in schools, focusing on how their complexity evolves across different grade levels. Using Word-Recurrence Graph Analysis, we examined a dataset of 1,627 literary texts spanning 13 years of education. The findings reveal significant exponential growth in connectedness, particularly during the first three years of schooling, mirroring patterns observed in children's oral narratives. These results highlight the potential of literary texts as a tool to support the development of literacy skills.

This study develops a transfer learning model for the automated classification of two species of fruit flies, Anastrepha fraterculus and Ceratitis capitata, in a controlled laboratory environment. The research addresses the need to optimize identification and classification, which are currently performed manually by experts, being affected by human factors and facing time challenges. The methodological process of this study includes the capture of high-quality images using a mobile phone camera and a stereo microscope, followed by segmentation to reduce size and focus on relevant morphological areas. The images were carefully labeled and preprocessed to ensure the quality and consistency of the dataset used to train the pre-trained convolutional neural network models VGG16, VGG19, and Inception-v3. The results were evaluated using the F1-score, achieving 82% for VGG16 and VGG19, while Inception-v3 reached an F1-score of 93%. Inception-v3's reliability was verified through model testing in uncontrolled environments, with positive results, complemented by the Grad-CAM technique, demonstrating its ability to capture essential morphological features. These findings indicate that Inception-v3 is an effective and replicable approach for classifying Anastrepha fraterculus and Ceratitis capitata, with potential for implementation in automated monitoring systems.

Automated benthic image annotation is crucial to efficiently monitor and protect coral reefs against climate change. Current machine learning approaches fail to capture the hierarchical nature of benthic organisms covering reef substrata, i.e., coral taxonomic levels and health condition. To address this limitation, we propose to annotate benthic images using hierarchical classification. Experiments on a custom dataset from a Northeast Brazilian coral reef show that our approach outperforms flat classifiers, improving both F1 and hierarchical F1 scores by approximately 2\% across varying amounts of training data. In addition, this hierarchical method aligns more closely with ecological objectives.

Neurological disorders that affect speech production, such as Alzheimer's Disease (AD), significantly impact the lives of both patients and caregivers, whether through social, psycho-emotional effects or other aspects not yet fully understood. Recent advancements in Large Language Model (LLM) architectures have developed many tools to identify representative features of neurological disorders through spontaneous speech. However, LLMs typically lack interpretability, meaning they do not provide clear and specific reasons for their decisions. Therefore, there is a need for methods capable of identifying the representative features of neurological disorders in speech and explaining clearly why these features are relevant. This paper presents an explainable LLM method, named SLIME (Statistical and Linguistic Insights for Model Explanation), capable of identifying lexical components representative of AD and indicating which components are most important for the LLM's decision. In developing this method, we used an English-language dataset consisting of transcriptions from the Cookie Theft picture description task. The LLM Bidirectional Encoder Representations from Transformers (BERT) classified the textual descriptions as either AD or control groups. To identify representative lexical features and determine which are most relevant to the model's decision, we used a pipeline involving Integrated Gradients (IG), Linguistic Inquiry and Word Count (LIWC), and statistical analysis. Our method demonstrates that BERT leverages lexical components that reflect a reduction in social references in AD and identifies which further improve the LLM's accuracy. Thus, we provide an explainability tool that enhances confidence in applying LLMs to neurological clinical contexts, particularly in the study of neurodegeneration.