mstyslavity/prisma_v3 huggingface dataset:
Cooperation underlies many aspects of the evolution of human and animal societies, where cooperators produce social goods to benefit others. Explaining the emergence of cooperation among selfish individuals has become a major research interest in evolutionary dynamics. Previous studies typically use complex networks to capture the interactions between individuals, and assume that cooperators distribute benefits equally to their neighbors. In practice, the distribution of social goods is often non-uniform, and individuals may selectively provide benefits to those they interact with based on their personal preferences. Here, we develop an efficient algorithm to optimize the placement of donation structure in any given network to minimize the threshold for the emergence of cooperation. We find when cooperators allocate the benefits preferentially compared to the traditional settings of donating to all neighbors, cooperation tends to be maximally promoted. Furthermore, the optimal donation structure is strongly disassortative -- the low-degree nodes tend to donate to high-degree ones preferentially and vice versa. Based on this finding, we offer a local heuristic strategy based on degree thresholds for personalizing the allocation of social goods and choosing each cooperator's recipient, which we use to prove its effectiveness in empirical datasets. Our findings advance the understanding of mechanisms for promoting cooperation with strategic allocations of social goods.
We consider the problem of preprocessing an $n\times n$ matrix M, and supporting queries that, for any vector v, returns the matrix-vector product Mv. This problem has been extensively studied in both theory and practice: on one side, practitioners have developed algorithms that are highly efficient in practice, whereas theoreticians have proven that the problem cannot be solved faster than naive multiplication in the worst-case. This lower bound holds even in the average-case, implying that existing average-case analyses cannot explain this gap between theory and practice. Therefore, we study the problem for structured matrices. We show that for $n\times n$ matrices of VC-dimension d, the matrix-vector multiplication problem can be solved with $\tilde{O}(n^2)$ preprocessing and $\tilde O(n^{2-1/d})$ query time. Given the low constant VC-dimensions observed in most real-world data, our results posit an explanation for why the problem can be solved so much faster in practice. Moreover, our bounds hold even if the matrix does not have a low VC-dimension, but is obtained by (possibly adversarially) corrupting at most a subquadratic number of entries of any unknown low VC-dimension matrix. Our results yield the first non-trivial upper bounds for many applications. In previous works, the online matrix-vector hypothesis (conjecturing that quadratic time is needed per query) was used to prove many conditional lower bounds, showing that it is impossible to compute and maintain high-accuracy estimates for shortest paths, Laplacian solvers, effective resistance, and triangle detection in graphs subject to node insertions and deletions in subquadratic time. Yet, via a reduction to our matrix-vector-multiplication result, we show we can maintain the aforementioned problems efficiently if the input is structured, providing the first subquadratic upper bounds in the high-accuracy regime.
In this work, we explain our approach employed in the BabyLM Challenge, which uses various methods of training language models (LMs) with significantly less data compared to traditional large language models (LLMs) and are inspired by how human children learn. While a human child is exposed to far less linguistic input than an LLM, they still achieve remarkable language understanding and generation abilities. To this end, we develop a model trained on a curated dataset consisting of 10 million words, primarily sourced from child-directed transcripts. The 2024 BabyLM Challenge initial dataset of 10M words is filtered to 8.5M. Next, it is supplemented with a randomly selected subset of TVR dataset consisting of 1.5M words of television dialogues. The latter dataset ensures that similar to children, the model is also exposed to language through media. Furthermore, we reduce the vocabulary size to 32,000 tokens, aligning it with the limited vocabulary of children in the early stages of language acquisition. We use curriculum learning and is able to match the baseline on certain benchmarks while surpassing the baseline on others. Additionally, incorporating common LLM training datasets, such as MADLAD-400, degrades performance. These findings underscore the importance of dataset selection, vocabulary scaling, and curriculum learning in creating more data-efficient language models that better mimic human learning processes.
In this paper I want to propose an argument to support Jerry Fodor's thesis (Fodor 1983) that input systems are modular and thus informationally encapsulated. The argument starts with the suggestion that there is a "grounding problem" in perception, i. e. that there is a problem in explaining how perception that can yield a visual experience is possible, how sensation can become meaningful perception of something for the subject. Given that visual experience is actually possible, this invites a transcendental argument that explains the conditions of its possibility. I propose that one of these conditions is the existence of a visual module in Fodor's sense that allows the step from sensation to object-identifying perception, thus enabling visual experience. It seems to follow that there is informationally encapsulated nonconceptual content in visual perception.
Background. Career abandonment, the process in which professionals leave the activity, assuming positions in another area, among software developers involves frustration with the lost investment and emotional and financial costs, even though being beneficial for the human being, depending on personal context. Previous studies have identified work-related motivators for career abandonment, such as the threat of obsolescence, unstable requirements, and low code quality, though these factors have primarily been examined in former developers. The relationship between these motivators and the intention to abandon among currently active developers remains unexplored. Goal. This article investigates the relationship between key work-related motivators and currently active software developers intention to abandon their careers. Method. We employed a quantitative approach, surveying 221 software developers to validate a theoretical model for career abandonment intention, based on an adaptation of the Investment Model, which incorporates satisfaction with technical aspects of the profession as well as the intention to abandon. Findings. Exploratory and confirmatory factor analyses, through structural equation modeling (SEM), provided robust support for the adapted Investment Model in explaining software developers intention to abandon their careers. Moreover, career commitment significantly impacts the intention to leave the profession, being positively influenced by satisfaction with technical work-related factors and negatively influenced by career alternatives and career investment. Conclusion. The paper offers valuable insights for organizational leaders and research, potentially guiding retention strategies to better support developers, and the adoption of theoretical models to explain career abandonment.
Functional brain network properties are heavily influenced by how the the network nodes are defined. A common approach uses Regions of Interest (ROIs), i.e., predetermined collections of functional magnetic resonance imaging (fMRI) measurement voxels, as nodes. Their definition is always a compromise, as static ROIs cannot capture the dynamics and temporal reconfigurations of the brain areas. Consequently, the ROIs do not align with the functionally homogeneous regions, which can explain the low functional homogeneity values observed for the ROIs. This is in violation of the underlying homogeneity assumption in functional brain network analysis pipelines, which can cause serious problems such as spurious network structure. We introduce the node-reconfiguring multilayer network model, where nodes represent ROIs with boundaries optimized for high functional homogeneity in each time window. In this representation, network layers correspond to time windows, intralayer links depict functional connectivity between ROIs, and interlayer links quantify the overlap between ROIs on different layers. The ROI optimization approach increases functional homogeneity notably, yielding an over 10-fold increase in the fraction of ROIs with high homogeneity compared to static ROIs from the Brainnetome atlas. The optimized ROIs reorganize non-trivially at short time scales of consecutive time windows and across several windows. The amount of reorganization across time windows is connected to intralayer hubness: ROIs with intermediate levels of reorganization have stronger intralayer links than extremely stable or unstable ROIs. Our results demonstrate that reconfiguring parcellations yield more accurate network models of brain function. This supports the ongoing paradigm shift towards the chronnectome that sees the brain as a set of sources with continuously reconfiguring spatial and connectivity profiles.
While XAI focuses on providing AI explanations to humans, can the reverse - humans explaining their judgments to AI - foster richer, synergistic human-AI systems? This paper explores various forms of human inputs to AI and examines how human explanations can guide machine learning models toward automated judgments and explanations that align more closely with human concepts.
Casimir effect, explained by Hendrik Casimir in 1948, is a macroscopic manifestation of quantum electrodynamics. Symmetry breaking due to space confinement of vacuum fluctuations in between two planar mirrors results in an attractive force acting between the two mirrors. Here, we show that spontaneous self-assembly of two-dimensional (2D) layered materials grown on molten metals (rheotaxy) is driven by the mechanical forces exerted by the liquid on the graphene domain and that these forces are subject to a Casimir-like effect. We present in situ environmental and ultrahigh vacuum scanning electron microscopy observations of chemical vapor deposition of graphene on both solid and molten gold and copper, which reveal that the self-assembly occurs via translational and rotational motions of graphene domains during growth on molten metals. Using high-temperature (~ 1300 K) atomic force microscopy measurements of graphene/molten-metal interfaces, coupled with density functional theory and continuum modelling of 2D layers floating on a liquid surface, we attribute the observed phenomena to Casimir-like effect of surface undulations and give further guidance for achieving seamless stitching of 2D layer domains into large scale monolayers.
We study LLM judgments of misinformation expressed with uncertainty. Our experiments study the response of three widely used LLMs (GPT-4o, LlaMA3, DeepSeek-v2) to misinformation propositions that have been verified false and then are transformed into uncertain statements according to an uncertainty typology. Our results show that after transformation, LLMs change their factchecking classification from false to not-false in 25% of the cases. Analysis reveals that the change cannot be explained by predictors to which humans are expected to be sensitive, i.e., modality, linguistic cues, or argumentation strategy. The exception is doxastic transformations, which use linguistic cue phrases such as "It is believed ...".To gain further insight, we prompt the LLM to make another judgment about the transformed misinformation statements that is not related to truth value. Specifically, we study LLM estimates of the frequency with which people make the uncertain statement. We find a small but significant correlation between judgment of fact and estimation of frequency.
In today's data-driven era, computational systems generate vast amounts of data that drive the digital transformation of industries, where Artificial Intelligence (AI) plays a key role. Currently, the demand for eXplainable AI (XAI) has increased to enhance the interpretability, transparency, and trustworthiness of AI models. However, evaluating XAI methods remains challenging: existing evaluation frameworks typically focus on quantitative properties such as fidelity, consistency, and stability without taking into account qualitative characteristics such as satisfaction and interpretability. In addition, practitioners face a lack of guidance in selecting appropriate datasets, AI models, and XAI methods -a major hurdle in human-AI collaboration. To address these gaps, we propose a framework that integrates quantitative benchmarking with qualitative user assessments through virtual personas based on the "Anthology" of backstories of the Large Language Model (LLM). Our framework also incorporates a content-based recommender system that leverages dataset-specific characteristics to match new input data with a repository of benchmarked datasets. This yields an estimated XAI score and provides tailored recommendations for both the optimal AI model and the XAI method for a given scenario.
Ensuring the security of critical infrastructure has become increasingly vital with the proliferation of Internet of Things (IoT) systems. However, the heterogeneous nature of IoT data and the lack of human-comprehensible insights from anomaly detection models remain significant challenges. This paper presents a hybrid framework that combines numerical anomaly detection using Autoencoders with Large Language Models (LLMs) for enhanced preprocessing and interpretability. Two preprocessing approaches are implemented: a traditional method utilizing Principal Component Analysis (PCA) to reduce dimensionality and an LLM-assisted method where GPT-4 dynamically recommends feature selection, transformation, and encoding strategies. Experimental results on the KDDCup99 10% corrected dataset demonstrate that the LLM-assisted preprocessing pipeline significantly improves anomaly detection performance. The macro-average F1 score increased from 0.49 in the traditional PCA-based approach to 0.98 with LLM-driven insights. Additionally, the LLM generates natural language explanations for detected anomalies, providing contextual insights into their causes and implications. This framework highlights the synergy between numerical AI models and LLMs, delivering an accurate, interpretable, and efficient solution for IoT cybersecurity in critical infrastructure.
Concern has recently been expressed by HCI researchers as to the inappropriate treatment of qualitative studies through a positivistic mode of evaluation that places emphasis on metrics and measurement. This contrasts with the nature of qualitative research, which privileges interpretation and understanding over quantification. This paper explains the difference between positivism and interpretivism, the limits of quantification in human science, the distinctive contribution of qualitative research, and how quality assurance might be provided for in the absence of numbers via five basic criteria that reviewers may use to evaluate qualitative studies on their own terms.
Security researchers grapple with the surge of malicious files, necessitating swift identification and classification of malware strains for effective protection. Visual classifiers and in particular Convolutional Neural Networks (CNNs) have emerged as vital tools for this task. However, issues of robustness and explainability, common in other high risk domain like medicine and autonomous vehicles, remain understudied in current literature. Although deep learning visualization classifiers presented in research obtain great results without the need for expert feature extraction, they have not been properly studied in terms of their replicability. Additionally, the literature is not clear on how these types of classifiers arrive to their answers. Our study addresses these gaps by replicating six CNN models and exploring their pitfalls. We employ Class Activation Maps (CAMs), like GradCAM and HiResCAM, to assess model explainability. We evaluate the CNNs' performance and interpretability on two standard datasets, MalImg and Big2015, and a newly created called VX-Zoo. We employ these different CAM techniques to gauge the explainability of each of the models. With these tools, we investigate the underlying factors contributing to different interpretations of inputs across the different models, empowering human researchers to discern patterns crucial for identifying distinct malware families and explain why CNN models arrive at their conclusions. Other then highlighting the patterns found in the interpretability study, we employ the extracted heatmpas to enhance Visual Transformers classifiers' performance and explanation quality. This approach yields substantial improvements in F1 score, ranging from 2% to 8%, across the datasets compared to benchmark values.
One of the primary goals of Human-Robot Interaction (HRI) research is to develop robots that can interpret human behavior and adapt their responses accordingly. Adaptive learning models, such as continual and reinforcement learning, play a crucial role in improving robots' ability to interact effectively in real-world settings. However, these models face significant challenges due to the limited availability of real-world data, particularly in sensitive domains like healthcare and well-being. This data scarcity can hinder a robot's ability to adapt to new situations. To address these challenges, causality provides a structured framework for understanding and modeling the underlying relationships between actions, events, and outcomes. By moving beyond mere pattern recognition, causality enables robots to make more explainable and generalizable decisions. This paper presents an exploratory causality-based analysis through a case study of an adaptive robotic coach delivering positive psychology exercises over four weeks in a workplace setting. The robotic coach autonomously adapts to multimodal human behaviors, such as facial valence and speech duration. By conducting both macro- and micro-level causal analyses, this study aims to gain deeper insights into how adaptability can enhance well-being during interactions. Ultimately, this research seeks to advance our understanding of how causality can help overcome challenges in HRI, particularly in real-world applications.
The language technology moonshot moment of Generative Large Language Models (GLLMs) was not limited to English: These models brought a surge of technological applications, investments, and hype to low-resource languages as well. However, the capabilities of these models in languages such as Danish were, until recently, difficult to verify beyond qualitative demonstrations due to a lack of applicable evaluation corpora. We present a GLLM benchmark to evaluate \emph{Danoliteracy}, a measure of Danish language and cultural competency across eight diverse scenarios such as Danish citizenship tests and abstractive social media question answering. This limited-size benchmark was found to produce a robust ranking that correlates to human feedback at $\rho \sim 0.8$ with GPT-4 and Claude Opus models achieving the highest rankings. Analyzing these model results across scenarios, we find one strong underlying factor explaining $95\%$ of scenario performance variance for GLLMs in Danish, suggesting a $g$ factor of model consistency in language adaptation.
Recent great advances in video generation models have demonstrated their potential to produce high-quality videos, bringing challenges to effective evaluation. Unlike human evaluation, existing automated evaluation metrics lack high-level semantic understanding and reasoning capabilities for video, thus making them infeasible and unexplainable. To fill this gap, we curate GRADEO-Instruct, a multi-dimensional T2V evaluation instruction tuning dataset, including 3.3k videos from over 10 existing video generation models and multi-step reasoning assessments converted by 16k human annotations. We then introduce GRADEO, one of the first specifically designed video evaluation models, which grades AI-generated videos for explainable scores and assessments through multi-step reasoning. Experiments show that our method aligns better with human evaluations than existing methods. Furthermore, our benchmarking reveals that current video generation models struggle to produce content that aligns with human reasoning and complex real-world scenarios. The models, datasets, and codes will be released soon.
We examine whether data generated by explanation techniques, which promote a process of self-reflection, can improve classifier performance. Our work is based on the idea that humans have the ability to make quick, intuitive decisions as well as to reflect on their own thinking and learn from explanations. To the best of our knowledge, this is the first time that the potential of mimicking this process by using explanations generated by explainability methods has been explored. We found that combining explanations with traditional labeled data leads to significant improvements in classification accuracy and training efficiency across multiple image classification datasets and convolutional neural network architectures. It is worth noting that during training, we not only used explanations for the correct or predicted class, but also for other classes. This serves multiple purposes, including allowing for reflection on potential outcomes and enriching the data through augmentation.
The advent of internet medicine provides patients with unprecedented convenience in searching and communicating with doctors relevant to their diseases and desired treatments online. However, the current doctor recommendation systems fail to fully ensure the professionalism and interpretability of the recommended results. In this work, we formulate doctor recommendation as a ranking task and develop a large language model (LLM)-based pointwise ranking framework. Our framework ranks doctors according to their relevance regarding specific diseases-treatment pairs in a zero-shot setting. The advantage of our framework lies in its ability to generate precise and explainable doctor ranking results. Additionally, we construct DrRank, a new expertise-driven doctor ranking dataset comprising over 38 disease-treatment pairs. Experiment results on the DrRank dataset demonstrate that our framework significantly outperforms the strongest cross-encoder baseline, achieving a notable gain of +5.45 in the NDCG@10 score while maintaining affordable latency consumption. Furthermore, we comprehensively present the fairness analysis results of our framework from three perspectives of different diseases, patient gender, and geographical regions. Meanwhile, the interpretability of our framework is rigorously verified by three human experts, providing further evidence of the reliability of our proposed framework for doctor recommendation.