pwc-archive/papers-with-abstracts huggingface dataset:
Convergence of Unadjusted Langevin in High Dimensions: Delocalization of Bias
Authors: Yifan Chen, Xiaoou Cheng, Jonathan Niles-Weed, Jonathan Weare
Date: 2024-08-20 00:00:00

The unadjusted Langevin algorithm is commonly used to sample probability distributions in extremely high-dimensional settings. However, existing analyses of the algorithm for strongly log-concave distributions suggest that, as the dimension $d$ of the problem increases, the number of iterations required to ensure convergence within a desired error in the $W_2$ metric scales in proportion to $d$ or $\sqrt{d}$. In this paper, we argue that, despite this poor scaling of the $W_2$ error for the full set of variables, the behavior for a small number of variables can be significantly better: a number of iterations proportional to $K$, up to logarithmic terms in $d$, often suffices for the algorithm to converge to within a desired $W_2$ error for all $K$-marginals. We refer to this effect as delocalization of bias. We show that the delocalization effect does not hold universally and prove its validity for Gaussian distributions and strongly log-concave distributions with certain sparse interactions. Our analysis relies on a novel $W_{2,\ell^\infty}$ metric to measure convergence. A key technical challenge we address is the lack of a one-step contraction property in this metric. Finally, we use asymptotic arguments to explore potential generalizations of the delocalization effect beyond the Gaussian and sparse interactions setting.
Structural Representation Learning and Disentanglement for Evidential Chinese Patent Approval Prediction
Authors: Jinzhi Shan, Qi Zhang, Chongyang Shi, Mengting Gui, Shoujin Wang, Usman Naseem
Date: 2024-08-23 00:00:00
Tasks: Decision Making, Disentanglement, Representation Learning, Retrieval

Automatic Chinese patent approval prediction is an emerging and valuable task in patent analysis. However, it involves a rigorous and transparent decision-making process that includes patent comparison and examination to assess its innovation and correctness. This resultant necessity of decision evidentiality, coupled with intricate patent comprehension presents significant challenges and obstacles for the patent analysis community. Consequently, few existing studies are addressing this task. This paper presents the pioneering effort on this task using a retrieval-based classification approach. We propose a novel framework called DiSPat, which focuses on structural representation learning and disentanglement to predict the approval of Chinese patents and offer decision-making evidence. DiSPat comprises three main components: base reference retrieval to retrieve the Top-k most similar patents as a reference base; structural patent representation to exploit the inherent claim hierarchy in patents for learning a structural patent representation; disentangled representation learning to learn disentangled patent representations that enable the establishment of an evidential decision-making process. To ensure a thorough evaluation, we have meticulously constructed three datasets of Chinese patents. Extensive experiments on these datasets unequivocally demonstrate our DiSPat surpasses state-of-the-art baselines on patent approval prediction, while also exhibiting enhanced evidentiality.
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
Authors: Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng
Date: 2023-10-29 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph Learning, Graph Representation Learning, Out-of-Distribution Generalization, Representation Learning

Invariant graph representation learning aims to learn the invariance among data from different environments for out-of-distribution generalization on graphs. As the graph environment partitions are usually expensive to obtain, augmenting the environment information has become the de facto approach. However, the usefulness of the augmented environment information has never been verified. In this work, we find that it is fundamentally impossible to learn invariant graph representations via environment augmentation without additional assumptions. Therefore, we develop a set of minimal assumptions, including variation sufficiency and variation consistency, for feasible invariant graph learning. We then propose a new framework Graph invAriant Learning Assistant (GALA). GALA incorporates an assistant model that needs to be sensitive to graph environment changes or distribution shifts. The correctness of the proxy predictions by the assistant model hence can differentiate the variations in spurious subgraphs. We show that extracting the maximally invariant subgraph to the proxy predictions provably identifies the underlying invariant subgraph for successful OOD generalization under the established minimal assumptions. Extensive experiments on datasets including DrugOOD with various graph distribution shifts confirm the effectiveness of GALA.
Unsupervised Change Detection Based on Image Reconstruction Loss with Segment Anything
Authors: Hyeoncheol Noh, Jingi Ju, YuHyun Kim, Minwoo Kim, Dong-Geol Choi
Date: 2024-08-18 00:00:00
Proceeding: Remote Sensing Letters 2024 8
Tasks: Change Detection, Image Reconstruction

In remote sensing, change detection based on deep learning shows promising performance. However, collecting multi-temporal paired images for training a change detection model is costly. To solve this problem, unsupervised change detection methodologies have been proposed, but their performance is still low. In this article, we introduce the Unsupervised Change Detection Based on Image Reconstruction Loss (CDRL) and CDRL with Segment Anything (CDRL-SA). Requiring only single-temporal unlabelled images for training, CDRL uses the original and a photometrically transformed image as an unchanged pair, input to a bi-temporal transformer-based network for reconstructing the original image. During inference, changed pairs result in significant reconstruction loss, highlighting change areas. To capture finer details, we change the structure of CDRL to a transformer-based model and introduce the CutSwap method for effective training. Furthermore, this output is fused with the results of a recently proposed Segment Anything (SA) model to improve the final output. We assessed the performance of CDRL and CDRL-SA using the LEVIR change detection dataset and CLCD dataset, and the method achieved competitive results of 88.9 and 91.6 ACC for the respective datasets, demonstrating its effectiveness in unsupervised change detection tasks.
Prediction of coastal vulnerability with machine learning techniques, Mediterranean coast of Tangier-Tetouan, Morocco
Authors: Youssef Fannassi, Zhour Ennouali, Mounir Hakkou, Aicha Benmohammadi, Motrih Al-Mutiry, Moussa S. Elbisy, Ali Masria
Date: 2023-04-13 00:00:00
Proceeding: Estuarine, Coastal and Shelf Science 2023 4

Coastal sites have been recognized to be among the most productive and dynamic regions. Nevertheless, these areas have been disrupted due to the rapid expansion of human interventions and climate change. Coastal ecosystems are becoming more endangered by the probable adverse effects of climate change as revealed by IPCC panel. The main objective of this paper is to integrate Machine Learning (ML) models to spatially predict coastal vulnerability along the Mediterranean coast of (Tangier-Tetouan), Morocco. Different models including Artificial neural network (ANN), Decision Tree (DT), logistic regression (LR), random forest (RF), and Support vector machine (SVM) are used on 10033 points to represent the vulnerability in terms of (high, moderate, and low), determined by the coastal vulnerability index (CVI). Eight coastal vulnerability predictors (Slope, Elevation, Tidal range, Sea level rise, maximum wave height, Natural habitat, Geomorphology, and Shoreline change rate) were considered as input for each model. The obtained dataset was split into two parts: 30% for testing and 70% for training. The presented models were examined on various parameters, such as accuracy, recall, F1 score, root mean square error (RMSE), and kappa index. The findings show that accuracy and Kappa statistics for RF and DT and SVM ranged from (92–99%) which indicates a better performance than other proposed models. The approach is considered an appropriate tool for predicting and mapping coastal vulnerability and can make significant contributions to the successful management of coastal zones. Additionally, it can aid in the development of public interest and promote the planning, creation, and execution of adaption measures. The prediction was applied over (∼124 km) of the shoreline. Results showed that about 19.95% (∼27.73 km) of the shoreline is highly vulnerable, while 20.92% (∼25.95 km) is moderately vulnerable and 59.1% (∼73.32 km) presents a low vulnerability.
Spanish and LLM Benchmarks: is MMLU Lost in Translation?
Authors: Irene Plaza, Nina Melero, Cristina del Pozo, Javier Conde, Pedro Reviriego, Marina Mayor-Rocher, María Grandury
Date: 2024-05-28 00:00:00
Tasks: MMLU, Translation

The evaluation of Large Language Models (LLMs) is a key element in their continuous improvement process and many benchmarks have been developed to assess the performance of LLMs in different tasks and topics. As LLMs become adopted worldwide, evaluating them in languages other than English is increasingly important. However, most LLM benchmarks are simply translated using an automated tool and then run in the target language. This means that the results depend not only on the LLM performance in that language but also on the quality of the translation. In this paper, we consider the case of the well-known Massive Multitask Language Understanding (MMLU) benchmark. Selected categories of the benchmark are translated into Spanish using Azure Translator and ChatGPT4 and run on ChatGPT4. Next, the results are processed to identify the test items that produce different answers in Spanish and English. Those are then analyzed manually to understand if the automatic translation caused the change. The results show that a significant fraction of the failing items can be attributed to mistakes in the translation of the benchmark. These results make a strong case for improving benchmarks in languages other than English by at least revising the translations of the items and preferably by adapting the tests to the target language by experts.
Can GPT-4 Models Detect Misleading Visualizations?
Authors: Jason Alexander, Priyal Nanda, Kai-Cheng Yang, Ali Sarvghad
Date: 2024-08-08 00:00:00
Tasks: Misinformation, Prompt Engineering

The proliferation of misleading visualizations online, particularly during critical events like public health crises and elections, poses a significant risk. This study investigates the capability of GPT-4 models (4V, 4o, and 4o mini) to detect misleading visualizations. Utilizing a dataset of tweet-visualization pairs containing various visual misleaders, we test these models under four experimental conditions with different levels of guidance. We show that GPT-4 models can detect misleading visualizations with moderate accuracy without prior training (naive zero-shot) and that performance notably improves when provided with definitions of misleaders (guided zero-shot). However, a single prompt engineering technique does not yield the best results for all misleader types. Specifically, providing the models with misleader definitions and examples (guided few-shot) proves more effective for reasoning misleaders, while guided zero-shot performs better for design misleaders. This study underscores the feasibility of using large vision-language models to detect visual misinformation and the importance of prompt engineering for optimized detection accuracy.
Artificial Intelligence in Election Campaigns: Perceptions, Penalties, and Implications
Authors: Andreas Jungherr, Adrian Rauchfleisch, Alexander Wuttke
Date: 2024-08-08 00:00:00
Tasks: Diversity

As political parties around the world experiment with Artificial Intelligence (AI) in election campaigns, concerns about deception and manipulation are rising. This article examines how the public reacts to different uses of AI in elections and the potential consequences for party evaluations and regulatory preferences. Across three preregistered studies with over 7,600 American respondents, we identify three categories of AI use -- campaign operations, voter outreach, and deception. While people generally dislike AI in campaigns, they are especially critical of deceptive uses, which they perceive as norm violations. However, parties engaging in AI-enabled deception face no significant drop in favorability, neither with supporters nor opponents. Instead, deceptive AI use increases public support for stricter AI regulation, including calls for an outright ban on AI development. These findings reveal a misalignment between public disapproval of deceptive AI and the political incentives of parties, underscoring the need for targeted regulatory oversight. Rather than banning AI in elections altogether, regulation should distinguish between harmful and beneficial applications to avoid stifling democratic innovation.
Using generative AI to support standardization work -- the case of 3GPP
Authors: Miroslaw Staron, Jonathan Strom, Albin Karlsson, Wilhelm Meding
Date: 2024-08-08 00:00:00
Tasks: Text Summarization

Standardization processes build upon consensus between partners, which depends on their ability to identify points of disagreement and resolving them. Large standardization organizations, like the 3GPP or ISO, rely on leaders of work packages who can correctly, and efficiently, identify disagreements, discuss them and reach a consensus. This task, however, is effort-, labor-intensive and costly. In this paper, we address the problem of identifying similarities, dissimilarities and discussion points using large language models. In a design science research study, we work with one of the organizations which leads several workgroups in the 3GPP standard. Our goal is to understand how well the language models can support the standardization process in becoming more cost-efficient, faster and more reliable. Our results show that generic models for text summarization correlate well with domain expert's and delegate's assessments (Pearson correlation between 0.66 and 0.98), but that there is a need for domain-specific models to provide better discussion materials for the standardization groups.
Sleeper Social Bots: a new generation of AI disinformation bots are already a political threat
Authors: Jaiv Doshi, Ines Novacic, Curtis Fletcher, Mats Borges, Elea Zhong, Mark C. Marino, Jason Gan, Sophia Mager, Dane Sprague, Melinda Xia
Date: 2024-08-07 00:00:00

This paper presents a study on the growing threat of "sleeper social bots," AI-driven social bots in the political landscape, created to spread disinformation and manipulate public opinion. We based the name sleeper social bots on their ability to pass as humans on social platforms, where they're embedded like political "sleeper" agents, making them harder to detect and more disruptive. To illustrate the threat these bots pose, our research team at the University of Southern California constructed a demonstration using a private Mastodon server, where ChatGPT-driven bots, programmed with distinct personalities and political viewpoints, engaged in discussions with human participants about a fictional electoral proposition. Our preliminary findings suggest these bots can convincingly pass as human users, actively participate in conversations, and effectively disseminate disinformation. Moreover, they can adapt their arguments based on the responses of human interlocutors, showcasing their dynamic and persuasive capabilities. College students participating in initial experiments failed to identify our bots, underscoring the urgent need for increased awareness and education about the dangers of AI-driven disinformation, and in particular, disinformation spread by bots. The implications of our research point to the significant challenges posed by social bots in the upcoming 2024 U.S. presidential election and beyond.
When are quarnets sufficient to reconstruct semi-directed phylogenetic networks?
Authors: Katharina T. Huber, Leo van Iersel, Mark Jones, Vincent Moulton, Leonie Veenema - Nipius
Date: 2024-08-23 00:00:00

Phylogenetic networks are graphs that are used to represent evolutionary relationships between different taxa. They generalize phylogenetic trees since for example, unlike trees, they permit lineages to combine. Recently, there has been rising interest in semi-directed phylogenetic networks, which are mixed graphs in which certain lineage combination events are represented by directed edges coming together, whereas the remaining edges are left undirected. One reason to consider such networks is that it can be difficult to root a network using real data. In this paper, we consider the problem of when a semi-directed phylogenetic network is defined or encoded by the smaller networks that it induces on the 4-leaf subsets of its leaf set. These smaller networks are called quarnets. We prove that semi-directed binary level-2 phylogenetic networks are encoded by their quarnets, but that this is not the case for level-3. In addition, we prove that the so-called blob tree of a semi-directed binary network, a tree that give the coarse-grained structure of the network, is always encoded by the quarnets of the network. These results are relevant for proving the statistical consistency of programs that are currently being developed for reconstructing phylogenetic networks from practical data, such as the recently developed Squirrel software tool.
The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks From Artificial Intelligence
Authors: Peter Slattery, Alexander K. Saeri, Emily A. C. Grundy, Jess Graham, Michael Noetel, Risto Uuk, James Dao, Soroush Pour, Stephen Casper, Neil Thompson
Date: 2024-08-14 00:00:00
Tasks: Misinformation

The risks posed by Artificial Intelligence (AI) are of considerable concern to academics, auditors, policymakers, AI companies, and the public. However, a lack of shared understanding of AI risks can impede our ability to comprehensively discuss, research, and react to them. This paper addresses this gap by creating an AI Risk Repository to serve as a common frame of reference. This comprises a living database of 777 risks extracted from 43 taxonomies, which can be filtered based on two overarching taxonomies and easily accessed, modified, and updated via our website and online spreadsheets. We construct our Repository with a systematic review of taxonomies and other structured classifications of AI risk followed by an expert consultation. We develop our taxonomies of AI risk using a best-fit framework synthesis. Our high-level Causal Taxonomy of AI Risks classifies each risk by its causal factors (1) Entity: Human, AI; (2) Intentionality: Intentional, Unintentional; and (3) Timing: Pre-deployment; Post-deployment. Our mid-level Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental, and (7) AI system safety, failures, & limitations. These are further divided into 23 subdomains. The AI Risk Repository is, to our knowledge, the first attempt to rigorously curate, analyze, and extract AI risk frameworks into a publicly accessible, comprehensive, extensible, and categorized risk database. This creates a foundation for a more coordinated, coherent, and complete approach to defining, auditing, and managing the risks posed by AI systems.
Fairness-Aware Streaming Feature Selection with Causal Graphs
Authors: Leizhen Zhang, Lusi Li, Di wu, Sheng Chen, Yi He
Date: 2024-08-17 00:00:00
Tasks: Fairness, Feature Correlation, feature selection

Its crux lies in the optimization of a tradeoff between accuracy and fairness of resultant models on the selected feature subset. The technical challenge of our setting is twofold: 1) streaming feature inputs, such that an informative feature may become obsolete or redundant for prediction if its information has been covered by other similar features that arrived prior to it, and 2) non-associational feature correlation, such that bias may be leaked from those seemingly admissible, non-protected features. To overcome this, we propose Streaming Feature Selection with Causal Fairness (SFCF) that builds two causal graphs egocentric to prediction label and protected feature, respectively, striving to model the complex correlation structure among streaming features, labels, and protected information. As such, bias can be eradicated from predictive modeling by removing those features being causally correlated with the protected feature yet independent to the labels. We theorize that the originally redundant features for prediction can later become admissible, when the learning accuracy is compromised by the large number of removed features (non-protected but can be used to reconstruct bias information). We benchmark SFCF\ on five datasets widely used in streaming feature research, and the results substantiate its performance superiority over six rival models in terms of efficiency and sparsity of feature selection and equalized odds of the resultant predictive models.
Semantic Variational Bayes Based on a Semantic Information Theory for Solving Latent Variables
Authors: Chenguang Lu
Date: 2024-08-12 00:00:00
Tasks: Data Compression

The Variational Bayesian method (VB) is used to solve the probability distributions of latent variables with the minimum free energy criterion. This criterion is not easy to understand, and the computation is complex. For these reasons, this paper proposes the Semantic Variational Bayes' method (SVB). The Semantic Information Theory the author previously proposed extends the rate-distortion function R(D) to the rate-fidelity function R(G), where R is the minimum mutual information for given semantic mutual information G. SVB came from the parameter solution of R(G), where the variational and iterative methods originated from Shannon et al.'s research on the rate-distortion function. The constraint functions SVB uses include likelihood, truth, membership, similarity, and distortion functions. SVB uses the maximum information efficiency (G/R) criterion, including the maximum semantic information criterion for optimizing model parameters and the minimum mutual information criterion for optimizing the Shannon channel. For the same tasks, SVB is computationally simpler than VB. The computational experiments in the paper include 1) using a mixture model as an example to show that the mixture model converges as G/R increases; 2) demonstrating the application of SVB in data compression with a group of error ranges as the constraint; 3) illustrating how the semantic information measure and SVB can be used for maximum entropy control and reinforcement learning in control tasks with given range constraints, providing numerical evidence for balancing control's purposiveness and efficiency. Further research is needed to apply SVB to neural networks and deep learning.