OpenRaiser/Intern-Atlas huggingface dataset:
Improving Adversarial Robustness via Decoupled Visual Representation Masking
Venue: arXiv cs.CV 2024
Year: 2024

Deep neural networks are proven to be vulnerable to fine-designed adversarial examples, and adversarial defense algorithms draw more and more attention nowadays. Pre-processing based defense is a major strategy, as well as learning robust feature representation has been proven an effective way to boost generalization. However, existing defense works lack considering different depth-level visual features in the training process. In this paper, we first highlight two novel properties of robust features from the feature distribution perspective: 1) \textbf{Diversity}. The robust feature of intra-class samples can maintain appropriate diversity; 2) \textbf{Discriminability}. The robust feature of inter-class samples should ensure adequate separation. We find that state-of-the-art defense methods aim to address both of these mentioned issues well. It motivates us to increase intra-class variance and decrease inter-class discrepancy simultaneously in adversarial training. Specifically, we propose a simple but effective defense based on decoupled visual representation masking. The designed Decoupled Visual Feature Masking (DFM) block can adaptively disentangle visual discriminative features and non-visual features with diverse mask strategies, while the suitable discarding information can disrupt adversarial noise to improve robustness. Our work provides a generic and easy-to-plugin block unit for any former adversarial training algorithm to achieve better protection integrally. Extensive experimental results prove the proposed method can achieve superior performance compared with state-of-the-art defense approaches. The code is publicly available at \href{https://github.com/chenboluo/Adversarial-defense}{https://github.com/chenboluo/Adversarial-defense}.
Understanding Understanding: A Pragmatic Framework Motivated by Large Language Models
Venue: arXiv cs.AI 2024
Year: 2024

Motivated by the rapid ascent of Large Language Models (LLMs) and debates about the extent to which they possess human-level qualities, we propose a framework for testing whether any agent (be it a machine or a human) understands a subject matter. In Turing-test fashion, the framework is based solely on the agent's performance, and specifically on how well it answers questions. Elements of the framework include circumscribing the set of questions (the "scope of understanding"), requiring general competence ("passing grade"), avoiding "ridiculous answers", but still allowing wrong and "I don't know" answers to some questions. Reaching certainty about these conditions requires exhaustive testing of the questions which is impossible for nontrivial scopes, but we show how high confidence can be achieved via random sampling and the application of probabilistic confidence bounds. We also show that accompanying answers with explanations can improve the sample complexity required to achieve acceptable bounds, because an explanation of an answer implies the ability to answer many similar questions. According to our framework, current LLMs cannot be said to understand nontrivial domains, but as the framework provides a practical recipe for testing understanding, it thus also constitutes a tool for building AI agents that do understand.
Joint Audio and Symbolic Conditioning for Temporally Controlled Text-to-Music Generation
Venue: arXiv cs.SD 2024
Year: 2024

We present JASCO, a temporally controlled text-to-music generation model utilizing both symbolic and audio-based conditions. JASCO can generate high-quality music samples conditioned on global text descriptions along with fine-grained local controls. JASCO is based on the Flow Matching modeling paradigm together with a novel conditioning method. This allows music generation controlled both locally (e.g., chords) and globally (text description). Specifically, we apply information bottleneck layers in conjunction with temporal blurring to extract relevant information with respect to specific controls. This allows the incorporation of both symbolic and audio-based conditions in the same text-to-music model. We experiment with various symbolic control signals (e.g., chords, melody), as well as with audio representations (e.g., separated drum tracks, full-mix). We evaluate JASCO considering both generation quality and condition adherence, using both objective metrics and human studies. Results suggest that JASCO is comparable to the evaluated baselines considering generation quality while allowing significantly better and more versatile controls over the generated music. Samples are available on our demo page https://pages.cs.huji.ac.il/adiyoss-lab/JASCO.
Enhancing Class Diagram Dynamics: A Natural Language Approach with ChatGPT
Venue: arXiv cs.SE 2024
Year: 2024

Integrating artificial intelligence (AI) into software engineering can transform traditional practices by enhancing efficiency, accuracy, and innovation. This study explores using ChatGPT, an advanced AI language model, to enhance UML class diagrams dynamically, an underexplored area. Traditionally, creating and maintaining class diagrams are manual, time-consuming, and error-prone processes. This research leverages natural language processing (NLP) techniques to automate the extraction of methods and interactions from detailed use case tables and integrate them into class diagrams. The methodology involves several steps: (1) developing detailed natural language use case tables by master's degree students for a "Waste Recycling Platform," (2) creating an initial static class diagram based on these tables, (3) iteratively enriching the class diagram through ChatGPT integration to analyze use cases and suggest methods, (4) reviewing and incorporating these methods into the class diagram, and (5) dynamically updating the PlantUML \cite{plantuml} class diagram, followed by evaluation and refinement. Findings indicate that the AI-driven approach significantly improves the accuracy and completeness of the class diagram. Additionally, dynamic enhancement aligns well with Agile development practices, facilitating rapid iterations and continuous improvement. Key contributions include demonstrating the feasibility and benefits of integrating AI into software modeling tasks, providing a comprehensive representation of system behaviors and interactions, and highlighting AI's potential to streamline and improve existing software engineering processes. Future research should address identified limitations and explore AI applications in other software models.
Optimized Speculative Sampling for GPU Hardware Accelerators
Venue: arXiv cs.LG 2024
Year: 2024

In this work, we optimize speculative sampling for parallel hardware accelerators to improve sampling speed. We notice that substantial portions of the intermediate matrices necessary for speculative sampling can be computed concurrently. This allows us to distribute the workload across multiple GPU threads, enabling simultaneous operations on matrix segments within thread blocks. This results in profiling time improvements ranging from 6% to 13% relative to the baseline implementation, without compromising accuracy. To further accelerate speculative sampling, probability distributions parameterized by softmax are approximated by sigmoid. This approximation approach results in significantly greater relative improvements in profiling time, ranging from 37% to 94%, with a minor decline in accuracy. We conduct extensive experiments on both automatic speech recognition and summarization tasks to validate the effectiveness of our optimization methods.
Scorecards for Synthetic Medical Data Evaluation and Reporting
Venue: arXiv cs.AI 2024
Year: 2024

Although interest in synthetic medical data (SMD) for training and testing AI methods is growing, the absence of a standardized framework to evaluate its quality and applicability hinders its wider adoption. Here, we outline an evaluation framework designed to meet the unique requirements of medical applications, and introduce SMD Card, which can serve as comprehensive reports that accompany artificially generated datasets. This card provides a transparent and standardized framework for evaluating and reporting the quality of synthetic data, which can benefit SMD developers, users, and regulators, particularly for AI models using SMD in regulatory submissions.
Federated Face Forgery Detection Learning with Personalized Representation
Venue: arXiv cs.CV 2024
Year: 2024

Deep generator technology can produce high-quality fake videos that are indistinguishable, posing a serious social threat. Traditional forgery detection methods directly centralized training on data and lacked consideration of information sharing in non-public video data scenarios and data privacy. Naturally, the federated learning strategy can be applied for privacy protection, which aggregates model parameters of clients but not original data. However, simple federated learning can't achieve satisfactory performance because of poor generalization capabilities for the real hybrid-domain forgery dataset. To solve the problem, the paper proposes a novel federated face forgery detection learning with personalized representation. The designed Personalized Forgery Representation Learning aims to learn the personalized representation of each client to improve the detection performance of individual client models. In addition, a personalized federated learning training strategy is utilized to update the parameters of the distributed detection model. Here collaborative training is conducted on multiple distributed client devices, and shared representations of these client models are uploaded to the server side for aggregation. Experiments on several public face forgery detection datasets demonstrate the superior performance of the proposed algorithm compared with state-of-the-art methods. The code is available at \emph{https://github.com/GANG370/PFR-Forgery.}
What Operations can be Performed Directly on Compressed Arrays, and with What Error?
Venue: arXiv cs.DC 2024
Year: 2024

In response to the rapidly escalating costs of computing with large matrices and tensors caused by data movement, several lossy compression methods have been developed to significantly reduce data volumes. Unfortunately, all these methods require the data to be decompressed before further computations are done. In this work, we develop a lossy compressor that allows a dozen fairly fundamental operations directly on compressed data while offering good compression ratios and modest errors. We implement a new compressor PyBlaz based on the familiar GPU-powered PyTorch framework, and evaluate it on three non-trivial applications, choosing different number systems for internal representation. Our results demonstrate that the compressed-domain operations achieve good scalability with problem sizes while incurring errors well within acceptable limits. To our best knowledge, this is the first such lossy compressor that supports compressed-domain operations while achieving acceptable performance as well as error.
Building another Spanish dictionary, this time with GPT-4
Venue: arXiv cs.CL 2024
Year: 2024

We present the "Spanish Built Factual Freectianary 2.0" (Spanish-BFF-2) as the second iteration of an AI-generated Spanish dictionary. Previously, we developed the inaugural version of this unique free dictionary employing GPT-3. In this study, we aim to improve the dictionary by using GPT-4-turbo instead. Furthermore, we explore improvements made to the initial version and compare the performance of both models.
A Mixed Tree-Cotree Gauge for the Reduced Basis Approximation of Maxwell's Eigenvalue Problem
Venue: arXiv cs.CE 2024
Year: 2024

Model order reduction methods are a powerful tool to drastically reduce the computational effort of problems which need to be evaluated repeatedly, i.e., when computing the same system for various parameter values. When applying a reduced basis approximation algorithm to the Maxwell eigenvalue problem, we encounter spurious solutions in the reduced system which hence need to be removed during the basis construction. In this paper, we discuss two tree-cotree gauge-based methods for the removal of the spurious eigenmodes.
Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding
Venue: arXiv cs.CV 2024
Year: 2024

The field of self-supervised 3D representation learning has emerged as a promising solution to alleviate the challenge presented by the scarcity of extensive, well-annotated datasets. However, it continues to be hindered by the lack of diverse, large-scale, real-world 3D scene datasets for source data. To address this shortfall, we propose Generalizable Representation Learning (GRL), where we devise a generative Bayesian network to produce diverse synthetic scenes with real-world patterns, and conduct pre-training with a joint objective. By jointly learning a coarse-to-fine contrastive learning task and an occlusion-aware reconstruction task, the model is primed with transferable, geometry-informed representations. Post pre-training on synthetic data, the acquired knowledge of the model can be seamlessly transferred to two principal downstream tasks associated with 3D scene understanding, namely 3D object detection and 3D semantic segmentation, using real-world benchmark datasets. A thorough series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed overview of datasets, evaluation metrics, and summarization methods before the LLM era, encompassing traditional statistical methods, deep learning approaches, and PLM fine-tuning techniques, and (2) the first detailed examination of recent advancements in benchmarking, modeling, and evaluating summarization in the LLM era. By synthesizing existing literature and presenting a cohesive overview, this survey also discusses research trends, open challenges, and proposes promising research directions in summarization, aiming to guide researchers through the evolving landscape of summarization research.
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants
Venue: arXiv cs.AI 2024
Year: 2024

The effective alignment of Large Language Models (LLMs) with precise instructions is essential for their application in diverse real-world scenarios. Current methods focus on enhancing the diversity and complexity of training and evaluation samples, yet they fall short in accurately assessing LLMs' ability to follow similar instruction variants. We introduce an effective data augmentation technique DeMoRecon that decomposes complex instructions into simpler sub-components, modifies these, and reconstructs them into new variants, thereby preserves the original instruction's context and complexity while introducing variability, which is critical for training and evaluating LLMs' instruction-following precision. Based on DeMoRecon, we developed the FGIV dataset which contains fine-grained instruction variants of 1,773 seed instructions to both fine-tune and evaluate LLMs. Our findings show that LLMs fine-tuned with FGIV will gain significant performance boost on both ours and commonly used instructions-following benchmarks.
Characterising Contributions that Coincide with Vulnerability Mitigation in NPM Libraries
Venue: arXiv cs.SE 2024
Year: 2024

With the urgent need to secure supply chains among Open Source libraries, attention has focused on mitigating vulnerabilities detected in these libraries. Although awareness has improved recently, most studies still report delays in the mitigation process. This suggests that developers still have to deal with other contributions that occur during the period of fixing vulnerabilities, such as coinciding Pull Requests (PRs) and Issues, yet the impact of these contributions remains unclear. To characterize these contributions, we conducted a mixed-method empirical study to analyze NPM GitHub projects affected by 554 different vulnerability advisories, mining a total of 4,699 coinciding PRs and Issues. We believe that tool development and improved workload management for developers have the potential to create a more efficient and effective vulnerability mitigation process.
Large Language Models and Knowledge Graphs for Astronomical Entity Disambiguation
Venue: arXiv cs.CL 2024
Year: 2024

This paper presents an experiment conducted during a hackathon, focusing on using large language models (LLMs) and knowledge graph clustering to extract entities and relationships from astronomical text. The study demonstrates an approach to disambiguate entities that can appear in various contexts within the astronomical domain. By collecting excerpts around specific entities and leveraging the GPT-4 language model, relevant entities and relationships are extracted. The extracted information is then used to construct a knowledge graph, which is clustered using the Leiden algorithm. The resulting Leiden communities are utilized to identify the percentage of association of unknown excerpts to each community, thereby enabling disambiguation. The experiment showcases the potential of combining LLMs and knowledge graph clustering techniques for information extraction in astronomical research. The results highlight the effectiveness of the approach in identifying and disambiguating entities, as well as grouping them into meaningful clusters based on their relationships.
Analysing the Behaviour of Tree-Based Neural Networks in Regression Tasks
Venue: arXiv cs.LG 2024
Year: 2024

The landscape of deep learning has vastly expanded the frontiers of source code analysis, particularly through the utilization of structural representations such as Abstract Syntax Trees (ASTs). While these methodologies have demonstrated effectiveness in classification tasks, their efficacy in regression applications, such as execution time prediction from source code, remains underexplored. This paper endeavours to decode the behaviour of tree-based neural network models in the context of such regression challenges. We extend the application of established models--tree-based Convolutional Neural Networks (CNNs), Code2Vec, and Transformer-based methods--to predict the execution time of source code by parsing it to an AST. Our comparative analysis reveals that while these models are benchmarks in code representation, they exhibit limitations when tasked with regression. To address these deficiencies, we propose a novel dual-transformer approach that operates on both source code tokens and AST representations, employing cross-attention mechanisms to enhance interpretability between the two domains. Furthermore, we explore the adaptation of Graph Neural Networks (GNNs) to this tree-based problem, theorizing the inherent compatibility due to the graphical nature of ASTs. Empirical evaluations on real-world datasets showcase that our dual-transformer model outperforms all other tree-based neural networks and the GNN-based models. Moreover, our proposed dual transformer demonstrates remarkable adaptability and robust performance across diverse datasets.
Calibrating Where It Matters: Constrained Temperature Scaling
Venue: arXiv cs.LG 2024
Year: 2024

We consider calibration of convolutional classifiers for diagnostic decision making. Clinical decision makers can use calibrated classifiers to minimise expected costs given their own cost function. Such functions are usually unknown at training time. If minimising expected costs is the primary aim, algorithms should focus on tuning calibration in regions of probability simplex likely to effect decisions. We give an example, modifying temperature scaling calibration, and demonstrate improved calibration where it matters using convnets trained to classify dermoscopy images.
Analysis of water injection heat recovery potential of abandoned oil wells to geothermal wells in northern Shaanxi
Venue: arXiv cs.CE 2024
Year: 2024

The Chang 2 bottom water reservoir area in the western part of northern Shaanxi is one of the core oil-producing areas in the Ordos Basin.One of the main reservoirs is the Chang 2 reservoir of the Triassic Yanchang Formation, which has good physical conditions, active edge and bottom water, and high geothermal gradient. In this paper, the reservoir numerical simulation software CMG is used to simulate the water intake and heat recovery in the target study area, and the heat recovery rate and heat recovery of the three water production methods of direct water production, four injection and one production and one injection and four production under different injection pressures are analyzed. The results show that it is difficult to realize the direct water extraction from the bottom water reservoir. The annual heat recovery of single well of four injection and one production and one injection and four production is converted to the standard coal production between 190 ~ 420 t, so the Chang 2 reservoir in the western part of northern Shaanxi has the potential of water injection and heat recovery.
Revisiting Spurious Correlation in Domain Generalization
Venue: arXiv cs.LG 2024
Year: 2024

Without loss of generality, existing machine learning techniques may learn spurious correlation dependent on the domain, which exacerbates the generalization of models in out-of-distribution (OOD) scenarios. To address this issue, recent works build a structural causal model (SCM) to describe the causality within data generation process, thereby motivating methods to avoid the learning of spurious correlation by models. However, from the machine learning viewpoint, such a theoretical analysis omits the nuanced difference between the data generation process and representation learning process, resulting in that the causal analysis based on the former cannot well adapt to the latter. To this end, we explore to build a SCM for representation learning process and further conduct a thorough analysis of the mechanisms underlying spurious correlation. We underscore that adjusting erroneous covariates introduces bias, thus necessitating the correct selection of spurious correlation mechanisms based on practical application scenarios. In this regard, we substantiate the correctness of the proposed SCM and further propose to control confounding bias in OOD generalization by introducing a propensity score weighted estimator, which can be integrated into any existing OOD method as a plug-and-play module. The empirical results comprehensively demonstrate the effectiveness of our method on synthetic and large-scale real OOD datasets.