OpenRaiser/Intern-Atlas huggingface dataset:
Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights
Venue: arXiv cs.CL 2024
Year: 2024

The Canadian air travel sector has seen a significant increase in flight delays, cancellations, and other issues concerning passenger rights. Recognizing this demand, we present a chatbot to assist passengers and educate them about their rights. Our system breaks a complex user input into simple queries which are used to retrieve information from a collection of documents detailing air travel regulations. The most relevant passages from these documents are presented along with links to the original documents and the generated queries, enabling users to dissect and leverage the information for their unique circumstances. The system successfully overcomes two predominant challenges: understanding complex user inputs, and delivering accurate answers, free of hallucinations, that passengers can rely on for making informed decisions. A user study comparing the chatbot to a Google search demonstrated the chatbot's usefulness and ease of use. Beyond the primary goal of providing accurate and timely information to air passengers regarding their rights, we hope that this system will also enable further research exploring the tradeoff between the user-friendly conversational interface of chatbots and the accuracy of retrieval systems.
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?
Venue: arXiv cs.CV 2024
Year: 2024

Foundation models pre-trained on web-scale vision-language data, such as CLIP, are widely used as cornerstones of powerful machine learning systems. While pre-training offers clear advantages for downstream learning, it also endows downstream models with shared adversarial vulnerabilities that can be easily identified through the open-sourced foundation model. In this work, we expose such vulnerabilities in CLIP's downstream models and show that foundation models can serve as a basis for attacking their downstream systems. In particular, we propose a simple yet effective adversarial attack strategy termed Patch Representation Misalignment (PRM). Solely based on open-sourced CLIP vision encoders, this method produces adversaries that simultaneously fool more than 20 downstream models spanning 4 common vision-language tasks (semantic segmentation, object detection, image captioning and visual question-answering). Our findings highlight the concerning safety risks introduced by the extensive usage of public foundational models in the development of downstream systems, calling for extra caution in these scenarios.
System Support for Environmentally Sustainable Computing in Data Centers
Venue: arXiv cs.AR 2024
Year: 2024

Modern data centers suffer from a growing carbon footprint due to insufficient support for environmental sustainability. While hardware accelerators and renewable energy have been utilized to enhance sustainability, addressing Quality of Service (QoS) degradation caused by renewable energy supply and hardware recycling remains challenging: (1) prior accelerators exhibit significant carbon footprints due to limited reconfigurability and inability to adapt to renewable energy fluctuations; (2) integrating recycled NAND flash chips in data centers poses challenges due to their short lifetime, increasing energy consumption; (3) the absence of a sustainability estimator impedes data centers and users in evaluating and improving their environmental impact. This study aims to improve system support for environmentally sustainable data centers by proposing a reconfigurable hardware accelerator for intensive computing primitives and developing a fractional NAND flash cell to extend the lifetime of recycled flash chips while supporting graceful capacity degradation. We also introduce a sustainability estimator to evaluate user task energy consumption and promote sustainable practices. We present our preliminary results and recognize this as an ongoing initiative with significant potential to advance environmentally sustainable computing in data centers and stimulate further exploration in this critical research domain.
Survey of Methods, Resources, and Formats for Teaching Constraint Programming
Venue: arXiv cs.CY 2024
Year: 2024

This paper provides an overview of the state of teaching for Constraint Programming, based on a survey of the community for the 2023 Workshop on Teaching Constraint Programming at the CP 2023 conference in Toronto. The paper presents the results of the survey, as well as lists of books, video courses and other tutorial materials for teaching Constraint Programming. The paper serves as a single location for current and public information on course resources, topics, formats, and methods.
Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
Venue: arXiv cs.LG 2024
Year: 2024

Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life. This challenge is tackled through a semi-supervised multi-modal diagnosis framework. In particular, we introduce a new hypergraph framework that enables higher-order relations between multi-modal data, while utilising minimal labels. We first introduce a bilevel hypergraph optimisation framework that jointly learns a graph augmentation policy and a semi-supervised classifier. This dual learning strategy is hypothesised to enhance the robustness and generalisation capabilities of the model by fostering new pathways for information propagation. Secondly, we introduce a novel strategy for generating pseudo-labels more effectively via a gradient-driven flow. Our experimental results demonstrate the superior performance of our framework over current techniques in diagnosing Alzheimer's disease.
Small Scale Reflection for the Working Lean User
Venue: arXiv cs.PL 2024
Year: 2024

We present the design and implementation of the Small Scale Reflection proof methodology and tactic language (a.k.a. SSR) for the Lean 4 proof assistant. Like its Coq predecessor SSReflect, our Lean 4 implementation, dubbed LeanSSR, provides powerful rewriting principles and means for effective management of hypotheses in the proof context. Unlike SSReflect for Coq, LeanSSR does not require explicit switching between the logical and symbolic representation of a goal, allowing for even more concise proof scripts that seamlessly combine deduction steps with proofs by computation. In this paper, we first provide a gentle introduction to the principles of structuring mechanised proofs using LeanSSR. Next, we show how the native support for metaprogramming in Lean 4 makes it possible to develop LeanSSR entirely within the proof assistant, greatly improving the overall experience of both tactic implementers and proof engineers. Finally, we demonstrate the utility of LeanSSR by conducting two case studies: (a) porting a collection of Coq lemmas about sequences from the widely used Mathematical Components library and (b) reimplementing proofs in the finite set library of Lean's mathlib4. Both case studies show significant reduction in proof sizes.
Controllable Face Synthesis with Semantic Latent Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

Semantic Image Synthesis (SIS) is among the most popular and effective techniques in the field of face generation and editing, thanks to its good generation quality and the versatility is brings along. Recent works attempted to go beyond the standard GAN-based framework, and started to explore Diffusion Models (DMs) for this task as these stand out with respect to GANs in terms of both quality and diversity. On the other hand, DMs lack in fine-grained controllability and reproducibility. To address that, in this paper we propose a SIS framework based on a novel Latent Diffusion Model architecture for human face generation and editing that is both able to reproduce and manipulate a real reference image and generate diversity-driven results. The proposed system utilizes both SPADE normalization and cross-attention layers to merge shape and style information and, by doing so, allows for a precise control over each of the semantic parts of the human face. This was not possible with previous methods in the state of the art. Finally, we performed an extensive set of experiments to prove that our model surpasses current state of the art, both qualitatively and quantitatively.
Multispectral Image Restoration by Generalized Opponent Transformation Total Variation
Venue: arXiv cs.CV 2024
Year: 2024

Multispectral images (MSI) contain light information in different wavelengths of objects, which convey spectral-spatial information and help improve the performance of various image processing tasks. Numerous techniques have been created to extend the application of total variation regularization in restoring multispectral images, for example, based on channel coupling and adaptive total variation regularization. The primary contribution of this paper is to propose and develop a new multispectral total variation regularization in a generalized opponent transformation domain instead of the original multispectral image domain. Here opponent transformations for multispectral images are generalized from a well-known opponent transformation for color images. We will explore the properties of generalized opponent transformation total variation (GOTTV) regularization and the corresponding optimization formula for multispectral image restoration. To evaluate the effectiveness of the new GOTTV method, we provide numerical examples that showcase its superior performance compared to existing multispectral image total variation methods, using criteria such as MPSNR and MSSIM.
Is open source software culture enough to make AI a common ?
Venue: arXiv cs.CY 2024
Year: 2024

Language models (LM or LLM) are increasingly deployed in the field of artificial intelligence (AI) and its applications, but the question arises as to whether they can be a common resource managed and maintained by a community of users. Indeed, the dominance of private companies with exclusive access to massive data and language processing resources can create inequalities and biases in LM, as well as obstacles to innovation for those who do not have the same resources necessary for their implementation. In this contribution, we examine the concept of the commons and its relevance for thinking about LM. We highlight the potential benefits of treating the data and resources needed to create LMs as commons, including increased accessibility, equity, and transparency in the development and use of AI technologies. Finally, we present a case study centered on the Hugging Face platform, an open-source platform for deep learning designed to encourage collaboration and sharing among AI designers.
Morse Theory for the k-NN Distance Function
Venue: arXiv cs.CG 2024
Year: 2024

We study the $k$-th nearest neighbor distance function from a finite point-set in $\mathbb{R}^d$. We provide a Morse theoretic framework to analyze the sub-level set topology. In particular, we present a simple combinatorial-geometric characterization for critical points and their indices, along with detailed information about the possible changes in homology at the critical levels. We conclude by computing the expected number of critical points for a homogeneous Poisson process. Our results deliver significant insights and tools for the analysis of persistent homology in order-$k$ Delaunay mosaics, and random $k$-fold coverage.
RelationVLM: Making Large Vision-Language Models Understand Visual Relations
Venue: arXiv cs.CV 2024
Year: 2024

The development of Large Vision-Language Models (LVLMs) is striving to catch up with the success of Large Language Models (LLMs), yet it faces more challenges to be resolved. Very recent works enable LVLMs to localize object-level visual contents and ground text to them. Nonetheless, current LVLMs still struggle to precisely understand visual relations due to the lack of relevant data. In this work, we present RelationVLM, a large vision-language model capable of comprehending various levels and types of relations whether across multiple images or within a video. Specifically, we devise a multi-stage relation-aware training scheme and a series of corresponding data configuration strategies to bestow RelationVLM with the capabilities of understanding semantic relations, temporal associations and geometric transforms. Extensive case studies and quantitative evaluations show RelationVLM has strong capability in understanding such relations and emerges impressive in-context capability of reasoning from few-shot examples by comparison. This work fosters the advancements of LVLMs by enabling them to support a wider range of downstream applications toward artificial general intelligence.
The Emergence of Hardware Fuzzing: A Critical Review of its Significance
Venue: arXiv cs.CR 2024
Year: 2024

In recent years, there has been a notable surge in attention towards hardware security, driven by the increasing complexity and integration of processors, SoCs, and third-party IPs aimed at delivering advanced solutions. However, this complexity also introduces vulnerabilities and bugs into hardware systems, necessitating early detection during the IC design cycle to uphold system integrity and mitigate re-engineering costs. While the Design Verification (DV) community employs dynamic and formal verification strategies, they encounter challenges such as scalability for intricate designs and significant human intervention, leading to prolonged verification durations. As an alternative approach, hardware fuzzing, inspired by software testing methodologies, has gained prominence for its efficacy in identifying bugs within complex hardware designs. Despite the introduction of various hardware fuzzing techniques, obstacles such as inefficient conversion of hardware modules into software models impede their effectiveness. This Systematization of Knowledge (SoK) initiative delves into the fundamental principles of existing hardware fuzzing, methodologies, and their applicability across diverse hardware designs. Additionally, it evaluates factors such as the utilization of golden reference models (GRMs), coverage metrics, and toolchains to gauge their potential for broader adoption, akin to traditional formal verification methods. Furthermore, this work examines the reliability of existing hardware fuzzing techniques in identifying vulnerabilities and identifies research gaps for future advancements in design verification techniques.
How Spammers and Scammers Leverage AI-Generated Images on Facebook for Audience Growth
Venue: arXiv cs.CY 2024
Year: 2024

Much of the research and discourse on risks from artificial intelligence (AI) image generators, such as DALL-E and Midjourney, has centered around whether they could be used to inject false information into political discourse. We show that spammers and scammers - seemingly motivated by profit or clout, not ideology - are already using AI-generated images to gain significant traction on Facebook. At times, the Facebook Feed is recommending unlabeled AI-generated images to users who neither follow the Pages posting the images nor realize that the images are AI-generated, highlighting the need for improved transparency and provenance standards as AI models proliferate.
The Interplay Between Symmetries and Impact Effects on Hybrid Mechanical Systems
Venue: arXiv cs.RO 2024
Year: 2024

Hybrid systems are dynamical systems with continuous-time and discrete-time components in their dynamics. When hybrid systems are defined on a principal bundle we are able to define two classes of impacts for the discrete-time transition of the dynamics: interior impacts and exterior impacts. In this paper we define hybrid systems on principal bundles, study the underlying geometry on the switching surface where impacts occur and we find conditions for which both exterior and interior impacts are preserved by the mechanical connection induced in the principal bundle.
D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation
Venue: arXiv cs.RO 2024
Year: 2024

Mastering dexterous robotic manipulation of deformable objects is vital for overcoming the limitations of parallel grippers in real-world applications. Current trajectory optimisation approaches often struggle to solve such tasks due to the large search space and the limited task information available from a cost function. In this work, we propose D-Cubed, a novel trajectory optimisation method using a latent diffusion model (LDM) trained from a task-agnostic play dataset to solve dexterous deformable object manipulation tasks. D-Cubed learns a skill-latent space that encodes short-horizon actions in the play dataset using a VAE and trains a LDM to compose the skill latents into a skill trajectory, representing a long-horizon action trajectory in the dataset. To optimise a trajectory for a target task, we introduce a novel gradient-free guided sampling method that employs the Cross-Entropy method within the reverse diffusion process. In particular, D-Cubed samples a small number of noisy skill trajectories using the LDM for exploration and evaluates the trajectories in simulation. Then, D-Cubed selects the trajectory with the lowest cost for the subsequent reverse process. This effectively explores promising solution areas and optimises the sampled trajectories towards a target task throughout the reverse diffusion process. Through empirical evaluation on a public benchmark of dexterous deformable object manipulation tasks, we demonstrate that D-Cubed outperforms traditional trajectory optimisation and competitive baseline approaches by a significant margin. We further demonstrate that trajectories found by D-Cubed readily transfer to a real-world LEAP hand on a folding task.
Regularization in Spider-Style Strategy Discovery and Schedule Construction
Venue: arXiv cs.AI 2024
Year: 2024

To achieve the best performance, automatic theorem provers often rely on schedules of diverse proving strategies to be tried out (either sequentially or in parallel) on a given problem. In this paper, we report on a large-scale experiment with discovering strategies for the Vampire prover, targeting the FOF fragment of the TPTP library and constructing a schedule for it, based on the ideas of Andrei Voronkov's system Spider. We examine the process from various angles, discuss the difficulty (or ease) of obtaining a strong Vampire schedule for the CASC competition, and establish how well a schedule can be expected to generalize to unseen problems and what factors influence this property.
mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding
Venue: arXiv cs.CV 2024
Year: 2024

Structure information is critical for understanding the semantics of text-rich images, such as documents, tables, and charts. Existing Multimodal Large Language Models (MLLMs) for Visual Document Understanding are equipped with text recognition ability but lack general structure understanding abilities for text-rich document images. In this work, we emphasize the importance of structure information in Visual Document Understanding and propose the Unified Structure Learning to boost the performance of MLLMs. Our Unified Structure Learning comprises structure-aware parsing tasks and multi-grained text localization tasks across 5 domains: document, webpage, table, chart, and natural image. To better encode structure information, we design a simple and effective vision-to-text module H-Reducer, which can not only maintain the layout information but also reduce the length of visual features by merging horizontal adjacent patches through convolution, enabling the LLM to understand high-resolution images more efficiently. Furthermore, by constructing structure-aware text sequences and multi-grained pairs of texts and bounding boxes for publicly available text-rich images, we build a comprehensive training set DocStruct4M to support structure learning. Finally, we construct a small but high-quality reasoning tuning dataset DocReason25K to trigger the detailed explanation ability in the document domain. Our model DocOwl 1.5 achieves state-of-the-art performance on 10 visual document understanding benchmarks, improving the SOTA performance of MLLMs with a 7B LLM by more than 10 points in 5/10 benchmarks. Our codes, models, and datasets are publicly available at https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl1.5.
Segment Anything for comprehensive analysis of grapevine cluster architecture and berry properties
Venue: arXiv cs.CV 2024
Year: 2024

Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, and yield. Evaluation methods for these traits include visual scoring, manual methodologies, and computer vision, with the latter being the most scalable approach. Most of the existing computer vision approaches for processing cluster images often rely on conventional segmentation or machine learning with extensive training and limited generalization. The Segment Anything Model (SAM), a novel foundation model trained on a massive image dataset, enables automated object segmentation without additional training. This study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2D cluster images. Using this model, we managed to segment approximately 3,500 cluster images, generating over 150,000 berry masks, each linked with spatial coordinates within their clusters. The correlation between human-identified berries and SAM predictions was very strong (Pearson r2=0.96). Although the visible berry count in images typically underestimates the actual cluster berry count due to visibility issues, we demonstrated that this discrepancy could be adjusted using a linear regression model (adjusted R2=0.87). We emphasized the critical importance of the angle at which the cluster is imaged, noting its substantial effect on berry counts and architecture. We proposed different approaches in which berry location information facilitated the calculation of complex features related to cluster architecture and compactness. Finally, we discussed SAM's potential integration into currently available pipelines for image generation and processing in vineyard conditions.
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
Venue: arXiv cs.LG 2024
Year: 2024

In offline reinforcement learning (RL), the absence of active exploration calls for attention on the model robustness to tackle the sim-to-real gap, where the discrepancy between the simulated and deployed environments can significantly undermine the performance of the learned policy. To endow the learned policy with robustness in a sample-efficient manner in the presence of high-dimensional state-action space, this paper considers the sample complexity of distributionally robust linear Markov decision processes (MDPs) with an uncertainty set characterized by the total variation distance using offline data. We develop a pessimistic model-based algorithm and establish its sample complexity bound under minimal data coverage assumptions, which outperforms prior art by at least $\widetilde{O}(d)$, where $d$ is the feature dimension. We further improve the performance guarantee of the proposed algorithm by incorporating a carefully-designed variance estimator.