OpenRaiser/Intern-Atlas huggingface dataset:
Learning Efficient Unsupervised Satellite Image-based Building Damage Detection
Venue: arXiv cs.CV 2023
Year: 2023

Existing Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions, hence largely limiting their applications. In this paper, we investigate a challenging yet practical scenario of BDD, Unsupervised Building Damage Detection (U-BDD), where only unlabelled pre- and post-disaster satellite image pairs are provided. As a pilot study, we have first proposed an advanced U-BDD baseline that leverages pre-trained vision-language foundation models (i.e., Grounding DINO, SAM and CLIP) to address the U-BDD task. However, the apparent domain gap between satellite and generic images causes low confidence in the foundation models used to identify buildings and their damages. In response, we further present a novel self-supervised framework, U-BDD++, which improves upon the U-BDD baseline by addressing domain-specific issues associated with satellite imagery. Furthermore, the new Building Proposal Generation (BPG) module and the CLIP-enabled noisy Building Proposal Selection (CLIP-BPS) module in U-BDD++ ensure high-quality self-training. Extensive experiments on the widely used building damage assessment benchmark demonstrate the effectiveness of the proposed method for unsupervised building damage detection. The presented annotation-free and foundation model-based paradigm ensures an efficient learning phase. This study opens a new direction for real-world BDD and sets a strong baseline for future research.
Scalable and Independent Learning of Nash Equilibrium Policies in $n$-Player Stochastic Games with Unknown Independent Chains
Venue: arXiv cs.GT 2023
Year: 2023

We study a subclass of $n$-player stochastic games, namely, stochastic games with independent chains and unknown transition matrices. In this class of games, players control their own internal Markov chains whose transitions do not depend on the states/actions of other players. However, players' decisions are coupled through their payoff functions. We assume players can receive only realizations of their payoffs, and that the players can not observe the states and actions of other players, nor do they know the transition probability matrices of their own Markov chain. Relying on a compact dual formulation of the game based on occupancy measures and the technique of confidence set to maintain high-probability estimates of the unknown transition matrices, we propose a fully decentralized mirror descent algorithm to learn an $ε$-NE for this class of games. The proposed algorithm has the desired properties of independence, scalability, and convergence. Specifically, under no assumptions on the reward functions, we show the proposed algorithm converges in polynomial time in a weaker distance (namely, the averaged Nikaido-Isoda gap) to the set of $ε$-NE policies with arbitrarily high probability. Moreover, assuming the existence of a variationally stable Nash equilibrium policy, we show that the proposed algorithm converges asymptotically to the stable $ε$-NE policy with arbitrarily high probability. In addition to Markov potential games and linear-quadratic stochastic games, this work provides another subclass of $n$-player stochastic games that, under some mild assumptions, admit polynomial-time learning algorithms for finding their stationary $ε$-NE policies.
Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning
Venue: arXiv cs.AI 2023
Year: 2023

Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline. The extrapolation of TKG is to predict unknowable facts happening in the future, holding significant practical value across diverse fields. Most extrapolation studies in TKGs focus on modeling global historical fact repeating and cyclic patterns, as well as local historical adjacent fact evolution patterns, showing promising performance in predicting future unknown facts. Yet, existing methods still face two major challenges: (1) They usually neglect the importance of historical information in KG snapshots related to the queries when encoding the local and global historical information; (2) They exhibit weak anti-noise capabilities, which hinders their performance when the inputs are contaminated with noise.To this end, we propose a novel \blue{Lo}cal-\blue{g}lobal history-aware \blue{C}ontrastive \blue{L}earning model (\blue{LogCL}) for TKG reasoning, which adopts contrastive learning to better guide the fusion of local and global historical information and enhance the ability to resist interference. Specifically, for the first challenge, LogCL proposes an entity-aware attention mechanism applied to the local and global historical facts encoder, which captures the key historical information related to queries. For the latter issue, LogCL designs four historical query contrast patterns, effectively improving the robustness of the model. The experimental results on four benchmark datasets demonstrate that LogCL delivers better and more robust performance than the state-of-the-art baselines.
Monte Carlo Experiments of Network Effects in Randomized Controlled Trials
Venue: arXiv cs.SI 2023
Year: 2023

I run Monte Carlo simulations of content production over random Watts-Strogatz graphs to show various effects relevant to modeling and understanding Randomized Controlled Trials on social networks: the network effect, spillover effect, experiment dampening effect, intrinsic dampening effect, clustering effect, degree distribution effect and the experiment size effect. I will also define some simple metrics to measure their strength. When running experiments these potentially unexpected effects must be understood and controlled for in some manner, such as modeling the underlying graph structure to establish a baseline.
Cooperative vs. Teleoperation Control of the Steady Hand Eye Robot with Adaptive Sclera Force Control: A Comparative Study
Venue: arXiv cs.RO 2023
Year: 2023

A surgeon's physiological hand tremor can significantly impact the outcome of delicate and precise retinal surgery, such as retinal vein cannulation (RVC) and epiretinal membrane peeling. Robot-assisted eye surgery technology provides ophthalmologists with advanced capabilities such as hand tremor cancellation, hand motion scaling, and safety constraints that enable them to perform these otherwise challenging and high-risk surgeries with high precision and safety. Steady-Hand Eye Robot (SHER) with cooperative control mode can filter out surgeon's hand tremor, yet another important safety feature, that is, minimizing the contact force between the surgical instrument and sclera surface for avoiding tissue damage cannot be met in this control mode. Also, other capabilities, such as hand motion scaling and haptic feedback, require a teleoperation control framework. In this work, for the first time, we implemented a teleoperation control mode incorporated with an adaptive sclera force control algorithm using a PHANTOM Omni haptic device and a force-sensing surgical instrument equipped with Fiber Bragg Grating (FBG) sensors attached to the SHER 2.1 end-effector. This adaptive sclera force control algorithm allows the robot to dynamically minimize the tool-sclera contact force. Moreover, for the first time, we compared the performance of the proposed adaptive teleoperation mode with the cooperative mode by conducting a vessel-following experiment inside an eye phantom under a microscope.
A text-dependent speaker verification application framework based on Chinese numerical string corpus
Venue: arXiv cs.SD 2023
Year: 2023

Researches indicate that text-dependent speaker verification (TD-SV) often outperforms text-independent verification (TI-SV) in short speech scenarios. However, collecting large-scale fixed text speech data is challenging, and as speech length increases, factors like sentence rhythm and pauses affect TDSV's sensitivity to text sequence. Based on these factors, We propose the hypothesis that strategies such as more fine-grained pooling methods on time scales and decoupled representations of speech speaker embedding and text embedding are more suitable for TD-SV. We have introduced an end-to-end TD-SV system based on a dataset comprising longer Chinese numerical string texts. It contains a text embedding network, a speaker embedding network, and back-end fusion. First, we recorded a dataset consisting of long Chinese numerical text named SHAL, which is publicly available on the Open-SLR website. We addressed the issue of dataset scarcity by augmenting it using Tacotron2 and HiFi-GAN. Next, we introduced a dual representation of speech with text embedding and speaker embedding. In the text embedding network, we employed an enhanced Transformer and introduced a triple loss that includes text classification loss, CTC loss, and decoder loss. For the speaker embedding network, we enhanced a sliding window attentive statistics pooling (SWASP), combined with attentive statistics pooling (ASP) to create a multi-scale pooling method. Finally, we fused text embedding and speaker embedding. Our pooling methods achieved an equal error rate (EER) performance improvement of 49.2% on Hi-Mia and 75.0% on SHAL, respectively.
Customize your NeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we target the adaptive source driven 3D scene editing task by proposing a CustomNeRF model that unifies a text description or a reference image as the editing prompt. However, obtaining desired editing results conformed with the editing prompt is nontrivial since there exist two significant challenges, including accurate editing of only foreground regions and multi-view consistency given a single-view reference image. To tackle the first challenge, we propose a Local-Global Iterative Editing (LGIE) training scheme that alternates between foreground region editing and full-image editing, aimed at foreground-only manipulation while preserving the background. For the second challenge, we also design a class-guided regularization that exploits class priors within the generation model to alleviate the inconsistency problem among different views in image-driven editing. Extensive experiments show that our CustomNeRF produces precise editing results under various real scenes for both text- and image-driven settings.
ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning
Venue: arXiv cs.CV 2023
Year: 2023

Nowadays, denoising diffusion probabilistic models have been adapted for many image segmentation tasks. However, existing end-to-end models have already demonstrated remarkable capabilities. Rather than using denoising diffusion probabilistic models alone, integrating the abilities of both denoising diffusion probabilistic models and existing end-to-end models can better improve the performance of image segmentation. Based on this, we implicitly introduce residual term into the diffusion process and propose ResEnsemble-DDPM, which seamlessly integrates the diffusion model and the end-to-end model through ensemble learning. The output distributions of these two models are strictly symmetric with respect to the ground truth distribution, allowing us to integrate the two models by reducing the residual term. Experimental results demonstrate that our ResEnsemble-DDPM can further improve the capabilities of existing models. Furthermore, its ensemble learning strategy can be generalized to other downstream tasks in image generation and get strong competitiveness.
Generalizing Political Leaning Inference to Multi-Party Systems: Insights from the UK Political Landscape
Venue: arXiv cs.SI 2023
Year: 2023

An ability to infer the political leaning of social media users can help in gathering opinion polls thereby leading to a better understanding of public opinion. While there has been a body of research attempting to infer the political leaning of social media users, this has been typically simplified as a binary classification problem (e.g. left vs right) and has been limited to a single location, leading to a dearth of investigation into more complex, multiclass classification and its generalizability to different locations, particularly those with multi-party systems. Our work performs the first such effort by studying political leaning inference in three of the UK's nations (Scotland, Wales and Northern Ireland), each of which has a different political landscape composed of multiple parties. To do so, we collect and release a dataset comprising users labelled by their political leaning as well as interactions with one another. We investigate the ability to predict the political leaning of users by leveraging these interactions in challenging scenarios such as few-shot learning, where training data is scarce, as well as assessing the applicability to users with different levels of political engagement. We show that interactions in the form of retweets between users can be a very powerful feature to enable political leaning inference, leading to consistent and robust results across different regions with multi-party systems. However, we also see that there is room for improvement in predicting the political leaning of users who are less engaged in politics.
On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat
Venue: arXiv cs.IR 2023
Year: 2023

Practitioners who wish to build real-world applications that rely on ranking models, need to decide which modelling paradigm to follow. This is not an easy choice to make, as the research literature on this topic has been shifting in recent years. In particular, whilst Gradient Boosted Decision Trees (GBDTs) have reigned supreme for more than a decade, the flexibility of neural networks has allowed them to catch up, and recent works report accuracy metrics that are on par. Nevertheless, practical systems require considerations beyond mere accuracy metrics to decide on a modelling approach. This work describes our experiences in balancing some of the trade-offs that arise, presenting a case study on a short-video recommendation application. We highlight (1) neural networks' ability to handle large training data size, user- and item-embeddings allows for more accurate models than GBDTs in this setting, and (2) because GBDTs are less reliant on specialised hardware, they can provide an equally accurate model at a lower cost. We believe these findings are of relevance to researchers in both academia and industry, and hope they can inspire practitioners who need to make similar modelling choices in the future.
Wild-Tab: A Benchmark For Out-Of-Distribution Generalization In Tabular Regression
Venue: arXiv cs.LG 2023
Year: 2023

Out-of-Distribution (OOD) generalization, a cornerstone for building robust machine learning models capable of handling data diverging from the training set's distribution, is an ongoing challenge in deep learning. While significant progress has been observed in computer vision and natural language processing, its exploration in tabular data, ubiquitous in many industrial applications, remains nascent. To bridge this gap, we present Wild-Tab, a large-scale benchmark tailored for OOD generalization in tabular regression tasks. The benchmark incorporates 3 industrial datasets sourced from fields like weather prediction and power consumption estimation, providing a challenging testbed for evaluating OOD performance under real-world conditions. Our extensive experiments, evaluating 10 distinct OOD generalization methods on Wild-Tab, reveal nuanced insights. We observe that many of these methods often struggle to maintain high-performance levels on unseen data, with OOD performance showing a marked drop compared to in-distribution performance. At the same time, Empirical Risk Minimization (ERM), despite its simplicity, delivers robust performance across all evaluations, rivaling the results of state-of-the-art methods. Looking forward, we hope that the release of Wild-Tab will facilitate further research on OOD generalization and aid in the deployment of machine learning models in various real-world contexts where handling distribution shifts is a crucial requirement.
SPECRUN: The Danger of Speculative Runahead Execution in Processors
Venue: arXiv cs.AR 2023
Year: 2023

Runahead execution is a continuously evolving microarchitectural technique for processor performance. This paper introduces the first transient execution attack on the runahead execution, called SPECRUN, which exploits the unresolved branch prediction during runahead execution. We show that SPECRUN eliminates the limitation on the number of transient instructions posed by the reorder buffer size, enhancing the exploitability and harmfulness of the attack. We concretely demonstrate a proof-of-concept attack that causes leaking secrets from a victim process, validate the merit of SPECRUN, and design a secure runahead execution scheme. This paper highlights the need to consider the security of potential optimization techniques before implementing them in a processor.
Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking
Venue: arXiv cs.SD 2023
Year: 2023

Dialogue state tracking plays a crucial role in extracting information in task-oriented dialogue systems. However, preceding research are limited to textual modalities, primarily due to the shortage of authentic human audio datasets. We address this by investigating synthetic audio data for audio-based DST. To this end, we develop cascading and end-to-end models, train them with our synthetic audio dataset, and test them on actual human speech data. To facilitate evaluation tailored to audio modalities, we introduce a novel PhonemeF1 to capture pronunciation similarity. Experimental results showed that models trained solely on synthetic datasets can generalize their performance to human voice data. By eliminating the dependency on human speech data collection, these insights pave the way for significant practical advancements in audio-based DST. Data and code are available at https://github.com/JihyunLee1/E2E-DST.
An In-Depth Survey on Virtualization Technologies in 6G Integrated Terrestrial and Non-Terrestrial Networks
Venue: arXiv cs.NI 2023
Year: 2023

6G networks are envisioned to deliver a large diversity of applications and meet stringent quality of service (QoS) requirements. Hence, integrated terrestrial and non-terrestrial networks (TN-NTNs) are anticipated to be key enabling technologies. However, the TN-NTNs integration faces a number of challenges that could be addressed through network virtualization technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV) and network slicing. In this survey, we provide a comprehensive review on the adaptation of these networking paradigms in 6G networks. We begin with a brief overview on NTNs and virtualization techniques. Then, we highlight the integral role of Artificial Intelligence in improving network virtualization by summarizing major research areas where AI models are applied. Building on this foundation, the survey identifies the main issues arising from the adaptation of SDN, NFV, and network slicing in integrated TN-NTNs, and proposes a taxonomy of integrated TN-NTNs virtualization offering a thorough review of relevant contributions. The taxonomy is built on a four-level classification indicating for each study the level of TN-NTNs integration, the used virtualization technology, the addressed problem, the type of the study and the proposed solution, which can be based on conventional or AI-enabled methods. Moreover, we present a summary on the simulation tools commonly used in the testing and validation of such networks. Finally, we discuss open issues and give insights on future research directions for the advancement of integrated TN-NTNs virtualization in the 6G era.
Mechanical Comparison of Arrangement Strategies for Topological Interlocking Assemblies
Venue: arXiv cs.CE 2023
Year: 2023

Topological Interlocking assemblies are arrangements of blocks kinematically constrained by a fixed frame, such that all rigid body motions of each block are constrained only by its permanent contact with other blocks and the frame. In the literature several blocks are introduced that can be arranged into different interlocking assemblies. In this study we investigate the influence of arrangement on the overall structural behaviour of the resulting interlocking assemblies. This is performed using the Versatile Block, as it can be arranged in three different doubly periodic ways given by wallpaper symmetries. Our focus lies on the load transfer mechanisms from the assembly onto the frame. For fast a priori evaluation of the assemblies we introduce a combinatorial model called Interlocking Flows. To investigate our assemblies from a mechanical point of view we conduct several finite element studies. These reveal a strong influence of arrangement on the structural behaviour, for instance, an impact on both the point and amount of maximum deflection. The results of the finite element analysis are in very good agreement with the predictions of the Interlocking Flow model. Our source code, data and examples are available under https://doi.org/10.5281/zenodo.10246034.
Optimizing Camera Configurations for Multi-View Pedestrian Detection
Venue: arXiv cs.CV 2023
Year: 2023

Jointly considering multiple camera views (multi-view) is very effective for pedestrian detection under occlusion. For such multi-view systems, it is critical to have well-designed camera configurations, including camera locations, directions, and fields-of-view (FoVs). Usually, these configurations are crafted based on human experience or heuristics. In this work, we present a novel solution that features a transformer-based camera configuration generator. Using reinforcement learning, this generator autonomously explores vast combinations within the action space and searches for configurations that give the highest detection accuracy according to the training dataset. The generator learns advanced techniques like maximizing coverage, minimizing occlusion, and promoting collaboration. Across multiple simulation scenarios, the configurations generated by our transformer-based model consistently outperform random search, heuristic-based methods, and configurations designed by human experts, shedding light on future camera layout optimization.
Learning Polynomial Problems with $SL(2,\mathbb{R})$ Equivariance
Venue: arXiv cs.LG 2023
Year: 2023

Optimizing and certifying the positivity of polynomials are fundamental primitives across mathematics and engineering applications, from dynamical systems to operations research. However, solving these problems in practice requires large semidefinite programs, with poor scaling in dimension and degree. In this work, we demonstrate for the first time that neural networks can effectively solve such problems in a data-driven fashion, achieving tenfold speedups while retaining high accuracy. Moreover, we observe that these polynomial learning problems are equivariant to the non-compact group $SL(2,\mathbb{R})$, which consists of area-preserving linear transformations. We therefore adapt our learning pipelines to accommodate this structure, including data augmentation, a new $SL(2,\mathbb{R})$-equivariant architecture, and an architecture equivariant with respect to its maximal compact subgroup, $SO(2, \mathbb{R})$. Surprisingly, the most successful approaches in practice do not enforce equivariance to the entire group, which we prove arises from an unusual lack of architecture universality for $SL(2,\mathbb{R})$ in particular. A consequence of this result, which is of independent interest, is that there exists an equivariant function for which there is no sequence of equivariant polynomials multiplied by arbitrary invariants that approximates the original function. This is a rare example of a symmetric problem where data augmentation outperforms a fully equivariant architecture, and provides interesting lessons in both theory and practice for other problems with non-compact symmetries.