OpenRaiser/Intern-Atlas huggingface dataset:
Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective
Venue: arXiv cs.CV 2024
Year: 2024

Recently, iteration-based stereo matching has shown great potential. However, these models optimize the disparity map using RNN variants. The discrete optimization process poses a challenge of information loss, which restricts the level of detail that can be expressed in the generated disparity map. In order to address these issues, we propose a novel training approach that incorporates diffusion models into the iterative optimization process. We designed a Time-based Gated Recurrent Unit (T-GRU) to correlate temporal and disparity outputs. Unlike standard recurrent units, we employ Agent Attention to generate more expressive features. We also designed an attention-based context network to capture a large amount of contextual information. Experiments on several public benchmarks show that we have achieved competitive stereo matching performance. Our model ranks first in the Scene Flow dataset, achieving over a 7% improvement compared to competing methods, and requires only 8 iterations to achieve state-of-the-art results.
Semantic In-Domain Product Identification for Search Queries
Venue: arXiv cs.IR 2024
Year: 2024

Accurate explicit and implicit product identification in search queries is critical for enhancing user experiences, especially at a company like Adobe which has over 50 products and covers queries across hundreds of tools. In this work, we present a novel approach to training a product classifier from user behavioral data. Our semantic model led to >25% relative improvement in CTR (click through rate) across the deployed surfaces; a >50% decrease in null rate; a 2x increase in the app cards surfaced, which helps drive product visibility.
InverseVis: Revealing the Hidden with Curved Sphere Tracing
Venue: arXiv cs.GR 2024
Year: 2024

Exploratory analysis of scalar fields on surface meshes presents significant challenges in identifying and visualizing important regions, particularly on the surface's backside. Previous visualization methods achieved only a limited visibility of significant features, i.e., regions with high or low scalar values, during interactive exploration. In response to this, we propose a novel technique, InverseVis, which leverages curved sphere tracing and uses the otherwise unused space to enhance visibility. Our approach combines direct and indirect rendering, allowing camera rays to wrap around the surface and reveal information from the backside. To achieve this, we formulate an energy term that guides the image synthesis in previously unused space, highlighting the most important regions of the backside. By quantifying the amount of visible important features, we optimize the camera position to maximize the visibility of the scalar field on both the front and backsides. InverseVis is benchmarked against state-of-the-art methods and a derived technique, showcasing its effectiveness in revealing essential features and outperforming existing approaches.
Learning Surface Terrain Classifications from Ground Penetrating Radar
Venue: arXiv cs.RO 2024
Year: 2024

Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of Ground Penetrating Radar (GPR) for terrain characterization for mobile robot platforms. Our approach leverages machine learning for surface terrain classification from GPR data. We collect a new dataset consisting of four different terrain types, and present qualitative and quantitative results. Our results demonstrate that classification networks can learn terrain categories from GPR signals. Additionally, we integrate our GPR-based classification approach into a multimodal semantic mapping framework to demonstrate a practical use case of GPR for surface terrain classification on mobile robots. Overall, this work extends the usability of GPR sensors deployed on robots to enable terrain classification in addition to GPR's existing scientific use cases.
Optimizing Disjunctive Queries with Tagged Execution
Venue: arXiv cs.DB 2024
Year: 2024

Despite decades of research into query optimization, optimizing queries with disjunctive predicate expressions remains a challenge. Solutions employed by existing systems (if any) are often simplistic and lead to much redundant work being performed by the execution engine. To address these problems, we propose a novel form of query execution called tagged execution. Tagged execution groups tuples into subrelations based on which predicates in the query they satisfy (or don't satisfy) and tags them with that information. These tags then provide additional context for query operators to take advantage of during runtime, allowing them to eliminate much of the redundant work performed by traditional engines and realize predicate pushdown optimizations for disjunctive predicates. However, tagged execution brings its own challenges, and the question of what tags to create is a nontrivial one. Careless creation of tags can lead to an exponential blowup in the tag space, with the overhead outweighing the benefits. To address this issue, we present a technique called tag generalization to minimize the space of tags. We implemented the tagged execution model with tag generalization in our system Basilisk, and our evaluation shows an average 2.7x speedup in runtime over the traditional execution model with up to a 19x speedup in certain situations.
Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation
Venue: arXiv cs.CL 2024
Year: 2024

Uncertainty estimation is a significant issue for current large language models (LLMs) that are generally poorly calibrated and over-confident, especially with reinforcement learning from human feedback (RLHF). Unlike humans, whose decisions and confidences not only stem from intrinsic beliefs but can also be adjusted through daily observations, existing calibration methods for LLMs focus on estimating or eliciting individual confidence without taking full advantage of the "Collective Wisdom": the interaction among multiple LLMs that can collectively improve both accuracy and calibration. In this work, we propose Collaborative Calibration, a post-hoc training-free calibration strategy that leverages the collaborative and expressive capabilities of multiple tool-augmented LLM agents in a simulated group deliberation process. We demonstrate the effectiveness of Collaborative Calibration on generative QA tasks across various domains, showing its potential in harnessing the rationalization of collectively calibrated confidence assessments and improving the reliability of model predictions.
Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions
Venue: arXiv cs.CL 2024
Year: 2024

Natural Language Processing (NLP) is witnessing a remarkable breakthrough driven by the success of Large Language Models (LLMs). LLMs have gained significant attention across academia and industry for their versatile applications in text generation, question answering, and text summarization. As the landscape of NLP evolves with an increasing number of domain-specific LLMs employing diverse techniques and trained on various corpus, evaluating performance of these models becomes paramount. To quantify the performance, it's crucial to have a comprehensive grasp of existing metrics. Among the evaluation, metrics which quantifying the performance of LLMs play a pivotal role. This paper offers a comprehensive exploration of LLM evaluation from a metrics perspective, providing insights into the selection and interpretation of metrics currently in use. Our main goal is to elucidate their mathematical formulations and statistical interpretations. We shed light on the application of these metrics using recent Biomedical LLMs. Additionally, we offer a succinct comparison of these metrics, aiding researchers in selecting appropriate metrics for diverse tasks. The overarching goal is to furnish researchers with a pragmatic guide for effective LLM evaluation and metric selection, thereby advancing the understanding and application of these large language models.
Evaluating the efficacy of haptic feedback, 360° treadmill-integrated Virtual Reality framework and longitudinal training on decision-making performance in a complex search-and-shoot simulation
Venue: arXiv cs.HC 2024
Year: 2024

Virtual Reality (VR) has made significant strides, offering users a multitude of ways to interact with virtual environments. Each sensory modality in VR provides distinct inputs and interactions, enhancing the user's immersion and presence. However, the potential of additional sensory modalities, such as haptic feedback and 360° locomotion, to improve decision-making performance has not been thoroughly investigated. This study addresses this gap by evaluating the impact of a haptic feedback, 360° locomotion-integrated VR framework and longitudinal, heterogeneous training on decision-making performance in a complex search-and-shoot simulation. The study involved 32 participants from a defence simulation base in India, who were randomly divided into two groups: experimental (haptic feedback, 360° locomotion-integrated VR framework with longitudinal, heterogeneous training) and placebo control (longitudinal, heterogeneous VR training without extrasensory modalities). The experiment lasted 10 days. On Day 1, all subjects executed a search-and-shoot simulation closely replicating the elements/situations in the real world. From Day 2 to Day 9, the subjects underwent heterogeneous training, imparted by the design of various complexity levels in the simulation using changes in behavioral attributes/artificial intelligence of the enemies. On Day 10, they repeated the search-and-shoot simulation executed on Day 1. The results showed that the experimental group experienced a gradual increase in presence, immersion, and engagement compared to the placebo control group. However, there was no significant difference in decision-making performance between the two groups on day 10. We intend to use these findings to design multisensory VR training frameworks that enhance engagement levels and decision-making performance.
A computational model for gender asset gap management with a focus on gender disparity in land acquisition and land tenure security
Venue: arXiv cs.CY 2024
Year: 2024

Gender inequality is a significant concern in many cultures, as women face significant barriers to asset acquisition particularly land ownership and control. Land acquisition and land tenure security are complex issues that affect various cultural groups differently, leading to disparities in access and ownership especially when superimposed with other socio-economic issues like gender inequality. Measuring the severity of these issues across different cultural groups is challenging due to variations in cultural norms, expectations and effectiveness of the measurement framework to correctly assess the level of severity. While nominal measures of gender asset gap provide valuable insights into land acquisition and tenure security issues, they do not fully capture the nuances of cultural differences and the impact of governmental and corporate policies that influence gender disparity in land ownership and control. The proposed framework aims to fill this gap by incorporating cultural and policy factors in developing a new measurement framework equipped with a more robust, comprehensive metric to standardize the approach to assessing the severity of gender asset disparity in a general sense but with a focus on land acquisition and tenure security to engender more effective interventions and policy recommendations.
LoopAnimate: Loopable Salient Object Animation
Venue: arXiv cs.CV 2024
Year: 2024

Research on diffusion model-based video generation has advanced rapidly. However, limitations in object fidelity and generation length hinder its practical applications. Additionally, specific domains like animated wallpapers require seamless looping, where the first and last frames of the video match seamlessly. To address these challenges, this paper proposes LoopAnimate, a novel method for generating videos with consistent start and end frames. To enhance object fidelity, we introduce a framework that decouples multi-level image appearance and textual semantic information. Building upon an image-to-image diffusion model, our approach incorporates both pixel-level and feature-level information from the input image, injecting image appearance and textual semantic embeddings at different positions of the diffusion model. Existing UNet-based video generation models require to input the entire videos during training to encode temporal and positional information at once. However, due to limitations in GPU memory, the number of frames is typically restricted to 16. To address this, this paper proposes a three-stage training strategy with progressively increasing frame numbers and reducing fine-tuning modules. Additionally, we introduce the Temporal E nhanced Motion Module(TEMM) to extend the capacity for encoding temporal and positional information up to 36 frames. The proposed LoopAnimate, which for the first time extends the single-pass generation length of UNet-based video generation models to 35 frames while maintaining high-quality video generation. Experiments demonstrate that LoopAnimate achieves state-of-the-art performance in both objective metrics, such as fidelity and temporal consistency, and subjective evaluation results.
TransformerFAM: Feedback attention is working memory
Venue: arXiv cs.LG 2024
Year: 2024

While Transformers have revolutionized deep learning, their quadratic attention complexity hinders their ability to process infinitely long inputs. We propose Feedback Attention Memory (FAM), a novel Transformer architecture that leverages a feedback loop to enable the network to attend to its own latent representations. This design fosters the emergence of working memory within the Transformer, allowing it to process indefinitely long sequences. TransformerFAM requires no additional weights, enabling seamless integration with pre-trained models. Our experiments show that TransformerFAM significantly improves Transformer performance on long-context tasks across various model sizes (1B, 8B, and 24B). These results showcase the potential to empower Large Language Models (LLMs) to process sequences of unlimited length.
Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning
Venue: arXiv cs.IT 2024
Year: 2024

This work deals with the problem of uplink communication and localization in an integrated sensing and communication system, where users are in the near field (NF) of antenna aperture due to the use of high carrier frequency and large antenna arrays at base stations. We formulate joint NF signal detection and localization as a problem of recovering signals with a sparse pattern. To solve the problem, we develop a message passing based sparse Bayesian learning (SBL) algorithm, where multiple unitary approximate message passing (UAMP)-based sparse signal estimators work jointly to recover the sparse signals with low complexity. Simulation results demonstrate the effectiveness of the proposed method.
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity
Venue: arXiv cs.CV 2024
Year: 2024

Federated learning (FL) facilitates a privacy-preserving neural network training paradigm through collaboration between edge clients and a central server. One significant challenge is that the distributed data is not independently and identically distributed (non-IID), typically including both intra-domain and inter-domain heterogeneity. However, recent research is limited to simply using averaged signals as a form of regularization and only focusing on one aspect of these non-IID challenges. Given these limitations, this paper clarifies these two non-IID challenges and attempts to introduce cluster representation to address them from both local and global perspectives. Specifically, we propose a dual-clustered feature contrast-based FL framework with dual focuses. First, we employ clustering on the local representations of each client, aiming to capture intra-class information based on these local clusters at a high level of granularity. Then, we facilitate cross-client knowledge sharing by pulling the local representation closer to clusters shared by clients with similar semantics while pushing them away from clusters with dissimilar semantics. Second, since the sizes of local clusters belonging to the same class may differ for each client, we further utilize clustering on the global side and conduct averaging to create a consistent global signal for guiding each local training in a contrastive manner. Experimental results on multiple datasets demonstrate that our proposal achieves comparable or superior performance gain under intra-domain and inter-domain heterogeneity.
A Reinforcement Learning Based Backfilling Strategy for HPC Batch Jobs
Venue: arXiv cs.DC 2024
Year: 2024

High Performance Computing (HPC) systems are used across a wide range of disciplines for both large and complex computations. HPC systems often receive many thousands of computational tasks at a time, colloquially referred to as jobs. These jobs must then be scheduled as optimally as possible so they can be completed within a reasonable timeframe. HPC scheduling systems often employ a technique called backfilling, wherein low-priority jobs are scheduled earlier to use the available resources that are waiting for the pending high-priority jobs. To make it work, backfilling largely relies on job runtime to calculate the start time of the ready-to-schedule jobs and avoid delaying them. It is a common belief that better estimations of job runtime will lead to better backfilling and more effective scheduling. However, our experiments show a different conclusion: there is a missing trade-off between prediction accuracy and backfilling opportunities. To learn how to achieve the best trade-off, we believe reinforcement learning (RL) can be effectively leveraged. Reinforcement Learning relies on an agent which makes decisions from observing the environment, and gains rewards or punishments based on the quality of its decision-making. Based on this idea, we designed RLBackfilling, a reinforcement learning-based backfilling algorithm. We show how RLBackfilling can learn effective backfilling strategies via trial-and-error on existing job traces. Our evaluation results show up to 59% better scheduling performance (based on average bounded job slowdown) compared to EASY backfilling using user-provided job runtime and 30% better performance compared with EASY using the ideal predicted job runtime (the actual job runtime).
Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learning
Venue: arXiv cs.CR 2024
Year: 2024

The popularity of Machine Learning (ML) makes the privacy of sensitive data more imperative than ever. Collaborative learning techniques like Split Learning (SL) aim to protect client data while enhancing ML processes. Though promising, SL has been proved to be vulnerable to a plethora of attacks, thus raising concerns about its effectiveness on data privacy. In this work, we introduce a hybrid approach combining SL and Function Secret Sharing (FSS) to ensure client data privacy. The client adds a random mask to the activation map before sending it to the servers. The servers cannot access the original function but instead work with shares generated using FSS. Consequently, during both forward and backward propagation, the servers cannot reconstruct the client's raw data from the activation map. Furthermore, through visual invertibility, we demonstrate that the server is incapable of reconstructing the raw image data from the activation map when using FSS. It enhances privacy by reducing privacy leakage compared to other SL-based approaches where the server can access client input information. Our approach also ensures security against feature space hijacking attack, protecting sensitive information from potential manipulation. Our protocols yield promising results, reducing communication overhead by over 2x and training time by over 7x compared to the same model with FSS, without any SL. Also, we show that our approach achieves >96% accuracy and remains equivalent to the plaintext models.
VRS-NeRF: Visual Relocalization with Sparse Neural Radiance Field
Venue: arXiv cs.CV 2024
Year: 2024

Visual relocalization is a key technique to autonomous driving, robotics, and virtual/augmented reality. After decades of explorations, absolute pose regression (APR), scene coordinate regression (SCR), and hierarchical methods (HMs) have become the most popular frameworks. However, in spite of high efficiency, APRs and SCRs have limited accuracy especially in large-scale outdoor scenes; HMs are accurate but need to store a large number of 2D descriptors for matching, resulting in poor efficiency. In this paper, we propose an efficient and accurate framework, called VRS-NeRF, for visual relocalization with sparse neural radiance field. Precisely, we introduce an explicit geometric map (EGM) for 3D map representation and an implicit learning map (ILM) for sparse patches rendering. In this localization process, EGP provides priors of spare 2D points and ILM utilizes these sparse points to render patches with sparse NeRFs for matching. This allows us to discard a large number of 2D descriptors so as to reduce the map size. Moreover, rendering patches only for useful points rather than all pixels in the whole image reduces the rendering time significantly. This framework inherits the accuracy of HMs and discards their low efficiency. Experiments on 7Scenes, CambridgeLandmarks, and Aachen datasets show that our method gives much better accuracy than APRs and SCRs, and close performance to HMs but is much more efficient.
Algorithm xxx: Faster Randomized SVD with Dynamic Shifts
Venue: arXiv cs.MS 2024
Year: 2024

Aiming to provide a faster and convenient truncated SVD algorithm for large sparse matrices from real applications (i.e. for computing a few of largest singular values and the corresponding singular vectors), a dynamically shifted power iteration technique is applied to improve the accuracy of the randomized SVD method. This results in a dynamic shifts based randomized SVD (dashSVD) algorithm, which also collaborates with the skills for handling sparse matrices. An accuracy-control mechanism is included in the dashSVD algorithm to approximately monitor the per vector error bound of computed singular vectors with negligible overhead. Experiments on real-world data validate that the dashSVD algorithm largely improves the accuracy of randomized SVD algorithm or attains same accuracy with fewer passes over the matrix, and provides an efficient accuracy-control mechanism to the randomized SVD computation, while demonstrating the advantages on runtime and parallel efficiency. A bound of the approximation error of the randomized SVD with the shifted power iteration is also proved.