OpenRaiser/Intern-Atlas huggingface dataset:
WiGNet: Windowed Vision Graph Neural Network
Venue: arXiv cs.CV 2024
Year: 2024

In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of-the-art performance in several computer vision tasks. However, their practical applicability is hindered by the computational complexity of constructing the graph, which scales quadratically with the image size. In this paper, we introduce a novel Windowed vision Graph neural Network (WiGNet) model for efficient image processing. WiGNet explores a different strategy from previous works by partitioning the image into windows and constructing a graph within each window. Therefore, our model uses graph convolutions instead of the typical 2D convolution or self-attention mechanism. WiGNet effectively manages computational and memory complexity for large image sizes. We evaluate our method in the ImageNet-1k benchmark dataset and test the adaptability of WiGNet using the CelebA-HQ dataset as a downstream task with higher-resolution images. In both of these scenarios, our method achieves competitive results compared to previous vision GNNs while keeping memory and computational complexity at bay. WiGNet offers a promising solution toward the deployment of vision GNNs in real-world applications. We publicly released the code at https://github.com/EIDOSLAB/WiGNet.
Developing a BLAS library for the AMD AI Engine
Venue: arXiv cs.DC 2024
Year: 2024

Spatial (dataflow) computer architectures can mitigate the control and performance overhead of classical von Neumann architectures such as traditional CPUs. Driven by the popularity of Machine Learning (ML) workloads, spatial devices are being marketed as ML inference accelerators. Despite providing a rich software ecosystem for ML practitioners, their adoption in other scientific domains is hindered by the steep learning curve and lack of reusable software, which makes them inaccessible to non-experts. We present our ongoing project AIEBLAS, an open-source, expandable implementation of Basic Linear Algebra Routines (BLAS) for the AMD AI Engine. Numerical routines are designed to be easily reusable, customized, and composed in dataflow programs, leveraging the characteristics of the targeted device without requiring the user to deeply understand the underlying hardware and programming model.
Energy-Quality-aware Variable Framerate Pareto-Front for Adaptive Video Streaming
Venue: arXiv cs.MM 2024
Year: 2024

Optimizing framerate for a given bitrate-spatial resolution pair in adaptive video streaming is essential to maintain perceptual quality while considering decoding complexity. Low framerates at low bitrates reduce compression artifacts and decrease decoding energy. We propose a novel method, Decoding-complexity aware Framerate Prediction (DECODRA), which employs a Variable Framerate Pareto-front approach to predict an optimized framerate that minimizes decoding energy under quality degradation constraints. DECODRA dynamically adjusts the framerate based on current bitrate and spatial resolution, balancing trade-offs between framerate, perceptual quality, and decoding complexity. Extensive experimentation with the Inter-4K dataset demonstrates DECODRA's effectiveness, yielding an average decoding energy reduction of up to 13.45%, with minimal VMAF reduction of 0.33 points at a low-quality degradation threshold, compared to the default 60 fps encoding. Even at an aggressive threshold, DECODRA achieves significant energy savings of 13.45% while only reducing VMAF by 2.11 points. In this way, DECODRA extends mobile device battery life and reduces the energy footprint of streaming services by providing a more energy-efficient video streaming pipeline.
Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis
Venue: arXiv cs.CL 2024
Year: 2024

Retrieval Augmented Generation (RAG) is a widely used approach for leveraging external context in several natural language applications such as question answering and information retrieval. Yet, the exact nature in which a Language Model (LM) leverages this non-parametric memory or retrieved context isn't clearly understood. This paper mechanistically examines the RAG pipeline to highlight that LMs demonstrate a "shortcut'' effect and have a strong bias towards utilizing the retrieved context to answer questions, while relying minimally on model priors. We propose (a) Causal Mediation Analysis; for proving that parametric memory is minimally utilized when answering a question and (b) Attention Contributions and Knockouts for showing the last token residual stream do not get enriched from the subject token in the question, but gets enriched from tokens of RAG-context. We find this pronounced "shortcut'' behaviour to be true across both LLMs (e.g.,LlaMa) and SLMs (e.g., Phi)
Do Music Generation Models Encode Music Theory?
Venue: arXiv cs.SD 2024
Year: 2024

Music foundation models possess impressive music generation capabilities. When people compose music, they may infuse their understanding of music into their work, by using notes and intervals to craft melodies, chords to build progressions, and tempo to create a rhythmic feel. To what extent is this true of music generation models? More specifically, are fundamental Western music theory concepts observable within the "inner workings" of these models? Recent work proposed leveraging latent audio representations from music generation models towards music information retrieval tasks (e.g. genre classification, emotion recognition), which suggests that high-level musical characteristics are encoded within these models. However, probing individual music theory concepts (e.g. tempo, pitch class, chord quality) remains under-explored. Thus, we introduce SynTheory, a synthetic MIDI and audio music theory dataset, consisting of tempos, time signatures, notes, intervals, scales, chords, and chord progressions concepts. We then propose a framework to probe for these music theory concepts in music foundation models (Jukebox and MusicGen) and assess how strongly they encode these concepts within their internal representations. Our findings suggest that music theory concepts are discernible within foundation models and that the degree to which they are detectable varies by model size and layer.
The Gradient of Health Data Privacy
Venue: arXiv cs.CY 2024
Year: 2024

In the era of digital health and artificial intelligence, the management of patient data privacy has become increasingly complex, with significant implications for global health equity and patient trust. This paper introduces a novel "privacy gradient" approach to health data governance, offering a more nuanced and adaptive framework than traditional binary privacy models. Our multidimensional concept considers factors such as data sensitivity, stakeholder relationships, purpose of use, and temporal aspects, allowing for context-sensitive privacy protections. Through policy analyses, ethical considerations, and case studies spanning adolescent health, integrated care, and genomic research, we demonstrate how this approach can address critical privacy challenges in diverse healthcare settings worldwide. The privacy gradient model has the potential to enhance patient engagement, improve care coordination, and accelerate medical research while safeguarding individual privacy rights. We provide policy recommendations for implementing this approach, considering its impact on healthcare systems, research infrastructures, and global health initiatives. This work aims to inform policymakers, healthcare leaders, and digital health innovators, contributing to a more equitable, trustworthy, and effective global health data ecosystem in the digital age.
Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation
Venue: arXiv cs.CV 2024
Year: 2024

Adaptation methods are developed to adapt depth foundation models to endoscopic depth estimation recently. However, such approaches typically under-perform training since they limit the parameter search to a low-rank subspace and alter the training dynamics. Therefore, we propose a full-parameter and parameter-efficient learning framework for endoscopic depth estimation. At the first stage, the subspace of attention, convolution and multi-layer perception are adapted simultaneously within different sub-spaces. At the second stage, a memory-efficient optimization is proposed for subspace composition and the performance is further improved in the united sub-space. Initial experiments on the SCARED dataset demonstrate that results at the first stage improves the performance from 10.2% to 4.1% for Sq Rel, Abs Rel, RMSE and RMSE log in the comparison with the state-of-the-art models.
Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Recent attention-based volumetric segmentation (VS) methods have achieved remarkable performance in the medical domain which focuses on modeling long-range dependencies. However, for voxel-wise prediction tasks, discriminative local features are key components for the performance of the VS models which is missing in attention-based VS methods. Aiming at resolving this issue, we deliberately incorporate the convolutional encoder branch with transformer backbone to extract local and global features in a parallel manner and aggregate them in Cross Feature Mixer Module (CFMM) for better prediction of segmentation mask. Consequently, we observe that the derived model, Y-CT-Net, achieves competitive performance on multiple medical segmentation tasks. For example, on multi-organ segmentation, Y-CT-Net achieves an 82.4% dice score, surpassing well-tuned VS Transformer/CNN-like baselines UNETR/ResNet-3D by 2.9%/1.4%. With the success of Y-CT-Net, we extend this concept with hybrid attention models, that derived Y-CH-Net model, which brings a 3% improvement in terms of HD95 score for same segmentation task. The effectiveness of both models Y-CT-Net and Y-CH-Net verifies our hypothesis and motivates us to initiate the concept of Y-CA-Net, a versatile generic architecture based upon any two encoders and a decoder backbones, to fully exploit the complementary strengths of both convolution and attention mechanisms. Based on experimental results, we argue Y-CA-Net is a key player in achieving superior results for volumetric segmentation.
Pose Estimation of Buried Deep-Sea Objects using 3D Vision Deep Learning Models
Venue: arXiv cs.CV 2024
Year: 2024

We present an approach for pose and burial fraction estimation of debris field barrels found on the seabed in the Southern California San Pedro Basin. Our computational workflow leverages recent advances in foundation models for segmentation and a vision transformer-based approach to estimate the point cloud which defines the geometry of the barrel. We propose BarrelNet for estimating the 6-DOF pose and radius of buried barrels from the barrel point clouds as input. We train BarrelNet using synthetically generated barrel point clouds, and qualitatively demonstrate the potential of our approach using remotely operated vehicle (ROV) video footage of barrels found at a historic dump site. We compare our method to a traditional least squares fitting approach and show significant improvement according to our defined benchmarks.
Inferring Kernel $ε$-Machines: Discovering Structure in Complex Systems
Venue: arXiv cs.LG 2024
Year: 2024

Previously, we showed that computational mechanic's causal states -- predictively-equivalent trajectory classes for a stochastic dynamical system -- can be cast into a reproducing kernel Hilbert space. The result is a widely-applicable method that infers causal structure directly from very different kinds of observations and systems. Here, we expand this method to explicitly introduce the causal diffusion components it produces. These encode the kernel causal-state estimates as a set of coordinates in a reduced dimension space. We show how each component extracts predictive features from data and demonstrate their application on four examples: first, a simple pendulum -- an exactly solvable system; second, a molecular-dynamic trajectory of $n$-butane -- a high-dimensional system with a well-studied energy landscape; third, the monthly sunspot sequence -- the longest-running available time series of direct observations; and fourth, multi-year observations of an active crop field -- a set of heterogeneous observations of the same ecosystem taken for over a decade. In this way, we demonstrate that the empirical kernel causal-states algorithm robustly discovers predictive structures for systems with widely varying dimensionality and stochasticity.
Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank
Venue: arXiv cs.LG 2024
Year: 2024

We study the problem of learning an approximate equilibrium in the offline multi-agent reinforcement learning (MARL) setting. We introduce a structural assumption -- the interaction rank -- and establish that functions with low interaction rank are significantly more robust to distribution shift compared to general ones. Leveraging this observation, we demonstrate that utilizing function classes with low interaction rank, when combined with regularization and no-regret learning, admits decentralized, computationally and statistically efficient learning in offline MARL. Our theoretical results are complemented by experiments that showcase the potential of critic architectures with low interaction rank in offline MARL, contrasting with commonly used single-agent value decomposition architectures.
Unifying the Scope of Bridging Anaphora Types in English: Bridging Annotations in ARRAU and GUM
Venue: arXiv cs.CL 2024
Year: 2024

Comparing bridging annotations across coreference resources is difficult, largely due to a lack of standardization across definitions and annotation schemas and narrow coverage of disparate text domains across resources. To alleviate domain coverage issues and consolidate schemas, we compare guidelines and use interpretable predictive models to examine the bridging instances annotated in the GUM, GENTLE and ARRAU corpora. Examining these cases, we find that there is a large difference in types of phenomena annotated as bridging. Beyond theoretical results, we release a harmonized, subcategorized version of the test sets of GUM, GENTLE and the ARRAU Wall Street Journal data to promote meaningful and reliable evaluation of bridging resolution across domains.
Exploring Fine-grained Task Parallelism on Simultaneous Multithreading Cores
Venue: arXiv cs.DC 2024
Year: 2024

Nowadays, latency-critical, high-performance applications are parallelized even on power-constrained client systems to improve performance. However, an important scenario of fine-grained tasking on simultaneous multithreading CPU cores in such systems has not been well researched in previous works. Hence, in this paper, we conduct performance analysis of state-of-the-art shared-memory parallel programming frameworks on simultaneous multithreading cores using real-world fine-grained application kernels. We introduce a specialized and simple software-only parallel programming framework called Relic to enable extremely fine-grained tasking on simultaneous multithreading cores. Using Relic framework, we increase performance speedups over serial implementations of benchmark kernels by 19.1% compared to LLVM OpenMP, by 31.0% compared to GNU OpenMP, by 20.2% compared to Intel OpenMP, by 33.2% compared to X-OpenMP, by 30.1% compared to oneTBB, by 23.0% compared to Taskflow, and by 21.4% compared to OpenCilk.
ParallelSFL: A Novel Split Federated Learning Framework Tackling Heterogeneity Issues
Venue: arXiv cs.DC 2024
Year: 2024

Mobile devices contribute more than half of the world's web traffic, providing massive and diverse data for powering various federated learning (FL) applications. In order to avoid the communication bottleneck on the parameter server (PS) and accelerate the training of large-scale models on resourceconstraint workers in edge computing (EC) system, we propose a novel split federated learning (SFL) framework, termed ParallelSFL. Concretely, we split an entire model into a bottom submodel and a top submodel, and divide participating workers into multiple clusters, each of which collaboratively performs the SFL training procedure and exchanges entire models with the PS. However, considering the statistical and system heterogeneity in edge systems, it is challenging to arrange suitable workers to specific clusters for efficient model training. To address these challenges, we carefully develop an effective clustering strategy by optimizing a utility function related to training efficiency and model accuracy. Specifically, ParallelSFL partitions workers into different clusters under the heterogeneity restrictions, thereby promoting model accuracy as well as training efficiency. Meanwhile, ParallelSFL assigns diverse and appropriate local updating frequencies for each cluster to further address system heterogeneity. Extensive experiments are conducted on a physical platform with 80 NVIDIA Jetson devices, and the experimental results show that ParallelSFL can reduce the traffic consumption by at least 21%, speed up the model training by at least 1.36x, and improve model accuracy by at least 5% in heterogeneous scenarios, compared to the baselines.
Deep learning for action spotting in association football videos
Venue: arXiv cs.CV 2024
Year: 2024

The task of action spotting consists in both identifying actions and precisely localizing them in time with a single timestamp in long, untrimmed video streams. Automatically extracting those actions is crucial for many sports applications, including sports analytics to produce extended statistics on game actions, coaching to provide support to video analysts, or fan engagement to automatically overlay content in the broadcast when specific actions occur. However, before 2018, no large-scale datasets for action spotting in sports were publicly available, which impeded benchmarking action spotting methods. In response, our team built the largest dataset and the most comprehensive benchmarks for sports video understanding, under the umbrella of SoccerNet. Particularly, our dataset contains a subset specifically dedicated to action spotting, called SoccerNet Action Spotting, containing more than 550 complete broadcast games annotated with almost all types of actions that can occur in a football game. This dataset is tailored to develop methods for automatic spotting of actions of interest, including deep learning approaches, by providing a large amount of manually annotated actions. To engage with the scientific community, the SoccerNet initiative organizes yearly challenges, during which participants from all around the world compete to achieve state-of-the-art performances. Thanks to our dataset and challenges, more than 60 methods were developed or published over the past five years, improving on the first baselines and making action spotting a viable option for the sports industry. This paper traces the history of action spotting in sports, from the creation of the task back in 2018, to the role it plays today in research and the sports industry.
CSLens: Towards Better Deploying Charging Stations via Visual Analytics -- A Coupled Networks Perspective
Venue: arXiv cs.HC 2024
Year: 2024

In recent years, the global adoption of electric vehicles (EVs) has surged, prompting a corresponding rise in the installation of charging stations. This proliferation has underscored the importance of expediting the deployment of charging infrastructure. Both academia and industry have thus devoted to addressing the charging station location problem (CSLP) to streamline this process. However, prevailing algorithms addressing CSLP are hampered by restrictive assumptions and computational overhead, leading to a dearth of comprehensive evaluations in the spatiotemporal dimensions. Consequently, their practical viability is restricted. Moreover, the placement of charging stations exerts a significant impact on both the road network and the power grid, which necessitates the evaluation of the potential post-deployment impacts on these interconnected networks holistically. In this study, we propose CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks. CSLens offers multiple visualizations and interactive features, empowering users to delve into the existing charging station layout, explore alternative deployment solutions, and assess the ensuring impact. To validate the efficacy of CSLens, we conducted two case studies and engaged in interviews with domain experts. Through these efforts, we substantiated the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment. Our findings underscore CSLens's potential to serve as a valuable asset in navigating the complexities of charging infrastructure planning.
Quantifying Cancer Likeness: A Statistical Approach for Pathological Image Diagnosis
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we present a new statistical approach to automatically identify cancer regions in pathological images. The proposed method is built from statistical theory in line with evidence-based medicine. The two core technologies are the classification information of image features, which was introduced based on information theory and which cancer features take positive values, normal features take negative values, and the calculation technique for determining their spatial distribution. This method then estimates areas where the classification information content shows a positive value as cancer areas in the pathological image. The method achieves AUCs of 0.95 or higher in cancer classification tasks. In addition, the proposed method has the practical advantage of not requiring a precise demarcation line between cancer and normal. This frees pathologists from the monotonous and tedious work of building consensus with other pathologists.
WiFi-CSI Sensing and Bearing Estimation in Multi-Robot Systems: An Open-Source Simulation Framework
Venue: arXiv cs.RO 2024
Year: 2024

Development and testing of multi-robot systems employing wireless signal-based sensing requires access to suitable hardware, such as channel monitoring WiFi transceivers, which can pose significant limitations. The WiFi Sensor for Robotics (WSR) toolbox, introduced by Jadhav et al. in 2022, provides a novel solution by using WiFi Channel State Information (CSI) to compute relative bearing between robots. The toolbox leverages the amplitude and phase of WiFi signals and creates virtual antenna arrays by exploiting the motion of mobile robots, eliminating the need for physical antenna arrays. However, the WSR toolbox's reliance on an obsoleting WiFi transceiver hardware has limited its operability and accessibility, hindering broader application and development of relevant tools. We present an open-source simulation framework that replicates the WSR toolbox's capabilities using Gazebo and Matlab. By simulating WiFi-CSI data collection, our framework emulates the behavior of mobile robots equipped with the WSR toolbox, enabling precise bearing estimation without physical hardware. We validate the framework through experiments with both simulated and real Turtlebot3 robots, showing a close match between the obtained CSI data and the resulting bearing estimates. This work provides a virtual environment for developing and testing WiFi-CSI-based multi-robot localization without relying on physical hardware. All code and experimental setup information are publicly available at https://github.com/BrendanxP/CSI-Simulation-Framework