OpenRaiser/Intern-Atlas huggingface dataset:
RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction
Venue: arXiv cs.CV 2023
Year: 2023

It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estimate the impact of its decisions. Recent approaches for conditional behavior prediction rely on a regression decoder, meaning that coordinates or polynomial coefficients are regressed. In this work we revisit set-based trajectory prediction, where the probability of each trajectory in a predefined trajectory set is determined by a classification model, and first-time employ it to the task of conditional behavior prediction. We propose RESET, which combines a new metric-driven algorithm for trajectory set generation with a graph-based encoder. For unconditional prediction, RESET achieves comparable performance to a regression-based approach. Due to the nature of set-based approaches, it has the advantageous property of being able to predict a flexible number of trajectories without influencing runtime or complexity. For conditional prediction, RESET achieves reasonable results with late fusion of the planned trajectory, which was not observed for regression-based approaches before. This means that RESET is computationally lightweight to combine with a planner that proposes multiple future plans of the autonomous vehicle, as large parts of the forward pass can be reused.
Scale-Equivariant Deep Learning for 3D Data
Venue: arXiv cs.CV 2023
Year: 2023

The ability of convolutional neural networks (CNNs) to recognize objects regardless of their position in the image is due to the translation-equivariance of the convolutional operation. Group-equivariant CNNs transfer this equivariance to other transformations of the input. Dealing appropriately with objects and object parts of different scale is challenging, and scale can vary for multiple reasons such as the underlying object size or the resolution of the imaging modality. In this paper, we propose a scale-equivariant convolutional network layer for three-dimensional data that guarantees scale-equivariance in 3D CNNs. Scale-equivariance lifts the burden of having to learn each possible scale separately, allowing the neural network to focus on higher-level learning goals, which leads to better results and better data-efficiency. We provide an overview of the theoretical foundations and scientific work on scale-equivariant neural networks in the two-dimensional domain. We then transfer the concepts from 2D to the three-dimensional space and create a scale-equivariant convolutional layer for 3D data. Using the proposed scale-equivariant layer, we create a scale-equivariant U-Net for medical image segmentation and compare it with a non-scale-equivariant baseline method. Our experiments demonstrate the effectiveness of the proposed method in achieving scale-equivariance for 3D medical image analysis. We publish our code at https://github.com/wimmerth/scale-equivariant-3d-convnet for further research and application.
Diffusion models with location-scale noise
Venue: arXiv cs.LG 2023
Year: 2023

Diffusion Models (DMs) are powerful generative models that add Gaussian noise to the data and learn to remove it. We wanted to determine which noise distribution (Gaussian or non-Gaussian) led to better generated data in DMs. Since DMs do not work by design with non-Gaussian noise, we built a framework that allows reversing a diffusion process with non-Gaussian location-scale noise. We use that framework to show that the Gaussian distribution performs the best over a wide range of other distributions (Laplace, Uniform, t, Generalized-Gaussian).
ReDWINE: A Clinical Datamart with Text Analytical Capabilities to Facilitate Rehabilitation Research
Venue: arXiv cs.CL 2023
Year: 2023

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face several challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.
A Deep Cybersickness Predictor through Kinematic Data with Encoded Physiological Representation
Venue: arXiv cs.HC 2023
Year: 2023

Users would experience individually different sickness symptoms during or after navigating through an immersive virtual environment, generally known as cybersickness. Previous studies have predicted the severity of cybersickness based on physiological and/or kinematic data. However, compared with kinematic data, physiological data rely heavily on biosensors during the collection, which is inconvenient and limited to a few affordable VR devices. In this work, we proposed a deep neural network to predict cybersickness through kinematic data. We introduced the encoded physiological representation to characterize the individual susceptibility; therefore, the predictor could predict cybersickness only based on a user's kinematic data without counting on biosensors. Fifty-three participants were recruited to attend the user study to collect multimodal data, including kinematic data (navigation speed, head tracking), physiological signals (e.g., electrodermal activity, heart rate), and Simulator Sickness Questionnaire (SSQ). The predictor achieved an accuracy of 97.8\% for cybersickness prediction by involving the pre-computed physiological representation to characterize individual differences, providing much convenience for the current cybersickness measurement.
KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme
Venue: arXiv cs.DC 2023
Year: 2023

The scale of the global edge AI market continues to grow. The current technical challenges that hinder the large-scale replication of edge AI are mainly small samples on the edge and heterogeneity of edge data. In addition, edge AI customers often have requirements for data security compliance and offline autonomy of edge AI services. Based on the lifelong learning method in the academic world, we formally define the problem of edge-cloud collaborative lifelong learning for the first time, and release the industry's first open-source edge-cloud collaborative lifelong learning. Edge-cloud collaborative lifelong learning adapts to data heterogeneity at different edge locations through (1) multi-task transfer learning to achieve accurate prediction of "thousands of people and thousands of faces"; (2) incremental processing of unknown tasks, the more systems learn and the smarter systems are with small samples, gradually realize AI engineering and automation; (3) Use the cloud-side knowledge base to remember new situational knowledge to avoid catastrophic forgetting; (4) The edge-cloud collaborative architecture enables data security compliance and edge AI services to be offline autonomy while applying cloud resources. This work hopes to help fundamentally solve the above-mentioned challenges of edge-cloud collaborative machine learning.
Auditing ICU Readmission Rates in an Clinical Database: An Analysis of Risk Factors and Clinical Outcomes
Venue: arXiv cs.LG 2023
Year: 2023

This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes. A range of ML models are used for classification and the fairness audit is conducted on the model predictions. The fairness audit uncovers disparities in equal opportunity, predictive parity, false positive rate parity, and false negative rate parity criteria on the MIMIC III dataset based on attributes such as gender, ethnicity, language, and insurance group. The results identify disparities in the model's performance across different groups and highlights the need for better fairness and bias mitigation strategies. The study suggests the need for collaborative efforts among researchers, policymakers, and practitioners to address bias and fairness in artificial intelligence (AI) systems.
Fast vehicle detection algorithm based on lightweight YOLO7-tiny
Venue: arXiv cs.CV 2023
Year: 2023

The swift and precise detection of vehicles plays a significant role in intelligent transportation systems. Current vehicle detection algorithms encounter challenges of high computational complexity, low detection rate, and limited feasibility on mobile devices. To address these issues, this paper proposes a lightweight vehicle detection algorithm based on YOLOv7-tiny (You Only Look Once version seven) called Ghost-YOLOv7. The width of model is scaled to 0.5 and the standard convolution of the backbone network is replaced with Ghost convolution to achieve a lighter network and improve the detection speed; then a self-designed Ghost bi-directional feature pyramid network (Ghost-BiFPN) is embedded into the neck network to enhance feature extraction capability of the algorithm and enriches semantic information; and a Ghost Decouoled Head (GDH) is employed for accurate prediction of vehicle location and species; finally, a coordinate attention mechanism is introduced into the output layer to suppress environmental interference. The WIoU loss function is employed to further enhance the detection accuracy. Ablation experiments results on the PASCAL VOC dataset demonstrate that Ghost-YOLOv7 outperforms the original YOLOv7-tiny model. It achieving a 29.8% reduction in computation, 37.3% reduction in the number of parameters, 35.1% reduction in model weights, 1.1% higher mean average precision (mAP), the detection speed is higher 27FPS compared with the original algorithm. Ghost-YOLOv7 was also compared on KITTI and BIT-vehicle datasets as well, and the results show that this algorithm has the overall best performance.
Tiered Mechanisms for Blockchain Transaction Fees
Venue: arXiv cs.GT 2023
Year: 2023

Blockchain systems come with the promise of being inclusive for a variety of decentralized applications (DApps) that can serve different purposes and have different urgency requirements. Despite this, the transaction fee mechanisms currently deployed in popular platforms as well as previous modeling attempts for the associated mechanism design problem focus on an approach that favors increasing prices in favor of those clients who value immediate service during periods of congestion. To address this issue, we introduce a model that captures the traffic diversity of blockchain systems and a tiered pricing mechanism that is capable of implementing more inclusive transaction policies. In this model, we demonstrate formally that EIP-1559, the transaction fee mechanism currently used in Ethereum, is not inclusive and demonstrate experimentally that its prices surge horizontally during periods of congestion. On the other hand, we prove formally that our mechanism achieves stable prices in expectation and we provide experimental results that establish that prices for transactions can be kept low for low urgency transactions, resulting in a diverse set of transaction types entering the blockchain. At the same time, perhaps surprisingly, our mechanism does not necessarily sacrifice revenue since the lowering of the prices for low urgency transactions can be covered from high urgency ones due to the price discrimination ability of the mechanism.
Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders
Venue: arXiv cs.CY 2023
Year: 2023

This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process also provides guidance on how to explain the always imperfect trade-offs in terms of bias to users.
Web 3.0: The Future of Internet
Venue: arXiv cs.CY 2023
Year: 2023

With the rapid growth of the Internet, human daily life has become deeply bound to the Internet. To take advantage of massive amounts of data and information on the internet, the Web architecture is continuously being reinvented and upgraded. From the static informative characteristics of Web 1.0 to the dynamic interactive features of Web 2.0, scholars and engineers have worked hard to make the internet world more open, inclusive, and equal. Indeed, the next generation of Web evolution (i.e., Web 3.0) is already coming and shaping our lives. Web 3.0 is a decentralized Web architecture that is more intelligent and safer than before. The risks and ruin posed by monopolists or criminals will be greatly reduced by a complete reconstruction of the Internet and IT infrastructure. In a word, Web 3.0 is capable of addressing web data ownership according to distributed technology. It will optimize the internet world from the perspectives of economy, culture, and technology. Then it promotes novel content production methods, organizational structures, and economic forms. However, Web 3.0 is not mature and is now being disputed. Herein, this paper presents a comprehensive survey of Web 3.0, with a focus on current technologies, challenges, opportunities, and outlook. This article first introduces a brief overview of the history of World Wide Web as well as several differences among Web 1.0, Web 2.0, Web 3.0, and Web3. Then, some technical implementations of Web 3.0 are illustrated in detail. We discuss the revolution and benefits that Web 3.0 brings. Finally, we explore several challenges and issues in this promising area.
TopTrack: Tracking Objects By Their Top
Venue: arXiv cs.CV 2023
Year: 2023

In recent years, the joint detection-and-tracking paradigm has been a very popular way of tackling the multi-object tracking (MOT) task. Many of the methods following this paradigm use the object center keypoint for detection. However, we argue that the center point is not optimal since it is often not visible in crowded scenarios, which results in many missed detections when the objects are partially occluded. We propose TopTrack, a joint detection-and-tracking method that uses the top of the object as a keypoint for detection instead of the center because it is more often visible. Furthermore, TopTrack processes consecutive frames in separate streams in order to facilitate training. We performed experiments to show that using the object top as a keypoint for detection can reduce the amount of missed detections, which in turn leads to more complete trajectories and less lost trajectories. TopTrack manages to achieve competitive results with other state-of-the-art trackers on two MOT benchmarks.
AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection
Venue: arXiv cs.CV 2023
Year: 2023

The short-form videos have explosive popularity and have dominated the new social media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou (Kwai), TikTok, Instagram Reels, and YouTube Shorts, have changed the way we consume and create content. For video content creation and understanding, the shot boundary detection (SBD) is one of the most essential components in various scenarios. In this work, we release a new public Short video sHot bOundary deTection dataset, named SHOT, consisting of 853 complete short videos and 11,606 shot annotations, with 2,716 high quality shot boundary annotations in 200 test videos. Leveraging this new data wealth, we propose to optimize the model design for video SBD, by conducting neural architecture search in a search space encapsulating various advanced 3D ConvNets and Transformers. Our proposed approach, named AutoShot, achieves higher F1 scores than previous state-of-the-art approaches, e.g., outperforming TransNetV2 by 4.2%, when being derived and evaluated on our newly constructed SHOT dataset. Moreover, to validate the generalizability of the AutoShot architecture, we directly evaluate it on another three public datasets: ClipShots, BBC and RAI, and the F1 scores of AutoShot outperform previous state-of-the-art approaches by 1.1%, 0.9% and 1.2%, respectively. The SHOT dataset and code can be found in https://github.com/wentaozhu/AutoShot.git .
Sensing the Pulse of the Pandemic: Geovisualizing the Demographic Disparities of Public Sentiment toward COVID-19 through Social Media
Venue: arXiv cs.CY 2023
Year: 2023

Social media offers a unique lens to observe large-scale, spatial-temporal patterns of users reactions toward critical events. However, social media use varies across demographics, with younger users being more prevalent compared to older populations. This difference introduces biases in data representativeness, and analysis based on social media without proper adjustment will lead to overlooking the voices of digitally marginalized communities and inaccurate estimations. This study explores solutions to pinpoint and alleviate the demographic biases in social media analysis through a case study estimating the public sentiment about COVID-19 using Twitter data. We analyzed the pandemic-related Twitter data in the U.S. during 2020-2021 to (1) elucidate the uneven social media usage among demographic groups and the disparities of their sentiments toward COVID-19, (2) construct an adjusted public sentiment measurement based on social media, the Sentiment Adjusted by Demographics (SAD) index, to evaluate the spatiotemporal varying public sentiment toward COVID-19. The results show higher proportions of female and adolescent Twitter users expressing negative emotions to COVID-19. The SAD index unveils that the public sentiment toward COVID-19 was most negative in January and February 2020 and most positive in April 2020. Vermont and Wyoming were the most positive and negative states toward COVID-19.
Performance Analysis for Near-Field MIMO: Discrete and Continuous Aperture Antennas
Venue: arXiv cs.IT 2023
Year: 2023

Performance analysis is carried out in a near-field multiple-input multiple-output (MIMO) system for both discrete and continuous aperture antennas. The effective degrees of freedom (EDoF) is first derived. It is shown that near-field MIMO systems have a higher EDoF than free-space far-field ones. Additionally, the near-field EDoF further depends on the communication distance. Based on the derived EDoF, closed-form expressions of channel capacity with a fixed distance are obtained. As a further advance, with randomly deployed receivers, ergodic capacity is derived. Simulation results reveal that near-field MIMO has an enhanced multiplexing gain even under line-of-sight transmissions. In addition, the performance of discrete MIMO converges to that of continuous aperture MIMO.
Locality via Global Ties: Stability of the 2-Core Against Misspecification
Venue: arXiv cs.DS 2023
Year: 2023

For many random graph models, the analysis of a related birth process suggests local sampling algorithms for the size of, e.g., the giant connected component, the $k$-core, the size and probability of an epidemic outbreak, etc. In this paper, we study the question of when these algorithms are robust against misspecification of the graph model, for the special case of the 2-core. We show that, for locally converging graphs with bounded average degrees, under a weak notion of expansion, a local sampling algorithm provides robust estimates for the size of both the 2-core and its largest component. Our weak notion of expansion generalizes the classical definition of expansion, while holding for many well-studied random graph models. Our method involves a two-step sprinkling argument. In the first step, we use sprinkling to establish the existence of a non-empty $2$-core inside the giant, while in the second, we use this non-empty $2$-core as seed for a second sprinkling argument to establish that the giant contains a linear sized $2$-core. The second step is based on a novel coloring scheme for the vertices in the tree-part. Our algorithmic results follow from the structural properties for the $2$-core established in the course of our sprinkling arguments. The run-time of our local algorithm is constant independent of the graph size, with the value of the constant depending on the desired asymptotic accuracy $ε$. But given the existential nature of local limits, our arguments do not give any bound on the functional dependence of this constant on $ε$, nor do they give a bound on how large the graph has to be for the asymptotic additive error bound $ε$ to hold.
SePEnTra: A secure and privacy-preserving energy trading mechanisms in transactive energy market
Venue: arXiv cs.CR 2023
Year: 2023

In this paper, we design and present a novel model called SePEnTra to ensure the security and privacy of energy data while sharing with other entities during energy trading to determine optimal price signals. Furthermore, the market operator can use this data to detect malicious activities of users in the later stage without violating privacy (e.g., deviation of actual energy generation/consumption from forecast beyond a threshold). We use two cryptographic primitives, additive secret sharing and Pedersen commitment, in SePEnTra. The performance of our model is evaluated theoretically and numerically. We compare the performance of SePEnTra with the same Transactive energy market (TEM) framework without security mechanisms. The result shows that even though using advanced cryptographic primitives in a large market framework, SePEnTra has very low computational complexity and communication overhead. Moreover, it is storage efficient for all parties.