pwc-archive/papers-with-abstracts huggingface dataset:
LunarNav: Crater-based Localization for Long-range Autonomous Lunar Rover Navigation
Authors: Shreyansh Daftry, Zhanlin Chen, Yang Cheng, Scott Tepsuporn, Brian Coltin, Ussama Naam, Lanssie Mingyue Ma, Shehryar Khattak, Matthew Deans, Larry Matthies
Date: 2023-01-03 00:00:00
Tasks: Position, Single Particle Analysis, State Estimation, Visual Odometry

The Artemis program requires robotic and crewed lunar rovers for resource prospecting and exploitation, construction and maintenance of facilities, and human exploration. These rovers must support navigation for 10s of kilometers (km) from base camps. A lunar science rover mission concept - Endurance-A, has been recommended by the new Decadal Survey as the highest priority medium-class mission of the Lunar Discovery and Exploration Program, and would be required to traverse approximately 2000 km in the South Pole-Aitkin (SPA) Basin, with individual drives of several kilometers between stops for downlink. These rover mission scenarios require functionality that provides onboard, autonomous, global position knowledge ( aka absolute localization). However, planetary rovers have no onboard global localization capability to date; they have only used relative localization, by integrating combinations of wheel odometry, visual odometry, and inertial measurements during each drive to track position relative to the start of each drive. In this work, we summarize recent developments from the LunarNav project, where we have developed algorithms and software to enable lunar rovers to estimate their global position and heading on the Moon with a goal performance of position error less than 5 meters (m) and heading error less than 3-degree, 3-sigma, in sunlit areas. This will be achieved autonomously onboard by detecting craters in the vicinity of the rover and matching them to a database of known craters mapped from orbit. The overall technical framework consists of three main elements: 1) crater detection, 2) crater matching, and 3) state estimation. In previous work, we developed crater detection algorithms for three different sensing modalities. Our results suggest that rover localization with an error less than 5 m is highly probable during daytime operations.
DADAgger: Disagreement-Augmented Dataset Aggregation
Authors: Akash Haridas, Karim Hamadeh, Samarendra Chandan Bindu Dash
Date: 2023-01-03 00:00:00
Tasks: Car Racing

DAgger is an imitation algorithm that aggregates its original datasets by querying the expert on all samples encountered during training. In order to reduce the number of samples queried, we propose a modification to DAgger, known as DADAgger, which only queries the expert for state-action pairs that are out of distribution (OOD). OOD states are identified by measuring the variance of the action predictions of an ensemble of models on each state, which we simulate using dropout. Testing on the Car Racing and Half Cheetah environments achieves comparable performance to DAgger but with reduced expert queries, and better performance than a random sampling baseline. We also show that our algorithm may be used to build efficient, well-balanced training datasets by running with no initial data and only querying the expert to resolve uncertainty.
Graphical House Allocation
Authors: Hadi Hosseini, Justin Payan, Rik Sengupta, Rohit Vaish, Vignesh Viswanathan
Date: 2023-01-03 00:00:00
Tasks: Fairness

The classical house allocation problem involves assigning $n$ houses (or items) to $n$ agents according to their preferences. A key criterion in such problems is satisfying some fairness constraints such as envy-freeness. We consider a generalization of this problem wherein the agents are placed along the vertices of a graph (corresponding to a social network), and each agent can only experience envy towards its neighbors. Our goal is to minimize the aggregate envy among the agents as a natural fairness objective, i.e., the sum of all pairwise envy values over all edges in a social graph. When agents have identical and evenly-spaced valuations, our problem reduces to the well-studied problem of linear arrangements. For identical valuations with possibly uneven spacing, we show a number of deep and surprising ways in which our setting is a departure from this classical problem. More broadly, we contribute several structural and computational results for various classes of graphs, including NP-hardness results for disjoint unions of paths, cycles, stars, or cliques, and fixed-parameter tractable (and, in some cases, polynomial-time) algorithms for paths, cycles, stars, cliques, and their disjoint unions. Additionally, a conceptual contribution of our work is the formulation of a structural property for disconnected graphs that we call separability which results in efficient parameterized algorithms for finding optimal allocations.
FATE in AI: Towards Algorithmic Inclusivity and Accessibility
Authors: Isa Inuwa-Dutse
Date: 2023-01-03 00:00:00
Tasks: Ethics, Fairness

Artificial Intelligence (AI) is at the forefront of modern technology, and its effects are felt in many areas of society. To prevent algorithmic disparities, fairness, accountability, transparency, and ethics (FATE) in AI are being implemented. However, the current discourse on these issues is largely dominated by more economically developed countries (MEDC), leaving out local knowledge, cultural pluralism, and global fairness. This study aims to address this gap by examining FATE-related desiderata, particularly transparency and ethics, in areas of the global South that are underserved by AI. A user study (n=43) and a participatory session (n=30) were conducted to achieve this goal. The results showed that AI models can encode bias and amplify stereotypes. To promote inclusivity, a community-led strategy is proposed to collect and curate representative data for responsible AI design. This will enable the affected community or individuals to monitor the increasing use of AI-powered systems. Additionally, recommendations based on public input are provided to ensure that AI adheres to social values and context-specific FATE needs.
Contextual Conservative Q-Learning for Offline Reinforcement Learning
Authors: Ke Jiang, Jiayu Yao, Xiaoyang Tan
Date: 2023-01-03 00:00:00
Tasks: MuJoCo, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Offline reinforcement learning learns an effective policy on offline datasets without online interaction, and it attracts persistent research attention due to its potential of practical application. However, extrapolation error generated by distribution shift will still lead to the overestimation for those actions that transit to out-of-distribution(OOD) states, which degrades the reliability and robustness of the offline policy. In this paper, we propose Contextual Conservative Q-Learning(C-CQL) to learn a robustly reliable policy through the contextual information captured via an inverse dynamics model. With the supervision of the inverse dynamics model, it tends to learn a policy that generates stable transition at perturbed states, for the fact that pertuebed states are a common kind of OOD states. In this manner, we enable the learnt policy more likely to generate transition that destines to the empirical next state distributions of the offline dataset, i.e., robustly reliable transition. Besides, we theoretically reveal that C-CQL is the generalization of the Conservative Q-Learning(CQL) and aggressive State Deviation Correction(SDC). Finally, experimental results demonstrate the proposed C-CQL achieves the state-of-the-art performance in most environments of offline Mujoco suite and a noisy Mujoco setting.
Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning
Authors: Lu Jiang, Yibin Wang, Jianan Wang, Pengyang Wang, Minghao Yin
Date: 2023-01-01 00:00:00
Tasks: Graph Representation Learning, Representation Learning

In this paper, we study the problem of MOOC quality evaluation which is essential for improving the course materials, promoting students' learning efficiency, and benefiting user services. While achieving promising performances, current works still suffer from the complicated interactions and relationships of entities in MOOC platforms. To tackle the challenges, we formulate the problem as a course representation learning task-based and develop an Information-aware Graph Representation Learning(IaGRL) for multi-view MOOC quality evaluation. Specifically, We first build a MOOC Heterogeneous Network (HIN) to represent the interactions and relationships among entities in MOOC platforms. And then we decompose the MOOC HIN into multiple single-relation graphs based on meta-paths to depict the multi-view semantics of courses. The course representation learning can be further converted to a multi-view graph representation task. Different from traditional graph representation learning, the learned course representations are expected to match the following three types of validity: (1) the agreement on expressiveness between the raw course portfolio and the learned course representations; (2) the consistency between the representations in each view and the unified representations; (3) the alignment between the course and MOOC platform representations. Therefore, we propose to exploit mutual information for preserving the validity of course representations. We conduct extensive experiments over real-world MOOC datasets to demonstrate the effectiveness of our proposed method.
Chatbots as Problem Solvers: Playing Twenty Questions with Role Reversals
Authors: David Noever, Forrest McKee
Date: 2023-01-01 00:00:00
Tasks: Chatbot

New chat AI applications like ChatGPT offer an advanced understanding of question context and memory across multi-step tasks, such that experiments can test its deductive reasoning. This paper proposes a multi-role and multi-step challenge, where ChatGPT plays the classic twenty-questions game but innovatively switches roles from the questioner to the answerer. The main empirical result establishes that this generation of chat applications can guess random object names in fewer than twenty questions (average, 12) and correctly guess 94% of the time across sixteen different experimental setups. The research introduces four novel cases where the chatbot fields the questions, asks the questions, both question-answer roles, and finally tries to guess appropriate contextual emotions. One task that humans typically fail but trained chat applications complete involves playing bilingual games of twenty questions (English answers to Spanish questions). Future variations address direct problem-solving using a similar inquisitive format to arrive at novel outcomes deductively, such as patentable inventions or combination thinking. Featured applications of this dialogue format include complex protein designs, neuroscience metadata, and child development educational materials.
Unpacking the "Black Box" of AI in Education
Authors: Nabeel Gillani, Rebecca Eynon, Catherine Chiabaut, Kelsey Finkel
Date: 2022-12-31 00:00:00

Recent advances in Artificial Intelligence (AI) have sparked renewed interest in its potential to improve education. However, AI is a loose umbrella term that refers to a collection of methods, capabilities, and limitations-many of which are often not explicitly articulated by researchers, education technology companies, or other AI developers. In this paper, we seek to clarify what "AI" is and the potential it holds to both advance and hamper educational opportunities that may improve the human condition. We offer a basic introduction to different methods and philosophies underpinning AI, discuss recent advances, explore applications to education, and highlight key limitations and risks. We conclude with a set of questions that educationalists may ask as they encounter AI in their research and practice. Our hope is to make often jargon-laden terms and concepts accessible, so that all are equipped to understand, interrogate, and ultimately shape the development of human centered AI in education.
Self-Supervised Video Forensics by Audio-Visual Anomaly Detection
Authors: Chao Feng, Ziyang Chen, Andrew Owens
Date: 2023-01-04 00:00:00
Proceeding: CVPR 2023 1
Tasks: Anomaly Detection, DeepFake Detection, Video Forensics

Manipulated videos often contain subtle inconsistencies between their visual and audio signals. We propose a video forensics method, based on anomaly detection, that can identify these inconsistencies, and that can be trained solely using real, unlabeled data. We train an autoregressive model to generate sequences of audio-visual features, using feature sets that capture the temporal synchronization between video frames and sound. At test time, we then flag videos that the model assigns low probability. Despite being trained entirely on real videos, our model obtains strong performance on the task of detecting manipulated speech videos. Project site: https://cfeng16.github.io/audio-visual-forensics
An Ensemble Mobile-Cloud Computing Method for Affordable and Accurate Glucometer Readout
Authors: Navidreza Asadi, Maziar Goudarzi
Date: 2023-01-04 00:00:00
Tasks: Cloud Computing, Ensemble Learning

Despite essential efforts towards advanced wireless medical devices for regular monitoring of blood properties, many such devices are not available or not affordable for everyone in many countries. Alternatively using ordinary devices, patients ought to log data into a mobile health-monitoring manually. It causes several issues: (1) clients reportedly tend to enter unrealistic data; (2) typing values several times a day is bothersome and causes clients to leave the mobile app. Thus, there is a strong need to use now-ubiquitous smartphones, reducing error by capturing images from the screen of medical devices and extracting useful information automatically. Nevertheless, there are a few challenges in its development: (1) data scarcity has led to impractical methods with very low accuracy: to our knowledge, only small datasets are available in this case; (2) accuracy-availability tradeoff: one can execute a less accurate algorithm on a mobile phone to maintain higher availability, or alternatively deploy a more accurate and more compute-intensive algorithm on the cloud, however, at the cost of lower availability in poor/no connectivity situations. We present an ensemble learning algorithm, a mobile-cloud computing service architecture, and a simple compression technique to achieve higher availability and faster response time while providing higher accuracy by integrating cloud- and mobile-side predictions. Additionally, we propose an algorithm to generate synthetic training data which facilitates utilizing deep learning models to improve accuracy. Our proposed method achieves three main objectives: (1) 92.1% and 97.7% accuracy on two different datasets, improving previous methods by 40%, (2) reducing required bandwidth by 45x with 1% drop in accuracy, (3) and providing better availability compared to mobile-only, cloud-only, split computing, and early exit service models.
Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging
Authors: Yusheng Zhou, Hao Li, Jianan Liu, Zhengmin Kong, Tao Huang, Euijoon Ahn, Zhihan Lv, Jinman Kim, David Dagan Feng
Date: 2023-01-04 00:00:00
Tasks: Diagnostic

Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. Obtaining such image pairs is difficult and therefore limits the application of supervised training. In this paper, we propose a novel UNsupervised Abnormality Extraction Network (UNAEN) to alleviate this problem. Our network is capable of working with unpaired MA-corrupted and MA-free images. It converts the MA-corrupted images to MA-reduced images by extracting abnormalities from the MA-corrupted images using a proposed artifact extractor, which intercepts the residual artifact maps from the MA-corrupted MR images explicitly, and a reconstructor to restore the original input from the MA-reduced images. The performance of UNAEN was assessed by experimenting with various publicly available MRI datasets and comparing them with state-of-the-art methods. The quantitative evaluation demonstrates the superiority of UNAEN over alternative MAR methods and visually exhibits fewer residual artifacts. Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies. Our codes are publicly available at https://github.com/YuSheng-Zhou/UNAEN.
COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images
Authors: Jessy Song, Ashkan Ebadi, Adrian Florea, Pengcheng Xi, Stéphane Tremblay, Alexander Wong
Date: 2023-01-04 00:00:00
Tasks: Medical Image Analysis

As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and assess severity through visual inspection of the chest ultrasound images. Combined with the recent advancements in computer science, applications of deep learning techniques in medical image analysis have shown promising results, demonstrating that artificial intelligence-based solutions can accelerate the diagnosis of COVID-19 and lower the burden on healthcare professionals. However, the lack of a huge amount of well-annotated data poses a challenge in building effective deep neural networks in the case of novel diseases and pandemics. Motivated by this, we present COVID-Net USPro, an explainable few-shot deep prototypical network, that monitors and detects COVID-19 positive cases with high precision and recall from minimal ultrasound images. COVID-Net USPro achieves 99.65% overall accuracy, 99.7% recall and 99.67% precision for COVID-19 positive cases when trained with only 5 shots. The analytic pipeline and results were verified by our contributing clinician with extensive experience in POCUS interpretation, ensuring that the network makes decisions based on actual patterns.
RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning
Authors: Kang Liao, Lang Nie, Chunyu Lin, Zishuo Zheng, Yao Zhao
Date: 2023-01-04 00:00:00
Proceeding: ICCV 2023 1

The wide-angle lens shows appealing applications in VR technologies, but it introduces severe radial distortion into its captured image. To recover the realistic scene, previous works devote to rectifying the content of the wide-angle image. However, such a rectification solution inevitably distorts the image boundary, which changes related geometric distributions and misleads the current vision perception models. In this work, we explore constructing a win-win representation on both content and boundary by contributing a new learning model, i.e., Rectangling Rectification Network (RecRecNet). In particular, we propose a thin-plate spline (TPS) module to formulate the non-linear and non-rigid transformation for rectangling images. By learning the control points on the rectified image, our model can flexibly warp the source structure to the target domain and achieves an end-to-end unsupervised deformation. To relieve the complexity of structure approximation, we then inspire our RecRecNet to learn the gradual deformation rules with a DoF (Degree of Freedom)-based curriculum learning. By increasing the DoF in each curriculum stage, namely, from similarity transformation (4-DoF) to homography transformation (8-DoF), the network is capable of investigating more detailed deformations, offering fast convergence on the final rectangling task. Experiments show the superiority of our solution over the compared methods on both quantitative and qualitative evaluations. The code and dataset are available at https://github.com/KangLiao929/RecRecNet.
StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection
Authors: Zhe Liu, Xiaoqing Ye, Xiao Tan, Errui Ding, Xiang Bai
Date: 2023-01-04 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection

In this paper, we propose a cross-modal distillation method named StereoDistill to narrow the gap between the stereo and LiDAR-based approaches via distilling the stereo detectors from the superior LiDAR model at the response level, which is usually overlooked in 3D object detection distillation. The key designs of StereoDistill are: the X-component Guided Distillation~(XGD) for regression and the Cross-anchor Logit Distillation~(CLD) for classification. In XGD, instead of empirically adopting a threshold to select the high-quality teacher predictions as soft targets, we decompose the predicted 3D box into sub-components and retain the corresponding part for distillation if the teacher component pilot is consistent with ground truth to largely boost the number of positive predictions and alleviate the mimicking difficulty of the student model. For CLD, we aggregate the probability distribution of all anchors at the same position to encourage the highest probability anchor rather than individually distill the distribution at the anchor level. Finally, our StereoDistill achieves state-of-the-art results for stereo-based 3D detection on the KITTI test benchmark and extensive experiments on KITTI and Argoverse Dataset validate the effectiveness.
Learning Decorrelated Representations Efficiently Using Fast Fourier Transform
Authors: Yutaro Shigeto, Masashi Shimbo, Yuya Yoshikawa, Akikazu Takeuchi
Date: 2023-01-04 00:00:00
Proceeding: CVPR 2023 1
Tasks: Representation Learning

Barlow Twins and VICReg are self-supervised representation learning models that use regularizers to decorrelate features. Although these models are as effective as conventional representation learning models, their training can be computationally demanding if the dimension d of the projected embeddings is high. As the regularizers are defined in terms of individual elements of a cross-correlation or covariance matrix, computing the loss for n samples takes O(n d^2) time. In this paper, we propose a relaxed decorrelating regularizer that can be computed in O(n d log d) time by Fast Fourier Transform. We also propose an inexpensive technique to mitigate undesirable local minima that develop with the relaxation. The proposed regularizer exhibits accuracy comparable to that of existing regularizers in downstream tasks, whereas their training requires less memory and is faster for large d. The source code is available.