OpenRaiser/Intern-Atlas huggingface dataset:
Best Axes Composition Extended: Multiple Gyroscopes and Accelerometers Data Fusion to Reduce Systematic Error
Venue: arXiv cs.RO 2022
Year: 2022

Multiple rigidly attached Inertial Measurement Unit (IMU) sensors provide a richer flow of data compared to a single IMU. State-of-the-art methods follow a probabilistic model of IMU measurements based on the random nature of errors combined under a Bayesian framework. However, affordable low-grade IMUs, in addition, suffer from systematic errors due to their imperfections not covered by their corresponding probabilistic model. In this paper, we propose a method, the Best Axes Composition (BAC) of combining Multiple IMU (MIMU) sensors data for accurate 3D-pose estimation that takes into account both random and systematic errors by dynamically choosing the best IMU axes from the set of all available axes. We evaluate our approach on our MIMU visual-inertial sensor and compare the performance of the method with a purely probabilistic state-of-the-art approach of MIMU data fusion. We show that BAC outperforms the latter and achieves up to 20% accuracy improvement for both orientation and position estimation in open loop, but needs proper treatment to keep the obtained gain.
NeuralMarker: A Framework for Learning General Marker Correspondence
Venue: arXiv cs.CV 2022
Year: 2022

We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker. Conventionally, this problem is addressed by fitting a homography model based on sparse feature matching. However, they are only able to handle plane-like markers and the sparse features do not sufficiently utilize appearance information. In this paper, we propose a novel framework NeuralMarker, training a neural network estimating dense marker correspondences under various challenging conditions, such as marker deformation, harsh lighting, etc. Besides, we also propose a novel marker correspondence evaluation method circumstancing annotations on real marker-image pairs and create a new benchmark. We show that NeuralMarker significantly outperforms previous methods and enables new interesting applications, including Augmented Reality (AR) and video editing.
Towards Standardized Mobility Reports with User-Level Privacy
Venue: arXiv cs.CR 2022
Year: 2022

The importance of human mobility analyses is growing in both research and practice, especially as applications for urban planning and mobility rely on them. Aggregate statistics and visualizations play an essential role as building blocks of data explorations and summary reports, the latter being increasingly released to third parties such as municipal administrations or in the context of citizen participation. However, such explorations already pose a threat to privacy as they reveal potentially sensitive location information, and thus should not be shared without further privacy measures. There is a substantial gap between state-of-the-art research on privacy methods and their utilization in practice. We thus conceptualize a standardized mobility report with differential privacy guarantees and implement it as open-source software to enable a privacy-preserving exploration of key aspects of mobility data in an easily accessible way. Moreover, we evaluate the benefits of limiting user contributions using three data sets relevant to research and practice. Our results show that even a strong limit on user contribution alters the original geospatial distribution only within a comparatively small range, while significantly reducing the error introduced by adding noise to achieve privacy guarantees.
Affine phase retrieval for sparse signals via $\ell_1$ minimization
Venue: arXiv cs.IT 2022
Year: 2022

Affine phase retrieval is the problem of recovering signals from the magnitude-only measurements with a priori information. In this paper, we use the $\ell_1$ minimization to exploit the sparsity of signals for affine phase retrieval, showing that $O(k\log(en/k))$ Gaussian random measurements are sufficient to recover all $k$-sparse signals by solving a natural $\ell_1$ minimization program, where $n$ is the dimension of signals. For the case where measurements are corrupted by noises, the reconstruction error bounds are given for both real-valued and complex-valued signals. Our results demonstrate that the natural $\ell_1$ minimization program for affine phase retrieval is stable.
An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images
Venue: arXiv cs.CV 2022
Year: 2022

Due to the reduction of technological costs and the increase of satellites launches, satellite images are becoming more popular and easier to obtain. Besides serving benevolent purposes, satellite data can also be used for malicious reasons such as misinformation. As a matter of fact, satellite images can be easily manipulated relying on general image editing tools. Moreover, with the surge of Deep Neural Networks (DNNs) that can generate realistic synthetic imagery belonging to various domains, additional threats related to the diffusion of synthetically generated satellite images are emerging. In this paper, we review the State of the Art (SOTA) on the generation and manipulation of satellite images. In particular, we focus on both the generation of synthetic satellite imagery from scratch, and the semantic manipulation of satellite images by means of image-transfer technologies, including the transformation of images obtained from one type of sensor to another one. We also describe forensic detection techniques that have been researched so far to classify and detect synthetic image forgeries. While we focus mostly on forensic techniques explicitly tailored to the detection of AI-generated synthetic contents, we also review some methods designed for general splicing detection, which can in principle also be used to spot AI manipulate images
Differentiable Collision Detection: a Randomized Smoothing Approach
Venue: arXiv cs.RO 2022
Year: 2022

Collision detection appears as a canonical operation in a large range of robotics applications from robot control to simulation, including motion planning and estimation. While the seminal works on the topic date back to the 80s, it is only recently that the question of properly differentiating collision detection has emerged as a central issue, thanks notably to the ongoing and various efforts made by the scientific community around the topic of differentiable physics. Yet, very few solutions have been suggested so far, and only with a strong assumption on the nature of the shapes involved. In this work, we introduce a generic and efficient approach to compute the derivatives of collision detection for any pair of convex shapes, by notably leveraging randomized smoothing techniques which have shown to be particularly adapted to capture the derivatives of non-smooth problems. This approach is implemented in the HPP-FCL and Pinocchio ecosystems, and evaluated on classic datasets and problems of the robotics literature, demonstrating few micro-second timings to compute informative derivatives directly exploitable by many real robotic applications including differentiable simulation.
F*** workflows: when parts of FAIR are missing
Venue: arXiv cs.DL 2022
Year: 2022

The FAIR principles for scientific data (Findable, Accessible, Interoperable, Reusable) are also relevant to other digital objects such as research software and scientific workflows that operate on scientific data. The FAIR principles can be applied to the data being handled by a scientific workflow as well as the processes, software, and other infrastructure which are necessary to specify and execute a workflow. The FAIR principles were designed as guidelines, rather than rules, that would allow for differences in standards for different communities and for different degrees of compliance. There are many practical considerations which impact the level of FAIR-ness that can actually be achieved, including policies, traditions, and technologies. Because of these considerations, obstacles are often encountered during the workflow lifecycle that trace directly to shortcomings in the implementation of the FAIR principles. Here, we detail some cases, without naming names, in which data and workflows were Findable but otherwise lacking in areas commonly needed and expected by modern FAIR methods, tools, and users. We describe how some of these problems, all of which were overcome successfully, have motivated us to push on systems and approaches for fully FAIR workflows.
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description
Venue: arXiv cs.LG 2022
Year: 2022

Most existing Time series classification (TSC) models lack interpretability and are difficult to inspect. Interpretable machine learning models can aid in discovering patterns in data as well as give easy-to-understand insights to domain specialists. In this study, we present Neuro-Symbolic Time Series Classification (NSTSC), a neuro-symbolic model that leverages signal temporal logic (STL) and neural network (NN) to accomplish TSC tasks using multi-view data representation and expresses the model as a human-readable, interpretable formula. In NSTSC, each neuron is linked to a symbolic expression, i.e., an STL (sub)formula. The output of NSTSC is thus interpretable as an STL formula akin to natural language, describing temporal and logical relations hidden in the data. We propose an NSTSC-based classifier that adopts a decision-tree approach to learn formula structures and accomplish a multiclass TSC task. The proposed smooth activation functions for wSTL allow the model to be learned in an end-to-end fashion. We test NSTSC on a real-world wound healing dataset from mice and benchmark datasets from the UCR time-series repository, demonstrating that NSTSC achieves comparable performance with the state-of-the-art models. Furthermore, NSTSC can generate interpretable formulas that match with domain knowledge.
DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network
Venue: arXiv cs.CV 2022
Year: 2022

Human motion prediction is a fundamental part of many human-robot applications. Despite the recent progress in human motion prediction, most studies simplify the problem by predicting the human motion relative to a fixed joint and/or only limit their model to predict one possible future motion. While due to the complex nature of human motion, a single output cannot reflect all the possible actions one can do. Also, for any robotics application, we need the full human motion including the user trajectory not a 3d pose relative to the hip joint. In this paper, we try to address these two issues by proposing a transformer-based generative model for forecasting multiple diverse human motions. Our model generates \textit{N} future possible motion by querying a history of human motion. Our model first predicts the pose of the body relative to the hip joint. Then the \textit{Hip Prediction Module} predicts the trajectory of the hip movement for each predicted pose frame. To emphasize on the diverse future motions we introduce a similarity loss that penalizes the pairwise sample distance. We show that our system outperforms the state-of-the-art in human motion prediction while it can predict diverse multi-motion future trajectories with hip movements
Machine Learning based Extraction of Boundary Conditions from Doppler Echo Images for Patient Specific Coarctation of the Aorta: Computational Fluid Dynamics Study
Venue: arXiv cs.CE 2022
Year: 2022

Purpose- Coarctation of the Aorta (CoA) patient-specific computational fluid dynamics (CFD) studies in resource constrained settings are limited by the available imaging modalities for geometry and velocity data acquisition. Doppler echocardiography has been seen as a suitable velocity acquisition modality due to its higher availability and safety. This study aimed to investigate the application of classical machine learning (ML) methods to create an adequate and robust approach for obtaining boundary conditions (BCs) from Doppler Echocardiography images, for haemodynamic modeling using CFD. Methods- Our proposed approach combines ML and CFD to model haemodynamic flow within the region of interest. With the key feature of the approach being the use of ML models to calibrate the inlet and outlet boundary conditions (BCs) of the CFD model. The key input variable for the ML model was the patients heart rate as this was the parameter that varied in time across the measured vessels within the study. ANSYS Fluent was used for the CFD component of the study whilst the scikit-learn python library was used for the ML component. Results- We validated our approach against a real clinical case of severe CoA before intervention. The maximum coarctation velocity of our simulations were compared to the measured maximum coarctation velocity obtained from the patient whose geometry is used within the study. Of the 5 ML models used to obtain BCs the top model was within 5\% of the measured maximum coarctation velocity. Conclusion- The framework demonstrated that it was capable of taking variations of the patients heart rate between measurements into account. Thus, enabling the calculation of BCs that were physiologically realistic when the heart rate was scaled across each vessel whilst providing a reasonably accurate solution.
Application of Neural Network in the Prediction of NOx Emissions from Degrading Gas Turbine
Venue: arXiv cs.LG 2022
Year: 2022

This paper is aiming to apply neural network algorithm for predicting the process response (NOx emissions) from degrading natural gas turbines. Nine different process variables, or predictors, are considered in the predictive modelling. It is found out that the model trained by neural network algorithm should use part of recent data in the training and validation sets accounting for the impact of the system degradation. R-Square values of the training and validation sets demonstrate the validity of the model. The residue plot, without any clear pattern, shows the model is appropriate. The ranking of the importance of the process variables are demonstrated and the prediction profile confirms the significance of the process variables. The model trained by using neural network algorithm manifests the optimal settings of the process variables to reach the minimum value of NOx emissions from the degrading gas turbine system.
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection
Venue: arXiv cs.LG 2022
Year: 2022

When evaluating the performance of clinical machine learning models, one must consider the deployment population. When the population of patients with observed labels is only a subset of the deployment population (label selection), standard model performance estimates on the observed population may be misleading. In this study we describe three classes of label selection and simulate five causally distinct scenarios to assess how particular selection mechanisms bias a suite of commonly reported binary machine learning model performance metrics. Simulations reveal that when selection is affected by observed features, naive estimates of model discrimination may be misleading. When selection is affected by labels, naive estimates of calibration fail to reflect reality. We borrow traditional weighting estimators from causal inference literature and find that when selection probabilities are properly specified, they recover full population estimates. We then tackle the real-world task of monitoring the performance of deployed machine learning models whose interactions with clinicians feed-back and affect the selection mechanism of the labels. We train three machine learning models to flag low-yield laboratory diagnostics, and simulate their intended consequence of reducing wasteful laboratory utilization. We find that naive estimates of AUROC on the observed population undershoot actual performance by up to 20%. Such a disparity could be large enough to lead to the wrongful termination of a successful clinical decision support tool. We propose an altered deployment procedure, one that combines injected randomization with traditional weighted estimates, and find it recovers true model performance.
Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer
Venue: arXiv cs.CV 2022
Year: 2022

The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade. Automated mitosis detection through deep learning-oriented image analysis often fails on unseen patient data due to inherent domain biases. This paper proposes a domain homogenizer for mitosis detection that attempts to alleviate domain differences in histology images via adversarial reconstruction of input images. The proposed homogenizer is based on a U-Net architecture and can effectively reduce domain differences commonly seen with histology imaging data. We demonstrate our domain homogenizer's effectiveness by observing the reduction in domain differences between the preprocessed images. Using this homogenizer, along with a subsequent retina-net object detector, we were able to outperform the baselines of the 2021 MIDOG challenge in terms of average precision of the detected mitotic figures.
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition
Venue: arXiv cs.CV 2022
Year: 2022

We present a learning algorithm for human activity recognition in videos. Our approach is designed for UAV videos, which are mainly acquired from obliquely placed dynamic cameras that contain a human actor along with background motion. Typically, the human actors occupy less than one-tenth of the spatial resolution. Our approach simultaneously harnesses the benefits of frequency domain representations, a classical analysis tool in signal processing, and data driven neural networks. We build a differentiable static-dynamic frequency mask prior to model the salient static and dynamic pixels in the video, crucial for the underlying task of action recognition. We use this differentiable mask prior to enable the neural network to intrinsically learn disentangled feature representations via an identity loss function. Our formulation empowers the network to inherently compute disentangled salient features within its layers. Further, we propose a cost-function encapsulating temporal relevance and spatial content to sample the most important frame within uniformly spaced video segments. We conduct extensive experiments on the UAV Human dataset and the NEC Drone dataset and demonstrate relative improvements of 5.72% - 13.00% over the state-of-the-art and 14.28% - 38.05% over the corresponding baseline model.
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization
Venue: arXiv cs.HC 2022
Year: 2022

Given thousands of equally accurate machine learning (ML) models, how can users choose among them? A recent ML technique enables domain experts and data scientists to generate a complete Rashomon set for sparse decision trees--a huge set of almost-optimal interpretable ML models. To help ML practitioners identify models with desirable properties from this Rashomon set, we develop TimberTrek, the first interactive visualization system that summarizes thousands of sparse decision trees at scale. Two usage scenarios highlight how TimberTrek can empower users to easily explore, compare, and curate models that align with their domain knowledge and values. Our open-source tool runs directly in users' computational notebooks and web browsers, lowering the barrier to creating more responsible ML models. TimberTrek is available at the following public demo link: https://poloclub.github.io/timbertrek.
PoxVerifi: An Information Verification System to Combat Monkeypox Misinformation
Venue: arXiv cs.CL 2022
Year: 2022

Following recent outbreaks, monkeypox-related misinformation continues to rapidly spread online. This negatively impacts response strategies and disproportionately harms LGBTQ+ communities in the short-term, and ultimately undermines the overall effectiveness of public health responses. In an attempt to combat monkeypox-related misinformation, we present PoxVerifi, an open-source, extensible tool that provides a comprehensive approach to assessing the accuracy of monkeypox related claims. Leveraging information from existing fact checking sources and published World Health Organization (WHO) information, we created an open-sourced corpus of 225 rated monkeypox claims. Additionally, we trained an open-sourced BERT-based machine learning model for specifically classifying monkeypox information, which achieved 96% cross-validation accuracy. PoxVerifi is a Google Chrome browser extension designed to empower users to navigate through monkeypox-related misinformation. Specifically, PoxVerifi provides users with a comprehensive toolkit to assess the veracity of headlines on any webpage across the Internet without having to visit an external site. Users can view an automated accuracy review from our trained machine learning model, a user-generated accuracy review based on community-member votes, and have the ability to see similar, vetted, claims. Besides PoxVerifi's comprehensive approach to claim-testing, our platform provides an efficient and accessible method to crowdsource accuracy ratings on monkeypox related-claims, which can be aggregated to create new labeled misinformation datasets.
S2TD: a Separation Logic Verifier that Supports Reasoning of the Absence and Presence of Bugs
Venue: arXiv cs.PL 2022
Year: 2022

Heap-manipulating programs are known to be challenging to reason about. We present a novel verifier for heap-manipulating programs called S2TD, which encodes programs systematically in the form of Constrained Horn Clauses (CHC) using a novel extension of separation logic (SL) with recursive predicates and dangling predicates. S2TD actively explores cyclic proofs to address the path explosion problem. S2TD differentiates itself from existing CHC-based verifiers by focusing on heap-manipulating programs and employing cyclic proof to efficiently verify or falsify them with counterexamples. Compared with existing SL-based verifiers, S2TD precisely specifies the heaps of de-allocated pointers to avoid false positives in reasoning about the presence of bugs. S2TD has been evaluated using a comprehensive set of benchmark programs from the SV-COMP repository. The results show that S2TD is more effective than state-of-art program verifiers and is more efficient than most of them.
Training an Assassin AI for The Resistance: Avalon
Venue: arXiv cs.LG 2022
Year: 2022

The Resistance: Avalon is a partially observable social deduction game. This area of AI game playing is fairly undeveloped. Implementing an AI for this game involves multiple components specific to each phase as well as role in the game. In this paper, we plan to iteratively develop the required components for each role/phase by first addressing the Assassination phase which can be modeled as a machine learning problem. Using a publicly available dataset from an online version of the game, we train classifiers that emulate an Assassin. After trying various classification techniques, we are able to achieve above average human performance using a simple linear support vector classifier. The eventual goal of this project is to pursue developing an intelligent and complete Avalon player that can play through each phase of the game as any role.