OpenRaiser/Intern-Atlas huggingface dataset:
The impact of the COVID-19 pandemic on daily rhythms
Venue: arXiv cs.HC 2023
Year: 2023

The COVID-19 pandemic has significantly impacted daily activity rhythms and life routines. Understanding the dynamics of these impacts on different groups of people is essential for creating environments where people's lives and well-being are least disturbed during such circumstances. Starting in June 2021, we conducted a year-long study to collect high-resolution data from fitness trackers as well as answers to monthly questionnaires from 128 working adults. Using questionnaires, we investigate how routines of exercising and working have changed throughout the pandemic for different people. In addition to that, for each person in the study, we build temporal distributions of daily step counts to quantify their daily movement rhythms and use the inverse of the Earth mover's distance between different movement rhythms to quantify the movement consistency over time. Throughout the pandemic, our cohort shows a shift in exercise routines, manifested in a decrease in time spent on non-walking physical exercises as opposed to the unchanged amount of time spent on walking. In terms of daily rhythms of movement, we show that migrants and those who live alone demonstrate a lower level of consistency of daily rhythms of movement compared to their counterparts. We also observe a relationship between movement and on-site work attendance, as participants who go to work (as opposed to working remotely) also tend to maintain more consistent daily rhythms of movement. Men and migrants show a faster pace in going back to work after the decrease in restriction measures that were set in place due to the pandemic. Our results quantitatively demonstrate the unequal effect of the pandemic among different sub-populations and inform organizations and policymakers to provide more adequate support and adapt to the different needs of different groups in the post-pandemic era.
Exploring Efficient-Tuned Learning Audio Representation Method from BriVL
Venue: arXiv cs.SD 2023
Year: 2023

Recently, researchers have gradually realized that in some cases, the self-supervised pre-training on large-scale Internet data is better than that of high-quality/manually labeled data sets, and multimodal/large models are better than single or bimodal/small models. In this paper, we propose a robust audio representation learning method WavBriVL based on Bridging-Vision-and-Language (BriVL). WavBriVL projects audio, image and text into a shared embedded space, so that multi-modal applications can be realized. We demonstrate the qualitative evaluation of the image generated from WavBriVL as a shared embedded space, with the main purposes of this paper:(1) Learning the correlation between audio and image;(2) Explore a new way of image generation, that is, use audio to generate pictures. Experimental results show that this method can effectively generate appropriate images from audio.
Overview of the Evaluation Methods for the Maximum EMF Exposure in 5G Networks
Venue: arXiv cs.NI 2023
Year: 2023

Instantaneous measurements of the electromagnetic field (EMF) strength do not reflect the maximum exposure levels possible in a given location. An extrapolation factor needs to be applied to the measurements before comparing them against the local exposure guidelines or recommendations for compliance evaluation. For the fifth generation (5G) networks, a standardized approach for extrapolating EMF values is yet to be defined. This work provides an overview of the state-of-the-art research that focuses on estimating the maximum EMF exposure caused by radiation from 5G base stations. It also considers current efforts by national and international organizations to establish standardized methods for extrapolating the EMF measurements which is necessary in investigating conformance with the EMF guidelines and regulations.
Robust Multimodal Fusion for Human Activity Recognition
Venue: arXiv cs.LG 2023
Year: 2023

The proliferation of IoT and mobile devices equipped with heterogeneous sensors has enabled new applications that rely on the fusion of time-series data generated by multiple sensors with different modalities. While there are promising deep neural network architectures for multimodal fusion, their performance falls apart quickly in the presence of consecutive missing data and noise across multiple modalities/sensors, the issues that are prevalent in real-world settings. We propose Centaur, a multimodal fusion model for human activity recognition (HAR) that is robust to these data quality issues. Centaur combines a data cleaning module, which is a denoising autoencoder with convolutional layers, and a multimodal fusion module, which is a deep convolutional neural network with the self-attention mechanism to capture cross-sensor correlation. We train Centaur using a stochastic data corruption scheme and evaluate it on three datasets that contain data generated by multiple inertial measurement units. Centaur's data cleaning module outperforms 2 state-of-the-art autoencoder-based models and its multimodal fusion module outperforms 4 strong baselines. Compared to 2 related robust fusion architectures, Centaur is more robust, achieving 11.59-17.52% higher accuracy in HAR, especially in the presence of consecutive missing data in multiple sensor channels.
Aberration-Aware Depth-from-Focus
Venue: arXiv cs.CV 2023
Year: 2023

Computer vision methods for depth estimation usually use simple camera models with idealized optics. For modern machine learning approaches, this creates an issue when attempting to train deep networks with simulated data, especially for focus-sensitive tasks like Depth-from-Focus. In this work, we investigate the domain gap caused by off-axis aberrations that will affect the decision of the best-focused frame in a focal stack. We then explore bridging this domain gap through aberration-aware training (AAT). Our approach involves a lightweight network that models lens aberrations at different positions and focus distances, which is then integrated into the conventional network training pipeline. We evaluate the generality of pretrained models on both synthetic and real-world data. Our experimental results demonstrate that the proposed AAT scheme can improve depth estimation accuracy without fine-tuning the model or modifying the network architecture.
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Venue: arXiv cs.AI 2023
Year: 2023

Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic background knowledge in the form of logic. It has been shown to aid learning in the limited data regime and to facilitate inference on out-of-distribution data. Probabilistic NeSy focuses on integrating neural networks with both logic and probability theory, which additionally allows learning under uncertainty. A major limitation of current probabilistic NeSy systems, such as DeepProbLog, is their restriction to finite probability distributions, i.e., discrete random variables. In contrast, deep probabilistic programming (DPP) excels in modelling and optimising continuous probability distributions. Hence, we introduce DeepSeaProbLog, a neural probabilistic logic programming language that incorporates DPP techniques into NeSy. Doing so results in the support of inference and learning of both discrete and continuous probability distributions under logical constraints. Our main contributions are 1) the semantics of DeepSeaProbLog and its corresponding inference algorithm, 2) a proven asymptotically unbiased learning algorithm, and 3) a series of experiments that illustrate the versatility of our approach.
SMaLL: A Software Framework for portable Machine Learning Libraries
Venue: arXiv cs.MS 2023
Year: 2023

Interest in deploying Deep Neural Network (DNN) inference on edge devices has resulted in an explosion of the number and types of hardware platforms to use. While the high-level programming interface, such as TensorFlow, can be readily ported across different devices, high-performance inference implementations rely on a good mapping of the high-level interface to the target hardware platform. Commonly, this mapping may use optimizing compilers to generate code at compile time or high-performance vendor libraries that have been specialized to the target platform. Both approaches rely on expert knowledge to produce the mapping, which may be time-consuming and difficult to extend to new architectures. In this work, we present a DNN library framework, SMaLL, that is easily extensible to new architectures. The framework uses a unified loop structure and shared, cache-friendly data format across all intermediate layers, eliminating the time and memory overheads incurred by data transformation between layers. Layers are implemented by simply specifying the layer's dimensions and a kernel -- the key computing operations of each layer. The unified loop structure and kernel abstraction allows us to reuse code across layers and computing platforms. New architectures only require the 100s of lines in the kernel to be redesigned. To show the benefits of our approach, we have developed software that supports a range of layer types and computing platforms, which is easily extensible for rapidly instantiating high performance DNN libraries. We evaluate our software by instantiating networks from the TinyMLPerf benchmark suite on 5 ARM platforms and 1 x86 platform ( an AMD Zen 2). Our framework shows end-to-end performance that is comparable to or better than ML Frameworks such as TensorFlow, TVM and LibTorch.
A Survey of Data Pricing for Data Marketplaces
Venue: arXiv cs.GT 2023
Year: 2023

A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data. A considerable number of studies on data pricing can be found in literature. This paper attempts to comprehensively review the state-of-the-art on existing data pricing studies to provide a general understanding of this emerging research area. Our key contribution lies in a new taxonomy of data pricing studies that unifies different attributes determining data prices. The basis of our framework categorises these studies by the kind of market structure, be it sell-side, buy-side, or two-sided. Then in a sell-side market, the studies are further divided by query type, which defines the way a data consumer accesses data, while in a buy-side market, the studies are divided according to privacy notion, which defines the way to quantify privacy of data owners. In a two-sided market, both privacy notion and query type are used as criteria. We systematically examine the studies falling into each category in our taxonomy. Lastly, we discuss gaps within the existing research and define future research directions.
High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data
Venue: arXiv cs.CV 2023
Year: 2023

Similar to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult due to privacy concerns. As a result, conditions like Rosacea are often understudied in computer-aided diagnosis. The limited availability of data for facial skin conditions has led to the investigation of alternative methods for computer-aided diagnosis. In recent years, Generative Adversarial Networks (GANs), mainly variants of StyleGANs, have demonstrated promising results in generating synthetic facial images. In this study, for the first time, a small dataset of Rosacea with 300 full-face images is utilized to further investigate the possibility of generating synthetic data. The preliminary experiments show how fine-tuning the model and varying experimental settings significantly affect the fidelity of the Rosacea features. It is demonstrated that $R_1$ Regularization strength helps achieve high-fidelity details. Additionally, this study presents qualitative evaluations of synthetic/generated faces by expert dermatologists and non-specialist participants. The quantitative evaluation is presented using a few validation metric(s). Furthermore a number of limitations and future directions are discussed. Code and generated dataset are available at: \url{https://github.com/thinkercache/stylegan2-ada-pytorch}
MetaMorph: Learning Metamorphic Image Transformation With Appearance Changes
Venue: arXiv cs.CV 2023
Year: 2023

This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i.e., caused by brain tumors). In contrast to previous learning-based registration methods that have little or no control over appearance-changes, our model introduces a new regularization that can effectively suppress the negative effects of appearance changing areas. In particular, we develop a piecewise regularization on the tangent space of diffeomorphic transformations (also known as initial velocity fields) via learned segmentation maps of abnormal regions. The geometric transformation and appearance changes are treated as joint tasks that are mutually beneficial. Our model MetaMorph is more robust and accurate when searching for an optimal registration solution under the guidance of segmentation, which in turn improves the segmentation performance by providing appropriately augmented training labels. We validate MetaMorph on real 3D human brain tumor magnetic resonance imaging (MRI) scans. Experimental results show that our model outperforms the state-of-the-art learning-based registration models. The proposed MetaMorph has great potential in various image-guided clinical interventions, e.g., real-time image-guided navigation systems for tumor removal surgery.
Energetic Analysis on the Optimal Bounding Gaits of Quadrupedal Robots
Venue: arXiv cs.RO 2023
Year: 2023

It is often overlooked by roboticists when designing locomotion controllers for their legged machines, that energy consumption plays an important role in selecting the best gaits for locomotion at high speeds or over long distances. The purpose of this study is to examine four similar asymmetrical quadrupedal gaits that are frequently observed in legged animals in nature. To understand how a specific footfall pattern will change the energetics of a legged system, we first developed a full body model of a quadrupedal robot called A1. And for each gait we created a hybrid system with desired footfall sequence and rigid impacts. In order to find the most energy efficient gait, we used optimal control methods to formulate the problem as a trajectory optimization problem with proper constraints and objective function. This problem was implemented and solved in a nonlinear programming framework called FROST. Based on the optimized trajectories for each gait, we investigated the values of cost of transport and the work done by all joints. Moreover, we analyzed the exchange of angular momentum in different components of the system during the whole stride cycle. According to the simulation results, bounding with two flight phases is likely to be the most energy efficient gait for A1 across a wide range of speed.
Towards Driving Policies with Personality: Modeling Behavior and Style in Risky Scenarios via Data Collection in Virtual Reality
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous driving research currently faces data sparsity in representation of risky scenarios. Such data is both difficult to obtain ethically in the real world, and unreliable to obtain via simulation. Recent advances in virtual reality (VR) driving simulators lower barriers to tackling this problem in simulation. We propose the first data collection framework for risky scenario driving data from real humans using VR, as well as accompanying numerical driving personality characterizations. We validate the resulting dataset with statistical analyses and model driving behavior with an eight-factor personality vector based on the Multi-dimensional Driving Style Inventory (MDSI). Our method, dataset, and analyses show that realistic driving personalities can be modeled without deep learning or large datasets to complement autonomous driving research.
Robotic Fabric Flattening with Wrinkle Direction Detection
Venue: arXiv cs.RO 2023
Year: 2023

Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc. Perception is considered one of the major challenges in DOM due to the complex dynamics and high degree of freedom of deformable objects. In this paper, we develop a novel image-processing algorithm based on Gabor filters to extract useful features from cloth, and based on this, devise a strategy for cloth flattening tasks. We also evaluate the overall framework experimentally and compare it with three human operators. The results show that our algorithm can determine the direction of wrinkles on the cloth accurately in simulation as well as in real robot experiments. Furthermore, our dewrinkling strategy compares favorably to baseline methods. The experiment video is available on https://sites.google.com/view/robotic-fabric-flattening/home
Parallel Strong Connectivity Based on Faster Reachability
Venue: arXiv cs.DC 2023
Year: 2023

Computing strongly connected components (SCC) is a fundamental problems in graph processing. As today's real-world graphs are getting larger and larger, parallel SCC is increasingly important. SCC is challenging in the parallel setting and is particularly hard on large-diameter graphs. Many existing parallel SCC implementations can be even slower than Tarjan's sequential algorithm on large-diameter graphs. To tackle this challenge, we propose an efficient parallel SCC implementation using a new parallel reachability algorithm. Our solution is based on a novel idea referred to as vertical granularity control (VGC). It breaks the synchronization barriers to increase parallelism and hide scheduling overhead. To use VGC in our SCC algorithm, we also design an efficient data structure called the \emph{parallel hash bag}. It uses parallel dynamic resizing to avoid redundant work in maintaining frontiers (vertices processed in a round). We implement the parallel SCC algorithm by Blelloch et al.\ (J.\ ACM, 2020) using our new parallel reachability algorithm. We compare our implementation to the state-of-the-art systems, including GBBS, iSpan, Multi-step, and our highly optimized Tarjan's (sequential) algorithm, on 18 graphs, including social, web, $k$-NN, and lattice graphs. On a machine with 96 cores, our implementation is the fastest on 16 out of 18 graphs. On average (geometric means) over all graphs, our SCC is 6.0$\times$ faster than the best previous parallel code (GBBS), 12.8$\times$ faster than Tarjan's sequential algorithms, and 2.7$\times$ faster than the \emph{best existing implementation on each graph}. We believe that our techniques are of independent interest. We also apply our parallel hash bag and VGC scheme to other graph problems, including connectivity and least-element lists (LE-lists).
UT-Net: Combining U-Net and Transformer for Joint Optic Disc and Cup Segmentation and Glaucoma Detection
Venue: arXiv cs.CV 2023
Year: 2023

Glaucoma is a chronic visual disease that may cause permanent irreversible blindness. Measurement of the cup-to-disc ratio (CDR) plays a pivotal role in the detection of glaucoma in its early stage, preventing visual disparities. Therefore, accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from retinal fundus images is a fundamental requirement. Existing CNN-based segmentation frameworks resort to building deep encoders with aggressive downsampling layers, which suffer from a general limitation on modeling explicit long-range dependency. To this end, in this paper, we propose a new segmentation pipeline, called UT-Net, availing the advantages of U-Net and transformer both in its encoding layer, followed by an attention-gated bilinear fusion scheme. In addition to this, we incorporate Multi-Head Contextual attention to enhance the regular self-attention used in traditional vision transformers. Thus low-level features along with global dependencies are captured in a shallow manner. Besides, we extract context information at multiple encoding layers for better exploration of receptive fields, and to aid the model to learn deep hierarchical representations. Finally, an enhanced mixing loss is proposed to tightly supervise the overall learning process. The proposed model has been implemented for joint OD and OC segmentation on three publicly available datasets: DRISHTI-GS, RIM-ONE R3, and REFUGE. Additionally, to validate our proposal, we have performed exhaustive experimentation on Glaucoma detection from all three datasets by measuring the Cup to Disc Ratio (CDR) value. Experimental results demonstrate the superiority of UT-Net as compared to the state-of-the-art methods.
RCABench: Open Benchmarking Platform for Root Cause Analysis
Venue: arXiv cs.CR 2023
Year: 2023

Fuzzing has contributed to automatically identifying bugs and vulnerabilities in the software testing field. Although it can efficiently generate crashing inputs, these inputs are usually analyzed manually. Several root cause analysis (RCA) techniques have been proposed to automatically analyze the root causes of crashes to mitigate this cost. However, outstanding challenges for realizing more elaborate RCA techniques remain unknown owing to the lack of extensive evaluation methods over existing techniques. With this problem in mind, we developed an end-to-end benchmarking platform, RCABench, that can evaluate RCA techniques for various targeted programs in a detailed and comprehensive manner. Our experiments with RCABench indicated that the evaluations in previous studies were not enough to fully support their claims. Moreover, this platform can be leveraged to evaluate emerging RCA techniques by comparing them with existing techniques.
Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation
Venue: arXiv cs.CV 2023
Year: 2023

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelong learning approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sensor-dependent, and depth maps in the real world exhibit domain-specific characteristics, leading to variations in depth ranges. These challenges limit existing methods to lifelong learning scenarios with small domain gaps and relative depth map estimation. To facilitate lifelong metric depth learning, we identify three crucial technical challenges that require attention: i) developing a model capable of addressing the depth scale variation through scale-aware depth learning, ii) devising an effective learning strategy to handle significant domain gaps, and iii) creating an automated solution for domain-aware depth inference in practical applications. Based on the aforementioned considerations, in this paper, we present i) a lightweight multi-head framework that effectively tackles the depth scale imbalance, ii) an uncertainty-aware lifelong learning solution that adeptly handles significant domain gaps, and iii) an online domain-specific predictor selection method for real-time inference. Through extensive numerical studies, we show that the proposed method can achieve good efficiency, stability, and plasticity, leading the benchmarks by 8% to 15%.