OpenRaiser/Intern-Atlas huggingface dataset:
Fully Dynamic Shortest Path Reporting Against an Adaptive Adversary
Venue: arXiv cs.DS 2023
Year: 2023

Algebraic data structures are the main subroutine for maintaining distances in fully dynamic graphs in subquadratic time. However, these dynamic algebraic algorithms generally cannot maintain the shortest paths, especially against adaptive adversaries. We present the first fully dynamic algorithm that maintains the shortest paths against an adaptive adversary in subquadratic update time. This is obtained via a combinatorial reduction that allows reconstructing the shortest paths with only a few distance estimates. Using this reduction, we obtain the following: On weighted directed graphs with real edge weights in $[1,W]$, we can maintain $(1+ε)$ approximate shortest paths in $\tilde{O}(n^{1.816}ε^{-2} \log W)$ update and $\tilde{O}(n^{1.741} ε^{-2} \log W)$ query time. This improves upon the approximate distance data structures from [v.d.Brand, Nanongkai, FOCS'19], which only returned a distance estimate, by matching their complexity and returning an approximate shortest path. On unweighted directed graphs, we can maintain exact shortest paths in $\tilde{O}(n^{1.823})$ update and $\tilde{O}(n^{1.747})$ query time. This improves upon [Bergamaschi, Henzinger, P.Gutenberg, V.Williams, Wein, SODA'21] who could report the path only against oblivious adversaries. We improve both their update and query time while also handling adaptive adversaries. On unweighted undirected graphs, our reduction holds not just against adaptive adversaries but is also deterministic. We maintain a $(1+ε)$-approximate $st$-shortest path in $O(n^{1.529} / ε^2)$ time per update, and $(1+ε)$-approximate single source shortest paths in $O(n^{1.764} / ε^2)$ time per update. Previous deterministic results by [v.d.Brand, Nazari, Forster, FOCS'22] could only maintain distance estimates but no paths.
Fairness in Visual Clustering: A Novel Transformer Clustering Approach
Venue: arXiv cs.CV 2023
Year: 2023

Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal. This is because of the limitation of large-scale balanced data with well-annotated labels for sensitive or protected attributes. In this paper, we first evaluate demographic bias in deep clustering models from the perspective of cluster purity, which is measured by the ratio of positive samples within a cluster to their correlation degree. This measurement is adopted as an indication of demographic bias. Then, a novel loss function is introduced to encourage a purity consistency for all clusters to maintain the fairness aspect of the learned clustering model. Moreover, we present a novel attention mechanism, Cross-attention, to measure correlations between multiple clusters, strengthening faraway positive samples and improving the purity of clusters during the learning process. Experimental results on a large-scale dataset with numerous attribute settings have demonstrated the effectiveness of the proposed approach on both clustering accuracy and fairness enhancement on several sensitive attributes.
Understanding and Mitigating Mental Health Misinformation on Video Sharing Platforms
Venue: arXiv cs.SI 2023
Year: 2023

Despite the ever-strong demand for mental health care globally, access to traditional mental health services remains severely limited expensive, and stifled by stigma and systemic barriers. Thus, over the last few years, young people are increasingly turning to content on video-sharing platforms (VSPs) like TikTok and YouTube to help them navigate their mental health journey. However, navigating towards trustworthy information relating to mental health on these platforms is challenging, given the uncontrollable and unregulated growth of dedicated mental health content and content creators catering to a wide array of mental health conditions on these platforms. In this paper, we attempt to define what constitutes as "mental health misinformation" through examples. In addition, we also suggest some open questions to answer and challenges to tackle regarding this important and timely research topic
Computer-assisted proofs of "Kariya's theorem" with computer algebra
Venue: arXiv cs.SC 2023
Year: 2023

We demonstrate computer-assisted proofs of "Kariya's theorem," a theorem in elementary geometry, with computer algebra. In the proof of geometry theorem with computer algebra, vertices of geometric figures that are subjects for the proof are expressed as variables. The variables are classified into two classes: arbitrarily given points and the points defined from the former points by constraints. We show proofs of Kariya's theorem with two formulations according to two ways for giving the arbitrary points: one is called "vertex formulation," and the other is called "incenter formulation," with two methods: one is Gröbner basis computation, and the other is Wu's method. Furthermore, we show computer-assisted proofs of the property that the point so-called "Kariya point" is located on the hyperbola so-called "Feuerbach's hyperbola", with two formulations and two methods.
BVIP Guiding System with Adaptability to Individual Differences
Venue: arXiv cs.RO 2023
Year: 2023

Guiding robots can not only detect close-range obstacles like other guiding tools, but also extend its range to perceive the environment when making decisions. However, most existing works over-simplified the interaction between human agents and robots, ignoring the differences between individuals, resulting in poor experiences for different users. To solve the problem, we propose a data-driven guiding system to cope with the effect brighten by individual differences. In our guiding system, we design a Human Motion Predictor (HMP) and a Robot Dynamics Model (RDM) based on deep neural network, the time convolutional network (TCN) is verified to have the best performance, to predict differences in interaction between different human agents and robots. To train our models, we collected datasets that records the interactions from different human agents. Moreover, given the predictive information of the specific user, we propose a waypoints selector that allows the robot to naturally adapt to the user's state changes, which are mainly reflected in the walking speed. We compare the performance of our models with previous works and achieve significant performance improvements. On this basis, our guiding system demonstrated good adaptability to different human agents. Our guiding system is deployed on a real quadruped robot to verify the practicability.
Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets
Venue: arXiv cs.CL 2023
Year: 2023

Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues is significant to the design of dialogue-based intelligent tutoring systems. Many prior studies employ machine learning models to classify DAs in tutoring dialogues and invest much effort to optimize the classification accuracy by using limited amounts of training data (i.e., low-resource data scenario). However, beyond the classification accuracy, the robustness of the classifier is also important, which can reflect the capability of the classifier on learning the patterns from different class distributions. We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution. The DA classifiers in these studies tend to prioritize accuracy on the majority class at the expense of the minority class which might not be robust to the data with imbalanced ratios of different DA classes. To optimize the robustness of classifiers on imbalanced class distributions, we propose to optimize the performance of the DA classifier by maximizing the area under the ROC curve (AUC) score (i.e., AUC maximization). Through extensive experiments, our study provides evidence that (i) by maximizing AUC in the training process, the DA classifier achieves significant performance improvement compared to the CE approach under low-resource data, and (ii) AUC maximization approaches can improve the robustness of the DA classifier under different class imbalance ratios.
Computing shortest 12-representants of labeled graphs
Venue: arXiv cs.DM 2023
Year: 2023

The notion of $12$-representable graphs was introduced as a variant of a well-known class of word-representable graphs. Recently, these graphs were shown to be equivalent to the complements of simple-triangle graphs. This indicates that a $12$-representant of a graph (i.e., a word representing the graph) can be obtained in polynomial time if it exists. However, the $12$-representant is not necessarily optimal (i.e., shortest possible). This paper proposes an $O(n^2)$-time algorithm to generate a shortest $12$-representant of a labeled graph, where $n$ is the number of vertices of the graph.
Multi-View Graph Representation Learning Beyond Homophily
Venue: arXiv cs.LG 2023
Year: 2023

Unsupervised graph representation learning(GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning methods usually adopt self-supervised learning, and embeddings are learned by solving a handcrafted auxiliary task(so-called pretext task). However, partially due to the irregular non-Euclidean data in graphs, the pretext tasks are generally designed under homophily assumptions and cornered in the low-frequency signals, which results in significant loss of other signals, especially high-frequency signals widespread in graphs with heterophily. Motivated by this limitation, we propose a multi-view perspective and the usage of diverse pretext tasks to capture different signals in graphs into embeddings. A novel framework, denoted as Multi-view Graph Encoder(MVGE), is proposed, and a set of key designs are identified. More specifically, a set of new pretext tasks are designed to encode different types of signals, and a straightforward operation is propxwosed to maintain both the commodity and personalization in both the attribute and the structural levels. Extensive experiments on synthetic and real-world network datasets show that the node representations learned with MVGE achieve significant performance improvements in three different downstream tasks, especially on graphs with heterophily. Source code is available at \url{https://github.com/G-AILab/MVGE}.
Within-Camera Multilayer Perceptron DVS Denoising
Venue: arXiv cs.CV 2023
Year: 2023

In-camera event denoising reduces the data rate of event cameras by filtering out noise at the source. A lightweight multilayer perceptron denoising filter (MLPF) provides state-of-the-art low-cost denoising accuracy. It processes a small neighborhood of pixels from the timestamp image around each event to discriminate signal and noise events. This paper proposes two digital logic implementations of the MLPF denoiser and quantifies their resource cost, power, and latency. The hardware MLPF quantizes the weights and hidden unit activations to 4 bits and has about 1k weights with about 40% sparsity. The Area-Under-Curve Receiver Operating Characteristic accuracy is nearly indistinguishable from that of the floating point network. The FPGA MLPF processes each event in 10 clock cycles. In FPGA, it uses 3.5k flip flops and 11.5k LUTs. Our ASIC implementation in 65nm digital technology for a 346x260 pixel camera occupies an area of 4.3mm^2 and consumes 4nJ of energy per event at event rates up to 25MHz. The MLPF can be easily integrated into an event camera using an FPGA or as an ASIC directly on the camera chip or in the same package. This denoising could dramatically reduce the energy consumed by the communication and host processor and open new areas of always-on event camera application under scavenged and battery power. Code: https://github.com/SensorsINI/dnd_hls
Shape is (almost) all!: Persistent homology features (PHFs) are an information rich input for efficient molecular machine learning
Venue: arXiv cs.LG 2023
Year: 2023

3-D shape is important to chemistry, but how important? Machine learning works best when the inputs are simple and match the problem well. Chemistry datasets tend to be very small compared to those generally used in machine learning so we need to get the most from each datapoint. Persistent homology measures the topological shape properties of point clouds at different scales and is used in topological data analysis. Here we investigate what persistent homology captures about molecular structure and create persistent homology features (PHFs) that encode a molecule's shape whilst losing most of the symbolic detail like atom labels, valence, charge, bonds etc. We demonstrate the usefulness of PHFs on a series of chemical datasets: QM7, lipophilicity, Delaney and Tox21. PHFs work as well as the best benchmarks. PHFs are very information dense and much smaller than other encoding methods yet found, meaning ML algorithms are much more energy efficient. PHFs success despite losing a large amount of chemical detail highlights how much of chemistry can be simplified to topological shape.
FSDNet-An efficient fire detection network for complex scenarios based on YOLOv3 and DenseNet
Venue: arXiv cs.CV 2023
Year: 2023

Fire is one of the common disasters in daily life. To achieve fast and accurate detection of fires, this paper proposes a detection network called FSDNet (Fire Smoke Detection Network), which consists of a feature extraction module, a fire classification module, and a fire detection module. Firstly, a dense connection structure is introduced in the basic feature extraction module to enhance the feature extraction ability of the backbone network and alleviate the gradient disappearance problem. Secondly, a spatial pyramid pooling structure is introduced in the fire detection module, and the Mosaic data augmentation method and CIoU loss function are used in the training process to comprehensively improve the flame feature extraction ability. Finally, in view of the shortcomings of public fire datasets, a fire dataset called MS-FS (Multi-scene Fire And Smoke) containing 11938 fire images was created through data collection, screening, and object annotation. To prove the effectiveness of the proposed method, the accuracy of the method was evaluated on two benchmark fire datasets and MS-FS. The experimental results show that the accuracy of FSDNet on the two benchmark datasets is 99.82% and 91.15%, respectively, and the average precision on MS-FS is 86.80%, which is better than the mainstream fire detection methods.
Teacher Network Calibration Improves Cross-Quality Knowledge Distillation
Venue: arXiv cs.CV 2023
Year: 2023

We investigate cross-quality knowledge distillation (CQKD), a knowledge distillation method where knowledge from a teacher network trained with full-resolution images is transferred to a student network that takes as input low-resolution images. As image size is a deciding factor for the computational load of computer vision applications, CQKD notably reduces the requirements by only using the student network at inference time. Our experimental results show that CQKD outperforms supervised learning in large-scale image classification problems. We also highlight the importance of calibrating neural networks: we show that with higher temperature smoothing of the teacher's output distribution, the student distribution exhibits a higher entropy, which leads to both, a lower calibration error and a higher network accuracy.
Understanding Developers Privacy Concerns Through Reddit Thread Analysis
Venue: arXiv cs.SE 2023
Year: 2023

With the growing global emphasis on regulating the protection of personal information and increasing user expectation of the same, developing with privacy in mind is becoming ever more important. In this paper, we study the concerns, questions, and solutions developers discuss on Reddit forums to enhance our understanding of their perceptions and challenges while developing applications in the current privacy-focused world. We perform various forms of Natural Language Processing (NLP) on 437,317 threads from subreddits such as r/webdev, r/androiddev, and r/iOSProgramming to identify both common points of discussion and how these points change over time as new regulations are passed around the globe. Our results show that there are common trends in privacy topics among the different subreddits while the frequency of those topics differs between web and mobile applications.
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging
Venue: arXiv cs.CV 2023
Year: 2023

Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare. However, conventional approaches that rely on precise vision-language alignment are not always feasible in complex clinical imaging modalities, such as cardiac magnetic resonance (CMR). CMR provides a comprehensive visualization of cardiac anatomy, physiology, and microstructure, making it challenging to interpret. Additionally, CMR reports require synthesizing information from sequences of images and different views, resulting in potentially weak alignment between the study and diagnosis report pair. To overcome these challenges, we propose \textbf{CMRformer}, a multimodal learning framework to jointly learn sequences of CMR images and associated cardiologist's reports. Moreover, one of the major obstacles to improving CMR study is the lack of large, publicly available datasets. To bridge this gap, we collected a large \textbf{CMR dataset}, which consists of 13,787 studies from clinical cases. By utilizing our proposed CMRformer and our collected dataset, we achieved remarkable performance in real-world clinical tasks, such as CMR image retrieval and diagnosis report retrieval. Furthermore, the learned representations are evaluated to be practically helpful for downstream applications, such as disease classification. Our work could potentially expedite progress in the CMR study and lead to more accurate and effective diagnosis and treatment.
Learning Empirical Bregman Divergence for Uncertain Distance Representation
Venue: arXiv cs.CV 2023
Year: 2023

Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks. However, classic approaches, which employ a fixed distance metric as a similarity function between two embeddings, may lead to suboptimal performance for capturing the complex data distribution. The Bregman divergence generalizes measures of various distance metrics and arises throughout many fields of deep metric learning. In this paper, we first show how deep metric learning loss can arise from the Bregman divergence. We then introduce a novel method for learning empirical Bregman divergence directly from data based on parameterizing the convex function underlying the Bregman divergence with a deep learning setting. We further experimentally show that our approach performs effectively on five popular public datasets compared to other SOTA deep metric learning methods, particularly for pattern recognition problems.
A Measurement Study of the Impact of Adjacent Channel Interference between C-band and CBRS
Venue: arXiv cs.NI 2023
Year: 2023

The 3.7 - 3.98 GHz frequency band (also known as C-band) was recently allocated in the US for the deployment of 5G cellular services. Prior to this, the lower adjacent band, 3.55 - 3.7 GHz, had been allocated to Citizens Broadband Radio Service (CBRS), where the entire 150 MHz can be used for free by Tier 3 General Authorized Access (GAA) users, but access to the spectrum needs to be authorized by the Spectrum Access System (SAS). GAA users are allowed on a channel only when there are no Tier 1 Incumbents (Navy radars) or Tier 2 Priority Access License (PAL) users in the area. However, since there are no guard bands between GAA and C-band, and both systems employ Time Division Duplexing (TDD) where the uplink/downlink configurations are not synchronized, adjacent channel interference can potentially reduce the performance of both systems. In this paper, we quantify the effect of this mutual interference by performing experiments with a real-world deployment. We observe significant downlink throughput reductions on both systems when two devices are in close proximity to each other, and one is transmitting uplink while the other is transmitting downlink: 60% for 4G CBRS and 43% for 5G C-band. We believe that this is the first paper to demonstrate this in a real deployment. This throughput degradation was reduced when the CBSD changed its channel and operated 20 MHz away from C-band, essentially creating a guard band between the channels. We also demonstrate the improvement in latency under adjacent channel interference by implementing MicroSlicing at the CBSD. Our results indicate that addressing adjacent channel interference due to the lack of guard bands and TDD configuration mismatch is crucial to improving the performance of both CBRS and C-band systems.
Non-exemplar Class-incremental Learning by Random Auxiliary Classes Augmentation and Mixed Features
Venue: arXiv cs.CV 2023
Year: 2023

Non-exemplar class-incremental learning refers to classifying new and old classes without storing samples of old classes. Since only new class samples are available for optimization, it often occurs catastrophic forgetting of old knowledge. To alleviate this problem, many new methods are proposed such as model distillation, class augmentation. In this paper, we propose an effective non-exemplar method called RAMF consisting of Random Auxiliary classes augmentation and Mixed Feature. On the one hand, we design a novel random auxiliary classes augmentation method, where one augmentation is randomly selected from three augmentations and applied on the input to generate augmented samples and extra class labels. By extending data and label space, it allows the model to learn more diverse representations, which can prevent the model from being biased towards learning task-specific features. When learning new tasks, it will reduce the change of feature space and improve model generalization. On the other hand, we employ mixed feature to replace the new features since only using new feature to optimize the model will affect the representation that was previously embedded in the feature space. Instead, by mixing new and old features, old knowledge can be retained without increasing the computational complexity. Extensive experiments on three benchmarks demonstrate the superiority of our approach, which outperforms the state-of-the-art non-exemplar methods and is comparable to high-performance replay-based methods.
Framework for Quality Evaluation of Smart Roadside Infrastructure Sensors for Automated Driving Applications
Venue: arXiv cs.CV 2023
Year: 2023

The use of smart roadside infrastructure sensors is highly relevant for future applications of connected and automated vehicles. External sensor technology in the form of intelligent transportation system stations (ITS-Ss) can provide safety-critical real-time information about road users in the form of a digital twin. The choice of sensor setups has a major influence on the downstream function as well as the data quality. To date, there is insufficient research on which sensor setups result in which levels of ITS-S data quality. We present a novel approach to perform detailed quality assessment for smart roadside infrastructure sensors. Our framework is multimodal across different sensor types and is evaluated on the DAIR-V2X dataset. We analyze the composition of different lidar and camera sensors and assess them in terms of accuracy, latency, and reliability. The evaluations show that the framework can be used reliably for several future ITS-S applications.