Reviewerly/exHarmony huggingface dataset:
Analyzing and Diagnosing Pose Estimation with Attributions

We present Pose Integrated Gradient (PoseIG), the first interpretability technique designed for pose estimation. We extend the concept of integrated gradients for pose estimation to generate pixel-level attribution maps. To enable comparison across different pose frameworks, we unify different pose outputs into a common output space, along with a likelihood approximation function for gradient back-propagation. To complement the qualitative insight from the attribution maps, we propose three indices for quantitative analysis. With these tools, we systematically compare different pose estimation frameworks to understand the impacts of network design, backbone and auxiliary tasks. Our analysis reveals an interesting shortcut of the knuckles (MCP joints) for hand pose estimation and an under-explored inversion error for keypoints in body pose estimation. Project page and code: https://qy-h00.github.io/poseig/.
Entanglement-Assisted Covert Communication via Qubit Depolarizing Channels

We consider entanglement-assisted communication over the qubit depolarizing channel under the security requirement of covert communication, where not only the information is kept secret, but the transmission itself must be concealed from detection by an adversary. Previous work showed that $O(\sqrt n )$ information bits can be reliably and covertly transmitted in n channel uses without entanglement assistance. However, Gagatsos et al. (2020) showed that entanglement assistance can increase this scaling to $O(\sqrt n \log n)$ for continuous-variable bosonic channels. Here, we present a finite-dimensional parallel, and show that $O(\sqrt n \log n)$ covert bits can be transmitted reliably over n uses of a qubit depolarizing channel.
OmniVidar: Omnidirectional Depth Estimation from Multi-Fisheye Images

Estimating depth from four large field of view (FoV) cameras has been a difficult and understudied problem. In this paper, we proposed a novel and simple system that can convert this difficult problem into easier binocular depth estimation. We name this system OmniVidar, as its results are similar to LiDAR, but rely only on vision. OmniVidar contains three components: (1) a new camera model to address the shortcomings of existing models, (2) a new multi-fisheye camera based epipolar rectification method for solving the image distortion and simplifying the depth estimation problem, (3) an improved binocular depth estimation network, which achieves a better balance between accuracy and efficiency. Unlike other omnidirectional stereo vision methods, OmniVidar does not contain any 3D convolution, so it can achieve higher resolution depth estimation at fast speed. Results demonstrate that OmniVidar outperforms all other methods in terms of accuracy and performance.
Principles of Forgetting in Domain-Incremental Semantic Segmentation in Adverse Weather Conditions

Deep neural networks for scene perception in automated vehicles achieve excellent results for the domains they were trained on. However, in real-world conditions, the domain of operation and its underlying data distribution are subject to change. Adverse weather conditions, in particular, can significantly decrease model performance when such data are not available during training. Additionally, when a model is incrementally adapted to a new domain, it suffers from catastrophic forgetting, causing a significant drop in performance on previously observed domains. Despite recent progress in reducing catastrophic forgetting, its causes and effects remain obscure. Therefore, we study how the representations of semantic segmentation models are affected during domain-incremental learning in adverse weather conditions. Our experiments and representational analyses indicate that catastrophic forgetting is primarily caused by changes to low-level features in domain-incremental learning and that learning more general features on the source domain using pre-training and image augmentations leads to efficient feature reuse in subsequent tasks, which drastically reduces catastrophic forgetting. These findings highlight the importance of methods that facilitate generalized features for effective continual learning algorithms.
On the Privacy of Social Networks with Personal Privacy Choices

We consider the problem of designing privacy mechanisms for users whose data is correlated based on a Markov random field over a graph, with social networks being a concrete application. Each user, modeled as a node in a graph, has its own personal data and can choose their privacy settings to be: ON or OFF, indicating whether the node requires privacy or not. We suppose that the users’ data is to be shared with a third party, such as electoral or ad campaigns, while respecting the different personal privacy choices of the users.The notion of privacy we use is a variation of differential privacy called dependent differential privacy that can handle the correlated nature of the data. The goal is to preserve the required privacy by releasing a noisy version of each user’s data while minimizing the expected error. We focus on the class of one-hop mechanisms in which each node’s released data depends on its own and its neighbors’ data. This class of mechanisms leads to scalable algorithms that are easily parallelizable. We present One-Hop Algorithm and show that it outputs the privatized data while respecting the privacy settings of each user in the presence of correlation. To give more insight, we consider two examples, star and complete graphs, and compare the privacy-utility tradeoffs of different versions of our algorithm.
On the Structure of Higher Order MDS Codes

A code of length n is said to be (combinatorially) (ρ, L)-list decodable if the Hamming ball of radius ρn around any vector in the ambient space does not contain more than L codewords. We study a recently introduced class of higher order MDS codes, which are closely related (via duality) to codes that achieve a generalized Singleton bound for list decodability. For some ℓ ≥ 1, higher order MDS codes of length n, dimension k, and order ℓ are denoted as (n, k)-MDS(ℓ) codes. We present a number of results on the structure of these codes, identifying the ‘extend-ability’ of their parameters in various scenarios. Specifically, for some parameter regimes, we identify conditions under which (n <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> , k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> )-MDS(ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ) codes can be obtained from (n <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> )-MDS(ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) codes, via various techniques. We believe that these results will aid in efficient constructions of higher order MDS codes. We also obtain a new field size upper bound for the existence of such codes, which arguably improves over the best known existing bound, in some parameter regimes.
Union Bound for Generalized Duplication Channels with DTW Decoding

In this paper, we calculate a union bound for dynamic time warping (DTW)-based decoding of piecewise constant signals corrupted by additive noise and time stretching due to sample duplications, as observed in raw measurement signals obtained from nanopore sequencers. We consider both finitely- and infinitely-supported duplications with geometric-like characteristic, which include discrete uniform distributions as a special case. First, we provide explicit algorithms that calculate the union bound in O(αk <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) time for the infinite-support case and in O(β <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for the finite-support case, where k is the codeword length, α is the minimum duplication, and β is the maximum duplication. Next, we show that a multi-read union bound exhibits a thresholding effect, where the error probability can be made arbitrarily close to zero by aggregating DTW distances from sufficiently many independent reads. Finally, we validate the calculated bounds relative to simulation results.
AUNet: Learning Relations Between Action Units for Face Forgery Detection

Face forgery detection becomes increasingly crucial due to the serious security issues caused by face manipulation techniques. Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same domain. However, the problem remains challenging when one tries to generalize the detector to forgeries created by unseen methods during training. Observing that face manipulation may alter the relation between different facial action units (AU), we propose the Action-Units Relation Learning framework to improve the generality of forgery detection. In specific, it consists of the Action Units Relation Transformer (ART) and the Tampered AU Prediction (TAP). The ART constructs the relation between different AUs with AU-agnostic Branch and AU-specific Branch, which complement each other and work together to exploit forgery clues. In the Tampered AU Prediction, we tamper AU-related regions at the image level and develop challenging pseudo samples at the feature level. The model is then trained to predict the tampered AU regions with the generated location-specific supervision. Experimental results demonstrate that our method can achieve state-of-the-art performance in both the in-dataset and cross-dataset evaluations.
Trading Communication for Computation in Byzantine-Resilient Gradient Coding

We consider gradient coding in the presence of an adversary controlling so-called malicious workers trying to corrupt the computations. Previous works propose the use of MDS codes to treat the inputs of the malicious workers as errors and correct them using the error-correction properties of the code. This comes at the expense of increasing the replication, i.e., the number of workers each partial gradient is computed by. In this work, we reduce replication by proposing a method that detects the erroneous inputs from the malicious workers, hence transforming them into erasures. For s malicious workers, our solution can reduce the replication to s+1 instead of 2s+1 for each partial gradient at the expense of only s additional computations at the main node and additional rounds of light communication between the main node and the workers. We give fundamental limits of the general framework for fractional repetition data allocation. Our scheme is optimal in terms of replication and local computation but incurs a communication cost that is asymptotically, in the size of the dataset, a multiplicative factor away from the derived bound.
Research on Underwater Robust Object Detection Method Based on Improved YOLOv5s

Due to the complex underwater environment, underwater biological occlusion and small objects are more common, resulting in lower accuracy and poor robustness of object detection algorithms, so an underwater robust object detection method based on improved YOLOv5s is proposed in this paper. First, GridMask data enhancement method is used to solve the problem of insufficient detection accuracy of YOLOv5s when obstacles block the target. Second, the NMS (Non-Maximum Suppression) in YOLOv5s is changed to the DIoU NMS algorithm for improving the detection accuracy of the overlapped objects. Finally, the structure of PAN+FPN in YOLOv5s is improved to RepGFPN to optimize the ability of the original algorithm to detect small objects. Compared with other object detection algorithms, the results show that the proposed method improves the robustness of underwater object detection.
Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning

Intermediate features of a pretrained model have been shown informative for making accurate predictions on downstream tasks, even if the model backbone is kept frozen. The key challenge is how to utilize these intermediate features given their gigantic amount. We propose visual query tuning (VQT), a simple yet effective approach to aggregate intermediate features of Vision Transformers. Through introducing a handful of learnable “query” tokens to each layer, VQT leverages the inner workings of Transformers to “summarize” rich intermediate features of each layer, which can then be used to train the prediction heads of downstream tasks. As VQT keeps the intermediate features intact and only learns to combine them, it enjoys memory efficiency in training, compared to many other parameter-efficient fine-tuning approaches that learn to adapt features and need back-propagation through the entire backbone. This also suggests the complementary role between VQT and those approaches in transfer learning. Empirically, VQT consistently surpasses the state-of-the-art approach that utilizes intermediate features for transfer learning and outperforms full fine-tuning in many cases. Compared to parameter-efficient approaches that adapt features, VQT achieves much higher accuracy under memory constraints. Most importantly, VQT is compatible with these approaches to attain even higher accuracy, making it a simple add-on to further boost transfer learning. Code is available at https://github.com/andytu28/VQT.
MFFPN: an Anchor-Free Method for Patent Drawing Object Detection

A patent document may contain meaningful drawings that can be used for image retrieval. However, labeling drawing locations manually is time-consuming. Since this work is similar to object detection, some object detection techniques can be employed to facilitate it. In this paper, we propose a new anchor-free object detection method for this purpose. The proposed method contains two parts, namely, max filtering feature pyramid network (MFFPN) and dilated sample selection loss (DSSL). We replace feature pyramid network and path aggregation network by 3D max pooling for multi-scale feature fusion with MFFPN. By using DSSL, we can adaptively select training samples according to the ground truth size. Experimental results show the proposed method can achieve a better performance compared with the state-of-the-art anchor-free methods on Taiwan patent dataset.
Small Object Detection for Birds with Swin Transformer

Object detection is the task of detecting objects in an image. In this task, the detection of small objects is particularly difficult. Other than the small size, it is also accompanied by difficulties due to blur, occlusion, and so on. Current small object detection methods are tailored to small and dense situations, such as pedestrians in a crowd or far objects in remote sensing scenarios. However, when the target object is small and sparse, there is a lack of objects available for training, making it more difficult to learn effective features. In this paper, we propose a specialized method for detecting a specific category of small objects; birds. Particularly, we improve the features learned by the neck; the sub-network between the backbone and the prediction head, to learn more effective features with a hierarchical design. We employ Swin Transformer to upsample the image features. Moreover, we change the shifted window size for adapting to small objects. Experiments show that the proposed Swin Transformer-based neck combined with CenterNet can lead to good performance by changing the window sizes. We further find that smaller window sizes (default 2) benefit mAPs for small object detection.
MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results

Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects. This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances, which is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The detail of the challenge with the SOD4SB dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the award-winning methods. The dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the baseline code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , and the website for evaluation on the public testset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> are publicly available.
An X3D Neural Network Analysis for Runner’s Performance Assessment in a Wild Sporting Environment

We present a transfer learning analysis on a sporting environment of the expanded 3D (X3D) neural networks. Inspired by action quality assessment methods in the literature, our method uses an action recognition network to estimate athletes’ cumulative race time (CRT) during an ultra-distance competition. We evaluate the performance considering the X3D, a family of action recognition networks that expand a small 2D image classification architecture along multiple network axes, including space, time, width, and depth. We demonstrate that the resulting neural network can provide remarkable performance for short input footage, with a mean absolute error of 12 minutes and a half when estimating the CRT for runners who have been active from 8 to 20 hours. Our most significant discovery is that X3D achieves state-of-the-art performance while requiring almost seven times less memory to achieve better precision than previous work.
Adaptive Real-Time Exploration and Optimization of Safety-Critical Industrial Systems with Ensemble Learning

Real-time optimization plays a key role in improving energy efficiency and the operational effectiveness of industrial systems. To deal with unknown process characteristics and safety constraints, a novel safe adaptive real-time exploration and optimization (ARTEO) algorithm is proposed recently for safety-critical industrial systems. ARTEO utilizes the Gaussian process (GP) regression to model unknown plant characteristics and enforces safety constraints using confidence intervals provided by the GP models. Due to changing process characteristics, the GP models need to be updated online by incorporating new observations and the computational complexity of model adaptation increases with a growing dataset. This work proposes an alternative ARTEO implementation by using ensemble learning, namely Ensemble-ARTEO. The Ensemble-ARTEO learns unknown plant characteristics through an ensemble of parametric regression models and calculates uncertainty by the variance of ensemble predictions. The predictive uncertainty is integrated into the optimization objective to further drive exploration. The ensemble members are updated efficiently online to capture the changing process characteristics. We demonstrate the effectiveness of our proposed Ensemble-ARTEO approach in an industrial refrigeration process. Experimental results show that our method enables tracking the desired cooling demand while satisfying the safety constraints.
Hot entanglement? — Parametrically coupled quantum oscillators in two heat baths: instability, squeezing and driving

A bstract Entanglement being a foundational cornerstone of quantum sciences and the primary resource in quantum information processing, understanding its dynamical evolution in realistic conditions is essential. Unfortunately, numerous model studies show that degradation of entanglement from a quantum system’s environment, especially thermal noise, is almost unavoidable. Thus the appellation ‘hot entanglement’ appears like a contradiction, until Galve et al. [Phys. Rev. Lett. 105 , 180501 (2010)] announced that entanglement can be kept at high temperatures if one considers a quantum system with time-dependent coupling between the two parties, each interacting with its individual bath. With the goal of understanding the sustenance of entanglement at high temperatures, working with the same model and set up as Galve et al, namely, parametrically-driven coupled harmonic oscillators interacting with their own Markovian baths, this work probes into the feasibility of ‘hot entanglement’ from three aspects listed in the subtitle. Our findings show that 1) hot entanglement functions only in the unstable regimes, 2) instability is a necessary but not sufficient condition, and 3) the power intake required by the drive operating in the unstable regime to sustain entanglement increases exponentially. The last factor indicates that hot entanglement under this modeling is theoretically untenable and its actual implementation likely unattainable.
The Impacts of Online Experience on Health and Well-Being: The Overlooked Aesthetic Dimension

It is well-recognized that online experience can carry profound impacts on health and well-being, particularly for young people. Research has already documented influences from cyberbullying, heightened feelings of inadequacy, and the relative decline of face-to-face interactions and active lifestyles. Less attention has been given to the health impacts of aesthetic experiences of online users, particularly gamers and other users of immersive virtual reality (VR) technologies. However, a significant body of research has begun to document the surprisingly strong yet previously unrecognized impacts of aesthetic experiences on health and well-being in other arenas of life. Other researchers have used both fixed laboratory and wearable sensors and, to a lesser extent, user surveys to measure indicators of activation level, mood, and stress level, which detect physiological markers for health. In this study, we assessed the evidence that online sensorial experience is no less important than in the physical world, with the capacity for both harmful effects and salutogenic benefits. We explore the implications for online design and propose an outline for further research.
The magnitude 6.1 earthquake at the border between China and Kazakhstan on December 1st, 2003, and its seismic damage characteristics

The magnitude 6.1 earthquake that occurred on December 1, 2003, at the border between China and Kazakhstan marked the first significant earthquake in the central Tianshan region of this century. This study presents an on-site investigation of this earthquake, including assessment of house damage and surface deformation, with a specific focus on the destructive impact on different types of houses. The Isoseismal map of the earthquake was drawn based on the survey data in China and the survey results of Kazakhstan's counterparts abroad. We also analyzed the tectonic structure of this earthquake based on geological data. The earthquake sequence's focal mechanism and characteristics were analyzed based on the seismic records. This study provides a comprehensive introduction to the main features of this earthquake and its disasters, which has important reference value for understanding the destructive effects of such earthquakes. Furthermore, it also sheds light on the emergency response work of Chinese earthquake workers during such earthquakes.