pwc-archive/papers-with-abstracts huggingface dataset:
Modelling Parasite-produced Marine Diseases: The case of the Mass Mortality Event of Pinna nobilis
Authors: Àlex Giménez-Romero, Amalia Grau, Iris E. Hendriks, Manuel A. Matias
Date: 2021-12-14 00:00:00

The state of the art of epidemic modelling in terrestrial ecosystems is the compartmental SIR model and its extensions from the now classical work of Kermack-Mackendrick. In contrast, epidemic modelling of marine ecosystems is a bit behind, and compartmental models have been introduced only recently. One of the reasons is that many epidemic processes in terrestrial ecosystems can be described through a contact process, while modelling marine epidemics is more subtle in many cases. Here we present a model describing disease outbreaks caused by parasites in bivalve populations. The SIRP model is a multicompartmental model with four compartments, three of which describe the different states of the host, susceptible (i.e. healthy), S, infected, I, and removed (dead), R, and one compartment for the parasite in the marine medium, P, written as a 4-dimensional dynamical system. Even if this is the simplest model one can write to describe this system, it is still too complicated for both direct analytical manipulation and direct comparison with experimental observations, as it depends on four parameters to be fitted. We show that it is possible to simplify the model, including a reduction to the standard SIR model if the parameters fulfil certain conditions. The model is validated with available data for the recent Mass Mortality Event of the noble pen shell Pinna nobilis, a disease caused by the parasite Haplosporidium pinnae, showing that the reduced SIR model is able to fit the data. So, we show that a model in which the species that suffers the epidemics (host) cannot move, and contagion occurs through parasites, can be reduced to the standard SIR model that represents epidemic transmission between mobile hosts. The fit indicates that the assumptions made to simplify the model are reasonable in practice, although it leads to an indeterminacy in three of the original parameters.
30.000 ways to reach 55% decarbonization of the European electricity sector
Authors: Tim T. Pedersen, Mikael Skou Andersen, Marta Victoria, Gorm B. Andresen
Date: 2021-12-14 00:00:00
Tasks: Fairness

Climate change mitigation is a global challenge that, however, needs to be resolved by national-level authorities, resembling a tragedy of the commons. This paradox is reflected at European scale, as climate commitments are made by the EU collectively, but implementation is the responsibility of individual Member States. Here, we investigate 30.000 near-optimal effort-sharing scenarios where the European electricity sector is decarbonized by at least 55% relative to 1990, in line with 2030 ambitions. Using a highly detailed brownfield electricity system optimization model, the optimal electricity system is simulated for a suite of effort-sharing scenarios. Results reveal large inequalities in the efforts required to decarbonize national electricity sectors, with some countries facing cost-optimal pathways to reach 55% emission reductions, while others are confronted with relatively high abatement costs. Specifically, we find that several countries with modest or low levels of GDP per capita will experience high abatement costs, and when passed over into electricity prices this may lead to increased energy poverty in certain parts of Europe
The RoboCup Rescue Victim Dataset
Authors: Peter Lorenz, Gerald Steinbauer-Wagner
Date: 2018-08-06 00:00:00
Proceeding: IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR) 2018 8
Tasks: Face Detection, Face Recognition

In this paper, we introduce a victim dataset for the RoboCup Rescue competitions. The RoboCup Rescue robots have to collect points  within several disciplines, e.g. a search task within an area to survey simulated baby doll (victim). When a robot comes across a victim, a  heat detector does not completely proof if this is a living being and not just a heat emitting somewhat else. Further investigations are  necessary so that a face detection could prove the existence of a victim. Lots of face detection approaches can be found in literature,
which manly are used for human face recognition. These cannot be straightforward used for victim faces which are, in case of the  RoboCup Rescue competitions, typically dolls. Thus we present the results of standard approaches and developed an own approach via bag-of-visual-words (BoVW).
Wind speed forecasting using the stationary wavelet transform and quaternion adaptivegradient methods
Authors: Lyes Saad Saoud, Hasan Al-Marzouqi, Mohamed Deriche
Date: 2021-09-10 00:00:00
Proceeding: journal 2021 9

Accurate wind speed forecasting is a fundamental requirement for advanced and economically 
viable large-scale wind power integration. The hybridization of the quaternion-valued neural networks and 
stationary wavelet transform has not been proposed before. In this paper, we propose a novel wind-speed 
forecasting model that combines the stationary wavelet transform with quaternion-valued neural networks. 
The proposed model represents wavelet subbands in quaternion vectors, which avoid separating the naturally
correlated subbands. The model consists of three main steps. First, the wind speed signal is decomposed using 
the stationary wavelet transform into sublevels. Second, a quaternion-valued neural network is used to 
forecast wind speed components in the stationary wavelet domain. Finally, the inverse stationary wavelet 
transform is applied to estimate the predicted wind speed. In addition, a softplus quaternion variant of the 
RMSProp learning algorithm is developed and used to improve the performance and convergence speed of 
the proposed model. The proposed model is tested on wind speed data collected from different sites in China 
and the United States, and the results demonstrate that it consistently outperforms similar models. In the 
meteorological terminal aviation routine (METAR) dataset experiment, the proposed wind speed forecasting 
model reduces the mean absolute error, and root mean squared error of predicted wind speed values by 26.5% 
and 33%, respectively, in comparison to several existing approaches.
Scatterbrained: A flexible and expandable pattern for decentralized machine learning
Authors: Miller Wilt, Jordan K. Matelsky, Andrew S. Gearhart
Date: 2021-12-14 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning

Federated machine learning is a technique for training a model across multiple devices without exchanging data between them. Because data remains local to each compute node, federated learning is well-suited for use-cases in fields where data is carefully controlled, such as medicine, or in domains with bandwidth constraints. One weakness of this approach is that most federated learning tools rely upon a central server to perform workload delegation and to produce a single shared model. Here, we suggest a flexible framework for decentralizing the federated learning pattern, and provide an open-source, reference implementation compatible with PyTorch.
Autonomous Navigation System from Simultaneous Localization and Mapping
Authors: Micheal Caracciolo, Owen Casciotti, Christopher Lloyd, Ernesto Sola-Thomas, Matthew Weaver, Kyle Bielby, Md Abdul Baset Sarker, Masudul H. Imtiaz
Date: 2021-12-14 00:00:00
Tasks: Autonomous Navigation, Simultaneous Localization and Mapping

This paper presents the development of a Simultaneous Localization and Mapping (SLAM) based Autonomous Navigation system. The motivation for this study was to find a solution for navigating interior spaces autonomously. Interior navigation is challenging as it can be forever evolving. Solving this issue is necessary for multitude of services, like cleaning, the health industry, and in manufacturing industries. The focus of this paper is the description of the SLAM-based software architecture developed for this proposed autonomous system. A potential application of this system, oriented to a smart wheelchair, was evaluated. Current interior navigation solutions require some sort of guiding line, like a black line on the floor. With this proposed solution, interiors do not require renovation to accommodate this solution. The source code of this application has been made open source so that it could be re-purposed for a similar application. Also, this open-source project is envisioned to be improved by the broad open-source community upon past its current state.
Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images
Authors: Binh M. Le, Simon S. Woo
Date: 2021-12-15 00:00:00

The rapid progression of Generative Adversarial Networks (GANs) has raised a concern of their misuse for malicious purposes, especially in creating fake face images. Although many proposed methods succeed in detecting GAN-based synthetic images, they are still limited by the need for large quantities of the training fake image dataset and challenges for the detector's generalizability to unknown facial images. In this paper, we propose a new approach that explores the asynchronous frequency spectra of color channels, which is simple but effective for training both unsupervised and supervised learning models to distinguish GAN-based synthetic images. We further investigate the transferability of a training model that learns from our suggested features in one source domain and validates on another target domains with prior knowledge of the features' distribution. Our experimental results show that the discrepancy of spectra in the frequency domain is a practical artifact to effectively detect various types of GAN-based generated images.
Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos
Authors: Pengfei Pei, Xianfeng Zhao, Yun Cao, Jinchuan Li, Xuyuan Lai
Date: 2021-12-15 00:00:00
Tasks: Retrieval, Triplet, Video Inpainting, Video Retrieval

In recent years, the spread of fake videos has brought great influence on individuals and even countries. It is important to provide robust and reliable results for fake videos. The results of conventional detection methods are not reliable and not robust for unseen videos. Another alternative and more effective way is to find the original video of the fake video. For example, fake videos from the Russia-Ukraine war and the Hong Kong law revision storm are refuted by finding the original video. We use an improved retrieval method to find the original video, named ViTHash. Specifically, tracing the source of fake videos requires finding the unique one, which is difficult when there are only small differences in the original videos. To solve the above problems, we designed a novel loss Hash Triplet Loss. In addition, we designed a tool called Localizator to compare the difference between the original traced video and the fake video. We have done extensive experiments on FaceForensics++, Celeb-DF and DeepFakeDetection, and we also have done additional experiments on our built three datasets: DAVIS2016-TL (video inpainting), VSTL (video splicing) and DFTL (similar videos). Experiments have shown that our performance is better than state-of-the-art methods, especially in cross-dataset mode. Experiments also demonstrated that ViTHash is effective in various forgery detection: video inpainting, video splicing and deepfakes. Our code and datasets have been released on GitHub: \url{https://github.com/lajlksdf/vtl}.
SeqFormer: Sequential Transformer for Video Instance Segmentation
Authors: Junfeng Wu, Yi Jiang, Song Bai, Wenqing Zhang, Xiang Bai
Date: 2021-12-15 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation, Video Instance Segmentation

In this work, we present SeqFormer for video instance segmentation. SeqFormer follows the principle of vision transformer that models instance relationships among video frames. Nevertheless, we observe that a stand-alone instance query suffices for capturing a time sequence of instances in a video, but attention mechanisms shall be done with each frame independently. To achieve this, SeqFormer locates an instance in each frame and aggregates temporal information to learn a powerful representation of a video-level instance, which is used to predict the mask sequences on each frame dynamically. Instance tracking is achieved naturally without tracking branches or post-processing. On YouTube-VIS, SeqFormer achieves 47.4 AP with a ResNet-50 backbone and 49.0 AP with a ResNet-101 backbone without bells and whistles. Such achievement significantly exceeds the previous state-of-the-art performance by 4.6 and 4.4, respectively. In addition, integrated with the recently-proposed Swin transformer, SeqFormer achieves a much higher AP of 59.3. We hope SeqFormer could be a strong baseline that fosters future research in video instance segmentation, and in the meantime, advances this field with a more robust, accurate, neat model. The code is available at https://github.com/wjf5203/SeqFormer.
RA V-Net: Deep learning network for automated liver segmentation
Authors: Zhiqi Lee, Sumin Qi, Chongchong Fan, Ziwei Xie
Date: 2021-12-15 00:00:00
Tasks: Deep Learning, Liver Segmentation, Segmentation

Accurate segmentation of the liver is a prerequisite for the diagnosis of disease. Automated segmentation is an important application of computer-aided detection and diagnosis of liver disease. In recent years, automated processing of medical images has gained breakthroughs. However, the low contrast of abdominal scan CT images and the complexity of liver morphology make accurate automatic segmentation challenging. In this paper, we propose RA V-Net, which is an improved medical image automatic segmentation model based on U-Net. It has the following three main innovations. CofRes Module (Composite Original Feature Residual Module) is proposed. With more complex convolution layers and skip connections to make it obtain a higher level of image feature extraction capability and prevent gradient disappearance or explosion. AR Module (Attention Recovery Module) is proposed to reduce the computational effort of the model. In addition, the spatial features between the data pixels of the encoding and decoding modules are sensed by adjusting the channels and LSTM convolution. Finally, the image features are effectively retained. CA Module (Channel Attention Module) is introduced, which used to extract relevant channels with dependencies and strengthen them by matrix dot product, while weakening irrelevant channels without dependencies. The purpose of channel attention is achieved. The attention mechanism provided by LSTM convolution and CA Module are strong guarantees for the performance of the neural network. The accuracy of U-Net network: 0.9862, precision: 0.9118, DSC: 0.8547, JSC: 0.82. The evaluation metrics of RA V-Net, accuracy: 0.9968, precision: 0.9597, DSC: 0.9654, JSC: 0.9414. The most representative metric for the segmentation effect is DSC, which improves 0.1107 over U-Net, and JSC improves 0.1214.
Textless Speech-to-Speech Translation on Real Data
Authors: Ann Lee, Hongyu Gong, Paul-Ambroise Duquenne, Holger Schwenk, Peng-Jen Chen, Changhan Wang, Sravya Popuri, Yossi Adi, Juan Pino, Jiatao Gu, Wei-Ning Hsu
Date: 2021-12-15 00:00:00
Proceeding: NAACL 2022 7
Tasks: Speech-to-Speech Translation, Translation

We present a textless speech-to-speech translation (S2ST) system that can translate speech from one language into another language and can be built without the need of any text data. Different from existing work in the literature, we tackle the challenge in modeling multi-speaker target speech and train the systems with real-world S2ST data. The key to our approach is a self-supervised unit-based speech normalization technique, which finetunes a pre-trained speech encoder with paired audios from multiple speakers and a single reference speaker to reduce the variations due to accents, while preserving the lexical content. With only 10 minutes of paired data for speech normalization, we obtain on average 3.2 BLEU gain when training the S2ST model on the VoxPopuli S2ST dataset, compared to a baseline trained on un-normalized speech target. We also incorporate automatically mined S2ST data and show an additional 2.0 BLEU gain. To our knowledge, we are the first to establish a textless S2ST technique that can be trained with real-world data and works for multiple language pairs. Audio samples are available at https://facebookresearch.github.io/speech_translation/textless_s2st_real_data/index.html .
Detail-aware Deep Clothing Animations Infused with Multi-source Attributes
Authors: TianXing Li, Rui Shi, Takashi Kanai
Date: 2021-12-15 00:00:00
Tasks: Attribute, Graph Neural Network

This paper presents a novel learning-based clothing deformation method to generate rich and reasonable detailed deformations for garments worn by bodies of various shapes in various animations. In contrast to existing learning-based methods, which require numerous trained models for different garment topologies or poses and are unable to easily realize rich details, we use a unified framework to produce high fidelity deformations efficiently and easily. To address the challenging issue of predicting deformations influenced by multi-source attributes, we propose three strategies from novel perspectives. Specifically, we first found that the fit between the garment and the body has an important impact on the degree of folds. We then designed an attribute parser to generate detail-aware encodings and infused them into the graph neural network, therefore enhancing the discrimination of details under diverse attributes. Furthermore, to achieve better convergence and avoid overly smooth deformations, we proposed output reconstruction to mitigate the complexity of the learning task. Experiment results show that our proposed deformation method achieves better performance over existing methods in terms of generalization ability and quality of details.
Rethinking Influence Functions of Neural Networks in the Over-parameterized Regime
Authors: Rui Zhang, Shihua Zhang
Date: 2021-12-15 00:00:00

Understanding the black-box prediction for neural networks is challenging. To achieve this, early studies have designed influence function (IF) to measure the effect of removing a single training point on neural networks. However, the classic implicit Hessian-vector product (IHVP) method for calculating IF is fragile, and theoretical analysis of IF in the context of neural networks is still lacking. To this end, we utilize the neural tangent kernel (NTK) theory to calculate IF for the neural network trained with regularized mean-square loss, and prove that the approximation error can be arbitrarily small when the width is sufficiently large for two-layer ReLU networks. We analyze the error bound for the classic IHVP method in the over-parameterized regime to understand when and why it fails or not. In detail, our theoretical analysis reveals that (1) the accuracy of IHVP depends on the regularization term, and is pretty low under weak regularization; (2) the accuracy of IHVP has a significant correlation with the probability density of corresponding training points. We further borrow the theory from NTK to understand the IFs better, including quantifying the complexity for influential samples and depicting the variation of IFs during the training dynamics. Numerical experiments on real-world data confirm our theoretical results and demonstrate our findings.
Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities
Authors: Eyke Hüllermeier
Date: 2021-12-15 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Ethics

Recent applications of machine learning (ML) reveal a noticeable shift from its use for predictive modeling in the sense of a data-driven construction of models mainly used for the purpose of prediction (of ground-truth facts) to its use for prescriptive modeling. What is meant by this is the task of learning a model that stipulates appropriate decisions about the right course of action in real-world scenarios: Which medical therapy should be applied? Should this person be hired for the job? As argued in this article, prescriptive modeling comes with new technical conditions for learning and new demands regarding reliability, responsibility, and the ethics of decision making. Therefore, to support the data-driven design of decision-making agents that act in a rational but at the same time responsible manner, a rigorous methodological foundation of prescriptive ML is needed. The purpose of this short paper is to elaborate on specific characteristics of prescriptive ML and to highlight some key challenges it implies. Besides, drawing connections to other branches of contemporary AI research, the grounding of prescriptive ML in a (generalized) decision-theoretic framework is advocated.
Est-ce que vous compute? Code-switching, cultural identity, and AI
Authors: Arianna Falbo, Travis LaCroix
Date: 2021-12-15 00:00:00

Cultural code-switching concerns how we adjust our overall behaviours, manners of speaking, and appearance in response to a perceived change in our social environment. We defend the need to investigate cultural code-switching capacities in artificial intelligence systems. We explore a series of ethical and epistemic issues that arise when bringing cultural code-switching to bear on artificial intelligence. Building upon Dotson's (2014) analysis of testimonial smothering, we discuss how emerging technologies in AI can give rise to epistemic oppression, and specifically, a form of self-silencing that we call 'cultural smothering'. By leaving the socio-dynamic features of cultural code-switching unaddressed, AI systems risk negatively impacting already-marginalised social groups by widening opportunity gaps and further entrenching social inequalities.
An Experimental Study of the Impact of Pre-training on the Pruning of a Convolutional Neural Network
Authors: Nathan Hubens, Matei Mancas, Bernard Gosselin, Marius Preda, Titus Zaharia
Date: 2021-12-15 00:00:00

In recent years, deep neural networks have known a wide success in various application domains. However, they require important computational and memory resources, which severely hinders their deployment, notably on mobile devices or for real-time applications. Neural networks usually involve a large number of parameters, which correspond to the weights of the network. Such parameters, obtained with the help of a training process, are determinant for the performance of the network. However, they are also highly redundant. The pruning methods notably attempt to reduce the size of the parameter set, by identifying and removing the irrelevant weights. In this paper, we examine the impact of the training strategy on the pruning efficiency. Two training modalities are considered and compared: (1) fine-tuned and (2) from scratch. The experimental results obtained on four datasets (CIFAR10, CIFAR100, SVHN and Caltech101) and for two different CNNs (VGG16 and MobileNet) demonstrate that a network that has been pre-trained on a large corpus (e.g. ImageNet) and then fine-tuned on a particular dataset can be pruned much more efficiently (up to 80% of parameter reduction) than the same network trained from scratch.