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6098446e-d70d-4b40-861b-cc83926125ff | using-multi-modal-data-for-improving | 2207.14781 | null | https://arxiv.org/abs/2207.14781v1 | https://arxiv.org/pdf/2207.14781v1.pdf | Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology | Traditional datasets for the radiological diagnosis tend to only provide the radiology image alongside the radiology report. However, radiology reading as performed by radiologists is a complex process, and information such as the radiologist's eye-fixations over the course of the reading has the potential to be an invaluable data source to learn from. Nonetheless, the collection of such data is expensive and time-consuming. This leads to the question of whether such data is worth the investment to collect. This paper utilizes the recently published Eye-Gaze dataset to perform an exhaustive study on the impact on performance and explainability of deep learning (DL) classification in the face of varying levels of input features, namely: radiology images, radiology report text, and radiologist eye-gaze data. We find that the best classification performance of X-ray images is achieved with a combination of radiology report free-text and radiology image, with the eye-gaze data providing no performance boost. Nonetheless, eye-gaze data serving as secondary ground truth alongside the class label results in highly explainable models that generate better attention maps compared to models trained to do classification and attention map generation without eye-gaze data. | ['Farzad Khalvati', 'Namdar', 'Khashayar', 'Sara Ketabi', 'Pranav Agnihotri'] | 2022-07-29 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 2.88762391e-01 6.39674366e-01 -1.07307255e-01 -5.90557456e-01
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29ec71d4-379b-4f45-a349-feaf86a16532 | out-of-distribution-generalization-via | 2306.11890 | null | https://arxiv.org/abs/2306.11890v1 | https://arxiv.org/pdf/2306.11890v1.pdf | Out of Distribution Generalization via Interventional Style Transfer in Single-Cell Microscopy | Real-world deployment of computer vision systems, including in the discovery processes of biomedical research, requires causal representations that are invariant to contextual nuisances and generalize to new data. Leveraging the internal replicate structure of two novel single-cell fluorescent microscopy datasets, we propose generally applicable tests to assess the extent to which models learn causal representations across increasingly challenging levels of OOD-generalization. We show that despite seemingly strong performance, as assessed by other established metrics, both naive and contemporary baselines designed to ward against confounding, collapse on these tests. We introduce a new method, Interventional Style Transfer (IST), that substantially improves OOD generalization by generating interventional training distributions in which spurious correlations between biological causes and nuisances are mitigated. We publish our code and datasets. | ['Juan C. Caicedo', 'Michio Hirano', 'Nicholas De Veaux', 'Sultan Kenjeyev', 'Aditya Pratapa', 'Alex Quach', 'Michael Doron', 'Wolfgang M. Pernice'] | 2023-06-15 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 7.39914715e-01 3.67015600e-01 -4.41709608e-02 -5.12727797e-01
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ad16b3d1-3b84-4237-af5b-4546ad559795 | scale-invariant-domain-generalization-image | 2110.03496 | null | https://arxiv.org/abs/2110.03496v1 | https://arxiv.org/pdf/2110.03496v1.pdf | Scale Invariant Domain Generalization Image Recapture Detection | Recapturing and rebroadcasting of images are common attack methods in insurance frauds and face identification spoofing, and an increasing number of detection techniques were introduced to handle this problem. However, most of them ignored the domain generalization scenario and scale variances, with an inferior performance on domain shift situations, and normally were exacerbated by intra-domain and inter-domain scale variances. In this paper, we propose a scale alignment domain generalization framework (SADG) to address these challenges. First, an adversarial domain discriminator is exploited to minimize the discrepancies of image representation distributions among different domains. Meanwhile, we exploit triplet loss as a local constraint to achieve a clearer decision boundary. Moreover, a scale alignment loss is introduced as a global relationship regularization to force the image representations of the same class across different scales to be undistinguishable. Experimental results on four databases and comparison with state-of-the-art approaches show that better performance can be achieved using our framework. | ['Hong Hui', 'Zheng Huang', 'Weidong Qiu', 'Jie Guo', 'Jinian Luo'] | 2021-10-07 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 2.92253345e-01 -2.33482912e-01 -1.86990485e-01 -4.18774486e-01
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79a6a5e5-e331-4291-9556-8e6ddb798309 | fast-and-high-quality-highlight-removal-from | 1512.00237 | null | http://arxiv.org/abs/1512.00237v1 | http://arxiv.org/pdf/1512.00237v1.pdf | Fast and High Quality Highlight Removal from A Single Image | Specular reflection exists widely in photography and causes the recorded
color deviating from its true value, so fast and high quality highlight removal
from a single nature image is of great importance. In spite of the progress in
the past decades in highlight removal, achieving wide applicability to the
large diversity of nature scenes is quite challenging. To handle this problem,
we propose an analytic solution to highlight removal based on an L2
chromaticity definition and corresponding dichromatic model. Specifically, this
paper derives a normalized dichromatic model for the pixels with identical
diffuse color: a unit circle equation of projection coefficients in two
subspaces that are orthogonal to and parallel with the illumination,
respectively. In the former illumination orthogonal subspace, which is
specular-free, we can conduct robust clustering with an explicit criterion to
determine the cluster number adaptively. In the latter illumination parallel
subspace, a property called pure diffuse pixels distribution rule (PDDR) helps
map each specular-influenced pixel to its diffuse component. In terms of
efficiency, the proposed approach involves few complex calculation, and thus
can remove highlight from high resolution images fast. Experiments show that
this method is of superior performance in various challenging cases. | ['Xiangyang Ji', 'Dongsheng An', 'Qionghai Dai', 'Jinli Suo', 'Haoqian Wang'] | 2015-12-01 | null | null | null | null | ['highlight-removal'] | ['computer-vision'] | [ 4.82114166e-01 -6.26317322e-01 2.15907514e-01 4.72648107e-02
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01650563-5666-4762-adf1-ec00d580d84f | empirical-analysis-of-a-segmentation | 2307.03266 | null | https://arxiv.org/abs/2307.03266v1 | https://arxiv.org/pdf/2307.03266v1.pdf | Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging | Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. These models, however, are often trained on narrowly-defined tasks in a supervised fashion, which requires expensive labeled datasets. Recent advances in several machine learning domains, such as natural language generation have demonstrated the feasibility and utility of building foundation models that can be customized for various downstream tasks with little to no labeled data. This likely represents a paradigm shift for medical imaging, where we expect that foundation models may shape the future of the field. In this paper, we consider a recently developed foundation model for medical image segmentation, UniverSeg. We conduct an empirical evaluation study in the context of prostate imaging and compare it against the conventional approach of training a task-specific segmentation model. Our results and discussion highlight several important factors that will likely be important in the development and adoption of foundation models for medical image segmentation. | ['Mert R. Sabuncu', 'Adrian V. Dalca', 'Victor Ion Butoi', 'Heejong Kim'] | 2023-07-06 | null | null | null | null | ['medical-image-segmentation', 'text-generation'] | ['medical', 'natural-language-processing'] | [ 7.22224653e-01 7.03115582e-01 -4.52021241e-01 -7.63031185e-01
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58a41937-b9dc-4d74-b4b7-3e332345b0f8 | towards-universal-texture-synthesis-by | 2203.04221 | null | https://arxiv.org/abs/2203.04221v1 | https://arxiv.org/pdf/2203.04221v1.pdf | Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2 | We present a new approach for universal texture synthesis by incorporating a multi-scale texton broadcasting module in the StyleGAN-2 framework. The texton broadcasting module introduces an inductive bias, enabling generation of broader range of textures, from those with regular structures to completely stochastic ones. To train and evaluate the proposed approach, we construct a comprehensive high-resolution dataset that captures the diversity of natural textures as well as stochastic variations within each perceptually uniform texture. Experimental results demonstrate that the proposed approach yields significantly better quality textures than the state of the art. The ultimate goal of this work is a comprehensive understanding of texture space. | ['Thrasyvoulos N. Pappas', 'Gaurav Sharma', 'Jue Lin'] | 2022-03-08 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 7.39868522e-01 7.02230036e-02 -1.83466852e-01 -2.49211445e-01
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54fce84a-e851-4cdd-afc1-139e2074f285 | refining-word-embeddings-for-sentiment | null | null | https://aclanthology.org/D17-1056 | https://aclanthology.org/D17-1056.pdf | Refining Word Embeddings for Sentiment Analysis | Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning context-based word embeddings typically fail to capture sufficient sentiment information. This may result in words with similar vector representations having an opposite sentiment polarity (e.g., good and bad), thus degrading sentiment analysis performance. Therefore, this study proposes a word vector refinement model that can be applied to any pre-trained word vectors (e.g., Word2vec and GloVe). The refinement model is based on adjusting the vector representations of words such that they can be closer to both semantically and sentimentally similar words and further away from sentimentally dissimilar words. Experimental results show that the proposed method can improve conventional word embeddings and outperform previously proposed sentiment embeddings for both binary and fine-grained classification on Stanford Sentiment Treebank (SST). | ['Xue-jie Zhang', 'K. Robert Lai', 'Liang-Chih Yu', 'Jin Wang'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['learning-word-embeddings'] | ['methodology'] | [-6.52118698e-02 -1.58371925e-01 -3.44888419e-01 -8.90055001e-01
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570efd42-e16e-48fc-8771-6bb2f601f81b | insta-yolo-real-time-instance-segmentation | 2102.06777 | null | https://arxiv.org/abs/2102.06777v2 | https://arxiv.org/pdf/2102.06777v2.pdf | INSTA-YOLO: Real-Time Instance Segmentation | Instance segmentation has gained recently huge attention in various computer vision applications. It aims at providing different IDs to different objects of the scene, even if they belong to the same class. Instance segmentation is usually performed as a two-stage pipeline. First, an object is detected, then semantic segmentation within the detected box area is performed which involves costly up-sampling. In this paper, we propose Insta-YOLO, a novel one-stage end-to-end deep learning model for real-time instance segmentation. Instead of pixel-wise prediction, our model predicts instances as object contours represented by 2D points in Cartesian space. We evaluate our model on three datasets, namely, Carvana,Cityscapes and Airbus. We compare our results to the state-of-the-art models for instance segmentation. The results show our model achieves competitive accuracy in terms of mAP at twice the speed on GTX-1080 GPU. | ['Mayada Hadhoud', 'Ahmad El-Sallab', 'Abdelrahman Shaker', 'Eslam Mohamed'] | 2021-02-12 | null | null | null | null | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 2.45029986e-01 1.58783615e-01 -3.73836905e-02 -5.61476827e-01
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24e63fc1-9c58-4438-9aeb-746b5aef2bca | geometry-guided-adversarial-facial-expression | 1712.03474 | null | http://arxiv.org/abs/1712.03474v1 | http://arxiv.org/pdf/1712.03474v1.pdf | Geometry Guided Adversarial Facial Expression Synthesis | Facial expression synthesis has drawn much attention in the field of computer
graphics and pattern recognition. It has been widely used in face animation and
recognition. However, it is still challenging due to the high-level semantic
presence of large and non-linear face geometry variations. This paper proposes
a Geometry-Guided Generative Adversarial Network (G2-GAN) for photo-realistic
and identity-preserving facial expression synthesis. We employ facial geometry
(fiducial points) as a controllable condition to guide facial texture synthesis
with specific expression. A pair of generative adversarial subnetworks are
jointly trained towards opposite tasks: expression removal and expression
synthesis. The paired networks form a mapping cycle between neutral expression
and arbitrary expressions, which also facilitate other applications such as
face transfer and expression invariant face recognition. Experimental results
show that our method can generate compelling perceptual results on various
facial expression synthesis databases. An expression invariant face recognition
experiment is also performed to further show the advantages of our proposed
method. | ['Zhihe Lu', 'Zhenan Sun', 'Tieniu Tan', 'Ran He', 'Lingxiao Song'] | 2017-12-10 | null | null | null | null | ['face-transfer'] | ['computer-vision'] | [ 4.09180552e-01 8.13048854e-02 1.74815625e-01 -5.31744719e-01
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28a25f08-84fd-48ed-91ea-b09f4a002bd2 | slothspeech-denial-of-service-attack-against | 2306.00794 | null | https://arxiv.org/abs/2306.00794v1 | https://arxiv.org/pdf/2306.00794v1.pdf | SlothSpeech: Denial-of-service Attack Against Speech Recognition Models | Deep Learning (DL) models have been popular nowadays to execute different speech-related tasks, including automatic speech recognition (ASR). As ASR is being used in different real-time scenarios, it is important that the ASR model remains efficient against minor perturbations to the input. Hence, evaluating efficiency robustness of the ASR model is the need of the hour. We show that popular ASR models like Speech2Text model and Whisper model have dynamic computation based on different inputs, causing dynamic efficiency. In this work, we propose SlothSpeech, a denial-of-service attack against ASR models, which exploits the dynamic behaviour of the model. SlothSpeech uses the probability distribution of the output text tokens to generate perturbations to the audio such that efficiency of the ASR model is decreased. We find that SlothSpeech generated inputs can increase the latency up to 40X times the latency induced by benign input. | ['Wei Yang', 'Cong Liu', 'Berrak Şişman', 'Simin Chen', 'Rutvij Shah', 'Mirazul Haque'] | 2023-06-01 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [-2.28746876e-01 -1.82334527e-01 2.43446156e-01 -9.15244594e-02
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4fe2f164-23b5-4b34-8d8d-aacb9bad7a91 | dual-branch-hybrid-learning-network-for | 2207.07913 | null | https://arxiv.org/abs/2207.07913v1 | https://arxiv.org/pdf/2207.07913v1.pdf | Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation | The current studies of Scene Graph Generation (SGG) focus on solving the long-tailed problem for generating unbiased scene graphs. However, most de-biasing methods overemphasize the tail predicates and underestimate head ones throughout training, thereby wrecking the representation ability of head predicate features. Furthermore, these impaired features from head predicates harm the learning of tail predicates. In fact, the inference of tail predicates heavily depends on the general patterns learned from head ones, e.g., "standing on" depends on "on". Thus, these de-biasing SGG methods can neither achieve excellent performance on tail predicates nor satisfying behaviors on head ones. To address this issue, we propose a Dual-branch Hybrid Learning network (DHL) to take care of both head predicates and tail ones for SGG, including a Coarse-grained Learning Branch (CLB) and a Fine-grained Learning Branch (FLB). Specifically, the CLB is responsible for learning expertise and robust features of head predicates, while the FLB is expected to predict informative tail predicates. Furthermore, DHL is equipped with a Branch Curriculum Schedule (BCS) to make the two branches work well together. Experiments show that our approach achieves a new state-of-the-art performance on VG and GQA datasets and makes a trade-off between the performance of tail predicates and head ones. Moreover, extensive experiments on two downstream tasks (i.e., Image Captioning and Sentence-to-Graph Retrieval) further verify the generalization and practicability of our method. | ['Heng Tao Shen', 'Abdulmotaleb El Saddik', 'Pengpeng Zeng', 'Xinyu Lyu', 'Lianli Gao', 'Chaofan Zheng'] | 2022-07-16 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 1.88122779e-01 4.82389122e-01 -3.86051178e-01 -5.52540541e-01
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0962290d-00c7-448d-92bb-8235dfa9420d | comparison-of-pedestrian-prediction-models | 2305.15942 | null | https://arxiv.org/abs/2305.15942v1 | https://arxiv.org/pdf/2305.15942v1.pdf | Comparison of Pedestrian Prediction Models from Trajectory and Appearance Data for Autonomous Driving | The ability to anticipate pedestrian motion changes is a critical capability for autonomous vehicles. In urban environments, pedestrians may enter the road area and create a high risk for driving, and it is important to identify these cases. Typical predictors use the trajectory history to predict future motion, however in cases of motion initiation, motion in the trajectory may only be clearly visible after a delay, which can result in the pedestrian has entered the road area before an accurate prediction can be made. Appearance data includes useful information such as changes of gait, which are early indicators of motion changes, and can inform trajectory prediction. This work presents a comparative evaluation of trajectory-only and appearance-based methods for pedestrian prediction, and introduces a new dataset experiment for prediction using appearance. We create two trajectory and image datasets based on the combination of image and trajectory sequences from the popular NuScenes dataset, and examine prediction of trajectories using observed appearance to influence futures. This shows some advantages over trajectory prediction alone, although problems with the dataset prevent advantages of appearance-based models from being shown. We describe methods for improving the dataset and experiment to allow benefits of appearance-based models to be captured. | ['Subramanian Ramamoorthy', 'John Redford', 'Morris Antonello', 'Anthony Knittel'] | 2023-05-25 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-9.57270190e-02 -2.76542217e-01 -2.60468155e-01 -6.50840163e-01
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07d67607-f21e-463b-a0fa-77dd27e8ba67 | benign-overparameterization-in-membership | 2205.14055 | null | https://arxiv.org/abs/2205.14055v2 | https://arxiv.org/pdf/2205.14055v2.pdf | A Blessing of Dimensionality in Membership Inference through Regularization | Is overparameterization a privacy liability? In this work, we study the effect that the number of parameters has on a classifier's vulnerability to membership inference attacks. We first demonstrate how the number of parameters of a model can induce a privacy--utility trade-off: increasing the number of parameters generally improves generalization performance at the expense of lower privacy. However, remarkably, we then show that if coupled with proper regularization, increasing the number of parameters of a model can actually simultaneously increase both its privacy and performance, thereby eliminating the privacy--utility trade-off. Theoretically, we demonstrate this curious phenomenon for logistic regression with ridge regularization in a bi-level feature ensemble setting. Pursuant to our theoretical exploration, we develop a novel leave-one-out analysis tool to precisely characterize the vulnerability of a linear classifier to the optimal membership inference attack. We empirically exhibit this "blessing of dimensionality" for neural networks on a variety of tasks using early stopping as the regularizer. | ['Richard G. Baraniuk', 'Hamid Javadi', 'Blake Mason', 'Daniel LeJeune', 'Jasper Tan'] | 2022-05-27 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 3.19872111e-01 -4.48126420e-02 -8.11665654e-02 -4.76306677e-01
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df288858-c134-4a78-b3a7-302bc85f8e7c | visgraphnet-a-complex-network-interpretation | 2108.12490 | null | https://arxiv.org/abs/2108.12490v1 | https://arxiv.org/pdf/2108.12490v1.pdf | VisGraphNet: a complex network interpretation of convolutional neural features | Here we propose and investigate the use of visibility graphs to model the feature map of a neural network. The model, initially devised for studies on complex networks, is employed here for the classification of texture images. The work is motivated by an alternative viewpoint provided by these graphs over the original data. The performance of the proposed method is verified in the classification of four benchmark databases, namely, KTHTIPS-2b, FMD, UIUC, and UMD and in a practical problem, which is the identification of plant species using scanned images of their leaves. Our method was competitive with other state-of-the-art approaches, confirming the potential of techniques used for data analysis in different contexts to give more meaningful interpretation to the use of neural networks in texture classification. | ['Marcelo K. Albertini', 'Gwanggil Jeon', 'Kyungkoo Jun', 'Young-Sup Lee', 'Joao B. Florindo'] | 2021-08-27 | null | null | null | null | ['texture-classification', 'network-interpretation'] | ['computer-vision', 'computer-vision'] | [ 3.75195324e-01 5.45149148e-02 5.45380376e-02 -2.05219612e-01
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3c9d2041-db5d-4928-8458-29d80ca20796 | modeling-tweet-arrival-times-using-log | null | null | https://aclanthology.org/D15-1028 | https://aclanthology.org/D15-1028.pdf | Modeling Tweet Arrival Times using Log-Gaussian Cox Processes | null | ['P. K. Srijith', 'Michal Lukasik', 'Kalina Bontcheva', 'Trevor Cohn'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['rumour-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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fbd78aac-22f3-48b5-9c1c-cf991dbdf454 | voxsrc-2022-the-fourth-voxceleb-speaker | 2302.10248 | null | https://arxiv.org/abs/2302.10248v2 | https://arxiv.org/pdf/2302.10248v2.pdf | VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge | This paper summarises the findings from the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22), which was held in conjunction with INTERSPEECH 2022. The goal of this challenge was to evaluate how well state-of-the-art speaker recognition systems can diarise and recognise speakers from speech obtained "in the wild". The challenge consisted of: (i) the provision of publicly available speaker recognition and diarisation data from YouTube videos together with ground truth annotation and standardised evaluation software; and (ii) a public challenge and hybrid workshop held at INTERSPEECH 2022. We describe the four tracks of our challenge along with the baselines, methods, and results. We conclude with a discussion on the new domain-transfer focus of VoxSRC-22, and on the progression of the challenge from the previous three editions. | ['Andrew Zisserman', 'Daniel Garcia-Romero', 'Arsha Nagrani', 'Joon Son Chung', 'Jee-weon Jung', 'Andrew Brown', 'Jaesung Huh'] | 2023-02-20 | null | null | null | null | ['speaker-recognition', 'speaker-verification'] | ['speech', 'speech'] | [ 2.87246089e-02 2.39714667e-01 3.42104226e-01 -6.10374987e-01
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42d969c5-20b1-4a83-8dfa-b50fe795ffa7 | representation-learning-through-multimodal | null | null | https://dl.acm.org/doi/abs/10.1145/3503161.3548018 | https://dl.acm.org/doi/pdf/10.1145/3503161.3548018 | Representation Learning through Multimodal Attention and Time-Sync Comments for Affective Video Content Analysis | Although temporal patterns inherent in visual and audio signals are crucial for affective video content analysis, they have not been thoroughly explored yet. In this paper, we propose a novel Temporal-Aware Multimodal (TAM) method to fully capture the temporal information. Specifically, we design a cross-temporal multimodal fusion module that applies attention-based fusion to different modalities within and across video segments. As a result, it fully captures the temporal relations between different modalities. Furthermore, a single emotion label lacks supervision for learning representation of each segment, making temporal pattern mining difficult. We leverage time-synchronized comments (TSCs) as auxiliary supervision, since these comments are easily accessible and contain rich emotional cues. Two TSC-based self-supervised tasks are designed: the first aims to predict the emotional words in a TSC from video representation and TSC contextual semantics, and the second predicts the segment in which the TSC appears by calculating the correlation between video representation and TSC embedding. These self-supervised tasks are used to pre-train the cross-temporal multimodal fusion module on a large-scale video-TSC dataset, which is crawled from the web without labeling costs. These self-supervised pre-training tasks prompt the fusion module to perform representation learning on segments including TSC, thus capturing more temporal affective patterns. Experimental results on three benchmark datasets show that the proposed fusion module achieves state-of-the-art results in affective video content analysis. Ablation studies verify that after TSC-based pre-training, the fusion module learns more segments' affective patterns and achieves better performance. | ['Lin Fang', 'Shangfei Wang', 'Jicai Pan'] | 2022-10-14 | null | null | null | acm-mm22-2022-10 | ['video-emotion-recognition'] | ['computer-vision'] | [ 2.16166601e-01 -3.76534075e-01 -4.46779817e-01 -4.21868145e-01
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0078f4a0-6e25-43f8-b329-ba6569d1eb3d | continuous-3d-multi-channel-sign-language | 2103.06982 | null | https://arxiv.org/abs/2103.06982v1 | https://arxiv.org/pdf/2103.06982v1.pdf | Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks | Sign languages are multi-channel visual languages, where signers use a continuous 3D space to communicate.Sign Language Production (SLP), the automatic translation from spoken to sign languages, must embody both the continuous articulation and full morphology of sign to be truly understandable by the Deaf community. Previous deep learning-based SLP works have produced only a concatenation of isolated signs focusing primarily on the manual features, leading to a robotic and non-expressive production. In this work, we propose a novel Progressive Transformer architecture, the first SLP model to translate from spoken language sentences to continuous 3D multi-channel sign pose sequences in an end-to-end manner. Our transformer network architecture introduces a counter decoding that enables variable length continuous sequence generation by tracking the production progress over time and predicting the end of sequence. We present extensive data augmentation techniques to reduce prediction drift, alongside an adversarial training regime and a Mixture Density Network (MDN) formulation to produce realistic and expressive sign pose sequences. We propose a back translation evaluation mechanism for SLP, presenting benchmark quantitative results on the challenging PHOENIX14T dataset and setting baselines for future research. We further provide a user evaluation of our SLP model, to understand the Deaf reception of our sign pose productions. | ['Richard Bowden', 'Necati Cihan Camgoz', 'Ben Saunders'] | 2021-03-11 | null | null | null | null | ['sign-language-production'] | ['natural-language-processing'] | [ 5.56380928e-01 2.05763772e-01 9.78769138e-02 -5.19594789e-01
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ccfb4eaf-1bd7-4661-8f4f-5b30f42eb4b6 | learning-based-multi-modality-image-and-video | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lu_Learning_Based_Multi-Modality_Image_and_Video_Compression_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lu_Learning_Based_Multi-Modality_Image_and_Video_Compression_CVPR_2022_paper.pdf | Learning Based Multi-Modality Image and Video Compression | Multi-modality (i.e., multi-sensor) data is widely used in various vision tasks for more accurate or robust perception. However, the increased data modalities bring new challenges for data storage and transmission. The existing data compression approaches usually adopt individual codecs for each modality without considering the correlation between different modalities. This work proposes a multi-modality compression framework for infrared and visible image pairs by exploiting the cross-modality redundancy. Specifically, given the image in the reference modality (e.g., the infrared image), we use the channel-wise alignment module to produce the aligned features based on the affine transform. Then the aligned feature is used as the context information for compressing the image in the current modality (e.g., the visible image), and the corresponding affine coefficients are losslessly compressed at negligible cost. Furthermore, we introduce the Transformer-based spatial alignment module to exploit the correlation between the intermediate features in the decoding procedures for different modalities. Our framework is very flexible and easily extended for multi-modality video compression. Experimental results show our proposed framework outperforms the traditional and learning-based single modality compression methods on the FLIR and KAIST datasets. | ['Dong Xu', 'Qiang Hu', 'Jing Geng', 'Tianxiong Zhong', 'Guo Lu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['data-compression'] | ['time-series'] | [ 6.25193894e-01 -7.24726737e-01 -2.39818528e-01 -1.24422550e-01
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bd131f4a-8fd2-4b11-9b1c-91a1b75da723 | monitoring-energy-trends-through-automatic | 2201.01559 | null | https://arxiv.org/abs/2201.01559v1 | https://arxiv.org/pdf/2201.01559v1.pdf | Monitoring Energy Trends through Automatic Information Extraction | Energy research is of crucial public importance but the use of computer science technologies like automatic text processing and data management for the energy domain is still rare. Employing these technologies in the energy domain will be a significant contribution to the interdisciplinary topic of ``energy informatics", just like the related progress within the interdisciplinary area of ``bioinformatics". In this paper, we present the architecture of a Web-based semantic system called EneMonIE (Energy Monitoring through Information Extraction) for monitoring up-to-date energy trends through the use of automatic, continuous, and guided information extraction from diverse types of media available on the Web. The types of media handled by the system will include online news articles, social media texts, online news videos, and open-access scholarly papers and technical reports as well as various numeric energy data made publicly available by energy organizations. The system will utilize and contribute to the energy-related ontologies and its ultimate form will comprise components for (i) text categorization, (ii) named entity recognition, (iii) temporal expression extraction, (iv) event extraction, (v) social network construction, (vi) sentiment analysis, (vii) information fusion and summarization, (viii) media interlinking, and (ix) Web-based information retrieval and visualization. Wits its diverse data sources, automatic text processing capabilities, and presentation facilities open for public use; EneMonIE will be an important source of distilled and concise information for decision-makers including energy generation, transmission, and distribution system operators, energy research centres, related investors and entrepreneurs as well as for academicians, students, other individuals interested in the pace of energy events and technologies. | ['Dilek Küçük'] | 2022-01-05 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-5.40771522e-03 -2.15541080e-01 -4.30351824e-01 1.24418527e-01
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4b7f4794-13bf-4a8d-94c2-dfe3d0818ed9 | text-based-person-search-without-parallel | 2305.12964 | null | https://arxiv.org/abs/2305.12964v1 | https://arxiv.org/pdf/2305.12964v1.pdf | Text-based Person Search without Parallel Image-Text Data | Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description. Existing methods are dominated by training models with parallel image-text pairs, which are very costly to collect. In this paper, we make the first attempt to explore TBPS without parallel image-text data ($\mu$-TBPS), in which only non-parallel images and texts, or even image-only data, can be adopted. Towards this end, we propose a two-stage framework, generation-then-retrieval (GTR), to first generate the corresponding pseudo text for each image and then perform the retrieval in a supervised manner. In the generation stage, we propose a fine-grained image captioning strategy to obtain an enriched description of the person image, which firstly utilizes a set of instruction prompts to activate the off-the-shelf pretrained vision-language model to capture and generate fine-grained person attributes, and then converts the extracted attributes into a textual description via the finetuned large language model or the hand-crafted template. In the retrieval stage, considering the noise interference of the generated texts for training model, we develop a confidence score-based training scheme by enabling more reliable texts to contribute more during the training. Experimental results on multiple TBPS benchmarks (i.e., CUHK-PEDES, ICFG-PEDES and RSTPReid) show that the proposed GTR can achieve a promising performance without relying on parallel image-text data. | ['Min Zhang', 'Liqiang Nie', 'Ziqiang Cao', 'Chen Chen', 'Min Cao', 'Jingyao Wang', 'Yang Bai'] | 2023-05-22 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 3.41186672e-01 -1.76663011e-01 2.18505666e-01 -4.89420444e-01
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b61c4208-85bc-4193-b3c4-ebe192c5fbab | svl-adapter-self-supervised-adapter-for | 2210.03794 | null | https://arxiv.org/abs/2210.03794v1 | https://arxiv.org/pdf/2210.03794v1.pdf | SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models | Vision-language models such as CLIP are pretrained on large volumes of internet sourced image and text pairs, and have been shown to sometimes exhibit impressive zero- and low-shot image classification performance. However, due to their size, fine-tuning these models on new datasets can be prohibitively expensive, both in terms of the supervision and compute required. To combat this, a series of light-weight adaptation methods have been proposed to efficiently adapt such models when limited supervision is available. In this work, we show that while effective on internet-style datasets, even those remedies under-deliver on classification tasks with images that differ significantly from those commonly found online. To address this issue, we present a new approach called SVL-Adapter that combines the complementary strengths of both vision-language pretraining and self-supervised representation learning. We report an average classification accuracy improvement of 10% in the low-shot setting when compared to existing methods, on a set of challenging visual classification tasks. Further, we present a fully automatic way of selecting an important blending hyperparameter for our model that does not require any held-out labeled validation data. Code for our project is available here: https://github.com/omipan/svl_adapter. | ['Oisin Mac Aodha', 'Kate Jones', 'Gabriel Brostow', 'Omiros Pantazis'] | 2022-10-07 | null | null | null | null | ['classification'] | ['methodology'] | [ 3.34554166e-01 -3.24916571e-01 -4.45985943e-01 -3.01577836e-01
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e00b9a11-8f2f-4c65-88c0-fc35f07cd1ea | research-on-the-application-of-contrastive | 2212.00552 | null | https://arxiv.org/abs/2212.00552v2 | https://arxiv.org/pdf/2212.00552v2.pdf | An Effective Employment of Contrastive Learning in Multi-label Text Classification | The effectiveness of contrastive learning technology in natural language processing tasks is yet to be explored and analyzed. How to construct positive and negative samples correctly and reasonably is the core challenge of contrastive learning. It is even harder to discover contrastive objects in multi-label text classification tasks. There are very few contrastive losses proposed previously. In this paper, we investigate the problem from a different angle by proposing five novel contrastive losses for multi-label text classification tasks. These are Strict Contrastive Loss (SCL), Intra-label Contrastive Loss (ICL), Jaccard Similarity Contrastive Loss (JSCL), Jaccard Similarity Probability Contrastive Loss (JSPCL), and Stepwise Label Contrastive Loss (SLCL). We explore the effectiveness of contrastive learning for multi-label text classification tasks by the employment of these novel losses and provide a set of baseline models for deploying contrastive learning techniques on specific tasks. We further perform an interpretable analysis of our approach to show how different components of contrastive learning losses play their roles. The experimental results show that our proposed contrastive losses can bring improvement to multi-label text classification tasks. Our work also explores how contrastive learning should be adapted for multi-label text classification tasks. | ['Dong Zhou', 'Aimin Yang', 'Jigang Wang', 'Guanqiu Qin', 'Nankai Lin'] | 2022-12-01 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 5.99146128e-01 -1.45961314e-01 -1.36845499e-01 -6.72377110e-01
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4a88e93b-21e3-4772-a6bb-d1ce5d928592 | uncertainty-quantification-in-deep-learning | 1907.13418 | null | https://arxiv.org/abs/1907.13418v1 | https://arxiv.org/pdf/1907.13418v1.pdf | Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement | Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, little consideration has been given to uncertainty quantification over the output image. Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution. Specifically, we propose to account for $intrinsic$ uncertainty through a heteroscedastic noise model and for $parameter$ uncertainty through approximate Bayesian inference, and integrate the two to quantify $predictive$ uncertainty over the output image. Moreover, we introduce a method to propagate the predictive uncertainty on a multi-channelled image to derived scalar parameters, and separately quantify the effects of intrinsic and parameter uncertainty therein. The methods are evaluated for super-resolution of two different signal representations of diffusion MR images---DTIs and Mean Apparent Propagator MRI---and their derived quantities such as MD and FA, on multiple datasets of both healthy and pathological human brains. Results highlight three key benefits of uncertainty modelling for improving the safety of DL-based image enhancement systems. Firstly, incorporating uncertainty improves the predictive performance even when test data departs from training data. Secondly, the predictive uncertainty highly correlates with errors, and is therefore capable of detecting predictive "failures". Results demonstrate that such an uncertainty measure enables subject-specific and voxel-wise risk assessment of the output images. Thirdly, we show that the method for decomposing predictive uncertainty into its independent sources provides high-level "explanations" for the performance by quantifying how much uncertainty arises from the inherent difficulty of the task or the limited training examples. | ['Stamatios N. Sotiropoulos', 'Ryutaro Tanno', 'Francesco Grussu', 'Daniel Worrall', 'Aurobrata Ghosh', 'Alberto Bizzi', 'Enrico Kaden', 'Daniel C. Alexander', 'Antonio Criminisi'] | 2019-07-31 | null | null | null | null | ['medical-image-enhancement'] | ['computer-vision'] | [ 3.51065338e-01 1.92920372e-01 2.01706961e-01 -5.55146217e-01
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246f4b13-f60b-43ad-9658-504cee10f5f9 | physics-informed-machine-learning-a-survey-on | 2211.08064 | null | https://arxiv.org/abs/2211.08064v2 | https://arxiv.org/pdf/2211.08064v2.pdf | Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications | Recent advances of data-driven machine learning have revolutionized fields like computer vision, reinforcement learning, and many scientific and engineering domains. In many real-world and scientific problems, systems that generate data are governed by physical laws. Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm. By integrating the data and mathematical physics models seamlessly, it can guide the machine learning model towards solutions that are physically plausible, improving accuracy and efficiency even in uncertain and high-dimensional contexts. In this survey, we present this learning paradigm called Physics-Informed Machine Learning (PIML) which is to build a model that leverages empirical data and available physical prior knowledge to improve performance on a set of tasks that involve a physical mechanism. We systematically review the recent development of physics-informed machine learning from three perspectives of machine learning tasks, representation of physical prior, and methods for incorporating physical prior. We also propose several important open research problems based on the current trends in the field. We argue that encoding different forms of physical prior into model architectures, optimizers, inference algorithms, and significant domain-specific applications like inverse engineering design and robotic control is far from being fully explored in the field of physics-informed machine learning. We believe that the interdisciplinary research of physics-informed machine learning will significantly propel research progress, foster the creation of more effective machine learning models, and also offer invaluable assistance in addressing long-standing problems in related disciplines. | ['Jun Zhu', 'Hang Su', 'Yao Feng', 'Chengyang Ying', 'Yichi Zhang', 'Songming Liu', 'Zhongkai Hao'] | 2022-11-15 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 9.46606696e-02 7.06595927e-02 -6.52922392e-01 -2.67510176e-01
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75f21168-f052-4925-b4bf-79b0f7bf563a | towards-solving-multimodal-comprehension | 2104.10139 | null | https://arxiv.org/abs/2104.10139v1 | https://arxiv.org/pdf/2104.10139v1.pdf | Towards Solving Multimodal Comprehension | This paper targets the problem of procedural multimodal machine comprehension (M3C). This task requires an AI to comprehend given steps of multimodal instructions and then answer questions. Compared to vanilla machine comprehension tasks where an AI is required only to understand a textual input, procedural M3C is more challenging as the AI needs to comprehend both the temporal and causal factors along with multimodal inputs. Recently Yagcioglu et al. [35] introduced RecipeQA dataset to evaluate M3C. Our first contribution is the introduction of two new M3C datasets- WoodworkQA and DecorationQA with 16K and 10K instructional procedures, respectively. We then evaluate M3C using a textual cloze style question-answering task and highlight an inherent bias in the question answer generation method from [35] that enables a naive baseline to cheat by learning from only answer choices. This naive baseline performs similar to a popular method used in question answering- Impatient Reader [6] that uses attention over both the context and the query. We hypothesized that this naturally occurring bias present in the dataset affects even the best performing model. We verify our proposed hypothesis and propose an algorithm capable of modifying the given dataset to remove the bias elements. Finally, we report our performance on the debiased dataset with several strong baselines. We observe that the performance of all methods falls by a margin of 8% - 16% after correcting for the bias. We hope these datasets and the analysis will provide valuable benchmarks and encourage further research in this area. | ['Ajay Divakaran', 'Karan Sikka', 'Pritish Sahu'] | 2021-04-20 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [ 5.71283460e-01 4.95756835e-01 3.30562681e-01 -4.15694058e-01
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1a6cf0e4-fa61-4ad8-9fd7-6496fd16d351 | deformable-medical-image-registration-using-a | 1908.00788 | null | https://arxiv.org/abs/1908.00788v1 | https://arxiv.org/pdf/1908.00788v1.pdf | Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior | We present deformable unsupervised medical image registration using a randomly-initialized deep convolutional neural network (CNN) as regularization prior. Conventional registration methods predict a transformation by minimizing dissimilarities between an image pair. The minimization is usually regularized with manually engineered priors, which limits the potential of the registration. By learning transformation priors from a large dataset, CNNs have achieved great success in deformable registration. However, learned methods are restricted to domain-specific data and the required amounts of medical data are difficult to obtain. Our approach uses the idea of deep image priors to combine convolutional networks with conventional registration methods based on manually engineered priors. The proposed method is applied to brain MRI scans. We show that our approach registers image pairs with state-of-the-art accuracy by providing dense, pixel-wise correspondence maps. It does not rely on prior training and is therefore not limited to a specific image domain. | ['Max-Heinrich Laves', 'Tobias Ortmaier', 'Sontje Ihler'] | 2019-08-02 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 6.04058802e-01 5.32816827e-01 -4.40454707e-02 -5.98907411e-01
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de92b3c1-3ce2-4df8-8a9b-be5181d9c55c | improved-word-embeddings-with-implicit | null | null | https://aclanthology.org/C16-1227 | https://aclanthology.org/C16-1227.pdf | Improved Word Embeddings with Implicit Structure Information | Distributed word representation is an efficient method for capturing semantic and syntactic word relations. In this work, we introduce an extension to the continuous bag-of-words model for learning word representations efficiently by using implicit structure information. Instead of relying on a syntactic parser which might be noisy and slow to build, we compute weights representing probabilities of syntactic relations based on the Huffman softmax tree in an efficient heuristic. The constructed {``}implicit graphs{''} from these weights show that these weights contain useful implicit structure information. Extensive experiments performed on several word similarity and word analogy tasks show gains compared to the basic continuous bag-of-words model. | ['Jie Shen', 'Cong Liu'] | 2016-12-01 | improved-word-embeddings-with-implicit-1 | https://aclanthology.org/C16-1227 | https://aclanthology.org/C16-1227.pdf | coling-2016-12 | ['learning-word-embeddings'] | ['methodology'] | [-1.18989693e-02 6.55260265e-01 -4.11759764e-01 -8.37315917e-01
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dfa375aa-0b21-4805-8d6e-1f86953eea93 | maximizing-soil-moisture-estimation-accuracy | 2305.15549 | null | https://arxiv.org/abs/2305.15549v1 | https://arxiv.org/pdf/2305.15549v1.pdf | Maximizing soil moisture estimation accuracy through simultaneous hydraulic parameter estimation using microwave remote sensing: Methodology and application | Improving the accuracy of soil moisture estimation is desirable from the perspectives of irrigation management and water conservation. To this end, this study proposes a systematic approach to select a subset of soil hydraulic parameters for estimation in large-scale agrohydrological systems to enhance soil moisture estimation accuracy. The proposed method involves simultaneous estimation of the selected parameters and the entire soil moisture distribution of the field, taking into account soil heterogeneity and using soil moisture observations obtained through microwave radiometers mounted on a center pivot irrigation system. At its core, the proposed method models the field with the cylindrical coordinate version of the Richards equation and addresses the issue of parameter estimability (quantitative parameter identifiability) through the sensitivity analysis and orthogonal projection approaches. Additionally, the study assimilates remotely sensed soil moisture observations into the field model using the extended Kalman filtering technique. The effectiveness of the proposed methodology is demonstrated through numerical simulations and a real field experiment, with cross-validation results showing a 24-43% improvement in soil moisture estimation accuracy. Overall, the study highlights the potential of this method to enhance soil moisture estimation in large-scale agricultural fields. | ['Sirish L. Shah', 'Jinfeng Liu', 'Maik Wolleben', 'Willemijn Appels', 'Mohamed Naouri', 'Erfan Orouskhani', 'Bernard T. Agyeman'] | 2023-05-24 | null | null | null | null | ['soil-moisture-estimation'] | ['computer-vision'] | [ 1.82588100e-01 -1.70841008e-01 -2.84361362e-01 1.71451718e-01
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afec22e3-6704-498a-b09a-0ec1d2154cf7 | denoising-and-prompt-tuning-for-multi | 2302.05862 | null | https://arxiv.org/abs/2302.05862v1 | https://arxiv.org/pdf/2302.05862v1.pdf | Denoising and Prompt-Tuning for Multi-Behavior Recommendation | In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional collaborative filtering techniques typically assume that users only have a single type of behavior with items, making it insufficient to utilize complex collaborative signals to learn informative representations and infer actual user preferences. Consequently, some pioneer studies explore modeling multi-behavior heterogeneity to learn better representations and boost the performance of recommendations for a target behavior. However, a large number of auxiliary behaviors (i.e., click and add-to-cart) could introduce irrelevant information to recommenders, which could mislead the target behavior (i.e., purchase) recommendation, rendering two critical challenges: (i) denoising auxiliary behaviors and (ii) bridging the semantic gap between auxiliary and target behaviors. Motivated by the above observation, we propose a novel framework-Denoising and Prompt-Tuning (DPT) with a three-stage learning paradigm to solve the aforementioned challenges. In particular, DPT is equipped with a pattern-enhanced graph encoder in the first stage to learn complex patterns as prior knowledge in a data-driven manner to guide learning informative representation and pinpointing reliable noise for subsequent stages. Accordingly, we adopt different lightweight tuning approaches with effectiveness and efficiency in the following stages to further attenuate the influence of noise and alleviate the semantic gap among multi-typed behaviors. Extensive experiments on two real-world datasets demonstrate the superiority of DPT over a wide range of state-of-the-art methods. The implementation code is available online at https://github.com/zc-97/DPT. | ['Li Li', 'Qilong Han', 'Xiangyu Zhao', 'Rui Chen', 'Chi Zhang'] | 2023-02-12 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 0.08500534 -0.2804696 -0.38899824 -0.6038336 -0.4350527 -0.3768517
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42afac95-996a-444c-9be1-c0c2585633b6 | hybrid-recommender-system-based-on-personal | 1607.02754 | null | http://arxiv.org/abs/1607.02754v1 | http://arxiv.org/pdf/1607.02754v1.pdf | Hybrid Recommender System Based on Personal Behavior Mining | Recommender systems are mostly well known for their applications in
e-commerce sites and are mostly static models. Classical personalized
recommender algorithm includes item-based collaborative filtering method
applied in Amazon, matrix factorization based collaborative filtering algorithm
from Netflix, etc. In this article, we hope to combine traditional model with
behavior pattern extraction method. We use desensitized mobile transaction
record provided by T-mall, Alibaba to build a hybrid dynamic recommender
system. The sequential pattern mining aims to find frequent sequential pattern
in sequence database and is applied in this hybrid model to predict customers'
payment behavior thus contributing to the accuracy of the model. | ['Chen Kun', 'Zhang Lingqi', 'Fang Zhiyuan'] | 2016-07-10 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [-2.27314785e-01 -5.52953660e-01 -2.80444175e-01 -3.81133109e-01
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65d4fa21-77c9-4a4e-80db-e82ed6a2deb2 | d2-net-weakly-supervised-action-localization | 2012.06440 | null | https://arxiv.org/abs/2012.06440v2 | https://arxiv.org/pdf/2012.06440v2.pdf | D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations | This work proposes a weakly-supervised temporal action localization framework, called D2-Net, which strives to temporally localize actions using video-level supervision. Our main contribution is the introduction of a novel loss formulation, which jointly enhances the discriminability of latent embeddings and robustness of the output temporal class activations with respect to foreground-background noise caused by weak supervision. The proposed formulation comprises a discriminative and a denoising loss term for enhancing temporal action localization. The discriminative term incorporates a classification loss and utilizes a top-down attention mechanism to enhance the separability of latent foreground-background embeddings. The denoising loss term explicitly addresses the foreground-background noise in class activations by simultaneously maximizing intra-video and inter-video mutual information using a bottom-up attention mechanism. As a result, activations in the foreground regions are emphasized whereas those in the background regions are suppressed, thereby leading to more robust predictions. Comprehensive experiments are performed on multiple benchmarks, including THUMOS14 and ActivityNet1.2. Our D2-Net performs favorably in comparison to the existing methods on all datasets, achieving gains as high as 2.3% in terms of mAP at IoU=0.5 on THUMOS14. Source code is available at https://github.com/naraysa/D2-Net | ['Ling Shao', 'Ming-Hsuan Yang', 'Fahad Shahbaz Khan', 'Munawar Hayat', 'Hisham Cholakkal', 'Sanath Narayan'] | 2020-12-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Narayan_D2-Net_Weakly-Supervised_Action_Localization_via_Discriminative_Embeddings_and_Denoised_Activations_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Narayan_D2-Net_Weakly-Supervised_Action_Localization_via_Discriminative_Embeddings_and_Denoised_Activations_ICCV_2021_paper.pdf | iccv-2021-1 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 2.77066410e-01 -1.43963933e-01 -6.66514099e-01 -1.64793178e-01
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c7bccb0e-97b2-48bc-86ef-d23175b3e4e2 | kptimes-a-large-scale-dataset-for-keyphrase-1 | 1911.12559 | null | https://arxiv.org/abs/1911.12559v1 | https://arxiv.org/pdf/1911.12559v1.pdf | KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents | Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https://github.com/ygorg/KPTimes . | ['Béatrice Daille', 'Florian Boudin', 'Ygor Gallina'] | 2019-11-28 | kptimes-a-large-scale-dataset-for-keyphrase | https://aclanthology.org/W19-8617 | https://aclanthology.org/W19-8617.pdf | ws-2019-10 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 4.99790683e-02 3.02326709e-01 -6.59343600e-01 3.07988584e-01
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672296a2-7822-43cf-9b0c-f0440daa26aa | affectmachine-classical-a-novel-system-for | 2304.04915 | null | https://arxiv.org/abs/2304.04915v1 | https://arxiv.org/pdf/2304.04915v1.pdf | AffectMachine-Classical: A novel system for generating affective classical music | This work introduces a new music generation system, called AffectMachine-Classical, that is capable of generating affective Classic music in real-time. AffectMachine was designed to be incorporated into biofeedback systems (such as brain-computer-interfaces) to help users become aware of, and ultimately mediate, their own dynamic affective states. That is, this system was developed for music-based MedTech to support real-time emotion self-regulation in users. We provide an overview of the rule-based, probabilistic system architecture, describing the main aspects of the system and how they are novel. We then present the results of a listener study that was conducted to validate the ability of the system to reliably convey target emotions to listeners. The findings indicate that AffectMachine-Classical is very effective in communicating various levels of Arousal ($R^2 = .96$) to listeners, and is also quite convincing in terms of Valence (R^2 = .90). Future work will embed AffectMachine-Classical into biofeedback systems, to leverage the efficacy of the affective music for emotional well-being in listeners. | ['Phoebe Chua', 'Adyasha Dash', 'Kat R. Agres'] | 2023-04-11 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [-1.62378833e-01 1.59151956e-01 -1.16423309e-01 -4.66369092e-01
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0600312e-4d2b-40f8-a372-4d98690e0f71 | constrained-bayesian-optimization-for-1 | 2302.14732 | null | https://arxiv.org/abs/2302.14732v2 | https://arxiv.org/pdf/2302.14732v2.pdf | Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design | Automatic underwater vehicle hull Design optimization is a complex engineering process for generating a UUV hull with optimized properties on a given requirement. First, it involves the integration of involved computationally complex engineering simulation tools. Second, it needs integration of a sample efficient optimization framework with the integrated toolchain. To this end, we integrated the CAD tool called FreeCAD with CFD tool openFoam for automatic design evaluation. For optimization, we chose Bayesian optimization (BO), which is a well-known technique developed for optimizing time-consuming expensive engineering simulations and has proven to be very sample efficient in a variety of problems, including hyper-parameter tuning and experimental design. During the optimization process, we can handle infeasible design as constraints integrated into the optimization process. By integrating domain-specific toolchain with AI-based optimization, we executed the automatic design optimization of underwater vehicle hull design. For empirical evaluation, we took two different use cases of real-world underwater vehicle design to validate the execution of our tool. | ['Will Hedgecock', 'Janos Sztipanovits', 'Peter Volgyesi', 'Harsh Vardhan'] | 2023-02-28 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-2.98114270e-01 2.40450218e-01 8.20193946e-01 -3.02090138e-01
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b716201f-9701-4bdf-b453-1262f190bae5 | examining-political-rhetoric-with-epistemic | 2212.14486 | null | https://arxiv.org/abs/2212.14486v2 | https://arxiv.org/pdf/2212.14486v2.pdf | Examining Political Rhetoric with Epistemic Stance Detection | Participants in political discourse employ rhetorical strategies -- such as hedging, attributions, or denials -- to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief holders). We first develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. Then we demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books, where we characterize trends in cited belief holders -- respected allies and opposed bogeymen -- across U.S. political ideologies. | ["Brendan O'Connor", 'Justin H Gross', 'Su Lin Blodgett', 'Ankita Gupta'] | 2022-12-29 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.02451973e-01 9.26855505e-01 -1.06511033e+00 -3.59876573e-01
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ce143871-0d7b-48fe-895c-15ad6c4a8167 | resolving-prepositional-phrase-attachment | null | null | https://aclanthology.org/2021.icon-main.40 | https://aclanthology.org/2021.icon-main.40.pdf | Resolving Prepositional Phrase Attachment Ambiguities with Contextualized Word Embeddings | This paper applies contextualized word embedding models to a long-standing problem in the natural language parsing community, namely prepositional phrase attachment. Following past formulations of this problem, we use data sets in which the attachment decision is both a binary-valued choice as well as a multi-valued choice. We present a deep learning architecture that fine-tunes the output of a contextualized word embedding model for the purpose of predicting attachment decisions. We present experiments on two commonly used datasets that outperform the previous best results, using only the original training data and the unannotated full sentence context. | ['Atul Kumar', 'Adwait Ratnaparkhi'] | null | null | null | null | icon-2021-12 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 7.89521262e-02 4.36358869e-01 -5.07143378e-01 -8.69846165e-01
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90e9c370-34ca-4c72-b537-f1661a5ac096 | deep-network-for-capacitive-ecg-denoising | 1903.12536 | null | http://arxiv.org/abs/1903.12536v1 | http://arxiv.org/pdf/1903.12536v1.pdf | Deep Network for Capacitive ECG Denoising | Continuous monitoring of cardiac health under free living condition is
crucial to provide effective care for patients undergoing post operative
recovery and individuals with high cardiac risk like the elderly. Capacitive
Electrocardiogram (cECG) is one such technology which allows comfortable and
long term monitoring through its ability to measure biopotential in conditions
without having skin contact. cECG monitoring can be done using many household
objects like chairs, beds and even car seats allowing for seamless monitoring
of individuals. This method is unfortunately highly susceptible to motion
artifacts which greatly limits its usage in clinical practice. The current use
of cECG systems has been limited to performing rhythmic analysis. In this paper
we propose a novel end-to-end deep learning architecture to perform the task of
denoising capacitive ECG. The proposed network is trained using motion
corrupted three channel cECG and a reference LEAD I ECG collected on
individuals while driving a car. Further, we also propose a novel joint loss
function to apply loss on both signal and frequency domain. We conduct
extensive rhythmic analysis on the model predictions and the ground truth. We
further evaluate the signal denoising using Mean Square Error(MSE) and Cross
Correlation between model predictions and ground truth. We report MSE of 0.167
and Cross Correlation of 0.476. The reported results highlight the feasibility
of performing morphological analysis using the filtered cECG. The proposed
approach can allow for continuous and comprehensive monitoring of the
individuals in free living conditions. | ['Sharath M. Shankaranarayana', 'Keerthi Ram', 'Vignesh Ravichandran', 'Mohanasankar Sivaprakasam', 'Jayaraj Joseph', 'Preejith S. P', 'Balamurali Murugesan'] | 2019-03-29 | null | null | null | null | ['ecg-denoising', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 4.35094386e-01 -8.94967467e-02 3.21298420e-01 -1.00970551e-01
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7b50a755-aea5-4fbe-b112-3e3e2039d496 | deep-q-learning-based-distribution-network | 2305.01180 | null | https://arxiv.org/abs/2305.01180v1 | https://arxiv.org/pdf/2305.01180v1.pdf | Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement | Distribution network reconfiguration (DNR) has proved to be an economical and effective way to improve the reliability of distribution systems. As optimal network configuration depends on system operating states (e.g., loads at each node), existing analytical and population-based approaches need to repeat the entire analysis and computation to find the optimal network configuration with a change in system operating states. Contrary to this, if properly trained, deep reinforcement learning (DRL)-based DNR can determine optimal or near-optimal configuration quickly even with changes in system states. In this paper, a Deep Q Learning-based framework is proposed for the optimal DNR to improve reliability of the system. An optimization problem is formulated with an objective function that minimizes the average curtailed power. Constraints of the optimization problem are radial topology constraint and all nodes traversing constraint. The distribution network is modeled as a graph and the optimal network configuration is determined by searching for an optimal spanning tree. The optimal spanning tree is the spanning tree with the minimum value of the average curtailed power. The effectiveness of the proposed framework is demonstrated through several case studies on 33-node and 69-node distribution test systems. | ['Mohammed Benidris', 'Narayan Bhusal', 'Mukesh Gautam'] | 2023-05-02 | null | null | null | null | ['q-learning'] | ['methodology'] | [-5.20262539e-01 -7.60876248e-03 -2.59026974e-01 -5.01880385e-02
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aeb9677e-4153-463c-b0a5-01383abd7dd8 | rcdnet-an-interpretable-rain-convolutional | 2107.06808 | null | https://arxiv.org/abs/2107.06808v2 | https://arxiv.org/pdf/2107.06808v2.pdf | RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining | As a common weather, rain streaks adversely degrade the image quality. Hence, removing rains from an image has become an important issue in the field. To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability. In specific, we first establish a RCD model for representing rain streaks and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model. By unfolding it, we then build the RCDNet in which every network module has clear physical meanings and corresponds to each operation involved in the algorithm. This good interpretability greatly facilitates an easy visualization and analysis on what happens inside the network and why it works well in inference process. Moreover, taking into account the domain gap issue in real scenarios, we further design a novel dynamic RCDNet, where the rain kernels can be dynamically inferred corresponding to input rainy images and then help shrink the space for rain layer estimation with few rain maps so as to ensure a fine generalization performance in the inconsistent scenarios of rain types between training and testing data. By end-to-end training such an interpretable network, all involved rain kernels and proximal operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers, and thus naturally lead to better deraining performance. Comprehensive experiments substantiate the superiority of our method, especially on its well generality to diverse testing scenarios and good interpretability for all its modules. Code is available in \emph{\url{https://github.com/hongwang01/DRCDNet}}. | ['Deyu Meng', 'Yefeng Zheng', 'Yong Liang', 'Yuexiang Li', 'Qian Zhao', 'Qi Xie', 'Hong Wang'] | 2021-07-14 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-2.77607113e-01 -2.06375852e-01 3.74451846e-01 -6.16801918e-01
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c6212cc7-1fb0-41be-9dcc-ace83da950d2 | on-out-of-distribution-detection-for-audio | 2210.15283 | null | https://arxiv.org/abs/2210.15283v2 | https://arxiv.org/pdf/2210.15283v2.pdf | On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors | Out-of-distribution (OOD) detection is concerned with identifying data points that do not belong to the same distribution as the model's training data. For the safe deployment of predictive models in a real-world environment, it is critical to avoid making confident predictions on OOD inputs as it can lead to potentially dangerous consequences. However, OOD detection largely remains an under-explored area in the audio (and speech) domain. This is despite the fact that audio is a central modality for many tasks, such as speaker diarization, automatic speech recognition, and sound event detection. To address this, we propose to leverage feature-space of the model with deep k-nearest neighbors to detect OOD samples. We show that this simple and flexible method effectively detects OOD inputs across a broad category of audio (and speech) datasets. Specifically, it improves the false positive rate (FPR@TPR95) by 17% and the AUROC score by 7% than other prior techniques. | ['Aaqib Saeed', 'Zaharah Bukhsh'] | 2022-10-27 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 3.14919591e-01 4.82269749e-02 -1.60724074e-01 -2.28886589e-01
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6a9b9fb6-a7b0-4c9a-bd5d-5bcf7271f2b0 | a-distributional-perspective-on-actor-critic | null | null | https://openreview.net/forum?id=jWXBUsWP7N | https://openreview.net/pdf?id=jWXBUsWP7N | A Distributional Perspective on Actor-Critic Framework | Recent distributional reinforcement learning methods, despite their successes, still contain fundamental problems that can lead to inaccurate representations of value distributions, such as distributional instability, action type restriction, and biased approximation. In this paper, we present a novel distributional actor-critic frame-work, GMAC, to address such problems. Adopting a stochastic policy removes the first two problems, and the bias in approximation is alleviated by minimizing the Cramer distance between the value distribution and its Bellman target distribution. In addition, GMAC improves data efficiency by generating the Bellman target distribution through Sample-Replacement algorithm, denoted by SR(λ), which provides a distributional generalization of multi-step policy evaluation algorithms.We empirically show that our method captures the multimodality of value distributions and improves the performance of conventional actor-critic methods with low computational cost in both discrete and continuous action spaces, using ArcadeLearning Environment (ALE) and PyBullet environment. | ['Chan Youn Park', 'Younghoon Kim', 'Daniel Wontae Nam'] | 2021-01-01 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.71658331e-01 1.44857578e-02 -4.31718946e-01 -2.00285725e-02
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ddc07a76-4ede-440d-a094-451938d82654 | learning-distributed-representations-of-code | 2012.07023 | null | https://arxiv.org/abs/2012.07023v2 | https://arxiv.org/pdf/2012.07023v2.pdf | InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees | Building deep learning models on source code has found many successful software engineering applications, such as code search, code comment generation, bug detection, code migration, and so on. Current learning techniques, however, have a major drawback that these models are mostly trained on datasets labeled for particular downstream tasks, and code representations may not be suitable for other tasks. While some techniques produce representations from unlabeled code, they are far from satisfactory when applied to downstream tasks. Although certain techniques generate representations from unlabeled code when applied to downstream tasks they are far from satisfactory. This paper proposes InferCode to overcome the limitation by adapting the self-supervised learning mechanism to build source code model. The key novelty lies in training code representations by predicting automatically identified subtrees from the context of the ASTs. Subtrees in ASTs are treated with InferCode as the labels for training code representations without any human labeling effort or the overhead of expensive graph construction, and the trained representations are no longer tied to any specific downstream tasks or code units. We trained an InferCode model instance using the Tree-based CNN as the encoder of a large set of Java code and applied it to downstream unsupervised tasks such as code clustering, code clone detection, cross-language code search or reused under a transfer learning scheme to continue training the model weights for supervised tasks such as code classification and method name prediction. Compared to previous code learning techniques applied to the same downstream tasks, such as Code2Vec, Code2Seq, ASTNN, higher performance results are achieved using our pre-trained InferCode model with a significant margin for most tasks including those involving different programming languages. | ['Lingxiao Jiang', 'Yijun Yu', 'Nghi D. Q. Bui'] | 2020-12-13 | null | null | null | null | ['code-classification', 'code-search', 'code-comment-generation', 'code-search', 'method-name-prediction', 'comment-generation'] | ['computer-code', 'computer-code', 'computer-code', 'computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-2.46324278e-02 3.20107341e-01 -4.54163402e-01 -5.09300470e-01
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afed14fa-ea47-46d5-b31e-5a8ab407756f | easicsdeep-a-deep-learning-model-for-cervical | 1812.04912 | null | http://arxiv.org/abs/1812.04912v1 | http://arxiv.org/pdf/1812.04912v1.pdf | EasiCSDeep: A deep learning model for Cervical Spondylosis Identification using surface electromyography signal | Cervical spondylosis (CS) is a common chronic disease that affects up to
two-thirds of the population and poses a serious burden on individuals and
society. The early identification has significant value in improving cure rate
and reducing costs. However, the pathology is complex, and the mild symptoms
increase the difficulty of the diagnosis, especially in the early stage.
Besides, the time-consuming and costliness of hospital medical service reduces
the attention to the CS identification. Thus, a convenient, low-cost
intelligent CS identification method is imperious demanded. In this paper, we
present an intelligent method based on the deep learning to identify CS, using
the surface electromyography (sEMG) signal. Faced with the complex, high
dimensionality and weak usability of the sEMG signal, we proposed and developed
a multi-channel EasiCSDeep algorithm based on the convolutional neural network,
which consists of the feature extraction, spatial relationship representation
and classification algorithm. To the best of our knowledge, this EasiCSDeep is
the first effort to employ the deep learning and the sEMG data to identify CS.
Compared with previous state-of-the-art algorithm, our algorithm achieves a
significant improvement. | ['Yingcong Xiang', 'Jing Xiao', 'Li Cui', 'Xi Huang', 'Nana Wang'] | 2018-12-12 | null | null | null | null | ['cervical-spondylosis-identification'] | ['medical'] | [ 1.12458132e-01 -3.82128626e-01 -2.60230869e-01 1.28824368e-01
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68e2f5c2-86b7-48cd-99b0-0b947db6806c | the-regretful-agent-heuristic-aided | 1903.01602 | null | http://arxiv.org/abs/1903.01602v1 | http://arxiv.org/pdf/1903.01602v1.pdf | The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation | As deep learning continues to make progress for challenging perception tasks,
there is increased interest in combining vision, language, and decision-making.
Specifically, the Vision and Language Navigation (VLN) task involves navigating
to a goal purely from language instructions and visual information without
explicit knowledge of the goal. Recent successful approaches have made in-roads
in achieving good success rates for this task but rely on beam search, which
thoroughly explores a large number of trajectories and is unrealistic for
applications such as robotics. In this paper, inspired by the intuition of
viewing the problem as search on a navigation graph, we propose to use a
progress monitor developed in prior work as a learnable heuristic for search.
We then propose two modules incorporated into an end-to-end architecture: 1) A
learned mechanism to perform backtracking, which decides whether to continue
moving forward or roll back to a previous state (Regret Module) and 2) A
mechanism to help the agent decide which direction to go next by showing
directions that are visited and their associated progress estimate (Progress
Marker). Combined, the proposed approach significantly outperforms current
state-of-the-art methods using greedy action selection, with 5% absolute
improvement on the test server in success rates, and more importantly 8% on
success rates normalized by the path length. Our code is available at
https://github.com/chihyaoma/regretful-agent . | ['Zsolt Kira', 'Ghassan AlRegib', 'Chih-Yao Ma', 'Caiming Xiong', 'Zuxuan Wu'] | 2019-03-05 | the-regretful-agent-heuristic-aided-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ma_The_Regretful_Agent_Heuristic-Aided_Navigation_Through_Progress_Estimation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ma_The_Regretful_Agent_Heuristic-Aided_Navigation_Through_Progress_Estimation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['vision-language-navigation'] | ['computer-vision'] | [-5.43131419e-02 4.80920300e-02 -3.13369811e-01 -3.14251125e-01
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24b0dbcf-5e34-45b6-81c7-9d63c877a5a7 | learning-to-simulate-natural-language | 2305.08195 | null | https://arxiv.org/abs/2305.08195v2 | https://arxiv.org/pdf/2305.08195v2.pdf | Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing | Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the traditional one-shot semantic parsing. However, prior work has heavily relied on human-annotated feedback data to train the interactive semantic parser, which is prohibitively expensive and not scalable. In this work, we propose a new task of simulating NL feedback for interactive semantic parsing. We accompany the task with a novel feedback evaluator. The evaluator is specifically designed to assess the quality of the simulated feedback, based on which we decide the best feedback simulator from our proposed variants. On a text-to-SQL dataset, we show that our feedback simulator can generate high-quality NL feedback to boost the error correction ability of a specific parser. In low-data settings, our feedback simulator can help achieve comparable error correction performance as trained using the costly, full set of human annotations. | ['Ziyu Yao', 'Wen-tau Yih', 'Sida I. Wang', 'Yintao Tai', 'Saurabh Srivastava', 'Hao Yan'] | 2023-05-14 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [ 4.11537945e-01 5.68292260e-01 3.08818161e-01 -6.21056378e-01
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9dfede04-599d-4507-824d-efafba61db53 | learning-to-order-natural-language-texts | null | null | https://aclanthology.org/P13-2016 | https://aclanthology.org/P13-2016.pdf | Learning to Order Natural Language Texts | null | ['Jiwei Tan', 'Xiaojun Wan', 'Jianguo Xiao'] | 2013-08-01 | null | null | null | acl-2013-8 | ['concept-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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bd3a2c7f-d13c-4cef-97fc-7ccb0d49f77e | languages-you-know-influence-those-you-learn | 2212.01757 | null | https://arxiv.org/abs/2212.01757v1 | https://arxiv.org/pdf/2212.01757v1.pdf | Languages You Know Influence Those You Learn: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer | Multi-lingual language models (LM), such as mBERT, XLM-R, mT5, mBART, have been remarkably successful in enabling natural language tasks in low-resource languages through cross-lingual transfer from high-resource ones. In this work, we try to better understand how such models, specifically mT5, transfer *any* linguistic and semantic knowledge across languages, even though no explicit cross-lingual signals are provided during pre-training. Rather, only unannotated texts from each language are presented to the model separately and independently of one another, and the model appears to implicitly learn cross-lingual connections. This raises several questions that motivate our study, such as: Are the cross-lingual connections between every language pair equally strong? What properties of source and target language impact the strength of cross-lingual transfer? Can we quantify the impact of those properties on the cross-lingual transfer? In our investigation, we analyze a pre-trained mT5 to discover the attributes of cross-lingual connections learned by the model. Through a statistical interpretation framework over 90 language pairs across three tasks, we show that transfer performance can be modeled by a few linguistic and data-derived features. These observations enable us to interpret cross-lingual understanding of the mT5 model. Through these observations, one can favorably choose the best source language for a task, and can anticipate its training data demands. A key finding of this work is that similarity of syntax, morphology and phonology are good predictors of cross-lingual transfer, significantly more than just the lexical similarity of languages. For a given language, we are able to predict zero-shot performance, that increases on a logarithmic scale with the number of few-shot target language data points. | ['Sachin Agarwal', 'David Vandyke', 'Jean-Philippe Fauconnier', 'Siddharth Patwardhan', 'Deepanshu Gupta', 'Benjamin Muller'] | 2022-12-04 | null | null | null | null | ['cross-lingual-transfer', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [-1.91633925e-01 -1.22006498e-01 -4.29599911e-01 -3.56030792e-01
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574e0a60-c4ca-446f-be4c-61fafeb51f3e | market2dish-health-aware-food-recommendation | 2012.06416 | null | https://arxiv.org/abs/2012.06416v1 | https://arxiv.org/pdf/2012.06416v1.pdf | Market2Dish: Health-aware Food Recommendation | With the rising incidence of some diseases, such as obesity and diabetes, a healthy diet is arousing increasing attention. However, most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, cooking assistance, or the nutrition and calorie estimation of dishes, ignoring the personalized health-aware food recommendation. Therefore, in this work, we present a personalized health-aware food recommendation scheme, namely Market2Dish, mapping the ingredients displayed in the market to the healthy dishes eaten at home. The proposed scheme comprises three components, namely recipe retrieval, user-health profiling, and health-aware food recommendation. In particular, recipe retrieval aims to acquire the ingredients available to the users, and then retrieve recipe candidates from a large-scale recipe dataset. User health profiling is to characterize the health conditions of users by capturing the textual health-related information crawled from social networks. Specifically, to solve the issue that the health-related information is extremely sparse, we incorporate a word-class interaction mechanism into the proposed deep model to learn the fine-grained correlations between the textual tweets and pre-defined health concepts. For the health-aware food recommendation, we present a novel category-aware hierarchical memory network-based recommender to learn the health-aware user-recipe interactions for better food recommendation. Moreover, extensive experiments demonstrate the effectiveness of the health-aware food recommendation scheme. | ['Liqiang Nie', 'Xuemeng Song', 'Peiguang Jing', 'Hao Jiang', 'Ling-Yu Duan', 'Wenjie Wang'] | 2020-12-11 | null | null | null | null | ['food-recommendation'] | ['miscellaneous'] | [-1.61234513e-01 -3.28431278e-01 -6.36102617e-01 -3.79348606e-01
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d8abf380-6f4e-49ee-b666-fd29168f2fc1 | how-to-democratise-and-protect-ai-fair-and | 2007.09370 | null | https://arxiv.org/abs/2007.09370v1 | https://arxiv.org/pdf/2007.09370v1.pdf | How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning | This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens and local credibility to ensure fairness, in combination with differential privacy to guarantee privacy. In particular, we build a fair and differentially private decentralised deep learning framework called FDPDDL, which enables parties to derive more accurate local models in a fair and private manner by using our developed two-stage scheme: during the initialisation stage, artificial samples generated by Differentially Private Generative Adversarial Network (DPGAN) are used to mutually benchmark the local credibility of each party and generate initial tokens; during the update stage, Differentially Private SGD (DPSGD) is used to facilitate collaborative privacy-preserving deep learning, and local credibility and tokens of each party are updated according to the quality and quantity of individually released gradients. Experimental results on benchmark datasets under three realistic settings demonstrate that FDPDDL achieves high fairness, yields comparable accuracy to the centralised and distributed frameworks, and delivers better accuracy than the standalone framework. | ['Xingjun Ma', 'Karthik Nandakumar', 'Yitong Li', 'Lingjuan Lyu', 'Jiangshan Yu'] | 2020-07-18 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-6.75514460e-01 9.75355580e-02 1.62090644e-01 -4.97168273e-01
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96b5a6eb-cb92-4a31-8717-f471b5bde712 | interpretative-computer-aided-lung-cancer | 2102.10919 | null | https://arxiv.org/abs/2102.10919v1 | https://arxiv.org/pdf/2102.10919v1.pdf | Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation | Background and Objective:Computer-aided diagnosis (CAD) systems promote diagnosis effectiveness and alleviate pressure of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based nodule malignancy evaluation depends on heuristic inference from low-dose computed tomography volume to malignant probability, which lacks clinical cognition. Methods:In this paper, we propose a joint radiology analysis and malignancy evaluation network (R2MNet) to evaluate the pulmonary nodule malignancy via radiology characteristics analysis. Radiological features are extracted as channel descriptor to highlight specific regions of the input volume that are critical for nodule malignancy evaluation. In addition, for model explanations, we propose channel-dependent activation mapping to visualize the features and shed light on the decision process of deep neural network. Results:Experimental results on the LIDC-IDRI dataset demonstrate that the proposed method achieved area under curve of 96.27% on nodule radiology analysis and AUC of 97.52% on nodule malignancy evaluation. In addition, explanations of CDAM features proved that the shape and density of nodule regions were two critical factors that influence a nodule to be inferred as malignant, which conforms with the diagnosis cognition of experienced radiologists. Conclusion:Incorporating radiology analysis with nodule malignant evaluation, the network inference process conforms to the diagnostic procedure of radiologists and increases the confidence of evaluation results. Besides, model interpretation with CDAM features shed light on the regions which DNNs focus on when they estimate nodule malignancy probabilities. | ['Liqin Huang', 'Bin Zheng', 'Lin Pan', 'Jiepeng Zheng', 'Haojin Lin', 'Wangbin Ding', 'Chenhao Peia', 'Zhiqiang Shen', 'Shaohua Zheng'] | 2021-02-22 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-1.13681471e-02 4.32730436e-01 -5.26246250e-01 -1.94878906e-01
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dc9553ef-ecaa-49cf-88bb-af1bf8bd3a82 | twitter-sentiment-analysis-1 | null | null | https://www.researchgate.net/publication/352780855_Twitter_Sentiment_Analysis | https://www.researchgate.net/publication/352780855_Twitter_Sentiment_Analysis | Twitter Sentiment Analysis | In this report, address the problem of sentiment classification on the Twitter dataset. used a number of
machine learning and deep learning methods to perform sentiment analysis. In the end, used a majority
vote ensemble method with 5 of our best models to achieve the classification accuracy of 83.58% on
Kaggle public leaderboard. | ['Vedurumudi Priyanka'] | 2021-06-13 | null | null | null | 2021-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-4.79255468e-01 -1.23356417e-01 -3.30431402e-01 -7.92189121e-01
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c5a006de-1885-4938-b89e-ff10b264f7bc | -rank-multi-agent-evaluation-by-evolution | 1903.01373 | null | http://arxiv.org/abs/1903.01373v1 | http://arxiv.org/pdf/1903.01373v1.pdf | α-Rank: Multi-Agent Evaluation by Evolution | We introduce {\alpha}-Rank, a principled evolutionary dynamics methodology,
for the evaluation and ranking of agents in large-scale multi-agent
interactions, grounded in a novel dynamical game-theoretic solution concept
called Markov-Conley chains (MCCs). The approach leverages continuous-time and
discrete-time evolutionary dynamical systems applied to empirical games, and
scales tractably in the number of agents, in the type of interactions (beyond
dyadic), and the type of empirical games (symmetric and asymmetric). Current
models are fundamentally limited in one or more of these dimensions, and are
not guaranteed to converge to the desired game-theoretic solution concept
(typically the Nash equilibrium). {\alpha}-Rank automatically provides a
ranking over the set of agents under evaluation and provides insights into
their strengths, weaknesses, and long-term dynamics in terms of basins of
attraction and sink components. This is a direct consequence of our new model's
direct correspondence to the dynamical MCC solution concept when its
ranking-intensity parameter, {\alpha}, is chosen to be large, which exactly
forms the basis of {\alpha}-Rank. In contrast to the Nash equilibrium, which is
a static solution concept based solely on fixed points, MCCs are a dynamical
solution concept based on the Markov chain formalism, Conley's Fundamental
Theorem of Dynamical Systems, and the core ingredients of dynamical systems:
fixed points, recurrent sets, periodic orbits, and limit cycles. Our
{\alpha}-Rank method runs in polynomial time with respect to the total number
of pure strategy profiles, whereas computing a Nash equilibrium for a
general-sum game is known to be intractable. We introduce mathematical proofs
that reveal the formal underpinnings of the {\alpha}-Rank methodology. We
illustrate the method in canonical games and in AlphaGo, AlphaZero, MuJoCo
Soccer, and Poker. | ['Marc Lanctot', 'Jean-Baptiste Lespiau', 'Christos Papadimitriou', 'Shayegan Omidshafiei', 'Wojciech M. Czarnecki', 'Mark Rowland', 'Karl Tuyls', 'Julien Perolat', 'Georgios Piliouras', 'Remi Munos'] | 2019-03-04 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-3.10568690e-01 -3.83707620e-02 1.44585013e-01 7.26666451e-01
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03a1e61f-316b-4d02-ae2b-115b4ebf95a1 | contrast-enhancement-estimation-for-digital | 1706.03875 | null | http://arxiv.org/abs/1706.03875v1 | http://arxiv.org/pdf/1706.03875v1.pdf | Contrast Enhancement Estimation for Digital Image Forensics | Inconsistency in contrast enhancement can be used to expose image forgeries.
In this work, we describe a new method to estimate contrast enhancement from a
single image. Our method takes advantage of the nature of contrast enhancement
as a mapping between pixel values, and the distinct characteristics it
introduces to the image pixel histogram. Our method recovers the original pixel
histogram and the contrast enhancement simultaneously from a single image with
an iterative algorithm. Unlike previous methods, our method is robust in the
presence of additive noise perturbations that are used to hide the traces of
contrast enhancement. Furthermore, we also develop an e effective method to to
detect image regions undergone contrast enhancement transformations that are
different from the rest of the image, and use this method to detect composite
images. We perform extensive experimental evaluations to demonstrate the
efficacy and efficiency of our method method. | ['Longyin Wen', 'Siwei Lyu', 'Honggang Qi'] | 2017-06-13 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 7.75313497e-01 -5.93594134e-01 2.87616462e-01 2.07548775e-02
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840510f5-5ebf-455c-a123-fcbadf7980dc | quantitative-planning-with-action-deception | 2301.01349 | null | https://arxiv.org/abs/2301.01349v2 | https://arxiv.org/pdf/2301.01349v2.pdf | Quantitative Planning with Action Deception in Concurrent Stochastic Games | We study a class of two-player competitive concurrent stochastic games on graphs with reachability objectives. Specifically, player 1 aims to reach a subset $F_1$ of game states, and player 2 aims to reach a subset $F_2$ of game states where $F_2\cap F_1=\emptyset$. Both players aim to satisfy their reachability objectives before their opponent does. Yet, the information players have about the game dynamics is asymmetric: P1 has a (set of) hidden actions unknown to P2 at the beginning of their interaction. In this setup, we investigate P1's strategic planning of action deception that decides when to deviate from the Nash equilibrium in P2's game model and employ a hidden action, so that P1 can maximize the value of action deception, which is the additional payoff compared to P1's payoff in the game where P2 has complete information. Anticipating that P2 may detect his misperception about the game and adapt his strategy during interaction in unpredictable ways, we construct a planning problem for P1 to augment the game model with an incomplete model about the theory of mind of the opponent P2. While planning in the augmented game, P1 can effectively influence P2's perception so as to entice P2 to take actions that benefit P1. We prove that the proposed deceptive planning algorithm maximizes a lower bound on the value of action deception and demonstrate the effectiveness of our deceptive planning algorithm using a robot motion planning problem inspired by soccer games. | ['Jie Fu', 'Shuo Han', 'Chongyang Shi'] | 2023-01-03 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 2.90178210e-01 1.06742096e+00 -1.55189773e-02 3.96975994e-01
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1d52e55c-f0ea-4984-9193-edf43f935647 | le2fusion-a-novel-local-edge-enhancement | 2305.17374 | null | https://arxiv.org/abs/2305.17374v1 | https://arxiv.org/pdf/2305.17374v1.pdf | LE2Fusion: A novel local edge enhancement module for infrared and visible image fusion | Infrared and visible image fusion task aims to generate a fused image which contains salient features and rich texture details from multi-source images. However, under complex illumination conditions, few algorithms pay attention to the edge information of local regions which is crucial for downstream tasks. To this end, we propose a fusion network based on the local edge enhancement, named LE2Fusion. Specifically, a local edge enhancement (LE2) module is proposed to improve the edge information under complex illumination conditions and preserve the essential features of image. For feature extraction, a multi-scale residual attention (MRA) module is applied to extract rich features. Then, with LE2, a set of enhancement weights are generated which are utilized in feature fusion strategy and used to guide the image reconstruction. To better preserve the local detail information and structure information, the pixel intensity loss function based on the local region is also presented. The experiments demonstrate that the proposed method exhibits better fusion performance than the state-of-the-art fusion methods on public datasets. | ['Xiaoning Song', 'Chunyang Cheng', 'Hui Li', 'Yongbiao Xiao'] | 2023-05-27 | null | null | null | null | ['image-reconstruction', 'infrared-and-visible-image-fusion'] | ['computer-vision', 'computer-vision'] | [ 3.21714699e-01 -6.15238369e-01 1.34365082e-01 -3.37721914e-01
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15f98a8f-d372-4762-8948-94fa0dbefedf | how-drones-look-crowdsourced-knowledge | 1811.05625 | null | https://arxiv.org/abs/1811.05625v2 | https://arxiv.org/pdf/1811.05625v2.pdf | Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction | As an emerging vision platform, a drone can look from many abnormal viewpoints which brings many new challenges into the classic vision task of video saliency prediction. To investigate these challenges, this paper proposes a large-scale video dataset for aerial saliency prediction, which consists of ground-truth salient object regions of 1,000 aerial videos, annotated by 24 subjects. To the best of our knowledge, it is the first large-scale video dataset that focuses on visual saliency prediction on drones. Based on this dataset, we propose a Model-guided Multi-path Network (MM-Net) that serves as a baseline model for aerial video saliency prediction. Inspired by the annotation process in eye-tracking experiments, MM-Net adopts multiple information paths, each of which is initialized under the guidance of a classic saliency model. After that, the visual saliency knowledge encoded in the most representative paths is selected and aggregated to improve the capability of MM-Net in predicting spatial saliency in aerial scenarios. Finally, these spatial predictions are adaptively combined with the temporal saliency predictions via a spatiotemporal optimization algorithm. Experimental results show that MM-Net outperforms ten state-of-the-art models in predicting aerial video saliency. | ['Kui Fu', 'Yonghong Tian', 'Hongze Shen', 'Yu Zhang', 'Jia Li'] | 2018-11-14 | null | null | null | null | ['aerial-video-saliency-prediction'] | ['computer-vision'] | [ 3.50446880e-01 -9.30309221e-02 -3.81445676e-01 -2.19109040e-02
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109fbe36-f388-4a51-89d9-9db3250a74f6 | evaluating-surgical-skills-from-kinematic | 1806.02750 | null | http://arxiv.org/abs/1806.02750v1 | http://arxiv.org/pdf/1806.02750v1.pdf | Evaluating surgical skills from kinematic data using convolutional neural networks | The need for automatic surgical skills assessment is increasing, especially
because manual feedback from senior surgeons observing junior surgeons is prone
to subjectivity and time consuming. Thus, automating surgical skills evaluation
is a very important step towards improving surgical practice. In this paper, we
designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by
extracting patterns in the surgeon motions performed in robotic surgery. The
proposed method is validated on the JIGSAWS dataset and achieved very
competitive results with 100% accuracy on the suturing and needle passing
tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate
its black-box effect using class activation map. This feature allows our method
to automatically highlight which parts of the surgical task influenced the
skill prediction and can be used to explain the classification and to provide
personalized feedback to the trainee. | ['Pierre-Alain Muller', 'Lhassane Idoumghar', 'Germain Forestier', 'Jonathan Weber', 'Hassan Ismail Fawaz'] | 2018-06-07 | null | null | null | null | ['skills-evaluation', 'skills-assessment', 'surgical-skills-evaluation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 6.91622868e-02 4.73142356e-01 -1.89675331e-01 -3.65940481e-01
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16ca2721-15d5-42fc-8a27-fadfb22af641 | vl-beit-generative-vision-language | 2206.01127 | null | https://arxiv.org/abs/2206.01127v2 | https://arxiv.org/pdf/2206.01127v2.pdf | VL-BEiT: Generative Vision-Language Pretraining | We introduce a vision-language foundation model called VL-BEiT, which is a bidirectional multimodal Transformer learned by generative pretraining. Our minimalist solution conducts masked prediction on both monomodal and multimodal data with a shared Transformer. Specifically, we perform masked vision-language modeling on image-text pairs, masked language modeling on texts, and masked image modeling on images. VL-BEiT is learned from scratch with one unified pretraining task, one shared backbone, and one-stage training. Our method is conceptually simple and empirically effective. Experimental results show that VL-BEiT obtains strong results on various vision-language benchmarks, such as visual question answering, visual reasoning, and image-text retrieval. Moreover, our method learns transferable visual features, achieving competitive performance on image classification, and semantic segmentation. | ['Furu Wei', 'Li Dong', 'Wenhui Wang', 'Hangbo Bao'] | 2022-06-02 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.74264365e-01 4.36514556e-01 -4.51677382e-01 -6.63908780e-01
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4c1581c1-fbe3-47b2-bb24-4d8b7232dbcd | feature-replacement-and-combination-for | 2104.04298 | null | https://arxiv.org/abs/2104.04298v3 | https://arxiv.org/pdf/2104.04298v3.pdf | On Architectures and Training for Raw Waveform Feature Extraction in ASR | With the success of neural network based modeling in automatic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly based on the raw waveform. Recently, one line of research has focused on unsupervised pre-training of feature extractors on audio-only data to improve downstream ASR performance. In this work, we investigate the usefulness of one of these front-end frameworks, namely wav2vec, in a setting without additional untranscribed data for hybrid ASR systems. We compare this framework both to the manually defined standard Gammatone feature set, as well as to features extracted as part of the acoustic model of an ASR system trained supervised. We study the benefits of using the pre-trained feature extractor and explore how to additionally exploit an existing acoustic model trained with different features. Finally, we systematically examine combinations of the described features in order to further advance the performance. | ['Hermann Ney', 'Ralf Schlüter', 'Wilfried Michel', 'Christoph Lüscher', 'Peter Vieting'] | 2021-04-09 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 4.73338783e-01 9.76738259e-02 4.81326848e-01 -4.26717401e-01
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b0f197dd-d048-45b6-9b07-816bc2b2fd0a | pgganet-pose-guided-graph-attention-network | 2111.14411 | null | https://arxiv.org/abs/2111.14411v2 | https://arxiv.org/pdf/2111.14411v2.pdf | PGGANet: Pose Guided Graph Attention Network for Person Re-identification | Person re-identification (reID) aims at retrieving a person from images captured by different cameras. For deep-learning-based reID methods, it has been proved that using local features together with global feature could help to give robust representation for person retrieval. Human pose information could provide the locations of human skeleton to effectively guide the network to pay more attention on these key areas and could also help to reduce the noise distractions from background or occlusion. However, methods proposed by previous pose-based works might not be able to fully exploit the benefits of pose information and few of them take into consideration the different contributions of separate local features. In this paper, we propose a pose guided graph attention network, a multi-branch architecture consisting of one branch for global feature, one branch for mid-granular body features and one branch for fine-granular key point features. We use a pre-trained pose estimator to generate the key-point heatmaps for local feature learning and carefully design a graph attention convolution layer to re-assign the contribution weights of extracted local features by modeling the similarities relations. Experiment results demonstrate the effectiveness of our approach on discriminative feature learning and we show that our model achieves state-of-the-art performances on several mainstream evaluation datasets. We also conduct a plenty of ablation studies and design different kinds of comparison experiments for our network to prove its effectiveness and robustness, including occluded experiments and cross-domain tests. | ['Wenquan Feng', 'Hongbo Zhao', 'Zhijun He'] | 2021-11-29 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [-3.57875854e-01 -2.45417252e-01 -1.50126992e-02 -4.70520884e-01
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6b83d6b9-ad5e-4464-a268-884124f5bccc | uavstereo-a-multiple-resolution-dataset-for | 2302.10082 | null | https://arxiv.org/abs/2302.10082v1 | https://arxiv.org/pdf/2302.10082v1.pdf | UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV Scenarios | Stereo matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, such as KITTI and Scene Flow. UAVs (Unmanned Aerial Vehicles) are commonly utilized for surface observation, and their captured images are frequently used for detailed 3D reconstruction due to high resolution and low-altitude acquisition. At present, the mainstream supervised learning network requires a significant amount of training data with ground-truth labels to learn model parameters. However, due to the scarcity of UAV stereo matching datasets, the learning-based network cannot be applied to UAV images. To facilitate further research, this paper proposes a novel pipeline to generate accurate and dense disparity maps using detailed meshes reconstructed by UAV images and LiDAR point clouds. Through the proposed pipeline, this paper constructs a multi-resolution UAV scenario dataset, called UAVStereo, with over 34k stereo image pairs covering 3 typical scenes. As far as we know, UAVStereo is the first stereo matching dataset of UAV low-altitude scenarios. The dataset includes synthetic and real stereo pairs to enable generalization from the synthetic domain to the real domain. Furthermore, our UAVStereo dataset provides multi-resolution and multi-scene images pairs to accommodate a variety of sensors and environments. In this paper, we evaluate traditional and state-of-the-art deep learning methods, highlighting their limitations in addressing challenges in UAV scenarios and offering suggestions for future research. The dataset is available at https://github.com/rebecca0011/UAVStereo.git | ['Quan Yujun', 'Li Zhenqi', 'Yu Wenshuai', 'Yu Anzhu', 'Cao Xuefeng', 'Zhang Xiaoyi'] | 2023-02-20 | null | null | null | null | ['3d-scene-reconstruction', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 1.27044395e-01 -5.19398808e-01 1.21177696e-02 -4.31476831e-01
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abc8ef61-f74c-41ac-85a3-4f077c144385 | how-can-voting-mechanisms-improve-the | 2209.08286 | null | https://arxiv.org/abs/2209.08286v1 | https://arxiv.org/pdf/2209.08286v1.pdf | How can voting mechanisms improve the robustness and generalizability of toponym disambiguation? | A vast amount of geographic information exists in natural language texts, such as tweets and news. Extracting geographic information from texts is called Geoparsing, which includes two subtasks: toponym recognition and toponym disambiguation, i.e., to identify the geospatial representations of toponyms. This paper focuses on toponym disambiguation, which is usually approached by toponym resolution and entity linking. Recently, many novel approaches have been proposed, especially deep learning-based approaches, such as CamCoder, GENRE, and BLINK. In this paper, a spatial clustering-based voting approach that combines several individual approaches is proposed to improve SOTA performance in terms of robustness and generalizability. Experiments are conducted to compare a voting ensemble with 20 latest and commonly-used approaches based on 12 public datasets, including several highly ambiguous and challenging datasets (e.g., WikToR and CLDW). The datasets are of six types: tweets, historical documents, news, web pages, scientific articles, and Wikipedia articles, containing in total 98,300 places across the world. The results show that the voting ensemble performs the best on all the datasets, achieving an average Accuracy@161km of 0.86, proving the generalizability and robustness of the voting approach. Also, the voting ensemble drastically improves the performance of resolving fine-grained places, i.e., POIs, natural features, and traffic ways. | ['Hongchao Fan', 'Friederike Klan', 'Zhiyong Zhou', 'Jens Kersten', 'Yeran Sun', 'Xuke Hu'] | 2022-09-17 | null | null | null | null | ['toponym-resolution'] | ['natural-language-processing'] | [-8.04873586e-01 -4.28876579e-01 -2.13161588e-01 -1.61470950e-01
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88dfbe18-7078-4dca-bbcb-4b35c4a87aae | vivesdebate-speech-a-corpus-of-spoken | 2302.12584 | null | https://arxiv.org/abs/2302.12584v1 | https://arxiv.org/pdf/2302.12584v1.pdf | VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining | In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic. Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline. The provided results can be used as a baseline for future research. | ['Javier Iranzo-Sánchez', 'Ramon Ruiz-Dolz'] | 2023-02-24 | null | null | null | null | ['argument-mining'] | ['natural-language-processing'] | [ 2.23222539e-01 7.72922099e-01 -6.53105751e-02 -4.66885448e-01
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e054f8fe-66dd-4349-bad3-9a488bc93185 | uncovering-surprising-event-boundaries-in-1 | null | null | https://aclanthology.org/2022.wnu-1.1 | https://aclanthology.org/2022.wnu-1.1.pdf | Uncovering Surprising Event Boundaries in Narratives | It is important to define meaningful and interpretable automatic evaluation metrics for open-domain dialog research. Standard language generation metrics have been shown to be ineffective for dialog. This paper introduces the FED metric (fine-grained evaluation of dialog), an automatic evaluation metric which uses DialoGPT, without any fine-tuning or supervision. It also introduces the FED dataset which is constructed by annotating a set of human-system and human-human conversations with eighteen fine-grained dialog qualities. The FED metric (1) does not rely on a ground-truth response, (2) does not require training data and (3) measures fine-grained dialog qualities at both the turn and whole dialog levels. FED attains moderate to strong correlation with human judgement at both levels. | ['Maarten Sap', 'Anna Jafarpour', 'Zhilin Wang'] | null | null | null | null | naacl-wnu-2022-7 | ['open-domain-dialog'] | ['natural-language-processing'] | [-3.31470490e-01 7.18336344e-01 1.29960254e-01 -8.96235943e-01
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e77cff33-161f-44a4-a16f-c5cfeba9352c | deep-learning-algorithms-with-applications-to | 1512.03131 | null | http://arxiv.org/abs/1512.03131v1 | http://arxiv.org/pdf/1512.03131v1.pdf | Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey | Deep learning has recently achieved very promising results in a wide range of
areas such as computer vision, speech recognition and natural language
processing. It aims to learn hierarchical representations of data by using deep
architecture models. In a smart city, a lot of data (e.g. videos captured from
many distributed sensors) need to be automatically processed and analyzed. In
this paper, we review the deep learning algorithms applied to video analytics
of smart city in terms of different research topics: object detection, object
tracking, face recognition, image classification and scene labeling. | ['Li Wang', 'Dennis Sng'] | 2015-12-10 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [-1.61140133e-02 -1.61423534e-01 -9.79210585e-02 -6.18529975e-01
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470b734c-4cbe-4a7a-be92-579f7147db64 | fast-and-lightweight-scene-regressor-for | 2212.01830 | null | https://arxiv.org/abs/2212.01830v1 | https://arxiv.org/pdf/2212.01830v1.pdf | Fast and Lightweight Scene Regressor for Camera Relocalization | Camera relocalization involving a prior 3D reconstruction plays a crucial role in many mixed reality and robotics applications. Estimating the camera pose directly with respect to pre-built 3D models can be prohibitively expensive for several applications with limited storage and/or communication bandwidth. Although recent scene and absolute pose regression methods have become popular for efficient camera localization, most of them are computation-resource intensive and difficult to obtain a real-time inference with high accuracy constraints. This study proposes a simple scene regression method that requires only a multi-layer perceptron network for mapping scene coordinates to achieve accurate camera pose estimations. The proposed approach uses sparse descriptors to regress the scene coordinates, instead of a dense RGB image. The use of sparse features provides several advantages. First, the proposed regressor network is substantially smaller than those reported in previous studies. This makes our system highly efficient and scalable. Second, the pre-built 3D models provide the most reliable and robust 2D-3D matches. Therefore, learning from them can lead to an awareness of equivalent features and substantially improve the generalization performance. A detailed analysis of our approach and extensive evaluations using existing datasets are provided to support the proposed method. The implementation detail is available at https://github.com/aislab/feat2map | ['Joo-Ho Lee', 'Dinh-Tuan Tran', 'Thuan B. Bui'] | 2022-12-04 | null | null | null | null | ['camera-localization', 'camera-relocalization', 'mixed-reality'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.69009734e-02 -4.40421492e-01 -3.18583757e-01 -5.09639680e-01
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2b3eb437-b85e-4be7-8955-a7fd5bb91954 | hang-time-har-a-benchmark-dataset-for | 2305.13124 | null | https://arxiv.org/abs/2305.13124v1 | https://arxiv.org/pdf/2305.13124v1.pdf | Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-worn Inertial Sensors | We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications toward game analysis, guided training, and personal physical activity tracking. The dataset was recorded for two teams from separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both repetitive basketball training sessions and full games. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels, since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset's features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures. | ['Qin Lv', 'Kristof Van Laerhoven', 'Marius Bock', 'Julia Lee Romero', 'Alexander Hoelzemann'] | 2023-05-22 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 4.01735157e-02 -2.43795365e-01 -5.76232374e-01 -6.60341531e-02
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f53a877b-bd73-4904-852d-366e69497d93 | oid-outlier-identifying-and-discarding-in | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5134_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700596.pdf | OID: Outlier Identifying and Discarding in Blind Image Deblurring | Blind deblurring methods are sensitive to outliers, such as saturated pixels and non-Gaussian noise. Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the linear formation of the blurring process. Prior arts develop sophisticated edge-selecting steps or noise filtering pre-processing steps to deal with outliers (i.e. indirect approaches). However, these indirect approaches may fail when massive outliers are presented, since informative details may be polluted by outliers or erased during the pre-processing steps. To address these problems, this paper develops a simple yet effective Outlier Identifying and Discarding (OID) method, which alleviates limitations in existing Maximum A Posteriori (MAP)- based deblurring models when significant outliers are presented. Unlike previous indirect outlier processing methods, OID tackles outliers directly by explicitly identifying and discarding them, when updating both the latent image and the blur kernel during the deblurring process, where the outliers are detected by using the sparse and entropy-based modules. OID is easy to implement and extendable for non-blind restoration. Extensive experiments demonstrate the superiority of OID against recent works both quantitatively and qualitatively. | ['Guixu Zhang', 'Faming Fang', 'Liang Chen', 'Jun Liu', 'Jiawei Zhang'] | null | null | null | null | eccv-2020-8 | ['blind-image-deblurring'] | ['computer-vision'] | [ 8.17272812e-02 -4.92221564e-01 3.42240095e-01 1.41686708e-01
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8c3ca26f-494b-4469-bb43-053053f8c85b | molxpt-wrapping-molecules-with-text-for | 2305.10688 | null | https://arxiv.org/abs/2305.10688v2 | https://arxiv.org/pdf/2305.10688v2.pdf | MolXPT: Wrapping Molecules with Text for Generative Pre-training | Generative pre-trained Transformer (GPT) has demonstrates its great success in natural language processing and related techniques have been adapted into molecular modeling. Considering that text is the most important record for scientific discovery, in this paper, we propose MolXPT, a unified language model of text and molecules pre-trained on SMILES (a sequence representation of molecules) wrapped by text. Briefly, we detect the molecule names in each sequence and replace them to the corresponding SMILES. In this way, the SMILES could leverage the information from surrounding text, and vice versa. The above wrapped sequences, text sequences from PubMed and SMILES sequences from PubChem are all fed into a language model for pre-training. Experimental results demonstrate that MolXPT outperforms strong baselines of molecular property prediction on MoleculeNet, performs comparably to the best model in text-molecule translation while using less than half of its parameters, and enables zero-shot molecular generation without finetuning. | ['Tie-Yan Liu', 'Ming Zhang', 'Tao Qin', 'Shufang Xie', 'Lijun Wu', 'Yingce Xia', 'Wei zhang', 'Zequn Liu'] | 2023-05-18 | null | null | null | null | ['molecule-captioning', 'text-based-de-novo-molecule-generation', 'molecular-property-prediction'] | ['medical', 'medical', 'miscellaneous'] | [ 7.92109549e-01 2.81059474e-01 -5.74964941e-01 -2.16338769e-01
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0d095300-65c3-4428-a813-84b8d00cb7b9 | text-flow-a-unified-text-detection-system-in | 1604.06877 | null | http://arxiv.org/abs/1604.06877v1 | http://arxiv.org/pdf/1604.06877v1.pdf | Text Flow: A Unified Text Detection System in Natural Scene Images | The prevalent scene text detection approach follows four sequential steps
comprising character candidate detection, false character candidate removal,
text line extraction, and text line verification. However, errors occur and
accumulate throughout each of these sequential steps which often lead to low
detection performance. To address these issues, we propose a unified scene text
detection system, namely Text Flow, by utilizing the minimum cost (min-cost)
flow network model. With character candidates detected by cascade boosting, the
min-cost flow network model integrates the last three sequential steps into a
single process which solves the error accumulation problem at both character
level and text line level effectively. The proposed technique has been tested
on three public datasets, i.e, ICDAR2011 dataset, ICDAR2013 dataset and a
multilingual dataset and it outperforms the state-of-the-art methods on all
three datasets with much higher recall and F-score. The good performance on the
multilingual dataset shows that the proposed technique can be used for the
detection of texts in different languages. | ['Shangxuan Tian', 'Chang Huang', 'Yifeng Pan', 'Shijian Lu', 'Kai Yu', 'Chew Lim Tan'] | 2016-04-23 | text-flow-a-unified-text-detection-system-in-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Tian_Text_Flow_A_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Tian_Text_Flow_A_ICCV_2015_paper.pdf | iccv-2015-12 | ['text-line-extraction'] | ['computer-vision'] | [ 2.76926428e-01 -6.92601562e-01 -5.28443605e-02 -1.28158942e-01
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8f53e823-4d62-47cc-800f-613d79b4e8df | adaptive-strategies-in-non-convex | 2306.10278 | null | https://arxiv.org/abs/2306.10278v2 | https://arxiv.org/pdf/2306.10278v2.pdf | Adaptive Strategies in Non-convex Optimization | An algorithm is said to be adaptive to a certain parameter (of the problem) if it does not need a priori knowledge of such a parameter but performs competitively to those that know it. This dissertation presents our work on adaptive algorithms in following scenarios: 1. In the stochastic optimization setting, we only receive stochastic gradients and the level of noise in evaluating them greatly affects the convergence rate. Tuning is typically required when without prior knowledge of the noise scale in order to achieve the optimal rate. Considering this, we designed and analyzed noise-adaptive algorithms that can automatically ensure (near)-optimal rates under different noise scales without knowing it. 2. In training deep neural networks, the scales of gradient magnitudes in each coordinate can scatter across a very wide range unless normalization techniques, like BatchNorm, are employed. In such situations, algorithms not addressing this problem of gradient scales can behave very poorly. To mitigate this, we formally established the advantage of scale-free algorithms that adapt to the gradient scales and presented its real benefits in empirical experiments. 3. Traditional analyses in non-convex optimization typically rely on the smoothness assumption. Yet, this condition does not capture the properties of some deep learning objective functions, including the ones involving Long Short-Term Memory networks and Transformers. Instead, they satisfy a much more relaxed condition, with potentially unbounded smoothness. Under this condition, we show that a generalized SignSGD algorithm can theoretically match the best-known convergence rates obtained by SGD with gradient clipping but does not need explicit clipping at all, and it can empirically match the performance of Adam and beat others. Moreover, it can also be made to automatically adapt to the unknown relaxed smoothness. | ['Zhenxun Zhuang'] | 2023-06-17 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-5.61444350e-02 -1.27841964e-01 1.96688343e-02 -4.90366995e-01
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d3564701-4419-4732-b02d-5a391ab87528 | kanglish-alli-names-named-entity-recognition | null | null | https://aclanthology.org/2022.wnut-1.17 | https://aclanthology.org/2022.wnut-1.17.pdf | “Kanglish alli names!” Named Entity Recognition for Kannada-English Code-Mixed Social Media Data | Code-mixing (CM) is a frequently observed phenomenon on social media platforms in multilingual societies such as India. While the increase in code-mixed content on these platforms provides good amount of data for studying various aspects of code-mixing, the lack of automated text analysis tools makes such studies difficult. To overcome the same, tools such as language identifiers and parts of-speech (POS) taggers for analysing code-mixed data have been developed. One such tool is Named Entity Recognition (NER), an important Natural Language Processing (NLP) task, which is not only a subtask of Information Extraction, but is also needed for downstream NLP tasks such as semantic role labeling. While entity extraction from social media data is generally difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete information. In this work, we present the first ever corpus for Kannada-English code-mixed social media data with the corresponding named entity tags for NER. We provide strong baselines with machine learning classification models such as CRF, Bi-LSTM, and Bi-LSTM-CRF on our corpus with word, character, and lexical features. | ['Manish Shrivastava', 'Sumukh S'] | null | null | null | null | coling-wnut-2022-10 | ['semantic-role-labeling'] | ['natural-language-processing'] | [-2.11552888e-01 1.00419492e-01 -3.61463606e-01 -2.45812967e-01
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5973a109-6d68-4ee4-bc7a-25b4ee690d76 | multiple-instance-learning-with-center | null | null | https://link.springer.com/chapter/10.1007/978-3-030-59722-1_50 | https://link.springer.com/chapter/10.1007/978-3-030-59722-1_50 | Multiple Instance Learning with Center Embeddings for Histopathology Classification | Histopathology image analysis plays an important role in the treatment and diagnosis of cancer. However, analysis of whole slide images (WSI) with deep learning is challenging given that the curation of pixel-level annotations is laborious and time consuming. To address this, recent methods have considered WSI classification as a Multiple Instance Learning (MIL) problem often with a multi-stage process for learning instance and slide level features. Currently, most methods focus on either instance-selection or instance prediction-aggregation that often fails to generalize and ignores instance relations. In this work, we propose a MIL-based method to jointly learn both instance- and bag-level embeddings in a single framework. In addition, we propose a center loss that maps embeddings of instances from the same bag to a single centroid and reduces intra-class variations. Consequently, our model can accurately predict instance labels and leverages robust hierarchical pooling of features to obtain bag-level features without sacrificing accuracy. Experimental results on curated colon datasets show the effectiveness of the proposed methods against recent state-of-the-art methods. | ['Sang Hyun Park', 'Heounjeong Go', 'Soo Jeong Nam', 'Meejeong Kim', 'Philip Chikontwe'] | 2020-09-29 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 5.21163046e-01 -3.61962467e-02 -4.51392382e-01 -6.57108247e-01
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39ff9013-021e-46b4-8bae-da19d2c45c19 | dehazenerf-multiple-image-haze-removal-and-3d | 2303.11364 | null | https://arxiv.org/abs/2303.11364v1 | https://arxiv.org/pdf/2303.11364v1.pdf | DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields | Neural radiance fields (NeRFs) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D shape reconstruction. However, these methods fail in adverse weather conditions. To address this challenge, we introduce DehazeNeRF as a framework that robustly operates in hazy conditions. DehazeNeRF extends the volume rendering equation by adding physically realistic terms that model atmospheric scattering. By parameterizing these terms using suitable networks that match the physical properties, we introduce effective inductive biases, which, together with the proposed regularizations, allow DehazeNeRF to demonstrate successful multi-view haze removal, novel view synthesis, and 3D shape reconstruction where existing approaches fail. | ['Gordon Wetzstein', 'Sy-Yen Kuo', 'Wang Yifan', 'Wei-Ting Chen'] | 2023-03-20 | null | null | null | null | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 1.83037758e-01 -3.86411250e-01 6.25741839e-01 -4.11545306e-01
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93c64078-3fce-49ad-a396-8145b2cb1412 | 3d-queryis-a-query-based-framework-for-3d | 2211.09375 | null | https://arxiv.org/abs/2211.09375v1 | https://arxiv.org/pdf/2211.09375v1.pdf | 3D-QueryIS: A Query-based Framework for 3D Instance Segmentation | Previous top-performing methods for 3D instance segmentation often maintain inter-task dependencies and the tendency towards a lack of robustness. Besides, inevitable variations of different datasets make these methods become particularly sensitive to hyper-parameter values and manifest poor generalization capability. In this paper, we address the aforementioned challenges by proposing a novel query-based method, termed as 3D-QueryIS, which is detector-free, semantic segmentation-free, and cluster-free. Specifically, we propose to generate representative points in an implicit manner, and use them together with the initial queries to generate the informative instance queries. Then, the class and binary instance mask predictions can be produced by simply applying MLP layers on top of the instance queries and the extracted point cloud embeddings. Thus, our 3D-QueryIS is free from the accumulated errors caused by the inter-task dependencies. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness and efficiency of our proposed 3D-QueryIS method. | ['Wanli Ouyang', 'Ke Xu', 'Hongcheng Guo', 'Junran Wu', 'Jiayi Tian', 'Rui Su', 'Honghui Yang', 'Tong He', 'Jiaheng Liu'] | 2022-11-17 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.06688857e-01 -1.06496908e-01 -2.29756221e-01 -6.30468249e-01
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7a7baf5a-6e2d-4ff0-9948-6e23e39ca25d | msr-vtt-a-large-video-description-dataset-for | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Xu_MSR-VTT_A_Large_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Xu_MSR-VTT_A_Large_CVPR_2016_paper.pdf | MSR-VTT: A Large Video Description Dataset for Bridging Video and Language | While there has been increasing interest in the task of describing video with natural language, current computer vision algorithms are still severely limited in terms of the variability and complexity of the videos and their associated language that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on specific fine-grained domains with limited videos and simple descriptions. While researchers have provided several benchmark datasets for image captioning, we are not aware of any large-scale video description dataset with comprehensive categories yet diverse video content. In this paper we present MSR-VTT (standing for "ABC-Video to Text") which is a new large-scale video benchmark for video understanding, especially the emerging task of translating video to text. This is achieved by collecting 257 popular queries from a commercial video search engine, with 118 videos for each query. In its current version, MSR-VTT provides 10K web video clips with 38.7 hours and 200K clip-sentence pairs in total, covering the most comprehensive categories and diverse visual content, and representing the largest dataset in terms of sentence and vocabulary. Each clip is annotated with about 20 natural sentences by 1,327 AMT workers. We present a detailed analysis of MSR-VTT in comparison to a complete set of existing datasets, together with a summarization of different state-of-the-art video-to-text approaches. We also provide an extensive evaluation of these approaches on this dataset, showing that the hybrid Recurrent Neural Network-based approach, which combines single-frame and motion representations with soft-attention pooling strategy, yields the best generalization capability on MSR-VTT. | ['Ting Yao', 'Yong Rui', 'Jun Xu', 'Tao Mei'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['video-description'] | ['computer-vision'] | [ 4.73879814e-01 -5.67336380e-01 -5.63627422e-01 -3.29401821e-01
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f8d7ef97-56d4-4ff3-bf21-d2131055d618 | document-level-event-role-filler-extraction | 2005.06579 | null | https://arxiv.org/abs/2005.06579v1 | https://arxiv.org/pdf/2005.06579v1.pdf | Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding | Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is spread across multiple sentences. We argue that document-level event extraction is a difficult task since it requires a view of a larger context to determine which spans of text correspond to event role fillers. We first investigate how end-to-end neural sequence models (with pre-trained language model representations) perform on document-level role filler extraction, as well as how the length of context captured affects the models' performance. To dynamically aggregate information captured by neural representations learned at different levels of granularity (e.g., the sentence- and paragraph-level), we propose a novel multi-granularity reader. We evaluate our models on the MUC-4 event extraction dataset, and show that our best system performs substantially better than prior work. We also report findings on the relationship between context length and neural model performance on the task. | ['Xinya Du', 'Claire Cardie'] | 2020-05-13 | document-level-event-role-filler-extraction-1 | https://aclanthology.org/2020.acl-main.714 | https://aclanthology.org/2020.acl-main.714.pdf | acl-2020-6 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 6.43974364e-01 1.73164174e-01 -4.04113203e-01 -3.68107021e-01
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c7cd31ca-a4d8-4e86-a731-f0bb1a5caaa8 | structural-health-monitoring-of-cantilever | 1908.06326 | null | https://arxiv.org/abs/1908.06326v1 | https://arxiv.org/pdf/1908.06326v1.pdf | Structural Health Monitoring of Cantilever Beam, a Case Study -- Using Bayesian Neural Network AND Deep Learning | The advancement of machine learning algorithms has opened a wide scope for vibration-based SHM (Structural Health Monitoring). Vibration-based SHM is based on the fact that damage will alter the dynamic properties viz., structural response, frequencies, mode shapes, etc of the structure. The responses measured using sensors, which are high dimensional in nature, can be intelligently analyzed using machine learning techniques for damage assessment. Neural networks employing multilayer architectures are expressive models capable of capturing complex relationships between input-output pairs but do not account for uncertainty in network outputs. A BNN (Bayesian Neural Network) refers to extending standard networks with posterior inference. It is a neural network with a prior distribution on its weights. Deep learning architectures like CNN (Convolutional neural network) and LSTM(Long Short Term Memory) are good candidates for representation learning from high dimensional data. The advantage of using CNN over multi-layer neural networks is that they are good feature extractors as well as classifiers, which eliminates the need for generating hand-engineered features. LSTM networks are mainly used for sequence modeling. This paper presents both a Bayesian multi-layer perceptron and deep learning-based approach for damage detection and location identification in beam-like structures. Raw frequency response data simulated using finite element analysis is fed as the input of the network. As part of this, frequency response was generated for a series of simulations in the cantilever beam involving different damage scenarios. This case study shows the effectiveness of the above approaches to predict bending rigidity with an acceptable error rate. | ['T. Sundararajan', 'D. Mohankumar', 'S. Sumitra', 'H. Viji', 'Rahul Vashisht'] | 2019-08-17 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [ 2.76619226e-01 -3.97148961e-03 4.13065463e-01 -1.35144293e-01
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f72f19a5-608e-4f83-bfef-a385b97b5a1a | dataset-of-fake-news-detection-and-fact | 2111.03299 | null | https://arxiv.org/abs/2111.03299v1 | https://arxiv.org/pdf/2111.03299v1.pdf | Dataset of Fake News Detection and Fact Verification: A Survey | The rapid increase in fake news, which causes significant damage to society, triggers many fake news related studies, including the development of fake news detection and fact verification techniques. The resources for these studies are mainly available as public datasets taken from Web data. We surveyed 118 datasets related to fake news research on a large scale from three perspectives: (1) fake news detection, (2) fact verification, and (3) other tasks; for example, the analysis of fake news and satire detection. We also describe in detail their utilization tasks and their characteristics. Finally, we highlight the challenges in the fake news dataset construction and some research opportunities that address these challenges. Our survey facilitates fake news research by helping researchers find suitable datasets without reinventing the wheel, and thereby, improves fake news studies in depth. | ['Taichi Murayama'] | 2021-11-05 | null | null | null | null | ['satire-detection'] | ['natural-language-processing'] | [-2.55444407e-01 -5.06718755e-02 -8.57129097e-01 -8.00043046e-02
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beeb7395-ff5a-4017-93d5-ac29e911df7a | unclonability-and-quantum-cryptanalysis-from | 2210.17545 | null | https://arxiv.org/abs/2210.17545v1 | https://arxiv.org/pdf/2210.17545v1.pdf | Unclonability and Quantum Cryptanalysis: From Foundations to Applications | The impossibility of creating perfect identical copies of unknown quantum systems is a fundamental concept in quantum theory and one of the main non-classical properties of quantum information. This limitation imposed by quantum mechanics, famously known as the no-cloning theorem, has played a central role in quantum cryptography as a key component in the security of quantum protocols. In this thesis, we look at Unclonability in a broader context in physics and computer science and more specifically through the lens of cryptography, learnability and hardware assumptions. We introduce new notions of unclonability in the quantum world, namely quantum physical unclonability, and study the relationship with cryptographic properties and assumptions such as unforgeability, and quantum pseudorandomness. The purpose of this study is to bring new insights into the field of quantum cryptanalysis and into the notion of unclonability itself. We also discuss several applications of this new type of unclonability as a cryptographic resource for designing provably secure quantum protocols. Furthermore, we present a new practical cryptanalysis technique concerning the problem of approximate cloning of quantum states. We design a quantum machine learning-based cryptanalysis algorithm to demonstrate the power of quantum learning tools as both attack strategies and powerful tools for the practical study of quantum unclonability. | ['Mina Doosti'] | 2022-10-31 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 3.49910915e-01 -9.82381925e-02 8.84590894e-02 -4.93625225e-03
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d96fa9a5-62ec-4527-b27f-b55c430a60a8 | building-concise-logical-patterns-by | 2301.08190 | null | https://arxiv.org/abs/2301.08190v1 | https://arxiv.org/pdf/2301.08190v1.pdf | Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size | Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classification, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We finally analyze CSC-TM power consumption and derive new convergence properties. | ['Xuan Zhang', 'Svein Anders Tunheim', 'Jivitesh Sharma', 'Rupsa Saha', 'Lei Jiao', 'Ole-Christoffer Granmo', 'Sondre Glimsdal', 'Charul Giri', 'Bimal Bhattarai', 'Ahmed Abdulrahem Othman Abouzeid', 'K. Darshana Abeyrathna'] | 2023-01-19 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 1.11068767e-02 2.09138751e-01 -6.78952634e-01 -4.13870275e-01
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b87ff443-2f99-426b-afa8-a7ba96ade530 | distributionally-robust-semi-supervised | 1811.05299 | null | http://arxiv.org/abs/1811.05299v1 | http://arxiv.org/pdf/1811.05299v1.pdf | Distributionally Robust Semi-Supervised Learning for People-Centric Sensing | Semi-supervised learning is crucial for alleviating labelling burdens in
people-centric sensing. However, human-generated data inherently suffer from
distribution shift in semi-supervised learning due to the diverse biological
conditions and behavior patterns of humans. To address this problem, we propose
a generic distributionally robust model for semi-supervised learning on
distributionally shifted data. Considering both the discrepancy and the
consistency between the labeled data and the unlabeled data, we learn the
latent features that reduce person-specific discrepancy and preserve
task-specific consistency. We evaluate our model in a variety of people-centric
recognition tasks on real-world datasets, including intention recognition,
activity recognition, muscular movement recognition and gesture recognition.
The experiment results demonstrate that the proposed model outperforms the
state-of-the-art methods. | ['Lina Yao', 'Xiaojun Chang', 'Sen Wang', 'Dalin Zhang', 'Kaixuan Chen', 'Guodong Long'] | 2018-11-12 | null | null | null | null | ['muscular-movement-recognition'] | ['medical'] | [ 4.81061161e-01 -1.04850844e-01 -6.55979931e-01 -8.31644714e-01
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38a2473e-e302-47ac-9aa1-09709c5dc9a9 | distributed-learning-of-neural-lyapunov | 2207.07731 | null | https://arxiv.org/abs/2207.07731v1 | https://arxiv.org/pdf/2207.07731v1.pdf | Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems | This paper considers the problem of characterizing the stability region of a large-scale networked system comprised of dissipative nonlinear subsystems, in a distributed and computationally tractable way. One standard approach to estimate the stability region of a general nonlinear system is to first find a Lyapunov function for the system and characterize its region of attraction as the stability region. However, classical approaches, such as sum-of-squares methods and quadratic approximation, for finding a Lyapunov function either do not scale to large systems or give very conservative estimates for the stability region. In this context, we propose a new distributed learning based approach by exploiting the dissipativity structure of the subsystems. Our approach has two parts: the first part is a distributed approach to learn the storage functions (similar to the Lyapunov functions) for all the subsystems, and the second part is a distributed optimization approach to find the Lyapunov function for the networked system using the learned storage functions of the subsystems. We demonstrate the superior performance of our proposed approach through extensive case studies in microgrid networks. | ['Le Xie', 'Dileep Kalathil', 'S. Sivaranjani', 'Tong Huang', 'Amit Jena'] | 2022-07-15 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-7.63257802e-01 -2.74683144e-02 1.86489642e-01 1.28286645e-01
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5b1e9799-1155-40df-8e8d-75f93b410e86 | pre-trained-sentence-embeddings-for-implicit | 2210.11005 | null | https://arxiv.org/abs/2210.11005v1 | https://arxiv.org/pdf/2210.11005v1.pdf | Pre-trained Sentence Embeddings for Implicit Discourse Relation Classification | Implicit discourse relations bind smaller linguistic units into coherent texts. Automatic sense prediction for implicit relations is hard, because it requires understanding the semantics of the linked arguments. Furthermore, annotated datasets contain relatively few labeled examples, due to the scale of the phenomenon: on average each discourse relation encompasses several dozen words. In this paper, we explore the utility of pre-trained sentence embeddings as base representations in a neural network for implicit discourse relation sense classification. We present a series of experiments using both supervised end-to-end trained models and pre-trained sentence encoding techniques - SkipThought, Sent2vec and Infersent. The pre-trained embeddings are competitive with the end-to-end model, and the approaches are complementary, with combined models yielding significant performance improvements on two of the three evaluations. | ['Jacob Eisenstein', 'Yangfeng Ji', 'Murali Raghu Babu Balusu'] | 2022-10-20 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'relation-classification', 'implicit-discourse-relation-classification', 'implicit-relations'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.20567697e-01 9.74514604e-01 -6.77944541e-01 -5.10694504e-01
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92db264a-a9a3-4ea2-83ca-0cc85da0302a | self-supervised-learning-for-audio-visual | 2002.05314 | null | https://arxiv.org/abs/2002.05314v1 | https://arxiv.org/pdf/2002.05314v1.pdf | Self-supervised learning for audio-visual speaker diarization | Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems. In this paper, we propose a self-supervised audio-video synchronization learning method to address the problem of speaker diarization without massive labeling effort. We improve the previous approaches by introducing two new loss functions: the dynamic triplet loss and the multinomial loss. We test them on a real-world human-computer interaction system and the results show our best model yields a remarkable gain of +8%F1-scoresas well as diarization error rate reduction. Finally, we introduce a new large scale audio-video corpus designed to fill the vacancy of audio-video datasets in Chinese. | ['Shi-Xiong Zhang', 'Yifan Ding', 'Yahuan Cong', 'Yong Xu', 'Liqiang Wang'] | 2020-02-13 | null | null | null | null | ['video-synchronization'] | ['computer-vision'] | [ 1.50463641e-01 -1.16971798e-01 -3.57686490e-01 -5.03663838e-01
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3c13067f-3a4c-408f-a55a-67a119128e22 | does-mbert-understand-romansh-evaluating-word | 2306.08702 | null | https://arxiv.org/abs/2306.08702v1 | https://arxiv.org/pdf/2306.08702v1.pdf | Does mBERT understand Romansh? Evaluating word embeddings using word alignment | We test similarity-based word alignment models (SimAlign and awesome-align) in combination with word embeddings from mBERT and XLM-R on parallel sentences in German and Romansh. Since Romansh is an unseen language, we are dealing with a zero-shot setting. Using embeddings from mBERT, both models reach an alignment error rate of 0.22, which outperforms fast_align, a statistical model, and is on par with similarity-based word alignment for seen languages. We interpret these results as evidence that mBERT contains information that can be meaningful and applicable to Romansh. To evaluate performance, we also present a new trilingual corpus, which we call the DERMIT (DE-RM-IT) corpus, containing press releases made by the Canton of Grisons in German, Romansh and Italian in the past 25 years. The corpus contains 4 547 parallel documents and approximately 100 000 sentence pairs in each language combination. We additionally present a gold standard for German-Romansh word alignment. The data is available at https://github.com/eyldlv/DERMIT-Corpus. | ['Eyal Liron Dolev'] | 2023-06-14 | null | null | null | null | ['word-embeddings', 'word-alignment', 'xlm-r'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-8.39764401e-02 -2.51281373e-02 1.70292961e-03 -3.36136371e-01
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514dac0d-76df-451f-8698-fa6a4f16040d | cst5-data-augmentation-for-code-switched-1 | 2211.07514 | null | https://arxiv.org/abs/2211.07514v1 | https://arxiv.org/pdf/2211.07514v1.pdf | CST5: Data Augmentation for Code-Switched Semantic Parsing | Extending semantic parsers to code-switched input has been a challenging problem, primarily due to a lack of supervised training data. In this work, we introduce CST5, a new data augmentation technique that finetunes a T5 model using a small seed set ($\approx$100 utterances) to generate code-switched utterances from English utterances. We show that CST5 generates high quality code-switched data, both intrinsically (per human evaluation) and extrinsically by comparing baseline models which are trained without data augmentation to models which are trained with augmented data. Empirically we observe that using CST5, one can achieve the same semantic parsing performance by using up to 20x less labeled data. To aid further research in this area, we are also releasing (a) Hinglish-TOP, the largest human annotated code-switched semantic parsing dataset to date, containing 10k human annotated Hindi-English (Hinglish) code-switched utterances, and (b) Over 170K CST5 generated code-switched utterances from the TOPv2 dataset. Human evaluation shows that both the human annotated data as well as the CST5 generated data is of good quality. | ['Rengarajan Aravamudhan', 'Pankaj Joshi', 'Shyam Upadhyay', 'Rahul Goel', 'Jigar Gupta', 'Anmol Agarwal'] | 2022-11-14 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.26807672e-01 7.51595497e-01 1.15891203e-01 -9.09366846e-01
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1a91fed9-6bae-4a6b-a133-bb2813a7fe5b | can-large-language-models-infer-causation | 2306.05836 | null | https://arxiv.org/abs/2306.05836v1 | https://arxiv.org/pdf/2306.05836v1.pdf | Can Large Language Models Infer Causation from Correlation? | Causal inference is one of the hallmarks of human intelligence. While the field of CausalNLP has attracted much interest in the recent years, existing causal inference datasets in NLP primarily rely on discovering causality from empirical knowledge (e.g., commonsense knowledge). In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs). Specifically, we formulate a novel task Corr2Cause, which takes a set of correlational statements and determines the causal relationship between the variables. We curate a large-scale dataset of more than 400K samples, on which we evaluate seventeen existing LLMs. Through our experiments, we identify a key shortcoming of LLMs in terms of their causal inference skills, and show that these models achieve almost close to random performance on the task. This shortcoming is somewhat mitigated when we try to re-purpose LLMs for this skill via finetuning, but we find that these models still fail to generalize -- they can only perform causal inference in in-distribution settings when variable names and textual expressions used in the queries are similar to those in the training set, but fail in out-of-distribution settings generated by perturbing these queries. Corr2Cause is a challenging task for LLMs, and would be helpful in guiding future research on improving LLMs' pure reasoning skills and generalizability. Our data is at https://huggingface.co/datasets/causalnlp/corr2cause. Our code is at https://github.com/causalNLP/corr2cause. | ['Bernhard Schölkopf', 'Mona Diab', 'Rada Mihalcea', 'Mrinmaya Sachan', 'Spencer Poff', 'Zhiheng Lyu', 'Jiarui Liu', 'Zhijing Jin'] | 2023-06-09 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 6.05481938e-02 3.61180067e-01 -7.30024278e-01 -4.96400416e-01
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703f4ec0-e6f3-453f-a49a-38fa90ab2677 | from-words-to-wires-generating-functioning | 2305.14874 | null | https://arxiv.org/abs/2305.14874v1 | https://arxiv.org/pdf/2305.14874v1.pdf | From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions | In this work, we show that contemporary language models have a previously unknown skill -- the capacity for electronic circuit design from high-level textual descriptions, akin to code generation. We introduce two benchmarks: Pins100, assessing model knowledge of electrical components, and Micro25, evaluating a model's capability to design common microcontroller circuits and code in the Arduino ecosystem that involve input, output, sensors, motors, protocols, and logic -- with models such as GPT-4 and Claude-V1 achieving between 60% to 96% Pass@1 on generating full devices. We include six case studies of using language models as a design assistant for moderately complex devices, such as a radiation-powered random number generator, an emoji keyboard, a visible spectrometer, and several assistive devices, while offering a qualitative analysis performance, outlining evaluation challenges, and suggesting areas of development to improve complex circuit design and practical utility. With this work, we aim to spur research at the juncture of natural language processing and electronic design. | ['Peter Jansen'] | 2023-05-24 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 4.16839659e-01 4.55085158e-01 -1.62539542e-01 -1.34328574e-01
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8e657c06-8c84-4b32-b65d-14d35cb19dae | representation-learning-with-information | 2207.01437 | null | https://arxiv.org/abs/2207.01437v1 | https://arxiv.org/pdf/2207.01437v1.pdf | Representation Learning with Information Theory for COVID-19 Detection | Successful data representation is a fundamental factor in machine learning based medical imaging analysis. Deep Learning (DL) has taken an essential role in robust representation learning. However, the inability of deep models to generalize to unseen data can quickly overfit intricate patterns. Thereby, we can conveniently implement strategies to aid deep models in discovering useful priors from data to learn their intrinsic properties. Our model, which we call a dual role network (DRN), uses a dependency maximization approach based on Least Squared Mutual Information (LSMI). The LSMI leverages dependency measures to ensure representation invariance and local smoothness. While prior works have used information theory measures like mutual information, known to be computationally expensive due to a density estimation step, our LSMI formulation alleviates the issues of intractable mutual information estimation and can be used to approximate it. Experiments on CT based COVID-19 Detection and COVID-19 Severity Detection benchmarks demonstrate the effectiveness of our method. | ['Hichem Sahli', 'Nikos Deligiannis', 'Matias Bossa', 'Tanmoy Mukherjee', 'Abel Díaz Berenguer'] | 2022-07-04 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 3.01550239e-01 2.38800168e-01 -2.70206720e-01 -5.58451176e-01
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6dda252a-6899-46ab-b459-cb5925edfd59 | difai-diverse-facial-inpainting-using | 2301.08443 | null | https://arxiv.org/abs/2301.08443v1 | https://arxiv.org/pdf/2301.08443v1.pdf | DIFAI: Diverse Facial Inpainting using StyleGAN Inversion | Image inpainting is an old problem in computer vision that restores occluded regions and completes damaged images. In the case of facial image inpainting, most of the methods generate only one result for each masked image, even though there are other reasonable possibilities. To prevent any potential biases and unnatural constraints stemming from generating only one image, we propose a novel framework for diverse facial inpainting exploiting the embedding space of StyleGAN. Our framework employs pSp encoder and SeFa algorithm to identify semantic components of the StyleGAN embeddings and feed them into our proposed SPARN decoder that adopts region normalization for plausible inpainting. We demonstrate that our proposed method outperforms several state-of-the-art methods. | ['Hanseok Ko', 'David Han', 'Yuanming Li', 'Jeong-gi Kwak', 'Dongsik Yoon'] | 2023-01-20 | null | null | null | null | ['facial-inpainting', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 6.96259618e-01 5.46983302e-01 -6.46988750e-02 -3.84172708e-01
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e7708880-84da-40b5-96cc-1865e33581bd | design-of-a-solver-for-multi-agent-epistemic | 1909.08259 | null | https://arxiv.org/abs/1909.08259v1 | https://arxiv.org/pdf/1909.08259v1.pdf | Design of a Solver for Multi-Agent Epistemic Planning | As the interest in Artificial Intelligence continues to grow it is becoming more and more important to investigate formalization and tools that allow us to exploit logic to reason about the world. In particular, given the increasing number of multi-agents systems that could benefit from techniques of automated reasoning, exploring new ways to define not only the world's status but also the agents' information is constantly growing in importance. This type of reasoning, i.e., about agents' perception of the world and also about agents' knowledge of her and others' knowledge, is referred to as epistemic reasoning. In our work we will try to formalize this concept, expressed through epistemic logic, for dynamic domains. In particular we will attempt to define a new action-based language for multi-agent epistemic planning and to implement an epistemic planner based on it. This solver should provide a tool flexible enough to be able to reason on different domains, e.g., economy, security, justice and politics, where reasoning about others' beliefs could lead to winning strategies or help in changing a group of agents' view of the world. | ['Francesco Fabiano'] | 2019-09-18 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-7.23234862e-02 8.25589299e-01 1.63369849e-01 -3.19151968e-01
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724ec9e7-20f0-4fbe-bca3-86beb932e5db | i-2-sb-image-to-image-schrodinger-bridge | 2302.05872 | null | https://arxiv.org/abs/2302.05872v3 | https://arxiv.org/pdf/2302.05872v3.pdf | I$^2$SB: Image-to-Image Schrödinger Bridge | We propose Image-to-Image Schr\"odinger Bridge (I$^2$SB), a new class of conditional diffusion models that directly learn the nonlinear diffusion processes between two given distributions. These diffusion bridges are particularly useful for image restoration, as the degraded images are structurally informative priors for reconstructing the clean images. I$^2$SB belongs to a tractable class of Schr\"odinger bridge, the nonlinear extension to score-based models, whose marginal distributions can be computed analytically given boundary pairs. This results in a simulation-free framework for nonlinear diffusions, where the I$^2$SB training becomes scalable by adopting practical techniques used in standard diffusion models. We validate I$^2$SB in solving various image restoration tasks, including inpainting, super-resolution, deblurring, and JPEG restoration on ImageNet 256x256 and show that I$^2$SB surpasses standard conditional diffusion models with more interpretable generative processes. Moreover, I$^2$SB matches the performance of inverse methods that additionally require the knowledge of the corruption operators. Our work opens up new algorithmic opportunities for developing efficient nonlinear diffusion models on a large scale. scale. Project page and codes: https://i2sb.github.io/ | ['Anima Anandkumar', 'Weili Nie', 'Evangelos A. Theodorou', 'De-An Huang', 'Arash Vahdat', 'Guan-Horng Liu'] | 2023-02-12 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 2.76812792e-01 -4.58929911e-02 1.21708460e-01 -2.96740271e-02
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82fba460-5f75-4ce8-82cd-f2bbf254b707 | an-efficient-feature-selection-in | 1404.1491 | null | http://arxiv.org/abs/1404.1491v1 | http://arxiv.org/pdf/1404.1491v1.pdf | An Efficient Feature Selection in Classification of Audio Files | In this paper we have focused on an efficient feature selection method in
classification of audio files. The main objective is feature selection and
extraction. We have selected a set of features for further analysis, which
represents the elements in feature vector. By extraction method we can compute
a numerical representation that can be used to characterize the audio using the
existing toolbox. In this study Gain Ratio (GR) is used as a feature selection
measure. GR is used to select splitting attribute which will separate the
tuples into different classes. The pulse clarity is considered as a subjective
measure and it is used to calculate the gain of features of audio files. The
splitting criterion is employed in the application to identify the class or the
music genre of a specific audio file from testing database. Experimental
results indicate that by using GR the application can produce a satisfactory
result for music genre classification. After dimensionality reduction best
three features have been selected out of various features of audio file and in
this technique we will get more than 90% successful classification result. | ['Jayita Mitra', 'Diganta Saha'] | 2014-03-24 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 1.72473282e-01 -4.95124131e-01 4.43757087e-01 -3.70810330e-01
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8ad13266-bbe7-4621-9e54-29b904d5a39d | local-life-stay-informed-around-you-a | 2305.07168 | null | https://arxiv.org/abs/2305.07168v1 | https://arxiv.org/pdf/2305.07168v1.pdf | Local Life: Stay Informed Around You, A Scalable Geoparsing and Geotagging Approach to Serve Local News Worldwide | Local news has become increasingly important in the news industry due to its various benefits. It offers local audiences information that helps them participate in their communities and interests. It also serves as a reliable source of factual reporting that can prevent misinformation. Moreover, it can influence national audiences as some local stories may have wider implications for politics, environment or crime. Hence, detecting the exact geolocation and impact scope of local news is crucial for news recommendation systems. There are two fundamental things required in this process, (1) classify whether an article belongs to local news, and (2) identify the geolocation of the article and its scope of influence to recommend it to appropriate users. In this paper, we focus on the second step and propose (1) an efficient approach to determine the location and radius of local news articles, (2) a method to reconcile the user's location with the article's location, and (3) a metric to evaluate the quality of the local news feed. We demonstrate that our technique is scalable and effective in serving hyperlocal news to users worldwide. | ['Radhika Bansal', 'Shiying He', 'Gosuddin Kamaruddin Siddiqi', 'Deven Santosh Shah'] | 2023-05-11 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-4.81557727e-01 -2.19245091e-01 -7.64177144e-01 -1.32571295e-01
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19a27622-1e40-4e50-837d-1d4c07163bf0 | concept2box-joint-geometric-embeddings-for | 2307.01933 | null | https://arxiv.org/abs/2307.01933v1 | https://arxiv.org/pdf/2307.01933v1.pdf | Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs | Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact many KGs contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities. They usually embed all nodes as vectors in one latent space. However, a single geometric representation fails to capture the structural differences between two views and lacks probabilistic semantics towards concepts' granularity. We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations. We model concepts with box embeddings, which learn the hierarchy structure and complex relations such as overlap and disjoint among them. Box volumes can be interpreted as concepts' granularity. Different from concepts, we model entities as vectors. To bridge the gap between concept box embeddings and entity vector embeddings, we propose a novel vector-to-box distance metric and learn both embeddings jointly. Experiments on both the public DBpedia KG and a newly-created industrial KG showed the effectiveness of Concept2Box. | ['Wei Wang', 'Yizhou Sun', 'Christos Faloutsos', 'Xian Li', 'Zhengyang Wang', 'Yan Liang', 'Jingbo Shang', 'Chenwei Zhang', 'Binxuan Huang', 'Daheng Wang', 'Zijie Huang'] | 2023-07-04 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graphs', 'knowledge-graph-embeddings'] | ['graphs', 'knowledge-base', 'methodology'] | [-5.77720284e-01 4.18674111e-01 -4.45542663e-01 -5.42756855e-01
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dcd6a064-a02a-4642-95df-6b087a160ce9 | the-starcraft-multi-agent-challenges-learning | 2207.02007 | null | https://arxiv.org/abs/2207.02007v2 | https://arxiv.org/pdf/2207.02007v2.pdf | The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions | In this paper, we propose a novel benchmark called the StarCraft Multi-Agent Challenges+, where agents learn to perform multi-stage tasks and to use environmental factors without precise reward functions. The previous challenges (SMAC) recognized as a standard benchmark of Multi-Agent Reinforcement Learning are mainly concerned with ensuring that all agents cooperatively eliminate approaching adversaries only through fine manipulation with obvious reward functions. This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control. This study covers both offensive and defensive scenarios. In the offensive scenarios, agents must learn to first find opponents and then eliminate them. The defensive scenarios require agents to use topographic features. For example, agents need to position themselves behind protective structures to make it harder for enemies to attack. We investigate MARL algorithms under SMAC+ and observe that recent approaches work well in similar settings to the previous challenges, but misbehave in offensive scenarios. Additionally, we observe that an enhanced exploration approach has a positive effect on performance but is not able to completely solve all scenarios. This study proposes new directions for future research. | ['Se-Young Yun', 'Song Chong', 'SeongHwan Kim', 'Joonkee Kim', 'Yongsik Lee', 'Jihwan Oh', 'Mingyu Kim'] | 2022-07-05 | null | null | null | null | ['smac-1'] | ['playing-games'] | [-1.76439676e-02 1.39165640e-01 3.40669267e-02 2.80394077e-01
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39161e85-16bf-4b81-9536-bb78290d8439 | conditions-for-estimation-of-sensitivities-of | 2212.01471 | null | https://arxiv.org/abs/2212.01471v2 | https://arxiv.org/pdf/2212.01471v2.pdf | Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections | Voltage phase angle measurements are often unavailable from sensors in distribution networks and transmission network boundaries. Therefore, this paper addresses the conditions for estimating sensitivities of voltage magnitudes with respect to complex (active and reactive) electric power injections based on sensor measurements. These sensitivities represent submatrices of the inverse power flow Jacobian. We extend previous results to show that the sensitivities of a bus voltage magnitude with respect to active power injections are unique and different from those with respect to reactive power. The classical Newton-Raphson power flow model is used to derive a novel representation of bus voltage magnitudes as an underdetermined linear operator of the active and reactive power injections; parameterized by the bus power factors. Two conditions that ensure the existence of unique complex power injections given voltage magnitudes are established for this underdetermined linear system, thereby compressing the solution space. The first is a sufficient condition based on the bus power factors. The second is a necessary and sufficient condition based on the system eigenvalues. We use matrix completion theory to develop estimation methods for recovering sensitivity matrices with varying levels of sensor availability. Simulations verify the results and demonstrate engineering use of the proposed methods. | ['Daniel K. Molzahn', 'Jorge Fernandez', 'Santiago Grijalva', 'Daniel Turizo', 'Samuel Talkington'] | 2022-12-02 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 8.45075771e-02 -2.12801591e-01 -9.87058133e-02 1.10762902e-01
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c49a8314-504f-4cce-be87-20a2deae93ab | real-time-radiance-fields-for-single-image | 2305.02310 | null | https://arxiv.org/abs/2305.02310v1 | https://arxiv.org/pdf/2305.02310v1.pdf | Real-Time Radiance Fields for Single-Image Portrait View Synthesis | We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e.g., face portrait) in real-time. Given a single RGB input, our image encoder directly predicts a canonical triplane representation of a neural radiance field for 3D-aware novel view synthesis via volume rendering. Our method is fast (24 fps) on consumer hardware, and produces higher quality results than strong GAN-inversion baselines that require test-time optimization. To train our triplane encoder pipeline, we use only synthetic data, showing how to distill the knowledge from a pretrained 3D GAN into a feedforward encoder. Technical contributions include a Vision Transformer-based triplane encoder, a camera data augmentation strategy, and a well-designed loss function for synthetic data training. We benchmark against the state-of-the-art methods, demonstrating significant improvements in robustness and image quality in challenging real-world settings. We showcase our results on portraits of faces (FFHQ) and cats (AFHQ), but our algorithm can also be applied in the future to other categories with a 3D-aware image generator. | ['Koki Nagano', 'Ravi Ramamoorthi', 'Manmohan Chandraker', 'Sameh Khamis', 'Zhiding Yu', 'Chao Liu', 'Eric R. Chan', 'Michael Stengel', 'Matthew Chan', 'Alex Trevithick'] | 2023-05-03 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 6.50602520e-01 4.42107767e-01 4.83900577e-01 -5.65909624e-01
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