pwc-archive/papers-with-abstracts huggingface dataset:
Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array Aperture
Authors: Guangbin Zhang, Tianyao Huang, Yimin Liu, Xiqin Wang, Yonina C. Eldar
Date: 2022-12-09 00:00:00
Tasks: blind source separation, Position

We consider the problem of direction finding using partly calibrated arrays, a distributed subarray with position errors between subarrays. The key challenge is to enhance angular resolution in the presence of position errors. To achieve this goal, existing algorithms, such as subspace separation and sparse recovery, have to rely on multiple snapshots, which increases the burden of data transmission and the processing delay. Therefore, we aim to enhance angular resolution using only a single snapshot. To this end, we exploit the orthogonality of the signals of partly calibrated arrays. Particularly, we transform the signal model into a special multiple-measurement model, show that there is approximate orthogonality between the source signals in this model, and then use blind source separation to exploit the orthogonality. Simulation and experiment results both verify that our proposed algorithm achieves high angular resolution as distributed arrays without position errors, inversely proportional to the whole array aperture.
An Attention-based Multi-Scale Feature Learning Network for Multimodal Medical Image Fusion
Authors: Meng Zhou, Xiaolan Xu, Yuxuan Zhang
Date: 2022-12-09 00:00:00
Tasks: Diagnostic

Medical images play an important role in clinical applications. Multimodal medical images could provide rich information about patients for physicians to diagnose. The image fusion technique is able to synthesize complementary information from multimodal images into a single image. This technique will prevent radiologists switch back and forth between different images and save lots of time in the diagnostic process. In this paper, we introduce a novel Dilated Residual Attention Network for the medical image fusion task. Our network is capable to extract multi-scale deep semantic features. Furthermore, we propose a novel fixed fusion strategy termed Softmax-based weighted strategy based on the Softmax weights and matrix nuclear norm. Extensive experiments show our proposed network and fusion strategy exceed the state-of-the-art performance compared with reference image fusion methods on four commonly used fusion metrics.
COVID-19 Activity Risk Calculator as a Gamified Public Health Intervention Tool
Authors: Shreyasvi Natraj, Malhar Bhide, Nathan Yap, Meng Liu, Agrima Seth, Jonathan Berman, Christin Glorioso
Date: 2022-12-05 00:00:00

The Coronavirus disease 2019 (COVID-19) pandemic, caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has impacted over 200 countries leading to hospitalizations and deaths of millions of people. Public health interventions, such as risk estimators, can reduce the spread of pandemics and epidemics through influencing behavior, which impacts risk of exposure and infection. Current publicly available COVID-19 risk estimation tools have had variable effectiveness during the pandemic due to their dependency on rapidly evolving factors such as community transmission levels and variants. There has also been confusion surrounding certain personal protective strategies such as risk reduction by mask-wearing and vaccination. In order to create a simple easy-to-use tool for estimating different individual risks associated with carrying out daily-life activity, we developed COVID-19 Activity Risk Calculator (CovARC). CovARC is a gamified public health intervention as users can "play with" how different risks associated with COVID-19 can change depending on several different factors when carrying out routine daily activities. Empowering the public to make informed, data-driven decisions about safely engaging in activities may help to reduce COVID-19 levels in the community. In this study, we demonstrate a streamlined, scalable and accurate COVID-19 risk calculation system. Our study also demonstrates the quantitative impact of vaccination and mask-wearing during periods of high case counts. Validation of this impact could inform and support policy decisions regarding case thresholds for mask mandates, and other public health interventions.
LoopDraw: a Loop-Based Autoregressive Model for Shape Synthesis and Editing
Authors: Nam Anh Dinh, Haochen Wang, Greg Shakhnarovich, Rana Hanocka
Date: 2022-12-09 00:00:00

There is no settled universal 3D representation for geometry with many alternatives such as point clouds, meshes, implicit functions, and voxels to name a few. In this work, we present a new, compelling alternative for representing shapes using a sequence of cross-sectional closed loops. The loops across all planes form an organizational hierarchy which we leverage for autoregressive shape synthesis and editing. Loops are a non-local description of the underlying shape, as simple loop manipulations (such as shifts) result in significant structural changes to the geometry. This is in contrast to manipulating local primitives such as points in a point cloud or a triangle in a triangle mesh. We further demonstrate that loops are intuitive and natural primitive for analyzing and editing shapes, both computationally and for users.
A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
Authors: Zhiheng Li, Ivan Evtimov, Albert Gordo, Caner Hazirbas, Tal Hassner, Cristian Canton Ferrer, Chenliang Xu, Mark Ibrahim
Date: 2022-12-09 00:00:00
Proceeding: CVPR 2023 1
Tasks: Domain Generalization, Image Classification, Out-of-Distribution Generalization

Machine learning models have been found to learn shortcuts -- unintended decision rules that are unable to generalize -- undermining models' reliability. Previous works address this problem under the tenuous assumption that only a single shortcut exists in the training data. Real-world images are rife with multiple visual cues from background to texture. Key to advancing the reliability of vision systems is understanding whether existing methods can overcome multiple shortcuts or struggle in a Whac-A-Mole game, i.e., where mitigating one shortcut amplifies reliance on others. To address this shortcoming, we propose two benchmarks: 1) UrbanCars, a dataset with precisely controlled spurious cues, and 2) ImageNet-W, an evaluation set based on ImageNet for watermark, a shortcut we discovered affects nearly every modern vision model. Along with texture and background, ImageNet-W allows us to study multiple shortcuts emerging from training on natural images. We find computer vision models, including large foundation models -- regardless of training set, architecture, and supervision -- struggle when multiple shortcuts are present. Even methods explicitly designed to combat shortcuts struggle in a Whac-A-Mole dilemma. To tackle this challenge, we propose Last Layer Ensemble, a simple-yet-effective method to mitigate multiple shortcuts without Whac-A-Mole behavior. Our results surface multi-shortcut mitigation as an overlooked challenge critical to advancing the reliability of vision systems. The datasets and code are released: https://github.com/facebookresearch/Whac-A-Mole.
Automatic Check-Out via Prototype-based Classifier Learning from Single-Product Exemplars
Authors: Hao Chen, Xiu-Shen Wei, Faen Zhang, Yang shen, Hui Xu, Liang Xiao
Date: 2022-10-20 00:00:00
Proceeding: The European Conference on Computer Vision (ECCV) 2022 10
Tasks: Re-Ranking

Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) and test data (check-out images). To mitigate the gap, we propose a method, termed as PSP, to perform Prototype-based classifier learning from Single-Product exemplars. In PSP, by revealing the advantages of representing category semantics, the prototype representation of each product category is firstly obtained from single-product exemplars. Based on the prototypes, it then generates categorical classifiers with a background classifier to not only recognize fine-grained product categories but also distinguish background upon product proposals derived from check-out images. To further improve the ACO accuracy, we develop discriminative re-ranking to both adjust the predicted scores of product proposals for bringing more discriminative ability in classifier learning and provide a reasonable sorting possibility by considering the fine-grained nature. Moreover, a multi-label recognition loss is also equipped for modeling co-occurrence of products in check-out images. Experiments are conducted on the large-scale RPC dataset for evaluations. Our ACO result achieves 86.69%, by 6.18% improvements over state-of-the-arts, which demonstrates the superiority of PSP. Our codes are available at https://github.com/Hao-Chen-NJUST/PSP.
Dual Attention Networks for Few-Shot Fine-Grained Recognition
Authors: Shu-Lin Xu, Faen Zhang, Xiu-Shen Wei, Jianhua Wang
Date: 2022-06-28 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2022 6
Tasks: Hard Attention, Meta-Learning

The task of few-shot fine-grained recognition is to classify images belonging to subordinate categories merely depending on few examples. Due to the fine-grained nature, it is desirable to capture subtle but discriminative part-level patterns from limited training data, which makes it a challenging problem. In this paper, to generate fine-grained tailored representations for few-shot recognition, we propose a Dual Attention Network (Dual Att-Net) consisting of two dual branches of both hard- and soft-attentions. Specifically, by producing attention guidance from deep activations of input images, our hard-attention is realized by keeping a few useful deep descriptors and forming them as a bag of multi-instance learning. Since these deep descriptors could correspond to objects' parts, the advantage of modeling as a multi-instance bag is able to exploit inherent correlation of these fine-grained parts. On the other side, a soft attended activation representation can be obtained by applying attention guidance upon original activations, which brings comprehensive attention information as the counterpart of hard-attention. After that, both outputs of dual branches are aggregated as a holistic image embedding w.r.t. input images. By performing meta-learning, we can learn a powerful image embedding in such a metric space to generalize to novel classes. Experiments on three popular fine-grained benchmark datasets show that our Dual Att-Net obviously outperforms other existing state-of-the-art methods.
A Channel Mix Method for Fine-Grained Cross-Modal Retrieval
Authors: Yang shen, Xuhao Sun, Xiu-Shen Wei, Hanxu Hu, Zhipeng Chen
Date: 2022-08-26 00:00:00
Proceeding: IEEE International Conference on Multimedia and Expo (ICME) 2022 8
Tasks: Cross-Modal Retrieval, Retrieval

In this paper, we propose a simple but effective method for dealing with the challenging fine-grained cross-modal retrieval task where it aims to enable flexible retrieval among subor-dinate categories across different modalities. Specifically, in order to enhance information interaction in different modalities for fine-grained objects, a channel mix method is developed and performed upon the channels of deep activations across dif-ferent modalities. After that, a 1 x 1 convolution is employed to aggregate the mixed channels into a unified feature vector. Moreover, equipped with a novel fine-grained cross-modal cen-ter loss, our method can further improve the intra-class separa-bility as well as inter-class compactness for multi-modalities. Experiments are conducted on the fine-grained cross-modal benchmark dataset and show our superiority over competing methods. Meanwhile, ablation studies also demonstrate the effectiveness of our proposals.
Analysis of Thai Capital Market Linkages: Part II. Bivariate Copula vs. Granger Causality, a Centrality Analysis
Authors: Poomjai Nacaskuli, Puvarith Veerabulyarithii, Isariyaporn Sukcharoenchaikul
Date: 2022-12-12 00:00:00
Proceeding: mimeo 2022 12

Analytically thorough understanding of financial-market dynamics is fundamental to the promotion of capital-market innovation, efficiency, and resilience; innovative, efficient, and resilient capital market, in turn, is fundamental to the sustainable economic development of the nation and the robust financial stability of its economy. As a follow-on of our earlier paper [21][20], which saw Bivariate Copula (semi-parametric statistics) techniques used to analyse probabilistic co-movement amongst 14 variables representing domestic (Thai) and international (US/Emerging Market/Asia) foreign-exchange, fixed-income, and equity market movements, as well as foreign portfolio-investment flows into Thai equity shares and bonds, this paper applies Variable-Lag Granger Causality [22][23], analysis to the same set of variables and comparatively discuss the results especially from the perspective of Centrality Analysis [24][25] performed on Adjacency Matrices induced by respective approaches.
Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding
Authors: Jay Bhanushali, Manivannan Muniyandi, PRANEETH CHAKRAVARTHULA
Date: 2022-12-09 00:00:00
Tasks: Autonomous Driving, Depth Estimation, Scene Understanding

We present a cross-domain inference technique that learns from synthetic data to estimate depth and normals for in-the-wild omnidirectional 3D scenes encountered in real-world uncontrolled settings. To this end, we introduce UBotNet, an architecture that combines UNet and Bottleneck Transformer elements to predict consistent scene normals and depth. We also introduce the OmniHorizon synthetic dataset containing 24,335 omnidirectional images that represent a wide variety of outdoor environments, including buildings, streets, and diverse vegetation. This dataset is generated from expansive, lifelike virtual spaces and encompasses dynamic scene elements, such as changing lighting conditions, different times of day, pedestrians, and vehicles. Our experiments show that UBotNet achieves significantly improved accuracy in depth estimation and normal estimation compared to existing models. Lastly, we validate cross-domain synthetic-to-real depth and normal estimation on real outdoor images using UBotNet trained solely on our synthetic OmniHorizon dataset, demonstrating the potential of both the synthetic dataset and the proposed network for real-world scene understanding applications.
Scalable Graph Convolutional Network Training on Distributed-Memory Systems
Authors: Gunduz Vehbi Demirci, Aparajita Haldar, Hakan Ferhatosmanoglu
Date: 2022-12-09 00:00:00
Tasks: Blocking, graph partitioning, hypergraph partitioning

Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the convolution operation on graphs induces irregular memory access patterns, designing a memory- and communication-efficient parallel algorithm for GCN training poses unique challenges. We propose a highly parallel training algorithm that scales to large processor counts. In our solution, the large adjacency and vertex-feature matrices are partitioned among processors. We exploit the vertex-partitioning of the graph to use non-blocking point-to-point communication operations between processors for better scalability. To further minimize the parallelization overheads, we introduce a sparse matrix partitioning scheme based on a hypergraph partitioning model for full-batch training. We also propose a novel stochastic hypergraph model to encode the expected communication volume in mini-batch training. We show the merits of the hypergraph model, previously unexplored for GCN training, over the standard graph partitioning model which does not accurately encode the communication costs. Experiments performed on real-world graph datasets demonstrate that the proposed algorithms achieve considerable speedups over alternative solutions. The optimizations achieved on communication costs become even more pronounced at high scalability with many processors. The performance benefits are preserved in deeper GCNs having more layers as well as on billion-scale graphs.
Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyond
Authors: Philipp Hacker, Johann Cordes, Janina Rochon
Date: 2022-12-09 00:00:00
Tasks: Fairness, Jurisprudence

Artificial intelligence is not only increasingly used in business and administration contexts, but a race for its regulation is also underway, with the EU spearheading the efforts. Contrary to existing literature, this article suggests, however, that the most far-reaching and effective EU rules for AI applications in the digital economy will not be contained in the proposed AI Act - but have just been enacted in the Digital Markets Act. We analyze the impact of the DMA and related EU acts on AI models and their underlying data across four key areas: disclosure requirements; the regulation of AI training data; access rules; and the regime for fair rankings. The paper demonstrates that fairness, in the sense of the DMA, goes beyond traditionally protected categories of non-discrimination law on which scholarship at the intersection of AI and law has so far largely focused on. Rather, we draw on competition law and the FRAND criteria known from intellectual property law to interpret and refine the DMA provisions on fair rankings. Moreover, we show how, based on CJEU jurisprudence, a coherent interpretation of the concept of non-discrimination in both traditional non-discrimination and competition law may be found. The final part sketches specific proposals for a comprehensive framework of transparency, access, and fairness under the DMA and beyond.
MOPRD: A multidisciplinary open peer review dataset
Authors: Jialiang Lin, Jiaxin Song, Zhangping Zhou, Yidong Chen, Xiaodong Shi
Date: 2022-12-09 00:00:00
Tasks: Comment Generation, Review Generation

Open peer review is a growing trend in academic publications. Public access to peer review data can benefit both the academic and publishing communities. It also serves as a great support to studies on review comment generation and further to the realization of automated scholarly paper review. However, most of the existing peer review datasets do not provide data that cover the whole peer review process. Apart from this, their data are not diversified enough as the data are mainly collected from the field of computer science. These two drawbacks of the currently available peer review datasets need to be addressed to unlock more opportunities for related studies. In response, we construct MOPRD, a multidisciplinary open peer review dataset. This dataset consists of paper metadata, multiple version manuscripts, review comments, meta-reviews, author's rebuttal letters, and editorial decisions. Moreover, we propose a modular guided review comment generation method based on MOPRD. Experiments show that our method delivers better performance as indicated by both automatic metrics and human evaluation. We also explore other potential applications of MOPRD, including meta-review generation, editorial decision prediction, author rebuttal generation, and scientometric analysis. MOPRD is a strong endorsement for further studies in peer review-related research and other applications.
Masked Lip-Sync Prediction by Audio-Visual Contextual Exploitation in Transformers
Authors: Yasheng Sun, Hang Zhou, Kaisiyuan Wang, Qianyi Wu, Zhibin Hong, Jingtuo Liu, Errui Ding, Jingdong Wang, Ziwei Liu, Hideki Koike
Date: 2022-12-09 00:00:00

Previous studies have explored generating accurately lip-synced talking faces for arbitrary targets given audio conditions. However, most of them deform or generate the whole facial area, leading to non-realistic results. In this work, we delve into the formulation of altering only the mouth shapes of the target person. This requires masking a large percentage of the original image and seamlessly inpainting it with the aid of audio and reference frames. To this end, we propose the Audio-Visual Context-Aware Transformer (AV-CAT) framework, which produces accurate lip-sync with photo-realistic quality by predicting the masked mouth shapes. Our key insight is to exploit desired contextual information provided in audio and visual modalities thoroughly with delicately designed Transformers. Specifically, we propose a convolution-Transformer hybrid backbone and design an attention-based fusion strategy for filling the masked parts. It uniformly attends to the textural information on the unmasked regions and the reference frame. Then the semantic audio information is involved in enhancing the self-attention computation. Additionally, a refinement network with audio injection improves both image and lip-sync quality. Extensive experiments validate that our model can generate high-fidelity lip-synced results for arbitrary subjects.
TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering
Authors: Meryem Banu Cavlak, Gagandeep Singh, Mohammed Alser, Can Firtina, Joël Lindegger, Mohammad Sadrosadati, Nika Mansouri Ghiasi, Can Alkan, Onur Mutlu
Date: 2022-12-09 00:00:00

Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, i.e., reads. State-of-the-art basecallers employ complex deep learning models to achieve high basecalling accuracy. This makes basecalling computationally inefficient and memory-hungry, bottlenecking the entire genome analysis pipeline. However, for many applications, the majority of reads do no match the reference genome of interest (i.e., target reference) and thus are discarded in later steps in the genomics pipeline, wasting the basecalling computation. To overcome this issue, we propose TargetCall, the first pre-basecalling filter to eliminate the wasted computation in basecalling. TargetCall's key idea is to discard reads that will not match the target reference (i.e., off-target reads) prior to basecalling. TargetCall consists of two main components: (1) LightCall, a lightweight neural network basecaller that produces noisy reads; and (2) Similarity Check, which labels each of these noisy reads as on-target or off-target by matching them to the target reference. Our thorough experimental evaluations show that TargetCall 1) improves the end-to-end basecalling runtime performance of the state-of-the-art basecaller by 3.31x while maintaining high (98.88%) recall in keeping on-target reads, 2) maintains high accuracy in downstream analysis, and 3) achieves better runtime performance, throughput, recall, precision, and generality compared to prior works. TargetCall is available at https://github.com/CMU-SAFARI/TargetCall.
Emergent Computations in Trained Artificial Neural Networks and Real Brains
Authors: Néstor Parga, Luis Serrano-Fernández, Joan Falcó-Roget
Date: 2022-12-09 00:00:00
Tasks: Decision Making

Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environments. How do cortical circuits use plasticity to acquire functions such as decision-making or working memory? Neurons are connected in complex ways, forming recurrent neural networks, and learning modifies the strength of their connections. Moreover, neurons communicate emitting brief discrete electric signals. Here we describe how to train recurrent neural networks in tasks like those used to train animals in neuroscience laboratories, and how computations emerge in the trained networks. Surprisingly, artificial networks and real brains can use similar computational strategies.