pwc-archive/papers-with-abstracts huggingface dataset:
The role of the vagus nerve during fetal development and its relationship with the environment
Authors: Francesco Cerritelli, Martin G. Frasch, Marta C. Antonelli, Chiara Viglione, Stefano Vecchi, Marco Chiera, Andrea Manzotti
Date: 2021-06-03 00:00:00
Tasks: Heart Rate Variability, Management

The autonomic nervous system (ANS) regulatory capacity begins before birth as the sympathetic and parasympathetic activity contributes significantly to the fetus' development. Several studies have shown how vagus nerve is involved in many vital processes during fetal, perinatal and postnatal life: from the regulation of inflammation through the anti-inflammatory cholinergic pathway, which may affect the functioning of each organ, to the production of hormones involved in bioenergetic metabolism. In addition, the vagus nerve has been recognized as the primary afferent pathway capable of transmitting information to the brain from every organ of the body. Therefore, this hypothesis paper aims to review the development of ANS during fetal and perinatal life, focusing particularly on the vagus nerve, to identify possible "critical windows" that could impact its maturation. These "critical windows" could help clinicians know when to monitor fetuses to effectively assess the developmental status of both ANS and specifically the vagus nerve. In addition, this paper will focus on which factors (i.e. fetal characteristics and behaviors, maternal lifestyle and pathologies, placental health and dysfunction, labor, incubator conditions, and drug exposure) may have an impact on the development of the vagus during the above-mentioned "critical window" and how. This analysis could help clinicians and stakeholders define precise guidelines for improving the management of fetuses and newborns, particularly to reduce the potential adverse environmental impacts on ANS development that may lead to persistent long-term consequences. Since the development of ANS and the vagus influence have been shown to be reflected in cardiac variability, this paper will rely in particular on studies using fetal heart rate variability (fHRV) to monitor the continued growth and health of both animal and human fetuses.
Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models
Authors: Biagio La Rosa, Roberto Capobianco, Daniele Nardi
Date: 2021-06-01 00:00:00
Tasks: image-classification, Image Classification

Due to their black-box and data-hungry nature, deep learning techniques are not yet widely adopted for real-world applications in critical domains, like healthcare and justice. This paper presents Memory Wrap, a plug-and-play extension to any image classification model. Memory Wrap improves both data-efficiency and model interpretability, adopting a content-attention mechanism between the input and some memories of past training samples. We show that Memory Wrap outperforms standard classifiers when it learns from a limited set of data, and it reaches comparable performance when it learns from the full dataset. We discuss how its structure and content-attention mechanisms make predictions interpretable, compared to standard classifiers. To this end, we both show a method to build explanations by examples and counterfactuals, based on the memory content, and how to exploit them to get insights about its decision process. We test our approach on image classification tasks using several architectures on three different datasets, namely CIFAR10, SVHN, and CINIC10.
Navigating the range of statistical tools for inferential network analysis
Authors: Skyler J Cranmer, Philip Leifeld, Scott McClurg, Meredith D Rolfe
Date: 2016-07-13 00:00:00
Tasks: Descriptive, valid

The last decade has seen substantial advances in statistical techniques for the analysis of network data, as well as a major increase in the frequency with which these tools are used. These techniques are designed to accomplish the same broad goal, statistically valid inference in the presence of highly interdependent relationships, but important differences remain between them. We review three approaches commonly used for inferential network analysis?the quadratic assignment procedure, exponential random graph models, and latent space network models?highlighting the strengths and weaknesses of the techniques relative to one another. An illustrative example using climate change policy network data shows that all three network models outperform standard logit estimates on multiple criteria. This article introduces political scientists to a class of network techniques beyond simple descriptive measures of network structure, and it helps researchers choose which model to use in their own research.
Detecting Malicious Websites by Learning IP Address Features
Authors: Daiki Chiba, Kazuhiro Tobe, T Mori, S Goto
Date: 2012-01-01 00:00:00

Web-based malware attacks have become one of the most serious threats that need to be addressed urgently. Several approaches that have attracted attention as promising ways of detecting such malware include employing various blacklists. However, these conventional approaches often fail to detect new attacks owing to the versatility of malicious websites. Thus, it is difficult to maintain up-to-date blacklists with information regarding new malicious websites. To tackle this problem, we propose a new method for detecting malicious websites using the characteristics of IP addresses. Our approach leverages the empirical observation that IP addresses are more stable than other metrics such as URL and DNS. While the strings that form URLs or domain names are highly variable, IP addresses are less variable, i.e., IPv4 address space is mapped onto 4-bytes strings. We develop a lightweight and scalable detection scheme based on the machine learning technique. The aim of this study is not to provide a single solution that effectively detects web-based malware but to develop a technique that compensates the drawbacks of existing approaches. We validate the effectiveness of our approach by using real IP address data from existing blacklists and real traffic data on a campus network. The results demonstrate that our method can expand the coverage/accuracy of existing blacklists and also detect unknown malicious websites that are not covered by conventional approaches.
Cascade Dynamics Modeling with Attention-based Recurrent Neural Network
Authors: Yongqing Wang, HuaWei Shen, Shenghua Liu, Jinhua Gao, and Xueqi Cheng
Date: 2017-05-01 00:00:00
Tasks: Marketing

An ability of modeling and predicting the cascades
of resharing is crucial to understanding information propagation and to launching campaign of viral
marketing. Conventional methods for cascade prediction heavily depend on the hypothesis of diffusion models, e.g., independent cascade model and
linear threshold model. Recently, researchers attempt to circumvent the problem of cascade prediction using sequential models (e.g., recurrent neural
network, namely RNN) that do not require knowing the underlying diffusion model. Existing sequential models employ a chain structure to capture
the memory effect. However, for cascade prediction, each cascade generally corresponds to a diffusion tree, causing cross-dependence in cascade—
one sharing behavior could be triggered by its
non-immediate predecessor in the memory chain.
In this paper, we propose to an attention-based
RNN to capture the cross-dependence in cascade.
Furthermore, we introduce a coverage strategy to
combat the misallocation of attention caused by
the memoryless of traditional attention mechanism.
Extensive experiments on both synthetic and real
world datasets demonstrate the proposed models
outperform state-of-the-art models at both cascade
prediction and inferring diffusion tree.
Compositionally restricted attention-based network for materials property predictions
Authors: Anthony Yu-Tung Wang, Steven K. Kauwe, Ryan J. Murdock, Taylor D. Sparks
Date: 2021-05-28 00:00:00

In this paper, we demonstrate an application of the Transformer self-attention mechanism in the context of materials science. Our network, the Compositionally Restricted Attention-Based network (CrabNet), explores the area of structure-agnostic materials property predictions when only a chemical formula is provided. Our results show that CrabNet’s performance matches or exceeds current best-practice methods on nearly all of 28 total benchmark datasets. We also demonstrate how CrabNet’s architecture lends itself towards model interpretability by showing different visualization approaches that are made possible by its design. We feel confident that CrabNet and its attention-based framework will be of keen interest to future materials informatics researchers.
An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation
Authors: L Huang，Y Ma，S Wang，Y Liu
Date: 2019-05-20 00:00:00

Next point-of-interest (POI) recommendation is recently proposed to predict user's next destination and has attracted considerable research interest. Most of the previous studies failed to incorporate the spatiotemporal contextual information, which plays a critical role in analyzing user check-in behavior, into recommending the next POI. In recent years, embedding learning and recurrent neural network (RNN) based approaches show promising performance for modeling sequential patterns of check-in behavior in next POI recommendation. However, not all of the historical check-in records contribute equally to the next-step check-in behavior. To provide better next POI recommendation performance, we first proposed a spatiotemporal long and short-term memory (ST-LSTM) network. By feeding the spatiotemporal contextual information into the LSTM network in each step, ST-LSTM can model the spatial and temporal information better. Also, we developed an attention-based spatiotemporal LSTM (ATST-LSTM) network for next POI recommendation. By using the attention
New Insights into Metric Optimization for Ranking-based Recommendation
Authors: Roger Zhe Li, Julián Urbano, Alan Hanjalic
Date: 2021-06-04 00:00:00
Tasks: Learning-To-Rank, Recommendation Systems

Direct optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach aim at optimizing the same metric being used for evaluation, under the assumption that this will lead to the best performance. A number of studies of this practice bring this assumption, however, into question. In this paper, we dig deeper into this issue in order to learn more about the effects of the choice of the metric to optimize on the performance of a ranking-based recommender system. We present an extensive experimental study conducted on different datasets in both pairwise and listwise learning-to-rank scenarios, to compare the relative merit of four popular IR metrics, namely RR, AP, nDCG and RBP, when used for optimization and assessment of recommender systems in various combinations. For the first three, we follow the practice of loss function formulation available in literature. For the fourth one, we propose novel loss functions inspired by RBP for both the pairwise and listwise scenario. Our results confirm that the best performance is indeed not necessarily achieved when optimizing the same metric being used for evaluation. In fact, we find that RBP-inspired losses perform at least as well as other metrics in a consistent way, and offer clear benefits in several cases. Interesting to see is that RBP-inspired losses, while improving the recommendation performance for all uses, may lead to an individual performance gain that is correlated with the activity level of a user in interacting with items. The more active the users, the more they benefit. Overall, our results challenge the assumption behind the current research practice of optimizing and evaluating the same metric, and point to RBP-based optimization instead as a promising alternative when learning to rank in the recommendation context.
Analysis of the robustness of NMF algorithms
Authors: Alex Díaz, Damian Steele
Date: 2021-06-04 00:00:00
Tasks: feature selection, Sensitivity

We examine three non-negative matrix factorization techniques; L2-norm, L1-norm, and L2,1-norm. Our aim is to establish the performance of these different approaches, and their robustness in real-world applications such as feature selection while managing computational complexity, sensitivity to noise and more. We thoroughly examine each approach from a theoretical perspective, and examine the performance of each using a series of experiments drawing on both the ORL and YaleB datasets. We examine the Relative Reconstruction Errors (RRE), Average Accuracy and Normalized Mutual Information (NMI) as criteria under a range of simulated noise scenarios.
Fre-GAN: Adversarial Frequency-consistent Audio Synthesis
Authors: Ji-Hoon Kim, Sang-Hoon Lee, Ji-Hyun Lee, Seong-Whan Lee
Date: 2021-06-04 00:00:00
Tasks: Audio Synthesis

Although recent works on neural vocoder have improved the quality of synthesized audio, there still exists a gap between generated and ground-truth audio in frequency space. This difference leads to spectral artifacts such as hissing noise or reverberation, and thus degrades the sample quality. In this paper, we propose Fre-GAN which achieves frequency-consistent audio synthesis with highly improved generation quality. Specifically, we first present resolution-connected generator and resolution-wise discriminators, which help learn various scales of spectral distributions over multiple frequency bands. Additionally, to reproduce high-frequency components accurately, we leverage discrete wavelet transform in the discriminators. From our experiments, Fre-GAN achieves high-fidelity waveform generation with a gap of only 0.03 MOS compared to ground-truth audio while outperforming standard models in quality.
CATs: Cost Aggregation Transformers for Visual Correspondence
Authors: Seokju Cho, Sunghwan Hong, Sangryul Jeon, Yunsung Lee, Kwanghoon Sohn, Seungryong Kim
Date: 2021-06-04 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Semantic correspondence

We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations. Cost aggregation is a highly important process in matching tasks, which the matching accuracy depends on the quality of its output. Compared to hand-crafted or CNN-based methods addressing the cost aggregation, in that either lacks robustness to severe deformations or inherit the limitation of CNNs that fail to discriminate incorrect matches due to limited receptive fields, CATs explore global consensus among initial correlation map with the help of some architectural designs that allow us to fully leverage self-attention mechanism. Specifically, we include appearance affinity modeling to aid the cost aggregation process in order to disambiguate the noisy initial correlation maps and propose multi-level aggregation to efficiently capture different semantics from hierarchical feature representations. We then combine with swapping self-attention technique and residual connections not only to enforce consistent matching but also to ease the learning process, which we find that these result in an apparent performance boost. We conduct experiments to demonstrate the effectiveness of the proposed model over the latest methods and provide extensive ablation studies. Code and trained models are available at https://github.com/SunghwanHong/CATs.
Learning to Draw: Emergent Communication through Sketching
Authors: Daniela Mihai, Jonathon Hare
Date: 2021-06-03 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Prehistory

Evidence that visual communication preceded written language and provided a basis for it goes back to prehistory, in forms such as cave and rock paintings depicting traces of our distant ancestors. Emergent communication research has sought to explore how agents can learn to communicate in order to collaboratively solve tasks. Existing research has focused on language, with a learned communication channel transmitting sequences of discrete tokens between the agents. In this work, we explore a visual communication channel between agents that are allowed to draw with simple strokes. Our agents are parameterised by deep neural networks, and the drawing procedure is differentiable, allowing for end-to-end training. In the framework of a referential communication game, we demonstrate that agents can not only successfully learn to communicate by drawing, but with appropriate inductive biases, can do so in a fashion that humans can interpret. We hope to encourage future research to consider visual communication as a more flexible and directly interpretable alternative of training collaborative agents.
Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention
Authors: Matthew R. Keaton, Ram J. Zaveri, Meghana Kovur, Cole Henderson, Donald A. Adjeroh, Gianfranco Doretto
Date: 2021-06-03 00:00:00
Tasks: Fine-Grained Image Classification, object-detection, Object Detection, Organ Detection

Plant species identification in the wild is a difficult problem in part due to the high variability of the input data, but also because of complications induced by the long-tail effects of the datasets distribution. Inspired by the most recent fine-grained visual classification approaches which are based on attention to mitigate the effects of data variability, we explore the idea of using object detection as a form of attention. We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers. We also curate a new dataset with a long-tail distribution for evaluating plant organ detection and organ-based species identification, which is publicly available.
Barcode Method for Generative Model Evaluation driven by Topological Data Analysis
Authors: Ryoungwoo Jang, Minjee Kim, Da-in Eun, Kyungjin Cho, Jiyeon Seo, Namkug Kim
Date: 2021-06-04 00:00:00
Tasks: Diversity, Image Generation, Topological Data Analysis

Evaluating the performance of generative models in image synthesis is a challenging task. Although the Fr\'echet Inception Distance is a widely accepted evaluation metric, it integrates different aspects (e.g., fidelity and diversity) of synthesized images into a single score and assumes the normality of embedded vectors. Recent methods such as precision-and-recall and its variants such as density-and-coverage have been developed to separate fidelity and diversity based on k-nearest neighborhood methods. In this study, we propose an algorithm named barcode, which is inspired by the topological data analysis and is almost free of assumption and hyperparameter selections. In extensive experiments on real-world datasets as well as theoretical approach on high-dimensional normal samples, it was found that the 'usual' normality assumption of embedded vectors has several drawbacks. The experimental results demonstrate that barcode outperforms other methods in evaluating fidelity and diversity of GAN outputs. Official codes can be found in https://github.com/minjeekim00/Barcode.
History Encoding Representation Design for Human Intention Inference
Authors: Zhuo Xu, Masayoshi Tomizuka
Date: 2021-06-04 00:00:00
Tasks: Prediction

In this extended abstract, we investigate the design of learning representation for human intention inference. In our designed human intention prediction task, we propose a history encoding representation that is both interpretable and effective for prediction. Through extensive experiments, we show our prediction framework with a history encoding representation design is successful on the human intention prediction problem.
X-volution: On the unification of convolution and self-attention
Authors: Xuanhong Chen, Hang Wang, Bingbing Ni
Date: 2021-06-04 00:00:00
Tasks: Image Classification, Instance Segmentation, Object Detection

Convolution and self-attention are acting as two fundamental building blocks in deep neural networks, where the former extracts local image features in a linear way while the latter non-locally encodes high-order contextual relationships. Though essentially complementary to each other, i.e., first-/high-order, stat-of-the-art architectures, i.e., CNNs or transformers lack a principled way to simultaneously apply both operations in a single computational module, due to their heterogeneous computing pattern and excessive burden of global dot-product for visual tasks. In this work, we theoretically derive a global self-attention approximation scheme, which approximates a self-attention via the convolution operation on transformed features. Based on the approximated scheme, we establish a multi-branch elementary module composed of both convolution and self-attention operation, capable of unifying both local and non-local feature interaction. Importantly, once trained, this multi-branch module could be conditionally converted into a single standard convolution operation via structural re-parameterization, rendering a pure convolution styled operator named X-volution, ready to be plugged into any modern networks as an atomic operation. Extensive experiments demonstrate that the proposed X-volution, achieves highly competitive visual understanding improvements (+1.2% top-1 accuracy on ImageNet classification, +1.7 box AP and +1.5 mask AP on COCO detection and segmentation).
Visual Question Rewriting for Increasing Response Rate
Authors: Jiayi Wei, Xilian Li, Yi Zhang, Xin Wang
Date: 2021-06-04 00:00:00
Tasks: 4k, Question Rewriting

When a human asks questions online, or when a conversational virtual agent asks human questions, questions triggering emotions or with details might more likely to get responses or answers. we explore how to automatically rewrite natural language questions to improve the response rate from people. In particular, a new task of Visual Question Rewriting(VQR) task is introduced to explore how visual information can be used to improve the new questions. A data set containing around 4K bland questions, attractive questions and images triples is collected. We developed some baseline sequence to sequence models and more advanced transformer based models, which take a bland question and a related image as input and output a rewritten question that is expected to be more attractive. Offline experiments and mechanical Turk based evaluations show that it is possible to rewrite bland questions in a more detailed and attractive way to increase the response rate, and images can be helpful.