pwc-archive/papers-with-abstracts huggingface dataset:
Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection
Authors: Jiajun Liang, Chuyang Ke, Jean Honorio
Date: 2021-01-29 00:00:00
Tasks: Community Detection, Stochastic Block Model

In this paper, we study the information theoretic bounds for exact recovery in sub-hypergraph models for community detection. We define a general model called the $m-$uniform sub-hypergraph stochastic block model ($m-$ShSBM). Under the $m-$ShSBM, we use Fano's inequality to identify the region of model parameters where any algorithm fails to exactly recover the planted communities with a large probability. We also identify the region where a Maximum Likelihood Estimation (MLE) algorithm succeeds to exactly recover the communities with high probability. Our bounds are tight and pertain to the community detection problems in various models such as the planted hypergraph stochastic block model, the planted densest sub-hypergraph model, and the planted multipartite hypergraph model.
Low Rank Forecasting
Authors: Shane Barratt, Yining Dong, Stephen Boyd
Date: 2021-01-29 00:00:00
Tasks: Time Series, Time Series Analysis

We consider the problem of forecasting multiple values of the future of a vector time series, using some past values. This problem, and related ones such as one-step-ahead prediction, have a very long history, and there are a number of well-known methods for it, including vector auto-regressive models, state-space methods, multi-task regression, and others. Our focus is on low rank forecasters, which break forecasting up into two steps: estimating a vector that can be interpreted as a latent state, given the past, and then estimating the future values of the time series, given the latent state estimate. We introduce the concept of forecast consistency, which means that the estimates of the same value made at different times are consistent. We formulate the forecasting problem in general form, and focus on linear forecasters, for which we propose a formulation that can be solved via convex optimization. We describe a number of extensions and variations, including nonlinear forecasters, data weighting, the inclusion of auxiliary data, and additional objective terms. We illustrate our methods with several examples.
Covariance Prediction via Convex Optimization
Authors: Shane Barratt, Stephen Boyd
Date: 2021-01-29 00:00:00
Tasks: Prediction

We consider the problem of predicting the covariance of a zero mean Gaussian vector, based on another feature vector. We describe a covariance predictor that has the form of a generalized linear model, i.e., an affine function of the features followed by an inverse link function that maps vectors to symmetric positive definite matrices. The log-likelihood is a concave function of the predictor parameters, so fitting the predictor involves convex optimization. Such predictors can be combined with others, or recursively applied to improve performance.
Simple Adaptive Estimation of Quadratic Functionals in Nonparametric IV Models
Authors: Christoph Breunig, Xiaohong Chen
Date: 2021-01-28 00:00:00

This paper considers adaptive, minimax estimation of a quadratic functional in a nonparametric instrumental variables (NPIV) model, which is an important problem in optimal estimation of a nonlinear functional of an ill-posed inverse regression with an unknown operator. We first show that a leave-one-out, sieve NPIV estimator of the quadratic functional can attain a convergence rate that coincides with the lower bound previously derived in Chen and Christensen [2018]. The minimax rate is achieved by the optimal choice of the sieve dimension (a key tuning parameter) that depends on the smoothness of the NPIV function and the degree of ill-posedness, both are unknown in practice. We next propose a Lepski-type data-driven choice of the key sieve dimension adaptive to the unknown NPIV model features. The adaptive estimator of the quadratic functional is shown to attain the minimax optimal rate in the severely ill-posed case and in the regular mildly ill-posed case, but up to a multiplicative $\sqrt{\log n}$ factor in the irregular mildly ill-posed case.
From Geometry to Topology: Inverse Theorems for Distributed Persistence
Authors: Elchanan Solomon, Alexander Wagner, Paul Bendich
Date: 2021-01-28 00:00:00

What is the "right" topological invariant of a large point cloud X? Prior research has focused on estimating the full persistence diagram of X, a quantity that is very expensive to compute, unstable to outliers, and far from a sufficient statistic. We therefore propose that the correct invariant is not the persistence diagram of X, but rather the collection of persistence diagrams of many small subsets. This invariant, which we call "distributed persistence," is perfectly parallelizable, more stable to outliers, and has a rich inverse theory. The map from the space of point clouds (with the quasi-isometry metric) to the space of distributed persistence invariants (with the Hausdorff-Bottleneck distance) is a global quasi-isometry. This is a much stronger property than simply being injective, as it implies that the inverse of a small neighborhood is a small neighborhood, and is to our knowledge the only result of its kind in the TDA literature. Moreover, the quasi-isometry bounds depend on the size of the subsets taken, so that as the size of these subsets goes from small to large, the invariant interpolates between a purely geometric one and a topological one. Lastly, we note that our inverse results do not actually require considering all subsets of a fixed size (an enormous collection), but a relatively small collection satisfying certain covering properties that arise with high probability when randomly sampling subsets. These theoretical results are complemented by two synthetic experiments demonstrating the use of distributed persistence in practice.
On the capacity of deep generative networks for approximating distributions
Authors: Yunfei Yang, Zhen Li, Yang Wang
Date: 2021-01-29 00:00:00

We study the efficacy and efficiency of deep generative networks for approximating probability distributions. We prove that neural networks can transform a low-dimensional source distribution to a distribution that is arbitrarily close to a high-dimensional target distribution, when the closeness are measured by Wasserstein distances and maximum mean discrepancy. Upper bounds of the approximation error are obtained in terms of the width and depth of neural network. Furthermore, it is shown that the approximation error in Wasserstein distance grows at most linearly on the ambient dimension and that the approximation order only depends on the intrinsic dimension of the target distribution. On the contrary, when $f$-divergences are used as metrics of distributions, the approximation property is different. We show that in order to approximate the target distribution in $f$-divergences, the dimension of the source distribution cannot be smaller than the intrinsic dimension of the target distribution.
Subgraph nomination: Query by Example Subgraph Retrieval in Networks
Authors: Al-Fahad M. Al-Qadhi, Carey E. Priebe, Hayden S. Helm, Vince Lyzinski
Date: 2021-01-29 00:00:00
Tasks: Recommendation Systems, Retrieval

This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting subgraphs. This type of problem appears time and again in real world problems connected to, for example, user recommendation systems and structural retrieval tasks in social and biological/connectomic networks. We formally define the subgraph nomination framework with an emphasis on the notion of a user-in-the-loop in the subgraph nomination pipeline. In this setting, a user can provide additional post-nomination light supervision that can be incorporated into the retrieval task. After introducing and formalizing the retrieval task, we examine the nuanced effect that user-supervision can have on performance, both analytically and across real and simulated data examples.
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
Authors: Fan-Keng Sun, Christopher I. Lang, Duane S. Boning
Date: 2021-01-28 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Time Series Regression

An increasing body of research focuses on using neural networks to model time series. A common assumption in training neural networks via maximum likelihood estimation on time series is that the errors across time steps are uncorrelated. However, errors are actually autocorrelated in many cases due to the temporality of the data, which makes such maximum likelihood estimations inaccurate. In this paper, in order to adjust for autocorrelated errors, we propose to learn the autocorrelation coefficient jointly with the model parameters. In our experiments, we verify the effectiveness of our approach on time series forecasting. Results across a wide range of real-world datasets with various state-of-the-art models show that our method enhances performance in almost all cases. Based on these results, we suggest empirical critical values to determine the severity of autocorrelated errors. We also analyze several aspects of our method to demonstrate its advantages. Finally, other time series tasks are also considered to validate that our method is not restricted to only forecasting.
Combining pre-trained language models and structured knowledge
Authors: Pedro Colon-Hernandez, Catherine Havasi, Jason Alonso, Matthew Huggins, Cynthia Breazeal
Date: 2021-01-28 00:00:00
Tasks: Knowledge Graphs

In recent years, transformer-based language models have achieved state of the art performance in various NLP benchmarks. These models are able to extract mostly distributional information with some semantics from unstructured text, however it has proven challenging to integrate structured information, such as knowledge graphs into these models. We examine a variety of approaches to integrate structured knowledge into current language models and determine challenges, and possible opportunities to leverage both structured and unstructured information sources. From our survey, we find that there are still opportunities at exploiting adapter-based injections and that it may be possible to further combine various of the explored approaches into one system.
Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning
Authors: Shengsheng Zhang, Yaping Huang, Yun Chen, Liner Yang, Chencheng Wang, Erhong Yang
Date: 2021-01-29 00:00:00
Tasks: Domain Adaptation, Grammatical Error Correction, Meta-Learning, Transfer Learning

Most existing Grammatical Error Correction (GEC) methods based on sequence-to-sequence mainly focus on how to generate more pseudo data to obtain better performance. Few work addresses few-shot GEC domain adaptation. In this paper, we treat different GEC domains as different GEC tasks and propose to extend meta-learning to few-shot GEC domain adaptation without using any pseudo data. We exploit a set of data-rich source domains to learn the initialization of model parameters that facilitates fast adaptation on new resource-poor target domains. We adapt GEC model to the first language (L1) of the second language learner. To evaluate the proposed method, we use nine L1s as source domains and five L1s as target domains. Experiment results on the L1 GEC domain adaptation dataset demonstrate that the proposed approach outperforms the multi-task transfer learning baseline by 0.50 $F_{0.5}$ score on average and enables us to effectively adapt to a new L1 domain with only 200 parallel sentences.
Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarization
Authors: Anurag Shandilya, Abhisek Dash, Abhijnan Chakraborty, Kripabandhu Ghosh, Saptarshi Ghosh
Date: 2021-01-29 00:00:00
Tasks: Fairness, Text Summarization

With the surge in user-generated textual information, there has been a recent increase in the use of summarization algorithms for providing an overview of the extensive content. Traditional metrics for evaluation of these algorithms (e.g. ROUGE scores) rely on matching algorithmic summaries to human-generated ones. However, it has been shown that when the textual contents are heterogeneous, e.g., when they come from different socially salient groups, most existing summarization algorithms represent the social groups very differently compared to their distribution in the original data. To mitigate such adverse impacts, some fairness-preserving summarization algorithms have also been proposed. All of these studies have considered normative notions of fairness from the perspective of writers of the contents, neglecting the readers' perceptions of the underlying fairness notions. To bridge this gap, in this work, we study the interplay between the fairness notions and how readers perceive them in textual summaries. Through our experiments, we show that reader's perception of fairness is often context-sensitive. Moreover, standard ROUGE evaluation metrics are unable to quantify the perceived (un)fairness of the summaries. To this end, we propose a human-in-the-loop metric and an automated graph-based methodology to quantify the perceived bias in textual summaries. We demonstrate their utility by quantifying the (un)fairness of several summaries of heterogeneous socio-political microblog datasets.
Meta-learning on Spectral Images of Electroencephalogram of Schizophenics
Authors: Maritza Tynes, Mahboobeh Parsapoor
Date: 2021-01-27 00:00:00
Tasks: Meta-Learning

Schizophrenia is a complex psychiatric disorder involving changes in thought patterns, perception, mood, and behavior. The diagnosis of schizophrenia is challenging and requires that patients show two or more positive symptoms for at least one month. Delays in identifying this debilitating disorder can impede a patient ability to receive much needed treatment. Advances in neuroimaging and machine learning algorithms can facilitate the diagnosis of schizophrenia and help clinicians to provide an accurate diagnosis of the disease. This paper presents a methodology for analyzing spectral images of Electroencephalography collected from patients with schizophrenia using convolutional neural networks. It also explains how we have developed accurate classifiers employing Model-Agnostic Meta-Learning and prototypical networks. Such classifiers have the capacity to distinguish people with schizophrenia from healthy controls based on their brain activity.
Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search
Authors: Rui Wang, Kai Gao, Daniel Nakhimovich, Jingjin Yu, Kostas E. Bekris
Date: 2021-01-28 00:00:00
Tasks: Object, Object Rearrangement

Object rearrangement is a widely-applicable and challenging task for robots. Geometric constraints must be carefully examined to avoid collisions and combinatorial issues arise as the number of objects increases. This work studies the algorithmic structure of rearranging uniform objects, where robot-object collisions do not occur but object-object collisions have to be avoided. The objective is minimizing the number of object transfers under the assumption that the robot can manipulate one object at a time. An efficiently computable decomposition of the configuration space is used to create a "region graph", which classifies all continuous paths of equivalent collision possibilities. Based on this compact but rich representation, a complete dynamic programming primitive DFSDP performs a recursive depth first search to solve monotone problems quickly, i.e., those instances that do not require objects to be moved first to an intermediate buffer. DFSDP is extended to solve single-buffer, non-monotone instances, given a choice of an object and a buffer. This work utilizes these primitives as local planners in an informed search framework for more general, non-monotone instances. The search utilizes partial solutions from the primitives to identify the most promising choice of objects and buffers. Experiments demonstrate that the proposed solution returns near-optimal paths with higher success rate, even for challenging non-monotone instances, than other leading alternatives.
Enabling Robots to Draw and Tell: Towards Visually Grounded Multimodal Description Generation
Authors: Ting Han, Sina Zarrieß
Date: 2021-01-14 00:00:00

Socially competent robots should be equipped with the ability to perceive the world that surrounds them and communicate about it in a human-like manner. Representative skills that exhibit such ability include generating image descriptions and visually grounded referring expressions. In the NLG community, these generation tasks are largely investigated in non-interactive and language-only settings. However, in face-to-face interaction, humans often deploy multiple modalities to communicate, forming seamless integration of natural language, hand gestures and other modalities like sketches. To enable robots to describe what they perceive with speech and sketches/gestures, we propose to model the task of generating natural language together with free-hand sketches/hand gestures to describe visual scenes and real life objects, namely, visually-grounded multimodal description generation. In this paper, we discuss the challenges and evaluation metrics of the task, and how the task can benefit from progress recently made in the natural language processing and computer vision realms, where related topics such as visually grounded NLG, distributional semantics, and photo-based sketch generation have been extensively studied.
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans
Authors: Navchetan Awasthi, Rohit Pardasani, Swati Gupta
Date: 2021-01-29 00:00:00
Tasks: Brain Tumor Segmentation, Prognosis, Segmentation, Tumor Segmentation

Gliomas are one of the most frequent brain tumors and are classified into high grade and low grade gliomas. The segmentation of various regions such as tumor core, enhancing tumor etc. plays an important role in determining severity and prognosis. Here, we have developed a multi-threshold model based on attention U-Net for identification of various regions of the tumor in magnetic resonance imaging (MRI). We propose a multi-path segmentation and built three separate models for the different regions of interest. The proposed model achieved mean Dice Coefficient of 0.59, 0.72, and 0.61 for enhancing tumor, whole tumor and tumor core respectively on the training dataset. The same model gave mean Dice Coefficient of 0.57, 0.73, and 0.61 on the validation dataset and 0.59, 0.72, and 0.57 on the test dataset.
Raspberry Pi Based Intelligent Robot that Recognizes and Places Puzzle Objects
Authors: Yakup Kutlu, Zülfü Alanoglu, Ahmet Gökçen, Mustafa Yeniad
Date: 2021-01-20 00:00:00

In this study; in order to diagnose congestive heart failure (CHF) patients, non-linear secondorder difference plot (SODP) obtained from raw 256 Hz sampled frequency and windowed record with different time of ECG records are used. All of the data rows are labelled with their belongings to classify much more realistically. SODPs are divided into different radius of quadrant regions and numbers of the points fall in the quadrants are computed in order to extract feature vectors. Fisher's linear discriminant, Naive Bayes, and artificial neural network are used as classifier. The results are considered in two step validation methods as general kfold cross-validation and patient based cross-validation. As a result, it is shown that using neural network classifier with features obtained from SODP, the constructed system could distinguish normal and CHF patients with 100% accuracy rate.
Personalization and Recommendation Technologies for MaaS
Authors: K. Arnaoutaki, E. Bothos, B. Magoutas, G. Mentzas
Date: 2021-01-27 00:00:00
Tasks: Autonomous Vehicles, Recommendation Systems

Over the last few years, MaaS has been extensively studied and evolved into offering a multitude of mobility services that continuously increase, from alternative car or bike-sharing modes to autonomous vehicles, that aspire to become a part of this novel ecosystem. MaaS provides end-users with multimodal, integrated, and digital mobility solutions, including a multitude of different choices able to cover users specific needs in a personalized manner. This practically leads to a range of novel MaaS products, that may have complex structures and the challenge of matching them to user preferences and needs so that suitable products can be provided to end-users. Moreover, in the everyday use of MaaS, travelers require support to identify routes to reach their destination that adhere to their personal preferences and are aligned to the MaaS product they have purchased. This paper tackles these two user-centric challenges by exploiting state-of-the-art techniques from the field of Personalization and Recommendation systems and integrating them in MaaS platforms and route planning applications.
Are Top School Students More Critical of Their Professors? Mining Comments on RateMyProfessor.com
Authors: Ziqi Tang, Yutong Wang, Jiebo Luo
Date: 2021-01-23 00:00:00
Tasks: Sentiment Analysis

Student reviews and comments on RateMyProfessor.com reflect realistic learning experiences of students. Such information provides a large-scale data source to examine the teaching quality of the lecturers. In this paper, we propose an in-depth analysis of these comments. First, we partition our data into different comparison groups. Next, we perform exploratory data analysis to delve into the data. Furthermore, we employ Latent Dirichlet Allocation and sentiment analysis to extract topics and understand the sentiments associated with the comments. We uncover interesting insights about the characteristics of both college students and professors. Our study proves that student reviews and comments contain crucial information and can serve as essential references for enrollment in courses and universities.
Deep Triplet Hashing Network for Case-based Medical Image Retrieval
Authors: Jiansheng Fang, Huazhu Fu, Jiang Liu
Date: 2021-01-29 00:00:00
Tasks: Classification, Deep Hashing, General Classification, Image Retrieval, Medical Image Retrieval, Retrieval, Triplet

Deep hashing methods have been shown to be the most efficient approximate nearest neighbor search techniques for large-scale image retrieval. However, existing deep hashing methods have a poor small-sample ranking performance for case-based medical image retrieval. The top-ranked images in the returned query results may be as a different class than the query image. This ranking problem is caused by classification, regions of interest (ROI), and small-sample information loss in the hashing space. To address the ranking problem, we propose an end-to-end framework, called Attention-based Triplet Hashing (ATH) network, to learn low-dimensional hash codes that preserve the classification, ROI, and small-sample information. We embed a spatial-attention module into the network structure of our ATH to focus on ROI information. The spatial-attention module aggregates the spatial information of feature maps by utilizing max-pooling, element-wise maximum, and element-wise mean operations jointly along the channel axis. The triplet cross-entropy loss can help to map the classification information of images and similarity between images into the hash codes. Extensive experiments on two case-based medical datasets demonstrate that our proposed ATH can further improve the retrieval performance compared to the state-of-the-art deep hashing methods and boost the ranking performance for small samples. Compared to the other loss methods, the triplet cross-entropy loss can enhance the classification performance and hash code-discriminability