pwc-archive/papers-with-abstracts huggingface dataset:
Mathematical models of COVID-19 spread
Authors: O. I. Krivorotko, S. I. Kabanikhin
Date: 2021-12-10 00:00:00
Tasks: Classification, Stochastic Optimization, Time Series, Time Series Analysis

The paper presents classification and analysis of the mathematical models of COVID-19 spread in different groups of populations such as the family, school, office (3-100 people), neighborhood (100-5000 people), city, region (0.5-15 million people), country, continent and the world. The classification covers the main types of models including time-series, differential, imitation ones, and their combinations. The time-series models are built from analysis of the time series derived using filtration, regression and network methods (Section 2). The differential models include those derived from systems of ordinary and stochastic differential equations as well as partial-derivative equations (Section 3). The imitation models include cellular automata and agent-based models (Section 4). The fourth group in the classification is combinations of nonlinear Markov chains and optimal control theory, derived within the framework of the mean-field game theory. Due to the novelty of the disease and the difficulties it causes, the parameters of most models are, as a rule, unknown, which necessitates one to solve the inverse problem, so the paper also analyses the main algorithms to solve the inverse problem such as stochastic optimization; nature-like algorithms (genetic; differential evolution; particle swarm, etc.); the understanding method; big-data analysis, and machine learning.
Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins
Authors: F. Emil Thomasen, Kresten Lindorff-Larsen
Date: 2021-12-10 00:00:00

Intrinsically disordered proteins (IDPs) and multidomain proteins with flexible linkers show a high level of structural heterogeneity and are best described by ensembles consisting of multiple conformations with associated thermodynamic weights. Determining conformational ensembles usually involves integration of biophysical experiments and computational models. In this review, we discuss current approaches to determining conformational ensembles of IDPs and multidomain proteins, including the choice of biophysical experiments, computational models used to sample protein conformations, models to calculate experimental observables from protein structure, and methods to refine ensembles against experimental data. We also provide examples of recent applications of integrative conformational ensemble determination to study IDPs and multidomain proteins and suggest future directions for research in the field.
Classification of genetic variants using machine learning
Authors: Abhinav Jain, Greg Slabaugh, Deepti Gurdasani
Date: 2021-12-09 00:00:00
Tasks: BIG-bench Machine Learning, Classification

Recent advances in genomic sequencing technology have resulted in an abundance of genome sequence data. Despite the progress in interpreting those data, there remains a broad scope for their translation into clinical and societal benefits. Loss-of-function variations in the human genome can be causal in disease development. Precise identification of such variations and pathogenicity prediction may lead to better drug targeting, among other benefits. Machine learning comes across as a promising method for its proven predictive ability. We have curated a novel dataset for the classification of LOF variants using high-quality databases of genetic variation. We trained and validated seven different classification algorithms using the new dataset to classify the variants as Benign, Pathogenic and Likely pathogenic. We recorded the best overall performance using the XG-Boost algorithm with an F1-score of 0.88 on the test set. We observed fair performance on Pathogenic samples with high recall and moderate precision and subpar performance on Likely pathogenic class, albeit with moderate precision. Overall, the encouraging results make our final model a promising candidate for further real-world tests.
Questioning the use of global estimates of reproduction numbers, with implications for policy
Authors: Pratyush K. Kollepara, Joel C. Miller
Date: 2021-12-10 00:00:00

The basic reproduction number, $R_0$ is an important and widely used concept in the study of infectious diseases. We briefly review the recent trend of calculating the average of various $R_0$ estimates in systematic reviews aimed at estimating the basic reproduction number of SARS-CoV-2, and discuss the drawbacks and implications of using such averaging methods. Additionally, we argue that even a theoretically grounded approach such as next generation matrix could have practical impediments in its use. More generally, the practice of associating an infectious disease with a single value of $R_0$ is problematic, when the disease can, in fact have different reproduction numbers in various populations.
Symmetry-Breaking in Plant Stems
Authors: Larry Harper, Greg Huey
Date: 2021-12-10 00:00:00

The purpose of this paper is to present a model of a phenomenon of plant stem morphogenesis observed by Cesar Gomez-Campo in 1970. We consider a simplified model of auxin dynamics in plant stems, showing that, after creation of the original primordium, it can represent random, distichous and spiral phyllotaxis (leaf arrangement) just by varying one parameter, the rate of diffusion. The same analysis extends to the $n$-jugate case where $n$ primordia are initiated at each plastochrone. Having validated the model, we consider how it can give rise to the Gomez-Campo phenomenon, showing how a stem with spiral phyllotaxis can produce branches of the same or opposite chirality. And finally, how the relationship can change from discordant to concordant over the course of a growing season.
Error Diagnosis of Deep Monocular Depth Estimation Models
Authors: Jagpreet Chawla, Nikhil Thakurdesai, Anuj Godase, Md Reza, David Crandall, Soon-Heung Jung
Date: 2021-11-15 00:00:00
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation

Estimating depth from a monocular image is an ill-posed problem: when the camera projects a 3D scene onto a 2D plane, depth information is inherently and permanently lost. Nevertheless, recent work has shown impressive results in estimating 3D structure from 2D images using deep learning. In this paper, we put on an introspective hat and analyze state-of-the-art monocular depth estimation models in indoor scenes to understand these models' limitations and error patterns. To address errors in depth estimation, we introduce a novel Depth Error Detection Network (DEDN) that spatially identifies erroneous depth predictions in the monocular depth estimation models. By experimenting with multiple state-of-the-art monocular indoor depth estimation models on multiple datasets, we show that our proposed depth error detection network can identify a significant number of errors in the predicted depth maps. Our module is flexible and can be readily plugged into any monocular depth prediction network to help diagnose its results. Additionally, we propose a simple yet effective Depth Error Correction Network (DECN) that iteratively corrects errors based on our initial error diagnosis.
An Embarrassingly Pragmatic Introduction to Vision-based Autonomous Robots
Authors: Marcos V. Conde
Date: 2021-11-15 00:00:00
Tasks: Autonomous Driving, Self-Driving Cars

Autonomous robots are currently one of the most popular Artificial Intelligence problems, having experienced significant advances in the last decade, from Self-driving cars and humanoids to delivery robots and drones. Part of the problem is to get a robot to emulate the perception of human beings, our sense of sight, replacing the eyes with cameras and the brain with mathematical models such as Neural Networks. Developing an AI able to drive a car without human intervention and a small robot to deliver packages in the city may seem like different problems, nevertheless from the point of view of perception and vision, both problems have several similarities. The main solutions we currently find focus on the environment perception through visual information using Computer Vision techniques, Machine Learning, and various algorithms to make the robot understand the environment or scene, move, adapt its trajectory and perform its tasks (maintenance, exploration, etc.) without the need for human intervention. In this work, we develop a small-scale autonomous vehicle from scratch, capable of understanding the scene using only visual information, navigating through industrial environments, detecting people and obstacles, or performing simple maintenance tasks. We review the state-of-the-art of fundamental problems and demonstrate that many methods employed at small-scale are similar to the ones employed in real Self-driving cars from companies like Tesla or Lyft. Finally, we discuss the current state of Robotics and autonomous driving and the technological and ethical limitations that we can find in this field.
Plurality and Quantification in Graph Representation of Meaning
Authors: Yu Cao
Date: 2021-12-13 00:00:00

In this thesis we present a semantic representation formalism based on directed graphs and explore its linguistic adequacy and explanatory benefits in the semantics of plurality and quantification. Our graph language covers the essentials of natural language semantics using only monadic second-order variables. We define its model-theoretical interpretation in terms of graph traversal, where the relative scope of variables arises from their order of valuation. We present a unification-based mechanism for constructing semantic graphs at a simple syntax-semantics interface, where syntax as a partition function on discourse referents is implemented with categorial grammars by establishing a partly deterministic relation between semantics and syntactic distribution. This mechanism is automated to facilitate future exploration. The present graph formalism is applied to linguistic issues in distributive predication, cross-categorial conjunction, and scope permutation of quantificational expressions, including the exceptional scoping behaviors of indefinites.
GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANs
Authors: Harshvardhan GM, Aanchal Sahu, Mahendra Kumar Gourisaria
Date: 2021-12-13 00:00:00
Tasks: Diversity

While generative adversarial networks (GAN) are popular for their higher sample quality as opposed to other generative models like the variational autoencoders (VAE) and Boltzmann machines, they suffer from the same difficulty of the evaluation of generated samples. Various aspects must be kept in mind, such as the quality of generated samples, the diversity of classes (within a class and among classes), the use of disentangled latent spaces, agreement of said evaluation metric with human perception, etc. In this paper, we propose a new score, namely, GM Score, which takes into various factors such as sample quality, disentangled representation, intra-class and inter-class diversity, and other metrics such as precision, recall, and F1 score are employed for discriminability of latent space of deep belief network (DBN) and restricted Boltzmann machine (RBM). The evaluation is done for different GANs (GAN, DCGAN, BiGAN, CGAN, CoupledGAN, LSGAN, SGAN, WGAN, and WGAN Improved) trained on the benchmark MNIST dataset.
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Authors: Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V Le, Yonghui Wu, Zhifeng Chen, Claire Cui
Date: 2021-12-13 00:00:00
Tasks: Common Sense Reasoning, In-Context Learning, Language Modeling, Language Modelling, Mixture-of-Experts, Question Answering

Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong results on in-context learning tasks. However, training these large dense models requires significant amounts of computing resources. In this paper, we propose and develop a family of language models named GLaM (Generalist Language Model), which uses a sparsely activated mixture-of-experts architecture to scale the model capacity while also incurring substantially less training cost compared to dense variants. The largest GLaM has 1.2 trillion parameters, which is approximately 7x larger than GPT-3. It consumes only 1/3 of the energy used to train GPT-3 and requires half of the computation flops for inference, while still achieving better overall zero-shot and one-shot performance across 29 NLP tasks.
The Hierarchical Organization of Syntax
Authors: Babak Ravandi, Valentina Concu
Date: 2021-11-27 00:00:00

Hierarchies are the hidden backbones of complex systems and their analysis allows for a deeper understanding of their structure and how they evolve. We consider languages also to be complex adaptive systems with several intricate networks that capture their structure and function. Hence, we decided to analyze the hierarchical organization of historical syntactic networks to understand how syntax evolves over time. We created these networks from a corpus of German texts from the 11th to 17th centuries, focusing on the hierarchical levels of these networks. diachronically and to map them to specific communicative needs of speakers. We developed a framework to empirically track the emergence of syntactic structures diachronically, enabling us to map the communicative needs of speakers with these structures. We named these syntactic structures "syntactic communicative hierarchies." We showed that the communicative needs of speakers are the organizational force of syntax. Thus, we argue that the emergence of syntactic communicative hierarchies plays a crucial role in shaping syntax over time. This may indicate that languages evolve not only to increase the efficiency of transferring information, but also to increase our capacity, as a species, to communicate our needs with more and more sophisticated abstractions.
Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification
Authors: Yinhua Piao, Sangseon Lee, Dohoon Lee, Sun Kim
Date: 2021-12-13 00:00:00
Tasks: Classification, Document Classification, Inductive Learning, Sentence

Recently, graph neural networks (GNNs) have been widely used for document classification. However, most existing methods are based on static word co-occurrence graphs without sentence-level information, which poses three challenges:(1) word ambiguity, (2) word synonymity, and (3) dynamic contextual dependency. To address these challenges, we propose a novel GNN-based sparse structure learning model for inductive document classification. Specifically, a document-level graph is initially generated by a disjoint union of sentence-level word co-occurrence graphs. Our model collects a set of trainable edges connecting disjoint words between sentences and employs structure learning to sparsely select edges with dynamic contextual dependencies. Graphs with sparse structures can jointly exploit local and global contextual information in documents through GNNs. For inductive learning, the refined document graph is further fed into a general readout function for graph-level classification and optimization in an end-to-end manner. Extensive experiments on several real-world datasets demonstrate that the proposed model outperforms most state-of-the-art results, and reveal the necessity to learn sparse structures for each document.
Computing Discrete Fr ́echet Distance
Authors: Eiter Thomas, Mannila Heikki
Date: 1994-04-25 00:00:00
Proceeding: journal 1994 4

The Fr ́echet distance between two curves in a metric space is a measure of the similarity between the curves.  We present a discrete variation of this measure. It provides good approximations of the continuous measure and can be efficiently computed using a simple algorithm. We also consider variants of discrete Fr ́echet distance, and find an interesting connection to measuring distance between theories.

keywords: Fr ́echet distance, metrics, curves, polygonal curves
Hallucinating Pose-Compatible Scenes
Authors: Tim Brooks, Alexei A. Efros
Date: 2021-12-13 00:00:00
Tasks: Generative Adversarial Network, Scene Generation

What does human pose tell us about a scene? We propose a task to answer this question: given human pose as input, hallucinate a compatible scene. Subtle cues captured by human pose -- action semantics, environment affordances, object interactions -- provide surprising insight into which scenes are compatible. We present a large-scale generative adversarial network for pose-conditioned scene generation. We significantly scale the size and complexity of training data, curating a massive meta-dataset containing over 19 million frames of humans in everyday environments. We double the capacity of our model with respect to StyleGAN2 to handle such complex data, and design a pose conditioning mechanism that drives our model to learn the nuanced relationship between pose and scene. We leverage our trained model for various applications: hallucinating pose-compatible scene(s) with or without humans, visualizing incompatible scenes and poses, placing a person from one generated image into another scene, and animating pose. Our model produces diverse samples and outperforms pose-conditioned StyleGAN2 and Pix2Pix/Pix2PixHD baselines in terms of accurate human placement (percent of correct keypoints) and quality (Frechet inception distance).
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Authors: Tong Zhu, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Min Zhang
Date: 2021-12-11 00:00:00
Tasks: Document-level Event Extraction, Event Extraction, GPU

Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference. In contrast to the previous studies, we propose a fast and lightweight model named as PTPCG. In our model, we design a novel strategy for event argument combination together with a non-autoregressive decoding algorithm via pruned complete graphs, which are constructed under the guidance of the automatically selected pseudo triggers. Compared to the previous systems, our system achieves competitive results with 19.8\% of parameters and much lower resource consumption, taking only 3.8\% GPU hours for training and up to 8.5 times faster for inference. Besides, our model shows superior compatibility for the datasets with (or without) triggers and the pseudo triggers can be the supplements for annotated triggers to make further improvements. Codes are available at https://github.com/Spico197/DocEE .
Shaping Visual Representations with Attributes for Few-Shot Recognition
Authors: Haoxing Chen, Huaxiong Li, Yaohui Li, Chunlin Chen
Date: 2021-12-13 00:00:00
Tasks: Attribute, Few-Shot Learning, Metric Learning, Representation Learning

Few-shot recognition aims to recognize novel categories under low-data regimes. Some recent few-shot recognition methods introduce auxiliary semantic modality, i.e., category attribute information, into representation learning, which enhances the feature discrimination and improves the recognition performance. Most of these existing methods only consider the attribute information of support set while ignoring the query set, resulting in a potential loss of performance. In this letter, we propose a novel attribute-shaped learning (ASL) framework, which can jointly perform query attributes generation and discriminative visual representation learning for few-shot recognition. Specifically, a visual-attribute predictor (VAP) is constructed to predict the attributes of queries. By leveraging the attributes information, an attribute-visual attention module (AVAM) is designed, which can adaptively utilize attributes and visual representations to learn more discriminative features. Under the guidance of attribute modality, our method can learn enhanced semantic-aware representation for classification. Experiments demonstrate that our method can achieve competitive results on CUB and SUN benchmarks. Our source code is available at: \url{https://github.com/chenhaoxing/ASL}.
Roof-Transformer: Divided and Joined Understanding with Knowledge Enhancement
Authors: Wei-Lin Liao, Cheng-En Su, Wei-Yun Ma
Date: 2021-12-13 00:00:00
Tasks: Knowledge Graphs, Reading Comprehension

Recent work on enhancing BERT-based language representation models with knowledge graphs (KGs) and knowledge bases (KBs) has yielded promising results on multiple NLP tasks. State-of-the-art approaches typically integrate the original input sentences with KG triples and feed the combined representation into a BERT model. However, as the sequence length of a BERT model is limited, such a framework supports little knowledge other than the original input sentences and is thus forced to discard some knowledge. This problem is especially severe for downstream tasks for which the input is a long paragraph or even a document, such as QA or reading comprehension tasks. We address this problem with Roof-Transformer, a model with two underlying BERTs and a fusion layer on top. One underlying BERT encodes the knowledge resources and the other one encodes the original input sentences, and the fusion layer integrates the two resultant encodings. Experimental results on a QA task and the GLUE benchmark attest the effectiveness of the proposed model.
Active learning with MaskAL reduces annotation effort for training Mask R-CNN
Authors: Pieter M. Blok, Gert Kootstra, Hakim Elchaoui Elghor, Boubacar Diallo, Frits K. van Evert, Eldert J. van Henten
Date: 2021-12-13 00:00:00
Tasks: Active Learning

The generalisation performance of a convolutional neural network (CNN) is influenced by the quantity, quality, and variety of the training images. Training images must be annotated, and this is time consuming and expensive. The goal of our work was to reduce the number of annotated images needed to train a CNN while maintaining its performance. We hypothesised that the performance of a CNN can be improved faster by ensuring that the set of training images contains a large fraction of hard-to-classify images. The objective of our study was to test this hypothesis with an active learning method that can automatically select the hard-to-classify images. We developed an active learning method for Mask Region-based CNN (Mask R-CNN) and named this method MaskAL. MaskAL involved the iterative training of Mask R-CNN, after which the trained model was used to select a set of unlabelled images about which the model was most uncertain. The selected images were then annotated and used to retrain Mask R-CNN, and this was repeated for a number of sampling iterations. In our study, MaskAL was compared to a random sampling method on a broccoli dataset with five visually similar classes. MaskAL performed significantly better than the random sampling. In addition, MaskAL had the same performance after sampling 900 images as the random sampling had after 2300 images. Compared to a Mask R-CNN model that was trained on the entire training set (14,000 images), MaskAL achieved 93.9% of that model's performance with 17.9% of its training data. The random sampling achieved 81.9% of that model's performance with 16.4% of its training data. We conclude that by using MaskAL, the annotation effort can be reduced for training Mask R-CNN on a broccoli dataset with visually similar classes. Our software is available on https://github.com/pieterblok/maskal.