pwc-archive/papers-with-abstracts huggingface dataset:
Tracé alternant detector for grading hypoxic-ischemic encephalopathy in neonatal EEG
Authors: Sumit A. Raurale, Geraldine B. Boylan, Sean R. Mathieson, William P. Marnane, Gordon Lightbody, John M. O'Toole
Date: 2021-05-31 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Electroencephalography (EEG) is an important clinical tool to capture sleep-wake cycling. It can also be used for grading injury, known as hypoxic-ischaemic encephalopathy(HIE), caused by lack of oxygen or blood to the brain during birth. Trac\'e alternant (TA) is a distinctive component of normal quiet sleep which consists of alternating periods of high-voltage activity (bursts) separated by lower-voltage activity (inter-bursts). This study presents an automated method to grade the severity of injury in HIE, using an automated method to first detect activity. The TA detector uses the output of an existing method to detect inter-bursts. Features are extracted from a processed output and then combined in a support vector machine (SVM). Next, we develop an HIE grading system using the TA detector by combining different features from the temporal organisation of the detected TA mask, again using an SVM. Training and testing for both models use a leave-one-baby-out cross-validation procedure, with model hyper-parameters selected from nested cross validations. The TA detector, tested on EEG from 71 healthy term neonates, has an accuracy of 79.1% (Cohen's \k{appa}=0.55). the grading system, tested on EEG from 54 term neonates in intensive care, has an accuracy of 81.5% (\k{appa}=0.74). These results validate how detecting the presence or absence of TA can be used to quantify the grade of HIE injury in neonatal EEG and open up the possibility of a clinically-meaningful grading system.
Weak target detection with multi-bit quantization in colocated MIMO radar
Authors: Hang Xiao, Shixing Yang, Wei Yi
Date: 2021-05-29 00:00:00
Tasks: Quantization

We consider the weak target detection problem with unknown parameter in colocated multiple-input multiple-output (MIMO) radar. To cope with the sheer amount of data for large-size systems, a multi-bit quantizer is utilized in the sampling process. As a low-complexity alternative to classic generalized likelihood ratio test (GLRT) for quantized data, we propose the multi-bit detector on Rao test with a closed-form test statistic, whose theoretical asymptotic distribution is provided to generalize the actual detection performance. Besides, we refine the design of quantizer by optimized quantization thresholds, which are obtained resorting to the popular particle swarm optimization algorithmthe (PSOA). The simulation is conducted to demonstrate the performance variations of detectors based on unquantized and quantized data. The numerical results corroborate our theoretical analyses and show that the performance with 3-bit quantization approaches the case without quantization.
$N$-player and Mean-field Games in Itô-diffusion Markets with Competitive or Homophilous Interaction
Authors: Ruimeng Hu, Thaleia Zariphopoulou
Date: 2021-06-01 00:00:00

In It\^{o}-diffusion environments, we introduce and analyze $N$-player and common-noise mean-field games in the context of optimal portfolio choice in a common market. The players invest in a finite horizon and also interact, driven either by competition or homophily. We study an incomplete market model in which the players have constant individual risk tolerance coefficients (CARA utilities). We also consider the general case of random individual risk tolerances and analyze the related games in a complete market setting. This randomness makes the problem substantially more complex as it leads to ($N$ or a continuum of) auxiliary ''individual'' It\^{o}-diffusion markets. For all cases, we derive explicit or closed-form solutions for the equilibrium stochastic processes, the optimal state processes, and the values of the games.
A Blueprint for the Study of the Brain's Spatiotemporal Patterns
Authors: Emmanuelle Tognoli, Daniela Benites, J. A. Scott Kelso
Date: 2021-06-01 00:00:00

The functioning of an organ such as the brain emerges from interactions between its constituent parts. Further, this interaction is not immutable in time but rather unfolds in a succession of patterns, thereby allowing the brain to adapt to constantly changing exterior and interior milieus. This calls for a framework able to study patterned spatiotemporal interactions between components of the brain. A theoretical and methodological framework is developed to study the brain's coordination dynamics. Here we present a toolset designed to decipher the continuous dynamics of electrophysiological data and its relation to (dys-) function. Understanding the spatiotemporal organization of brain patterns and their association with behavioral, cognitive and clinically-relevant variables is an important challenge for the fields of neuroscience and biologically-inspired engineering. It is hoped that such a comprehensive framework will shed light not only on human behavior and the human mind but also help in understanding the growing number of pathologies that are linked to disorders of brain connectivity.
Study on the multiple characteristics of M3 generation of pea mutants obtained by neutron irradiation
Authors: Dapeng Xu, Ze'en Yao, Jianbin Pan, Huyuan Feng, Zhiqi Guo, Xiaolong Lu
Date: 2021-06-01 00:00:00

Irradiation breeding is an important technique in the effort to solve food shortages and improve the quality of agricultural products. In this study, a field test was implemented on the M3 generation of two mutant pea plants gained from previous neutron radiation of pea seeds. The relationship between agronomic characteristics and yields of the mutants was investigated. Moreover, differences in physiological and biochemical properties and seed nutrients were analyzed. The results demonstrated that the plant height, effective pods per plant, and yield per plant of mutant Leaf-M1 were 45.0%, 43.2%, and 50.9% higher than those of the control group. Further analysis attributed the increase in yield per plant to the increased branching number. The yield per plant of mutant Leaf-M2 was 7.8% higher than that of the control group, which could be related with the increased chlorophyll content in the leaves. There was a significant difference between the two mutants in the increase of yield per plant owing to morphological variation between the two mutants. There were significant differences in SOD activity and MDA content between the two mutants and the control, indicating that the physiological regulation of the two mutants also changed. In addition, the iron element content of seeds of the two mutants were about 10.9% lower than in the seeds of the control group, a significant difference. These findings indicate that the mutants Leaf-M1 and Leaf-M2 have breeding value and material value for molecular biological studies.
A review of matrix SIR Arino epidemic models
Authors: Florin Avram, Rim Adenane, David I. Ketcheson
Date: 2021-06-01 00:00:00
Tasks: Epidemiology

Many of the models used nowadays in mathematical epidemiology, in particular in COVID-19 research, belong to a certain sub-class of compartmental models whose classes may be divided into three "(x, y, z)" groups, which we will call respectively "susceptible/entrance, diseased, and output" (in the classic SIR case, there is only one class of each type). Roughly, the ODE dynamics of these models contain only linear terms, with the exception of products between x and y terms. It has long been noticed that the basic reproduction number R has a very simple formula (3.3) in terms of the matrices which define the model, and an explicit first integral formula (3.8) is also available. These results can be traced back at least to [ABvdD+07] and [Fen07], respectively, and may be viewed as the "basic laws of SIR-type epidemics"; however many papers continue to reprove them in particular instances (by the next-generation matrix method or by direct computations, which are unnecessary). This motivated us to redraw the attention to these basic laws and provide a self-contained reference of related formulas for (x, y, z) models. We propose to rebaptize the class to which they apply as matrix SIR epidemic models, abbreviated as SYR, to emphasize the similarity to the classic SIR case. For the case of one susceptible class, we propose to use the name SIR-PH, due to a simple probabilistic interpretation as SIR models where the exponential infection time has been replaced by a PH-type distribution. We note that to each SIR-PH model, one may associate a scalar quantity Y(t) which satisfies "classic SIR relations", see (3.8). In the case of several susceptible classes, this generalizes to (5.10); in a future paper, we will show that (3.8), (5.10) may be used to obtain approximate control policies which compare well with the optimal control of the original model.
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
Authors: Xuanyu Zhu, Yang Gao, Feng Liu, Stuart Crozier, Hongfu Sun
Date: 2021-06-01 00:00:00

Introduction: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can substantially shorten the scan time or enhance the spatial resolution without increasing scan time; however, this may lead to significant DGM susceptibility underestimation. Method: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages. Pre-processed magnetic field maps are extended symmetrically from the centre of globus pallidus in the coronal plane to simulate QSM acquisitions of difference spatial coverages.Results: The proposed xQSM network led to the lowest DGM contrast lost with the smallest susceptibility variation range across all spatial coverages. For the digital brain phantom simulation, xQSM improved the DGM susceptibility underestimation more than 20% in small spatial coverages. For the in vivo acquisition, less than 5% DGM susceptibility error was achieved in 48 mm axial slabs using the xQSM network, while a minimum of 112 mm coverage was required for conventional methods. It is also shown that the background field removal process performed worse in reduced brain coverages, which further deteriorated the subsequent dipole inversion. Conclusion: The recently proposed deep learning-based xQSM method significantly improves the accuracy of DGM QSM from small spatial coverages as compared with conventional QSM algorithms, which can shorten DGM QSM acquisition time substantially.
A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting
Authors: Chao Wang, Richard Gerlach
Date: 2021-06-01 00:00:00

This paper proposes an innovative threshold measurement equation to be employed in a Realized-GARCH framework. The proposed framework incorporates a nonlinear threshold regression specification to consider the leverage effect and model the contemporaneous dependence between the observed realized measure and hidden volatility. A Bayesian Markov Chain Monte Carlo method is adapted and employed for model estimation, with its validity assessed via a simulation study. The validity of incorporating the proposed measurement equation in Realized-GARCH type models is evaluated via an empirical study, forecasting the 1% and 2.5% Value-at-Risk and Expected Shortfall on six market indices with two different out-of-sample sizes. The proposed framework is shown to be capable of producing competitive tail risk forecasting results in comparison to the GARCH and Realized-GARCH type models.
Integer-Only Neural Network Quantization Scheme Based on Shift-Batch-Normalization
Authors: Qingyu Guo, YuAn Wang, Xiaoxin Cui
Date: 2021-05-28 00:00:00
Tasks: Quantization

Neural networks are very popular in many areas, but great computing complexity makes it hard to run neural networks on devices with limited resources. To address this problem, quantization methods are used to reduce model size and computation cost, making it possible to use neural networks on embedded platforms or mobile devices. In this paper, an integer-only-quantization scheme is introduced. This scheme uses one layer that combines shift-based batch normalization and uniform quantization to implement 4-bit integer-only inference. Without big integer multiplication(which is used in previous integer-only-quantization methods), this scheme can achieve good power and latency efficiency, and is especially suitable to be deployed on co-designed hardware platforms. Tests have proved that this scheme works very well for easy tasks. And for tough tasks, performance loss can be tolerated for its inference efficiency. Our work is available on github: https://github.com/hguq/IntegerNet.
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video
Authors: Ruoyu Xue, Jingyuan Chen, Yajun Fang
Date: 2021-05-11 00:00:00
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, Time Series, Time Series Analysis

The intelligent surveillance system urgently needs the real-time machine recognition of abnormal events to solve
the extremely uneven human supervision resource and digital cameras. Besides, the number of anomaly types that real-time
machine monitoring could recognize has not met the need. This paper presents a fast and robust methodology for real-time
anomaly detection under different scenarios. We created the Video-Energy-Vector(VEV) to significantly reduce the dimension of feature maps while maintaining the spatial-temporal information. We applied the proposed method on different computer vision features to evaluate the effectiveness of common features to different types of abnormal events based on SVM. Also, we adopted the voting model among different features, which significantly increased the performance. Further More, the small video size to be trained guaranteed the real-time efficiency. The result of the modified UCF-Crime Dataset has proved that our approach has achieved robust results and had the generalization ability on new anomaly types.
Evaluation of Transportation Systems and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness and Sustainability
Authors: Lixin Xu, Lin Li, Kunniang Liu, Jiarui Zhang, Yuanning Chang, Yunpeng Fang, Hao Yuan, Zhiyuan Yang, Jingyuan Chen, Yiyao Wang, Yajun Fang
Date: 2021-05-11 00:00:00
Tasks: Decision Making, energy management, Management

ransportation systems have revolutionized the form of society. With modern transportation methods, countries and cities are becoming more interconnected, allowing for less traveling time and efficiency. However, the transportation system nowadays is also facing great challenges. The limited resources cannot meet the ever-growing demand for transportation, the expensive modern lifestyle puts the environment we rely on in jeopardy, the exhaustion of traditional transportation tools damages the health of all living creatures, and the current transportation system even brings about many safety and security issues. Although the Intelligent Transportation System (ITS) has been proposed to address these issues in the last century, many connectivity problems are still not considered. In this paper, we evaluate, from the UV perspective, the challenges of the transportation system and the current status of ITS based on a framework of closed feedback control loop: data acquisition, communication, decision making, and action. We propose that an effective ITS should take into consideration the interaction between the ITS and other seven smart city subsystems: smart home and community, smart medicine and healthcare, smart energy management, smart city infrastructure, smart environmental protection, smart response system for city emergency, and smart humanity, and also study how the ITS would be affected by four major impacting factors of smart cities: information flow, material cycle, lifestyle, and community. This systematic study will help us explore in-depth the complicated dynamic relationship between multiple impacting factors and propose a UV-oriented, integrated, resilient, inclusive, and sustainable development framework design to address current imminent challenges and to improve our modern intelligent transportation system through our innovative methods of accurate static and dynamic information matching based on the redundancy in data acquisition, novel communication mechanism based on multi-dimensional layers of information that ensures the information effectiveness, robustness and self-adaptive methods, and demand-response management.
Few-shot Pseudo-Labeling for Intent Detection
Authors: Thomas Dopierre, Christophe Gravier, Julien Subercaze, Wilfried Logerais
Date: 2020-12-08 00:00:00
Proceeding: COLING 2020 8
Tasks: Clustering, Intent Detection, text-classification, Text Classification

In this paper, we introduce a state-of-the-art pseudo-labeling technique for few-shot intent detection. We devise a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances. We show that our two-step method yields significant improvement over existing solutions. This performance is achieved on multiple intent detection datasets, even in more challenging situations where the number of classes is large or when the dataset is highly imbalanced. Moreover, we confirm this results on the more general text classification task. We also demonstrate that our approach nicely complements existing solutions, thereby providing an even stronger state-of-the-art ensemble method.
Systematic Generation of Diverse Benchmarks for DNN Verification
Authors: Dong Xu, David Shriver, Matthew B. Dwyer, Sebastian Elbaum
Date: 2020-07-14 00:00:00

The field of verification has advanced due to the interplay of theoretical development and empirical evaluation. Benchmarks play an important role in this by supporting the assessment of the state-of-the-art and comparison of alternative verification approaches. Recent years have witnessed significant developments in the verification of deep neural networks, but diverse benchmarks representing the range of verification problems in this domain do not yet exist. This paper describes a neural network verification benchmark generator, GDVB, that systematically varies aspects of problems in the benchmark that influence verifier performance. Through a series of studies, we illustrate how GDVB can assist in advancing the sub-field of neural network verification by more efficiently providing richer and less biased sets of verification problems.
Opening the Black Box of Deep Neural Networks in Physical Layer Communication
Authors: Jun Liu, Haitao Zhao, Dongtang Ma, Kai Mei, Jibo Wei
Date: 2021-06-02 00:00:00

Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems. However, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques and their cost in terms of computational complexity. We further investigate and also experimentally validate how information is flown in a DNN-based communication system under the information theoretic concepts.
A Differentiable Point Process with Its Application to Spiking Neural Networks
Authors: Hiroshi Kajino
Date: 2021-06-02 00:00:00
Tasks: Variational Inference

This paper is concerned about a learning algorithm for a probabilistic model of spiking neural networks (SNNs). Jimenez Rezende & Gerstner (2014) proposed a stochastic variational inference algorithm to train SNNs with hidden neurons. The algorithm updates the variational distribution using the score function gradient estimator, whose high variance often impedes the whole learning algorithm. This paper presents an alternative gradient estimator for SNNs based on the path-wise gradient estimator. The main technical difficulty is a lack of a general method to differentiate a realization of an arbitrary point process, which is necessary to derive the path-wise gradient estimator. We develop a differentiable point process, which is the technical highlight of this paper, and apply it to derive the path-wise gradient estimator for SNNs. We investigate the effectiveness of our gradient estimator through numerical simulation.
Cleaning and Structuring the Label Space of the iMet Collection 2020
Authors: Vivien Nguyen, Sunnie S. Y. Kim
Date: 2021-06-01 00:00:00
Tasks: Attribute

The iMet 2020 dataset is a valuable resource in the space of fine-grained art attribution recognition, but we believe it has yet to reach its true potential. We document the unique properties of the dataset and observe that many of the attribute labels are noisy, more than is implied by the dataset description. Oftentimes, there are also semantic relationships between the labels (e.g., identical, mutual exclusion, subsumption, overlap with uncertainty) which we believe are underutilized. We propose an approach to cleaning and structuring the iMet 2020 labels, and discuss the implications and value of doing so. Further, we demonstrate the benefits of our proposed approach through several experiments. Our code and cleaned labels are available at https://github.com/sunniesuhyoung/iMet2020cleaned.
LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations
Authors: Ruisheng Cao, Lu Chen, Zhi Chen, Yanbin Zhao, Su Zhu, Kai Yu
Date: 2021-06-02 00:00:00
Proceeding: ACL 2021 5
Tasks: Text to SQL, Text-To-SQL

This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task. Previous methods are typically node-centric and merely utilize different weight matrices to parameterize edge types, which 1) ignore the rich semantics embedded in the topological structure of edges, and 2) fail to distinguish local and non-local relations for each node. To this end, we propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying relational features without constructing meta-paths. By virtue of the line graph, messages propagate more efficiently through not only connections between nodes, but also the topology of directed edges. Furthermore, both local and non-local relations are integrated distinctively during the graph iteration. We also design an auxiliary task called graph pruning to improve the discriminative capability of the encoder. Our framework achieves state-of-the-art results (62.8% with Glove, 72.0% with Electra) on the cross-domain text-to-SQL benchmark Spider at the time of writing.
DynaEval: Unifying Turn and Dialogue Level Evaluation
Authors: Chen Zhang, Yiming Chen, Luis Fernando D'Haro, Yan Zhang, Thomas Friedrichs, Grandee Lee, Haizhou Li
Date: 2021-06-02 00:00:00
Proceeding: ACL 2021 5
Tasks: Dialogue Evaluation

A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are focused very much on the turn-level quality, while ignoring such dynamics. To this end, we propose DynaEval, a unified automatic evaluation framework which is not only capable of performing turn-level evaluation, but also holistically considers the quality of the entire dialogue. In DynaEval, the graph convolutional network (GCN) is adopted to model a dialogue in totality, where the graph nodes denote each individual utterance and the edges represent the dependency between pairs of utterances. A contrastive loss is then applied to distinguish well-formed dialogues from carefully constructed negative samples. Experiments show that DynaEval significantly outperforms the state-of-the-art dialogue coherence model, and correlates strongly with human judgements across multiple dialogue evaluation aspects at both turn and dialogue level.