pwc-archive/papers-with-abstracts huggingface dataset:
Financial Markets and the Phase Transition between Water and Steam
Authors: Christof Schmidhuber
Date: 2021-07-08 00:00:00

Motivated by empirical observations on the interplay of trends and reversion, a lattice gas model of financial markets is presented. The shares of an asset are modeled by gas molecules that are distributed across a hidden social network of investors. The model is equivalent to the Ising model on this network, whose magnetization represents the deviation of the asset price from its value. Moreover, the system should drive itself to its critical temperature in efficient markets. There, it is characterized by universal critical exponents, in analogy with the second-order phase transition between water and steam. These critical exponents imply predictions for the auto-correlations of financial market returns and for Hurst exponents. For a simple network topology, consistency with empirical observations implies a fractal network dimension near 3, and a correlation time at least as long as the economic cyle. To also explain the observed market auto-correlations at intermediate scales, the model should be extended beyond the critical domain, to other network topologies, and to other models of critical dynamics.
CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search
Authors: Seyed Mojtaba Marvasti-Zadeh, Javad Khaghani, Li Cheng, Hossein Ghanei-Yakhdan, Shohreh Kasaei
Date: 2021-07-02 00:00:00
Tasks: Neural Architecture Search, Semantic Segmentation, Visual Tracking

A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results. Not surprisingly, the manual design process becomes a particularly challenging barrier, as it demands sufficient prior experience, enormous effort, intuition, and perhaps some good luck. Meanwhile, neural architecture search has gaining grounds in practical applications as a promising method in tackling the issue of automated search of feasible network structures. In this work, we propose a novel cell-level differentiable architecture search mechanism with early stopping to automate the network design of the tracking module, aiming to adapt backbone features to the objective of Siamese tracking networks during offline training. Besides, the proposed early stopping strategy avoids over-fitting and performance collapse problems leading to generalization improvement. The proposed approach is simple, efficient, and with no need to stack a series of modules to construct a network. Our approach is easy to be incorporated into existing trackers, which is empirically validated using different differentiable architecture search-based methods and tracking objectives. Extensive experimental evaluations demonstrate the superior performance of our approach over five commonly-used benchmarks.
NUS-HLT Report for ActivityNet Challenge 2021 AVA (Speaker)
Authors: Ruijie Tao, Zexu Pan, Rohan Kumar Das, Xinyuan Qian, Mike Zheng Shou, Haizhou Li
Date: 2021-06-01 00:00:00
Proceeding: The ActivityNet Large-Scale Activity Recognition Challenge Workshop, CVPR 2021 6
Tasks: Active Speaker Detection, Audio-Visual Active Speaker Detection

Active speaker detection (ASD) seeks to detect who is speaking in a visual scene of one or more speakers. The successful ASD depends on accurate interpretation of short-term and long-term audio and visual information, as well as audiovisual interaction. Unlike the prior work where systems makedecision instantaneously using short-term features, we propose a novel framework, named TalkNet, that makes decision by taking both short-term and long-term features into consideration. TalkNet consists of audio and visual temporal encoders for feature representation, audio-visual cross-attention mechanism for
inter-modality interaction, and a self-attention mechanism to capture long-term speaking evidence. The experiments demonstrate that TalkNet achieves 3.5% and 3.0% improvement over the state-of-the-art systems on the AVA-ActiveSpeaker validation and test dataset, respectively. We will release the codes, the models and data logs.
AAA: Fair Evaluation for Abuse Detection Systems Wanted
Authors: Agostina Calabrese, Michele Bevilacqua, Björn Ross, Rocco Tripodi, Roberto Navigli
Date: 2021-06-21 00:00:00
Proceeding: ACM Web Science 2021 6
Tasks: Abuse Detection, Abusive Language, Hate Speech Detection, Selection bias

User-generated web content is rife with abusive language that can harm others and discourage participation. Thus, a primary research aim is to develop abuse detection systems that can be used to alert and support human moderators of online communities. Such systems are notoriously hard to develop and evaluate. Even when they appear to achieve satisfactory performance on current evaluation metrics, they may fail in practice on new data. This is partly because datasets commonly used in this field suffer from selection bias, and consequently, existing supervised models overrely on cue words such as group identifiers (e.g., gay and black) which are not inherently abusive. Although there are attempts to mitigate this bias, current evaluation metrics do not adequately quantify their progress. In this work, we introduce Adversarial Attacks against Abuse (AAA), a new evaluation strategy and associated metric that better captures a model’s performance on certain classes of hard-to-classify microposts, and for example penalises systems which are biased on low-level lexical features. It does so by adversarially modifying the model developer’s training and test data to generate plausible test samples dynamically. We make AAA available as an easy-to-use tool, and show its effectiveness in error analysis by comparing the AAA performance of several state-of-the-art models on multiple datasets. This work will inform the development of detection systems and contribute to the fight against abusive language online.
Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network
Authors: Hongzhi Shi, Quanming Yao, Qi Guo, Yaguang Li, Lingyu Zhang, Jieping Ye, Yong Li, Yan Liu
Date: 2020-04-01 00:00:00
Proceeding: 2020 IEEE 36th International Conference on Data Engineering (ICDE) 2020 4
Tasks: Philosophy

Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation. However, it is extremely challenging because of three reasons: first, correlations exist between both origins and destinations; second, the correlations are dynamic across the time; at last, there are multiple correlations from different aspects. To the best of our knowledge, existing models for OD flow prediction cannot tackle all of these three issues simultaneously. We propose Multi-Perspective Graph Convolutional Networks (MPGCN) to capture the complex dependencies. Our proposed model first utilizes long short-term memory (LSTM) network to extract temporal features for each OD pair and then learns the spatial dependency of origins and destinations by a two-dimensional graph convolutional network. Furthermore, we design a dynamic graph together with two static graphs to capture the complicated spatial dependencies and use an average strategy to obtain the final predicted OD flow. We conduct extensive experiments on two large-scale and real-world datasets, which not only demonstrate our design philosophy but also validate the effectiveness of the proposed model.
Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks
Authors: Zhaonan Wang, Tianqi Xia, Renhe Jiang, Xin Liu, Kyoung-Sook Kim, Xuan Song, Ryosuke Shibasaki
Date: 2021-04-01 00:00:00
Proceeding: 2021 IEEE 37th International Conference on Data Engineering (ICDE) 2021 4

Forecasting regional ambulance demand plays a fundamental part in dynamic fleet allocation and redeployment. This topic has been gaining increasing significance, as virtually every country is experiencing an aging population, with generally higher level of vulnerability and demand for the emergency medical service (EMS). Although exploring the spatial and temporal correlations in EMS historical records, the existing methods principally consider the former time-invariant, which does not necessarily hold in reality. Moreover, this assumption ignores the fact that the behind-the-scenes dynamics are people, whose demographic profiles and activity patterns could be determinants of regional EMS demands. In this paper, we are therefore motivated to mine the collective daily routines in human mobility, to further represent the evolving spatial correlations. Particularly, we model profiled mobility groups as multiple random walkers and propose a novel bicomponent neural network, including a heterogeneous multi-graph convolution layer and spatio-temporal interlacing attention module, to perform the prediction task. Experimental results on the real-world data verify the effectiveness of introducing dynamic human mobility and the advantage of our approach over the state-of-the-art models.
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events
Authors: Renhe Jiang, Xuan Song, Dou Huang, Xiaoya Song, Tianqi Xia, Zekun Cai, Zhaonan Wang, Kyoung-Sook Kim, Ryosuke Shibasaki
Date: 2019-08-01 00:00:00
Proceeding: 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019 8
Tasks: Management, Prediction, Video Prediction

Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g. police) and public service operators (e.g. subway/bus operator) to protect people’s safety or maintain the operation of public infrastructures. However, under such event situations, human behavior will become very different from daily routines, which makes prediction of crowd dynamics at big events become highly challenging, especially at a citywide level. Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. Motivated by these, we build an online system called DeepUrbanEvent which can iteratively take citywide crowd dynamics from the current one hour as input and report the prediction results for the next one hour as output. A novel deep learning architecture built with recurrent neural networks is designed to effectively model these highly-complex sequential data in an analogous manner to video prediction tasks. Experimental results demonstrate the superior performance of our proposed methodology to the existing approaches. Lastly, we apply our prototype system to multiple big real-world events and show that it is highly deployable as an online crowd management system.
DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
Authors: Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Quanjun Chen, Kota Tsubouchi, Xuan Song, Ryosuke Shibasaki
Date: 2021-05-03 00:00:00
Proceeding: IEEE Transactions on Knowledge and Data Engineering 2021 5
Tasks: Management

Predicting the density and flow of the crowd or traffic at a citywide level becomes possible by using the big data and
cutting-edge AI technologies. It has been a very significant research topic with high social impact, which can be widely applied to
emergency management, traffic regulation, and urban planning. In particular, by meshing a large urban area to a number of
fine-grained mesh-grids, citywide crowd and traffic information in a continuous time period can be represented with 4D tensor
(Timestep, Height, Width, Channel). Based on this idea, a series of methods have been proposed to address grid-based prediction for
citywide crowd and traffic. In this study, we revisit the density and in-out flow prediction problem and publish a new aggregated human
mobility dataset generated from a real-world smartphone application. Comparing with the existing ones, our dataset holds several
advantages including large mesh-grid number, fine-grained mesh size, and high user sample. Towards this large-scale crowd dataset,
we propose a novel deep learning model called DeepCrowd by designing pyramid architectures and high-dimensional attention
mechanism based on Convolutional LSTM. Lastly, thorough and comprehensive performance evaluations are conducted to
demonstrate the superiority of the proposed DeepCrowd comparing to multiple state-of-the-art methods.
DataDepsGenerators. jl: making reusing data easy by automatically generating DataDeps. jl registration code
Authors: Lyndon White, Sebastin Santy
Date: 2018-11-06 00:00:00
Proceeding: The Journal of Open Source Software 2018 11

DataDepsGenerators.jl is a tool written to help users of the Julia programming language(Bezanson, Edelman, Karpinski, & Shah, 2017), to observe best practices when making use of published datasets. Using the metadata present in published datasets, it generates the code for the data dependency registration blocks required by DataDeps.jl (White, Togneri, Liu, & Bennamoun, 2018). These registration blocks are effectively executable metadata, which can be resolved by DataDeps.jl to download the dataset. They include a message that is displayed to the user whenever the data set is automatically downloaded. This message should include provenance information on the dataset, so that downstream users know its original source and details on its processing
Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
Authors: Long T., Fortunato N.M., Opahle I., Zhang Y., Samathrakis I., Shen C., Gutfleisch O., Zhang H.
Date: 2021-05-10 00:00:00
Proceeding: npj Computational Materials 2021 5
Tasks: Formation Energy, Generative Adversarial Network

Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model, which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backward propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range, and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium. The method can be extended to multicomponent systems for multi-objective optimization, which paves the way to achieve the inverse design of materials with optimal properties.
Constructive Observer Design for Visual Simultaneous Localisation and Mapping
Authors: Pieter van Goor, Robert Mahony, Tarek Hamel, Jochen Trumpf
Date: 2020-06-09 00:00:00

Visual Simultaneous Localisation and Mapping (VSLAM) is a well-known problem in robotics with a large range of applications. This paper presents a novel approach to VSLAM by lifting the observer design to a novel Lie group on which the system output is equivariant. The perspective gained from this analysis facilitates the design of a non-linear observer with almost semi-globally asymptotically stable error dynamics. Simulations are provided to illustrate the behaviour of the proposed observer and experiments on data gathered using a fixed-wing UAV flying outdoors demonstrate its performance.
An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions
Authors: Ramin Toosi, Mohammad Ali Akhaee, Mohammad-Reza A. Dehaqani
Date: 2021-07-06 00:00:00
Proceeding: Scientific Reports 2021 7
Tasks: Spike Sorting

Developing high-density electrodes for recording large ensembles of neurons provides a unique opportunity for understanding the mechanism of the neuronal circuits. Nevertheless, the change of brain tissue around chronically implanted neural electrodes usually causes spike wave-shape distortion and raises the crucial issue of spike sorting with an unstable structure. The automatic spike sorting algorithms have been developed to extract spikes from these big extracellular data. However, due to the spike wave-shape instability, there have been a lack of robust spike detection procedures and clustering to overcome the spike loss problem. Here, we develop an automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions to address these distortions and instabilities. The adaptive detection procedure applies to the detected spikes, consists of multi-point alignment and statistical filtering for removing mistakenly detected spikes. The detected spikes are clustered based on the mixture of skew-t distributions to deal with non-symmetrical clusters and spike loss problems. The proposed algorithm improves the performance of the spike sorting in both terms of precision and recall, over a broad range of signal-to-noise ratios. Furthermore, the proposed algorithm has been validated on different datasets and demonstrates a general solution to precise spike sorting, in vitro and in vivo.
One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline
Authors: Michele Bevilacqua, Rexhina Blloshmi, Roberto Navigli
Date: 2021-05-18 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2021 5
Tasks: AMR Parsing, AMR-to-Text Generation, Decoder, Semantic Parsing, Text Generation

In Text-to-AMR parsing, current state-of-the-art semantic parsers 
use cumbersome pipelines integrating several different modules or components, and exploit graph recategorization, i.e., a set of content-specific heuristics that are developed on the basis of the training set. However, the generalizability of graph recategorization in an out-of-distribution setting is unclear.
In contrast, state-of-the-art AMR-to-Text generation, which can be seen as the inverse to parsing, is based on simpler seq2seq.
In this paper, we cast Text-to-AMR and AMR-to-Text as a symmetric transduction task and show that by devising a careful graph linearization and extending a pretrained encoder-decoder model, it is possible to obtain state-of-the-art performances in both tasks using the very same seq2seq approach, i.e., SPRING (\textit{\acl{spring}}). Our model does not require complex pipelines, nor heuristics built on heavy assumptions. In fact, we drop the need for graph recategorization, showing that this technique is actually harmful outside of the standard benchmark.
Finally, we outperform the previous state of the art on the English AMR 2.0 dataset by a large margin: on Text-to-AMR we obtain an improvement of 3.6 Smatch points,  while on AMR-to-Text we outperform the state of the art by 11.2 BLEU points. 
We release the software at github.com/SapienzaNLP/spring.
Comparison of Stochastic Forecasting Models
Authors: Shalin Shah
Date: 2021-07-11 00:00:00
Proceeding: OSF Preprints 2021 7
Tasks: All

In this work, we compare several stochastic forecasting techniques like Stochastic Differential Equations (SDE), ARIMA, the Bayesian filter, Geometric Brownian motion (GBM), and the Kalman filter. We use historical daily stock prices of Microsoft (MSFT), Target (TGT) and Tesla (TSLA) and apply all algorithms to try to predict 54 days ahead. We find that there are instances in which all algorithms do well, or do poorly. We find that all three stocks have a strong auto-correlation and a high Hurst factor which shows that it is possible to predict future prices based on a short history of past prices. In our geometric Brownian motion model, we have two parameters for drift and diffusion which are not time dependent. In our more general SDE model (TDNGBM), we have time-dependent drift and time-dependent diffusion terms which makes it more effective than GBM. We measure all algorithms on the correlation between the predicted and actual values, the mean absolute error (MAE) and also the confidence bounds generated by the methods. Confidence intervals are more important than point forecasts, and we see that TDNGBM and ARIMA produce good bounds.
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference
Authors: Wenting Jiang, Yicheng Jiang, Lu Zhang, Changmiao Wang, Xiaoguang Han, Shuixing Zhang, Xiang Wan, Shuguang Cui
Date: 2021-07-09 00:00:00
Tasks: Segmentation

Automatic segmentation of hepatocellular carcinoma (HCC) in Digital Subtraction Angiography (DSA) videos can assist radiologists in efficient diagnosis of HCC and accurate evaluation of tumors in clinical practice. Few studies have investigated HCC segmentation from DSA videos. It shows great challenging due to motion artifacts in filming, ambiguous boundaries of tumor regions and high similarity in imaging to other anatomical tissues. In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset. We also propose a novel segmentation network called DSA-LTDNet, including a segmentation sub-network, a temporal difference learning (TDL) module and a liver region segmentation (LRS) sub-network for providing additional guidance. DSA-LTDNet is preferable for learning the latent motion information from DSA videos proactively and boosting segmentation performance. All of experiments are conducted on our self-collected dataset. Experimental results show that DSA-LTDNet increases the DICE score by nearly 4% compared to the U-Net baseline.
Joint Matrix Decomposition for Deep Convolutional Neural Networks Compression
Authors: Shaowu Chen, Jiahao Zhou, Weize Sun, Lei Huang
Date: 2021-07-09 00:00:00
Tasks: Efficient Neural Network, Matrix Factorization / Decomposition, Neural Network Compression

Deep convolutional neural networks (CNNs) with a large number of parameters require intensive computational resources, and thus are hard to be deployed in resource-constrained platforms. Decomposition-based methods, therefore, have been utilized to compress CNNs in recent years. However, since the compression factor and performance are negatively correlated, the state-of-the-art works either suffer from severe performance degradation or have relatively low compression factors. To overcome this problem, we propose to compress CNNs and alleviate performance degradation via joint matrix decomposition, which is different from existing works that compressed layers separately. The idea is inspired by the fact that there are lots of repeated modules in CNNs. By projecting weights with the same structures into the same subspace, networks can be jointly compressed with larger ranks. In particular, three joint matrix decomposition schemes are developed, and the corresponding optimization approaches based on Singular Value Decomposition are proposed. Extensive experiments are conducted across three challenging compact CNNs for different benchmark data sets to demonstrate the superior performance of our proposed algorithms. As a result, our methods can compress the size of ResNet-34 by 22X with slighter accuracy degradation compared with several state-of-the-art methods.