pwc-archive/papers-with-abstracts huggingface dataset:
Characterizing and comparing external measures for the assessment of cluster analysis and community detection
Authors: Nejat Arinik, Vincent Labatut, Rosa Figueiredo
Date: 2021-02-01 00:00:00
Tasks: Community Detection, graph partitioning

In the context of cluster analysis and graph partitioning, many external evaluation measures have been proposed in the literature to compare two partitions of the same set. This makes the task of selecting the most appropriate measure for a given situation a challenge for the end user. However, this issue is overlooked in the literature. Researchers tend to follow tradition and use the standard measures of their field, although they often became standard only because previous researchers started consistently using them. In this work, we propose a new empirical evaluation framework to solve this issue, and help the end user selecting an appropriate measure for their application. For a collection of candidate measures, it first consists in describing their behavior by computing them for a generated dataset of partitions, obtained by applying a set of predefined parametric partition transformations. Second, our framework performs a regression analysis to characterize the measures in terms of how they are affected by these parameters and transformations. This allows both describing and comparing the measures. Our approach is not tied to any specific measure or application, so it can be applied to any situation. We illustrate its relevance by applying it to a selection of standard measures, and show how it can be put in practice through two concrete use cases.
Pyramid scheme in stock market: a kind of financial market simulation
Authors: Yong Shi, Bo Li, Guangle Du
Date: 2021-02-03 00:00:00

Artificial stock market simulation based on agent is an important means to study financial market. Based on the assumption that the investors are composed of a main fund, small trend and contrarian investors characterized by four parameters, we simulate and research a kind of financial phenomenon with the characteristics of pyramid schemes. Our simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors, the small investors' parameters of taking profit and stopping loss, the order size of the main fund and the strategies adopted by the main fund. Our work are helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets, design trading rules for regulators and develop trading strategies for investors.
Clearing prices under margin calls and the short squeeze
Authors: Zachary Feinstein
Date: 2021-02-03 00:00:00

In this paper, we propose a clearing model for prices in a financial markets due to margin calls on short sold assets. In doing so, we construct an explicit formulation for the prices that would result immediately following asset purchases and a margin call. The key result of this work is the determination of a threshold short interest ratio which, if exceeded, results in the discontinuity of the clearing prices due to a feedback loop.
Learning Models of Model Predictive Controllers using Gradient Data
Authors: Rebecka Winqvist, Arun Venkitaraman, Bo Wahlberg
Date: 2021-02-03 00:00:00
Tasks: Experimental Design

This paper investigates controller identification given data from a Model Predictive Controller (MPC) with constraints. We propose an approach for learning MPC that explicitly uses the gradient information in the training process. This is motivated by the observation that recent differentiable convex optimization MPC solvers can provide both the optimal feedback law from the state to control input as well as the corresponding gradient. As a proof of concept, we apply this approach to explicit MPC (eMPC), for which the feedback law is a piece-wise affine function of the state, but the number of pieces grows rapidly with the state dimension. Controller identification can here be used to find an approximate lower complexity functional approximation of the controller. The eMPC is modelled with a Neural Network (NN) with Rectified Linear Units (ReLUs), since such NN can represent any piece-wise affine function. A motivation is to replace on-line solvers with neural networks to implement MPC and to simplify the evaluation of the function in larger input dimensions. We also study experimental design and model evaluation in this framework, and propose a hit and run sampling algorithm for input design. The proposed algorithm are illustrated and numerically evaluated on a second order MPC problem.
The Great Equalizer: Medicare and the Geography of Consumer Financial Strain
Authors: Paul Goldsmith-Pinkham, Maxim Pinkovskiy, Jacob Wallace
Date: 2021-02-03 00:00:00
Tasks: regression

We use a five percent sample of Americans' credit bureau data, combined with a regression discontinuity approach, to estimate the effect of universal health insurance at age 65-when most Americans become eligible for Medicare-at the national, state, and local level. We find a 30 percent reduction in debt collections-and a two-thirds reduction in the geographic variation in collections-with limited effects on other financial outcomes. The areas that experienced larger reductions in collections debt at age 65 were concentrated in the Southern United States, and had higher shares of black residents, people with disabilities, and for-profit hospitals.
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
Authors: Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma
Date: 2020-07-01 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Memorization, Recommendation Systems

Knowledge graph (KG) contains well-structured external information and has shown to be effective for high-quality recommendation. However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG. While for model learning, these methods mainly rely on Negative Sampling (NS) to optimize the models for both KG embedding task and recommendation task. Since NS is not robust (e.g., sampling a small fraction ofnegative instances may lose lots ofuseful information), it is reasonable to argue that these methods are insufficient to capture collaborative information among users, items, and entities.
In this paper, we propose a novel Jointly Non-Sampling learning model for Knowledge graph enhanced Recommendation (JNSKR). Specifically, we first design a new efficient NS optimization algorithm for knowledge graph embedding learning. The subgraphs are then encoded by the proposed attentive neural network to better characterize user preference over items. Through novel designs of memorization strategies and joint learning framework, JNSKR not only models the fine-grained connections among users, items, and entities, but also efficiently learns model parameters from the whole training data (including all non-observed data) with a rather low time complexity. Experimental results on two public benchmarks show that JNSKR significantly outperforms the state-of-the-art methods like RippleNet and KGAT. Remarkably, JNSKR also shows significant advantages in training efficiency (about 20 times faster than KGAT), which makes it more applicable to real-world largescale systems.
CRF-based Single-stage Acoustic Modeling with CTC Topology
Authors: Hongyu Xiang, Zhijian Ou
Date: 2019-04-16 00:00:00
Tasks: Benchmarking, Speech Recognition

In this paper, we develop conditional random field (CRF) based single-stage (SS) acoustic modeling with connectionist temporal classification (CTC) inspired state topology, which is called CTC-CRF for short.
CTC-CRF is conceptually simple, which basically implements a CRF layer on top of features generated by the bottom neural network with the special state topology.
Like SS-LF-MMI (lattice-free maximum-mutual-information), CTC-CRFs can be trained from scratch (flat-start), eliminating GMM-HMM pre-training and tree-building.
Evaluation experiments are conducted on the WSJ, Switchboard and Librispeech datasets.
In a head-to-head comparison, the CTC-CRF model using simple Bidirectional LSTMs consistently outperforms the strong SS-LF-MMI, across all the three benchmarking datasets and in both cases of mono-phones and mono-chars.
Additionally, CTC-CRFs avoid some ad-hoc operation in SS-LF-MMI.
An evolutionary view on the emergence of Artificial Intelligence
Authors: Matheus E. Leusin, Bjoern Jindra, Daniel S. Hain
Date: 2021-01-30 00:00:00

This paper draws upon the evolutionary concepts of technological relatedness and knowledge complexity to enhance our understanding of the long-term evolution of Artificial Intelligence (AI). We reveal corresponding patterns in the emergence of AI - globally and in the context of specific geographies of the US, Japan, South Korea, and China. We argue that AI emergence is associated with increasing related variety due to knowledge commonalities as well as increasing complexity. We use patent-based indicators for the period between 1974-2018 to analyse the evolution of AI's global technological space, to identify its technological core as well as changes to its overall relatedness and knowledge complexity. At the national level, we also measure countries' overall specialisations against AI-specific ones. At the global level, we find increasing overall relatedness and complexity of AI. However, for the technological core of AI, which has been stable over time, we find decreasing related variety and increasing complexity. This evidence points out that AI innovations related to core technologies are becoming increasingly distinct from each other. At the country level, we find that the US and Japan have been increasing the overall relatedness of their innovations. The opposite is the case for China and South Korea, which we associate with the fact that these countries are overall less technologically developed than the US and Japan. Finally, we observe a stable increasing overall complexity for all countries apart from China, which we explain by the focus of this country in technologies not strongly linked to AI.
GraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs
Authors: Mugilan Mariappan, Keval Vora
Date: 2020-04-01 00:00:00

Efficient streaming graph processing systems leverage incremental processing by updating computed results to reflect the change in graph structure for the latest graph snapshot. Although certain monotonic path-based algorithms produce correct results by refining intermediate values via numerical comparisons, directly reusing values that were computed before mutation does not work correctly for algorithms that require BSP semantics. Since structural mutations in streaming graphs render the intermediate results unusable, exploiting incremental computation while simultaneously providing synchronous processing guarantees is challenging.

In this paper we develop GraphBolt which incrementally processes streaming graphs while guaranteeing BSP semantics. GraphBolt incorporates dependency-driven incremental processing where it first tracks dependencies to capture how intermediate values get computed, and then uses this information to incrementally propagate the impact of change across intermediate values. To support wide variety of graph-based analytics, GraphBolt provides a generalized incremental programming model that enables development of incremental versions of complex aggregations. Our evaluation shows that GraphBolt's incremental processing eliminates redundant computations and efficiently processes streaming graphs with varying mutation rates, starting from just a single edge mutation all the way up to 1 million edge mutations at a time. Furthermore, being specialized for graph computations, GraphBolt extracts high performance compared to Differential Dataflow.
Discretizing Unobserved Heterogeneity
Authors: Stéphane Bonhomme Thibaut Lamadon Elena Manresa
Date: 2021-02-03 00:00:00
Tasks: Clustering

We study discrete panel data methods where unobserved heterogeneity is revealed in a first step, in environments where population heterogeneity is not discrete. We focus on two-step grouped fixed-effects (GFE) estimators, where individuals are first classified into groups using kmeans clustering, and the model is then estimated allowing for group-specific heterogeneity. Our framework relies on two key properties: heterogeneity is a function - possibly nonlinear and time-varying - of a low-dimensional continuous latent type, and informative moments are available for classification. We illustrate the method in a model of wages and labor market participation, and in a probit model with time-varying heterogeneity. We derive asymptotic expansions of two-step GFE estimators as the number of groups grows with the two dimensions of the panel. We propose a data-driven rule for the number of groups, and discuss bias reduction and inference.
Artificial intelligence applied to bailout decisions in financial systemic risk management
Authors: Daniele Petrone, Neofytos Rodosthenous, Vito Latora
Date: 2021-02-03 00:00:00
Tasks: Management

We describe the bailout of banks by governments as a Markov Decision Process (MDP) where the actions are equity investments. The underlying dynamics is derived from the network of financial institutions linked by mutual exposures, and the negative rewards are associated to the banks' default. Each node represents a bank and is associated to a probability of default per unit time (PD) that depends on its capital and is increased by the default of neighbouring nodes. Governments can control the systemic risk of the network by providing additional capital to the banks, lowering their PD at the expense of an increased exposure in case of their failure. Considering the network of European global systemically important institutions, we find the optimal investment policy that solves the MDP, providing direct indications to governments and regulators on the best way of action to limit the effects of financial crises.
The VIX index under scrutiny of machine learning techniques and neural networks
Authors: Ali Hirsa, Joerg Osterrieder, Branka Hadji Misheva, Wenxin Cao, Yiwen Fu, Hanze Sun, Kin Wai Wong
Date: 2021-02-03 00:00:00
Tasks: BIG-bench Machine Learning

The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the market's expected volatility on the SP 500 Index, calculated and published by the Chicago Board Options Exchange (CBOE). It is also often referred to as the fear index or the fear gauge. The current VIX index value quotes the expected annualized change in the SP 500 index over the following 30 days, based on options-based theory and current options-market data. Despite its theoretical foundation in option price theory, CBOE's Volatility Index is prone to inadvertent and deliberate errors because it is weighted average of out-of-the-money calls and puts which could be illiquid. Many claims of market manipulation have been brought up against VIX in recent years. This paper discusses several approaches to replicate the VIX index as well as VIX futures by using a subset of relevant options as well as neural networks that are trained to automatically learn the underlying formula. Using subset selection approaches on top of the original CBOE methodology, as well as building machine learning and neural network models including Random Forests, Support Vector Machines, feed-forward neural networks, and long short-term memory (LSTM) models, we will show that a small number of options is sufficient to replicate the VIX index. Once we are able to actually replicate the VIX using a small number of SP options we will be able to exploit potential arbitrage opportunities between the VIX index and its underlying derivatives. The results are supposed to help investors to better understand the options market, and more importantly, to give guidance to the US regulators and CBOE that have been investigating those manipulation claims for several years.
Group Consensus of Linear Multi-agent Systems under Nonnegative Directed Graphs
Authors: Zhongchang Liu, Wing Shing Wong
Date: 2021-02-03 00:00:00
Tasks: Clustering

Group consensus implies reaching multiple groups where agents belonging to the same cluster reach state consensus. This paper focuses on linear multi-agent systems under nonnegative directed graphs. A new necessary and sufficient condition for ensuring group consensus is derived, which requires the spanning forest of the underlying directed graph and that of its quotient graph induced with respect to a clustering partition to contain equal minimum number of directed trees. This condition is further shown to be equivalent to containing cluster spanning trees, a commonly used topology for the underlying graph in the literature. Under a designed controller gain, lower bound of the overall coupling strength for achieving group consensus is specified. Moreover, the pattern of the multiple consensus states formed by all clusters is characterized when the overall coupling strength is large enough.
Analysis and Experimental Demonstration of Orthant-Symmetric Four-dimensional 7 bit/4D-sym Modulation for Optical Fiber Communication
Authors: Bin Chen, Alex Alvarado, Sjoerd van der Heide, Menno van den Hout, Hartmut Hafermann, Chigo Okonkwo
Date: 2020-03-28 00:00:00

We propose a new four-dimensional orthant-symmetric 128-ary modulation format (4D-OS128) with a spectral efficiency of 7bit/4D-sym. The proposed format fills the gap between polarization-multiplexed 8- and 16-ary quadrature-amplitude modulation (PM-8QAM and PM-16QAM).Numerical simulations show that 4D-OS128 outperforms two well-studied 4D geometrically-shaped modulation formats: 128SP-16QAM and 7b4D-2A8PSK by up to 0.65dB for bit-interleaved coded modulation at the same spectral efficiency. These gains are experimentally demonstrated in a 11$\times$233Gbit/s wavelength division multiplexing (WDM) transmission system operating at 5.95 bit/4D-sym over 6000km and 9000km for both EDFA-only and hybrid amplification scenarios, respectively. A reach increase of 15% is achieved with respect to 128-ary set-partitioning 16QAM. Furthermore, the proposed 4D-OS128 is also compared to $\boldsymbol{D}_4$ lattice-based constellation and probabilistically-shaped 16QAM with finite blocklength via simulation.
Multi-interactive Encoder-decoder Network for RGBT Salient Object Detection
Authors: Zhengzheng Tu, Zhun Li, Chenglong Li, Yang Lang, Jin Tang
Date: 2020-06-05 00:00:00
Tasks: Decoder, object-detection, Object Detection, Salient Object Detection

RGBT salient object detection (SOD) aims to segment the common prominent regions of visible and thermal infrared images. Existing RGBT SOD methods don't fully explore and exploit the potentials of complementarity of different modalities and the global context of image contents, which play a vital role in achieving accurate results. In this paper, we propose a multi-interactive Siamese decoder to mine and model the multi-type interactions for accurate RGBT SOD. In specific, we first encode RGB and thermal image pair into multi-level multi-modal representation. Then, we design a novel Siamese decoder to integrate the multi-level interactions of dual modalities and global contexts. With these interactions, our method works well in diversely challenging scenarios even in the presence of invalid modality. Moreover, the Siamese decoder employs label supervision to drive feature learning in each modality and the modality prejudice is thus suppressed. Finally, we carry out extensive experiments on several benchmark datasets, and the results show that the proposed method achieves the outstanding performance against state-of-the-art algorithms. The source code has released at
MeInGame: Create a Game Character Face from a Single Portrait
Authors: Jiangke Lin, Yi Yuan, Zhengxia Zou
Date: 2021-02-04 00:00:00
Tasks: 3D Face Reconstruction, Face Model, Face Reconstruction

Many deep learning based 3D face reconstruction methods have been proposed recently, however, few of them have applications in games. Current game character customization systems either require players to manually adjust considerable face attributes to obtain the desired face, or have limited freedom of facial shape and texture. In this paper, we propose an automatic character face creation method that predicts both facial shape and texture from a single portrait, and it can be integrated into most existing 3D games. Although 3D Morphable Face Model (3DMM) based methods can restore accurate 3D faces from single images, the topology of 3DMM mesh is different from the meshes used in most games. To acquire fidelity texture, existing methods require a large amount of face texture data for training, while building such datasets is time-consuming and laborious. Besides, such a dataset collected under laboratory conditions may not generalized well to in-the-wild situations. To tackle these problems, we propose 1) a low-cost facial texture acquisition method, 2) a shape transfer algorithm that can transform the shape of a 3DMM mesh to games, and 3) a new pipeline for training 3D game face reconstruction networks. The proposed method not only can produce detailed and vivid game characters similar to the input portrait, but can also eliminate the influence of lighting and occlusions. Experiments show that our method outperforms state-of-the-art methods used in games.
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency
Authors: Seokju Lee, Sunghoon Im, Stephen Lin, In So Kweon
Date: 2021-02-04 00:00:00
Tasks: Instance Segmentation, Monocular Depth Estimation, Motion Estimation, Optical Flow Estimation, Semantic Segmentation, Unsupervised Monocular Depth Estimation, Video Instance Segmentation

We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision. Our technical contributions are three-fold. First, we highlight the fundamental difference between inverse and forward projection while modeling the individual motion of each rigid object, and propose a geometrically correct projection pipeline using a neural forward projection module. Second, we design a unified instance-aware photometric and geometric consistency loss that holistically imposes self-supervisory signals for every background and object region. Lastly, we introduce a general-purpose auto-annotation scheme using any off-the-shelf instance segmentation and optical flow models to produce video instance segmentation maps that will be utilized as input to our training pipeline. These proposed elements are validated in a detailed ablation study. Through extensive experiments conducted on the KITTI and Cityscapes dataset, our framework is shown to outperform the state-of-the-art depth and motion estimation methods. Our code, dataset, and models are available at https://github.com/SeokjuLee/Insta-DM .