pwc-archive/papers-with-abstracts huggingface dataset:
Mesoscopic Structure of the Stock Market and Portfolio Optimization
Authors: Sebastiano Michele Zema, Giorgio Fagiolo, Tiziano Squartini, Diego Garlaschelli
Date: 2021-12-13 00:00:00
Tasks: Management, Portfolio Optimization

The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic `equally-weighted' portfolio. In this paper, we exploit clustering techniques derived from Random Matrix Theory (RMT) to study a third, intermediate (mesoscopic) market structure that turns out to be the most stable over time and provides important practical insights from a portfolio management perspective. First, we illustrate the benefits, in terms of predicted and realized risk profiles, of constructing portfolios by filtering out both random and systemic co-movements from the correlation matrix. Second, we redefine the portfolio optimization problem in terms of stock clusters that emerge after filtering. Finally, we propose a new wealth allocation scheme that attaches equal importance to stocks belonging to the same community and show that it further increases the reliability of the constructed portfolios. Results are robust across different time spans, cross-sectional dimensions and set of constraints defining the optimization problem
Hedging Cryptocurrency Options
Authors: Jovanka Lili Matic, Natalie Packham, Wolfgang Karl Härdle
Date: 2021-11-23 00:00:00
Tasks: Density Estimation, Management

The cryptocurrency market is volatile, non-stationary and non-continuous. Together with liquid derivatives markets, this poses a unique opportunity to study risk management, especially the hedging of options, in a turbulent market. We study the hedge behaviour and effectiveness for the class of affine jump diffusion models and infinite activity Levy processes. First, market data is calibrated to stochastic volatility inspired (SVI)-implied volatility surfaces to price options. To cover a wide range of market dynamics, we generate Monte Carlo price paths using an SVCJ model (stochastic volatility with correlated jumps), a close-to-actual-market GARCH-filtered kernel density estimation as well as a historical backtest. In all three settings, options are dynamically hedged with Delta, Delta-Gamma, Delta-Vega and Minimum Variance strategies. Including a wide range of market models allows to understand the trade-off in the hedge performance between complete, but overly parsimonious models, and more complex, but incomplete models. The calibration results reveal a strong indication for stochastic volatility, low jump frequency and evidence of infinite activity. Short-dated options are less sensitive to volatility or Gamma hedges. For longer-dated options, tail risk is consistently reduced by multiple-instrument hedges, in particular by employing complete market models with stochastic volatility.
Time-consistent mean-variance reinsurance-investment problem with long-range dependent mortality rate
Authors: Ling Wang, Mei Choi Chiu, Hoi Ying Wong
Date: 2021-12-13 00:00:00

This paper investigates the time-consistent mean-variance reinsurance-investment (RI) problem faced by life insurers. Inspired by recent findings that mortality rates exhibit long-range dependence (LRD), we examine the effect of LRD on RI strategies. We adopt the Volterra mortality model proposed in Wang et al.(2021) to incorporate LRD into the mortality rate process and describe insurance claims using a compound Poisson process with the intensity represented by stochastic mortality rate. Under the open-loop equilibrium mean-variance criterion, we derive explicit equilibrium RI controls and study the uniqueness of these controls in cases of constant and state-dependent risk aversion. We simultaneously resolve difficulties arising from unbounded non-Markovian parameters and sudden increases in the insurer's wealth process. We also use a numerical study to reveal the influence of LRD on equilibrium strategies.
Semantic Interaction in Augmented Reality Environments for Microsoft HoloLens
Authors: Peer Schüett, Max Schwarz, Sven Behnke
Date: 2021-11-18 00:00:00
Tasks: 2D Semantic Segmentation, Object, Semantic Segmentation

Augmented Reality is a promising technique for human-machine interaction. Especially in robotics, which always considers systems in their environment, it is highly beneficial to display visualizations and receive user input directly in exactly that environment. We explore this idea using the Microsoft HoloLens, with which we capture indoor environments and display interaction cues with known object classes. The 3D mesh recorded by the HoloLens is annotated on-line, as the user moves, with semantic classes using a projective approach, which allows us to use a state-of-the-art 2D semantic segmentation method. The results are fused onto the mesh; prominent object segments are identified and displayed in 3D to the user. Finally, the user can trigger actions by gesturing at the object. We both present qualitative results and analyze the accuracy and performance of our method in detail on an indoor dataset.
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation
Authors: Pouria Mehrabi, Hamid D. Taghirad
Date: 2021-12-01 00:00:00
Tasks: Bayesian Inference, Gaussian Processes

Autonomous Land Vehicles (ALV) shall efficiently recognize the ground in unknown environments. A novel $\mathcal{GP}$-based method is proposed for the ground segmentation task in rough driving scenarios. A non-stationary covariance function is utilized as the kernel for the $\mathcal{GP}$. The ground surface behavior is assumed to only demonstrate local-smoothness. Thus, point estimates of the kernel's length-scales are obtained. Thus, two Gaussian processes are introduced to separately model the observation and local characteristics of the data. While, the \textit{observation process} is used to model the ground, the \textit{latent process} is put on length-scale values to estimate point values of length-scales at each input location. Input locations for this latent process are chosen in a physically-motivated procedure to represent an intuition about ground condition. Furthermore, an intuitive guess of length-scale value is represented by assuming the existence of hypothetical surfaces in the environment that every bunch of data points may be assumed to be resulted from measurements from this surfaces. Bayesian inference is implemented using \textit{maximum a Posteriori} criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame. Simulation results shows the effectiveness of the proposed method even in an uneven, rough scene which outperforms similar Gaussian process based ground segmentation methods. While adjacent segments do not have similar ground structure in an uneven scene, the proposed method gives an efficient ground estimation based on a whole-frame viewpoint instead of just estimating segment-wise probable ground surfaces.
Neural Network Quantization for Efficient Inference: A Survey
Authors: Olivia Weng
Date: 2021-12-08 00:00:00
Tasks: Quantization, Survey

As neural networks have become more powerful, there has been a rising desire to deploy them in the real world; however, the power and accuracy of neural networks is largely due to their depth and complexity, making them difficult to deploy, especially in resource-constrained devices. Neural network quantization has recently arisen to meet this demand of reducing the size and complexity of neural networks by reducing the precision of a network. With smaller and simpler networks, it becomes possible to run neural networks within the constraints of their target hardware. This paper surveys the many neural network quantization techniques that have been developed in the last decade. Based on this survey and comparison of neural network quantization techniques, we propose future directions of research in the area.
Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings
Authors: Kingsley Nweye, Bo Liu, Peter Stone, Zoltan Nagy
Date: 2021-11-25 00:00:00
Tasks: Model Predictive Control, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Building upon prior research that highlighted the need for standardizing environments for building control research, and inspired by recently introduced challenges for real life reinforcement learning control, here we propose a non-exhaustive set of nine real world challenges for reinforcement learning control in grid-interactive buildings. We argue that research in this area should be expressed in this framework in addition to providing a standardized environment for repeatability. Advanced controllers such as model predictive control and reinforcement learning (RL) control have both advantages and disadvantages that prevent them from being implemented in real world problems. Comparisons between the two are rare, and often biased. By focusing on the challenges, we can investigate the performance of the controllers under a variety of situations and generate a fair comparison. As a demonstration, we implement the offline learning challenge in CityLearn and study the impact of different levels of domain knowledge and complexity of RL algorithms. We show that the sequence of operations utilized in a rule based controller (RBC) used for offline training affects the performance of the RL agents when evaluated on a set of four energy flexibility metrics. Longer offline learning from an optimized RBC leads to improved performance in the long run. RL agents that learn from a simplified RBC risk poorer performance as the offline learning period increases. We also observe no impact on performance from information sharing amongst agents. We call for a more interdisciplinary effort of the research community to address the real world challenges, and unlock the potential of grid-interactive building
On reducing the order of arm-passes bandit streaming algorithms under memory bottleneck
Authors: Santanu Rathod
Date: 2021-11-30 00:00:00

In this work we explore multi-arm bandit streaming model, especially in cases where the model faces resource bottleneck. We build over existing algorithms conditioned by limited arm memory at any instance of time. Specifically, we improve the amount of streaming passes it takes for a bandit algorithm to incur a $O(\sqrt{T\log(T)})$ regret by a logarithmic factor, and also provide 2-pass algorithms with some initial conditions to incur a similar order of regret.
Bayesian Nonparametric View to Spawning
Authors: Bahman Moraffah
Date: 2021-12-03 00:00:00

In tracking multiple objects, it is often assumed that each observation (measurement) is originated from one and only one object. However, we may encounter a situation that each measurement may or may not be associated with multiple objects at each time step --spawning. Therefore, the association of each measurement to multiple objects is a crucial task to perform in order to track multiple objects with birth and death. In this paper, we introduce a novel Bayesian nonparametric approach that models a scenario where each observation may be drawn from an unknown number of objects for which it provides a tractable Markov chain Monte Carlo (MCMC) approach to sample from the posterior distribution. The number of objects at each time step, itself, is also assumed to be unknown. We, then, show through experiments the advantage of nonparametric modeling to scenarios with spawning events. Our experiment results also demonstrate the advantages of our framework over the existing methods.
Representation Learning for Conversational Data using Discourse Mutual Information Maximization
Authors: Bishal Santra, Sumegh Roychowdhury, Aishik Mandal, Vasu Gurram, Atharva Naik, Manish Gupta, Pawan Goyal
Date: 2021-12-04 00:00:00
Proceeding: NAACL 2022 7
Tasks: Language Modeling, Language Modelling, Representation Learning

Although many pretrained models exist for text or images, there have been relatively fewer attempts to train representations specifically for dialog understanding. Prior works usually relied on finetuned representations based on generic text representation models like BERT or GPT-2. But such language modeling pretraining objectives do not take the structural information of conversational text into consideration. Although generative dialog models can learn structural features too, we argue that the structure-unaware word-by-word generation is not suitable for effective conversation modeling. We empirically demonstrate that such representations do not perform consistently across various dialog understanding tasks. Hence, we propose a structure-aware Mutual Information based loss-function DMI (Discourse Mutual Information) for training dialog-representation models, that additionally captures the inherent uncertainty in response prediction. Extensive evaluation on nine diverse dialog modeling tasks shows that our proposed DMI-based models outperform strong baselines by significant margins.
The 2018 Signal Separation Evaluation Campaign
Authors: Fabian-Robert Stöter, Antoine Liutkus, Nobutaka Ito
Date: 2018-04-17 00:00:00

This paper reports the organization and results for the 2018 community-based Signal Separation Evaluation Campaign (SiSEC 2018). This year's edition was focused on audio and pursued the effort towards scaling up and making it easier to prototype audio separation software in an era of machine-learning based systems. For this purpose, we prepared a new music separation database: MUSDB18, featuring close to 10h of audio. Additionally, open-source software was released to automatically load, process and report performance on MUSDB18. Furthermore, a new official Python version for the BSSEval toolbox was released, along with reference implementations for three oracle separation methods: ideal binary mask, ideal ratio mask, and multichannel Wiener filter. We finally report the results obtained by the participants.
Automatic micro-expression apex spotting using Cubic-LBP
Authors: Vida Esmaeili & Seyed Omid Shahdi
Date: 2020-04-15 00:00:00
Proceeding: Multimedia Tools and Applications, volume 79, pages 20221–20239 (2020) 2020 4

The main way to communicate is through non-verbal expressions, although it could totally be manipulated by the person to give false expression. Unlike ordinary facial expressions, facial micro-expression has characterized by subtle movement and short duration of appearance which unleashes the true expression beyond the control of the person. Due to the nature of micro-expression which is very brief in time and low in intensity, prevalent methods could not come up with its challenges. One of the well-known dynamic texture descriptors is Local Binary Patterns on Three Orthogonal Planes (LBP-TOP) which mainly lacks in grabbing most vital information. To address this issue in this paper, we propose a novel feature extractor called Cubic-LBP that computes LBP on fifteen introduced planes. We demonstrate the effectiveness of these planes to find the apex frame where maximum facial movements within video sequences have occurred. Moreover, the whole process of spotting the apex frame in this paper is done automatically. Achieving results of apex frame spotting is satisfying on CASME and CASME II databases in comparison with most relevant state-of-the-art methods.
ContrXT: Generating Contrastive Explanations from any Text Classifier
Authors: Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Andrea Seveso, Navid Nobani
Date: 2021-12-11 00:00:00
Proceeding: Information Fusion 2021 12
Tasks: Decision Making, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Interpretable Machine Learning, Text Classification

The need for explanations of ML systems is growing as new models outperform their predecessors while becoming more complex and less comprehensible for their end-users. Though several XAI methods have been proposed in recent years, not enough attention was paid to explaining how models change their behaviour in contrast with previous ones (e.g., due to retraining). In such cases, an XAI system should explain why the model changes its predictions concerning past outcomes. Capturing and understanding such differences is crucial, as the need for trust is key in any application to support human-AI decision-making processes.

This is the idea of ContrXT, a novel approach that (i) traces the decision criteria of a black box text classifier by encoding the changes in the decision logic through Binary Decision Diagrams. Then (ii) it provides global, model-agnostic, Time-Contrastive (T-contrast) explanations in natural language, estimating why – and to what extent – the model has modified its behaviour over time. We implemented and evaluated ContrXT over several supervised ML models trained on a benchmark dataset and a real-life application, showing it is effective in catching majorly changed classes and in explaining their variation through a user study.

The approach has been implemented, and it is available to the community both as a python package and through REST API, providing contrastive explanations as a service.
PantheonRL: A MARL Library for Dynamic Training Interactions
Authors: Bidipta Sarkar, Aditi Talati, Andy Shih, Dorsa Sadigh
Date: 2021-12-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We present PantheonRL, a multiagent reinforcement learning software package for dynamic training interactions such as round-robin, adaptive, and ad-hoc training. Our package is designed around flexible agent objects that can be easily configured to support different training interactions, and handles fully general multiagent environments with mixed rewards and n agents. Built on top of StableBaselines3, our package works directly with existing powerful deep RL algorithms. Finally, PantheonRL comes with an intuitive yet functional web user interface for configuring experiments and launching multiple asynchronous jobs. Our package can be found at https://github.com/Stanford-ILIAD/PantheonRL.
Co-training Transformer with Videos and Images Improves Action Recognition
Authors: BoWen Zhang, Jiahui Yu, Christopher Fifty, Wei Han, Andrew M. Dai, Ruoming Pang, Fei Sha
Date: 2021-12-14 00:00:00
Tasks: Action Classification, Action Recognition, Action Recognition In Videos, Object Recognition, Video Classification

In learning action recognition, models are typically pre-trained on object recognition with images, such as ImageNet, and later fine-tuned on target action recognition with videos. This approach has achieved good empirical performance especially with recent transformer-based video architectures. While recently many works aim to design more advanced transformer architectures for action recognition, less effort has been made on how to train video transformers. In this work, we explore several training paradigms and present two findings. First, video transformers benefit from joint training on diverse video datasets and label spaces (e.g., Kinetics is appearance-focused while SomethingSomething is motion-focused). Second, by further co-training with images (as single-frame videos), the video transformers learn even better video representations. We term this approach as Co-training Videos and Images for Action Recognition (CoVeR). In particular, when pretrained on ImageNet-21K based on the TimeSFormer architecture, CoVeR improves Kinetics-400 Top-1 Accuracy by 2.4%, Kinetics-600 by 2.3%, and SomethingSomething-v2 by 2.3%. When pretrained on larger-scale image datasets following previous state-of-the-art, CoVeR achieves best results on Kinetics-400 (87.2%), Kinetics-600 (87.9%), Kinetics-700 (79.8%), SomethingSomething-v2 (70.9%), and Moments-in-Time (46.1%), with a simple spatio-temporal video transformer.
Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting
Authors: Donghui Chen, Ling Chen, Zongjiang Shang, Youdong Zhang, Bo Wen, Chenghu Yang
Date: 2021-12-14 00:00:00
Tasks: Graph Learning, Multivariate Time Series Forecasting, Neural Architecture Search, Time Series, Time Series Analysis, Time Series Forecasting

Multivariate time series (MTS) forecasting has attracted much attention in many intelligent applications. It is not a trivial task, as we need to consider both intra-variable dependencies and inter-variable dependencies. However, existing works are designed for specific scenarios, and require much domain knowledge and expert efforts, which is difficult to transfer between different scenarios. In this paper, we propose a scale-aware neural architecture search framework for MTS forecasting (SNAS4MTF). A multi-scale decomposition module transforms raw time series into multi-scale sub-series, which can preserve multi-scale temporal patterns. An adaptive graph learning module infers the different inter-variable dependencies under different time scales without any prior knowledge. For MTS forecasting, a search space is designed to capture both intra-variable dependencies and inter-variable dependencies at each time scale. The multi-scale decomposition, adaptive graph learning, and neural architecture search modules are jointly learned in an end-to-end framework. Extensive experiments on two real-world datasets demonstrate that SNAS4MTF achieves a promising performance compared with the state-of-the-art methods.
EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices
Authors: Siwei Zhang, Qianli Ma, Yan Zhang, Zhiyin Qian, Taein Kwon, Marc Pollefeys, Federica Bogo, Siyu Tang
Date: 2021-12-14 00:00:00
Tasks: Motion Estimation

Understanding social interactions from egocentric views is crucial for many applications, ranging from assistive robotics to AR/VR. Key to reasoning about interactions is to understand the body pose and motion of the interaction partner from the egocentric view. However, research in this area is severely hindered by the lack of datasets. Existing datasets are limited in terms of either size, capture/annotation modalities, ground-truth quality, or interaction diversity. We fill this gap by proposing EgoBody, a novel large-scale dataset for human pose, shape and motion estimation from egocentric views, during interactions in complex 3D scenes. We employ Microsoft HoloLens2 headsets to record rich egocentric data streams (including RGB, depth, eye gaze, head and hand tracking). To obtain accurate 3D ground truth, we calibrate the headset with a multi-Kinect rig and fit expressive SMPL-X body meshes to multi-view RGB-D frames, reconstructing 3D human shapes and poses relative to the scene, over time. We collect 125 sequences, spanning diverse interaction scenarios, and propose the first benchmark for 3D full-body pose and shape estimation of the social partner from egocentric views. We extensively evaluate state-of-the-art methods, highlight their limitations in the egocentric scenario, and address such limitations leveraging our high-quality annotations. Data and code are available at https://sanweiliti.github.io/egobody/egobody.html.