pwc-archive/papers-with-abstracts huggingface dataset:
Liquidity Providers Greeks and Impermanent Gain
Authors: Niccolò Bardoscia, Alessandro Nodari
Date: 2023-02-23 00:00:00
Tasks: Position

In traditional finance, the Black & Scholes model has guided almost 50 years of derivatives pricing, defining a standard to model any volatility-based product. With the rise of Decentralized Finance (DeFi) and constant product Automated Market Makers (AMMs), Liquidity Providers (LPs) are playing an increasingly important role in markets functioning, but, as the recent bear market highlighted, they are exposed to important risks such as Impermanent Loss (IL). In this paper, we tailor the formulas introduced by Black & Scholes to DeFi, proposing a method to calculate the greeks of an LP. We also introduce Impermanent Gain, a product that LPs can use to hedge their position and traders can use to bet on a rise in volatility and benefit from large market moves.
Pairwise counter-monotonicity
Authors: Jean-Gabriel Lauzier, Liyuan Lin, Ruodu Wang
Date: 2023-02-22 00:00:00

We systematically study pairwise counter-monotonicity, an extremal notion of negative dependence. A stochastic representation and an invariance property are established for this dependence structure. We show that pairwise counter-monotonicity implies negative association, and it is equivalent to joint mix dependence if both are possible for the same marginal distributions. We find an intimate connection between pairwise counter-monotonicity and risk sharing problems for quantile agents. This result highlights the importance of this extremal negative dependence structure in optimal allocations for agents who are not risk averse in the classic sense.
Multi-kernel property in high-frequency price dynamics under Hawkes model
Authors: Kyungsub Lee
Date: 2023-02-23 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This study investigates and uses multi-kernel Hawkes models to describe a high-frequency mid-price process. Each kernel represents a different responsive speed of market participants. Using the conditional Hessian, we examine whether the numerical optimizer effectively finds the global maximum of the log-likelihood function under complicated modeling. Empirical studies that use stock prices in the US equity market show the existence of multi-kernels classified as ultra-high-frequency (UHF), very-high-frequency (VHF), and high-frequency (HF). We estimate the conditional expectations of arrival times and the degree of contribution to the high-frequency activities for each kernel.
Master's Thesis: Out-of-distribution Detection with Energy-based Models
Authors: Sven Elflein
Date: 2023-01-28 00:00:00
Tasks: Autonomous Driving, Dimensionality Reduction, Medical Diagnosis, Out-of-Distribution Detection

Today, deep learning is increasingly applied in security-critical situations such as autonomous driving and medical diagnosis. Despite its success, the behavior and robustness of deep networks are not fully understood yet, posing a significant risk. In particular, researchers recently found that neural networks are overly confident in their predictions, even on data they have never seen before. To tackle this issue, one can differentiate two approaches in the literature. One accounts for uncertainty in the predictions, while the second estimates the underlying density of the training data to decide whether a given input is close to the training data, and thus the network is able to perform as expected.In this thesis, we investigate the capabilities of EBMs at the task of fitting the training data distribution to perform detection of out-of-distribution (OOD) inputs. We find that on most datasets, EBMs do not inherently outperform other density estimators at detecting OOD data despite their flexibility. Thus, we additionally investigate the effects of supervision, dimensionality reduction, and architectural modifications on the performance of EBMs. Further, we propose Energy-Prior Network (EPN) which enables estimation of various uncertainties within an EBM for classification, bridging the gap between two approaches for tackling the OOD detection problem. We identify a connection between the concentration parameters of the Dirichlet distribution and the joint energy in an EBM. Additionally, this allows optimization without a held-out OOD dataset, which might not be available or costly to collect in some applications. Finally, we empirically demonstrate that Energy-Prior Network (EPN) is able to detect OOD inputs, datasets shifts, and adversarial examples. Theoretically, EPN offers favorable properties for the asymptotic case when inputs are far from the training data.
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions
Authors: Qiyuan Liu, Qi Zhou, Rui Yang, Jie Wang
Date: 2023-02-12 00:00:00
Tasks: Clustering, Reinforcement Learning (RL), Representation Learning

Recent work has shown that representation learning plays a critical role in sample-efficient reinforcement learning (RL) from pixels. Unfortunately, in real-world scenarios, representation learning is usually fragile to task-irrelevant distractions such as variations in background or viewpoint. To tackle this problem, we propose a novel clustering-based approach, namely Clustering with Bisimulation Metrics (CBM), which learns robust representations by grouping visual observations in the latent space. Specifically, CBM alternates between two steps: (1) grouping observations by measuring their bisimulation distances to the learned prototypes; (2) learning a set of prototypes according to the current cluster assignments. Computing cluster assignments with bisimulation metrics enables CBM to capture task-relevant information, as bisimulation metrics quantify the behavioral similarity between observations. Moreover, CBM encourages the consistency of representations within each group, which facilitates filtering out task-irrelevant information and thus induces robust representations against distractions. An appealing feature is that CBM can achieve sample-efficient representation learning even if multiple distractions exist simultaneously.Experiments demonstrate that CBM significantly improves the sample efficiency of popular visual RL algorithms and achieves state-of-the-art performance on both multiple and single distraction settings. The code is available at https://github.com/MIRALab-USTC/RL-CBM.
Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing System
Authors: Zhangyue Shi, YuXuan Li, Chenang Liu
Date: 2023-02-08 00:00:00
Tasks: Knowledge Distillation

In advanced manufacturing, the incorporation of sensing technology provides an opportunity to achieve efficient in-situ process monitoring using machine learning methods. Meanwhile, the advances of information technologies also enable a connected and decentralized environment for manufacturing systems, making different manufacturing units in the system collaborate more closely. In a decentralized manufacturing system, the involved units may fabricate same or similar products and deploy their own machine learning model for online process monitoring. However, due to the possible inconsistency of task progress during the operation, it is also common that some units have more informative data while some have less informative data. Thus, the monitoring performance of machine learning model for each unit may highly vary. Therefore, it is extremely valuable to achieve efficient and secured knowledge sharing among the units in a decentralized manufacturing system for enhancement of poorly performed models. To realize this goal, this paper proposes a novel knowledge distillation-based information sharing (KD-IS) framework, which could distill informative knowledge from well performed models to improve the monitoring performance of poorly performed models. To validate the effectiveness of this method, a real-world case study is conducted in a connected fused filament fabrication (FFF)-based additive manufacturing (AM) platform. The experimental results show that the developed method is very efficient in improving model monitoring performance at poorly performed models, with solid protection on potential data privacy.
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis
Authors: Rabel Guharoy, Nanda Dulal Jana, Suparna Biswas, Lalit Garg
Date: 2023-02-17 00:00:00
Tasks: Dimensionality Reduction, EEG, Electroencephalogram (EEG), Seizure Detection, Specificity

An Electroencephalogram (EEG) is a non-invasive exam that records the brain's electrical activity. This is used to help diagnose conditions such as different brain problems. EEG signals are taken for epilepsy detection, and with Discrete Wavelet Transform (DWT) and machine learning classifier, they perform epilepsy detection. In Epilepsy seizure detection, machine learning classifiers and statistical features are mainly used. The hidden information in the EEG signal helps detect diseases affecting the brain. Sometimes it is complicated to identify the minimum changes in the EEG in the time and frequency domain's purpose. The DWT can give a suitable decomposition of the signals in different frequency bands and feature extraction. We use the tri-dimensionality reduction algorithm, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). Finally, features are selected by using a fusion rule and at the last step, three different classifiers, Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest-Neighbor (KNN) have been used individually for the classification. The proposed framework is tested on the Bonn dataset. The simulation results provide 100% accuracy for the LDA and NB combination outperforming accuracy with other classifiers combinations, including 89.17% for LDA and SVM, 80.42% for LDA and KNN, 89.92% for PCA and NB, 85.58% PCA and SVM, 80.42% PCA and KNN, 82.33% for ICA and NB, 90.42% for ICA and SVM, 90% for ICA and KNN. Also, the LDA and NB combination shows the sensitivity, specificity, accuracy, Precision, and Recall of 100%, 100%, 100%, 100%, and 100%. The results prove the effectiveness of this model.
Orders-of-coupling representation with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
Authors: Sergei Manzhos, Manabu Ihara
Date: 2023-02-11 00:00:00

Representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates (corresponding of different orders of coupling) are useful in quantum dynamics and other applications, especially where integration is needed. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Comm. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces.
normflows: A PyTorch Package for Normalizing Flows
Authors: Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato
Date: 2023-01-26 00:00:00
Tasks: Image Generation, Variational Inference

Normalizing flows model probability distributions through an expressive tractable density. They transform a simple base distribution, such as a Gaussian, through a sequence of invertible functions, which are referred to as layers. These layers typically use neural networks to become very expressive. Flows are ubiquitous in machine learning and have been applied to image generation, text modeling, variational inference, approximating Boltzmann distributions, and many other problems. Here, we present normflows, a Python package for normalizing flows. It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks. The package is implemented in the popular deep learning framework PyTorch, which simplifies the integration of flows in larger machine learning models or pipelines. It supports most of the common normalizing flow architectures, such as Real NVP, Glow, Masked Autoregressive Flows, Neural Spline Flows, Residual Flows, and many more. The package can be easily installed via pip and the code is publicly available on GitHub.
Gaussian Switch Sampling: A Second Order Approach to Active Learning
Authors: Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib, Armin Pacharmi, Enrique Corona
Date: 2023-02-16 00:00:00
Tasks: Active Learning, Informativeness

In active learning, acquisition functions define informativeness directly on the representation position within the model manifold. However, for most machine learning models (in particular neural networks) this representation is not fixed due to the training pool fluctuations in between active learning rounds. Therefore, several popular strategies are sensitive to experiment parameters (e.g. architecture) and do not consider model robustness to out-of-distribution settings. To alleviate this issue, we propose a grounded second-order definition of information content and sample importance within the context of active learning. Specifically, we define importance by how often a neural network "forgets" a sample during training - artifacts of second order representation shifts. We show that our definition produces highly accurate importance scores even when the model representations are constrained by the lack of training data. Motivated by our analysis, we develop Gaussian Switch Sampling (GauSS). We show that GauSS is setup agnostic and robust to anomalous distributions with exhaustive experiments on three in-distribution benchmarks, three out-of-distribution benchmarks, and three different architectures. We report an improvement of up to 5% when compared against four popular query strategies.
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule
Authors: Maor Ivgi, Oliver Hinder, Yair Carmon
Date: 2023-02-08 00:00:00
Tasks: Transfer Learning

We propose a tuning-free dynamic SGD step size formula, which we call Distance over Gradients (DoG). The DoG step sizes depend on simple empirical quantities (distance from the initial point and norms of gradients) and have no ``learning rate'' parameter. Theoretically, we show that a slight variation of the DoG formula enjoys strong parameter-free convergence guarantees for stochastic convex optimization assuming only \emph{locally bounded} stochastic gradients. Empirically, we consider a broad range of vision and language transfer learning tasks, and show that DoG's performance is close to that of SGD with tuned learning rate. We also propose a per-layer variant of DoG that generally outperforms tuned SGD, approaching the performance of tuned Adam. A PyTorch implementation is available at https://github.com/formll/dog
Forecasting with Deep Learning
Authors: Gissel Velarde
Date: 2023-02-17 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis, Time Series Forecasting

This paper presents a method for time series forecasting with deep learning and its assessment on two datasets. The method starts with data preparation, followed by model training and evaluation. The final step is a visual inspection. Experimental work demonstrates that a single time series can be used to train deep learning networks if time series in a dataset contain patterns that repeat even with a certain variation. However, for less structured time series such as stock market closing prices, the networks perform just like a baseline that repeats the last observed value. The implementation of the method as well as the experiments are open-source.
Explainable AI does not provide the explanations end-users are asking for
Authors: Savio Rozario, George Čevora
Date: 2023-01-25 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence (XAI) techniques are frequently required by users in many AI systems with the goal of understanding complex models, their associated predictions, and gaining trust. While suitable for some specific tasks during development, their adoption by organisations to enhance trust in machine learning systems has unintended consequences. In this paper we discuss XAI's limitations in deployment and conclude that transparency alongside with rigorous validation are better suited to gaining trust in AI systems.
Scaling Up Computer Vision Neural Networks Using Fast Fourier Transform
Authors: Siddharth Agrawal
Date: 2023-02-02 00:00:00

Deep Learning-based Computer Vision field has recently been trying to explore larger kernels for convolution to effectively scale up Convolutional Neural Networks. Simultaneously, new paradigm of models such as Vision Transformers find it difficult to scale up to larger higher resolution images due to their quadratic complexity in terms of input sequence. In this report, Fast Fourier Transform is utilised in various ways to provide some solutions to these issues.
Hierarchically Regularized Entropy Balancing
Authors: Yiqing Xu
Date: 2022-06-22 00:00:00
Proceeding: ACM 2022 6

We introduce hierarchically regularized entropy balancing as an extension to entropy balancing, a reweighting method that adjusts weights for control group units to achieve covariate balance in observational studies with binary treatments. Our proposed extension expands the feature space by including higher-order terms (such as squared and cubic terms and interactions) of covariates and then achieves approximate balance on the expanded features using ridge penalties with a hierarchical structure. Compared with entropy balancing, this extension relaxes model dependency and improves the robustness of causal estimates while avoiding optimization failure or highly concentrated weights. It prevents specification searches by minimizing user discretion in selecting features to balance on and is also computationally more efficient than kernel balancing, a kernel-based covariate balancing method. We demonstrate its performance through simulations and an empirical example. We develop an open-source R package, hbal, to facilitate implementation.
Automatic Background Filtering Method for Roadside LiDAR Data
Authors: Jianqing Wu, Hao Xu, Yuan Sun, Jianying Zheng, and Rui Yue
Date: 2018-12-01 00:00:00
Proceeding: Transportation Research Record, v 2672, n 45, p 106-114, 2018 12

Abstract
The high-resolution micro traffic data (HRMTD) of all roadway users is important for serving the connected-vehicle system
in mixed traffic situations. The roadside LiDAR sensor gives a solution to providing HRMTD from real-time 3D point clouds
of its scanned objects. Background filtering is the preprocessing step to obtain the HRMTD of different roadway users from
roadside LiDAR data. It can significantly reduce the data processing time and improve the vehicle/pedestrian identification
accuracy. An algorithm is proposed in this paper, based on the spatial distribution of laser points, which filters both static and
moving background efficiently. Various thresholds of point density are applied in this algorithm to exclude background at dif
ferent distances from the roadside sensor. The case study shows that the algorithm can filter background LiDAR points in dif
ferent situations (different road geometries, different traffic demands, day/night time, different speed limits). Vehicle and
pedestrian shape can be retained well after background filtering. The low computational load guarantees this method can be
applied for real-time data processing such as vehicle monitoring and pedestrian tracking.
Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals
Authors: Hao Shan, Jianping Yuan, Na Wang, Zhen Wang
Date: 2022-08-22 00:00:00
Proceeding: The Astrophysical Journal 2022 8
Tasks: compressed sensing

In pulsar signal processing, two primary difficulties are (1) radio-frequency interference (RFI) mitigation and (2)
information loss due to preprocessing and mitigation itself. Linear mitigation methods have a difficulty in RFI
modeling, and accommodate a limited range of RFI morphologies. Thresholding methods suffer from manual
factors and adaptability. There is also a distinct lack of methods dedicated to information loss. In this paper, a novel
method “CS-Pulsar” is proposed. It carries out compressed sensing (CS) on time-frequency signals to accomplish
RFI mitigation and signal restoration simultaneously. Curvelets allow an optimal sparse representation for
multichannel pulsar signals containing the time-of-arrival dispersion relationship. CS-Pulsar mitigation is
implemented using a regularized least-squares framework that does not require the statistics of RFI to be known
beforehand. CS-Pulsar implements channel restoration, and useful signal contents are retrieved from the
measurement error by a morphological component analysis aided by the root-mean-square envelope. These two
steps allow CS-Pulsar to provide key signal details for special astrophysical purposes. Experiments of signal
restoration for pulsar data from the Nanshan 26 m radio telescope reveal the advantage of CS-Pulsar. The method
successfully removes false peaks due to on-pulse RFI in multipeaked pulsar profiles. CS-Pulsar also increases the
timing accuracy and signal-to-noise ratio proving its feasibilities and prospects in astrophysical measurements.
Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework
Authors: Eric Rapp, Soyong Shin, Wolf Thomsen, Reed Ferber, Eni Halilaj
Date: 2021-02-12 00:00:00
Proceeding: Journal of Biomechanics 2021 2
Tasks: Data Augmentation, Sensor Fusion

The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings. We trained deep neural networks on a large set of synthetic inertial data derived from a clinical marker-based motion-tracking database of hundreds of subjects. We used data augmentation techniques and an automated calibration approach to reduce error due to variability in sensor placement and limb alignment. On left-out subjects, lower extremity kinematics could be predicted with a mean (±STD) root mean squared error of less than 1.27° (±0.38°) in flexion/extension, less than 2.52° (±0.98°) in ad/abduction, and less than 3.34° (±1.02°) internal/external rotation, across walking and running trials. Errors decreased exponentially with the amount of training data, confirming the need for large datasets when training deep neural networks. While this framework remains to be validated with true inertial measurement unit data, the results presented here are a promising advance toward convenient estimation of gait kinematics in natural environments. Progress in this direction could enable large-scale studies and offer new perspective into disease progression, patient recovery, and sports biomechanics.