pwc-archive/papers-with-abstracts huggingface dataset:
Decomposing LIBOR in Transition: Evidence from the Futures Markets
Authors: David Skovmand, Jacob Bjerre Skov
Date: 2022-01-18 00:00:00

Applying historical data from the USD LIBOR transition period, we estimate a joint model for SOFR, Fed Funds, and Eurodollar futures rates as well as spot USD LIBOR and term repo rates. The framework endogenously models basis spreads between each of the benchmark rates and allows for the decomposition of spreads. Modelling the LIBOR-OIS spread as credit and funding-liquidity roll-over risk, we find that the spike in the LIBOR-OIS spread during the onset of COVID-19 was mainly due to credit risk, while on average credit and funding-liquidity risk contribute equally to the spread.
Internal multi-portfolio rebalancing processes: Linking resource allocation models and biproportional matrix techniques to portfolio management
Authors: Kelli Francis-Staite
Date: 2022-01-17 00:00:00
Tasks: Management

This paper describes multi-portfolio `internal' rebalancing processes used in the finance industry. Instead of trading with the market to `externally' rebalance, these internal processes detail how portfolio managers buy and sell between their portfolios to rebalance. We give an overview of currently used internal rebalancing processes, including one known as the `banker' process and another known as the `linear' process. We prove the banker process disadvantages the nominated banker portfolio in volatile markets, while the linear process may advantage or disadvantage portfolios. We describe an alternative process that uses the concept of `market-invariance'. We give analytic solutions for small cases, while in general show that the $n$-portfolio solution and its corresponding `market-invariant' algorithm solve a system of nonlinear polynomial equations. It turns out this algorithm is a rediscovery of the RAS algorithm (also called the `iterative proportional fitting procedure') for biproportional matrices. We show that this process is more equitable than the banker and linear processes, and demonstrate this with empirical results. The market-invariant process has already been implemented by industry due to the significance of these results.
Augmented Dynamic Gordon Growth Model
Authors: Battulga Gankhuu
Date: 2022-01-16 00:00:00
Tasks: model

In this paper, we introduce a dynamic Gordon growth model, which is augmented by a time--varying spot interest rate and the Gordon growth model for dividends. Using the risk--neutral valuation method and locally risk--minimizing strategy, we obtain pricing and hedging formulas for the dividend--paying European call and put options and equity--linked life insurance products. Also, we provide ML estimator of the model.
Optimal trend following portfolios
Authors: Sebastien Valeyre
Date: 2022-01-17 00:00:00

This paper derives an optimal portfolio that is based on trend-following signal. Building on an earlier related article, it provides a unifying theoretical setting to introduce an autocorrelation model with the covariance matrix of trends and risk premia. We specify practically relevant models for the covariance matrix of trends. The optimal portfolio is decomposed into four basic components that yield four basic portfolios: Markowitz, risk parity, agnostic risk parity, and trend following on risk parity. The overperformance of the proposed optimal portfolio, applied to cross-asset trading universe, is confirmed by empirical backtests. We provide thus a unifying framework to describe and rationalize earlier developed portfolios.
Model Aggregation for Risk Evaluation and Robust Optimization
Authors: Tiantian Mao, Ruodu Wang, Qinyu Wu
Date: 2022-01-17 00:00:00
Tasks: model, Portfolio Optimization

We introduce a new approach for prudent risk evaluation based on stochastic dominance, which will be called the model aggregation (MA) approach. In contrast to the classic worst-case risk (WR) approach, the MA approach produces not only a robust value of risk evaluation but also a robust distributional model, independent of any specific risk measure. The MA risk evaluation can be computed through explicit formulas in the lattice theory of stochastic dominance, and under some standard assumptions, the MA robust optimization admits a convex-program reformulation. The MA approach for Wasserstein and mean-variance uncertainty sets admits explicit formulas for the obtained robust models. Via an equivalence property between the MA and the WR approaches, new axiomatic characterizations are obtained for the Value-at-Risk (VaR) and the Expected Shortfall (ES, also known as CVaR). The new approach is illustrated with various risk measures and examples from portfolio optimization.
Multinomial Backtesting of Distortion Risk Measures
Authors: Sören Bettels, Sojung Kim, Stefan Weber
Date: 2022-01-17 00:00:00

We extend the scope of risk measures for which backtesting models are available by proposing a multinomial backtesting method for general distortion risk measures. The method relies on a stratification and randomization of risk levels. We illustrate the performance of our methods in numerical case studies.
A Novel Approach to Topological Graph Theory with R-K Diagrams and Gravitational Wave Analysis
Authors: Animikh Roy, Andor Kesselman
Date: 2021-12-14 00:00:00

Graph Theory and Topological Data Analytics, while powerful, have many drawbacks related to their sensitivity and consistency with TDA & Graph Network Analytics. In this paper, we aim to propose a novel approach for encoding vectorized associations between data points for the purpose of enabling smooth transitions between Graph and Topological Data Analytics. We conclusively reveal effective ways of converting such vectorized associations to simplicial complexes representing micro-states in a Phase-Space, resulting in filter specific, homotopic self-expressive, event-driven unique topological signatures which we have referred as Roy-Kesselman Diagrams or R-K Diagrams with persistent homology, which emerge from filter-based encodings of R-K Models. The validity and impact of this approach were tested specifically on high-dimensional raw and derived measures of Gravitational Wave Data from the latest LIGO datasets published by the LIGO Open Science Centre along with testing a generalized approach for a non-scientific use-case, which has been demonstrated using the Tableau Superstore Sales dataset. We believe the findings of our work will lay the foundation for many future scientific and engineering applications of stable, high-dimensional data analysis with the combined effectiveness of Topological Graph Theory transformations.
Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps
Authors: Kevin Moran, Carlos Bernal-Cárdenas, Michael Curcio, Richard Bonett, Denys Poshyvanyk
Date: 2018-02-07 00:00:00
Tasks: BIG-bench Machine Learning, Component Classification

It is common practice for developers of user-facing software to transform a mock-up of a graphical user interface (GUI) into code. This process takes place both at an application's inception and in an evolutionary context as GUI changes keep pace with evolving features. Unfortunately, this practice is challenging and time-consuming. In this paper, we present an approach that automates this process by enabling accurate prototyping of GUIs via three tasks: detection, classification, and assembly. First, logical components of a GUI are detected from a mock-up artifact using either computer vision techniques or mock-up metadata. Then, software repository mining, automated dynamic analysis, and deep convolutional neural networks are utilized to accurately classify GUI-components into domain-specific types (e.g., toggle-button). Finally, a data-driven, K-nearest-neighbors algorithm generates a suitable hierarchical GUI structure from which a prototype application can be automatically assembled. We implemented this approach for Android in a system called ReDraw. Our evaluation illustrates that ReDraw achieves an average GUI-component classification accuracy of 91% and assembles prototype applications that closely mirror target mock-ups in terms of visual affinity while exhibiting reasonable code structure. Interviews with industrial practitioners illustrate ReDraw's potential to improve real development workflows.
AugmentedNet: A Roman Numeral Analysis Network with Synthetic Training Examples and Additional Tonal Tasks
Authors: Nestor Napoles Lopez, Mark Gotham, Ichiro Fujinaga
Date: 2021-11-07 00:00:00
Proceeding: ISMIR 2021 11
Tasks: Chord Recognition, Data Augmentation

AugmentedNet is a new convolutional recurrent neural network for predicting Roman numeral labels. The network architecture is characterized by a separate convolutional block for bass and chromagram inputs. This layout is further enhanced by using synthetic training examples for data augmentation, and a greater number of tonal tasks to solve simultaneously via multitask learning. This paper reports the improved performance achieved by combining these ideas. The additional tonal tasks strengthen the shared representation learned through multitask learning. The synthetic examples, in turn, complement key transposition, which is often the only technique used for data augmentation in similar problems related to tonal music. The name "AugmentedNet" speaks to the increased number of both training examples and tonal tasks. We report on tests across six relevant and publicly available datasets: ABC, BPS, HaydnSun, TAVERN, When-in-Rome, and WTC. In our tests, our model outperforms recent methods of functional harmony, such as other convolutional neural networks and Transformer-based models. Finally, we show a new method for reconstructing the full Roman numeral label, based on common Roman numeral classes, which leads to better results compared to previous methods.
WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking
Authors: Chunhui Zhang, Guanjie Huang, Li Liu, Shan Huang, Yinan Yang, Xiang Wan, Shiming Ge, DaCheng Tao
Date: 2022-01-19 00:00:00

Unmanned aerial vehicle (UAV) tracking is of great significance for a wide range of applications, such as delivery and agriculture. Previous benchmarks in this area mainly focused on small-scale tracking problems while ignoring the amounts of data, types of data modalities, diversities of target categories and scenarios, and evaluation protocols involved, greatly hiding the massive power of deep UAV tracking. In this work, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers. WebUAV-3M contains over 3.3 million frames across 4,500 videos and offers 223 highly diverse target categories. Each video is densely annotated with bounding boxes by an efficient and scalable semiautomatic target annotation (SATA) pipeline. Importantly, to take advantage of the complementary superiority of language and audio, we enrich WebUAV-3M by innovatively providing both natural language specifications and audio descriptions. We believe that such additions will greatly boost future research in terms of exploring language features and audio cues for multimodal UAV tracking. In addition, a fine-grained UAV tracking-under-scenario constraint (UTUSC) evaluation protocol and seven challenging scenario subtest sets are constructed to enable the community to develop, adapt and evaluate various types of advanced trackers. We provide extensive evaluations and detailed analyses of 43 representative trackers and envision future research directions in the field of deep UAV tracking and beyond. The dataset, toolkits and baseline results are available at \url{https://github.com/983632847/WebUAV-3M}.
Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features
Authors: Alireza Rezazadeh, Yasamin Jafarian, Ali Kord
Date: 2022-01-17 00:00:00
Tasks: image-classification, Image Classification

Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis based on ultrasound images. We extract first- and second-order texture features of the ultrasound images and use them to build a probabilistic ensemble of decision tree classifiers. Each decision tree learns to classify the input ultrasound image by learning a set of robust decision thresholds for texture features of the image. The decision path of the model predictions can then be interpreted by decomposing the learned decision trees. Our results show that our proposed framework achieves high predictive performance while being explainable.
EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates
Authors: Mushahid Khan, Jean Paul Latyr Faye, Udson C. Mendes, Andriy Miranskyy
Date: 2022-01-10 00:00:00
Tasks: Attribute, Retrieval

Machine learning (ML) classification tasks can be carried out on a quantum computer (QC) using Probabilistic Quantum Memory (PQM) and its extension, Parameteric PQM (P-PQM) by calculating the Hamming distance between an input pattern and a database of $r$ patterns containing $z$ features with $a$ distinct attributes. For accurate computations, the feature must be encoded using one-hot encoding, which is memory-intensive for multi-attribute datasets with $a>2$. We can easily represent multi-attribute data more compactly on a classical computer by replacing one-hot encoding with label encoding. However, replacing these encoding schemes on a QC is not straightforward as PQM and P-PQM operate at the quantum bit level. We present an enhanced P-PQM, called EP-PQM, that allows label encoding of data stored in a PQM data structure and reduces the circuit depth of the data storage and retrieval procedures. We show implementations for an ideal QC and a noisy intermediate-scale quantum (NISQ) device. Our complexity analysis shows that the EP-PQM approach requires $O\left(z \log_2(a)\right)$ qubits as opposed to $O(za)$ qubits for P-PQM. EP-PQM also requires fewer gates, reducing gate count from $O\left(rza\right)$ to $O\left(rz\log_2(a)\right)$. For five datasets, we demonstrate that training an ML classification model using EP-PQM requires 48% to 77% fewer qubits than P-PQM for datasets with $a>2$. EP-PQM reduces circuit depth in the range of 60% to 96%, depending on the dataset. The depth decreases further with a decomposed circuit, ranging between 94% and 99%. EP-PQM requires less space; thus, it can train on and classify larger datasets than previous PQM implementations on NISQ devices. Furthermore, reducing the number of gates speeds up the classification and reduces the noise associated with deep quantum circuits. Thus, EP-PQM brings us closer to scalable ML on a NISQ device.
Using Particle Swarm Optimization as Pathfinding Strategy in a Space with Obstacles
Authors: David, Budi Adiperdana
Date: 2021-12-16 00:00:00

Particle swarm optimization (PSO) is a search algorithm based on stochastic and population-based adaptive optimization. In this paper, a pathfinding strategy is proposed to improve the efficiency of path planning for a broad range of applications. This study aims to investigate the effect of PSO parameters (numbers of particle, weight constant, particle constant, and global constant) on algorithm performance to give solution paths. Increasing the PSO parameters makes the swarm move faster to the target point but takes a long time to converge because of too many random movements, and vice versa. From a variety of simulations with different parameters, the PSO algorithm is proven to be able to provide a solution path in a space with obstacles.
ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes
Authors: Rahul Sajnani, Adrien Poulenard, Jivitesh Jain, Radhika Dua, Leonidas J. Guibas, Srinath Sridhar
Date: 2022-01-19 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Canonicalization, 3D Geometry Perception, 3D Part Segmentation, 3D Pose Estimation, Position

Progress in 3D object understanding has relied on manually canonicalized shape datasets that contain instances with consistent position and orientation (3D pose). This has made it hard to generalize these methods to in-the-wild shapes, eg., from internet model collections or depth sensors. ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds. We build on top of Tensor Field Networks (TFNs), a class of permutation- and rotation-equivariant, and translation-invariant 3D networks. During inference, our method takes an unseen full or partial 3D point cloud at an arbitrary pose and outputs an equivariant canonical pose. During training, this network uses self-supervision losses to learn the canonical pose from an un-canonicalized collection of full and partial 3D point clouds. ConDor can also learn to consistently co-segment object parts without any supervision. Extensive quantitative results on four new metrics show that our approach outperforms existing methods while enabling new applications such as operation on depth images and annotation transfer.
When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data
Authors: Edmond Awad, Sydney Levine, Andrea Loreggia, Nicholas Mattei, Iyad Rahwan, Francesca Rossi, Kartik Talamadupula, Joshua Tenenbaum, Max Kleiman-Weiner
Date: 2022-01-19 00:00:00

One of the most remarkable things about the human moral mind is its flexibility. We can make moral judgments about cases we have never seen before. We can decide that pre-established rules should be broken. We can invent novel rules on the fly. Capturing this flexibility is one of the central challenges in developing AI systems that can interpret and produce human-like moral judgment. This paper details the results of a study of real-world decision makers who judge whether it is acceptable to break a well-established norm: ``no cutting in line.'' We gather data on how human participants judge the acceptability of line-cutting in a range of scenarios. Then, in order to effectively embed these reasoning capabilities into a machine, we propose a method for modeling them using a preference-based structure, which captures a novel modification to standard ``dual process'' theories of moral judgment.
Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets
Authors: Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini
Date: 2022-01-19 00:00:00
Tasks: BIG-bench Machine Learning

One of the potential solutions for model interpretation is to train a surrogate model: a more transparent model that approximates the behavior of the model to be explained. Typically, classification rules or decision trees are used due to the intelligibility of their logic-based expressions. However, decision trees can grow too deep and rule sets can become too large to approximate a complex model. Unlike paths on a decision tree that must share ancestor nodes (conditions), rules are more flexible. However, the unstructured visual representation of rules makes it hard to make inferences across rules. To address these issues, we present a workflow that includes novel algorithmic and interactive solutions. First, we present Hierarchical Surrogate Rules (HSR), an algorithm that generates hierarchical rules based on user-defined parameters. We also contribute SuRE, a visual analytics (VA) system that integrates HSR and interactive surrogate rule visualizations. Particularly, we present a novel feature-aligned tree to overcome the shortcomings of existing rule visualizations. We evaluate the algorithm in terms of parameter sensitivity, time performance, and comparison with surrogate decision trees and find that it scales reasonably well and outperforms decision trees in many respects. We also evaluate the visualization and the VA system by a usability study with 24 volunteers and an observational study with 7 domain experts. Our investigation shows that the participants can use feature-aligned trees to perform non-trivial tasks with very high accuracy. We also discuss many interesting observations that can be useful for future research on designing effective rule-based VA systems.
Improving Behavioural Cloning with Human-Driven Dynamic Dataset Augmentation
Authors: Federico Malato, Joona Jehkonen, Ville Hautamäki
Date: 2022-01-19 00:00:00
Tasks: Behavioural cloning

Behavioural cloning has been extensively used to train agents and is recognized as a fast and solid approach to teach general behaviours based on expert trajectories. Such method follows the supervised learning paradigm and it strongly depends on the distribution of the data. In our paper, we show how combining behavioural cloning with human-in-the-loop training solves some of its flaws and provides an agent task-specific corrections to overcome tricky situations while speeding up the training time and lowering the required resources. To do this, we introduce a novel approach that allows an expert to take control of the agent at any moment during a simulation and provide optimal solutions to its problematic situations. Our experiments show that this approach leads to better policies both in terms of quantitative evaluation and in human-likeliness.
Debiased Graph Neural Networks with Agnostic Label Selection Bias
Authors: Shaohua Fan, Xiao Wang, Chuan Shi, Kun Kuang, Nian Liu, Bai Wang
Date: 2022-01-19 00:00:00
Tasks: parameter estimation, Selection bias

Most existing Graph Neural Networks (GNNs) are proposed without considering the selection bias in data, i.e., the inconsistent distribution between the training set with test set. In reality, the test data is not even available during the training process, making selection bias agnostic. Training GNNs with biased selected nodes leads to significant parameter estimation bias and greatly impacts the generalization ability on test nodes. In this paper, we first present an experimental investigation, which clearly shows that the selection bias drastically hinders the generalization ability of GNNs, and theoretically prove that the selection bias will cause the biased estimation on GNN parameters. Then to remove the bias in GNN estimation, we propose a novel Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation regularizer. The differentiated decorrelation regularizer estimates a sample weight for each labeled node such that the spurious correlation of learned embeddings could be eliminated. We analyze the regularizer in causal view and it motivates us to differentiate the weights of the variables based on their contribution on the confounding bias. Then, these sample weights are used for reweighting GNNs to eliminate the estimation bias, thus help to improve the stability of prediction on unknown test nodes. Comprehensive experiments are conducted on several challenging graph datasets with two kinds of label selection biases. The results well verify that our proposed model outperforms the state-of-the-art methods and DGNN is a flexible framework to enhance existing GNNs.