pwc-archive/papers-with-abstracts huggingface dataset:
SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data
Authors: Hyunsung Kim, Bit Kim, Dongwook Chung, Jinsung Yoon, Sang-Ki Ko
Date: 2022-06-22 00:00:00
Tasks: Change Point Detection

In fluid team sports such as soccer and basketball, analyzing team formation is one of the most intuitive ways to understand tactics from domain participants' point of view. However, existing approaches either assume that team formation is consistent throughout a match or assign formations frame-by-frame, which disagree with real situations. To tackle this issue, we propose a change-point detection framework named SoccerCPD that distinguishes tactically intended formation and role changes from temporary changes in soccer matches. We first assign roles to players frame-by-frame and perform two-step change-point detections: (1) formation change-point detection based on the sequence of role-adjacency matrices and (2) role change-point detection based on the sequence of role permutations. The evaluation of SoccerCPD using the ground truth annotated by domain experts shows that our method accurately detects the points of tactical changes and estimates the formation and role assignment per segment. Lastly, we introduce practical use-cases that domain participants can easily interpret and utilize.
Proximity Graph Maintenance for Fast Online Nearest Neighbor Search
Authors: Zhaozhuo Xu, Weijie Zhao, Shulong Tan, Zhixin Zhou, Ping Li
Date: 2022-06-22 00:00:00
Tasks: Quantization, Recommendation Systems

Approximate Nearest Neighbor (ANN) search is a fundamental technique for (e.g.,) the deployment of recommender systems. Recent studies bring proximity graph-based methods into practitioners' attention -- proximity graph-based methods outperform other solutions such as quantization, hashing, and tree-based ANN algorithm families. In current recommendation systems, data point insertions, deletions, and queries are streamed into the system in an online fashion as users and items change dynamically. As proximity graphs are constructed incrementally by inserting data points as new vertices into the graph, online insertions and queries are well-supported in proximity graph. However, a data point deletion incurs removing a vertex from the proximity graph index, while no proper graph index updating mechanisms are discussed in previous studies. To tackle the challenge, we propose an incremental proximity graph maintenance (IPGM) algorithm for online ANN. IPGM supports both vertex deletion and insertion on proximity graphs. Given a vertex deletion request, we thoroughly investigate solutions to update the connections of the vertex. The proposed updating scheme eliminates the performance drop in online ANN methods on proximity graphs, making the algorithm suitable for practical systems.
RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval
Authors: Yihan Wu, Hongyang Zhang, Heng Huang
Date: 2022-06-17 00:00:00
Tasks: Image Retrieval, Retrieval

Recent research works have shown that image retrieval models are vulnerable to adversarial attacks, where slightly modified test inputs could lead to problematic retrieval results. In this paper, we aim to design a provably robust image retrieval model which keeps the most important evaluation metric Recall@1 invariant to adversarial perturbation. We propose the first 1-nearest neighbor (NN) image retrieval algorithm, RetrievalGuard, which is provably robust against adversarial perturbations within an $\ell_2$ ball of calculable radius. The challenge is to design a provably robust algorithm that takes into consideration the 1-NN search and the high-dimensional nature of the embedding space. Algorithmically, given a base retrieval model and a query sample, we build a smoothed retrieval model by carefully analyzing the 1-NN search procedure in the high-dimensional embedding space. We show that the smoothed retrieval model has bounded Lipschitz constant and thus the retrieval score is invariant to $\ell_2$ adversarial perturbations. Experiments on image retrieval tasks validate the robustness of our RetrievalGuard method.
Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models
Authors: Francis X. Diebold, Maximilian Goebel, Philippe Goulet Coulombe
Date: 2022-06-21 00:00:00

We use "glide charts" (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice. We first use them to evaluate the simple feature-engineered linear regression (FELR) forecasts of Diebold and Goebel (2021), and to compare FELR forecasts to naive pure-trend benchmark forecasts. Then we introduce a much more sophisticated feature-engineered machine learning (FEML) model, and we use glide charts to evaluate FEML forecasts and compare them to a FELR benchmark. Our substantive results include the frequent appearance of predictability thresholds, which differ across months, meaning that accuracy initially fails to improve as the target date is approached but then increases progressively once a threshold lead time is crossed. Also, we find that FEML can improve appreciably over FELR when forecasting "turning point" months in the annual cycle at horizons of one to three months ahead.
Learning Color Representations for Low-Light Image Enhancement
Authors: Bomi Kim, Sunhyeok Lee, Nahyun Kim, Donggon Jang, Dae-shik Kim
Date: 2022-01-03 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022 1
Tasks: Image Enhancement, Low-Light Image Enhancement, Representation Learning

Color conveys important information about the visible world. However, under low-light conditions, both pixel intensity, as well as true color distribution, can be significantly shifted. Moreover, most of such distortions are non-recoverable due to inverse problems. In the present study, we utilized recent advancements in learning-based methods for low-light image enhancement. However, while most "deep learning" methods aim to restore high-level and object-oriented visual information, we hypothesized that learning-based methods can also be used for restoring color-based information. To address this question, we propose a novel color representation learning method for low-light image enhancement. More specifically, we used a channel-aware residual network and a differentiable intensity histogram to capture color features. Experimental results using synthetic and natural datasets suggest that the proposed learning scheme achieves state-of-the-art performance. We conclude from our study that inter-channel dependency and color distribution matching are crucial factors for learning color representations under low-light conditions.
Optimal Covariance Steering for Continuous-Time Linear Stochastic Systems With Additive Noise
Authors: Fengjiao Liu, Panagiotis Tsiotras
Date: 2022-06-22 00:00:00

In this paper, we study the problem of how to optimally steer the state covariance of a general continuous-time linear stochastic system over a finite time interval subject to additive noise. Optimality here means reaching a target state covariance with minimal control energy. The additive noise may include a combination of white Gaussian noise and abrupt "jump noise" that is discontinuous in time. We first establish the controllability of the state covariance for linear time-varying stochastic systems. We then turn to the derivation of the optimal control, which entails solving two dynamically coupled matrix ordinary differential equations (ODEs) with split boundary conditions. We show the existence and uniqueness of the solution to these coupled matrix ODEs, and thus those of the optimal control.
Event-triggered and distributed model predictive control for guaranteed collision avoidance in UAV swarms
Authors: Alexander Gräfe, Joram Eickhoff, Sebastian Trimpe
Date: 2022-06-22 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

Distributed model predictive control (DMPC) is often used to tackle path planning for unmanned aerial vehicle (UAV) swarms. However, it requires considerable computations on-board the UAV, leading to increased weight and power consumption. In this work, we propose to offload path planning computations to multiple ground-based computation units. As simultaneously communicating and recomputing all trajectories is not feasible for a large swarm with tight timing requirements, we develop a novel event-triggered DMPC that selects a subset of most relevant UAV trajectories to be replanned. The resulting architecture reduces UAV weight and power consumption, while the active redundancy provides robustness against computation unit failures. Moreover, the DMPC guarantees feasible and collision-free trajectories for UAVs with linear dynamics. In simulations, we demonstrate that our method can reliably plan trajectories, while saving 60% of network traffic and required computational power. Hardware-in-the-loop experiments show that it is suitable to control real quadcopter swarms.
A Multimodal Perceived Stress Classification Framework using Wearable Physiological Sensors
Authors: Muhammad Majid, Aamir Arsalan, Syed Muhammad Anwar
Date: 2022-06-22 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), feature selection, Photoplethysmography (PPG)

Mental stress is a largely prevalent condition known to affect many people and could be a serious health concern. The quality of human life can be significantly improved if mental health is properly managed. Towards this, we propose a robust method for perceived stress classification, which is based on using multimodal data, acquired from forty subjects, including three (electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG)) physiological modalities. The data is acquired for three minutes duration in an open eyes condition. A perceived stress scale (PSS) questionnaire is used to record the stress of participants, which is then used to assign stress labels (two- and three classes). Time (four from GSR and PPG signals) and frequency (four from EEG signal) domain features are extracted. Among EEG based features, using a frequency band selection algorithm for selecting the optimum EEG frequency subband, the theta band was selected. Further, a wrapper-based method is used for optimal feature selection. Human stress level classification is performed using three different classifiers, which are fed with a fusion of the selected set of features from three modalities. A significant accuracy (95% for two classes, and 77.5% for three classes) was achieved using the multilayer perceptron classifier.
Frequency Domain Identifiability and Sloppiness of Descriptor Systems with an LFT Structure
Authors: Tong Zhou
Date: 2022-06-22 00:00:00

Identifiability and sloppiness are investigated in this paper for the parameters of a descriptor system based on its frequency response samples. Two metrics are suggested respectively for measuring absolute and relative sloppiness of the parameter vector at a prescribed value. In this descriptor system, system matrices are assumed to depend on its parameters through a linear fractional transformation (LFT). When an associated transfer function matrix (TFM) is of full normal row rank, a matrix rank based necessary and sufficient condition is derived for parameter identifiability with a set of finitely many frequency responses. This condition can be verified recursively which is computationally quite appealing, especially when the system is of a large scale. From this condition, an algorithm is suggested to find a set of frequencies with which the frequency responses of the system are capable to uniquely determine its parameters. An ellipsoid approximation is given for the set consisting of all the parameter values with which the associated descriptor system has a frequency response that deviates within a prescribed distance, from that corresponding to a globally identifiable parameter vector value. Explicit formulas are also derived for the suggested absolute and relative sloppiness metrics.
An Overview of Drone Energy Consumption Factors and Models
Authors: Pedram Beigi, Mohammad Sadra Rajabi, Sina Aghakhani
Date: 2022-06-21 00:00:00

At present, there is a growing demand for drones with diverse capabilities that can be used in both civilian and military applications, and this topic is receiving increasing attention. When it comes to drone operations, the amount of energy they consume is a determining factor in their ability to achieve their full potential. According to this, it appears that it is necessary to identify the factors affecting the energy consumption of the unmanned air vehicle (UAV) during the mission process, as well as examine the general factors that influence the consumption of energy. This chapter aims to provide an overview of the current state of research in the area of UAV energy consumption and provide general categorizations of factors affecting UAV's energy consumption as well as an investigation of different energy models.
Drone Delivery Systems and Energy Management: A Review and Future Trends
Authors: Mohammad Sadra Rajabi, Pedram Beigi, Sina Aghakhani
Date: 2022-06-21 00:00:00
Tasks: energy management, Management

Advanced technological breakthroughs and exceptional levels of innovation are enhancing the capabilities and potential of autonomous unmanned aerial vehicles (UAVs or drones), and in so doing attracting the interest of a broader swath of logistic companies, online retailers, and governmental agencies. These technology advancements and their impact on regulatory agencies may soon pave the way for the widespread use of drones for delivery and monitoring purposes. Moreover, increasingly urgent environmental factors that include CO2 emissions reductions and other energy-saving approaches are intensifying to need to reduce vehicular usage and congestion, which could further spur their usage. To optimize these systems, drones often employ a hybrid power supply system architecture to boost endurance and performance. Fuel cells, batteries, solar cells, and supercapacitors are examples of power sources that may be combined in a hybrid power architecture. To enable todays drones (and those of the future) to work efficiently, the appropriate energy management system must be selected based on optimal and accurate modeling techniques This chapter provides a comprehensive review of drone energy-supply management and strategic systems to identify their plusses and minuses, as well as suggests recommendations for future research.
Recursive Overbetting of a Satellite Investment Account
Authors: Alex Garivaltis
Date: 2022-06-22 00:00:00

This paper builds a core-satellite model of semi-static Kelly betting and log-optimal investment. We study the problem of a saver whose core portfolio consists in unlevered (1x) retirement plans with no access to margin debt. However, the agent has a satellite investment account with recourse to significant, but not unlimited, leverage; accordingly, we study optimal controllers for the satellite gearing ratio. On a very short time horizon, the best policy is to overbet the satellite, whereby the overriding objective is to raise the aggregate beta toward a growth-optimal level. On an infinite horizon, by contrast, the correct behavior is to blithely ignore the core and optimize the exponential growth rate of the satellite, which will anyways come to dominate the entire bankroll in the limit. For time horizons strictly between zero and infinity, the optimal strategy is not so simple: there is a key trade-off between the instantaneous growth rate of the composite bankroll, and that of the satellite itself, which suffers ongoing volatility drag from the overbetting. Thus, a very perspicacious policy is called for, since any losses in the satellite will constrain the agent's access to leverage in the continuation problem. We characterize the optimal feedback controller, and compute it in earnest by solving the corresponding HJB equation recursively and backward in time. This solution is then compared to the best open-loop controller, which, in spite of its relative simplicity, is expected to perform similarly in practical situations.
Revisiting Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech
Authors: Paul Hofmarcher, Jan Vávra, Sourav Adhikari, Bettina Grün
Date: 2022-06-22 00:00:00
Tasks: Language Modeling, Language Modelling, Position, Topic Models

Gentzkow, Shapiro and Taddy, Econometrica Vol 87, No 4, 2019 (henceforth GST) use a supervised text-based regression model to assess changes in partisanship in U.S. congressional speech over time. Their estimates imply that partisanship is far greater in recent years than in the past, and that it increased sharply in the early 1990s. The paper at hand provides a replication in the wide sense of GST by complementing their analysis in three ways. First, we propose an alternative unsupervised language model, which combines ideas of topic models and ideal point models, to analyze the change in partisanship over time. We apply this model to the Senate speech data used in GST ranging from 1981-2017. Using our model we replicate their results on the specific evolution of partisanship. Second, our model provides additional insights such as the data-driven estimation of evolvement of topical contents over time. Third, we identify key phrases of partisanship on topic level.
Generational Differences in Automobility: Comparing America's Millennials and Gen Xers Using Gradient Boosting Decision Trees
Authors: Kailai Wang, Xize Wang
Date: 2022-06-19 00:00:00

Whether the Millennials are less auto-centric than the previous generations has been widely discussed in the literature. Most existing studies use regression models and assume that all factors are linear-additive in contributing to the young adults' driving behaviors. This study relaxes this assumption by applying a non-parametric statistical learning method, namely the gradient boosting decision trees (GBDT). Using U.S. nationwide travel surveys for 2001 and 2017, this study examines the non-linear dose-response effects of lifecycle, socio-demographic and residential factors on daily driving distances of Millennial and Gen-X young adults. Holding all other factors constant, Millennial young adults had shorter predicted daily driving distances than their Gen-X counterparts. Besides, residential and economic factors explain around 50% of young adults' daily driving distances, while the collective contributions for life course events and demographics are about 33%. This study also identifies the density ranges for formulating effective land use policies aiming at reducing automobile travel demand.
The lattice of envy-free many-to-many matchings with contracts
Authors: Agustin G. Bonifacio, Nadia Guinazu, Noelia Juarez, Pablo Neme, Jorge Oviedo
Date: 2022-06-21 00:00:00
Tasks: Blocking, Position

We study envy-free allocations in a many-to-many matching model with contracts in which agents on one side of the market (doctors) are endowed with substitutable choice functions and agents on the other side of the market (hospitals) are endowed with responsive preferences. Envy-freeness is a weakening of stability that allows blocking contracts involving a hospital with a vacant position and a doctor that does not envy any of the doctors that the hospital currently employs. We show that the set of envy-free allocations has a lattice structure. Furthermore, we define a Tarski operator on this lattice and use it to model a vacancy chain dynamic process by which, starting from any envy-free allocation, a stable one is reached.
Conformer with dual-mode chunked attention for joint online and offline ASR
Authors: Felix Weninger, Marco Gaudesi, Md Akmal Haidar, Nicola Ferri, Jesús Andrés-Ferrer, Puming Zhan
Date: 2022-06-22 00:00:00
Tasks: Knowledge Distillation

In this paper, we present an in-depth study on online attention mechanisms and distillation techniques for dual-mode (i.e., joint online and offline) ASR using the Conformer Transducer. In the dual-mode Conformer Transducer model, layers can function in online or offline mode while sharing parameters, and in-place knowledge distillation from offline to online mode is applied in training to improve online accuracy. In our study, we first demonstrate accuracy improvements from using chunked attention in the Conformer encoder compared to autoregressive attention with and without lookahead. Furthermore, we explore the efficient KLD and 1-best KLD losses with different shifts between online and offline outputs in the knowledge distillation. Finally, we show that a simplified dual-mode Conformer that only has mode-specific self-attention performs equally well as the one also having mode-specific convolutions and normalization. Our experiments are based on two very different datasets: the Librispeech task and an internal corpus of medical conversations. Results show that the proposed dual-mode system using chunked attention yields 5% and 4% relative WER improvement on the Librispeech and medical tasks, compared to the dual-mode system using autoregressive attention with similar average lookahead.
Intensity-Sensitive Similarity Indexes for Image Quality Assessment
Authors: X. Li, W. Armour
Date: 2022-06-22 00:00:00
Tasks: Image Quality Assessment, SSIM

The importance of Image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. However, SSIM methods are insufficiently sensitive, when images contain low information, where the important information only occupies a low proportion of the image while most of the image is noise-like, which is common in scientific data. Therefore, we propose two new IQA methods, InTensity Weighted SSIM index and Low-Information Similarity Index, for such low information images. In addition, auxiliary indexes are proposed to assist with the assessment. The application of these new IQA methods to natural images and field-specific images, such as radio astronomical images, medical images, and remote sensing images, are also demonstrated. The results show that our IQA methods perform better than state-of-the-art SSIM methods for differences in high-intensity parts of the input images and have similar performance to that of the original and gradient-based SSIM for differences in low-intensity parts. Different similarity indexes are suitable for different applications, which we demonstrate in our results.