pwc-archive/papers-with-abstracts huggingface dataset:
Extensible Logging and Empirical Attainment Function for IOHexperimenter
Authors: Johann Dreo, Manuel López-Ibáñez
Date: 2021-09-28 00:00:00
Tasks: Benchmarking

In order to allow for large-scale, landscape-aware, per-instance algorithm selection, a benchmarking platform software is key. IOHexperimenter provides a large set of synthetic problems, a logging system, and a fast implementation. In this work, we refactor IOHexperimenter's logging system, in order to make it more extensible and modular. Using this new system, we implement a new logger, which aims at computing performance metrics of an algorithm across a benchmark. The logger computes the most generic view on an anytime stochastic heuristic performances, in the form of the Empirical Attainment Function (EAF). We also provide some common statistics on the EAF and its discrete counterpart, the Empirical Attainment Histogram. Our work has eventually been merged in the IOHexperimenter codebase.
Grassmannian diffusion maps based surrogate modeling via geometric harmonics
Authors: Ketson R. M. dos Santos, Dimitrios G. Giovanis, Katiana Kontolati, Dimitrios Loukrezis, Michael D. Shields
Date: 2021-09-28 00:00:00
Tasks: Uncertainty Quantification

In this paper, a novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics is developed for predicting the response of engineering systems and complex physical phenomena. The method utilizes the GDMaps to obtain a low-dimensional representation of the underlying behavior of physical/mathematical systems with respect to uncertainties in the input parameters. Using this representation, geometric harmonics, an out-of-sample function extension technique, is employed to create a global map from the space of input parameters to a Grassmannian diffusion manifold. Geometric harmonics is also employed to locally map points on the diffusion manifold onto the tangent space of a Grassmann manifold. The exponential map is then used to project the points in the tangent space onto the Grassmann manifold, where reconstruction of the full solution is performed. The performance of the proposed surrogate modeling is verified with three examples. The first problem is a toy example used to illustrate the development of the technique. In the second example, errors associated with the various mappings employed in the technique are assessed by studying response predictions of the electric potential of a dielectric cylinder in a homogeneous electric field. The last example applies the method for uncertainty prediction in the strain field evolution in a model amorphous material using the shear transformation zone (STZ) theory of plasticity. In all examples, accurate predictions are obtained, showing that the present technique is a strong candidate for the application of uncertainty quantification in large-scale models.
Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution
Authors: Daniel Mork, Marianthi-Anna Kioumourtzoglou, Marc Weisskopf, Brent A Coull, Ander Wilson
Date: 2021-09-28 00:00:00

Children's health studies support an association between maternal environmental exposures and children's birth outcomes. A common goal is to identify critical windows of susceptibility--periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different levels of individual, family, and neighborhood characteristics. Using an administrative Colorado birth cohort we estimate the individualized relationship between weekly exposures to fine particulate matter (PM$_{2.5}$) during gestation and birth weight. To achieve this goal, we propose a statistical learning method combining distributed lag models and Bayesian additive regression trees to estimate critical windows at the individual level and identify characteristics that induce heterogeneity from a high-dimensional set of potential modifying factors. We find evidence of heterogeneity in the PM$_{2.5}$-birth weight relationship, with some mother-child dyads showing a 3 times larger decrease in birth weight for an IQR increase in exposure (5.9 to 8.5 $\mu g/m^3$ PM$_{2.5}$) compared to the population average. Specifically, we find increased susceptibility for non-Hispanic mothers who are either younger, have higher body mass index or lower educational attainment. Our case study is the first precision health study of critical windows.
Dynamic Ranking with the BTL Model: A Nearest Neighbor based Rank Centrality Method
Authors: Eglantine Karlé, Hemant Tyagi
Date: 2021-09-28 00:00:00
Tasks: Recommendation Systems

Many applications such as recommendation systems or sports tournaments involve pairwise comparisons within a collection of $n$ items, the goal being to aggregate the binary outcomes of the comparisons in order to recover the latent strength and/or global ranking of the items. In recent years, this problem has received significant interest from a theoretical perspective with a number of methods being proposed, along with associated statistical guarantees under the assumption of a suitable generative model. While these results typically collect the pairwise comparisons as one comparison graph $G$, however in many applications - such as the outcomes of soccer matches during a tournament - the nature of pairwise outcomes can evolve with time. Theoretical results for such a dynamic setting are relatively limited compared to the aforementioned static setting. We study in this paper an extension of the classic BTL (Bradley-Terry-Luce) model for the static setting to our dynamic setup under the assumption that the probabilities of the pairwise outcomes evolve smoothly over the time domain $[0,1]$. Given a sequence of comparison graphs $(G_{t'})_{t' \in \mathcal{T}}$ on a regular grid $\mathcal{T} \subset [0,1]$, we aim at recovering the latent strengths of the items $w_t^* \in \mathbb{R}^n$ at any time $t \in [0,1]$. To this end, we adapt the Rank Centrality method - a popular spectral approach for ranking in the static case - by locally averaging the available data on a suitable neighborhood of $t$. When $(G_{t'})_{t' \in \mathcal{T}}$ is a sequence of Erd\"os-Renyi graphs, we provide non-asymptotic $\ell_2$ and $\ell_{\infty}$ error bounds for estimating $w_t^*$ which in particular establishes the consistency of this method in terms of $n$, and the grid size $\lvert\mathcal{T}\rvert$. We also complement our theoretical analysis with experiments on real and synthetic data.
Optimal Orthogonal Group Synchronization and Rotation Group Synchronization
Authors: Chao GAO, Anderson Y. Zhang
Date: 2021-09-28 00:00:00

We study the statistical estimation problem of orthogonal group synchronization and rotation group synchronization. The model is $Y_{ij} = Z_i^* Z_j^{*T} + \sigma W_{ij}\in\mathbb{R}^{d\times d}$ where $W_{ij}$ is a Gaussian random matrix and $Z_i^*$ is either an orthogonal matrix or a rotation matrix, and each $Y_{ij}$ is observed independently with probability $p$. We analyze an iterative polar decomposition algorithm for the estimation of $Z^*$ and show it has an error of $(1+o(1))\frac{\sigma^2 d(d-1)}{2np}$ when initialized by spectral methods. A matching minimax lower bound is further established which leads to the optimality of the proposed algorithm as it achieves the exact minimax risk.
The eDiscovery Medicine Show
Authors: Maura R. Grossman, Gordon V. Cormack
Date: 2021-09-28 00:00:00
Tasks: Information Retrieval, Retrieval

The practice of bloodletting gradually fell into disfavor as a growing body of scientific evidence showed its ineffectiveness and demonstrated the effectiveness of various pharmaceuticals for the prevention and treatment of certain diseases. At the same time, the patent medicine industry promoted ineffective remedies at medicine shows featuring entertainment, testimonials, and pseudo-scientific claims with all the trappings--but none of the methodology--of science. Today, many producing parties and eDiscovery vendors similarly promote obsolete technology as well as unvetted tools labeled "artificial intelligence" or "technology-assisted review," along with unsound validation protocols. This situation will end only when eDiscovery technologies and tools are subject to testing using the methods of information retrieval.
Fake or Credible? Towards Designing Services to Support Users' Credibility Assessment of News Content
Authors: Enrico Bunde, Niklas Kühl, Christian Meske
Date: 2021-09-21 00:00:00
Tasks: Fake News Detection

Fake news has become omnipresent in digitalized areas such as social media platforms. While being disseminated online, it also poses a threat to individuals and societies offline, for example, in the context of democratic elections. Research and practice have investigated the detection of fake news with behavioral science or method-related perspectives. However, to date, we lack design knowledge on presenting fake news warnings to users to support their individual news credibility assessment. We present the journey through the first design cycle on developing a fake news detection service focusing on the user interface design. The design is grounded in concepts from the field of source credibility theory and instantiated in a prototype that was qualitatively evaluated. The 13 participants communicated their interest in a lightweight application that aids in the news credibility assessment and rated the design features as useful as well as desirable.
CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
Authors: Daniel Adolfsson, Martin Magnusson, Anas Alhashimi, Achim J. Lilienthal, Henrik Andreasson
Date: 2021-09-16 00:00:00
Proceeding: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021 9
Tasks: CPU, Radar odometry, Translation

This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.
Index Modulation with Circularly-Shifted Chirps for Dual-Function Radar and Communications
Authors: Alphan Sahin, Safi Shams Muhtasimul Hoque, Chao-Yu Chen
Date: 2021-09-28 00:00:00

In this study, we propose index modulation (IM) with circularly-shifted chirps (CSCs) (CSC-IM) for dual-function radar and communication (DFRC) systems. The proposed scheme encodes the information bits with the CSC indices and the phase-shift keying (PSK) symbols. It allows the receiver to exploit the frequency selectivity naturally in fading channels by combining IM and wideband CSCs. It also leverages the fact that a CSC is a constant-envelope signal to achieve a controllable peak-to-mean envelope power ratio (PMEPR). For radar functionality, CSC-IM maintains the good autocorrelation (AC) properties of a chirp by ensuring that the transmitted CSCs are separated apart sufficiently in the time domain through index separation (IS). We investigate the impact of IS on spectral efficiency (SE) and obtain the corresponding mapping functions. For theoretical results, we derive the union bound (UB) of the block error rate (BLER) for arbitrary chirps and the Cramer-Rao lower bounds (CRLBs) for the range and reflection coefficients for the matched filter (MF)-based estimation. We also prove that complementary sequences (CSs) can be constructed through CSCs by linearly combining the Fourier series of CSCs. Finally, through comprehensive comparisons, we demonstrate the efficacy of the proposed scheme for DFRC scenarios.
Domain Generalization for Vision-based Driving Trajectory Generation
Authors: Yunkai Wang, Dongkun Zhang, Yuxiang Cui, Zexi Chen, Wei Jing, Junbo Chen, Rong Xiong, Yue Wang
Date: 2021-09-22 00:00:00
Tasks: Autonomous Vehicles, Decoder, Domain Generalization

One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems. We leverage an adversarial learning approach to train a trajectory generator as the decoder. Based on the pre-trained decoder, we infer the latent variables corresponding to the trajectories, and pre-train the encoder by regressing the inferred latent variable. Finally, we fix the decoder but fine-tune the encoder with the final trajectory loss. We compare our proposed method with the state-of-the-art trajectory generation method and some recent domain generalization methods on both datasets and simulation, demonstrating that our method has better generalization ability.
NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction
Authors: Chahat Deep Singh, Nitin J. Sanket, Chethan M. Parameshwara, Cornelia Fermüller, Yiannis Aloimonos
Date: 2021-09-22 00:00:00
Tasks: Motion Segmentation, Object, Segmentation, Semantic Segmentation

Recent advances in object segmentation have demonstrated that deep neural networks excel at object segmentation for specific classes in color and depth images. However, their performance is dictated by the number of classes and objects used for training, thereby hindering generalization to never seen objects or zero-shot samples. To exacerbate the problem further, object segmentation using image frames rely on recognition and pattern matching cues. Instead, we utilize the 'active' nature of a robot and their ability to 'interact' with the environment to induce additional geometric constraints for segmenting zero-shot samples. In this paper, we present the first framework to segment unknown objects in a cluttered scene by repeatedly 'nudging' at the objects and moving them to obtain additional motion cues at every step using only a monochrome monocular camera. We call our framework NudgeSeg. These motion cues are used to refine the segmentation masks. We successfully test our approach to segment novel objects in various cluttered scenes and provide an extensive study with image and motion segmentation methods. We show an impressive average detection rate of over 86% on zero-shot objects.
Articulatory Coordination for Speech Motor Tracking in Huntington Disease
Authors: Matthew Perez, Amrit Romana, Angela Roberts, Noelle Carlozzi, Jennifer Ann Miner, Praveen Dayalu, Emily Mower Provost
Date: 2021-09-28 00:00:00

Huntington Disease (HD) is a progressive disorder which often manifests in motor impairment. Motor severity (captured via motor score) is a key component in assessing overall HD severity. However, motor score evaluation involves in-clinic visits with a trained medical professional, which are expensive and not always accessible. Speech analysis provides an attractive avenue for tracking HD severity because speech is easy to collect remotely and provides insight into motor changes. HD speech is typically characterized as having irregular articulation. With this in mind, acoustic features that can capture vocal tract movement and articulatory coordination are particularly promising for characterizing motor symptom progression in HD. In this paper, we present an experiment that uses Vocal Tract Coordination (VTC) features extracted from read speech to estimate a motor score. When using an elastic-net regression model, we find that VTC features significantly outperform other acoustic features across varied-length audio segments, which highlights the effectiveness of these features for both short- and long-form reading tasks. Lastly, we analyze the F-value scores of VTC features to visualize which channels are most related to motor score. This work enables future research efforts to consider VTC features for acoustic analyses which target HD motor symptomatology tracking.
Establishment and placement of a Multi-purpose Phasor measurement unit to improve parallel state estimation in distribution Networks
Authors: Zeyad Khashroum, Ali Dehghan Chaharabi, Lorena Palmero, Keiichiro Yasukawa
Date: 2021-09-28 00:00:00
Tasks: State Estimation

Today, microgrids in distribution networks are in dire need of improvement to cope with economic challenges, human losses, and equipment placement issues. Today, there is the issue of scattered resources in distribution systems, which has created many problems in the areas of environment, economy, and human and animal losses. The most important challenge in this section is the existence of voltage and frequency fluctuations during the occurrence of possible events such as severe load changes or errors in distribution networks. Having such a big problem can call a distribution network into question and destroy it. Therefore, it is necessary to provide an optimal method that can meet and cover these challenges. For this purpose, the present research deals with the problem of establishing and placing a multifunctional phasor measurement unit to improve the parallel state estimation in distribution networks, which offers a control approach. This approach determines the time of occurrence of internal and external disturbances after using the phasor unit. The approach of this research is to use a neural-fuzzy method because there is uncertainty in the distribution network due to the mentioned challenges, and training in the system is needed to accurately deploy and place possible errors. Do not occur. When setting up and placing the phasor measuring unit, the most important issue is the proper distribution of the load in the distribution network. The simulation results in the MATLAB / Simulink environment show the improvement of the results according to the proposed approach. Keywords: Distribution Network, Neural-Fuzzy Network, Optimal Load Distribution, Parallel State Estimation, Phasor Measurement Unit.
Optimal Sensor Gain Control for Minimum-Information Estimation of Continuous-Time Gauss-Markov Processes
Authors: Vrushabh Zinage, Takashi Tanaka, Valeri Ugrinovskii
Date: 2021-09-28 00:00:00
Tasks: Scheduling

We consider the scenario in which a continuous-time Gauss-Markov process is estimated by the Kalman-Bucy filter over a Gaussian channel (sensor) with a variable sensor gain. The problem of scheduling the sensor gain over a finite time interval to minimize the weighted sum of the data rate (the mutual information between the sensor output and the underlying Gauss-Markov process) and the distortion (the mean-square estimation error) is formulated as an optimal control problem. A necessary optimality condition for a scheduled sensor gain is derived based on Pontryagin's minimum principle. For a scalar problem, we show that an optimal sensor gain control is of bang-bang type, except the possibility of taking an intermediate value when there exists a stationary point on the switching surface in the phase space of canonical dynamics. Furthermore, we show that the number of switches is at most two and the time instants at which the optimal gain must be switched can be computed from the analytical solutions to the canonical equations.
Cross-layer Design for Real-Time Grid Operation: Estimation, Optimization and Power Flow
Authors: Miguel Picallo, Dominic Liao-McPherson, Saverio Bolognani, Florian Dörfler
Date: 2021-09-28 00:00:00

In this paper, we propose a combined Online Feedback Optimization (OFO) and dynamic estimation approach for a real-time power grid operation under time-varying conditions. A dynamic estimation uses grid measurements to generate the information required by an OFO controller, that incrementally steers the controllable power injections set-points towards the solutions of a time-varying AC Optimal Power Flow (AC-OPF) problem. More concretely, we propose a quadratic programming-based OFO that guarantees satisfying the grid operational constraints, like admissible voltage limits. Within the estimation, we design an online power flow solver that efficiently computes power flow approximations in real time. Finally, we certify the stability and convergence of this combined approach under time-varying conditions, and we validate its effectiveness on a simulation with a test feeder and high resolution consumption data.
Convex Optimization of Speed and Energy Management System for Fuel Cell Hybrid Trains
Authors: Rabee Jibrin, Stuart Hillmansen, Clive Roberts, Ning Zhao, Zhongbei Tian
Date: 2021-09-28 00:00:00
Tasks: energy management, Management

We look into minimizing the hydrogen fuel consumption of hydrogen hybrid trains by optimizing their operation. The powertrain considered is a fuel cell charge-sustaining hybrid. Convex optimization is utilized to compute optimal speed and energy management trajectories. The barrier method is used to solve the optimization problems quickly on the order of tens of seconds for the entire journey. Simulations show a considerable reduction in fuel consumption when both trajectories -- speed and energy management -- are optimized concurrently within a single optimization problem in comparison to being optimized separately in a sequential manner -- optimizing energy management after optimizing speed. It is concluded that the concurrent method greatly benefits from its holistic powertrain knowledge while optimizing all trajectories together within a single optimization problem.
Cluster Synchronization of Networks via a Canonical Transformation for Simultaneous Block Diagonalization of Matrices
Authors: Shirin Panahi, Isaac Klickstein, Francesco Sorrentino
Date: 2021-09-28 00:00:00

We study cluster synchronization of networks and propose a canonical transformation for simultaneous block diagonalization of matrices that we use to analyze stability of the cluster synchronous solution. Our approach has several advantages as it allows us to: (1) decouple the stability problem into subproblems of minimal dimensionality while preserving physically meaningful information; (2) study stability of both orbital and equitable partitions of the network nodes and (3) obtain a parametrization of the problem in a small number of parameters. For the last point, we show how the canonical transformation decouples the problem into blocks that preserve key physical properties of the original system. We also apply our proposed algorithm to analyze several real networks of interest, and we find that it runs faster than alternative algorithms from the literature.