pwc-archive/papers-with-abstracts huggingface dataset:
Contributor-Aware Defenses Against Adversarial Backdoor Attacks
Authors: Glenn Dawson, Muhammad Umer, Robi Polikar
Date: 2022-05-28 00:00:00
Tasks: Backdoor Attack, image-classification, Image Classification

Deep neural networks for image classification are well-known to be vulnerable to adversarial attacks. One such attack that has garnered recent attention is the adversarial backdoor attack, which has demonstrated the capability to perform targeted misclassification of specific examples. In particular, backdoor attacks attempt to force a model to learn spurious relations between backdoor trigger patterns and false labels. In response to this threat, numerous defensive measures have been proposed; however, defenses against backdoor attacks focus on backdoor pattern detection, which may be unreliable against novel or unexpected types of backdoor pattern designs. We introduce a novel re-contextualization of the adversarial setting, where the presence of an adversary implicitly admits the existence of multiple database contributors. Then, under the mild assumption of contributor awareness, it becomes possible to exploit this knowledge to defend against backdoor attacks by destroying the false label associations. We propose a contributor-aware universal defensive framework for learning in the presence of multiple, potentially adversarial data sources that utilizes semi-supervised ensembles and learning from crowds to filter the false labels produced by adversarial triggers. Importantly, this defensive strategy is agnostic to backdoor pattern design, as it functions without needing -- or even attempting -- to perform either adversary identification or backdoor pattern detection during either training or inference. Our empirical studies demonstrate the robustness of the proposed framework against adversarial backdoor attacks from multiple simultaneous adversaries.
Two Ways of Understanding Social Dynamics: Analyzing the Predictability of Emergence of Objects in Reddit r/place Dependent on Locality in Space and Time
Authors: Alyssa M Adams, Javier Fernandez, Olaf Witkowski
Date: 2022-06-02 00:00:00

Lately, studying social dynamics in interacting agents has been boosted by the power of computer models, which bring the richness of qualitative work, while offering the precision, transparency, extensiveness, and replicability of statistical and mathematical approaches. A particular set of phenomena for the study of social dynamics is Web collaborative platforms. A dataset of interest is r/place, a collaborative social experiment held in 2017 on Reddit, which consisted of a shared online canvas of 1000 pixels by 1000 pixels co-edited by over a million recorded users over 72 hours. In this paper, we designed and compared two methods to analyze the dynamics of this experiment. Our first method consisted in approximating the set of 2D cellular-automata-like rules used to generate the canvas images and how these rules change over time. The second method consisted in a convolutional neural network (CNN) that learned an approximation to the generative rules in order to generate the complex outcomes of the canvas. Our results indicate varying context-size dependencies for the predictability of different objects in r/place in time and space. They also indicate a surprising peak in difficulty to statistically infer behavioral rules towards the middle of the social experiment, while user interactions did not drop until before the end. The combination of our two approaches, one rule-based and the other statistical CNN-based, shows the ability to highlight diverse aspects of analyzing social dynamics.
Compressive Sensing with Wigner $D$-functions on Subsets of the Sphere
Authors: Marc Andrew Valdez, Alex J. Yuffa, Michael B. Wakin
Date: 2022-06-07 00:00:00
Tasks: Compressive Sensing

In this paper, we prove a compressive sensing guarantee for restricted measurement domains on the rotation group, $\mathrm{SO}(3)$. We do so by first defining Slepian functions on a measurement sub-domain $R$ of the rotation group $\mathrm{SO}(3)$. Then, we transform the inverse problem from the measurement basis, the bounded orthonormal system of band-limited Wigner $D$-functions on $\mathrm{SO}(3)$, to the Slepian functions in a way that limits increases to signal sparsity. Contrasting methods using Wigner $D$-functions that require measurements on all of $\mathrm{SO}(3)$, we show that the orthogonality structure of the Slepian functions only requires measurements on the sub-domain $R$, which is select-able. Due to the particulars of this approach and the inherent presence of Slepian functions with low concentrations on $R$, our approach gives the highest accuracy when the signal under study is well concentrated on $R$. We provide numerical examples of our method in comparison with other classical and compressive sensing approaches. In terms of reconstruction quality, we find that our method outperforms the other compressive sensing approaches we test and is at least as good as classical approaches but with a significant reduction in the number of measurements.
Structure-Preserving Model Order Reduction for Index Two Port-Hamiltonian Descriptor Systems
Authors: Tim Moser, Paul Schwerdtner, Volker Mehrmann, Matthias Voigt
Date: 2022-06-08 00:00:00

We present a new optimization-based structure-preserving model order reduction (MOR) method for port-Hamiltonian descriptor systems (pH-DAEs) with differentiation index two. Our method is based on a novel parameterization that allows us to represent any linear time-invariant pH-DAE with a minimal number of parameters, which makes it well-suited to model reduction. We propose two algorithms which directly optimize the parameters of a reduced model to approximate a given large-scale model with respect to either the H-infinity or the H-2 norm. This approach has several benefits. Our parameterization ensures that the reduced model is again a pH-DAE system and enables a compact representation of the algebraic part of the large-scale model, which in projection-based methods often requires a more involved treatment. The direct optimization is entirely based on transfer function evaluations of the large-scale model and is therefore independent of the system matrices' structure. Numerical experiments are conducted to illustrate the high accuracy and small reduced model orders in comparison to other structure-preserving MOR methods.
Motion control with optimal nonlinear damping: from theory to experiment
Authors: Michael Ruderman
Date: 2022-06-08 00:00:00

Optimal nonlinear damping control was recently introduced for the second-order SISO systems, showing some advantages over a classical PD feedback controller. This paper summarizes the main theoretical developments and properties of the optimal nonlinear damping controller and demonstrates, for the first time, its practical experimental evaluation. An extended analysis and application to more realistic (than solely the double-integrator) motion systems are also given in the theoretical part of the paper. As comparative linear feedback controller, a PD one is taken, with the single tunable gain and direct compensation of the plant time constant. The second, namely experimental, part of the paper includes the voice-coil drive system with relatively high level of the process and measurement noise, for which the standard linear model is first identified in frequency domain. The linear approximation by two-parameters model forms the basis for designing the PD reference controller, which fixed feedback gain is the same as for the optimal nonlinear damping control. A robust sliding-mode based differentiator is used in both controllers for a reliable velocity estimation required for the feedback. The reference PD and the proposed optimal nonlinear damping controller, both with the same single design parameter, are compared experimentally with respect to trajectory tracking and disturbance rejection.
Event-Triggered Control for Nonlinear Time-Delay Systems
Authors: Kexue Zhang, Bahman Gharesifard, Elena Braverman
Date: 2022-06-08 00:00:00

This article studies the event-triggered control problem of general nonlinear systems with time delay. A novel event-triggering scheme is presented with two tunable design parameters, based on a Lyapunov functional result for the input-to-state stability of time-delay systems. The proposed event-triggered control algorithm guarantees the resulting closed-loop systems to be globally asymptotically stable, uniformly bounded, and/or globally attractive for different choices of these parameters. Sufficient conditions on the parameters are derived to exclude Zeno behavior. Two illustrative examples are studied to demonstrate our theoretical results.
A Model-Based Reinforcement Learning Approach for PID Design
Authors: Hozefa Jesawada, Amol Yerudkar, Carmen Del Vecchio, Navdeep Singh
Date: 2022-06-07 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Proportional-integral-derivative (PID) controller is widely used across various industrial process control applications because of its straightforward implementation. However, it can be challenging to fine-tune the PID parameters in practice to achieve robust performance. The paper proposes a model-based reinforcement learning (RL) framework to design PID controllers leveraging the probabilistic inference for learning control (PILCO) method and Kullback-Leibler divergence (KLD). Since PID controllers have a much more interpretable control structure than a network basis function, an optimal policy given by PILCO is transformed into a set of robust PID tuning parameters for underactuated mechanical systems. The presented method is general and can blend with several model-based and model-free algorithms. The performance of the devised PID controllers is demonstrated with simulation studies for a benchmark cart-pole system under disturbances and system parameter uncertainties.
CPES-QSM: A Quantitative Method Towards the Secure Operation of Cyber-Physical Energy Systems
Authors: Juan Ospina, Venkatesh Venkataramanan, Charalambos Konstantinou
Date: 2022-06-07 00:00:00
Tasks: Decision Making

Power systems are evolving into cyber-physical energy systems (CPES) due to the integration of modern communication and Internet-of-Things (IoT) devices. CPES security evaluation is challenging since the physical and cyber layers are often not considered holistically. Existing literature focuses on only optimizing the operation of either the physical or cyber layer while ignoring the interactions between them. This paper proposes a metric, the Cyber-Physical Energy System Quantitative Security Metric (CPES-QSM), that quantifies the interaction between the cyber and physical layers across three domains: electrical, cyber-risk, and network topology. A method for incorporating the proposed cyber-metric into operational decisions is also proposed by formulating a cyber-constrained AC optimal power flow (C-ACOPF) that considers the status of all the CPES layers. The cyber-constrained ACOPF considers the vulnerabilities of physical and cyber networks by incorporating factors such as voltage stability, contingencies, graph-theory, and IoT cyber risks, while using a multi-criteria decision-making technique. Simulation studies are conducted using standard IEEE test systems to evaluate the effectiveness of the proposed metric and the C-ACOPF formulation.
A Multiplex Approach Against Disturbance Propagation in Nonlinear Networks with Delays
Authors: Shihao Xie, Giovanni Russo
Date: 2022-06-07 00:00:00

We consider both leaderless and leader-follower, possibly nonlinear, networks affected by time-varying communication delays. For such systems, we give a set of sufficient conditions that guarantee the convergence of the network towards some desired behaviour while simultaneously ensuring the rejection of polynomial disturbances and the non-amplification of other classes of disturbances across the network. To fulfill these desired properties, and prove our main results, we propose the use of a control protocol that implements a multiplex architecture. The use of our results for control protocol design is then illustrated in the context of formation control. The protocols are validated both in-silico and via an experimental set-up with real robots. All experiments confirm the effectiveness of our approach.
The influence of color on prices of abstract paintings
Authors: Maksim Borisov, Valeria Kolycheva, Alexander Semenov, Dmitry Grigoriev
Date: 2022-06-08 00:00:00
Tasks: Diversity

Determination of price of an artwork is a fundamental problem in cultural economics. In this work we investigate what impact visual characteristics of a painting have on its price. We construct a number of visual features measuring complexity of the painting, its points of interest, segmentation-based features, local color features, and features based on Itten and Kandinsky theories, and utilize mixed-effects model to study impact of these features on the painting price. We analyze the influence of the color on the example of the most complex art style - abstractionism, by created Kandinsky, for which the color is the primary basis. We use Itten's theory - the most recognized color theory in art history, from which the largest number of subtheories was born. For this day it is taken as the base for teaching artists. We utilize novel dataset of 3885 paintings collected from Christie's and Sotheby's and find that color harmony has some explanatory power, color complexity metrics are insignificant and color diversity explains price well.
Can Mobile Technology Improve Female Entrepreneurship? Evidence from Nepal
Authors: Conner Mullally, Sarah Janzen, Nicholas Magnan, Shruti Sharma, Bhola Shrestha
Date: 2022-06-08 00:00:00

Gender norms may constrain the ability of women to develop their entrepreneurial skills, particularly in rural areas. By bringing entrepreneurial training to women rather than requiring extended time away from home, mobile technology could open doors that would otherwise be closed. We randomly selected Nepali women to be trained as veterinary service providers known as community animal health workers. Half of the selected candidates were randomly assigned to a traditional training course requiring 35 consecutive days away from home, and half were assigned to a hybrid distance learning course requiring two shorter stays plus a table-based curriculum to be completed at home. Distance learning strongly increases women's ability to complete training as compared to traditional training. Distance learning has a larger effect than traditional training on boosting the number of livestock responsibilities women carry out at home, while also raising aspirations. Both training types increase women's control over income. Our results indicate that if anything, distance learning produced more effective community animal health workers.
Time-varying Cost of Distancing: Distancing Fatigue and Lockdowns
Authors: Christoph Carnehl, Satoshi Fukuda, Nenad Kos
Date: 2022-06-08 00:00:00

We study a behavioral SIR model with time-varying costs of distancing. The two main causes of the variation in the cost of distancing we explore are distancing fatigue and public policies (lockdowns). We show that for a second wave of an epidemic to arise, a steep increase in distancing cost is necessary. Distancing fatigue cannot increase the distancing cost sufficiently fast to create a second wave. However, public policies that discontinuously affect the distancing cost can create a second wave. With that in mind, we characterize the largest change in the distancing cost (due to, for example, lifting a public policy) that will not cause a second wave. Finally, we provide a numerical analysis of public policies under distancing fatigue and show that a strict lockdown at the beginning of an epidemic (as, for example, recently in China) can lead to unintended adverse consequences. When the policy is lifted the disease spreads very fast due to the accumulated distancing fatigue of the individuals causing high prevalence levels.
Controlling replication via the belief system in multi-unit organizations
Authors: Ravshanbek Khodzhimatov, Stephan Leitner, Friederike Wall
Date: 2022-06-08 00:00:00

Multi-unit organizations such as retail chains are interested in the diffusion of best practices throughout all divisions. However, the strict guidelines or incentive schemes may not always be effective in promoting the replication of a practice. In this paper we analyze how the individual belief systems, namely the desire of individuals to conform, may be used to spread knowledge between departments. We develop an agent-based simulation of an organization with different network structures between divisions through which the knowledge is shared, and observe the resulting synchrony. We find that the effect of network structures on the diffusion of knowledge depends on the interdependencies between divisions, and that peer-to-peer exchange of information is more effective in reaching synchrony than unilateral sharing of knowledge from one division. Moreover, we find that centralized network structures lead to lower performance in organizations.
Welfare and Distributional Effects of Joint Intervention in Networks
Authors: Ryan Kor, Junjie Zhou
Date: 2022-06-08 00:00:00

We study a planner's optimal interventions in both the standalone marginal utilities of players on a network and weights on the links that connect players. The welfare-maximizing joint intervention exhibits the following properties: (a) when the planner's budget is moderate (so that optimal interventions are interior), the change in weight on any link connecting a pair of players is proportional to the product of eigen-centralities of the pair; (b) when the budget is sufficiently large, the optimal network takes a simple form: It is either a complete network under strategic complements or a complete balanced bipartite network under strategic substitutes. We show that the welfare effect of joint intervention is shaped by the principal eigenvalue, while the distributional effect is captured by the dispersion of the corresponding eigenvalues, i.e., the eigen-centralities. Comparing joint intervention in our setting with single intervention solely on the standalone marginal utilities, as studied by Galeotti et al. (2020), we find that joint intervention always yields a higher aggregate welfare, but may lead to greater inequality, which highlights a possible trade-off between the welfare and distributional impacts of joint intervention.
On the Integration of Acoustics and LiDAR: a Multi-Modal Approach to Acoustic Reflector Estimation
Authors: Ellen Riemens, Pablo Martínez-Nuevo, Jorge Martinez, Martin Møller, Richard C. Hendriks
Date: 2022-06-08 00:00:00
Tasks: Boundary Detection

Having knowledge on the room acoustic properties, e.g., the location of acoustic reflectors, allows to better reproduce the sound field as intended. Current state-of-the-art methods for room boundary detection using microphone measurements typically focus on a two-dimensional setting, causing a model mismatch when employed in real-life scenarios. Detection of arbitrary reflectors in three dimensions encounters practical limitations, e.g., the need for a spherical array and the increased computational complexity. Moreover, loudspeakers may not have an omnidirectional directivity pattern, as usually assumed in the literature, making the detection of acoustic reflectors in some directions more challenging. In the proposed method, a LiDAR sensor is added to a loudspeaker to improve wall detection accuracy and robustness. This is done in two ways. First, the model mismatch introduced by horizontal reflectors can be resolved by detecting reflectors with the LiDAR sensor to enable elimination of their detrimental influence from the 2D problem in pre-processing. Second, a LiDAR-based method is proposed to compensate for the challenging directions where the directive loudspeaker emits little energy. We show via simulations that this multi-modal approach, i.e., combining microphone and LiDAR sensors, improves the robustness and accuracy of wall detection.
Low-complexity acoustic scene classification in DCASE 2022 Challenge
Authors: Irene Martín-Morató, Francesco Paissan, Alberto Ancilotto, Toni Heittola, Annamaria Mesaros, Elisabetta Farella, Alessio Brutti, Tuomas Virtanen
Date: 2022-06-08 00:00:00
Tasks: Acoustic Scene Classification, Classification, Quantization, Scene Classification

This paper presents an analysis of the Low-Complexity Acoustic Scene Classification task in DCASE 2022 Challenge. The task was a continuation from the previous years, but the low-complexity requirements were changed to the following: the maximum number of allowed parameters, including the zero-valued ones, was 128 K, with parameters being represented using INT8 numerical format; and the maximum number of multiply-accumulate operations at inference time was 30 million. The provided baseline system is a convolutional neural network which employs post-training quantization of parameters, resulting in 46.5 K parameters, and 29.23 million multiply-and-accumulate operations (MMACs). Its performance on the evaluation data is 44.2% accuracy and 1.532 log-loss. In comparison, the top system in the challenge obtained an accuracy of 59.6% and a log loss of 1.091, having 121 K parameters and 28 MMACs. The task received 48 submissions from 19 different teams, most of which outperformed the baseline system.
A High Resolution Optimum 2D Coprime Planar Array
Authors: Kretika Goel, Monika Aggarwal, Subrat Kar
Date: 2022-06-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Designing a new class of rectangular two-dimensional sparse array to enhance the signal resolving capabilities with a limited number of sensors has always been a challenge. We explore the non-uniformity of the sparse arrays to enhance the Degrees of Freedom (DOF) by considering the under-determined cases using the concept of the virtual array. In this paper, we propose a two-dimensional novel sparse array configurations to estimate both azimuth and elevation angle of arrival of the signal. We propose a new class of rectangular two-dimensional arrays with sensors on a plane, whose difference co-array can give rise to a virtual two-dimensional planar array with a much larger number of elements, leading to an increase in the fully augmentable range of the array. The difference co-array of the proposed rectangular array gives a contiguous dense structure which leads to a reduction in holes within the planar array as compared to the traditional methods of CPA geometry in which there are many holes present in the outer dimension of the array structure as well as within the array as well. Moreover, we know that the angular resolution of an array is highly dependent on its beamwidth which itself is inversely proportional to array size or effective array aperture. Hence by increasing the array dimension a higher resolution can be achieved. The proposed arrays provide a higher DOF for the given number of physical sensors. Optimization is performed on the proposed geometry to maximize the directivity in the array steering look-up direction by suppressing the sidelobe levels to a greater extent.