pwc-archive/papers-with-abstracts huggingface dataset:
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence
Authors: Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, Astro Teller
Date: 2022-10-31 00:00:00

In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Peter Stone of the University of Texas at Austin. The report, entitled "Artificial Intelligence and Life in 2030," examines eight domains of typical urban settings on which AI is likely to have impact over the coming years: transportation, home and service robots, healthcare, education, public safety and security, low-resource communities, employment and workplace, and entertainment. It aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential and to help guide decisions in industry and governments, as well as to inform research and development in the field. The charge for this report was given to the panel by the AI100 Standing Committee, chaired by Barbara Grosz of Harvard University.
A Sign That Spells: DALL-E 2, Invisual Images and The Racial Politics of Feature Space
Authors: Fabian Offert, Thao Phan
Date: 2022-10-26 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Semantic Compression

In this paper, we examine how generative machine learning systems produce a new politics of visual culture. We focus on DALL-E 2 and related models as an emergent approach to image-making that operates through the cultural techniques of feature extraction and semantic compression. These techniques, we argue, are inhuman, invisual, and opaque, yet are still caught in a paradox that is ironically all too human: the consistent reproduction of whiteness as a latent feature of dominant visual culture. We use Open AI's failed efforts to 'debias' their system as a critical opening to interrogate how systems like DALL-E 2 dissolve and reconstitute politically salient human concepts like race. This example vividly illustrates the stakes of this moment of transformation, when so-called foundation models reconfigure the boundaries of visual culture and when 'doing' anti-racism means deploying quick technical fixes to mitigate personal discomfort, or more importantly, potential commercial loss.
Deep Appearance Prefiltering
Authors: Steve Bako, Pradeep Sen, Anton Kaplanyan
Date: 2022-11-08 00:00:00

Physically based rendering of complex scenes can be prohibitively costly with a potentially unbounded and uneven distribution of complexity across the rendered image. The goal of an ideal level of detail (LoD) method is to make rendering costs independent of the 3D scene complexity, while preserving the appearance of the scene. However, current prefiltering LoD methods are limited in the appearances they can support due to their reliance of approximate models and other heuristics. We propose the first comprehensive multi-scale LoD framework for prefiltering 3D environments with complex geometry and materials (e.g., the Disney BRDF), while maintaining the appearance with respect to the ray-traced reference. Using a multi-scale hierarchy of the scene, we perform a data-driven prefiltering step to obtain an appearance phase function and directional coverage mask at each scale. At the heart of our approach is a novel neural representation that encodes this information into a compact latent form that is easy to decode inside a physically based renderer. Once a scene is baked out, our method requires no original geometry, materials, or textures at render time. We demonstrate that our approach compares favorably to state-of-the-art prefiltering methods and achieves considerable savings in memory for complex scenes.
Slightly Altruistic Nash Equilibrium for Multi-agent Pursuit-Evasion Games With Input Constraints
Authors: Dongting Li
Date: 2022-11-10 00:00:00

This is an initial manuscript that presents the basic idea of "slightly altruistic Nash equilibrium", "bi-layer game topology", "rolling horizon target selection". This manuscript is just used for peer discussion and joint Ph.D. application affairs rather than submission to any journal. Thus some references are not all provided. The complete paper for submission will be provided in the next version.
Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions
Authors: Wei zhang, Yanjun Han, Zhengyuan Zhou, Aaron Flores, Tsachy Weissman
Date: 2022-11-05 00:00:00
Tasks: Marketing

With the advent and increasing consolidation of e-commerce, digital advertising has very recently replaced traditional advertising as the main marketing force in the economy. In the past four years, a particularly important development in the digital advertising industry is the shift from second-price auctions to first-price auctions for online display ads. This shift immediately motivated the intellectually challenging question of how to bid in first-price auctions, because unlike in second-price auctions, bidding one's private value truthfully is no longer optimal. Following a series of recent works in this area, we consider a differentiated setup: we do not make any assumption about other bidders' maximum bid (i.e. it can be adversarial over time), and instead assume that we have access to a hint that serves as a prediction of other bidders' maximum bid, where the prediction is learned through some blackbox machine learning model. We consider two types of hints: one where a single point-prediction is available, and the other where a hint interval (representing a type of confidence region into which others' maximum bid falls) is available. We establish minimax optimal regret bounds for both cases and highlight the quantitatively different behavior between the two settings. We also provide improved regret bounds when the others' maximum bid exhibits the further structure of sparsity. Finally, we complement the theoretical results with demonstrations using real bidding data.
Joint Spatio-Temporal Precoding for Practical Non-Stationary Wireless Channels
Authors: Zhibin Zou, Maqsood Careem, Aveek Dutta, Ngwe Thawdar
Date: 2022-11-11 00:00:00

The high mobility, density and multi-path evident in modern wireless systems makes the channel highly non-stationary. This causes temporal variation in the channel distribution that leads to the existence of time-varying joint interference across multiple degrees of freedom (DoF, e.g., users, antennas, frequency and symbols), which renders conventional precoding sub-optimal in practice. In this work, we derive a High-Order Generalization of Mercer's Theorem (HOGMT), which decomposes the multi-user non-stationary channel into two (dual) sets of jointly orthogonal subchannels (eigenfunctions), that result in the other set when one set is transmitted through the channel. This duality and joint orthogonality of eigenfuntions ensure transmission over independently flat-fading subchannels. Consequently, transmitting these eigenfunctions with optimally derived coefficients eventually mitigates any interference across its degrees of freedoms and forms the foundation of the proposed joint spatio-temporal precoding. The transferred dual eigenfuntions and coefficients directly reconstruct the data symbols at the receiver upon demodulation, thereby significantly reducing its computational burden, by alleviating the need for any complementary post-coding. Additionally, the eigenfunctions decomposed from the time-frequency delay-Doppler channel kernel are paramount to extracting the second-order channel statistics, and therefore completely characterize the underlying channel. We evaluate this using a realistic non-stationary channel framework built in Matlab and show that our precoding achieves ${\geqslant}$4 orders of reduction in BER at SNR${\geqslant}15$dB in OFDM systems for higher-order modulations and less complexity compared to the state-of-the-art precoding.
Topology in Sound Synthesis and Digital Signal Processing -- DAFx2022 Lecture Notes
Authors: Georg Essl
Date: 2022-11-10 00:00:00

Lecture notes of a tutorial on topology in sound synthesis and digital signal processing held at international conference for digital audio effects (DAFx-22) in Vienna, Austria.
Fraudulent User Detection Via Behavior Information Aggregation Network (BIAN) On Large-Scale Financial Social Network
Authors: Hanyi Hu, Long Zhang, Shuan Li, Zhi Liu, Yao Yang, Chongning Na
Date: 2022-11-04 00:00:00
Tasks: Attribute, Fraud Detection, Graph Attention

Financial frauds cause billions of losses annually and yet it lacks efficient approaches in detecting frauds considering user profile and their behaviors simultaneously in social network . A social network forms a graph structure whilst Graph neural networks (GNN), a promising research domain in Deep Learning, can seamlessly process non-Euclidean graph data . In financial fraud detection, the modus operandi of criminals can be identified by analyzing user profile and their behaviors such as transaction, loaning etc. as well as their social connectivity. Currently, most GNNs are incapable of selecting important neighbors since the neighbors' edge attributes (i.e., behaviors) are ignored. In this paper, we propose a novel behavior information aggregation network (BIAN) to combine the user behaviors with other user features. Different from its close "relatives" such as Graph Attention Networks (GAT) and Graph Transformer Networks (GTN), it aggregates neighbors based on neighboring edge attribute distribution, namely, user behaviors in financial social network. The experimental results on a real-world large-scale financial social network dataset, DGraph, show that BIAN obtains the 10.2% gain in AUROC comparing with the State-Of-The-Art models.
Data-driven Nonlinear Predictive Control for Feedback Linearizable Systems
Authors: Mohammad Alsalti, Victor G. Lopez, Julian Berberich, Frank Allgöwer, Matthias A. Müller
Date: 2022-11-11 00:00:00

We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems. The scheme uses a non-parametric predictive model based only on input and noisy output data along with a set of basis functions that approximate the unknown nonlinearities. Despite the noisy output data as well as the mismatch caused by the use of basis functions, we show that the proposed multistep robust data-driven nonlinear predictive control scheme is recursively feasible and renders the closed-loop system practically exponentially stable. We illustrate our results on a model of a fully-actuated double inverted pendulum.
Reference Governor Design in the Presence of Uncertain Polynomial Constraints
Authors: Rick Schieni, Chengwei Zhao, Michael Malisoff, Laurent Burlion
Date: 2022-11-11 00:00:00

Reference governors are add-on schemes that are used to modify trajectories to prevent controlled dynamical systems from violating constraints and so are playing an increasingly important role in aerospace, robotic, and other engineering applications. Here we present a novel reference governor design for systems whose polynomial constraints depend on unknown bounded parameters. This is a significant departure from earlier treatments of reference governors, where the constraints were linear or known, because here we transfer the uncertainties into the constraints instead of having them in the closed loop dynamics, which greatly simplifies the task of determining future evolution of the constraints. Unlike our earlier treatment of reference governors with polynomial constraints, which transformed the constraints into linear ones that depend on an augmented state of the system, here we transform the constraints into linear ones that depend on both the system's state and uncertain parameters. Convexity allows us to compute the maximal output admissible set for an uncertain pre-stabilized linear system. We show that it is sufficient to only consider the extreme values of the uncertain parameters when computing and propagating the polynomial constraints. We illustrate our method using an uncertain longitudinal dynamics for civilian aircraft, which is controlled using a disturbance compensation method and needs to satisfy input and state constraints, and where our reference governor method ensures that safety constraints are always satisfied.
Unbalanced OPF Modelling for Mixed Monopolar and Bipolar HVDC Grid Configurations
Authors: Chandra Kant Jat, Jay Dave, Dirk Van Hertem, Hakan Ergun
Date: 2022-11-11 00:00:00

HVDC is a critically important technology for the large-scale integration of renewable resources such as offshore wind farms. Currently, only point-to-point and multi-terminal HVDC connections exist in real-life operation. However, with the advancement of VSC-based converter technologies, future HVDC systems are foreseen to develop into meshed HVDC grids. Bipolar HVDC grids can be operated in an unbalanced way during single pole outages or in form of mixed monopolar and bipolar grids. However, currently, there are no (optimal) power flow tools to study the feasibility of such systems. Therefore, we develop an optimal power flow (OPF) model for hybrid AC-DC grids to capture the DC side unbalances and allowing to efficiently plan and operate such future grids. In this paper, we present a multi-conductor OPF model with separate modeling of the positive pole, negative pole, metallic return conductors, and ground return. The capabilities of the model are demonstrated on a small test case, including monopolar tapping over a bipolar DC link. It is demonstrated that the developed OPF model can capture the loop flows between the different poles in unbalanced conditions, as opposed to the existing single-wire representations in the literature. Further, numerical results are presented for multiple test cases with various system sizes, starting from an 11-bus system to a 3120-bus system to demonstrate the computational tractability of the chosen model formulation.
Linear Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design
Authors: Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani, Nathan van de Wouw
Date: 2022-11-11 00:00:00

This paper addresses the problem of robust process and sensor fault reconstruction for nonlinear systems. The proposed method augments the system dynamics with an approximated internal linear model of the combined contribution of known nonlinearities and unknown faults -- leading to an approximated linear model in the augmented state. We exploit the broad modeling power of ultra-local models to characterize this internal dynamics. We use a linear filter to reconstruct the augmented state (simultaneously estimating the state of the original system and the sum of nonlinearities and faults). Having this combined estimate, we can simply subtract the analytic expression of nonlinearities from that of the corresponding estimate to reconstruct the fault vector. Because the nonlinearity does not play a role in the filter dynamics (it is only used as a static nonlinear output to estimate the fault), we can avoid standard restrictive assumptions like globally (one-sided) Lipschitz nonlinearities and/or the need for Lipschitz constants to carry out the filter design. The filter synthesis is posed as a mixed H2/Hinf optimization problem where the effect of disturbances and model mismatches is minimized in the Hinf sense, for an acceptable H2 performance with respect to measurement noise.
Control Lyapunov-Barrier Function Based Model Predictive Control for Stochastic Nonlinear Affine Systems
Authors: Weijiang Zheng, Bing Zhu
Date: 2022-11-11 00:00:00
Tasks: Model Predictive Control

A stochastic model predictive control (MPC) framework is presented in this paper for nonlinear affine systems with stability and feasibility guarantee. We first introduce the concept of stochastic control Lyapunov-barrier function (CLBF) and provide a method to construct CLBF by combining an unconstrained control Lyapunov function (CLF) and control barrier functions. The unconstrained CLF is obtained from its corresponding semi-linear system through dynamic feedback linearization. Based on the constructed CLBF, we utilize sampled-data MPC framework to deal with states and inputs constraints, and to analyze stability of closed-loop systems. Moreover, event-triggering mechanisms are integrated into MPC framework to improve performance during sampling intervals. The proposed CLBF based stochastic MPC is validated via an obstacle avoidance example.
Coherent Equalization of Linear Quantum Systems
Authors: V. Ugrinovskii, M. R. James
Date: 2022-11-11 00:00:00

This paper introduces a $H_\infty$-like methodology of coherent filtering for equalization of passive linear quantum systems to help mitigate degrading effects of quantum communication channels. For such systems, which include a wide range of linear quantum optical devices and signals, we seek to find a near optimal equalizing filter which is itself a passive quantum system. The problem amounts to solving an optimization problem subject to constraints dictated by the requirement for the equalizer to be physically realizable. By formulating these constraints in the frequency domain, we show that the problem admits a convex $H_\infty$-like formulation. This allows us to derive a set of suboptimal coherent equalizers using $J$-spectral factorization. An additional semidefinite relaxation combined with the Nevanlinna-Pick interpolation is shown to lead to a tractable algorithm for the design of a near optimal coherent equalizer.
BattX: An Equivalent Circuit Model for Lithium-Ion Batteries Over Broad Current Ranges
Authors: Nikhil Biju, Huazhen Fang
Date: 2022-11-11 00:00:00
Tasks: Management

Advanced battery management is to lithium-ion battery systems as the brain is to the human body. Its performance rests on the use of battery models that are both fast and accurate. However, mainstream equivalent circuit models and electrochemical models have yet to meet this need well, due to struggle with either predictive accuracy or computational complexity. This problem has acquired urgency as some emerging battery applications running across broad current ranges, e.g., electric vertical take-off and landing aircraft, can hardly find usable models from the literature. Motivated to address the problem, we develop an innovative model in this study. Called BattX, the model is an equivalent circuit model but draws comparisons to a single particle model with electrolyte and thermal dynamics, thus combining their respective merits to be computationally efficient, accurate, and physically interpretable. The model design pivots on leveraging multiple circuits to approximate major electrochemical and physical processes in charging/discharging. Given the model, we develop a multipronged approach to design experiments and identify its parameters in groups from experimental data. Experimental validation proves that the BattX model is capable of accurate voltage prediction for charging/discharging across low to high C-rates.
Frequency and Amplitude Optimizations for Magnetic Particle Spectroscopy Applications
Authors: Vinit Kumar Chugh, Arturo di Girolamo, Venkatramana D. Krishna, Kai Wu, Maxim C-J Cheeran, Jian-Ping Wang
Date: 2022-11-11 00:00:00

Nowadays, there is a growing interest in the field of magnetic particle spectroscopy (MPS)-based bioassays. MPS monitors the dynamic magnetic response of surface-functionalized magnetic nanoparticles (MNPs) upon excitation by an alternating magnetic field (AMF) to detect various target analytes. This technology has flourished in the past decade due to its low cost, low background magnetic noise interference from biomatrix, and fast response time. A large number of MPS variants have been reported by different groups around the world, with applications ranging from disease diagnosis to foodborne pathogen detection, and virus detection. However, there is an urgent need for guidance on how to optimize the sensitivity of MPS detection by choosing different types of MNPs, AMF modalities, and MPS assay strategies (i.e., volume- and surface-based assays). In this work, we systematically study the effect of AMF frequencies and amplitudes on the responses of single- and multi-core MNPs under two extreme conditions, namely, the bound and unbound states. Our results show that some modalities such as dual-frequency MPS utilizing multicore MNPs are more suitable for surface-based bioassay applications, whereas, single-frequency MPS systems using single- or multi-core MNPs are better suited for volumetric bioassay applications. Furthermore, the bioassay sensitivities for these modalities can be further improved by careful selection of AMF frequencies and amplitudes.
Structured Singular Value of a Repeated Complex Full-Block Uncertainty
Authors: Talha Mushtaq, Diganta Bhattacharjee, Peter Seiler, Maziar S. Hemati
Date: 2022-11-11 00:00:00
Tasks: Computational Efficiency

The structured singular value (SSV), or mu, is used to assess the robust stability and performance of an uncertain linear time-invariant system. Existing algorithms compute upper and lower bounds on the SSV for structured uncertainties that contain repeated (real or complex) scalars and/or non-repeated complex full blocks. This paper presents algorithms to compute bounds on the SSV for the case of repeated complex full blocks. This specific class of uncertainty is relevant for the input output analysis of many convective systems, such as fluid flows. Specifically, we present a power iteration to compute a lower bound on SSV for the case of repeated complex full blocks. This generalizes existing power iterations for repeated complex scalar and non-repeated complex full blocks. The upper bound can be formulated as a semi-definite program (SDP), which we solve using a standard interior-point method to compute optimal scaling matrices associated with the repeated full blocks. Our implementation of the method only requires gradient information, which improves the computational efficiency of the method. Finally, we test our proposed algorithms on an example model of incompressible fluid flow. The proposed methods provide less conservative bounds as compared to prior results, which ignore the repeated full block structure.