pwc-archive/papers-with-abstracts huggingface dataset:
HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation
Authors: Han Liu, Yinwei Wei, Jianhua Yin, Liqiang Nie
Date: 2023-01-13 00:00:00
Tasks: Recommendation Systems

An efficient solution to the large-scale recommender system is to represent users and items as binary hash codes in the Hamming space. Towards this end, existing methods tend to code users by modeling their Hamming similarities with the items they historically interact with, which are termed as the first-order similarities in this work. Despite their efficiency, these methods suffer from the suboptimal representative capacity, since they forgo the correlation established by connecting multiple first-order similarities, i.e., the relation among the indirect instances, which could be defined as the high-order similarity. To tackle this drawback, we propose to model both the first- and the high-order similarities in the Hamming space through the user-item bipartite graph. Therefore, we develop a novel learning to hash framework, namely Hamming Spatial Graph Convolutional Networks (HS-GCN), which explicitly models the Hamming similarity and embeds it into the codes of users and items. Extensive experiments on three public benchmark datasets demonstrate that our proposed model significantly outperforms several state-of-the-art hashing models, and obtains performance comparable with the real-valued recommendation models.
Price impact in equity auctions: zero, then linear
Authors: Mohammed Salek, Damien Challet, Ioane Muni Toke
Date: 2023-01-13 00:00:00

Using high-quality data, we report several statistical regularities of equity auctions in the Paris stock exchange. First, the average order book density is linear around the auction price at the time of auction clearing and has a large peak at the auction price. While the peak is due to slow traders, the order density shape is the result of subtle dynamics. The impact of a new market order or cancellation at the auction time can be decomposed into three parts as a function of the size of the additional order: (1) zero impact, caused by the discrete nature of prices, sometimes up to a surprisingly large additional volume relative to the auction volume (2) linear impact for additional orders up to a large fraction of the auction volume (3) for even larger orders price impact is non-linear, frequently super-linear.
Partial entropy decomposition reveals higher-order structures in human brain activity
Authors: Thomas F Varley, Maria Pope, Maria Grazia Puxeddu, Joshua Faskowitz, Olaf Sporns
Date: 2023-01-12 00:00:00
Tasks: Functional Connectivity

The standard approach to modeling the human brain as a complex system is with a network, where the basic unit of interaction is a pairwise link between two brain regions. While powerful, this approach is limited by the inability to assess higher-order interactions involving three or more elements directly. In this work, we present a method for capturing higher-order dependencies in discrete data based on partial entropy decomposition (PED). Our approach decomposes the joint entropy of the whole system into a set of strictly non-negative partial entropy atoms that describe the redundant, unique, and synergistic interactions that compose the system's structure. We begin by showing how the PED can provide insights into the mathematical structure of both the FC network itself, as well as established measures of higher-order dependency such as the O-information. When applied to resting state fMRI data, we find robust evidence of higher-order synergies that are largely invisible to standard functional connectivity analyses. This synergistic structure distinct from structural features based on redundancy that have previously dominated FC analyses. Our approach can also be localized in time, allowing a frame-by-frame analysis of how the distributions of redundancies and synergies change over the course of a recording. We find that different ensembles of regions can transiently change from being redundancy-dominated to synergy-dominated, and that the temporal pattern is structured in time. These results provide strong evidence that there exists a large space of unexplored structures in human brain data that have been largely missed by a focus on bivariate network connectivity models. This synergistic "shadow structures" is dynamic in time and, likely will illuminate new and interesting links between brain and behavior.
Planning Visual Inspection Tours for a 3D Dubins Airplane Model in an Urban Environment
Authors: Collin Hague, Andrew Willis, Dipankar Maity, Artur Wolek
Date: 2023-01-12 00:00:00

This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environment. Objects are 2.5D extruded polygons representing buildings or other structures. A visibility volume defines the set of admissible (occlusion-free) viewing locations for each target that satisfy feasible airspace and imaging constraints. The Dubins traveling salesperson problem with neighborhoods (DTSPN) is extended to three dimensions with visibility volumes that are approximated by triangular meshes. Four sampling algorithms are proposed for sampling vehicle configurations within each visibility volume to define vertices of the underlying DTSPN. Additionally, a heuristic approach is proposed to improve computation time by approximating edge costs of the 3D Dubins airplane with a lower bound that is used to solve for a sequence of viewing locations. The viewing locations are then assigned pitch and heading angles based on their relative geometry. The proposed sampling methods and heuristics are compared through a Monte-Carlo experiment that simulates view planning tours over a realistic urban environment.
Rock Guitar Tablature Generation via Natural Language Processing
Authors: Josue Casco-Rodriguez
Date: 2023-01-12 00:00:00

Deep learning has recently empowered and democratized generative modeling of images and text, with additional concurrent works exploring the possibility of generating more complex forms of data, such as audio. However, the high dimensionality, long-range dependencies, and lack of standardized datasets currently makes generative modeling of audio and music very challenging. We propose to model music as a series of discrete notes upon which we can use autoregressive natural language processing techniques for successful generative modeling. While previous works used similar pipelines on data such as sheet music and MIDI, we aim to extend such approaches to the under-studied medium of guitar tablature. Specifically, we develop the first work to our knowledge that models one specific genre as guitar tablature: heavy rock. Unlike other works in guitar tablature generation, we have a freely available public demo at https://huggingface.co/spaces/josuelmet/Metal_Music_Interpolator
Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approach
Authors: Qinghua Lu, Yuxiu Luo, Liming Zhu, Mingjian Tang, Xiwei Xu, Jon Whittle
Date: 2023-01-03 00:00:00
Tasks: Chatbot, Fairness

The recent release of ChatGPT has gained huge attention and discussion worldwide, with responsible AI being a key topic of discussion. How can we ensure that AI systems, including ChatGPT, are developed and adopted in a responsible way? To tackle the responsible AI challenges, various ethical principles have been released by governments, organisations, and companies. However, those principles are very abstract and not practical enough. Further, significant efforts have been put on algorithm-level solutions that only address a narrow set of principles, such as fairness and privacy. To fill the gap, we adopt a pattern-oriented responsible AI engineering approach and build a Responsible AI Pattern Catalogue to operationalise responsible AI from a system perspective. In this article, we first summarise the major challenges in operationalising responsible AI at scale and introduce how we use the Responsible AI Pattern Catalogue to address those challenges. We then examine the risks at each stage of the chatbot development process and recommend pattern-driven mitigations to evaluate the the usefulness of the Responsible AI Pattern Catalogue in a real-world setting.
Data-Assisted Control -- A Framework Development by Exploiting NASA GTM Platform
Authors: Mostafa Eslami, Afshin Banazadeh
Date: 2023-01-13 00:00:00

Today's focus on expanding the capabilities of control systems, resulting from the abundance of data and computational resources, requires data-based alternatives over model-based ones. These alternatives may become the sole tool for analysis and synthesis. Nevertheless, mathematical models are available to some extent, especially for air and space vehicles. Hypothetically, data assistance would be the approach to meet the requirements in collaboration with the model. In this paper, a framework of Data-Assisted Control (DAC) for aerospace vehicles is proposed. NASA Generic Transport Model (GTM) is the platform for the study and the data supports the model-based controller in extending performance over a damage event. The framework requires real-time decisions to override the control law with the information obtained from the data, while the model-based controller does not show regular performance. The closed-loop system is shown to be stable in the transition phase between the data and the model. The fixed dynamic parameters are estimated using the Dual Unscented Kalman Filter (DUKF) and the evolution of the generalized force moments is estimated using the Koopman estimator. Simulations have shown that the purely model-based robust control leads to degradation of the closed-loop performance in case of damage, suggesting the need for data assistance.
Router for wireless power packet transmission: Design and application to intersystem power management
Authors: Takahiro Mamiya, Shiu Mochiyama, Takashi Hikihara
Date: 2023-01-13 00:00:00
Tasks: energy management, Management

Power supply for small-scale battery-powered systems such as electric vehicles (EVs and mobile robots) is being actively researched. We are particularly interested in energy management, which considers the interconnection of such systems close to each other. This allows for overall redundancy to be maintained without assuming excessive redundancy with individual power sources. Its implementation necessitates a high level of integration between power management and information and communication technology. As one of these methods, this study investigates energy management based on power packetization. When the individual systems to be connected have moving parts or are mobile, wireless power transmission is a promising method for power sharing. However, power packetization has so far only been considered for wired transmission. In this paper, we address the integration of power and information in wireless channels using power packetization. We propose a power packet router circuit that can wirelessly transmit power over multiple channels selectively. Furthermore, we demonstrate that the developed system can handle both wired intrasystem power management and wireless intersystem power sharing in a unified manner.
Concentration in Gossip Opinion Dynamics over Random Graphs
Authors: Yu Xing, Karl Henrik Johansson
Date: 2023-01-13 00:00:00
Tasks: Stochastic Block Model

We study concentration inequalities in gossip opinion dynamics over random graphs. In the model, a network is generated from a random graph model with independent edges, and agents interact pairwise randomly over the network. During the process, regular agents average neighbors' opinions and then update, whereas stubborn agents do not change opinions. To approximate the original process, we introduce a gossip model over an expected graph, obtained by averaging all possible networks generated from the random graph model. Using concentration inequalities, we derive high-probability bounds for the distance between the expected final opinion vectors over the random graph and over the expected graph. Leveraging matrix perturbation results, we show how such concentration can help study the effect of network structure on the expected final opinions in two cases: (i) When the influence of stubborn agents is large, the expected final opinions polarize and are close to stubborn agents' opinions. (ii) When the influence of stubborn agents is small, the expected final opinions are close to each other. With the help of concentration inequalities for Markov chains, we obtain high-probability bounds for the distance between time-averaged opinions and the expected final opinions over the expected graph. In simulation, we validate the theoretical findings, and study a gossip model over a stochastic block model that has community structure.
Data-driven Moving Horizon Estimation for Angular Velocity of Space Noncooperative Target in Eddy Current De-tumbling Mission
Authors: Xiyao Liu, Haitao Chang, Zhenyu Lu, Panfeng Huang
Date: 2023-01-13 00:00:00
Tasks: LEMMA, State Estimation

Angular velocity estimation is critical for eddy current de-tumbling of noncooperative space targets. However, unknown model of the noncooperative target and few observation data make the model-based estimation methods challenged. In this paper, a Data-driven Moving Horizon Estimation method is proposed to estimate the angular velocity of the noncooperative target with de-tumbling torque. In this method, model-free state estimation of the angular velocity can be achieved using only one historical trajectory data that satisfies the rank condition. With local linear approximation, the Willems fundamental lemma is extended to nonlinear autonomous systems, and the rank condition for the historical trajectory data is deduced. Then, a data-driven moving horizon estimation algorithm based on the M step Lyapunov function is designed, and the time-discount robust stability of the algorithm is given. In order to illustrate the effectiveness of the proposed algorithm, experiments and simulations are performed to estimate the angular velocity in eddy current de-tumbling with only de-tumbling torque measurement.
Reinforcement Learning-based Joint Handover and Beam Tracking in Millimeter-wave Networks
Authors: Sara Khosravi, Hossein S. Ghadikolaei, Jens Zander, Marina Petrova
Date: 2023-01-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

In this paper, we develop an algorithm for joint handover and beam tracking in millimeter-wave (mmWave) networks. The aim is to provide a reliable connection in terms of the achieved throughput along the trajectory of the mobile user while preventing frequent handovers. We model the association problem as an optimization problem and propose a reinforcement learning-based solution. Our approach learns whether and when beam tracking and handover should be performed and chooses the target base stations. In the case of beam tracking, we propose a tracking algorithm based on measuring a small spatial neighbourhood of the optimal beams in the previous time slot. Simulation results in an outdoor environment show the superior performance of our proposed solution in achievable throughput and the number of handovers needed in comparison to a multi-connectivity baseline and a learning-based handover baseline.
Probabilistic Constraint Construction for Network-safe Load Coordination
Authors: Sunho Jang, Necmiye Ozay, Johanna L Mathieu
Date: 2023-01-12 00:00:00

Distributed Energy Resources (DERs) can provide balancing services to the grid, but their power variations might cause voltage and current constraint violations in the distribution network, compromising network safety. This could be avoided by including network constraints within DER control formulations, but the entities coordinating DERs (e.g., aggregators) may not have access to network information, which typically is known only to the utility. Therefore, it is challenging to develop network-safe DER control algorithms when the aggregator is not the utility; it requires these entities to coordinate with each other. In this paper, we develop an aggregator-utility coordination framework that enables network-safe control of thermostatically-controlled loads to provide frequency regulation. In our framework, the utility sends a network-safe constraint set on the aggregator's command without directly sharing any network information. We propose a constraint set construction algorithm that guarantees satisfaction of a chance constraint on network safety. Assuming monotonicity of the probability of network safety with respect to the aggregator's command, we leverage the bisection method to find the largest possible constraint set, providing maximum flexibility to the aggregator. Simulations show that, compared to two benchmark algorithms, the proposed approach provides a good balance between service quality and network safety.
Discontinuous Jump Behavior of the Energy Conversion in Wind Energy Systems
Authors: Pyei Phyo Lin, Matthias Waechter, M. Reza Rahimi Tabar, Joachim Peinke
Date: 2023-01-09 00:00:00

The power conversion process of a wind turbine can be characterized by a stochastic differential equation (SDE) of the power output conditioned to certain fixed wind speeds. An analogous approach can also be applied to the mechanical loads on a wind turbine, such as generator torque. The constructed SDE consists of the deterministic and stochastic terms, the latter corresponding to the highly fluctuating behavior of the wind turbine. Here we show how advanced stochastic analysis of the noise contribution can be used to show different operating modes of the conversion process of a wind turbine. The parameters of the SDE, known as Kramers-Moyal (KM) coefficients, are estimated directly from the measurement data. Clear evidence is found that both, continuous diffusion noise and discontinuous jump noise are present. The difference in the noise contributions indicates different operational regions. In particular, we observe that the jump character or discontinuity in power production has a significant contribution in the regions where the control system switches strategies. We find that there is a high increase in jump amplitude near the transition to the rated region, and the switching strategies cannot result in a smooth transition. The proposed analysis provides new insights to the control strategies of the wind turbine.
Effect of Adult-Born Immature Granule Cells on Pattern Separation in The Hippocampal Dentate Gyrus
Authors: Sang-Yoon Kim, Woochang Lim
Date: 2023-01-13 00:00:00

Young immature granule cells (imGCs) appear via adult neurogenesis in the hippocampal dentate gyrus (DG). In comparison to mature GCs (mGCs) (born during development), the imGCs exhibit two competing distinct properties such as high excitability and low excitatory innervation. We develop a spiking neural network for the DG, incorporating the imGCs, and investigate their effect on pattern separation. We first consider the effect of high excitability. The imGCs become very highly active due to their low firing threshold. Then, strong pattern correlation occurs, which results in pattern integration. On the other hand, the mGCs exhibit very sparse firing activity due to strongly increased feedback inhibition. As a result of high sparsity, the pattern separation efficacy (PSE) of the mGCs becomes very high. Thus, the whole population of GCs becomes a heterogeneous one, composed of a (major) subpopulation of mGCs (i.e., pattern separators) with very low activation degree and a (minor) subpopulation of imGCs (i.e., pattern integrators) with very high activation degree. In the whole heterogeneous population, the overall activation degree of all the GCs is a little reduced in comparison to the activation degree in the presence of only mGCs without imGCs. However, no pattern separation occurs, due to heterogeneous sparsity, in contrast to the usual intuitive thought that sparsity could improve PSE. Next, we consider the effect of low excitatory innervation for the imGCs. In this case, the imGCs perform pattern integration. The PSE of the mGCs decreases from a high value to a limit value. In the whole population of all the GCs, when passing through a threshold, pattern separation starts, the overall PSE increases and approaches that of the mGCs. However, due to heterogeneity caused by the imGCs, the overall PSE becomes deteriorated, in comparison with that in the presence of only mGCs.
Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach
Authors: Shuai Wang, ChiYung Yam, Shuguang Chen, Lihong Hu, Liping Li, Faan-Fung Hung, Jiaqi Fan, Chi-Ming Che, Guanhua Chen
Date: 2023-01-08 00:00:00
Tasks: Ensemble Learning, Triplet

Phosphorescent metal complexes have been under intense investigations as emissive dopants for energy efficient organic light emitting diodes (OLEDs). Among them, cyclometalated Pt(II) complexes are widespread triplet emitters with color-tunable emissions. To render their practical applications as OLED emitters, it is in great need to develop Pt(II) complexes with high radiative decay rate constant ($k_r$) and photoluminescence (PL) quantum yield. Thus, an efficient and accurate prediction tool is highly desirable. Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration. A new dataset concerning phosphorescent Pt(II) emitters is constructed, with more than two hundred samples collected from the literature. Features containing pertinent electronic properties of the complexes are chosen. Our results demonstrate that ensemble learning models combined with stacking-based approaches exhibit the best performance, where the values of squared correlation coefficients ($R^2$), mean absolute error (MAE), and root mean square error (RMSE) are 0.96, 7.21 nm and 13.00 nm for emission wavelength prediction, and 0.81, 0.11 and 0.15 for PL quantum yield prediction. For radiative decay rate constant ($k_r$), the obtained value of $R^2$ is 0.67 while MAE and RMSE are 0.21 and 0.25 (both in log scale), respectively. The accuracy of the protocol is further confirmed using 24 recently reported Pt(II) complexes, which demonstrates its reliability for a broad palette of Pt(II) emitters.We expect this protocol will become a valuable tool, accelerating the rational design of novel OLED materials with desired properties.