pwc-archive/papers-with-abstracts huggingface dataset:
Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence
Authors: Adrien Basdevant, Camille François, Victor Storchan, Kevin Bankston, Ayah Bdeir, Brian Behlendorf, Merouane Debbah, Sayash Kapoor, Yann Lecun, Mark Surman, Helen King-Turvey, Nathan Lambert, Stefano Maffulli, Nik Marda, Govind Shivkumar, Justine Tunney
Date: 2024-05-17 00:00:00
Tasks: Descriptive

Over the past year, there has been a robust debate about the benefits and risks of open sourcing foundation models. However, this discussion has often taken place at a high level of generality or with a narrow focus on specific technical attributes. In part, this is because defining open source for foundation models has proven tricky, given its significant differences from traditional software development. In order to inform more practical and nuanced decisions about opening AI systems, including foundation models, this paper presents a framework for grappling with openness across the AI stack. It summarizes previous work on this topic, analyzes the various potential reasons to pursue openness, and outlines how openness varies in different parts of the AI stack, both at the model and at the system level. In doing so, its authors hope to provide a common descriptive framework to deepen a nuanced and rigorous understanding of openness in AI and enable further work around definitions of openness and safety in AI.
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens
Authors: Félix Marcoccia, Cédric Adjih, Paul Mühlethaler
Date: 2024-05-28 00:00:00
Tasks: Denoising, Graph Generation, Link Prediction

This work introduces Cross-Attentive Modulation (CAM) tokens, which are tokens whose initial value is learned, gather information through cross-attention, and modulate the nodes and edges accordingly. These tokens are meant to improve the global awareness of link predictions models which, based on graph neural networks, can struggle to capture graph-level features. This lack of ability to feature high level representations is particularly limiting when predicting multiple or entire sets of links. We implement CAM tokens in a simple attention-based link prediction model and in a graph transformer, which we also use in a denoising diffusion framework. A brief introduction to our toy datasets will then be followed by benchmarks which prove that CAM token improve the performance of the model they supplement and outperform a baseline with diverse statistical graph attributes.
A modular and scalable web platform for computational phylogenetics
Authors: Nyckollas Brandão, André Jesus, André Páscoa, Alexandre P. Francisco, Mário Ramirez, Cátia Vaz
Date: 2024-05-25 00:00:00

Phylogenetic analysis, which allow to understand the evolution of bacterial and viral epidemics, requires large quantities of data to be analysed and processed for knowledge extraction. One of the major challenges consists on the integration of the results from typing and phylogenetic inference methods with epidemiological data, namely in what concerns their integrated and simultaneous analysis and visualization. Numerous approaches to support phylogenetic analysis have been proposed, varying from standalone tools to integrative web applications that include tools and/or algorithms for executing the common analysis tasks for this kind of data. However, most of them lack the capacity to integrate epidemiological data. Others provide the ability for visualizing and analyzing such data, allowing the integration of epidemiological data but they do not scale for large data analysis and visualization. Namely, most of them run inference and/or visualization optimization tasks on the client side, which becomes often unfeasible for large amounts of data, usually implying transferring data from existing databases in order to be analysed. Moreover, the results and optimizations are not stored for reuse. We propose the PHYLOViZ Web Platform, a cloud based tool for phylogenetic analysis, that not only unifies the features of both existing versions of PHYLOViZ, but also supports structured and customized workflows for executing data processing and analyses tasks, and promotes the reproducibility of previous phylogenetic analyses. This platform supports large scale analyses by relying on a workflow system that enables the distribution of parallel computations on cloud and HPC environments. Moreover, it has a modular architecture, allowing easy integration of new methods and tools, as well as customized workflows, making it flexible and extensible.
Repeat-Aware Neighbor Sampling for Dynamic Graph Learning
Authors: Tao Zou, Yuhao Mao, Junchen Ye, Bowen Du
Date: 2024-05-24 00:00:00
Tasks: Graph Learning, Link Prediction, Recommendation Systems, Traffic Prediction

Dynamic graph learning equips the edges with time attributes and allows multiple links between two nodes, which is a crucial technology for understanding evolving data scenarios like traffic prediction and recommendation systems. Existing works obtain the evolving patterns mainly depending on the most recent neighbor sequences. However, we argue that whether two nodes will have interaction with each other in the future is highly correlated with the same interaction that happened in the past. Only considering the recent neighbors overlooks the phenomenon of repeat behavior and fails to accurately capture the temporal evolution of interactions. To fill this gap, this paper presents RepeatMixer, which considers evolving patterns of first and high-order repeat behavior in the neighbor sampling strategy and temporal information learning. Firstly, we define the first-order repeat-aware nodes of the source node as the destination nodes that have interacted historically and extend this concept to high orders as nodes in the destination node's high-order neighbors. Then, we extract neighbors of the source node that interacted before the appearance of repeat-aware nodes with a slide window strategy as its neighbor sequence. Next, we leverage both the first and high-order neighbor sequences of source and destination nodes to learn temporal patterns of interactions via an MLP-based encoder. Furthermore, considering the varying temporal patterns on different orders, we introduce a time-aware aggregation mechanism that adaptively aggregates the temporal representations from different orders based on the significance of their interaction time sequences. Experimental results demonstrate the superiority of RepeatMixer over state-of-the-art models in link prediction tasks, underscoring the effectiveness of the proposed repeat-aware neighbor sampling strategy.
Goal-Oriented Sensor Reporting Scheduling for Non-linear Dynamic System Monitoring
Authors: Prasoon Raghuwanshi, Onel Luis Alcaraz López, Vimal Bhatia, Matti Latva-aho
Date: 2024-05-31 00:00:00
Tasks: Deep Reinforcement Learning, Scheduling, Semantic Communication

Goal-oriented communication (GoC) is a form of semantic communication where the effectiveness of information transmission is measured by its impact on achieving the desired goal. In the context of the Internet of Things (IoT), GoC can make IoT sensors to selectively transmit data pertinent to the intended goals of the receiver. Therefore, GoC holds significant value for IoT networks as it facilitates timely decision-making at the receiver, reduces network congestion, and enhances spectral efficiency. In this paper, we consider a scenario where an edge node polls sensors monitoring the state of a non-linear dynamic system (NLDS) to respond to the queries of several clients. Our work delves into the foregoing GoC problem, which we term goal-oriented scheduling (GoS). Our proposed GoS utilizes deep reinforcement learning (DRL) with meticulously devised action space, state space, and reward function. The devised action space and reward function play a pivotal role in reducing the number of sensor transmissions. Meanwhile, the devised state space empowers our DRL scheduler to poll the sensor whose observation is expected to minimize the mean square error (MSE) of the query responses. Our numerical analysis demonstrates that the proposed GoS can either effectively minimize the query response MSE further or obtain a resembling MSE compared to benchmark scheduling methods, depending on the type of query. Furthermore, the proposed GoS proves to be energy-efficient for the sensors and of lower complexity compared to benchmark scheduling methods.
Dynamic Microgrid Formation Considering Time-dependent Contingency: A Distributionally Robust Approach
Authors: Ziang Liu, Sheng Cai, Qiuwei Wu, Xinwei Shen, Xuan Zhang, Nikos Hatziargyriou
Date: 2024-05-31 00:00:00

The increasing frequency of extreme weather events has posed significant risks to the operation of power grids. During long-duration extreme weather events, microgrid formation (MF) is an essential solution to enhance the resilience of the distribution systems by proactively partitioning the distribution system into several microgrids to mitigate the impact of contingencies. This paper proposes a distributionally robust dynamic microgrid formation (DR-DMF) approach to fully consider the temporal characteristics of line failure probability during long-duration extreme weather events like typhoons. The boundaries of each microgrid are dynamically adjusted to enhance the resilience of the system. Furthermore, the expected load shedding is minimized by a distributionally robust optimization model considering the uncertainty of line failure probability regarding the worst-case distribution of contingencies. The effectiveness of the proposed model is verified by numerical simulations on a modified IEEE 37-node system.
A flexible numerical tool for large dynamic DC networks
Authors: Erwin Luesink, Juan Giraldo, Bernard Geurts, Johann Hurink, Hans Zwart
Date: 2024-05-31 00:00:00
Tasks: Decision Making

DC networks play an important role within the ongoing energy transition. In this context, simulations of designed and existing networks and their corresponding assets are a core tool to get insights and form a support to decision-making. Hereby, these simulations of DC networks are executed in the time domain. Due to the involved high frequencies and the used controllers, the equations that model these DC networks are stiff and highly oscillatory differential equations. By exploiting sparsity, we show that conventional adaptive time stepping schemes can be used efficiently for the time domain simulation of very large DC networks and that this scales linearly in the computational cost as the size of the networks increase.
Impact of Phase Selection on Accuracy and Scalability in Calculating Distributed Energy Resources Hosting Capacity
Authors: Tomislav Antic, Andrew Keane, Tomislav Capuder
Date: 2024-05-31 00:00:00

Hosting capacity (HC) and dynamic operating envelopes (DOEs), defined as dynamic, time-varying HC, are calculated using three-phase optimal power flow (OPF) formulations. Due to the computational complexity of such optimisation problems, HC and DOE are often calculated by introducing certain assumptions and approximations, including the linearised OPF formulation, which we implement in the Python-based tool ppOPF. Furthermore, we investigate how assumptions of the distributed energy resource (DER) connection phase impact the objective function value and computational time in calculating HC and DOE in distribution networks of different sizes. The results are not unambiguous and show that it is not possible to determine the optimal connection phase without introducing binary variables since, no matter the case study, the highest objective function values are calculated with mixed integer OPF formulations. The difference is especially visible in a real-world low-voltage network in which the difference between different scenarios is up to 14 MW in a single day. However, binary variables make the problem computationally complex and increase computational time to several hours in the DOE calculation, even when the optimality gap different from zero is set.
Chance-Constrained Economic Dispatch with Flexible Loads and RES
Authors: Tian Liu, Bo Sun, Danny H. K. Tsang
Date: 2024-05-24 00:00:00

With the increasing penetration of intermittent renewable energy sources (RESs), it becomes increasingly challenging to maintain the supply-demand balance of power systems by solely relying on the generation side. To combat the volatility led by the uncertain RESs, demand-side management by leveraging the multi-dimensional flexibility (MDF) has been recognized as an economic and efficient approach. Thus, it is important to integrate MDF into existing power systems. In this paper, we propose an enhanced day-ahead energy market, where the MDFs of aggregate loads are traded to minimize the generation cost and mitigate the volatility of locational marginal prices (LMPs) in the transmission network. We first explicitly capture the negative impact of the uncertainty from RESs on the day-ahead market by a chance-constrained economic dispatch problem (CEDP). Then, we propose a bidding mechanism for the MDF of the aggregate loads and combine this mechanism into the CEDP for the day-ahead market. Through multiple case studies, we show that MDF from load aggregators can reduce the volatility of LMPs. In addition, we identify the values of the different flexibilities in the MDF bids, which provide useful insights into the design of more complex MDF markets.
Robust Economic Dispatch with Flexible Demand and Adjustable Uncertainty Set
Authors: Tian Liu, Su Wang, Danny H. K. Tsang
Date: 2024-05-24 00:00:00

With more renewable energy sources (RES) integrated into the power system, the intermittency of RES places a heavy burden on the system. The uncertainty of RES is traditionally handled by controllable generators to balance the real time wind power deviation. As the demand side management develops, the flexibility of aggregate loads can be leveraged to mitigate the negative impact of the wind power. In view of this, we study the problem of how to exploit the multi-dimensional flexibility of elastic loads to balance the trade-off between a low generation cost and a low system risk related to the wind curtailment and the power deficiency. These risks are captured by the conditional value-at-risk. Also, unlike most of the existing studies, the uncertainty set of the wind power output in our model is not fixed. By contrast, it is undetermined and co-optimized based on the available load flexibility. We transform the original optimization problem into a convex one using surrogate affine approximation such that it can be solved efficiently. In case studies, we apply our model on a six-bus transmission network and demonstrate that how flexible load aggregators can help to determine the optimal admissible region for the wind power deviations.
Discrete-Time Implementation of Explicit Reference Governor
Authors: Mu'taz A. Momani, Mehdi Hosseinzadeh
Date: 2024-05-31 00:00:00

Explicit reference governor (ERG) is an add-on unit that provides constraint handling capability to pre-stabilized systems. The main idea behind ERG is to manipulate the derivative of the applied reference in continuous time such that the satisfaction of state and input constraints is guaranteed at all times. However, ERG should be practically implemented in discrete-time. This paper studies the discrete-time implementation of ERG, and provides conditions under which the feasibility and convergence properties of the ERG framework are maintained when the updates of the applied reference are performed in discrete time. The proposed approach is validated via extensive simulation and experimental studies.
Reach-Avoid Control Synthesis for a Quadrotor UAV with Formal Safety Guarantees
Authors: Mohamed Serry, Haocheng Chang, Jun Liu
Date: 2024-05-30 00:00:00

Reach-avoid specifications are one of the most common tasks in autonomous aerial vehicle (UAV) applications. Despite the intensive research and development associated with control of aerial vehicles, generating feasible trajectories though complex environments and tracking them with formal safety guarantees remain challenging. In this paper, we propose a control framework for a quadrotor UAV that enables accomplishing reach-avoid tasks with formal safety guarantees. In this proposed framework, we integrate geometric control theory for tracking and polynomial trajectory generation using Bezier curves, where tracking errors are accounted for in the trajectory synthesis process. To estimate the tracking errors, we revisit the stability analysis of the closed-loop quadrotor system, when geometric control is implemented. We show that the tracking error dynamics exhibit local exponential stability when geometric control is implemented with any positive control gains, and we derive tight uniform bounds of the tracking error. We also introduce sufficient conditions to be imposed on the desired trajectory utilizing the derived uniform bounds to ensure the well-definedness of the closed-loop system. For the trajectory synthesis, we present an efficient algorithm that enables constructing a safe tube by means of sampling-based planning and safe hyper-rectangular set computations. Then, we compute the trajectory, given as a piecewise continuous Bezier curve, through the safe tube, where a heuristic efficient approach that utilizes iterative linear programming is employed. We present extensive numerical simulations with a cluttered environment to illustrate the effectiveness of the proposed framework in reach-avoid planning scenarios.
A case study comparing both stochastic and worst-case robust control co-design under different control structures
Authors: Saeed Azad, Daniel R Herber
Date: 2024-05-30 00:00:00

As uncertainty considerations become increasingly important aspects of concurrent plant and control optimization, it is imperative to identify and compare the impact of uncertain control co-design (UCCD) formulations on their associated solutions. While previous work developed the theory for various UCCD formulations, their implementation, along with an in-depth discussion of the structure of UCCD problems, implicit assumptions, method-dependent considerations, and practical insights, is currently missing from the literature. Therefore, in this study, we address some of these limitations by focusing on UCCD formulations, with an emphasis on optimal control structures, and uncertainty propagation techniques. Specifically, we propose three optimal control structures for UCCD problems: (i) open-loop multiple-control (OLMC), (ii) multi-stage control (MSC), and (iii) open-loop single-control (OLSC). Stochastic in expectation UCCD (SE-UCCD) and worst-case robust UCCD (WCR-UCCD) formulations, which are motivated by probabilistic and crisp representations of uncertainties, respectively, are implemented for a simplified strain-actuated solar array case study. Solutions to the OLMC SE-UCCD problem are obtained using two uncertainty propagation techniques: generalized Polynomial Chaos expansion (gPC) and Monte Carlo simulation (MCS). The OLMC and MSC WCR-UCCD problems are solved by leveraging the structure of the linear program, leading to polytopic uncertainties. To highlight the importance of uncertainty in early-stage design, the closed-loop reference-tracking response of the systems is also investigated. Insights from such studies underscore the role of the control structure in managing the trade-offs between risk and performance, as well as meeting problem requirements. The results also emphasize the benefits of efficient uncertainty propagation techniques for dynamic optimization problems.
Stability-Constrained Learning for Frequency Regulation in Power Grids with Variable Inertia
Authors: Jie Feng, Manasa Muralidharan, Rodrigo Henriquez-Auba, Patricia Hidalgo-Gonzalez, Yuanyuan Shi
Date: 2024-05-30 00:00:00

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and neural network controller for inverter-based primary frequency control that is stable at time-varying levels of inertia. We model the time-variance in inertia via a switched affine hybrid system model. We derive stability certificates for the proposed controller via a quadratic candidate Lyapunov function. We test the proposed control on a 12-bus 3-area test network, and compare its performance with a base case linear controller, optimized linear controller, and finite-horizon Linear Quadratic Regulator (LQR). Our proposed controller achieves faster mean settling time and over 50% reduction in average control cost across $100$ inertia scenarios compared to the optimized linear controller. Unlike LQR which requires complete knowledge of the inertia trajectories and system dynamics over the entire control time horizon, our proposed controller is real-time tractable, and achieves comparable performance to LQR.
Equivalent External Noise Temperature of Time-Varying Receivers
Authors: Kurt Schab, K. C. Kerby-Patel
Date: 2024-05-30 00:00:00

The equivalent external noise temperature of time-varying antennas is studied using the concept of cross-frequency effective aperture, which quantifies the intermodulation conversion of external noise across the frequency spectrum into a receiver's operational bandwidth. The theoretical tools for this approach are laid out following the classical method for describing external noise temperature of linear time-invariant antennas, with generalizations made along the way to capture the effects of time-varying components or materials. The results demonstrate the specific ways that a time-varying system's noise characteristics are dependent on its cross-frequency effective aperture and the broadband noise environment. The general theory is applied to several examples, including abstract models of hypothetical systems, antennas integrated with parametric amplification, and time-modulated arrays.
Contingency-Aware Station-Keeping Control of Halo Orbits
Authors: Fausto Vega, Zachary Manchester, Martin Lo, Ricardo Restrepo
Date: 2024-05-30 00:00:00

We present an algorithm to perform fuel-optimal stationkeeping for spacecraft in unstable halo orbits with additional constraints to ensure safety in the event of a control failure. We formulate a convex trajectory-optimization problem to generate impulsive spacecraft maneuvers to loosely track a halo orbit using a receding-horizon controller. Our solution also provides a safe exit strategy in the event that propulsion is lost at any point in the mission. We validate our algorithm in simulations of the three-body Earth-Moon and Saturn-Enceladus systems, demonstrating both low total delta-v and a safe contingency plan throughout the mission.