pwc-archive/papers-with-abstracts huggingface dataset:
Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks
Authors: Kamesh Korangi, Christophe Mues, Cristián Bravo
Date: 2024-07-22 00:00:00
Tasks: Graph Attention, Portfolio Optimization

Apart from assessing individual asset performance, investors in financial markets also need to consider how a set of firms performs collectively as a portfolio. Whereas traditional Markowitz-based mean-variance portfolios are widespread, network-based optimisation techniques offer a more flexible tool to capture complex interdependencies between asset values. However, most of the existing studies do not contain firms at risk of default and remove any firms that drop off indices over a certain time. This is the first study to also incorporate such firms in portfolio optimisation on a large scale. We propose and empirically test a novel method that leverages Graph Attention networks (GATs), a subclass of Graph Neural Networks (GNNs). GNNs, as deep learning-based models, can exploit network data to uncover nonlinear relationships. Their ability to handle high-dimensional data and accommodate customised layers for specific purposes makes them appealing for large-scale problems such as mid- and small-cap portfolio optimisation. This study utilises 30 years of data on mid-cap firms, creating graphs of firms using distance correlation and the Triangulated Maximally Filtered Graph approach. These graphs are the inputs to a GAT model incorporating weight and allocation constraints and a loss function derived from the Sharpe ratio, thus focusing on maximising portfolio risk-adjusted returns. This new model is benchmarked against a network characteristic-based portfolio, a mean variance-based portfolio, and an equal-weighted portfolio. The results show that the portfolio produced by the GAT-based model outperforms all benchmarks and is consistently superior to other strategies over a long period, while also being informative of market dynamics.
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs
Authors: Huanjing Zhao, Beining Yang, Yukuo Cen, Junyu Ren, Chenhui Zhang, Yuxiao Dong, Evgeny Kharlamov, Shu Zhao, Jie Tang
Date: 2024-07-22 00:00:00
Tasks: Few-Shot Learning, Graph Neural Network, Language Modeling, Language Modelling, Masked Language Modeling, Node Classification, TAG

The text-attributed graph (TAG) is one kind of important real-world graph-structured data with each node associated with raw texts. For TAGs, traditional few-shot node classification methods directly conduct training on the pre-processed node features and do not consider the raw texts. The performance is highly dependent on the choice of the feature pre-processing method. In this paper, we propose P2TAG, a framework designed for few-shot node classification on TAGs with graph pre-training and prompting. P2TAG first pre-trains the language model (LM) and graph neural network (GNN) on TAGs with self-supervised loss. To fully utilize the ability of language models, we adapt the masked language modeling objective for our framework. The pre-trained model is then used for the few-shot node classification with a mixed prompt method, which simultaneously considers both text and graph information. We conduct experiments on six real-world TAGs, including paper citation networks and product co-purchasing networks. Experimental results demonstrate that our proposed framework outperforms existing graph few-shot learning methods on these datasets with +18.98% ~ +35.98% improvements.
A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech
Authors: Gaurav Verma, Rynaa Grover, Jiawei Zhou, Binny Mathew, Jordan Kraemer, Munmun De Choudhury, Srijan Kumar
Date: 2024-07-21 00:00:00

Violence-provoking speech -- speech that implicitly or explicitly promotes violence against the members of the targeted community, contributed to a massive surge in anti-Asian crimes during the pandemic. While previous works have characterized and built tools for detecting other forms of harmful speech, like fear speech and hate speech, our work takes a community-centric approach to studying anti-Asian violence-provoking speech. Using data from ~420k Twitter posts spanning a 3-year duration (January 1, 2020 to February 1, 2023), we develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection using state-of-the-art classifiers. We contrast the capabilities of natural language processing classifiers, ranging from BERT-based to LLM-based classifiers, in detecting violence-provoking speech with their capabilities to detect anti-Asian hateful speech. In contrast to prior work that has demonstrated the effectiveness of such classifiers in detecting hateful speech ($F_1 = 0.89$), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task ($F_1 = 0.69$). We discuss the implications of our findings, particularly the need for proactive interventions to support Asian communities during public health crises. The resources related to the study are available at https://claws-lab.github.io/violence-provoking-speech/.
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
Authors: Yonghao Liu, Mengyu Li, Ximing Li, Lan Huang, Fausto Giunchiglia, Yanchun Liang, Xiaoyue Feng, Renchu Guan
Date: 2024-07-20 00:00:00
Tasks: Contrastive Learning, Graph Learning, Meta-Learning, Node Classification

Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning with graph neural networks to solve few-shot node classification on graphs. Despite their promising performance, some limitations remain. First, they employ the node encoding mechanism of homophilic graphs to learn node embeddings, even in heterophilic graphs. Second, existing models based on meta-learning ignore the interference of randomness in the learning process. Third, they are trained using only limited labeled nodes within the specific task, without explicitly utilizing numerous unlabeled nodes. Finally, they treat almost all sampled tasks equally without customizing them for their uniqueness. To address these issues, we propose a novel framework for few-shot node classification called Meta-GPS++. Specifically, we first adopt an efficient method to learn discriminative node representations on homophilic and heterophilic graphs. Then, we leverage a prototype-based approach to initialize parameters and contrastive learning for regularizing the distribution of node embeddings. Moreover, we apply self-training to extract valuable information from unlabeled nodes. Additionally, we adopt S$^2$ (scaling & shifting) transformation to learn transferable knowledge from diverse tasks. The results on real-world datasets show the superiority of Meta-GPS++. Our code is available here.
The Software Complexity of Nations
Authors: Sándor Juhász, Johannes Wachs, Jermain Kaminski, César A. Hidalgo
Date: 2024-07-18 00:00:00

Despite the growing importance of the digital sector, research on economic complexity and its implications continues to rely mostly on administrative records, e.g. data on exports, patents, and employment, that fail to capture the nuances of the digital economy. In this paper we use data on the geography of programming languages used in open-source software projects to extend economic complexity ideas to the digital economy. We estimate a country's software economic complexity and show that it complements the ability of measures of complexity based on trade, patents, and research papers to account for international differences in GDP per capita, income inequality, and emissions. We also show that open-source software follows the principle of relatedness, meaning that a country's software entries and exits are explained by specialization in related programming languages. We conclude by exploring the diversification and development of countries in open-source software in the context of large language models. Together, these findings help extend economic complexity methods and their policy considerations to the digital sector.
How to Shrink Confidence Sets for Many Equivalent Discrete Distributions?
Authors: Odalric-Ambrym Maillard, Mohammad Sadegh Talebi
Date: 2024-07-22 00:00:00

We consider the situation when a learner faces a set of unknown discrete distributions $(p_k)_{k\in \mathcal K}$ defined over a common alphabet $\mathcal X$, and can build for each distribution $p_k$ an individual high-probability confidence set thanks to $n_k$ observations sampled from $p_k$. The set $(p_k)_{k\in \mathcal K}$ is structured: each distribution $p_k$ is obtained from the same common, but unknown, distribution q via applying an unknown permutation to $\mathcal X$. We call this \emph{permutation-equivalence}. The goal is to build refined confidence sets \emph{exploiting} this structural property. Like other popular notions of structure (Lipschitz smoothness, Linearity, etc.) permutation-equivalence naturally appears in machine learning problems, and to benefit from its potential gain calls for a specific approach. We present a strategy to effectively exploit permutation-equivalence, and provide a finite-time high-probability bound on the size of the refined confidence sets output by the strategy. Since a refinement is not possible for too few observations in general, under mild technical assumptions, our finite-time analysis establish when the number of observations $(n_k)_{k\in \mathcal K}$ are large enough so that the output confidence sets improve over initial individual sets. We carefully characterize this event and the corresponding improvement. Further, our result implies that the size of confidence sets shrink at asymptotic rates of $O(1/\sqrt{\sum_{k\in \mathcal K} n_k})$ and $O(1/\max_{k\in K} n_{k})$, respectively for elements inside and outside the support of q, when the size of each individual confidence set shrinks at respective rates of $O(1/\sqrt{n_k})$ and $O(1/n_k)$. We illustrate the practical benefit of exploiting permutation equivalence on a reinforcement learning task.
Vehicle-to-Everything: Looking into the Future of Flexibility Services
Authors: Omid Homaee, Chaimaa Essayeh, Vahid Vahidinasab, Ville Tikka, Goncalo Mendes, Jim Fawcett
Date: 2024-07-22 00:00:00

The primary aim of this paper is to illuminate potential Vehicle-to-Everything (V2X) flexibility services that can be activated at the three levels of home, community, and grid. To do this, the potential practical services that can be provided by EVs, flexibility requesters, and the required exchange mechanisms at these three levels are identified. At the home level, the two main services that EVs can provide to households are explored. The initial service focuses on cost reduction for homes by employing smart charging and discharging methods, and the second service underscores the capability of an EV equipped with bidirectional chargers to function as a backup resource during grid outages. At the community level, three flexibility services are introduced and outlined: community cost reduction, energy sharing, and backup resources. There is more than one flexibility requester, including the community manager, EV owners, and other end users. Accordingly, at this level, having a fair and transparent market mechanism can optimise the activation of different V2X flexibility services. At the grid level, flexibility providers can offer two main flexibility services, namely load profile adjustment and real-time voltage and frequency control, to a wide range of flexibility requesters, including distribution network/systems operators (DNOs/DSOs) to overcome technical challenges of the distribution network, energy suppliers to manage their energy portfolio, and TSOs to support transmission network. In addition, a review of trial V2X projects and V2X supporting regulations is presented to contextualize the feasibility and regulatory framework for implementing these services. This analysis offers insights into the challenges and opportunities that need to be addressed for the effective integration of V2X flexibility services across the three identified levels.
A Methodology for Power Dispatch Based on Traction Station Clusters in the Flexible Traction Power Supply System
Authors: Ruofan Li, Qianhao Sun, Qifang Chen, Mingchao Xia
Date: 2024-07-22 00:00:00

The flexible traction power supply system (FTPSS) eliminates the neutral zone but leads to increased complexity in power flow coordinated control and power mismatch. To address these challenges, the methodology for power dispatch (PD) based on traction station clusters (TSCs) in FTPSS is proposed, in which each TSC with a consistent structure performs independent local phase angle control. First, to simplify the PD problem of TSCs, the system is transformed into an equivalent model with constant topology, resulting in it can be solved by univariate numerical optimization with higher computational performance. Next, the calculation method of the feasible phase angle domain under strict and relaxed power circulation constraints are described, respectively, which ensures that power circulation can be either eliminated or precisely controlled. Finally, the PD method with three unique modes for uncertain train loads is introduced to enhance power flow flexibility: specified power distribution coefficients between traction substations (TSs), constant output power of TSs, and maximum consumption of renewable resources within TSs. In the experimental section, the performance of the TSC methodology for PD is verified through detailed train operation scenarios.
Should we use model-free or model-based control? A case study of battery management systems
Authors: Mohamad Fares El Hajj Chehade, Young-ho Cho, Sandeep Chinchali, Hao Zhu
Date: 2024-07-22 00:00:00
Tasks: Management, model, Model Predictive Control, Reinforcement Learning (RL)

Reinforcement learning (RL) and model predictive control (MPC) each offer distinct advantages and limitations when applied to control problems in power and energy systems. Despite various studies on these methods, benchmarks remain lacking and the preference for RL over traditional controls is not well understood. In this work, we put forth a comparative analysis using RL- and MPC-based controllers for optimizing a battery management system (BMS). The BMS problem aims to minimize costs while adhering to operational limits. by adjusting the battery (dis)charging in response to fluctuating electricity prices over a time horizon. The MPC controller uses a learningbased forecast of future demand and price changes to formulate a multi-period linear program, that can be solved using off-the-shelf solvers. Meanwhile, the RL controller requires no timeseries modeling but instead is trained from the sample trajectories using the proximal policy optimization (PPO) algorithm. Numerical tests compare these controllers across optimality, training time, testing time, and robustness, providing a comprehensive evaluation of their efficacy. RL not only yields optimal solutions quickly but also ensures robustness to shifts in customer behavior, such as changes in demand distribution. However, as expected, training the RL agent is more time-consuming than MPC.
Weyl Calculus and Exactly Solvable Schrödinger Bridges with Quadratic State Cost
Authors: Alexis M. H. Teter, Wenqing Wang, Abhishek Halder
Date: 2024-07-21 00:00:00

Schr\"{o}dinger bridge--a stochastic dynamical generalization of optimal mass transport--exhibits a learning-control duality. Viewed as a stochastic control problem, the Schr\"{o}dinger bridge finds an optimal control policy that steers a given joint state statistics to another while minimizing the total control effort subject to controlled diffusion and deadline constraints. Viewed as a stochastic learning problem, the Schr\"{o}dinger bridge finds the most-likely distribution-valued trajectory connecting endpoint distributional observations, i.e., solves the two point boundary-constrained maximum likelihood problem over the manifold of probability distributions. Recent works have shown that solving the Schr\"{o}dinger bridge problem with state cost requires finding the Markov kernel associated with a reaction-diffusion PDE where the state cost appears as a state-dependent reaction rate. We explain how ideas from Weyl calculus in quantum mechanics, specifically the Weyl operator and the Weyl symbol, can help determine such Markov kernels. We illustrate these ideas by explicitly finding the Markov kernel for the case of quadratic state cost via Weyl calculus, recovering our earlier results but avoiding tedious computation with Hermite polynomials.
Variation Bayesian Interference for Multiple Extended Targets or Unresolved Group Targets Tracking
Authors: Yuanhao Cheng, Yunhe Cao, Tat-Soon Yeo, Yulin Zhang, Fu Jie
Date: 2024-07-21 00:00:00
Tasks: Bayesian Inference

In this work, we propose a tracking method for multiple extended targets or unresolvable group targets based on the Variational Bayesian Inference (VBI). Firstly, based on the most commonly used Random Matrix Model (RMM), the joint states of a single target are modeled as a Gamma Gaussian Inverse Wishart (GGIW) distribution, and the multi-target joint association variables are involved in the estimation together as unknown information with a prior distribution. A shape evolution model and VBI are employed to address the shortcomings of the RMM. Through the VBI, we can derive the approximate variational posterior for the exact multi-target posterior. Furthermore, to demonstrate the applicability of the method in real-world tracking scenarios, we present two potential lightweight schemes. The first is based on clustering, which effectively prunes the joint association events. The second is a simplification of the variational posterior through marginal association probabilities. We demonstrate the effectiveness of the proposed method using simulation experiments, and the proposed method outperforms current state-of-the-art methods in terms of accuracy and adaptability. This manuscript is only a preprint version, a completer and more official version will be uploaded as soon as possible
More-than-Moore Microacoustics: A Scalable Fabrication Process for Suspended Lamb Wave Resonators
Authors: Marco Liffredo, Federico Peretti, Nan Xu, Silvan Stettler, Luis Guillermo Villanueva
Date: 2024-07-21 00:00:00

Deep Ultraviolet (DUV) Photolithography is currently used to fabricate mass-scale integrated circuits (ICs). Its high throughput and resolution could benefit large-scale RF MEMS production for the telecommunication market. We present a process flow to fabricate suspended acoustic resonators using DUV Photolithography. This method allows for scalable production of resonators with critical dimensions of 250 nm and alignment accuracy of less than 100 nm. We show how photoresists and anti-reflective coatings integrate with the process, help with deposition quality and resolution, and how Ion Beam Etching allows for vertical sidewalls of the resonators. We measure resonance frequencies (fr) up to 7.5 GHz and electromechanical couplings up to 8%, and we investigate the uniformity of this process by analyzing the deviation of fs over the wafer surface for four main resonance modes. We show that the deviation of the S0 mode can be kept below 1%. These results indicate the suitability of this process for quick scale-up of Lamb wave resonator technology, bridging the gap from research to industry.
Measurements of the Safety Function Response Time on a Private 5G and IO-Link Wireless Testbed
Authors: Henry Beuster, Kevin Tebbe, Thomas Doebbert, Gerd Scholl
Date: 2024-07-21 00:00:00

In the past few years, there has been a growing significance of interactions between human workers and automated systems throughout the factory floor. Wherever static or mobile robots, such as automated guided vehicles, operate autonomously, a protected environment for personnel and machines must be provided by, e.g., safe, deterministic and low-latency technologies. Another trend in this area is the increased use of wireless communication, offering a high flexibility, modularity, and reduced installation and maintenance efforts. This work presents a testbed implementation that integrates a wireless framework, employing IO-Link Wireless (IOLW) and a private 5G cellular network, to orchestrate a complete example process from sensors and actuators up into the edge, represented by a programmable logic controller (PLC). Latency assessments identify the systems cycle time as well as opportunities for improvement. A worst-case estimation shows the attainable safety function response time for practical applications in the context of functional safety.
An Interface Method for Co-simulation of EMT Model and Shifted Frequency EMT Model Based on Rotational Invariance Techniques
Authors: Shilin Gao, Ying Chen, Zhitong Yu, WenSheng Chen, Yankan Song
Date: 2024-07-21 00:00:00
Tasks: Computational Efficiency, model

The shifted frequency-based electromagnetic transient (SFEMT) simulation has greatly improved the computational efficiency of traditional electromagnetic transient (EMT) simulation for the ac grid. This letter proposes a novel interface for the co-simulation of the SFEMT model and the traditional EMT model. The general form of SFEMT modeling and the principle of analytical signal construction are first derived. Then, an interface for the co-simulation of EMT and SFEMT simulation is proposed based on rotational invariance techniques. Theoretical analyses and test results demonstrate the effectiveness of the proposed method.
UAV Active Perception and Motion Control for Improving Navigation Using Low-Cost Sensors
Authors: Konstantinos Gounis, Nikolaos Passalis, Anastasios Tefas
Date: 2024-07-21 00:00:00
Tasks: Navigate

In this study a model pipeline is proposed that combines computer vision with control-theoretic methods and utilizes low cost sensors. The proposed work enables perception-aware motion control for a quadrotor UAV to detect and navigate to objects of interest such as wind turbines and electric towers. The distance to the object of interest was estimated utilizing RGB as the primary sensory input. For the needs of the study, the Microsoft AirSim simulator was used. As a first step, a YOLOv8 model was integrated providing the basic position setpoints towards the detection. From the YOLOv8 inference, a target yaw angle was derived. The subsequent algorithms, combining performant in computational terms computer vision methods and YOLOv8, actively drove the drone to measure the height of the detection. Based on the height, an estimate of the depth was retrieved. In addition to this step, a convolutional neural network was developed, namely ActvePerceptionNet aiming at active YOLOv8 inference. The latter was validated for wind turbines where the rotational motion of the propeller was found to affect object confidence in a near periodical fashion. The results of the simulation experiments conducted in this study showed efficient object height and distance estimation and effective localization.