pwc-archive/papers-with-abstracts huggingface dataset:
Movable-Antenna Position Optimization for Physical-Layer Security via Discrete Sampling
Authors: Weidong Mei, Xin Wei, Yijie Liu, Boyu Ning, Zhi Chen
Date: 2024-08-01 00:00:00
Tasks: Position

Fluid antennas (FAs) and mobile antennas (MAs) are innovative technologies in wireless communications that are able to proactively improve channel conditions by dynamically adjusting the transmit/receive antenna positions within a given spatial region. In this paper, we investigate an MA-enhanced multiple-input single-output (MISO) secure communication system, aiming to maximize the secrecy rate by jointly optimizing the positions of multiple MAs. Instead of continuously searching for the optimal MA positions as in prior works, we propose to discretize the transmit region into multiple sampling points, thereby converting the continuous antenna position optimization into a discrete sampling point selection problem. However, this point selection problem is combinatory and thus difficult to be optimally solved. To tackle this challenge, we ingeniously transform this combinatory problem into a recursive path selection problem in graph theory and propose a partial enumeration algorithm to obtain its optimal solution without the need for high-complexity exhaustive search. To further reduce the complexity, a linear-time sequential update algorithm is also proposed to obtain a high-quality suboptimal solution. Numerical results show that our proposed algorithms yield much higher secrecy rates as compared to the conventional FPA and other baseline schemes.
Fiber neural networks for the intelligent optical fiber communications
Authors: Yubin Zang, Zuxing Zhang, Simin Li, Fangzheng Zhang, Hongwei Chen
Date: 2024-08-07 00:00:00
Tasks: Intelligent Communication

Optical neural networks have long cast attention nowadays. Like other optical structured neural networks, fiber neural networks which utilize the mechanism of light transmission to compute can take great advantages in both computing efficiency and power cost. Though the potential ability of optical fiber was demonstrated via the establishing of fiber neural networks, it will be of great significance of combining both fiber transmission and computing functions so as to cater the needs of future beyond 5G intelligent communication signal processing. Thus, in this letter, the fiber neural networks and their related optical signal processing methods will be both developed. In this way, information derived from the transmitted signals can be directly processed in the optical domain rather than being converted to the electronic domain. As a result, both prominent gains in processing efficiency and power cost can be further obtained. The fidelity of the whole structure and related methods is demonstrated by the task of modulation format recognition which plays important role in fiber optical communications without losing the generality.
Coarse-to-fine Alignment Makes Better Speech-image Retrieval
Authors: Lifeng Zhou, Yuke Li
Date: 2024-08-15 00:00:00
Tasks: cross-modal alignment, Image Retrieval, Retrieval

In this paper, we propose a novel framework for speech-image retrieval. We utilize speech-image contrastive (SIC) learning tasks to align speech and image representations at a coarse level and speech-image matching (SIM) learning tasks to further refine the fine-grained cross-modal alignment. SIC and SIM learning tasks are jointly trained in a unified manner. To optimize the learning process, we utilize an embedding queue that facilitates efficient sampling of high-quality and diverse negative representations during SIC learning. Additionally, it enhances the learning of SIM tasks by effectively mining hard negatives based on contrastive similarities calculated in SIC tasks. To further optimize learning under noisy supervision, we incorporate momentum distillation into the training process. Experimental results show that our framework outperforms the state-of-the-art method by more than 4% in R@1 on two benchmark datasets for the speech-image retrieval tasks. Moreover, as observed in zero-shot experiments, our framework demonstrates excellent generalization capabilities.
Quadratic estimation for stochastic systems in the presence of random parameter matrices, time-correlated additive noise and deception attacks
Authors: Raquel Caballero-Águila, Josefa Linares-Pérez
Date: 2024-08-23 00:00:00

Networked systems usually face different random uncertainties that make the performance of the least-squares (LS) linear filter decline significantly. For this reason, great attention has been paid to the search for other kinds of suboptimal estimators. Among them, the LS quadratic estimation approach has attracted considerable interest in the scientific community for its balance between computational complexity and estimation accuracy. When it comes to stochastic systems subject to different random uncertainties and deception attacks, the quadratic estimator design has not been deeply studied. In this paper, using covariance information, the LS quadratic filtering and fixed-point smoothing problems are addressed under the assumption that the measurements are perturbed by a time-correlated additive noise, as well as affected by random parameter matrices and exposed to random deception attacks. The use of random parameter matrices covers a wide range of common uncertainties and random failures, thus better reflecting the engineering reality. The signal and observation vectors are augmented by stacking the original vectors with their second-order Kronecker powers; then, the linear estimator of the original signal based on the augmented observations provides the required quadratic estimator. A simulation example illustrates the superiority of the proposed quadratic estimators over the conventional linear ones and the effect of the deception attacks on the estimation performance.
Dual Grid-Forming Converter
Authors: Federico Milano
Date: 2024-08-23 00:00:00

This letter proposes a dual model for grid-forming (GFM) controlled converters. The model is inspired from the observation that the structures of the active and reactive power equations of lossy synchronous machine models are almost symmetrical in terms of armature resistance and transient reactance. The proposed device is able to compensate grid power unbalance without requiring a frequency signal. In fact, the active power control is based on the rate of change of the voltage magnitude. On the other hand, synchronization and frequency control is obtained through the reactive power support. The letter shows that the proposed dual-GFM control is robust and capable of recovering a normal operating condition following large contingencies, such as load outages and three-phase faults.
Towards learning digital twin: case study on an anisotropic non-ideal rotor system
Authors: Zhibo Zhou, Michael Walther, Alexander Verl
Date: 2024-08-23 00:00:00
Tasks: Lifelong learning, Object

In the manufacturing industry, the digital twin (DT) is becoming a central topic. It has the potential to enhance the efficiency of manufacturing machines and reduce the frequency of errors. In order to fulfill its purpose, a DT must be an exact enough replica of its corresponding physical object. Nevertheless, the physical object endures a lifelong process of degradation. As a result, the digital twin must be modified accordingly in order to satisfy the accuracy requirement. This article introduces the novel concept of "learning digital twin (LDT)," which concentrates on the temporal behavior of the physical object and highlights the digital twin's capacity for lifelong learning. The structure of a LDT is first described. Then, in-depth descriptions of various algorithms for implementing each component of a LDT are provided. The proposed LDT is validated on the simulated degradation process of an anisotropic non-ideal rotor system.
From Time-Invariant to Uniformly Time-Varying Control Barrier Functions: A Constructive Approach
Authors: Adrian Wiltz, Dimos V. Dimarogonas
Date: 2024-08-23 00:00:00

In this paper, we define and analyze a subclass of (time-invariant) Control Barrier Functions (CBF) that have favorable properties for the construction of uniformly timevarying CBFs and thereby for the satisfaction of uniformly time-varying constraints. We call them {\Lambda}-shiftable CBFs where {\Lambda} states the extent by which the CBF can be varied by adding a time-varying function. Moreover, we derive sufficient conditions under which a time-varying CBF can be obtained from a time-invariant one, and we propose a systematic construction method. Advantageous about our approach is that a {\Lambda}-shiftable CBF, once constructed, can be reused for various control objectives. In the end, we relate the class of {\Lambda}-shiftable CBFs to Control Lyapunov Functions (CLF), and we illustrate the application of our results with a relevant simulation example.
Minimizing Movement Delay for Movable Antennas via Trajectory Optimization
Authors: Qingliang Li, Weidong Mei, Boyu Ning, Rui Zhang
Date: 2024-08-23 00:00:00
Tasks: Position

Movable antennas (MAs) have received increasing attention in wireless communications due to their capability of antenna position adjustment to reconfigure wireless channels. However, moving MAs results in non-negligible delay, which may decrease the effective data transmission time. To reduce the movement delay, we study in this paper a new MA trajectory optimization problem. In particular, given the desired destination positions of multiple MAs, we aim to jointly optimize their associations with the initial MA positions and the trajectories for moving them from their respective initial to destination positions within a given two-dimensional (2D) region, such that the delay of antenna movement is minimized, subject to the inter-MA minimum distance constraints in the movement. However, this problem is a continuous-time mixed-integer linear programming (MILP) problem that is challenging to solve. To tackle this challenge, we propose a two-stage optimization framework that sequentially optimizes the MAs' position associations and trajectories, respectively. First, we relax the inter-MA distance constraints and optimally solve the resulted delay minimization problem. Next, we check if the obtained MA association and trajectory solutions satisfy the inter-MA distance constraints. If not satisfied, we then employ a successive convex approximation (SCA) algorithm to adjust the MAs' trajectories until they satisfy the given constraints. Simulation results are provided to show the effectiveness of our proposed trajectory optimization method in reducing the movement delay as well as draw useful insights.
A sufficient condition for 2-contraction of a feedback interconnection
Authors: Ron Ofir, Francesco Bullo, Michael Margaliot
Date: 2024-08-23 00:00:00

Multistationarity - the existence of multiple equilibrium points - is a common phenomenon in dynamical systems from a variety of fields, including neuroscience, opinion dynamics, systems biology, and power systems. A recently proposed generalization of contraction theory, called $k$-contraction, is a promising approach for analyzing the asymptotic behaviour of multistationary systems. In particular, all bounded trajectories of a time-invariant 2-contracting system converge to an equilibrium point, but the system may have multiple equilibrium points where more than one is locally stable. An important challenge is to study $k$-contraction in large-scale interconnected systems. Inspired by a recent small-gain theorem for 2-contraction by Angeli et al., we derive a new sufficient condition for 2-contraction of a feedback interconnection of two nonlinear dynamical systems. Our condition is based on (i) deriving new formulas for the 2-multiplicative [2-additive] compound of block matrices using block Kronecker products [sums], (ii) a hierarchical approach for proving standard contraction, and (iii) a network small-gain theorem for Metzler matrices. We demonstrate our results by deriving a simple sufficient condition for 2-contraction in a network of FitzHugh-Nagumo neurons.
Autonomous Grid-Forming Inverter Exponential Droop Control for Improved Frequency Stability
Authors: Rick Wallace Kenyon, Amirhossein Sajadi, Bri-Mathias Hodge
Date: 2024-08-22 00:00:00

This paper introduces the novel Droop-e grid-forming power electronic converter control strategy, which establishes a non-linear, active power--frequency droop relationship based on an exponential function of the power output. A primary advantage of Droop-e is an increased utilization of available power headroom that directly mitigates system frequency excursions and reduces the rate of change of frequency. The motivation for Droop-e as compared to a linear grid-forming control is first established, and then the full controller is described, including the mirrored inversion at the origin, the linearization at a parameterized limit, and the auxiliary autonomous power sharing controller. The analytic stability of the controller, including synchronization criteria and a small signal stability analysis, is assessed. Electromagnetic transient time domain simulations of the Droop-e controller with full order power electronic converters and accompanying DC-side dynamics, connected in parallel with synchronous generators, are executed at a range of dispatches on a simple 3-bus system. Finally, IEEE 39-bus system simulations highlight the improved frequency stability of the system with multiple, Droop-e controlled grid-forming inverters.
Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
Authors: Gianni de Fabritiis
Date: 2024-08-17 00:00:00

Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore their potential to dramatically improve accuracy and scalability in simulating complex molecular systems. I discuss key challenges that must be addressed to fully realize their transformative potential in chemical biology and related fields.
StringNET: Neural Network based Variational Method for Transition Pathways
Authors: Jiayue Han, Shuting Gu, Xiang Zhou
Date: 2024-08-12 00:00:00
Tasks: ARC, Computational chemistry

Rare transition events in meta-stable systems under noisy fluctuations are crucial for many non-equilibrium physical and chemical processes. In these processes, the primary contributions to reactive flux are predominantly near the transition pathways that connect two meta-stable states. Efficient computation of these paths is essential in computational chemistry. In this work, we examine the temperature-dependent maximum flux path, the minimum energy path, and the minimum action path at zero temperature. We propose the StringNET method for training these paths using variational formulations and deep learning techniques. Unlike traditional chain-of-state methods, StringNET directly parametrizes the paths through neural network functions, utilizing the arc-length parameter as the main input. The tasks of gradient descent and re-parametrization in the string method are unified into a single framework using loss functions to train deep neural networks. More importantly, the loss function for the maximum flux path is interpreted as a softmax approximation to the numerically challenging minimax problem of the minimum energy path. To compute the minimum energy path efficiently and robustly, we developed a pre-training strategy that includes the maximum flux path loss in the early training stage, significantly accelerating the computation of minimum energy and action paths. We demonstrate the superior performance of this method through various analytical and chemical examples, as well as the two- and four-dimensional Ginzburg-Landau functional energy.
Causal Hierarchy in the Financial Market Network -- Uncovered by the Helmholtz-Hodge-Kodaira Decomposition
Authors: Tobias Wand, Oliver Kamps, Hiroshi Iyetomi
Date: 2024-08-23 00:00:00
Tasks: Time Series

Granger causality can uncover the cause and effect relationships in financial networks. However, such networks can be convoluted and difficult to interpret, but the Helmholtz-Hodge-Kodaira decomposition can split them into a rotational and gradient component which reveals the hierarchy of Granger causality flow. Using Kenneth French's business sector return time series, it is revealed that during the Covid crisis, precious metals and pharmaceutical products are causal drivers of the financial network. Moreover, the estimated Granger causality network shows a high connectivity during crisis which means that the research presented here can be especially useful to better understand crises in the market by revealing the dominant drivers of the crisis dynamics.
General Intelligent Imaging and Uncertainty Quantification by Deterministic Diffusion Model
Authors: Weiru Fan, Xiaobin Tang, Yiyi Liao, Da-Wei Wang
Date: 2024-08-23 00:00:00
Tasks: Autonomous Driving, Bayesian Inference, Denoising, Image Reconstruction, Uncertainty Quantification

Computational imaging is crucial in many disciplines from autonomous driving to life sciences. However, traditional model-driven and iterative methods consume large computational power and lack scalability for imaging. Deep learning (DL) is effective in processing local-to-local patterns, but it struggles with handling universal global-to-local (nonlocal) patterns under current frameworks. To bridge this gap, we propose a novel DL framework that employs a progressive denoising strategy, named the deterministic diffusion model (DDM), to facilitate general computational imaging at a low cost. We experimentally demonstrate the efficient and faithful image reconstruction capabilities of DDM from nonlocal patterns, such as speckles from multimode fiber and intensity patterns of second harmonic generation, surpassing the capability of previous state-of-the-art DL algorithms. By embedding Bayesian inference into DDM, we establish a theoretical framework and provide experimental proof of its uncertainty quantification. This advancement ensures the predictive reliability of DDM, avoiding misjudgment in high-stakes scenarios. This versatile and integrable DDM framework can readily extend and improve the efficacy of existing DL-based imaging applications.
Difference-in-differences with as few as two cross-sectional units -- A new perspective to the democracy-growth debate
Authors: Gilles Koumou, Emmanuel Selorm Tsyawo
Date: 2024-08-23 00:00:00

Pooled panel analyses often mask heterogeneity in unit-specific treatment effects. This challenge, for example, crops up in studies of the impact of democracy on economic growth, where findings vary substantially due to differences in country composition. To address this challenge, this paper introduces a Difference-in-Differences (DiD) estimator that leverages the temporal dimension of the data to estimate unit-specific average treatment effects on the treated (ATT) with as few as two cross-sectional units. Under weak identification and temporal dependence conditions, the proposed DiD estimator is shown to be asymptotically normal. The method is further complemented with an identification test that, unlike pre-trends tests, is more powerful and can detect violations of parallel trends in post-treatment periods. Empirical results using the DiD estimator suggest Benin's economy would have been 6.3% smaller on average over the 1993-2018 period had she not democratised.
Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching
Authors: Siyu Bie, Francis X. Diebold, Jingyu He, Junye Li
Date: 2024-08-23 00:00:00

We explore tree-based macroeconomic regime-switching in the context of the dynamic Nelson-Siegel (DNS) yield-curve model. In particular, we customize the tree-growing algorithm to partition macroeconomic variables based on the DNS model's marginal likelihood, thereby identifying regime-shifting patterns in the yield curve. Compared to traditional Markov-switching models, our model offers clear economic interpretation via macroeconomic linkages and ensures computational simplicity. In an empirical application to U.S. Treasury yields, we find (1) important yield-curve regime switching, and (2) evidence that macroeconomic variables have predictive power for the yield curve when the federal funds rate is high, but not in other regimes, thereby refining the notion of yield curve ''macro-spanning''.
Understanding the Effect of Market Risks on New Pension System and Government Responsibility
Authors: Sourish Das, Bikramaditya Datta, Shiv Ratan Tiwari
Date: 2024-08-23 00:00:00

This study examines how market risks impact the sustainability and performance of the New Pension System (NPS). NPS relies on defined contributions from both employees and employers to build a corpus during the employee's service period. Upon retirement, employees use the corpus fund to sustain their livelihood. A critical concern for individuals is whether the corpus will grow sufficiently to be sustainable or if it will deplete, leaving them financially vulnerable at an advanced age. We explore the impact of market risks on the performance of the corpus resulting from the NPS. To address this, we quantify market risks using Monte Carlo simulations with historical data to model their impact on NPS. We quantify the risk of pension corpus being insufficient and the cost to the Government to hedge the risk arising from guaranteeing the pension.
Education Opportunities for Rural Areas: Evidence from China's Higher Education Expansion
Authors: Ande Shen, Jiwei Zhou
Date: 2024-08-23 00:00:00

This paper explores the causal impact of education opportunities on rural areas by exploiting the higher education expansion (HEE) in China in 1999. By utilizing the detailed census data, the cohort-based difference-in-differences design indicates that the HEE increased college attendance and encouraged more people to attend senior high schools and that the effect is more significant in rural areas. Then we apply a similar approach to a novel panel data set of rural villages and households to examine the effect of education opportunities on rural areas. We find contrasting impacts on income and life quality between villages and households. Villages in provinces with higher HEE magnitudes underwent a drop in the average income and worse living facilities. On the contrary, households sending out migrants after the HEE experienced an increase in their per capita income. The phenomenon where villages experienced a ``brain drain'' and households with migrants gained after the HEE is explained by the fact that education could serve as a way to overcome the barrier of rural-urban migration. Our findings highlight the opposed impacts of education opportunities on rural development and household welfare in rural areas.
Inference-Adaptive Neural Steering for Real-Time Area-Based Sound Source Separation
Authors: Martin Strauss, Wolfgang Mack, María Luis Valero, Okan Köpüklü
Date: 2024-08-23 00:00:00

We propose a novel Neural Steering technique that adapts the target area of a spatial-aware multi-microphone sound source separation algorithm during inference without the necessity of retraining the deep neural network (DNN). To achieve this, we first train a DNN aiming to retain speech within a target region, defined by an angular span, while suppressing sound sources stemming from other directions. Afterward, a phase shift is applied to the microphone signals, allowing us to shift the center of the target area during inference at negligible additional cost in computational complexity. Further, we show that the proposed approach performs well in a wide variety of acoustic scenarios, including several speakers inside and outside the target area and additional noise. More precisely, the proposed approach performs on par with DNNs trained explicitly for the steered target area in terms of DNSMOS and SI-SDR.