pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Differentially Private Structural Entropy Minimization for Unsupervised Social Event Detection
Authors: Zhiwei Yang, Yuecen Wei, Haoran Li, Qian Li, Lei Jiang, Li Sun, Xiaoyan Yu, Chunming Hu, Hao Peng
Date: 2024-07-23 00:00:00
Tasks: Event Detection

Social event detection refers to extracting relevant message clusters from social media data streams to represent specific events in the real world. Social event detection is important in numerous areas, such as opinion analysis, social safety, and decision-making. Most current methods are supervised and require access to large amounts of data. These methods need prior knowledge of the events and carry a high risk of leaking sensitive information in the messages, making them less applicable in open-world settings. Therefore, conducting unsupervised detection while fully utilizing the rich information in the messages and protecting data privacy remains a significant challenge. To this end, we propose a novel social event detection framework, ADP-SEMEvent, an unsupervised social event detection method that prioritizes privacy. Specifically, ADP-SEMEvent is divided into two stages, i.e., the construction stage of the private message graph and the clustering stage of the private message graph. In the first stage, an adaptive differential privacy approach is used to construct a private message graph. In this process, our method can adaptively apply differential privacy based on the events occurring each day in an open environment to maximize the use of the privacy budget. In the second stage, to address the reduction in data utility caused by noise, a novel 2-dimensional structural entropy minimization algorithm based on optimal subgraphs is used to detect events in the message graph. The highlight of this process is unsupervised and does not compromise differential privacy. Extensive experiments on two public datasets demonstrate that ADP-SEMEvent can achieve detection performance comparable to state-of-the-art methods while maintaining reasonable privacy budget parameters.
Leader-Follower Formation and Tracking Control of Underactuated Surface Vessels
Authors: Bo wang, Antonio Loria
Date: 2024-07-26 00:00:00

This Technical Note presents a simple control approach for global trajectory tracking and formation control of underactuated surface vessels equipped with only two propellers. The control approach exploits the inherent cascaded structure of the vehicle dynamics and is divided into control designs at the kinematics and kinetics levels. A controller with a low-gain feature is designed at the kinematics level by incorporating the cascaded system method, persistency of excitation, and the small-gain theorem. Furthermore, a PD+ controller is designed to achieve the velocity tracking at the kinetics level. The proposed control laws are partially linear and saturated linear and easy to implement. Based on a leader-follower scheme, our control approach applies to the formation tracking control problem of multi-vehicle systems under a directed spanning tree topology. Our main results guarantee uniform global asymptotic stability for the closed-loop system, which implies robustness with respect to bounded disturbances in the sense of Malkin's total stability, also known as local input-to-state stability.
Piecewise constant tuning gain based singularity-free MRAC with application to aircraft control systems
Authors: Zhipeng Zhang, Yanjun Zhang, Jian Sun
Date: 2024-07-26 00:00:00
Tasks: Form, parameter estimation, regression

This paper introduces an innovative singularity-free output feedback model reference adaptive control (MRAC) method applicable to a wide range of continuous-time linear time-invariant (LTI) systems with general relative degrees. Unlike existing solutions such as Nussbaum and multiple-model-based methods, which manage unknown high-frequency gains through persistent switching and repeated parameter estimation, the proposed method circumvents these issues without prior knowledge of the high-frequency gain or additional design conditions. The key innovation of this method lies in transforming the estimation error equation into a linear regression form via a modified MRAC law with a piecewise constant tuning gain developed in this work. This represents a significant departure from existing MRAC systems, where the estimation error equation is typically in a bilinear regression form. The linear regression form facilitates the direct estimation of all unknown parameters, thereby simplifying the adaptive control process. The proposed method preserves closed-loop stability and ensures asymptotic output tracking, overcoming some of the limitations associated with existing methods like Nussbaum and multiple-model based methods. The practical efficacy of the developed MRAC method is demonstrated through detailed simulation results within an aircraft control system scenario.
On the Number of Observation Nodes in Boolean Networks
Authors: Liangjie Sun, Wai-Ki Ching, Tatsuya Akutsu
Date: 2024-07-26 00:00:00

A Boolean network (BN) is called observable if any initial state can be uniquely determined from the output sequence. In the existing literature on observability of BNs, there is almost no research on the relationship between the number of observation nodes and the observability of BNs, which is an important and practical issue. In this paper, we mainly focus on three types of BNs with $n$ nodes (i.e., $K$-AND-OR-BNs, $K$-XOR-BNs, and $K$-NC-BNs, where $K$ is the number of input nodes for each node and NC means nested canalyzing) and study the upper and lower bounds of the number of observation nodes for these BNs. First, we develop a novel technique using information entropy to derive a general lower bound of the number of observation nodes, and conclude that the number of observation nodes cannot be smaller than $\left[(1-K)+\frac{2^{K}-1}{2^{K}}\log_{2}(2^{K}-1)\right]n$ to ensure that any $K$-AND-OR-BN is observable, and similarly, some lower bound is also obtained for $K$-NC-BNs. Then for any type of BN, we also develop two new techniques to infer the general lower bounds, using counting identical states at time 1 and counting the number of fixed points, respectively. On the other hand, we derive nontrivial upper bounds of the number of observation nodes by combinatorial analysis of several types of BNs. Specifically, we indicate that $\left(\frac{2^{K}-K-1}{2^{K}-1}\right)n,~1$, and $\lceil \frac{n}{K}\rceil$ are the best case upper bounds for $K$-AND-OR-BNs, $K$-XOR-BNs, and $K$-NC-BN, respectively.
Integrating Posture Control in Speech Motor Models: A Parallel-Structured Simulation Approach
Authors: Yadong Liu, Sidney Fels, Arian Shamei, Najeeb Khan, Bryan Gick
Date: 2024-07-26 00:00:00

Posture is an essential aspect of motor behavior, necessitating continuous muscle activation to counteract gravity. It remains stable under perturbation, aiding in maintaining bodily balance and enabling movement execution. Similarities have been observed between gross body postures and speech postures, such as those involving the jaw, tongue, and lips, which also exhibit resilience to perturbations and assist in equilibrium and movement. Although postural control is a recognized element of human movement and balance, particularly in broader motor skills, it has not been adequately incorporated into existing speech motor control models, which typically concentrate on the gestures or motor commands associated with specific speech movements, overlooking the influence of postural control and gravity. Here we introduce a model that aligns speech posture and movement, using simulations to explore whether speech posture within this framework mirrors the principles of bodily postural control. Our findings indicate that, akin to body posture, speech posture is also robust to perturbation and plays a significant role in maintaining local segment balance and enhancing speech production.
Finite-time and bumpless transfer control of asynchronously switched systems: An output feedback control approach
Authors: Mo-Ran Liu, Zhen Wu, Xian Du, Zhongyang Fei
Date: 2024-07-26 00:00:00

In this paper, the finite-time control and bumpless transfer control are investigated for switched systems under asynchronously switching. First, a class of dynamic output feedback controllers are designed to stabilize the switched system with measurable system outputs. Considering the improvement of transient performance, the bumpless transfer control and finite-time control are further studied in the controller design. To avoid the control bumps, a practical filter is introduced to make the control signal smoother and continuous. Furthermore, to derive a finite-time bounded system state over short-time intervals, the finite-time analysis is considered in managing the switching process with the average dwell time. New criteria are proposed to analyze the finite-time stability and finite-time boundedness for the closed-loop system and solvable conditions are newly proposed to optimize the controller gain. Finally, the superiorities of the proposed method are validated through an application to a boost converter.
Regret-Optimal Defense Against Stealthy Adversaries: A System Level Approach
Authors: Hiroyasu Tsukamoto, Joudi Hajar, Soon-Jo Chung, Fred Y. Hadaegh
Date: 2024-07-26 00:00:00

Modern control designs in robotics, aerospace, and cyber-physical systems rely heavily on real-world data obtained through system outputs. However, these outputs can be compromised by system faults and malicious attacks, distorting critical system information needed for secure and reliable operation. In this paper, we introduce a novel regret-optimal control framework for designing controllers that make a linear system robust against stealthy attacks, including both sensor and actuator attacks. Specifically, we present (a) a convex optimization-based system metric to quantify the regret under the worst-case stealthy attack (the difference between actual performance and optimal performance with hindsight of the attack), which adapts and improves upon the $\mathcal{H}_2$ and $\mathcal{H}_{\infty}$ norms in the presence of stealthy adversaries, (b) an optimization problem for minimizing the regret of (a) in system-level parameterization, enabling localized and distributed implementation in large-scale systems, and (c) a rank-constrained optimization problem equivalent to the optimization of (b), which can be solved using convex rank minimization methods. We also present numerical simulations that demonstrate the effectiveness of our proposed framework.
Robust Optimal Network Topology Switching for Zero Dynamics Attacks
Authors: Hiroyasu Tsukamoto, Joshua D. Ibrahim, Joudi Hajar, James Ragan, Soon-Jo Chung, Fred Y. Hadaegh
Date: 2024-07-26 00:00:00

The intrinsic, sampling, and enforced zero dynamics attacks (ZDAs) are among the most detrimental stealthy attacks in robotics, aerospace, and cyber-physical systems. They exploit internal dynamics, discretization, redundancy/asynchronous actuation and sensing, to construct disruptive attacks that are completely stealthy in the measurement. They work even when the systems are both controllable and observable. This paper presents a novel framework to robustly and optimally detect and mitigate ZDAs for networked linear control systems. We utilize controllability, observability, robustness, and sensitivity metrics written explicitly in terms of the system topology, thereby proposing a robust and optimal switching topology formulation for resilient ZDA detection and mitigation. Our main contribution is the reformulation of this problem into an equivalent rank-constrained optimization problem (i.e., optimization with a convex objective function subject to convex constraints and rank constraints), which can be solved using convex rank minimization approaches. The effectiveness of our method is demonstrated using networked double integrators subject to ZDAs.
Thermodynamics-Consistent Graph Neural Networks
Authors: Jan G. Rittig, Alexander Mitsos
Date: 2024-07-08 00:00:00

We propose excess Gibbs free energy graph neural networks (GE-GNNs) for predicting composition-dependent activity coefficients of binary mixtures. The GE-GNN architecture ensures thermodynamic consistency by predicting the molar excess Gibbs free energy and using thermodynamic relations to obtain activity coefficients. As these are differential, automatic differentiation is applied to learn the activity coefficients in an end-to-end manner. Since the architecture is based on fundamental thermodynamics, we do not require additional loss terms to learn thermodynamic consistency. As the output is a fundamental property, we neither impose thermodynamic modeling limitations and assumptions. We demonstrate high accuracy and thermodynamic consistency of the activity coefficient predictions.
Coevolutionary game dynamics with localized environmental resource feedback
Authors: Yi-Duo Chen, Jian-Yue Guan, Zhi-Xi Wu
Date: 2024-07-26 00:00:00

Dynamic environments shape diverse dynamics in evolutionary game systems. We introduce spatial heterogeneity of resources into the prisoner's dilemma game model to explore coevolutionary game dynamics with environmental feedback. The availability of resources significantly affects the survival competitiveness of surrounding individuals. Feedback between individuals' strategies and the resources they can use leads to the oscillating dynamic known as the "oscillatory tragedy of the commons". Our findings indicate that when the influence of individuals' strategies on the update rate of resources is significantly high in systems characterized by environmental heterogeneity, they can attain an equilibrium state that avoids the oscillatory tragedy. In contrast to the numerical results obtained in well-mixed structures, self-organized clustered patterns emerge in simulations utilizing square lattices, further enhancing the stability of the system. We discuss critical phenomena in detail, demonstrating that the aforementioned transition is robust across various system parameters, including the strength of cooperators in restoring the environment, initial distributions of cooperators, system size and structures, and noise.
Diffusion-based subsurface CO$_2$ multiphysics monitoring and forecasting
Authors: Xinquan Huang, Fu Wang, Tariq Alkhalifah
Date: 2024-07-25 00:00:00

Carbon capture and storage (CCS) plays a crucial role in mitigating greenhouse gas emissions, particularly from industrial outputs. Using seismic monitoring can aid in an accurate and robust monitoring system to ensure the effectiveness of CCS and mitigate associated risks. However, conventional seismic wave equation-based approaches are computationally demanding, which hinders real-time applications. In addition to efficiency, forecasting and uncertainty analysis are not easy to handle using such numerical-simulation-based approaches. To this end, we propose a novel subsurface multiphysics monitoring and forecasting framework utilizing video diffusion models. This approach can generate high-quality representations of CO$2$ evolution and associated changes in subsurface elastic properties. With reconstruction guidance, forecasting and inversion can be achieved conditioned on historical frames and/or observational data. Meanwhile, due to the generative nature of the approach, we can quantify uncertainty in the prediction. Tests based on the Compass model show that the proposed method successfully captured the inherently complex physical phenomena associated with CO$_2$ monitoring, and it can predict and invert the subsurface elastic properties and CO$_2$ saturation with consistency in their evolution.
Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization
Authors: Yuanpu Cao, Tianrong Zhang, Bochuan Cao, Ziyi Yin, Lu Lin, Fenglong Ma, Jinghui Chen
Date: 2024-05-28 00:00:00
Tasks: Hallucination

Researchers have been studying approaches to steer the behavior of Large Language Models (LLMs) and build personalized LLMs tailored for various applications. While fine-tuning seems to be a direct solution, it requires substantial computational resources and may significantly affect the utility of the original LLM. Recent endeavors have introduced more lightweight strategies, focusing on extracting "steering vectors" to guide the model's output toward desired behaviors by adjusting activations within specific layers of the LLM's transformer architecture. However, such steering vectors are directly extracted from the activations of human preference data and thus often lead to suboptimal results and occasional failures, especially in alignment-related scenarios. This work proposes an innovative approach that could produce more effective steering vectors through bi-directional preference optimization. Our method is designed to allow steering vectors to directly influence the generation probability of contrastive human preference data pairs, thereby offering a more precise representation of the target behavior. By carefully adjusting the direction and magnitude of the steering vector, we enabled personalized control over the desired behavior across a spectrum of intensities. Extensive experimentation across various open-ended generation tasks, particularly focusing on steering AI personas, has validated the efficacy of our approach. Moreover, we comprehensively investigate critical alignment-concerning scenarios, such as managing truthfulness, mitigating hallucination, and addressing jailbreaking attacks. Remarkably, our method can still demonstrate outstanding steering effectiveness across these scenarios. Furthermore, we showcase the transferability of our steering vectors across different models/LoRAs and highlight the synergistic benefits of applying multiple vectors simultaneously.
Order-theoretical fixed point theorems for correspondences and application in game theory
Authors: Lu Yu
Date: 2024-07-26 00:00:00

For an ascending correspondence $F:X\to 2^X$ with chain-complete values on a complete lattice $X$, we prove that the set of fixed points is a complete lattice. This strengthens Zhou's fixed point theorem. For chain-complete posets that are not necessarily lattices, we generalize the Abian-Brown and the Markowsky fixed point theorems from single-valued maps to multivalued correspondences. We provide an application in game theory.
VoxSim: A perceptual voice similarity dataset
Authors: Junseok Ahn, Youkyum Kim, Yeunju Choi, Doyeop Kwak, Ji-Hoon Kim, Seongkyu Mun, Joon Son Chung
Date: 2024-07-26 00:00:00
Tasks: Benchmarking, Speaker Recognition, Speech Synthesis, Voice Similarity

This paper introduces VoxSim, a dataset of perceptual voice similarity ratings. Recent efforts to automate the assessment of speech synthesis technologies have primarily focused on predicting mean opinion score of naturalness, leaving speaker voice similarity relatively unexplored due to a lack of extensive training data. To address this, we generate about 41k utterance pairs from the VoxCeleb dataset, a widely utilised speech dataset for speaker recognition, and collect nearly 70k speaker similarity scores through a listening test. VoxSim offers a valuable resource for the development and benchmarking of speaker similarity prediction models. We provide baseline results of speaker similarity prediction models on the VoxSim test set and further demonstrate that the model trained on our dataset generalises to the out-of-domain VCC2018 dataset.
Matlab-based Epoch Extraction for Speaker Differentiation
Authors: Kunlun Li, Daniel Ferro, Xu Zhao, Abdul Jabbar Syed, Anil K Vuppala, Azeemuddin Syed
Date: 2024-07-26 00:00:00

Epoch extraction has become increasingly popular in recent years for speech analysis research because accurately detecting the location of the Epoch is crucial for analyzing speech signals. The Epoch, occurring at the instant of excitation in the vocal tract system, particularly during glottal closure, plays a significant role in differentiating speakers in multi-speaker conversations. However, the extraction of the Epoch poses a challenge due to the time-varying factors in the vocal tract system, which makes deconvolution for obtaining the original excitation location more complex. In this paper, various methods for Epoch extraction, including Zero Frequency Filtering (ZFF) and Zero Frequency Resonator (ZFR), will be discussed, and their pros and cons evaluated. In addition, the stability, accuracy, and feasibility of each method will be compared. The evaluation will involve a Matlab-based locking algorithm, and a proposed hardware implementation using Raspberry pi for speaker differentiation. The experiment includes six individuals uttering the phrase "The University of Mississippi," with one person acting as the reference or "lock" speaker. The number of epochs occurring at similar positions to the reference speaker will be counted as Delta, with larger Delta values indicating greater speaker similarity. Experimental results demonstrate that when the speaker remains the same, the average number of Delta is 7.5, while for different speakers, the average number of Delta decreases to 3, 2, 2, and 1, respectively, representing a decrease of approximately 73% in the number of epochs at similar positions compared to the reference speaker.
Three-dimensional ultrasound-based online system for automated ovarian follicle measurement
Authors: Pedro Royo, Elkin Muñoz, José-Enrique Romero, José-Vicente Manjón, Catalina Roig, Carmen Fernández-Delgado, Nuria Muñiz, Antonio Requena, Nicolás Garrido, Juan Antonio García- Velasco, Antonio Pellicer
Date: 2024-07-26 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Ultrasound follicle tracking is an important part of cycle monitoring. OSIS Ovary (Online System for Image Segmentation for the Ovary) has been conceived aiming to aid the management of the workflow in follicle tracking, one of the most iterative procedures in cycle monitoring during ovarian stimulation. In the present study, we compared OSIS Ovary (as three-dimensional ultrasound-based automated system) with the two-dimensional manual standard measurement method, in order to assess the reliability of the main measurements obtained to track follicle growth during ovarian stimulation cycles, the follicle size and count. Based on the mean follicle diameter and follicle count values obtained, the Pearson/intraclass correlation coefficients were 0.976/0.987 and 0.804/0.889 in >=10mm follicles, 0.989/0.994 and 0.809/0.867 in >=13mm follicles and 0.995/0.997 and 0.791/0.840 in >=16mm follicles. The mean difference (MnD) for the mean diameter and follicle count was, respectively, 0.759/0.161 in >=10mm follicles, 0.486/1.033 in >=13mm follicles and 0.784/0.486 in >=16mm follicles. The upper and lower limits of agreement (ULA and LLA) were 3.641/2.123 and 5.392/3.070 in >=10mm follicles, 3.496/2.522 and 4.285/2.218 in >=13mm follicles, and 3.723/2.153 and 2.432/1.459 in >=16mm follicles. The limits of agreement range (LoAR) were 5.764/8.462 in >=10mm follicles, 6.048/6.503 in >=13mm follicles and 5.876/3.891 in >=16mm follicles. P<0.05 was considered for all calculations. As three-dimensional ultrasound-based automated system in comparison with two-dimensional manual method standard, we found OSIS Ovary as a reliable tool to track follicle growth during ovarian stimulation cycles