pwc-archive/papers-with-abstracts huggingface dataset:
How Prevalent is Gender Bias in ChatGPT? -- Exploring German and English ChatGPT Responses
Authors: Stefanie Urchs, Veronika Thurner, Matthias Aßenmacher, Christian Heumann, Stephanie Thiemichen
Date: 2023-09-21 00:00:00

With the introduction of ChatGPT, OpenAI made large language models (LLM) accessible to users with limited IT expertise. However, users with no background in natural language processing (NLP) might lack a proper understanding of LLMs. Thus the awareness of their inherent limitations, and therefore will take the systems' output at face value. In this paper, we systematically analyse prompts and the generated responses to identify possible problematic issues with a special focus on gender biases, which users need to be aware of when processing the system's output. We explore how ChatGPT reacts in English and German if prompted to answer from a female, male, or neutral perspective. In an in-depth investigation, we examine selected prompts and analyse to what extent responses differ if the system is prompted several times in an identical way. On this basis, we show that ChatGPT is indeed useful for helping non-IT users draft texts for their daily work. However, it is absolutely crucial to thoroughly check the system's responses for biases as well as for syntactic and grammatical mistakes.
穿越稠密障碍物的自适应动态窗口法
Authors: 王永雄, 田永永, 李 璇, 李梁华
Date: 2019-05-01 00:00:00
Proceeding: 控制与决策 2019 5

针对应用广泛的局部避障算法 —– 动态窗口法 (DWA) 穿越稠密障碍物时存在路径不合理、速度和安全性不能兼顾等问题, 提出参数自适应的 DWA 算法, 根据机器人与障碍物距离和障碍物的密集度自动调整目标函数中的权值, 以自适应环境的动态变化, 从而获得移动机器人的最佳运行速度和合理路径. 该方法可明显改善机器人穿越稠密障碍物区域时的性能; 同时, 该方法还可避免机器人从密集障碍物区域外绕行以及轨迹不平滑现象. 仿真实验表明: 改进的 DWA 算法在复杂环境中通过逐步优化可使运行轨迹更加合理, 能够同时兼顾路径平滑性和安全性; 机器人在离稠密障碍物较远处保持高速, 通过狭窄通道或者稠密障碍物区域时速度适当降低, 安全性更高, 实验中总迭代次数和运行时间可缩短 20 % 以上.
A quantum moving target segmentation algorithm for grayscale video
Authors: Wenjie Liu, Lu Wang, Qingshan Wu
Date: 2023-10-01 00:00:00
Tasks: Binarization, Segmentation

The moving target segmentation (MTS) aims to segment out moving targets in the video, however, the classical algorithm faces the huge challenge of real-time processing in the current video era. Some scholars have successfully demonstrated the quantum advantages in some video processing tasks, but not concerning moving target segmentation. In this paper, a quantum moving target segmentation algorithm for grayscale video is proposed, which can use quantum mechanism to simultaneously calculate the difference of all pixels in all adjacent frames and then quickly segment out the moving target. In addition, a feasible quantum comparator is designed to distinguish the grayscale values with the threshold. Then several quantum circuit units, including three-frame difference, binarization and AND operation, are designed in detail, and then are combined together to construct the complete quantum circuits for segmenting the moving target. For a quantum video with $2^m$ frames (every frame is a $2^n\times 2^n$ image with $q$ grayscale levels), the complexity of our algorithm can be reduced to O$(n^2 + q)$. Compared with the classic counterpart, it is an exponential speedup, while its complexity is also superior to the existing quantum algorithms. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
Quantum image edge detection based on eight-direction Sobel operator for NEQR
Authors: Wenjie Liu, Lu Wang
Date: 2023-10-01 00:00:00
Tasks: Edge Detection

Quantum Sobel edge detection (QSED) is a kind of algorithm for image edge detection using quantum mechanism, which can solve the real-time problem encountered by classical algorithms. However, the existing QSED algorithms only consider two- or four-direction Sobel operator, which leads to a certain loss of edge detail information in some high-definition images. In this paper, a novel QSED algorithm based on eight-direction Sobel operator is proposed, which not only reduces the loss of edge information, but also simultaneously calculates eight directions' gradient values of all pixel in a quantum image. In addition, the concrete quantum circuits, which consist of gradient calculation, non-maximum suppression, double threshold detection and edge tracking units, are designed in details. For a 2^n x 2^n image with q gray scale, the complexity of our algorithm can be reduced to O(n^2 + q^2), which is lower than other existing classical or quantum algorithms. And the simulation experiment demonstrates that our algorithm can detect more edge information, especially diagonal edges, than the two- and four-direction QSED algorithms.
A quantum system control method based on enhanced reinforcement learning
Authors: Wenjie Liu, Bosi Wang, Jihao Fan, Yebo Ge, Mohammed Zidan
Date: 2023-09-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. To learn a satisfactory control strategy under the condition of limited resources, a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. The states and actions in reinforcement learning are mapped to quantum states and control operations in quantum systems. By using new enhanced neural networks, reinforcement learning can quickly achieve the maximization of long-term cumulative rewards, and a quantum state can be evolved accurately from an initial state to a target state. According to the number of candidate unitary operations, the three-switch control is used for simulation experiments. Compared with other methods, the QSC-ERL achieves close to 1 fidelity learning control of quantum systems, and takes fewer episodes to quantum state evolution under the condition of limited resources.
Competitive and Revenue-Optimal Pricing with Budgets
Authors: Simon Finster, Paul W. Goldberg, Edwin Lock
Date: 2023-10-05 00:00:00

In markets with budget-constrained buyers, competitive equilibria need not be efficient in the utilitarian sense, or maximise the seller's revenue. We consider a setting with multiple divisible goods. Competitive equilibrium outcomes, and only those, are constrained utilitarian efficient, a notion of utilitarian efficiency that respects buyers' demands and budgets. Our main contribution establishes that, when buyers have linear valuations, competitive equilibrium prices are unique and revenue-optimal for a zero-cost seller.
Beyond Diagonal Reconfigurable Intelligent Surfaces with Mutual Coupling: Modeling and Optimization
Authors: Hongyu Li, Shanpu Shen, Matteo Nerini, Marco Di Renzo, Bruno Clerckx
Date: 2023-10-04 00:00:00

This work studies the modeling and optimization of beyond diagonal reconfigurable intelligent surface (BD-RIS) aided wireless communication systems in the presence of mutual coupling among the RIS elements. Specifically, we first derive the mutual coupling aware BD-RIS aided communication model using scattering and impedance parameter analysis. Based on the obtained communication model, we propose a general BD-RIS optimization algorithm applicable to different architectures of BD-RIS to maximize the channel gain. Numerical results validate the effectiveness of the proposed design and demonstrate that the larger the mutual coupling the larger the gain offered by BD-RIS over conventional diagonal RIS.
Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoising
Authors: Hui Shi, Yann Traonmilin, J-F Aujol
Date: 2023-10-02 00:00:00
Tasks: Denoising, Image Denoising

We consider the problem of denoising with the help of prior information taken from a database of clean signals or images. Denoising with variational methods is very efficient if a regularizer well adapted to the nature of the data is available. Thanks to the maximum a posteriori Bayesian framework, such regularizer can be systematically linked with the distribution of the data. With deep neural networks (DNN), complex distributions can be recovered from a large training database.To reduce the computational burden of this task, we adapt the compressive learning framework to the learning of regularizers parametrized by DNN. We propose two variants of stochastic gradient descent (SGD) for the recovery of deep regularization parameters from a heavily compressed database. These algorithms outperform the initially proposed method that was limited to low-dimensional signals, each iteration using information from the whole database. They also benefit from classical SGD convergence guarantees. Thanks to these improvements we show that this method can be applied for patch based image denoising.}
Enhancing Secrecy Capacity in PLS Communication with NORAN based on Pilot Information Codebooks
Authors: Yebo Gu, Tao Shen, Jian Song, Qingbo Wang
Date: 2023-10-02 00:00:00

In recent research, non-orthogonal artificial noise (NORAN) has been proposed as an alternative to orthogonal artificial noise (AN). However, NORAN introduces additional noise into the channel, which reduces the capacity of the legitimate channel (LC). At the same time, selecting a NORAN design with ideal security performance from a large number of design options is also a challenging problem. To address these two issues, a novel NORAN based on a pilot information codebook is proposed in this letter. The codebook associates different suboptimal NORANs with pilot information as the key under different channel state information (CSI). The receiver interrogates the codebook using the pilot information to obtain the NORAN that the transmitter will transmit in the next moment, in order to eliminate the NORAN when receiving information. Therefore, NORAN based on pilot information codebooks can improve the secrecy capacity (SC) of the communication system by directly using suboptimal NORAN design schemes without increasing the noise in the LC. Numerical simulations and analyses show that the introduction of NORAN with a novel design using pilot information codebooks significantly enhances the security and improves the SC of the communication system.
Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign Recognition
Authors: Andreea Postovan, Mădălina Eraşcu
Date: 2023-09-25 00:00:00
Tasks: Autonomous Driving, Benchmarking, Traffic Sign Recognition

Traffic signs play a critical role in road safety and traffic management for autonomous driving systems. Accurate traffic sign classification is essential but challenging due to real-world complexities like adversarial examples and occlusions. To address these issues, binary neural networks offer promise in constructing classifiers suitable for resource-constrained devices. In our previous work, we proposed high-accuracy BNN models for traffic sign recognition, focusing on compact size for limited computation and energy resources. To evaluate their local robustness, this paper introduces a set of benchmark problems featuring layers that challenge state-of-the-art verification tools. These layers include binarized convolutions, max pooling, batch normalization, fully connected. The difficulty of the verification problem is given by the high number of network parameters (905k - 1.7 M), of the input dimension (2.7k-12k), and of the number of regions (43) as well by the fact that the neural networks are not sparse. The proposed BNN models and local robustness properties can be checked at https://github.com/ChristopherBrix/vnncomp2023_benchmarks/tree/main/benchmarks/traffic_signs_recognition. The results of the 4th International Verification of Neural Networks Competition (VNN-COMP'23) revealed the fact that 4, out of 7, solvers can handle many of our benchmarks randomly selected (minimum is 6, maximum is 36, out of 45). Surprisingly, tools output also wrong results or missing counterexample (ranging from 1 to 4). Currently, our focus lies in exploring the possibility of achieving a greater count of solved instances by extending the allotted time (previously set at 8 minutes). Furthermore, we are intrigued by the reasons behind the erroneous outcomes provided by the tools for certain benchmarks.
Robust Integral Consensus Control of Multi-Agent Networks Perturbed by Matched and Unmatched Disturbances: The Case of Directed Graphs
Authors: Jose Guadalupe Romero, David Navarro-Alarcon
Date: 2023-09-30 00:00:00

This work presents a new method to design consensus controllers for perturbed double integrator systems whose interconnection is described by a directed graph containing a rooted spanning tree. We propose new robust controllers to solve the consensus and synchronization problems when the systems are under the effects of matched and unmatched disturbances. In both problems, we present simple continuous controllers, whose integral actions allow us to handle the disturbances. A rigorous stability analysis based on Lyapunov's direct method for unperturbed networked systems is presented. To assess the performance of our result, a representative simulation study is presented.
Towards a control-theoretic trivialization of ABR video streaming
Authors: Michel Fliess, Cédric Join
Date: 2023-10-02 00:00:00

Combining flatness-based control, model-free control and algebraic estimation techniques permits to trivialize several key issues in the adaptive bitrate (ABR) setting, now the dominant industry approach in video streaming: 1) A straightforward open-loop strategy for the bitrate and the buffer level; 2) closing the loop permits not only to take into account unavoidable mismatches and disturbances, like Internet fluctuations, but also the inherently discrete nature of the bitrate; 3) an easily implementable closed-form estimate for a bandwidth allows to handle severe variations of the channel capacity. Several computer experiments and metrics for evaluating the Quality of Experience (QoE) are displayed and discussed.
Open Gimbal: A 3 Degrees of Freedom Open Source Sensing and Testing Platform for Nano and Micro UAVs
Authors: Suryansh Sharma, Tristan Dijkstra, R. Venkatesha Prasad
Date: 2023-10-04 00:00:00

Testing the aerodynamics of micro- and nano-UAVs without actually flying is highly challenging. To address this issue, we introduce Open Gimbal, a specially designed 3 Degrees of Freedom platform that caters to the unique requirements of micro- and nano-UAVs. This platform allows for unrestricted and free rotational motion, enabling comprehensive experimentation and evaluation of these UAVs. Our approach focuses on simplicity and accessibility. We developed an open-source, 3D printable electro-mechanical design that has minimal size and low complexity. This design facilitates easy replication and customization, making it widely accessible to researchers and developers. Addressing the challenges of sensing flight dynamics at a small scale, we have devised an integrated wireless batteryless sensor subsystem. Our innovative solution eliminates the need for complex wiring and instead uses wireless power transfer for sensor data reception. To validate the effectiveness of open gimbal, we thoroughly evaluate and test its communication link and sensing performance using a typical nano-quadrotor. Through comprehensive testing, we verify the reliability and accuracy of open gimbal in real-world scenarios. These advancements provide valuable tools and insights for researchers and developers working with mUAVs and nUAVs, contributing to the progress of this rapidly evolving field.
Distributed end-effector formation control for mixed fully- and under-actuated manipulators with flexible joints
Authors: Zhiyu Peng, Bayu Jayawardhana, Xin Xin
Date: 2023-10-02 00:00:00

The presence of faulty or underactuated manipulators can disrupt the end-effector formation keeping of a team of manipulators. Based on two-link planar manipulators, we investigate this end-effector formation keeping problem for mixed fully- and under-actuated manipulators with flexible joints. In this case, the underactuated manipulators can comprise of active-passive (AP) manipulators, passive-active (PA) manipulators, or a combination thereof. We propose distributed control laws for the different types of manipulators to achieve and maintain the desired formation shape of the end-effectors. It is achieved by assigning virtual springs to the end-effectors for the fully-actuated ones and to the virtual end-effectors for the under-actuated ones. We study further the set of all desired and reachable shapes for the networked manipulators' end-effectors. Finally, we validate our analysis via numerical simulations.
Safety-Critical Control of Nonholonomic Vehicles in Dynamic Environments using Velocity Obstacles
Authors: Aurora Haraldsen, Martin S. Wiig, Aaron D. Ames, Kristin Y. Pettersen
Date: 2023-10-01 00:00:00
Tasks: Collision Avoidance

This paper considers collision avoidance for vehicles with first-order nonholonomic constraints maintaining nonzero forward speeds, moving within dynamic environments. We leverage the concept of control barrier functions (CBFs) to synthesize control inputs that prioritize safety, where the safety criteria are derived from the velocity obstacle principle. Existing instantiations of CBFs for collision avoidance, e.g., based on maintaining a minimal distance, can result in control inputs that make the vehicle stop or even reverse. The proposed formulation effectively separates speed control from steering, allowing the vehicle to maintain a forward motion without compromising safety. This is beneficial for ensuring that the vehicle advances towards its desired destination, and it is moreover an underlying requirement for certain vehicles such as marine vessels and fixed-wing UAVs. Theoretical safety guarantees are provided, and numerical simulations demonstrate the efficiency of the strategy in environments containing moving obstacles.
Speaker localization using direct path dominance test based on sound field directivity
Authors: Boaz Rafaely, Koby Alhaiany
Date: 2023-10-05 00:00:00
Tasks: Audio Signal Processing

Estimation of the direction-of-arrival (DoA) of a speaker in a room is important in many audio signal processing applications. Environments with reverberation that masks the DoA information are particularly challenging. Recently, a DoA estimation method that is robust to reverberation has been developed. This method identifies time-frequency bins dominated by the contribution from the direct path, which carries the correct DoA information. However, its implementation is computationally demanding as it requires frequency smoothing to overcome the effect of coherent early reflections and matrix decomposition to apply the direct-path dominance (DPD) test. In this work, a novel computationally-efficient alternative to the DPD test is proposed, based on the directivity measure for sensor arrays, which requires neither frequency smoothing nor matrix decomposition, and which has been reformulated for sound field directivity with spherical microphone arrays. The paper presents the proposed method and a comparison to previous methods under a range of reverberation and noise conditions. Result demonstrate that the proposed method shows comparable performance to the original method in terms of robustness to reverberation and noise, and is about four times more computationally efficient for the given experiment.
Performance and energy balance: a comprehensive study of state-of-the-art sound event detection systems
Authors: Francesca Ronchini, Romain Serizel
Date: 2023-10-05 00:00:00
Tasks: Event Detection, Sound Event Detection

In recent years, deep learning systems have shown a concerning trend toward increased complexity and higher energy consumption. As researchers in this domain and organizers of one of the Detection and Classification of Acoustic Scenes and Events challenges tasks, we recognize the importance of addressing the environmental impact of data-driven SED systems. In this paper, we propose an analysis focused on SED systems based on the challenge submissions. This includes a comparison across the past two years and a detailed analysis of this year's SED systems. Through this research, we aim to explore how the SED systems are evolving every year in relation to their energy efficiency implications.
VaSAB: The variable size adaptive information bottleneck for disentanglement on speech and singing voice
Authors: Frederik Bous, Axel Roebel
Date: 2023-10-05 00:00:00
Tasks: Disentanglement, Quantization

The information bottleneck auto-encoder is a tool for disentanglement commonly used for voice transformation. The successful disentanglement relies on the right choice of bottleneck size. Previous bottleneck auto-encoders created the bottleneck by the dimension of the latent space or through vector quantization and had no means to change the bottleneck size of a specific model. As the bottleneck removes information from the disentangled representation, the choice of bottleneck size is a trade-off between disentanglement and synthesis quality. We propose to build the information bottleneck using dropout which allows us to change the bottleneck through the dropout rate and investigate adapting the bottleneck size depending on the context. We experimentally explore into using the adaptive bottleneck for pitch transformation and demonstrate that the adaptive bottleneck leads to improved disentanglement of the F0 parameter for both, speech and singing voice leading to improved synthesis quality. Using the variable bottleneck size, we were able to achieve disentanglement for singing voice including extremely high pitches and create a universal voice model, that works on both speech and singing voice with improved synthesis quality.
Discriminative Training of VBx Diarization
Authors: Dominik Klement, Mireia Diez, Federico Landini, Lukáš Burget, Anna Silnova, Marc Delcroix, Naohiro Tawara
Date: 2023-10-04 00:00:00
Tasks: Bayesian Inference

Bayesian HMM clustering of x-vector sequences (VBx) has become a widely adopted diarization baseline model in publications and challenges. It uses an HMM to model speaker turns, a generatively trained probabilistic linear discriminant analysis (PLDA) for speaker distribution modeling, and Bayesian inference to estimate the assignment of x-vectors to speakers. This paper presents a new framework for updating the VBx parameters using discriminative training, which directly optimizes a predefined loss. We also propose a new loss that better correlates with the diarization error rate compared to binary cross-entropy $\unicode{x2013}$ the default choice for diarization end-to-end systems. Proof-of-concept results across three datasets (AMI, CALLHOME, and DIHARD II) demonstrate the method's capability of automatically finding hyperparameters, achieving comparable performance to those found by extensive grid search, which typically requires additional hyperparameter behavior knowledge. Moreover, we show that discriminative fine-tuning of PLDA can further improve the model's performance. We release the source code with this publication.