pwc-archive/papers-with-abstracts huggingface dataset:
ROM-Based Stochastic Optimization for a Continuous Manufacturing Process
Authors: Raul Cruz-Oliver, Luis Monzon, Edgar Ramirez-Laboreo, Jose-Manuel Rodriguez-Fortun
Date: 2023-10-24 00:00:00
Tasks: Bayesian Optimization, Stochastic Optimization

This paper proposes a model-based optimization method for the production of automotive seals in an extrusion process. The high production throughput, coupled with quality constraints and the inherent uncertainty of the process, encourages the search for operating conditions that minimize nonconformities. The main uncertainties arise from the process variability and from the raw material itself. The proposed method, which is based on Bayesian optimization, takes these factors into account and obtains a robust set of process parameters. Due to the high computational cost and complexity of performing detailed simulations, a reduced order model is used to address the optimization. The proposal has been evaluated in a virtual environment, where it has been verified that it is able to minimize the impact of process uncertainties. In particular, it would significantly improve the quality of the product without incurring additional costs, achieving a 50% tighter dimensional tolerance compared to a solution obtained by a deterministic optimization algorithm.
The Evolution from Design to Verification of the Antenna System and Mechanisms in the AcubeSAT mission
Authors: Panagiotis Bountzioukas, Georgios Kikas, Christoforos Tsiolakis, Dimitrios Stoupis, Eleftheria Chatziargyriou, Alkis Hatzopoulos, Vasiliki Kourampa-Gottfroh, Ilektra Karakosta-Amarantidou, Aggelos Mavropoulos, Ioannis-Nikolaos Komis, Afroditi Kita, David Palma, Loris Franchi
Date: 2023-10-24 00:00:00

AcubeSAT is an open-source CubeSat mission aiming to explore the effects of microgravity and radiation on eukaryotic cells using a compact microfluidic LoC platform. It is developed by SpaceDot, a volunteer, interdisciplinary student team at the Aristotle University of Thessaloniki and supported by the "Fly Your Satellite! 3" program of the ESA Education Office. The scientific data of the mission is comprised of microscope images captured through the on-board integrated camera setup. As the total size of the payload data is expected to be close to 2GB over 12 months, a fast and efficient downlink fulfilling the restrictive power, cost and complexity budgets is required. Currently, there is no open-source communications system design which fully supports these specific constraints, so we opted to develop our own solutions. The antenna system underwent multiple iterations as the design matured, a process highly aided by the feedback received from the ESA experts. The final communications system configuration consists of an S-band microstrip antenna operating at 2.4GHz and a UHF deployable antenna, for the payload data and TM&TC respectively, both in-house designed. In this paper, we will present AcubeSAT's antenna system iterations that span over 3 years, as well as the rationale and analysis results behind each. The development decisions will be highlighted throughout the paper in an effort to aid in the future development of such a low-cost CubeSat mission communications system.
Adaptive Fuzzy Tracking Control for Nonlinear State Constrained Pure-Feedback Systems With Input Delay via Dynamic Surface Technique
Authors: Ju Wu, Tong Wang
Date: 2023-10-23 00:00:00

This brief constructs the adaptive backstepping control scheme for a class of pure-feedback systems with input delay and full state constraints. With the help of Mean Value Theorem, the pure-feedback system is transformed into strict-feedback one. Barrier Lyapunov functions are employed to guarantee all of the states remain constrained within predefined sets. By introducing the Pade approximation method and corresponding intermediate, the impact generated by input delay on the output tracking performance of the system can be eliminated. Furthermore, a low-pass filter driven by a newly-defined control input, is employed to generate the actual control input, which facilitates the design of backstepping control. To approximate the unknown functions with a desired level of accuracy, the fuzzy logic systems (FLSs) are utilized by choosing appropriate fuzzy rules, logics and so on. The minimal learning parameter (MLP) technique is employed to decrease the number of nodes and parameters in FLSs, and dynamic surface control (DSC) technique is leveraged to avoid so-called "explosion of complexity". Moreover, smooth robust compensators are introduced to circumvent the influences of external disturbance and approximation errors. By stability analysis, it is proved that all of signals in the closed-loop system are semi-globally ultimately uniform bounded, and the tracking error can be within a arbitrary small neighbor of origin via selecting appropriate parameters of controllers. Finally, the results of numerical illustration are provided to demonstrate the effectiveness of the designed method.
Improving Robust Decisions with Data
Authors: Xiaoyu Cheng
Date: 2023-10-25 00:00:00

A decision-maker faces uncertainty governed by a data-generating process (DGP), which is only known to belong to a set of sequences of independent but possibly non-identical distributions. A robust decision maximizes the expected payoff against the worst possible DGP in this set. This paper characterizes when and how such robust decisions can be improved with data, measured by the expected payoff under the true DGP, no matter which possible DGP is the truth. It further develops novel and simple inference methods to achieve it, as common methods (e.g., maximum likelihood) may fail to deliver such an improvement.
Climate-related Agricultural Productivity Losses through a Poverty Lens
Authors: Alkis Blanz
Date: 2023-10-25 00:00:00

In this paper, we analyze the long-term distributive impact of climate change through rising food prices. We use a standard incomplete markets model and account for non-linear Engel curves for food consumption. For the calibration of our model, we rely on household data from 92 developing countries, representing 4.5 billion people. The results indicate that the short-term and long-term distributive impact of climate change differs. Including general equilibrium effects change the welfare outcome especially for the poorest quintile. In the presence of idiosyncratic risk, higher food prices increase precautionary savings, which through general equilibrium affect labor income of all agents. Furthermore, this paper studies the impact on inequality for different allocations of productivity losses across sectors. When climate impacts affects total factor productivity in both sectors of the economy, climate impacts increase also wealth inequality.
Elevating Women in the Workplace: The Dual Influence of Spiritual Intelligence and Ethical Environments on Job Satisfaction
Authors: Ali Bai, Morteza Vahedian, Rashin Ghahreman, Hasan Piri
Date: 2023-10-25 00:00:00

In today's rapidly evolving workplace, the dynamics of job satisfaction and its determinants have become a focal point of organizational studies. This research offers a comprehensive examination of the nexus between spiritual intelligence and job satisfaction among female employees, with particular emphasis on the moderating role of ethical work environments. Beginning with an exploration of the multifaceted nature of human needs, the study delves deep into the psychological underpinnings that drive job satisfaction. It elucidates how various tangible and intangible motivators, such as salary benefits and recognition, play pivotal roles in shaping employee attitudes and behaviors. Moreover, the research spotlights the unique challenges and experiences of female employees, advocating for a more inclusive understanding of their needs. An extensive review of the literature and empirical analysis culminates in the pivotal finding that integrating spiritual intelligence and ethical considerations within organizational practices can significantly enhance job satisfaction. Such a holistic approach, the paper posits, not only bolsters the well-being and contentment of female employees but also augments overall organizational productivity, retention rates, and morale.
A Note on the Continuity of Expected Utility Functions
Authors: Yuhki Hosoya
Date: 2023-10-25 00:00:00

In this paper, we study the continuity of expected utility functions, and derive a necessary and sufficient condition for a weak order on the space of simple probabilities to have a continuous expected utility function. We also verify that almost the same condition is necessary and sufficient for a weak order on the space of probabilities with compact-support to have a continuous expected utility function.
UniX-Encoder: A Universal $X$-Channel Speech Encoder for Ad-Hoc Microphone Array Speech Processing
Authors: Zili Huang, Yiwen Shao, Shi-Xiong Zhang, Dong Yu
Date: 2023-10-25 00:00:00
Tasks: speaker-diarization, Speaker Diarization, Speaker Recognition, speech-recognition, Speech Recognition

The speech field is evolving to solve more challenging scenarios, such as multi-channel recordings with multiple simultaneous talkers. Given the many types of microphone setups out there, we present the UniX-Encoder. It's a universal encoder designed for multiple tasks, and worked with any microphone array, in both solo and multi-talker environments. Our research enhances previous multi-channel speech processing efforts in four key areas: 1) Adaptability: Contrasting traditional models constrained to certain microphone array configurations, our encoder is universally compatible. 2) Multi-Task Capability: Beyond the single-task focus of previous systems, UniX-Encoder acts as a robust upstream model, adeptly extracting features for diverse tasks including ASR and speaker recognition. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integration: In contrast to models that first beamform then process single-channels, our encoder offers an end-to-end solution, bypassing explicit beamforming or separation. To validate its effectiveness, we tested the UniX-Encoder on a synthetic multi-channel dataset from the LibriSpeech corpus. Across tasks like speech recognition and speaker diarization, our encoder consistently outperformed combinations like the WavLM model with the BeamformIt frontend.
Covariance Blocking and Whitening Method for Successive Relative Transfer Function Vector Estimation in Multi-Speaker Scenarios
Authors: Henri Gode, Simon Doclo
Date: 2023-10-25 00:00:00
Tasks: Blocking

This paper addresses the challenge of estimating the relative transfer function (RTF) vectors of multiple speakers in a noisy and reverberant environment. More specifically, we consider a scenario where two speakers activate successively. In this scenario, the RTF vector of the first speaker can be estimated in a straightforward way and the main challenge lies in estimating the RTF vector of the second speaker during segments where both speakers are simultaneously active. To estimate the RTF vector of the second speaker the so-called blind oblique projection (BOP) method determines the oblique projection operator that optimally blocks the second speaker. Instead of blocking the second speaker, in this paper we propose a covariance blocking and whitening (CBW) method, which first blocks the first speaker and applies whitening using the estimated noise covariance matrix and then estimates the RTF vector of the second speaker based on a singular value decomposition. When using the estimated RTF vectors of both speakers in a linearly constrained minimum variance beamformer, simulation results using real-world recordings for multiple speaker positions demonstrate that the proposed CBW method outperforms the conventional BOP and covariance whitening methods in terms of signal-to-interferer-and-noise ratio improvement.
Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation
Authors: Daniel Saragih, Atsuhiro Hibi, Pascal Tyrrell
Date: 2023-10-25 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, MS-SSIM, Segmentation, Semantic Segmentation, SSIM

Medical image analysis has become a prominent area where machine learning has been applied. However, high quality, publicly available data is limited either due to patient privacy laws or the time and cost required for experts to annotate images. In this retrospective study, we designed and evaluated a pipeline to generate synthetic labeled polyp images for augmenting medical image segmentation models with the aim of reducing this data scarcity. In particular, we trained diffusion models on the HyperKvasir dataset, comprising 1000 images of polyps in the human GI tract from 2008 to 2016. Qualitative expert review, Fr\'echet Inception Distance (FID), and Multi-Scale Structural Similarity (MS-SSIM) were tested for evaluation. Additionally, various segmentation models were trained with the generated data and evaluated using Dice score and Intersection over Union. We found that our pipeline produced images more akin to real polyp images based on FID scores, and segmentation performance also showed improvements over GAN methods when trained entirely, or partially, with synthetic data, despite requiring less compute for training. Moreover, the improvement persists when tested on different datasets, showcasing the transferability of the generated images.
TILT: topological interface recovery in limited-angle tomography
Authors: Elli Karvonen, Matti Lassas, Pekka Pankka, Samuli Siltanen
Date: 2023-10-25 00:00:00

A novel reconstruction method is introduced for the severely ill-posed inverse problem of limited-angle tomography. It is well known that, depending on the available measurement, angles specify a subset of the wavefront set of the unknown target, while some oriented singularities remain invisible in the data. Topological Interface recovery for Limited-angle Tomography, or TILT, is based on lifting the visible part of the wavefront set under a universal covering map. In the space provided, it is possible to connect the appropriate pieces of the lifted wavefront set correctly using dual-tree complex wavelets, a dedicated metric, and persistent homology. The result is not only a suggested invisible boundary but also a computational representation for all interfaces in the target.
How to Extend 3D GBSM to Integrated Sensing and Communication Channel with Sharing Feature?
Authors: Yameng Liu, Jianhua Zhang, Yuxiang Zhang, Huiwen Gong, Tao Jiang, Guangyi Liu
Date: 2023-10-25 00:00:00
Tasks: 3D geometry, Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC) is a promising technology in 6G systems. The existing 3D Geometry-Based Stochastic Model (GBSM), as standardized for 5G systems, addresses solely communication channels and lacks consideration of the integration with sensing channel. Therefore, this letter extends 3D GBSM to support ISAC research, with a particular focus on capturing the sharing feature of both channels, including shared scatterers, clusters, paths, and similar propagation param-eters, which have been experimentally verified in the literature. The proposed approach can be summarized as follows: Firstly, an ISAC channel model is proposed, where shared and non-shared components are superimposed for both communication and sensing. Secondly, sensing channel is characterized as a cascade of TX-target, radar cross section, and target-RX, with the introduction of a novel parameter S for shared target extraction. Finally, an ISAC channel implementation framework is proposed, allowing flexible configuration of sharing feature and the joint generation of communication and sensing channels. The proposed ISAC channel model can be compatible with the 3GPP standards and offers promising support for ISAC technology evaluation.
Power Optimization in Satellite Communication Using Multi-Intelligent Reflecting Surfaces
Authors: Muhammad Ihsan Khalil
Date: 2023-10-25 00:00:00

This study introduces two innovative methodologies aimed at augmenting energy efficiency in satellite-to-ground communication systems through the integration of multiple Reflective Intelligent Surfaces (RISs). The primary objective of these methodologies is to optimize overall energy efficiency under two distinct scenarios. In the first scenario, denoted as Ideal Environment (IE), we enhance energy efficiency by decomposing the problem into two sub-optimal tasks. The initial task concentrates on maximizing power reception by precisely adjusting the phase shift of each RIS element, followed by the implementation of Selective Diversity to identify the RIS element delivering maximal power. The second task entails minimizing power consumption, formulated as a binary linear programming problem, and addressed using the Binary Particle Swarm Optimization (BPSO) technique. The IE scenario presupposes an environment where signals propagate without any path loss, serving as a foundational benchmark for theoretical evaluations that elucidate the systems optimal capabilities. Conversely, the second scenario, termed Non-Ideal Environment (NIE), is designed for situations where signal transmission is subject to path loss. Within this framework, the Adam algorithm is utilized to optimize energy efficiency. This non ideal setting provides a pragmatic assessment of the systems capabilities under conventional operational conditions. Both scenarios emphasize the potential energy savings achievable by the satellite RIS system. Empirical simulations further corroborate the robustness and effectiveness of our approach, highlighting its potential to enhance energy efficiency in satellite-to-ground communication systems.
Pilot-Based Uplink Power Control in Single-UE Massive MIMO Systems With 1-Bit ADCs
Authors: Amila Ravinath, Bikshapathi Gouda, Italo Atzeni, Antti Tölli
Date: 2023-10-25 00:00:00

We propose uplink power control (PC) methods for massive multiple-input multiple-output systems with 1-bit analog-to-digital converters, which are specifically tailored to address the non-monotonic data detection performance with respect to the transmit power of the user equipment (UE). Considering a single UE, we design a multi-amplitude pilot sequence to capture the aforementioned non-monotonicity, which is utilized at the base station to derive UE transmit power adjustments via single-shot or differential power control (DPC) techniques. Both methods enable closed-loop uplink PC using different feedback approaches. The single-shot method employs one-time multi-bit feedback, while the DPC method relies on continuous adjustments with 1-bit feedback. Numerical results demonstrate the superiority of the proposed schemes over conventional closed-loop uplink PC techniques.
Transmitting Data Through Reconfigurable Intelligent Surface: A Spatial Sigma-Delta Modulation Approach
Authors: Wai-Yiu Keung, Hei Victor Cheng, Wing-Kin Ma
Date: 2023-10-25 00:00:00
Tasks: Quantization

Transmitting data using the phases on reconfigurable intelligent surfaces (RIS) is a promising solution for future energy-efficient communication systems. Recent work showed that a virtual phased massive multiuser multiple-input-multiple-out (MIMO) transmitter can be formed using only one active antenna and a large passive RIS. In this paper, we are interested in using such a system to perform MIMO downlink precoding. In this context, we may not be able to apply conventional MIMO precoding schemes, such as the simple zero-forcing (ZF) scheme, and we typically need to design the phase signals by solving optimization problems with constant modulus constraints or with discrete phase constraints, which pose challenges with high computational complexities. In this work, we propose an alternative approach based on Sigma-Delta ($\Sigma\Delta$) modulation, which is classically famous for its noise-shaping ability. Specifically, first-order $\Sigma\Delta$ modulation is applied in the spatial domain to handle phase quantization in generating constant envelope signals. Under some mild assumptions, the proposed phased $\Sigma\Delta$ modulator allows us to use the ZF scheme to synthesize the RIS reflection phases with negligible complexity. The proposed approach is empirically shown to achieve comparable bit error rate performance to the unquantized ZF scheme.
Resource Allocation for UAV-Assisted Industrial IoT User with Finite Blocklength
Authors: Atefeh Rezaei, Ata Khalili, Falko Dressler
Date: 2023-10-24 00:00:00

We consider a relay system empowered by an unmanned aerial vehicle (UAV) that facilitates downlink information delivery while adhering to finite blocklength requirements. The setup involves a remote controller transmitting information to both a UAV and an industrial Internet of Things (IIoT) or remote device, employing the non-orthogonal multiple access (NOMA) technique in the first phase. Subsequently, the UAV decodes and forwards this information to the remote device in the second phase. Our primary objective is to minimize the decoding error probability (DEP) at the remote device, which is influenced by the DEP at the UAV. To achieve this goal, we optimize the blocklength, transmission power, and location of the UAV. However, the underlying problem is highly non-convex and generally intractable to be solved directly. To overcome this challenge, we adopt an alternative optimization (AO) approach and decompose the original problem into three sub-problems. This approach leads to a sub-optimal solution, which effectively mitigates the non-convexity issue. In our simulations, we compare the performance of our proposed algorithm with baseline schemes. The results reveal that the proposed framework outperforms the baseline schemes, demonstrating its superiority in achieving lower DEP at the remote device. Furthermore, the simulation results illustrate the rapid convergence of our proposed algorithm, indicating its efficiency and effectiveness in solving the optimization problem.
Cortical Functional architectures as contact and sub-Riemannian geometry
Authors: Giovanna Citti, Alessandro Sarti
Date: 2023-10-16 00:00:00
Tasks: Position

In a joint paper, Jean Petitot together with the authors of the present paper described the functional geometry of the visual cortex as the symplectization of a contact form to describe the family of cells sensitive to position, orientation and scale. In the present paper, as a "homage" to the enormous contribution of Jean Petitot to neurogeometry, we will extend this approach to more complex functional architectures built as a sequence of contactization or a symplectization process, able to extend the dimension of the space. We will also outline a few examples where a sub-Riemannian lifting is needed.