pwc-archive/papers-with-abstracts huggingface dataset:
How to maintain compliance among host country employees who are less anxious after strict government regulations are lifted: An attempt to apply conservation of resources theory to the workplace amid the still-unending COVID-19 pandemic
Authors: Keisuke Kokubun, Yoshiaki Ino, Kazuyoshi Ishimura
Date: 2023-11-03 00:00:00

Design/methodology/approach We compared the awareness of 813 people in Wuhan city from January to March 2023 (Wuhan 2023) and 2,973 people in East and South China from February to May 2020 (China 2020) using responses to questionnaires conducted at Japanese local subsidiaries during each period. Purpose As the coronavirus pandemic becomes less terrifying than before, there is a trend in countries around the world to abolish strict behavioral restrictions imposed by governments. How should overseas subsidiaries change the way they manage human resources in response to these system changes? To find an answer to this question, this paper examines what changes occurred in the mindset of employees working at local subsidiaries after the government's strict behavioral restrictions were introduced and lifted during the COVID-19 pandemic. Findings The results showed that the analytical model based on conservation of resources (COR) theory can be applied to both China 2020 and Wuhan 2023. However, the relationship between anxiety, fatigue, compliance, turnover intention, and psychological and social resources of employees working at local subsidiaries changed after the initiation and removal of government behavioral restrictions during the pandemic, indicating that managers need to adjust their human resource management practices in response to these changes. Originality/value This is the first study that compares data after the start of government regulations and data after the regulations were lifted. Therefore, this research proposes a new analytical framework that companies, especially foreign-affiliated companies that lack local information, can refer to respond appropriately to disasters, which expand damage while changing its nature and influence while anticipating changes in employee awareness.
Labour Absorption In Manufacturing Industry In Indonesia: Anomalous And Regressive Phenomena
Authors: Tongam Sihol Nababan, Elvis Fresly Purba
Date: 2023-11-03 00:00:00

The manufacturing industry sector was expected to generate new employment opportunities and take on labour. Gradually, however, it emerged as a menace to the sustenance of its workers. According to the findings of this study, 24 manufacturing subsectors with ISIC 2 digits in Indonesia exhibited regressive and abnormal patterns in the period 2012-2020. This suggests that, to a great extent, labour absorption has been limited and, in some cases, even shown a decline. Anomalous occurrences were observed in three subsectors: ISIC 12 (tobacco products), ISIC 26 (computer, electronic and optical products), and ISIC 31 (furniture). In contrast, regressive phenomena were present in the remaining 21 ISIC subsectors. Furthermore, the manufacturing industry displayed a negative correlation between employment and efficiency index, demonstrating this anomalous and regressive phenomenon. This implies that as the efficiency index of the manufacturing industry increases, the index of labour absorption decreases
A Model of Enclosures: Coordination, Conflict, and Efficiency in the Transformation of Land Property Rights
Authors: Matthew J. Baker, Jonathan Conning
Date: 2023-11-02 00:00:00

Economists, historians, and social scientists have long debated how open-access areas, frontier regions, and customary landholding regimes came to be enclosed or otherwise transformed into private property. This paper analyzes decentralized enclosure processes using the theory of aggregative games, examining how population density, enclosure costs, potential productivity gains, and the broader physical, institutional, and policy environment jointly determine the property regime. Changes to any of these factors can lead to smooth or abrupt changes in equilibria that can result in inefficiently high, inefficiently low, or efficient levels of enclosure and associated technological transformation. Inefficient outcomes generally fall short of second-best. While policies to strengthen customary governance or compensate displaced stakeholders can realign incentives, addressing one market failure while neglecting others can worsen outcomes. Our analysis provides a unified framework for evaluating mechanisms emphasized in Neoclassical, Neo-institutional, and Marxian interpretations of historical enclosure processes and contemporary land formalization policies.
SE Territory: Monaural Speech Enhancement Meets the Fixed Virtual Perceptual Space Mapping
Authors: Xinmeng Xu, Yuhong Yang, Weiping tu
Date: 2023-11-03 00:00:00
Tasks: Multi-Task Learning, Speech Enhancement

Monaural speech enhancement has achieved remarkable progress recently. However, its performance has been constrained by the limited spatial cues available at a single microphone. To overcome this limitation, we introduce a strategy to map monaural speech into a fixed simulation space for better differentiation between target speech and noise. Concretely, we propose SE-TerrNet, a novel monaural speech enhancement model featuring a virtual binaural speech mapping network via a two-stage multi-task learning framework. In the first stage, monaural noisy input is projected into a virtual space using supervised speech mapping blocks, creating binaural representations. These blocks synthesize binaural noisy speech from monaural input via an ideal binaural room impulse response. The synthesized output assigns speech and noise sources to fixed directions within the perceptual space. In the second stage, the obtained binaural features from the first stage are aggregated. This aggregation aims to decrease pattern discrepancies between the mapped binaural and original monaural features, achieved by implementing an intermediate fusion module. Furthermore, this stage incorporates the utilization of cross-attention to capture the injected virtual spatial information to improve the extraction of the target speech. Empirical studies highlight the effectiveness of virtual spatial cues in enhancing monaural speech enhancement. As a result, the proposed SE-TerrNet significantly surpasses the recent monaural speech enhancement methods in terms of both speech quality and intelligibility.
Adapting Frechet Audio Distance for Generative Music Evaluation
Authors: Azalea Gui, Hannes Gamper, Sebastian Braun, Dimitra Emmanouilidou
Date: 2023-11-02 00:00:00
Tasks: FAD

The growing popularity of generative music models underlines the need for perceptually relevant, objective music quality metrics. The Frechet Audio Distance (FAD) is commonly used for this purpose even though its correlation with perceptual quality is understudied. We show that FAD performance may be hampered by sample size bias, poor choice of audio embeddings, or the use of biased or low-quality reference sets. We propose reducing sample size bias by extrapolating scores towards an infinite sample size. Through comparisons with MusicCaps labels and a listening test we identify audio embeddings and music reference sets that yield FAD scores well-correlated with acoustic and musical quality. Our results suggest that per-song FAD can be useful to identify outlier samples and predict perceptual quality for a range of music sets and generative models. Finally, we release a toolkit that allows adapting FAD for generative music evaluation.
Quantitative Evaluation of a Multi-Modal Camera Setup for Fusing Event Data with RGB Images
Authors: Julian Moosmann, Jakub Mandula, Philipp Mayer, Luca Benini, Michele Magno
Date: 2023-11-03 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection

Event-based cameras, also called silicon retinas, potentially revolutionize computer vision by detecting and reporting significant changes in intensity asynchronous events, offering extended dynamic range, low latency, and low power consumption, enabling a wide range of applications from autonomous driving to longtime surveillance. As an emerging technology, there is a notable scarcity of publicly available datasets for event-based systems that also feature frame-based cameras, in order to exploit the benefits of both technologies. This work quantitatively evaluates a multi-modal camera setup for fusing high-resolution DVS data with RGB image data by static camera alignment. The proposed setup, which is intended for semi-automatic DVS data labeling, combines two recently released Prophesee EVK4 DVS cameras and one global shutter XIMEA MQ022CG-CM RGB camera. After alignment, state-of-the-art object detection or segmentation networks label the image data by mapping boundary boxes or labeled pixels directly to the aligned events. To facilitate this process, various time-based synchronization methods for DVS data are analyzed, and calibration accuracy, camera alignment, and lens impact are evaluated. Experimental results demonstrate the benefits of the proposed system: the best synchronization method yields an image calibration error of less than 0.90px and a pixel cross-correlation deviation of1.6px, while a lens with 8mm focal length enables detection of objects with size 30cm at a distance of 350m against homogeneous background.
3-Dimensional residual neural architecture search for ultrasonic defect detection
Authors: Shaun McKnight, Christopher MacKinnon, S. Gareth Pierce, Ehsan Mohseni, Vedran Tunukovic, Charles N. MacLeod, Randika K. W. Vithanage, Tom OHare
Date: 2023-11-03 00:00:00
Tasks: Defect Detection, Dimensionality Reduction, Neural Architecture Search, Synthetic Data Generation

This study presents a deep learning methodology using 3-dimensional (3D) convolutional neural networks to detect defects in carbon fiber reinforced polymer composites through volumetric ultrasonic testing data. Acquiring large amounts of ultrasonic training data experimentally is expensive and time-consuming. To address this issue, a synthetic data generation method was extended to incorporate volumetric data. By preserving the complete volumetric data, complex preprocessing is reduced, and the model can utilize spatial and temporal information that is lost during imaging. This enables the model to utilise important features that might be overlooked otherwise. The performance of three architectures were compared. The first two architectures were hand-designed to address the high aspect ratios between the spatial and temporal dimensions. The first architecture reduced dimensionality in the time domain and used cubed kernels for feature extraction. The second architecture used cuboidal kernels to account for the large aspect ratios. The evaluation included comparing the use of max pooling and convolutional layers for dimensionality reduction, with the fully convolutional layers consistently outperforming the models using max pooling. The third architecture was generated through neural architecture search from a modified 3D Residual Neural Network (ResNet) search space. Additionally, domain-specific augmentation methods were incorporated during training, resulting in significant improvements in model performance for all architectures. The mean accuracy improvements ranged from 8.2% to 22.4%. The best performing models achieved mean accuracies of 91.8%, 92.2%, and 100% for the reduction, constant, and discovered architectures, respectively. Whilst maintaining a model size smaller than most 2-dimensional (2D) ResNets.
Neural SPDE solver for uncertainty quantification in high-dimensional space-time dynamics
Authors: Maxime Beauchamp, Ronan Fablet, Hugo Georgenthum
Date: 2023-11-03 00:00:00
Tasks: Gaussian Processes, parameter estimation, Uncertainty Quantification

Historically, the interpolation of large geophysical datasets has been tackled using methods like Optimal Interpolation (OI) or model-based data assimilation schemes. However, the recent connection between Stochastic Partial Differential Equations (SPDE) and Gaussian Markov Random Fields (GMRF) introduced a novel approach to handle large datasets making use of sparse precision matrices in OI. Recent advancements in deep learning also addressed this issue by incorporating data assimilation into neural architectures: it treats the reconstruction task as a joint learning problem involving both prior model and solver as neural networks. Though, it requires further developments to quantify the associated uncertainties. In our work, we leverage SPDEbased Gaussian Processes to estimate complex prior models capable of handling nonstationary covariances in space and time. We develop a specific architecture able to learn both state and SPDE parameters as a neural SPDE solver, while providing the precisionbased analytical form of the SPDE sampling. The latter is used as a surrogate model along the data assimilation window. Because the prior is stochastic, we can easily draw samples from it and condition the members by our neural solver, allowing flexible estimation of the posterior distribution based on large ensemble. We demonstrate this framework on realistic Sea Surface Height datasets. Our solution improves the OI baseline, aligns with neural prior while enabling uncertainty quantification and online parameter estimation.
Terahertz Communication Testbeds: Challenges and Opportunities
Authors: Eray Guven, Gunes Karabulut Kurt
Date: 2023-11-03 00:00:00

This study investigates an experimental software defined radio (SDR) implementation on 180 GHz. Rate scarcity and frequency sparsity are discussed as hardware bottlenecks. Experimental challenges are explained along with the derived system model of such a cascaded structure. Multiple error metrics for the terahertz (THz) signal are acquired, and various case scenarios are subsequently compared. The SDR-THz testbed reaches 3.2 Mbps with < 1 degree skew error. The use of a reflector plate can fine-tune the frequency error and gain imbalance in the expense of at least 14.91 dB signal-to-noise ratio. The results demonstrate the complete feasibility of SDR-based baseband signal generation in THz communication, revealing abundant opportunities to overcome hardware limitations in experimental research.
Reconfigurable Intelligent Surface & Edge -- An Introduction of an EM manipulation structure on obstacles' edge
Authors: Tianqi Xiang, Zhiwei Jiang, Weijun Hong, Xin Zhang, Yuehong Gao
Date: 2023-11-03 00:00:00

Reconfigurable Intelligent Surface (RIS) or metasurface is one of the important enabling technologies in mobile cellular networks that can effectively enhance the signal coverage performance in obstructed regions, and it is generally deployed on surfaces different from obstacles to redirect electromagnetic (EM) waves by reflection, or covered on objects' surfaces to manipulate EM waves by refraction. In this paper, Reconfigurable Intelligent Surface & Edge (RISE) is proposed to extend RIS' abilities of reflection and refraction over surfaces to diffraction around obstacles' edge for better adaptation to specific coverage scenarios. Based on that, this paper analyzes the performance of several different deployment locations and EM manipulation structure designs for different coverage scenarios. Then a novel EM manipulation structure deployed at the obstacles' edge is proposed to achieve static EM environment modification. Simulations validate the preference of the schemes for different scenarios and the new structure achieves better coverage performance than other typical structures in the static scheme.
Carrier Frequency Offset Estimation for OCDM with Null Subchirps
Authors: Sidong Guo, Yiyin Wang, Xiaoli Ma
Date: 2023-11-03 00:00:00
Tasks: Diversity

In this paper, we investigate the carrier frequency offset (CFO) identifiability problem in orthogonal chirp division multiplexing (OCDM) systems. We propose a transmission scheme by inserting consecutive null subchirps. A CFO estimator is accordingly developed to achieve a full acquisition range. We further demonstrate that the proposed transmission scheme not only help to resolve CFO identifiability issues but also enable multipath diversity for OCDM systems. Simulation results corroborate our theoretical findings.
Moving Target Sensing for ISAC Systems in Clutter Environment
Authors: Dongqi Luo, Huihui Wu, Hongliang Luo, Bo Lin, Feifei Gao
Date: 2023-11-03 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we consider the moving target sensing problem for integrated sensing and communication (ISAC) systems in clutter environment. Scatterers produce strong clutter, deteriorating the performance of ISAC systems in practice. Given that scatterers are typically stationary and the targets of interest are usually moving, we here focus on sensing the moving targets. Specifically, we adopt a scanning beam to search for moving target candidates. For the received signal in each scan, we employ high-pass filtering in the Doppler domain to suppress the clutter within the echo, thereby identifying candidate moving targets according to the power of filtered signal. Then, we adopt root-MUSIC-based algorithms to estimate the angle, range, and radial velocity of these candidate moving targets. Subsequently, we propose a target detection algorithm to reject false targets. Simulation results validate the effectiveness of these proposed methods.
Integrated Sensing and Communications in Clutter Environment
Authors: Hongliang Luo, Yucong Wang, Dongqi Luo, Jianwei Zhao, Huihui Wu, Shaodan Ma, Feifei Gao
Date: 2023-11-03 00:00:00
Tasks: ISAC

In this paper, we propose a practical integrated sensing and communications (ISAC) framework to sense dynamic targets from clutter environment while ensuring users communications quality. To implement communications function and sensing function simultaneously, we design multiple communications beams that can communicate with the users as well as one sensing beam that can rotate and scan the entire space. To minimize the interference of sensing beam on existing communications systems, we divide the service area into sensing beam for sensing (S4S) sector and communications beam for sensing (C4S) sector, and provide beamforming design and power allocation optimization strategies for each type sector. Unlike most existing ISAC studies that ignore the interference of static environmental clutter on target sensing, we construct a mixed sensing channel model that includes both static environment and dynamic targets. When base station receives the echo signals, the mean phasor cancellation (MPC) method is employed to filter out the interference from static environmental clutter and to extract the effective dynamic target echoes. Then a complete and practical dynamic target sensing scheme is designed to detect the presence of dynamic targets and to estimate their angles, distances, and velocities. In particular, dynamic target detection and angle estimation are realized through angle-Doppler spectrum estimation (ADSE) and joint detection over multiple subcarriers (MSJD), while distance and velocity estimation are realized through the extended subspace algorithm. Simulation results demonstrate the effectiveness of the proposed scheme and its superiority over the existing methods that ignore environmental clutter.
Intelligent QoS aware slice resource allocation with user association parameterization for beyond 5G ORAN based architecture using DRL
Authors: Suvidha Mhatre, Ferran Adelantado, Kostas Ramantas, Christos Verikoukis
Date: 2023-11-02 00:00:00
Tasks: Deep Reinforcement Learning

The diverse requirements of beyond 5G services increase design complexity and demand dynamic adjustments to the network parameters. This can be achieved with slicing and programmable network architectures such as the open radio access network (ORAN). It facilitates the tuning of the network components exactly to the demands of future-envisioned applications as well as intelligence at the edge of the network. Artificial intelligence (AI) has recently drawn a lot of interest for its potential to solve challenging issues in wireless communication. Due to the non-deterministic, random, and complex behavior of models and parameters involved in the process, radio resource management is one of the topics that needs to be addressed with such techniques. The study presented in this paper proposes quality of service (QoS)-aware intra-slice resource allocation that provides superior performance compared to baseline and state of the art strategies. The slice-dedicated intelligent agents learn how to handle resources at near-RT RIC level time granularities while optimizing various key performance indicators (KPIs) and meeting QoS requirements for each end user. In order to improve KPIs and system performance with various reward functions, the study discusses Markov's decision process (MDP) and deep reinforcement learning (DRL) techniques, notably the deep Q network (DQN). The simulation evaluates the efficacy of the algorithm under dynamic conditions and various network characteristics. Results and analysis demonstrate the improvement in the performance of the network for enhanced mobile broadband (eMBB) and ultra-reliable low latency (URLLC) slice categories.
The Role of Soil Surface in a Sustainable Semiarid Ecosystem
Authors: Swadesh Pal, Malay Banerjee, Roderick Melnik
Date: 2023-11-03 00:00:00
Tasks: Management

Patterns in a semiarid ecosystem are important because they directly and indirectly affect ecological processes, biodiversity, and ecosystem resilience. Understanding the causes and effects of these patterns is critical for long-term land surface management and conservation efforts in semiarid regions, which are especially sensitive to climate change and human-caused disturbances. It is known that there is a regular connection between the vegetation and the living species in a habitat since some animals evolved to live in a semiarid ecosystem and rely on plants for food. In this work, we have constructed a coupled mathematical model to connect the water resource, vegetation and living organisms and have investigated how the soil surface affects the resulting patterns for the long term. This study contributes to a better understanding of ecological patterns and processes in semiarid environments by shedding light on the complex interaction mechanisms that depend on the structure of semiarid ecosystems. The findings provide further critical insight into the influence of efforts for improving ecosystem resilience and adjusting to the challenges posed by climate change and human activities.