pwc-archive/papers-with-abstracts huggingface dataset:
Testing an instrument to measure the BPMS-KM Support Model
Authors: Alicia Martin-Navarro, Maria Paula Lechuga Sancho, Jose Aurelio Medina-Garrido
Date: 2023-11-24 00:00:00
Tasks: Management, valid

BPMS (Business Process Management System) represents a type of software that automates the organizational processes looking for efficiency. Since the knowledge of organizations lies in their processes, it seems probable that a BPMS can be used to manage the knowledge applied in these processes. Through the BPMS-KM Support Model, this study aims to determine the reliability and validity of a 65-item instrument to measure the utility and the use of a BPMS for knowledge management (KM). A questionnaire was sent to 242 BPMS users and to determine its validity, a factorial analysis was conducted. The results showed that the measuring instrument is trustworthy and valid. It represents implications for research, since it provides an instrument validated for research on the success of a BPMS for KM. There would also be practical implications, since managers can evaluate the use of BPMS, in addition to automating processes to manage knowledge.
Impact of effectual propensity on entrepreneurial intention
Authors: Alicia Martin-Navarro, Felix Velicia-Martin, Jose Aurelio Medina-Garrido, Pedro R. Palos-Sanchez
Date: 2023-11-24 00:00:00

For decades, entrepreneurship has been promoted in academia and tourism sector and has it been seen as an opportunity for new business ventures. In terms of entrepreneurial behavior, effectual logic shows how the individual uses his or her resources to create new opportunities. In this context, this paper aims to determine effectual propensity as an antecedent of entrepreneurial intentions. For this purpose, and based on the TPB model, we conducted our research with tourism students from Cadiz and Seville (Spain) universities with Smart PLS 3. The results show that effectual propensity influences entrepreneurial intentions and that attitude and perceived behavioral control mediate between subjective norms and intentions. Our research has a great added value since we have studied for the first time efficacious propensity as an antecedent of intentions in people who have never been entrepreneurs.
Consumption Smoothing in Metropolis: Evidence from Working-class Households in Prewar Tokyo
Authors: Kota Ogasawara
Date: 2023-11-24 00:00:00

I analyzed the risk-coping strategies among factory worker households in early 20th-century Tokyo. I digitized and analyzed a unique daily longitudinal household budget survey to determine how consumption was impacted by idiosyncratic shocks. I found that while the households were so vulnerable that the shocks impacted their consumption levels, the estimated income elasticity for food consumption was relatively low in the short run. The event-study analysis using adverse health shock confirms the robustness of the results. The result of mechanism analysis suggests that credit purchases with local retailers smoothed short-run food consumption. Despite the potential loss of profit, retailers in a competitive situation allowed consumers to trade on credit. This shows the roles of informal credit institutions in mitigating vulnerability among urban worker households.
Organizational support for work-family life balance as an antecedent to the well-being of tourism employees in Spain
Authors: Jose Aurelio Medina-Garrido, Jose Maria Biedma-Ferrer, Maria Bogren
Date: 2023-11-23 00:00:00
Tasks: Management, valid

The study of work-family conflict (WFC) and work-family policies (WFP) and their impact on the well-being of employees in the tourism sector is increasingly attracting the attention of researchers. To overcome the adverse effects of WFC, managers should promote WFP, which contribute to increased well-being at work and employees' commitment. This paper aims to analyze the impact of WFP accessibility and organizational support on well-being directly and by mediating the organizational commitment that these policies might encourage. In addition, we also study whether these relationships vary according to gender and employee seniority. To test the hypotheses derived from this objective, we collected 530 valid and completed questionnaires from workers in the tourism sector in Spain, which we analyzed using structural equation modeling based on the PLS-SEM approach. The results show that human resource management must consider the importance of organizational support for workers to make WFP accessible and generate organizational commitment and well-being at work.
Interactive Dual-Conformer with Scene-Inspired Mask for Soft Sound Event Detection
Authors: Han Yin, Jisheng Bai, Mou Wang, Dongyuan Shi, Woon-Seng Gan, Jianfeng Chen
Date: 2023-11-23 00:00:00
Tasks: Event Detection, Sound Event Detection

Traditional binary hard labels for sound event detection (SED) lack details about the complexity and variability of sound event distributions. Recently, a novel annotation workflow is proposed to generate fine-grained non-binary soft labels, resulting in a new real-life dataset named MAESTRO Real for SED. In this paper, we first propose an interactive dual-conformer (IDC) module, in which a cross-interaction mechanism is applied to effectively exploit the information from soft labels. In addition, a novel scene-inspired mask (SIM) based on soft labels is incorporated for more precise SED predictions. The SIM is initially generated through a statistical approach, referred as SIM-V1. However, the fixed artificial mask may mismatch the SED model, resulting in limited effectiveness. Therefore, we further propose SIM-V2, which employs a word embedding model for adaptive SIM estimation. Experimental results show that the proposed IDC module can effectively utilize the information from soft labels, and the integration of SIM-V1 can further improve the accuracy. In addition, the impact of different word embedding dimensions on SIM-V2 is explored, and the results show that the appropriate dimension can enable SIM-V2 achieve superior performance than SIM-V1. In DCASE 2023 Challenge Task4B, the proposed system achieved the top ranking performance on the evaluation dataset of MAESTRO Real.
End-to-end transfer learning for speaker-independent cross-language and cross-corpus speech emotion recognition
Authors: Duowei Tang, Peter Kuppens, Luc Geurts, Toon van Waterschoot
Date: 2023-11-22 00:00:00
Tasks: Cross-corpus, Emotion Recognition, Speech Emotion Recognition, Transfer Learning

Data-driven models achieve successful results in Speech Emotion Recognition (SER). However, these models, which are often based on general acoustic features or end-to-end approaches, show poor performance when the testing set has a different language than the training set or when these sets are taken from different datasets. To alleviate these problems, this paper presents an end-to-end Deep Neural Network (DNN) model based on transfer learning for cross-language and cross-corpus SER. We use the wav2vec 2.0 pre-trained model to transform audio time-domain waveforms from different languages, different speakers and different recording conditions into a feature space shared by multiple languages, thereby reducing the language variabilities in the speech embeddings. Next, we propose a new Deep-Within-Class Covariance Normalisation (Deep-WCCN) layer that can be inserted into the DNN model and aims to reduce other variabilities including speaker variability, channel variability and so on. The entire model is fine-tuned in an end-to-end manner on a combined loss and is validated on datasets from three languages (i.e. English, German, Chinese). Experimental results show that our proposed method outperforms the baseline model that is based on common acoustic feature sets for SER in the within-language setting and the cross-language setting. In addition, we also experimentally validate the effectiveness of Deep-WCCN, which can further improve the model performance. Next, we show that the proposed transfer learning method has good data efficiency when merging target language data into the fine-tuning process. The model speaker-independent SER performance increases with up to 15.6% when only 160s of target language data is used. Finally, our proposed model shows significantly better performance than other state-of-the-art models in cross-language SER.
Lightweight Framework for Automated Kidney Stone Detection using coronal CT images
Authors: Fangyijie Wang, Guenole Silvestre, Kathleen M. Curran
Date: 2023-11-24 00:00:00
Tasks: Computed Tomography (CT), CPU

Kidney stone disease results in millions of annual visits to emergency departments in the United States. Computed tomography (CT) scans serve as the standard imaging modality for efficient detection of kidney stones. Various approaches utilizing convolutional neural networks (CNNs) have been proposed to implement automatic diagnosis of kidney stones. However, there is a growing interest in employing fast and efficient CNNs on edge devices in clinical practice. In this paper, we propose a lightweight fusion framework for kidney detection and kidney stone diagnosis on coronal CT images. In our design, we aim to minimize the computational costs of training and inference while implementing an automated approach. The experimental results indicate that our framework can achieve competitive outcomes using only 8\% of the original training data. These results include an F1 score of 96\% and a False Negative (FN) error rate of 4\%. Additionally, the average detection time per CT image on a CPU is 0.62 seconds. Reproducibility: Framework implementation and models available on GitHub.
Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping
Authors: Jack Breen, Katie Allen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar
Date: 2023-11-23 00:00:00
Tasks: Multiple Instance Learning

Artificial intelligence has found increasing use for ovarian cancer morphological subtyping from histopathology slides, but the optimal magnification for computational interpretation is unclear. Higher magnifications offer abundant cytological information, whereas lower magnifications give a broader histoarchitectural overview. Using attention-based multiple instance learning, we performed the most extensive analysis of ovarian cancer tissue magnifications to date, with data at six magnifications subjected to the same preprocessing, hyperparameter tuning, cross-validation and hold-out testing procedures. The lowest magnifications (1.25x and 2.5x) performed best in cross-validation, and intermediate magnifications (5x and 10x) performed best in hold-out testing (62% and 61% accuracy, respectively). Lower magnification models were also significantly faster, with the 5x model taking 5% as long to train and 31% as long to evaluate slides compared to 40x. This indicates that the standard usage of high magnifications for computational ovarian cancer subtyping may be unnecessary, with lower magnifications giving faster, more accurate alternatives.
Cooperative Multi-Monostatic Sensing for Object Localization in 6G Networks
Authors: Maximiliano Rivera Figueroa, Pradyumna Kumar Bishoyi, Marina Petrova
Date: 2023-11-24 00:00:00
Tasks: Object Localization

Enabling passive sensing of the environment using cellular base stations (BSs) will be one of the disruptive features of the sixth-generation (6G) networks. However, accurate localization and positioning of objects are challenging to achieve as multipath significantly degrades the reflected echos. Existing localization techniques perform well under the assumption of large bandwidth available but perform poorly in bandwidth-limited scenarios. To alleviate this problem, in this work, we introduce a 5G New Radio (NR)-based cooperative multi-monostatic sensing framework for passive target localization that operates in the Frequency Range 1 (FR1) band. We propose a novel fusion-based estimation process that can mitigate the effect of multipath by assigning appropriate weight to the range estimation of each BS. Extensive simulation results using ray-tracing demonstrate the efficacy of the proposed multi-sensing framework in bandwidth-limited scenarios.
SNN Architecture for Differential Time Encoding Using Decoupled Processing Time
Authors: Daniel Windhager, Bernhard A. Moser, Michael Lunglmayr
Date: 2023-11-24 00:00:00
Tasks: Quantization

Spiking neural networks (SNNs) have gained attention in recent years due to their ability to handle sparse and event-based data better than regular artificial neural networks (ANNs). Since the structure of SNNs is less suited for typically used accelerators such as GPUs than conventional ANNs, there is a demand for custom hardware accelerators for processing SNNs. In the past, the main focus was on platforms that resemble the structure of multiprocessor systems. In this work, we propose a lightweight neuron layer architecture that allows network structures to be directly mapped onto digital hardware. Our approach is based on differential time coding of spike sequences and the decoupling of processing time and spike timing that allows the SNN to be processed on different hardware platforms. We present synthesis and performance results showing that this architecture can be implemented for networks of more than 1000 neurons with high clock speeds on a State-of-the-Art FPGA. We furthermore show results on the robustness of our approach to quantization. These results demonstrate that high-accuracy inference can be performed with bit widths as low as 4.
An ADMM-Based Geometric Configuration Optimization in RSSD-Based Source Localization By UAVs with Spread Angle Constraint
Authors: Xin Cheng, Guangjie Han, Jinlin Peng, Jinfang Jiang, Yu He, Weiqiang Zhu, Feng Shu, Jiangzhou Wang
Date: 2023-11-24 00:00:00

Deploying multiple unmanned aerial vehicles (UAVs) to locate a signal-emitting source covers a wide range of military and civilian applications like rescue and target tracking. It is well known that the UAVs-source (sensors-target) geometry, namely geometric configuration, significantly affects the final localization accuracy. This paper focuses on the geometric configuration optimization for received signal strength difference (RSSD)-based passive source localization by drone swarm. Different from prior works, this paper considers a general measuring condition where the spread angle of drone swarm centered on the source is constrained. Subject to this constraint, a geometric configuration optimization problem with the aim of maximizing the determinant of Fisher information matrix (FIM) is formulated. After transforming this problem using matrix theory, an alternating direction method of multipliers (ADMM)-based optimization framework is proposed. To solve the subproblems in this framework, two global optimal solutions based on the Von Neumann matrix trace inequality theorem and majorize-minimize (MM) algorithm are proposed respectively. Finally, the effectiveness as well as the practicality of the proposed ADMM-based optimization algorithm are demonstrated by extensive simulations.
3D Printed Discrete Dielectric Lens With Improved Matching Layers
Authors: Juan Andrés Vásquez-Peralvo, José Manuel Fernández-González, Thomas Wong
Date: 2023-11-23 00:00:00

This paper presents a non-zoned discrete dielectric lens comprising two or three matching layers to reduce the 50-110 GHz frequency range reflections. Based on Chebyshev and binomial multi-section transformers, the designed models use matching layers at the top and bottom. In addition, the presented designs use pins instead of the conventional slots for the matching layers, thus easing the manufacturing process. The results show that the broadband realized gain obtained using the proposed design is higher for both the two- and three-layer design than the commonly used quarter-wave transformer. A Binomial lens with two matchings layers using 38 unit cells is fabricated and illuminated by an open-ended waveguide to validate the simulation results obtained using CST Microwave Studio. The fabrication process uses stereolithography additive manufacturing.
A Fast Power Spectrum Sensing Solution for Generalized Coprime Sampling
Authors: Kaili Jiang, Dechang Wang, Kailun Tian, HanCong Feng, Yuxin Zhao, Junyu Yuan, Bin Tang
Date: 2023-11-23 00:00:00

The growing scarcity of spectrum resources, wideband spectrum sensing is required to process a prohibitive volume of data at a high sampling rate. For some applications, spectrum estimation only requires second-order statistics. In this case, a fast power spectrum sensing solution is proposed based on the generalized coprime sampling. By exploring the sensing vector inherent structure, the autocorrelation sequence of inputs can be reconstructed from sub-Nyquist samples by only utilizing the parallel Fourier transform and simple multiplication operations. Thus, it takes less time than the state-of-the-art methods while maintaining the same performance, and it achieves higher performance than the existing methods within the same execution time, without the need for pre-estimating the number of inputs. Furthermore, the influence of the model mismatch has only a minor impact on the estimation performance, which allows for more efficient use of the spectrum resource in a distributed swarm scenario. Simulation results demonstrate the low complexity in sampling and computation, making it a more practical solution for real-time and distributed wideband spectrum sensing applications.
On optimal tracking portfolio in incomplete markets: The reinforcement learning approach
Authors: Lijun Bo, YiJie Huang, Xiang Yu
Date: 2023-11-24 00:00:00
Tasks: Q-Learning

This paper studies an infinite horizon optimal tracking portfolio problem using capital injection in incomplete market models. The benchmark process is modelled by a geometric Brownian motion with zero drift driven by some unhedgeable risk. The relaxed tracking formulation is adopted where the fund account compensated by the injected capital needs to outperform the benchmark process at any time, and the goal is to minimize the cost of the discounted total capital injection. When model parameters are known, we formulate the equivalent auxiliary control problem with reflected state dynamics, for which the classical solution of the HJB equation with Neumann boundary condition is obtained explicitly. When model parameters are unknown, we introduce the exploratory formulation for the auxiliary control problem with entropy regularization and develop the continuous-time q-learning algorithm in models of reflected diffusion processes. In some illustrative numerical example, we show the satisfactory performance of the q-learning algorithm.
Calibration of the Bass Local Volatility model
Authors: Beatrice Acciaio, Antonio Marini, Gudmund Pammer
Date: 2023-11-24 00:00:00
Tasks: model

The Bass local volatility model introduced by Backhoff-Veraguas--Beiglb\"ock--Huesmann--K\"allblad is a Markov model perfectly calibrated to vanilla options at finitely many maturities, that approximates the Dupire local volatility model. Conze and Henry-Labord\`ere show that its calibration can be achieved by solving a fixed-point equation. In this paper we complement the analysis and show existence and uniqueness of the solution to this equation, and that the fixed-point iteration scheme converges at a linear rate.
Hierarchical Structure of Uncertainty
Authors: Takanori Adachi
Date: 2023-11-23 00:00:00

We introduce a new concept called uncertainty spaces which is an extended concept of probability spaces. Then, we express n-layer uncertainty which we call hierarchical uncertainty by a hierarchically constructed sequence of uncertainty spaces, called a U-sequence. We use U-sequences for providing examples that illustrate Ellsberg's paradox. We will use the category theory to get a bird's eye view of the hierarchical structure of uncertainty. We discuss maps between uncertainty spaces and maps between U-sequences, seeing that they form categories of uncertainty spaces and the category of U-sequences, respectively. We construct an endofunctor of the category of measurable spaces in order to embed a given U-sequence into it. Then, by the iterative application of the endofunctor, we construct the universal uncertainty space which may be able to serve as a basis for multi-layer uncertainty theory.
A selective review of recent developments in spatially variable gene detection for spatial transcriptomics
Authors: Sikta Das Adhikari, Jiaxin Yang, Jianrong Wang, Yuehua Cui
Date: 2023-11-23 00:00:00

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.
Incomplete resection of the icEEG seizure onset zone is not associated with post-surgical outcomes
Authors: Sarah J. Gascoigne, Nathan Evans, Gerard Hall, Csaba Kozma, Mariella Panagiotopoulou, Gabrielle M. Schroeder, Callum Simpson, Christopher Thornton, Frances Turner, Heather Woodhouse, Jess Blickwedel, Fahmida Chowdhury, Beate Diehl, John S. Duncan, Ryan Faulder, Rhys H. Thomas, Kevin Wilson, Peter N. Taylor, Yujiang Wang
Date: 2023-11-24 00:00:00
Tasks: EEG

Delineation of seizure onset regions from EEG is important for effective surgical workup. However, it is unknown if their complete resection is required for seizure freedom, or in other words, if post-surgical seizure recurrence is due to incomplete removal of the seizure onset regions. Retrospective analysis of icEEG recordings from 63 subjects (735 seizures) identified seizure onset regions through visual inspection and algorithmic delineation. We analysed resection of onset regions and correlated this with post-surgical seizure control. Most subjects had over half of onset regions resected (70.7% and 60.5% of subjects for visual and algorithmic methods, respectively). In investigating spatial extent of onset or resection, and presence of diffuse onsets, we found no substantial evidence of association with post-surgical seizure control (all AUC<0.7, p>0.05). Seizure onset regions tends to be at least partially resected, however a less complete resection is not associated with worse post-surgical outcome. We conclude that seizure recurrence after epilepsy surgery is not necessarily a result of failing to completely resect the seizure onset zone, as defined by icEEG. Other network mechanisms must be involved, which are not limited to seizure onset regions alone.