pwc-archive/papers-with-abstracts huggingface dataset:
The Price of Empire: Unrest Location and Sovereign Risk in Tsarist Russia
Authors: Christopher A. Hartwell, Paul M. Vaaler
Date: 2023-09-13 00:00:00

Research on politically motivated unrest and sovereign risk overlooks whether and how unrest location matters for sovereign risk in geographically extensive states. Intuitively, political violence in the capital or nearby would seem to directly threaten the state's ability to pay its debts. However, it is possible that the effect on a government could be more pronounced the farther away the violence is, connected to the longer-term costs of suppressing rebellion. We use Tsarist Russia to assess these differences in risk effects when unrest occurs in Russian homeland territories versus more remote imperial territories. Our analysis of unrest events across the Russian imperium from 1820 to 1914 suggests that unrest increases risk more in imperial territories. Echoing current events, we find that unrest in Ukraine increases risk most. The price of empire included higher costs in projecting force to repress unrest and retain the confidence of the foreign investors financing those costs.
How to foster innovation in the social sciences? Qualitative evidence from focus group workshops at Oxford University
Authors: Fabian Braesemann, Moritz Marpe
Date: 2023-09-13 00:00:00

This report addresses challenges and opportunities for innovation in the social sciences at the University of Oxford. It summarises findings from two focus group workshops with innovation experts from the University ecosystem. Experts included successful social science entrepreneurs and professional service staff from the University. The workshops focused on four different dimensions related to innovative activities and commercialisation. The findings show several challenges at the institutional and individual level, together with features of the social scientific discipline that impede more innovation in the social sciences. Based on identifying these challenges, we present potential solutions and ways forward identified in the focus group discussions to foster social science innovation. The report aims to illustrate the potential of innovation and commercialisation of social scientific research for both researchers and the university.
A Reexamination of Proof Approaches for the Impossibility Theorem
Authors: Kazuya Yamamoto
Date: 2023-09-13 00:00:00
Tasks: valid

The decisive-set and pivotal-voter approaches have been used to prove Arrow's impossibility theorem. This study presents a proof using a proof calculus in logic. A valid deductive inference between the premises, the axioms and conditions of the theorem, and the conclusion, dictatorship, guarantees that every profile of all possible social welfare functions is examined, thereby establishing the theorem.
Not obviously manipulable allotment rules
Authors: R. Pablo Arribillaga, Agustin G. Bonifacio
Date: 2023-09-12 00:00:00
Tasks: Fairness

In the problem of allocating a single non-disposable commodity among agents whose preferences are single-peaked, we study a weakening of strategy-proofness called not obvious manipulability (NOM). If agents are cognitively limited, then NOM is sufficient to describe their strategic behavior. We characterize a large family of own-peak-only rules that satisfy efficiency, NOM, and a minimal fairness condition. We call these rules "simple". In economies with excess demand, simple rules fully satiate agents whose peak amount is less than or equal to equal division and assign, to each remaining agent, an amount between equal division and his peak. In economies with excess supply, simple rules are defined symmetrically. These rules can be thought of as a two-step procedure that involves solving a claims problem. We also show that the single-plateaued domain is maximal for the characterizing properties of simple rules. Therefore, even though replacing strategy-proofness with NOM greatly expands the family of admissible rules, the maximal domain of preferences involved remains basically unaltered.
Improving HEVC Encoding of Rendered Video Data Using True Motion Information
Authors: Christian Herglotz, David Müller, Andreas Weinlich, Frank Bauer, Michael Ortner, Marc Stamminger, André Kaup
Date: 2023-09-13 00:00:00
Tasks: Motion Estimation

This paper shows that motion vectors representing the true motion of an object in a scene can be exploited to improve the encoding process of computer generated video sequences. Therefore, a set of sequences is presented for which the true motion vectors of the corresponding objects were generated on a per-pixel basis during the rendering process. In addition to conventional motion estimation methods, it is proposed to exploit the computer generated motion vectors to enhance the ratedistortion performance. To this end, a motion vector mapping method including disocclusion handling is presented. It is shown that mean rate savings of 3.78% can be achieved.
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies
Authors: Jorge Vasquez, Hemant K. Sharma, Tomotake Furuhata, Kenji Shimada
Date: 2023-09-13 00:00:00
Tasks: Defect Detection, Image Augmentation, Image Enhancement

Detecting subtle defects in window frames, including dents and scratches, is vital for upholding product integrity and sustaining a positive brand perception. Conventional machine vision systems often struggle to identify these defects in challenging environments like construction sites. In contrast, modern vision systems leveraging machine and deep learning (DL) are emerging as potent tools, particularly for cosmetic inspections. However, the promise of DL is yet to be fully realized. A few manufacturers have established a clear strategy for AI integration in quality inspection, hindered mainly by issues like scarce clean datasets and environmental changes that compromise model accuracy. Addressing these challenges, our study presents an innovative approach that amplifies defect detection in DL models, even with constrained data resources. The paper proposes a new defect detection pipeline called InspectNet (IPT-enhanced UNET) that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset and a Unet model tuned for window frame defect detection and segmentation. Experiments were carried out using a Spot Robot doing window frame inspections . 16 variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that, on average, across all proposed evaluation measures, Unet outperformed all other algorithms when IPT-enhanced augmentations were applied. In particular, when using the best dataset, the average Intersection over Union (IoU) values achieved were IPT-enhanced Unet, reaching 0.91 of mIoU.
Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration
Authors: Fanwen Wang, Pedro F. Ferreira, Yinzhe Wu, Camila Munoz, Ke Wen, Yaqing Luo, Jiahao Huang, Dudley J. Pennell, Andrew D. Scott, Sonia Nielles-Vallespin, Guang Yang
Date: 2023-09-12 00:00:00
Tasks: Decoder, Image Registration

Diffusion tensor cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure. Image registration is essential to correct image shifts due to intra and inter breath-hold motion and imperfect cardiac triggering. Registration is challenging in DT-CMR due to the low signal-to-noise and various contrasts induced by the diffusion encoding in the myocardium and surrounding organs. Traditional deformable registration corrects through-plane motion but at the risk of destroying the texture information while rigid registration inefficiently discards frames with local deformation. In this study, we explored the possibility of deep learning-based deformable registration on DT-CMR. Based on the noise suppression using low-rank features and diffusion encoding suppression using variational auto encoder-decoder, a B-spline based registration network extracted the displacement fields and maintained the texture features of DT-CMR. In this way, our method improved the efficiency of frame utilization, manual cropping, and computational speed.
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation
Authors: Xiaoyong Ni, Guy Revach, Nir Shlezinger
Date: 2023-09-13 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, State Estimation

Combining the classical Kalman filter (KF) with a deep neural network (DNN) enables tracking in partially known state space (SS) models. A major limitation of current DNN-aided designs stems from the need to train them to filter data originating from a specific distribution and underlying SS model. Consequently, changes in the model parameters may require lengthy retraining. While the KF adapts through parameter tuning, the black-box nature of DNNs makes identifying tunable components difficult. Hence, we propose Adaptive KalmanNet (AKNet), a DNN-aided KF that can adapt to changes in the SS model without retraining. Inspired by recent advances in large language model fine-tuning paradigms, AKNet uses a compact hypernetwork to generate context-dependent modulation weights. Numerical evaluation shows that AKNet provides consistent state estimation performance across a continuous range of noise distributions, even when trained using data from limited noise settings.
6G Radio Testbeds: Requirements, Trends, and Approaches
Authors: Gilles Callebaut, Liang Liu, Thomas Eriksson, Liesbet Van der Perre, Ove Edfors, Christian Fager
Date: 2023-09-13 00:00:00

The proof of the pudding is in the eating - that is why 6G testbeds are essential in the progress towards the next generation of wireless networks. Theoretical research towards 6G wireless networks is proposing advanced technologies to serve new applications and drastically improve the energy performance of the network. Testbeds are indispensable to validate these new technologies under more realistic conditions. This paper clarifies the requirements for 6G radio testbeds, reveals trends, and introduces approaches towards their development.
Intelligent Reflective Surface Assisted Integrated Sensing and Wireless Power Transfer
Authors: Zheng Li, Zhengyu Zhu, Zheng Chu, Yingying Guan, De Mi, Fan Liu, Lie-Liang Yang
Date: 2023-09-13 00:00:00

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. In light of the performance tradeoff between energy harvesting and sensing, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first one involves a semi-definite program technique, while the second one features a low-complexity optimization algorithm based on successive convex approximation and majorization minimization. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems.
Low-complexity hardware and algorithm for joint communication and sensing
Authors: Andrea Bedin, Shaghayegh Shahcheraghi, Traian E. Abrudan, Arash Asadi
Date: 2023-09-13 00:00:00

Joint Communication and Sensing (JCAS) is foreseen as one very distinctive feature of the emerging 6G systems providing, in addition to fast end reliable communication, the ability to obtain an accurate perception of the physical environment. In this paper, we propose a JCAS algorithm that exploits a novel beamforming architecture, which features a combination of wideband analog and narrowband digital beamforming. This allows accurate estimation of Time of Arrival (ToA), exploiting the large bandwidth and Angle of Arrival (AoA), exploiting the high-rank digital beamforming. In our proposal, we separately estimate the ToA and AoA. The association between ToA and AoA is solved by acquiring multiple non-coherent frames and adding up the signal from each frame such that a specific component is combined coherently before the AoA estimation. Consequently, this removes the need to use 2D and 3D joint estimation methods, thus significantly lowering complexity. The resolution performance of the method is compared with that of 2D MUltiple SIgnal Classification (2D-MUSIC) algorithm, using a fully-digital wideband beamforming architecture. The results show that the proposed method can achieve performance similar to a fully-digital high-bandwidth system, while requiring a fraction of the total aggregate sampling rate and having much lower complexity.
Mobile Object Tracking in Panoramic Video and LiDAR for Radiological Source-Object Attribution and Improved Source Detection
Authors: M. R. Marshall, R. J. Cooper, J. C. Curtis, D. Hellfeld, T. H. Y. Joshi, M. Salathe, K. Vetter
Date: 2023-09-12 00:00:00
Tasks: Object, object-detection, Object Detection, Object Tracking, Sensitivity

The addition of contextual sensors to mobile radiation sensors provides valuable information about radiological source encounters that can assist in adjudication of alarms. This study explores how computer-vision based object detection and tracking analyses can be used to augment radiological data from a mobile detector system. We study how contextual information (streaming video and LiDAR) can be used to associate dynamic pedestrians or vehicles with radiological alarms to enhance both situational awareness and detection sensitivity. Possible source encounters were staged in a mock urban environment where participants included pedestrians and vehicles moving in the vicinity of an intersection. Data was collected with a vehicle equipped with 6 NaI(Tl) 2 inch times 4 inch times 16 inch detectors in a hexagonal arrangement and multiple cameras, LiDARs, and an IMU. Physics-based models that describe the expected count rates from tracked objects are used to correlate vehicle and/or pedestrian trajectories to measured count-rate data through the use of Poisson maximum likelihood estimation and to discern between source-carrying and non-source-carrying objects. In this work, we demonstrate the capabilities of our source-object attribution approach as applied to a mobile detection system in the presence of moving sources to improve both detection sensitivity and situational awareness in a mock urban environment.
Homeostasis in Gene Regulatory Networks
Authors: Fernando Antoneli, Martin Golubitsky, Jiaxin Jin, Ian Stewart
Date: 2023-09-12 00:00:00

In this paper, we use the framework of infinitesimal homeostasis to study general design principles for the occurrence of homeostasis in gene regulatory networks. We assume that the dynamics of the genes explicitly includes both transcription and translation, keeping track of both mRNA and protein concentrations. Given a GRN we construct an associated Protein-mRNA Network (PRN), where each individual (mRNA and protein) concentration corresponds to a node and the edges are defined in such a way that the PRN becomes a bipartite directed graph. By simultaneously working with the GRN and the PRN we are able to apply our previous results about the classification of homeostasis types (i.e., topologically defined homeostasis generating mechanism) and their corresponding homeostasis patterns. Given an arbitrarily large and complex GRN $\mathcal{G}$ and its associated PRN $\mathcal{R}$, we obtain a correspondence between all the homeostasis types (and homeostasis patterns) of $\mathcal{G}$ and a subset the homeostasis types (and homeostasis patterns) of $\mathcal{R}$. Moreover, we completely characterize the homeostasis types of the PRN that do not have GRN counterparts.
Matrix-analytic methods for the evolution of species trees, gene trees, and their reconciliation
Authors: Albert C. Soewongsono, Jiahao Diao, Tristan Stark, Amanda E. Wilson, David A. Liberles, Barbara R. Holland, Malgorzata M. O'Reilly
Date: 2023-09-12 00:00:00

We consider the reconciliation problem, in which the task is to find a mapping of a gene tree into a species tree, so as to maximize the likelihood of such fitting, given the available data. We describe a model for the evolution of the species tree, a subfunctionalisation model for the evolution of the gene tree, and provide an algorithm to compute the likelihood of the reconciliation. We derive our results using the theory of matrix-analytic methods and describe efficient algorithms for the computation of a range of useful metrics. We illustrate the theory with examples and provide the physical interpretations of the discussed quantities, with a focus on the practical applications of the theory to incomplete data.
Regulation of store-operated calcium entry
Authors: Goutham Kodakandla, Askar Akimzhanov, Darren Boehning
Date: 2023-09-13 00:00:00

Plasma membrane calcium influx through ion channels is crucial for many events in cellular physiology. Cell surface stimuli lead to the production of inositol 1,4,5-trisphosphate (IP3), which binds to IP3 receptors in the endoplasmic reticulum (ER) to release calcium pools from the ER lumen. This leads to depletion of ER calcium pools which has been termed store-depletion. Store-depletion leads the dissociation of calcium ions from the EF-hand motif of the ER calcium sensor Stromal Interaction Molecule 1 (STIM1). This leads to a conformational change in STIM1 which helps it to interact with a plasma membrane (PM) at ER:PM junctions. At these ER:PM junctions, STIM1 binds to and activates a calcium channel known as Orai1 to form calcium-release activated calcium (CRAC) channels. Activation of Orai1 leads to calcium influx, known as store-operated calcium entry (SOCE). In addition to Orai1 and STIM1, the homologs of Orai1 and STIM1, such as Orai2/3 and STIM2 also play a crucial role in calcium homeostasis. The influx of calcium through the Orai channel activates a calcium current that has been termed CRAC currents. CRAC channels form multimers and cluster together in large macromolecular assemblies termed puncta. How these CRAC channels form puncta has been contentious since their discovery. In this review, we will outline the history of SOCE, the molecular players involved in this process (Orai and STIM proteins, TRP channels, SOCE-associated regulatory factor etc.), as well as the models that have been proposed to explain this important mechanism in cellular physiology.
Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models
Authors: Natalie M. Isenberg, Susan D. Mertins, Byung-Jun Yoon, Kristofer Reyes, Nathan M. Urban
Date: 2023-09-12 00:00:00
Tasks: Experimental Design, Prediction, Uncertainty Quantification

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.
Modelling individual motion sickness accumulation in vehicles and driving simulators
Authors: Varun Kotian, Daan M. Pool, Riender Happee
Date: 2023-09-13 00:00:00

Users of automated vehicles will move away from being drivers to passengers, preferably engaged in other activities such as reading or using laptops and smartphones, which will strongly increase susceptibility to motion sickness. Similarly, in driving simulators, the presented visual motion with scaled or even without any physical motion causes an illusion of passive motion, creating a conflict between perceived and expected motion, and eliciting motion sickness. Given the very large differences in sickness susceptibility between individuals, we need to consider sickness at an individual level. This paper combines a group-averaged sensory conflict model with an individualized accumulation model to capture individual differences in motion sickness susceptibility across various vision conditions. The model framework can be used to develop personalized models for users of automated vehicles and improve the design of new motion cueing algorithms for simulators. The feasibility and accuracy of this model framework are verified using two existing datasets with sickening. Both datasets involve passive motion, representative of being driven by an automated vehicle. The model is able to fit an individuals motion sickness responses using only 2 parameters (gain K1 and time constant T1), as opposed to the 5 parameters in the original model. This ensures unique parameters for each individual. Better fits, on average by a factor of 1.7 of an individuals motion sickness levels, are achieved as compared to using only the group-averaged model. Thus, we find that models predicting group-averaged sickness incidence cannot be used to predict sickness at an individual level. On the other hand, the proposed combined model approach predicts individual motion sickness levels and thus can be used to control sickness.
Schizophrenia research under the framework of predictive coding: body, language, and others
Authors: Lingyu Li, Chunbo Li
Date: 2023-09-13 00:00:00

Although there have been so many studies on schizophrenia under the framework of predictive coding, works focusing on treatment are very preliminary. A model-oriented, operationalist, and comprehensive understanding of schizophrenia would promote the therapy turn of further research. We summarize predictive coding models of embodiment, co-occurrence of over- and under-weighting priors, subjective time processing, language production or comprehension, self-or-other inference, and social interaction. Corresponding impairments and clinical manifestations of schizophrenia are reviewed under these models at the same time. Finally, we discuss why and how to inaugurate a therapy turn of further research under the framework of predictive coding.