pwc-archive/papers-with-abstracts huggingface dataset:
Toward environmental sustainability: an empirical study on airports efficiency
Authors: Riccardo Gianluigi Serio, Maria Michela Dickson, Diego Giuliani, Giuseppe Espa
Date: 2022-10-06 00:00:00
Tasks: Management

The transition to more environmentally sustainable production processes and managerial practices is an increasingly important topic. Many industries need to undergo radical change to meet environmental sustainability requirements; the tourism industry is no exception. In this respect, a particular aspect that needs further attention is the relationship between airport performances and investments in environmental sustainability policies. This work represents a first attempt to provide empirical evidences about this relationship. Through the application of a non-parametrical method, we first assess the efficiency of the Italian airports industry. Secondly, we investigated the relationship between airports performance and management commitment toward the ecological transition using a Tobit regression model. The results show that airports adherence to formal multi-year ecological transition programs has a positive and consistent impact on their performance.
The Local to Unity Dynamic Tobit Model
Authors: Anna Bykhovskaya, James A. Duffy
Date: 2022-10-05 00:00:00
Tasks: model, Time Series, Time Series Analysis, Unity

This paper considers highly persistent time series that are subject to nonlinearities in the form of censoring or an occasionally binding constraint, such as are regularly encountered in macroeconomics. A tractable candidate model for such series is the dynamic Tobit with a root local to unity. We show that this model generates a process that converges weakly to a non-standard limiting process, that is constrained (regulated) to be positive. Surprisingly, despite the presence of censoring, the OLS estimators of the model parameters are consistent. We show that this allows OLS-based inferences to be drawn on the overall persistence of the process (as measured by the sum of the autoregressive coefficients), and for the null of a unit root to be tested in the presence of censoring. Our simulations illustrate that the conventional ADF test substantially over-rejects when the data is generated by a dynamic Tobit with a unit root, whereas our proposed test is correctly sized. We provide an application of our methods to testing for a unit root in the Swiss franc / euro exchange rate, during a period when this was subject to an occasionally binding lower bound.
Bikeability and the induced demand for cycling
Authors: Mogens Fosgerau, Miroslawa Lukawska, Mads Paulsen, Thomas Kjær Rasmussen
Date: 2022-10-05 00:00:00
Tasks: counterfactual

To what extent is the volume of urban bicycle traffic affected by the provision of bicycle infrastructure? In this study, we exploit a large dataset of observed bicycle trajectories in combination with a fine-grained representation of the Copenhagen bicycle-relevant network. We apply a novel model for bicyclists' choice of route from origin to destination that takes the complete network into account. This enables us to determine bicyclists' preferences for a range of infrastructure and land-use types. We use the estimated preferences to compute a subjective cost of bicycle travel, which we correlate with the number of bicycle trips across a large number of origin-destination pairs. Simulations suggest that the extensive Copenhagen bicycle lane network has caused the number of bicycle trips and the bicycle kilometers traveled to increase by 60% and 90%, respectively, compared with a counterfactual without the bicycle lane network. This translates into an annual benefit of EUR 0.4M per km of bicycle lane owing to changes in subjective travel cost, health, and accidents. Our results thus strongly support the provision of bicycle infrastructure.
From Rules to Regs: A Structural Topic Model of Collusion Research
Authors: W. Benedikt Schmal
Date: 2022-10-06 00:00:00
Tasks: Time Series, Time Series Analysis

Collusive practices of firms continue to be a major threat to competition and consumer welfare. Academic research on this topic aims at understanding the economic drivers and behavioral patterns of cartels, among others, to guide competition authorities on how to tackle them. Utilizing topical machine learning techniques in the domain of natural language processing enables me to analyze the publications on this issue over more than 20 years in a novel way. Coming from a stylized oligopoly-game theory focus, researchers recently turned toward empirical case studies of bygone cartels. Uni- and multivariate time series analyses reveal that the latter did not supersede the former but filled a gap the decline in rule-based reasoning has left. Together with a tendency towards monocultures in topics covered and an endogenous constriction of the topic variety, the course of cartel research has changed notably: The variety of subjects included has grown, but the pluralism in economic questions addressed is in descent. It remains to be seen whether this will benefit or harm the cartel detection capabilities of authorities in the future.
Fully Unsupervised Training of Few-shot Keyword Spotting
Authors: Dongjune Lee, Minchan Kim, Sung Hwan Mun, Min Hyun Han, Nam Soo Kim
Date: 2022-10-06 00:00:00
Tasks: Keyword Spotting, Metric Learning, Speech Synthesis

For training a few-shot keyword spotting (FS-KWS) model, a large labeled dataset containing massive target keywords has known to be essential to generalize to arbitrary target keywords with only a few enrollment samples. To alleviate the expensive data collection with labeling, in this paper, we propose a novel FS-KWS system trained only on synthetic data. The proposed system is based on metric learning enabling target keywords to be detected using distance metrics. Exploiting the speech synthesis model that generates speech with pseudo phonemes instead of texts, we easily obtain a large collection of multi-view samples with the same semantics. These samples are sufficient for training, considering metric learning does not intrinsically necessitate labeled data. All of the components in our framework do not require any supervision, making our method unsupervised. Experimental results on real datasets show our proposed method is competitive even without any labeled and real datasets.
LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation
Authors: Muhammad F. A. Chaudhary, Sarah E. Gerard, Gary E. Christensen, Christopher B. Cooper, Joyce D. Schroeder, Eric A. Hoffman, Joseph M. Reinhardt
Date: 2022-10-06 00:00:00
Tasks: Computed Tomography (CT), Image-to-Image Translation, Style Transfer

Chest computed tomography (CT) at inspiration is often complemented by an expiratory CT to identify peripheral airways disease. Additionally, co-registered inspiratory-expiratory volumes can be used to derive various markers of lung function. Expiratory CT scans, however, may not be acquired due to dose or scan time considerations or may be inadequate due to motion or insufficient exhale; leading to a missed opportunity to evaluate underlying small airways disease. Here, we propose LungViT - a generative adversarial learning approach using hierarchical vision transformers for translating inspiratory CT intensities to corresponding expiratory CT intensities. LungViT addresses several limitations of the traditional generative models including slicewise discontinuities, limited size of generated volumes, and their inability to model texture transfer at volumetric level. We propose a shifted-window hierarchical vision transformer architecture with squeeze-and-excitation decoder blocks for modeling dependencies between features. We also propose a multiview texture similarity distance metric for texture and style transfer in 3D. To incorporate global information into the training process and refine the output of our model, we use ensemble cascading. LungViT is able to generate large 3D volumes of size 320 x 320 x 320. We train and validate our model using a diverse cohort of 1500 subjects with varying disease severity. To assess model generalizability beyond the development set biases, we evaluate our model on an out-of-distribution external validation set of 200 subjects. Clinical validation on internal and external testing sets shows that synthetic volumes could be reliably adopted for deriving clinical endpoints of chronic obstructive pulmonary disease.
SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction
Authors: Yuyang Hu, Weijie Gan, Chunwei Ying, Tongyao Wang, Cihat Eldeniz, Jiaming Liu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov
Date: 2022-10-05 00:00:00
Tasks: Self-Supervised Learning, Sensitivity

Deep model-based architectures (DMBAs) integrating physical measurement models and learned image regularizers are widely used in parallel magnetic resonance imaging (PMRI). Traditional DMBAs for PMRI rely on pre-estimated coil sensitivity maps (CSMs) as a component of the measurement model. However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled. Additionally, traditional training of DMBAs requires high-quality groundtruth images, limiting their use in applications where groundtruth is difficult to obtain. This paper addresses these issues by presenting SPICE as a new method that integrates self-supervised learning and automatic coil sensitivity estimation. Instead of using pre-estimated CSMs, SPICE simultaneously reconstructs accurate MR images and estimates high-quality CSMs. SPICE also enables learning from undersampled noisy measurements without any groundtruth. We validate SPICE on experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to 10x).
Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks
Authors: Xiaofeng Lei, Shaohua Li, Xinxing Xu, Huazhu Fu, Yong liu, Yih-Chung Tham, Yangqin Feng, Mingrui Tan, Yanyu Xu, Jocelyn Hui Lin Goh, Rick Siow Mong Goh, Ching-Yu Cheng
Date: 2022-09-25 00:00:00
Tasks: Medical Image Analysis, object-detection, Object Detection

Localizing anatomical landmarks are important tasks in medical image analysis. However, the landmarks to be localized often lack prominent visual features. Their locations are elusive and easily confused with the background, and thus precise localization highly depends on the context formed by their surrounding areas. In addition, the required precision is usually higher than segmentation and object detection tasks. Therefore, localization has its unique challenges different from segmentation or detection. In this paper, we propose a zoom-in attentive network (ZIAN) for anatomical landmark localization in ocular images. First, a coarse-to-fine, or "zoom-in" strategy is utilized to learn the contextualized features in different scales. Then, an attentive fusion module is adopted to aggregate multi-scale features, which consists of 1) a co-attention network with a multiple regions-of-interest (ROIs) scheme that learns complementary features from the multiple ROIs, 2) an attention-based fusion module which integrates the multi-ROIs features and non-ROI features. We evaluated ZIAN on two open challenge tasks, i.e., the fovea localization in fundus images and scleral spur localization in AS-OCT images. Experiments show that ZIAN achieves promising performances and outperforms state-of-the-art localization methods. The source code and trained models of ZIAN are available at https://github.com/leixiaofeng-astar/OMIA9-ZIAN.
Evaluation of stationarity regions in measured non-WSSUS 60 GHz mmWave V2V channels
Authors: Danilo Radovic, Herbert Groll, Christoph F. Mecklenbräuker
Date: 2022-10-06 00:00:00

Due to high mobility in multipath propagation environments, vehicle-to-vehicle (V2V) channels are generally time and frequency variant. Therefore, the criteria for wide-sense stationarity (WSS) and uncorrelated scattering (US) are just satisfied over very limited intervals in the time and frequency domains, respectively. We test the validity of these criteria in measured vehicular 60 GHz millimeter wave (mmWave) channels, by estimating the local scattering functions (LSFs) from the measured data. Based on the variation of the LSFs, we define time-frequency stationarity regions, over which the WSSUS assumption is assumed to be fulfilled approximately. We analyze and compare both line-of-sight (LOS) and non-LOS (NLOS) V2V communication conditions. We observe large stationarity regions for channels with a dominant LOS connection, without relative movement between the transmitting and receiving vehicle. In the same measured urban driving scenario, modified by eliminating the LOS component in the post-processing, the channel is dominated by specular components reflected from an overpassing vehicle with a relative velocity of 56 km/h. Here, we observe a stationarity bandwidth of 270 MHz. Furthermore, the NLOS channel, dominated by a single strong specular component, shows a relatively large average stationarity time of 16 ms, while the stationarity time for the channel with a rich multipath profile is much shorter, in the order of 5 ms.
On Integrated Sensing and Communication Waveforms with Tunable PAPR
Authors: Ahmad Bazzi, Marwa Chafii
Date: 2022-10-06 00:00:00
Tasks: Integrated sensing and communication

We present a novel approach to the problem of dual-functional radar and communication (DFRC) waveform design with adjustable peak-to-average power ratio (PAPR), while minimizing the multi-user communication interference and maintaining a similarity constraint towards a radar chirp signal. The approach is applicable to generic radar chirp signals and for different constellation sizes. We formulate the waveform design problem as a non convex optimization problem. As a solution, we adopt the alternating direction method of multipliers (ADMM), hence iterating towards a stable waveform for both radar and communication purposes. Additionally, we prove convergence of the proposed method and analyze its computational complexity. Moreover, we offer an extended version of the method to cope with imperfect channel state information (CSI). Finally, we demonstrate its superior performance through simulations, in comparison to state-of-the-art radar-communication waveform designs.
Stochastic Resonance in Climate Reddening Increases the Risk of Cyclic Ecosystem Extinction via Phase-tipping
Authors: Hassan Alkhayuon, Jessa Marley, Sebastian Wieczorek, Rebecca C. Tyson
Date: 2022-10-06 00:00:00

Human activity is leading to changes in the mean and variability of climatic parameters in most locations around the world. The changing mean has received considerable attention from scientists and climate policy makers. However, recent work indicates that the changing variability, that is, the amplitude and the temporal autocorrelation of deviations from the mean, may have greater and more imminent impact on ecosystems. In this paper, we demonstrate that changes in climate variability alone could drive cyclic predator-prey ecosystems to extinction via so-called phase tipping (P-tipping), a new type of instability that occurs only from certain phases of the predator-prey cycle. We construct a mathematical model of a variable climate and couple it to two self-oscillating paradigmatic predator-prey models. Most importantly, we combine realistic parameter values for the Canada lynx and snowshoe hare with actual climate data from the boreal forest. In this way, we demonstrate that critically important species in the boreal forest have increased likelihood of P-tipping to extinction under predicted changes in climate variability, and are most vulnerable during stages of the cycle when the predator population is near its maximum. Furthermore, our analysis reveals that stochastic resonance is the underlying mechanism for the increased likelihood of P-tipping to extinction.
Dueling Convex Optimization with General Preferences
Authors: Aadirupa Saha, Tomer Koren, Yishay Mansour
Date: 2022-09-27 00:00:00

We address the problem of \emph{convex optimization with dueling feedback}, where the goal is to minimize a convex function given a weaker form of \emph{dueling} feedback. Each query consists of two points and the dueling feedback returns a (noisy) single-bit binary comparison of the function values of the two queried points. The translation of the function values to the single comparison bit is through a \emph{transfer function}. This problem has been addressed previously for some restricted classes of transfer functions, but here we consider a very general transfer function class which includes all functions that can be approximated by a finite polynomial with a minimal degree $p$. Our main contribution is an efficient algorithm with convergence rate of $\smash{\widetilde O}(\epsilon^{-4p})$ for a smooth convex objective function, and an optimal rate of $\smash{\widetilde O}(\epsilon^{-2p})$ when the objective is smooth and strongly convex.
Nanoscopic distribution of VAChT and VGLUT3 in striatal cholinergic varicosities suggests colocalization and segregation of the two transporters in synaptic vesicles
Authors: Paola Cristofari, Mazarine Desplanque, Odile Poirel, Alison Hébert, Sylvie Dumas, Etienne Herzog, Lydia Danglot, David Geny, Jean-François Gilles, Audrey Geeverding, Susanne Bolte, Alexis Canette, Michaël Trichet, Véronique Fabre, Stéphanie Daumas, Nicolas Pietrancosta, Salah El Mestikawy, Véronique Bernard
Date: 2022-10-06 00:00:00
Tasks: Open-Ended Question Answering, Super-Resolution

Striatal cholinergic interneurons (CINs) use acetylcholine (ACh) and glutamate (Glut) to regulate the striatal network since they express vesicular transporters for ACh (VAChT) and Glut (VGLUT3). However, whether ACh and Glut are released simultaneously and/or independently from cholinergic varicosities is an open question. The answer to that question requires the multichannel detection of vesicular transporters at the level of single synaptic vesicle (SV). Here, we used super-resolution STimulated Emission Depletion microscopy (STED) to characterize and quantify the distribution of VAChT and VGLUT3 in CINs SVs. Nearest-neighbor distances analysis between VAChT and VGLUT3-immunofluorescent spots revealed that 34% of CINs SVs contain both VAChT and VGLUT3. In addition, 40% of SVs expressed only VAChT while 26% of SVs contain only VGLUT3. These results suggest that SVs from CINs have the potential to store simultaneously or independently ACh and/or Glut. Overall, these morphological findings support the notion that CINs varicosities can signal with either ACh or Glut or both with an unexpected level of complexity.
See and Copy: Generation of complex compositional movements from modular and geometric RNN representations
Authors: Sunny Duan, Mikail Khona, Adrian Bertagnoli, Sarthak Chandra, Ila Fiete
Date: 2022-10-05 00:00:00

A hallmark of biological intelligence and control is combinatorial generalization: animals are able to learn various things, then piece them together in new combinations to produce appropriate outputs for new tasks. Inspired by the ability of primates to readily imitate seen movement sequences, we present a model of motor control using a realistic model of arm dynamics, tasked with imitating a guide that makes arbitrary two-segment drawings. We hypothesize that modular organization is one of the keys to such flexible and generalizable control. We construct a modular control model consisting of separate encoding and motor RNNs and a scheduler, which we train end-to-end on the task. We show that the modular structure allows the model to generalize not only to unseen two-segment trajectories, but to new drawings consisting of many more segments than it was trained on, and also allows for rapid adaptation to perturbations. Finally, our model recapitulates experimental observations of the preparatory and execution-related processes unfolding during motor control, providing a normative explanation for functional segregation of preparatory and execution-related activity within the motor cortex.
Plant evolution on rock outcrops and cliffs: contrasting patterns of diversification following edaphic specialization
Authors: Isaac Lichter-Marck
Date: 2022-10-05 00:00:00
Tasks: Management

Sheer cliffs on mountains and in deep canyons are among the worlds most iconic landmarks but our understanding of the enigmatic flora that lives in these vertical rock landscapes remains fragmented. In this article, I review and synthesize recent studies on the evolution of specialization onto bare rock and its consequences for plant diversification. Putative adaptations commonly associated with growth on bare rock include specialized root structures, stress tolerant leaf traits, and reduced dispersibility. Fitness tradeoffs are a principal explanation for edaphic specialization, but adaptation to bare environments stands apart as a precursor environment to specialization in other stressful habitats, such as chemically harsh soils. In species level phylogenies, many rock specialist plants are evolutionarily isolated and form the sister lineage to congeners found on decomposed substrates, suggesting that specialization onto bare rock may be an evolutionary trap or dead end. In other cases, rock specialists form diverse clades, suggesting archipelago speciation or ecological release enabled by innovations in stress tolerance. Today, in environments severely impacted by poor management and introduced organisms, such as islands, cliffs serve as important refuges for stress tolerant endangered plants. New technology for climbing safety and drone assisted reconnaissance open new possibilities for the discovery and conservation of cliff plants in these landscapes but come with the inherent risks of increasing their accessibility. The future success of efforts to save plants from extinction may depend on our understanding of the unique and resilient flora endemic to cliffy places.
Morpho-anatomical characterization of the urogenital schistosmiasis vector Bulinus truncatus (Audouin, 1827) (Heterobranchia : Bulinidae) from Southwestern Europe
Authors: Alberto Martínez-Ortí, Sonia Adam, Giovanni Garippa, Jérôme Boissier, M Dolores Bargues, Santiago Mas-Coma
Date: 2022-10-05 00:00:00

Urogenital schistosomiasis has been present naturally in the South of Europe since the beginning of the 20 th century and nowadays its presence is also known, at least imported by Sub-Saharan emigrants and tourists, in France, Italy, Portugal and Spain. One of the intermediate hosts of this trematode present in Europe is the bulinid mollusc Bulinus truncatus, non-native species that can be reached to Europe by humans and birds. In order to know this mollusc better, we carried out a morpho-anatomical study, of the shell, the reproductive system, radula, the respiratory organs and pseudobranch of several populations from Italy, France and Spain. Spanish conchological material studied comes from different populations, from material deposited in the "Museo Nacional de Ciencias Naturales" of Madrid and the "Museu de Ci{\`e}ncies Naturals" of Barcelona, as well as from its own material deposited in the "Museu Valenci{\`a} d'Hist{\`o}ria Natural" of Alginet (Valencia). The shell growth in captivity and the estimation of the population age of B. truncatus from El Ejido (Almer{\'i}a, Spain), has also been studied. Finally, the finding of aphallic and euphallic specimens in the different populations of southern Europe studied is presented and taxonomic and ecological data of the genus Bulinus are shown.
Absent daddy, but important father
Authors: Nathanaël Jacquier, Thomas Widiez
Date: 2022-10-06 00:00:00

Mixing maternal and paternal genomes is the base of plant sexual reproduction, but some so-called 'haploid inducer lines' lead to the formation of seeds bearing well-developed embryos with solely the maternal genome. A recent study adds a new piece to the puzzle of this enigmatic in planta haploid embryo induction process.