pwc-archive/papers-with-abstracts huggingface dataset:
Climate change heterogeneity: A new quantitative approach
Authors: Maria Dolores Gadea, Jesus Gonzalo
Date: 2023-01-06 00:00:00

Climate change is a non-uniform phenomenon. This paper proposes a new quantitative methodology to characterize, measure, and test the existence of climate change heterogeneity. It consists of three steps. First, we introduce a new testable warming typology based on the evolution of the trend of the whole temperature distribution and not only on the average. Second, we define the concepts of warming acceleration and warming amplification in a testable format. And third, we introduce the new testable concept of warming dominance to determine whether region A is suffering a worse warming process than region B. Applying this three-step methodology, we find that Spain and the Globe experience a clear distributional warming process (beyond the standard average) but of different types. In both cases, this process is accelerating over time and asymmetrically amplified. Overall, warming in Spain dominates the Globe in all the quantiles except the lower tail of the global temperature distribution that corresponds to the Arctic region. Our climate change heterogeneity results open the door to the need for a non-uniform causal-effect climate analysis that goes beyond the standard causality in mean as well as for a more efficient design of the mitigation-adaptation policies. In particular, the heterogeneity we find suggests that these policies should contain a common global component and a clear local-regional element. Future climate agreements should take the whole temperature distribution into account.
Cognitive Endurance, Talent Selection, and the Labor Market Returns to Human Capital
Authors: Germán Reyes
Date: 2023-01-06 00:00:00

Cognitive endurance -- the ability to sustain performance on a cognitively-demanding task over time -- is thought to be a crucial productivity determinant. However, a lack of data on this variable has limited researchers' ability to understand its role for success in college and the labor market. This paper uses college-admission-exam records from 15 million Brazilian high school students to measure cognitive endurance based on changes in performance throughout the exam. By exploiting exogenous variation in the order of exam questions, I show that students are 7.1 percentage points more likely to correctly answer a given question when it appears at the beginning of the day versus the end (relative to a sample mean of 34.3%). I develop a method to decompose test scores into fatigue-adjusted ability and cognitive endurance. I then merge these measures into a higher-education census and the earnings records of the universe of Brazilian formal-sector workers to quantify the association between endurance and long-run outcomes. I find that cognitive endurance has a statistically and economically significant wage return. Controlling for fatigue-adjusted ability and other student characteristics, a one-standard-deviation higher endurance predicts a 5.4% wage increase. This wage return to endurance is sizable, equivalent to a third of the wage return to ability. I also document positive associations between endurance and college attendance, college quality, college graduation, firm quality, and other outcomes. Finally, I show how systematic differences in endurance across students interact with the exam design to determine the sorting of students to colleges. I discuss the implications of these findings for the use of cognitive assessments for talent selection and investments in interventions that build cognitive endurance.
Regret theory, Allais' Paradox, and Savage's omelet
Authors: Vardan G. Bardakhchyan, Armen E. Allahverdyan
Date: 2023-01-06 00:00:00

We study a sufficiently general regret criterion for choosing between two probabilistic lotteries. For independent lotteries, the criterion is consistent with stochastic dominance and can be made transitive by a unique choice of the regret function. Together with additional (and intuitively meaningful) super-additivity property, the regret criterion resolves the Allais' paradox including the cases were the paradox disappears, and the choices agree with the expected utility. This superadditivity property is also employed for establishing consistency between regret and stochastic dominance for dependent lotteries. Furthermore, we demonstrate how the regret criterion can be used in Savage's omelet, a classical decision problem in which the lottery outcomes are not fully resolved. The expected utility cannot be used in such situations, as it discards important aspects of lotteries.
Learning Deep MRI Reconstruction Models from Scratch in Low-Data Regimes
Authors: Salman UH Dar, Şaban Öztürk, Muzaffer Özbey, Tolga Çukur
Date: 2023-01-06 00:00:00
Tasks: Diagnostic, MRI Reconstruction, Rolling Shutter Correction

Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan times. Reconstruction methods can alleviate this limitation by recovering clinically usable images from accelerated acquisitions. In particular, learning-based methods promise performance leaps by employing deep neural networks as data-driven priors. A powerful approach uses scan-specific (SS) priors that leverage information regarding the underlying physical signal model for reconstruction. SS priors are learned on each individual test scan without the need for a training dataset, albeit they suffer from computationally burdening inference with nonlinear networks. An alternative approach uses scan-general (SG) priors that instead leverage information regarding the latent features of MRI images for reconstruction. SG priors are frozen at test time for efficiency, albeit they require learning from a large training dataset. Here, we introduce a novel parallel-stream fusion model (PSFNet) that synergistically fuses SS and SG priors for performant MRI reconstruction in low-data regimes, while maintaining competitive inference times to SG methods. PSFNet implements its SG prior based on a nonlinear network, yet it forms its SS prior based on a linear network to maintain efficiency. A pervasive framework for combining multiple priors in MRI reconstruction is algorithmic unrolling that uses serially alternated projections, causing error propagation under low-data regimes. To alleviate error propagation, PSFNet combines its SS and SG priors via a novel parallel-stream architecture with learnable fusion parameters. Demonstrations are performed on multi-coil brain MRI for varying amounts of training data. PSFNet outperforms SG methods in low-data regimes, and surpasses SS methods with few tens of training samples.
Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics
Authors: Haoqing Li, Bhavya Duvvuri, Ricardo Borsoi, Tales Imbiriba, Edward Beighley, Deniz Erdogmus, Pau Closas
Date: 2023-01-06 00:00:00
Tasks: State Estimation

Real-time satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena such as floods, earthquakes, etc. One important constraint of satellite imaging is the trade-off between spatial/spectral resolution and their revisiting time, a consequence of design and physical constraints imposed by satellite orbit among other technical limitations. In this paper, we focus on fusing multi-temporal, multi-spectral images where data acquired from different instruments with different spatial resolutions is used. We leverage the spatial relationship between images at multiple modalities to generate high-resolution image sequences at higher revisiting rates. To achieve this goal, we formulate the fusion method as a recursive state estimation problem and study its performance in filtering and smoothing contexts. Furthermore, a calibration strategy is proposed to estimate the time-varying temporal dynamics of the image sequence using only a small amount of historical image data. Differently from the training process in traditional machine learning algorithms, which usually require large datasets and computation times, the parameters of the temporal dynamical model are calibrated based on an analytical expression that uses only two of the images in the historical dataset. A distributed version of the Bayesian filtering and smoothing strategies is also proposed to reduce its computational complexity. To evaluate the proposed methodology we consider a water mapping task where real data acquired by the Landsat and MODIS instruments are fused generating high spatial-temporal resolution image estimates. Our experiments show that the proposed methodology outperforms the competing methods in both estimation accuracy and water mapping tasks.
MR Elastography with Optimization-Based Phase Unwrapping and Traveling Wave Expansion-based Neural Network (TWENN)
Authors: Shengyuan Ma, Runke Wang, Suhao Qiu, Ruokun Li, Qi Yue, Qingfang Sun, Liang Chen, Fuhua Yan, Guang-Zhong Yang, Yuan Feng
Date: 2023-01-06 00:00:00

Magnetic Resonance Elastography (MRE) can characterize biomechanical properties of soft tissue for disease diagnosis and treatment planning. However, complicated wavefields acquired from MRE coupled with noise pose challenges for accurate displacement extraction and modulus estimation. Here we propose a pipeline for processing MRE images using optimization-based displacement extraction and Traveling Wave Expansion-based Neural Network (TWENN) modulus estimation. Phase unwrapping and displacement extraction were achieved by optimization of an objective function with Dual Data Consistency (Dual-DC). A complex-valued neural network using displacement covariance as input has been constructed for the estimation of complex wavenumbers. A model of traveling wave expansion is used to generate training datasets with different levels of noise for the network. The complex shear modulus map is obtained by a fusion of multifrequency and multidirectional data. Validation using images of brain and liver simulation demonstrates the practical value of the proposed pipeline, which can estimate the biomechanical properties with minimum root-mean-square-errors compared with state-of-the-art methods. Applications of the proposed method for processing MRE images of phantom, brain, and liver show clear anatomical features and that the pipeline is robust to noise and has a good generalization capability.
Hawkeye: Hectometer-range Subcentimeter Localization for Large-scale mmWave Backscatter
Authors: Kang Min Bae, Hankyeol Moon, Sung-Min Sohn, Song Min Kim
Date: 2023-01-06 00:00:00
Tasks: TAG

Accurate localization of a large number of objects over a wide area is one of the keys to the pervasive interaction with the Internet of Things. This paper presents Hawkeye, a new mmWave backscatter that, for the first time, offers over (i) hundred-scale simultaneous 3D localization at (ii) subcentimeter accuracy for over an (iii) hectometer distance. Hawkeye generally applies to indoors and outdoors as well as under mobility. Hawkeye tag's Van Atta Array design with retro-reflectivity in both elevation and azimuth planes offers 3D localization and effectively suppresses the multipath. Hawkeye localization algorithm is a lightweight signal processing compatible with the commodity FMCW radar. It uniquely leverages the interplay between the tag signal and clutter, and leverages the spetral leakage for fine-grained positioning. Prototype evaluations in corridor, lecture room, and soccer field reveal 6.7 mm median accuracy at 160 m range, and simultaneously localizes 100 tags in only 33.2 ms. Hawkeye is reliable under temperature change with significant oscillator frequency offset.
Spectrum Monitoring and Analysis in Urban and Rural Environments at Different Altitudes
Authors: Amir Hossein Fahim Raouf, Sung Joon Maeng, Ismail Guvenc, Ozgur Ozdemir, Mihail Sichitiu
Date: 2023-01-06 00:00:00

Due to the scarcity of spectrum resources, the emergence of new technologies and ever-increasing number of wireless devices operating in the radio frequency spectrum lead to data congestion and interference. In this work, we study the effect of altitude on sub-6 GHz spectrum measurement results obtained at a Helikite flying over two distinct scenarios; i.e., urban and rural environments. Specifically, we aim at investigating the spectrum occupancy of various long-term evolution (LTE), $5^{\text{th}}$ generation (5G) and citizens broadband radio service (CBRS) bands utilized in the United States for both uplink and downlink at altitudes up to 180 meters. Our results reveal that generally the mean value of the measured power increases as the altitude increases where the line-of-sight links with nearby base stations is more available. SigMF-compliant spectrum measurement datasets used in this paper covering all the bands between 100~MHz to 6~GHz are also provided.
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks
Authors: Wangyang Xu, Jiancheng An, Hongbin Li, Lu Gan, Chau Yuen
Date: 2023-01-06 00:00:00
Tasks: Distributed Optimization, Rolling Shutter Correction

The user-centric cell-free network has emerged as an appealing technology to improve the next-generation wireless network's capacity thanks to its ability to eliminate inter-cell interference effectively. However, the cell-free network inevitably brings in higher hardware cost and backhaul overhead as a larger number of base stations (BSs) are deployed. Additionally, severe channel fading in high-frequency bands constitutes another crucial issue that limits the practical application of the cell-free network. In order to address the above challenges, we amalgamate the cell-free system with another emerging technology, namely reconfigurable intelligent surface (RIS), which can provide high spectrum and energy efficiency with low hardware cost by reshaping the wireless propagation environment intelligently. To this end, we formulate a weighted sum-rate (WSR) maximization problem for RIS-assisted cell-free systems by jointly optimizing the BS precoding matrix and the RIS reflection coefficient vector. Subsequently, we transform the complicated WSR problem to a tractable optimization problem and propose a distributed cooperative alternating direction method of multipliers (ADMM) to fully utilize parallel computing resources. Inspired by the model-based algorithm unrolling concept, we unroll our solver to a learning-based deep distributed ADMM (D-ADMM) network framework. To improve the efficiency of the D-ADMM in distributed BSs, we develop a monodirectional information exchange strategy with a small signaling overhead. In addition to benefiting from domain knowledge, D-ADMM adaptively learns hyper-parameters and non-convex solvers of the intractable RIS design problem through data-driven end-to-end training.
The Growing Liberality Observed in Primary Animal and Plant Cultures is Common to the Social Amoeba
Authors: Norichika Ogata
Date: 2023-01-06 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Tissue culture environment liberates cells from ordinary laws of multi-cellular organisms. This liberation enables cells several behaviors, such as proliferation, dedifferentiation, acquisition of pluripotency, immortalization, and reprogramming. Recently, the quantitative value of cellular dedifferentiation and differentiation was defined as liberality, which is measurable as Shannon entropy of numerical transcriptome data and Lempel-Zip complexity of nucleotide sequence transcriptome data. The increasing liberality induced by the culture environment had first been observed in animal cells and had reconfirmed in plant cells. The phenomena may be common across the kingdom, also in a social amoeba. We measured the liberality of the social amoeba which disaggregated from multicellular aggregates and transferred into a liquid medium.
Graph Contrastive Learning for Multi-omics Data
Authors: Nishant Rajadhyaksha, Aarushi Chitkara
Date: 2023-01-03 00:00:00
Tasks: Classification, Contrastive Learning

Advancements in technologies related to working with omics data require novel computation methods to fully leverage information and help develop a better understanding of human diseases. This paper studies the effects of introducing graph contrastive learning to help leverage graph structure and information to produce better representations for downstream classification tasks for multi-omics datasets. We present a learnining framework named Multi-Omics Graph Contrastive Learner(MOGCL) which outperforms several aproaches for integrating multi-omics data for supervised learning tasks. We show that pre-training graph models with a contrastive methodology along with fine-tuning it in a supervised manner is an efficient strategy for multi-omics data classification.
Does Gaia Play Dice? : Simple Models of non-Darwinian Selection
Authors: Rudy Arthur, Arwen Nicholson
Date: 2023-01-06 00:00:00

In this paper we introduce some simple models, based on rolling dice, to explore mechanisms proposed to explain planetary habitability. The idea is to study these selection mechanisms in an analytically tractable setting, isolating their consequences from other details which can confound or obscure their effect in more realistic models. We find that while the observable of interest, the face value shown on the die, `improves' over time in all models, for two of the more popular ideas, Selection by Survival and Sequential Selection, this is down to sampling effects. A modified version of Sequential Selection, Sequential Selection with Memory, implies a statistical tendency for systems to improve over time. We discuss the implications of this and its relationship to the ideas of the `Inhabitance Paradox' and the `Gaian bottleneck'.
Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A Wavelet Approach
Authors: Yury E. Garcia, Shu-Wei Chou-Chen, Luis A. Barboza, Maria L. Daza-Torres, J. Cricelio Montesinos-Lopez, Paola Vasquez, Juan G. Calvo, Miriam Nuno, Fabio Sanchez
Date: 2023-01-03 00:00:00
Tasks: Time Series, Time Series Analysis

Throughout history, prevention and control of dengue transmission have challenged public health authorities worldwide. In the last decades, the interaction of multiple factors, such as environmental and climate variability, has influenced increments in incidence and geographical spread of the virus. In Costa Rica, a country characterized by multiple microclimates separated by short distances, dengue has been endemic since its introduction in 1993. Understanding the role of climatic and environmental factors in the seasonal and inter-annual variability of disease spread is essential to develop effective surveillance and control efforts. In this study, we conducted a wavelet time series analysis of weekly climate, local environmental variables, and dengue cases (2001-2019) from 32 cantons in Costa Rica to identify significant periods (e.g., annual, biannual) in which climate and environmental variables co-varied with dengue cases. Wavelet coherence analysis was used to characterize seasonality, multi-year outbreaks, and relative delays between the time series. Results show that dengue outbreaks occurring every 3 years in cantons located in the country's Central, North, and South Pacific regions were highly coherent with the Oceanic Ni\~no 3.4 and the Tropical North Caribbean Index (TNA). Dengue cases were in phase with El Ni\~no 3.4 and TNA, with El Ni\~no 3.4 ahead of dengue cases by roughly nine months and TNA ahead by less than three months. Annual dengue outbreaks were coherent with local environmental variables (NDWI, EVI, Evapotranspiration, and Precipitation) in most cantons except those located in the Central, South Pacific, and South Caribbean regions of the country. The local environmental variables were in phase with dengue cases and were ahead by around three months.
Deep leakage from gradients
Authors: Yaqiong Mu
Date: 2022-12-15 00:00:00
Tasks: Federated Learning

With the development of artificial intelligence technology, Federated Learning (FL) model has been widely used in many industries for its high efficiency and confidentiality. Some researchers have explored its confidentiality and designed some algorithms to attack training data sets, but these algorithms all have their own limitations. Therefore, most people still believe that local machine learning gradient information is safe and reliable. In this paper, an algorithm based on gradient features is designed to attack the federated learning model in order to attract more attention to the security of federated learning systems. In federated learning system, gradient contains little information compared with the original training data set, but this project intends to restore the original training image data through gradient information. Convolutional Neural Network (CNN) has excellent performance in image processing. Therefore, the federated learning model of this project is equipped with Convolutional Neural Network structure, and the model is trained by using image data sets. The algorithm calculates the virtual gradient by generating virtual image labels. Then the virtual gradient is matched with the real gradient to restore the original image. This attack algorithm is written in Python language, uses cat and dog classification Kaggle data sets, and gradually extends from the full connection layer to the convolution layer, thus improving the universality. At present, the average squared error between the data recovered by this algorithm and the original image information is approximately 5, and the vast majority of images can be completely restored according to the gradient information given, indicating that the gradient of federated learning system is not absolutely safe and reliable.
Logically at Factify 2: A Multi-Modal Fact Checking System Based on Evidence Retrieval techniques and Transformer Encoder Architecture
Authors: Pim Jordi Verschuuren, Jie Gao, Adelize van Eeden, Stylianos Oikonomou, Anil Bandhakavi
Date: 2023-01-09 00:00:00
Tasks: Avg, Benchmarking, Fact Checking, Fact Verification, Retrieval

In this paper, we present the Logically submissions to De-Factify 2 challenge (DE-FACTIFY 2023) on the task 1 of Multi-Modal Fact Checking. We describes our submissions to this challenge including explored evidence retrieval and selection techniques, pre-trained cross-modal and unimodal models, and a cross-modal veracity model based on the well established Transformer Encoder (TE) architecture which is heavily relies on the concept of self-attention. Exploratory analysis is also conducted on this Factify 2 data set that uncovers the salient multi-modal patterns and hypothesis motivating the architecture proposed in this work. A series of preliminary experiments were done to investigate and benchmarking different pre-trained embedding models, evidence retrieval settings and thresholds. The final system, a standard two-stage evidence based veracity detection system, yields weighted avg. 0.79 on both val set and final blind test set on the task 1, which achieves 3rd place with a small margin to the top performing system on the leaderboard among 9 participants.