pwc-archive/papers-with-abstracts huggingface dataset:
Big Bad Banks? The Winners and Losers from Bank Deregulation in the United States
Authors: Thorsten Beck, Ross Levine, and Alexey Levkov
Date: 2009-10-07 00:00:00
Proceeding: The Journal of Finance 2009 10

Abstract: We assess the impact of bank deregulation on the distribution of income in the United 
States. From the 1970s through the 1990s, most states removed restrictions on intrastate 
branching, which intensified bank competition and improved bank performance. Exploiting the 
cross-state, cross-time variation in the timing of branch deregulation, we find that deregulation 
materially tightened the distribution of income by boosting incomes in the lower part of the 
income distribution while having little impact on incomes above the median. The results suggest 
that regulatory impediments to competition among banks during the 20th century were 
disproportionally harmful to lower income workers. We also find that the impact of bank 
deregulation on income inequality is accounted for by an increase in the relative wage rates and 
working hours of unskilled workers, and not by lower-income households financing additional 
education or starting new businesses.
CEREBRUM‐7T: Fast and Fully Volumetric Brain Segmentation of 7 Tesla MR Volumes
Authors: Svanera, M., Benini, S., Bontempi, D., Muckli, L
Date: 2021-01-28 00:00:00
Proceeding: Human Brain Mapping 2021 1
Tasks: Brain Segmentation, Segmentation

Ultra high-field MRI enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures. Despite recent efforts in brain imaging analysis, the literature lacks in accurate and fast methods for segmenting 7 Tesla (7T) brain MRI. We here present CEREBRUM-7T, an optimised end-to-end Convolutional Neural Network (CNN), that allows fully automatic segmentation of a whole 7T T1w MRI brain volume at once, without partitioning the volume, preprocessing, nor aligning it to an atlas. The trained model is able to produce accurate multi-structure segmentation masks on six different classes plus background in only a few seconds. The experimental part, a combination of objective numerical evaluations and subjective analysis, confirms that the proposed solution outperforms the training labels it was trained on, and is suitable for neuroimaging studies, such as layer fMRI studies. Taking advantage of a fine-tuning operation on a reduced set of volumes, we also show how it is possible to effectively apply CEREBRUM-7T to different sites data. Furthermore, we release the code, 7T data, and other materials, including the training labels and the Turing test. For more, please visit: https://rocknroll87q.github.io/cerebrum7t/.
Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network
Authors: Khaled Almezhghwi, Sertan Serte
Date: 2020-07-09 00:00:00
Proceeding: Computational Intelligence and Neuroscience 2020 7
Tasks: Data Augmentation, Feature Engineering, Generative Adversarial Network

White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.
Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning
Authors: Hitesh Sapkota, Qi Yu
Date: 2021-04-13 00:00:00
Proceeding: AISTATS 2021 4
Tasks: Anomaly Detection, Gaussian Processes, Multiple Instance Learning, Video Anomaly Detection

Multiple Instance Learning (MIL) provides a promising solution to many real-world problems, where labels are only available at the bag level but missing for instances due to a high labeling cost. As a powerful Bayesian non-parametric model, Gaussian Processes (GP) have been extended from classical supervised learning to MIL settings, aiming to identify the most likely positive (or least negative) instance from a positive (or negative) bag using only the bag-level labels. However, solely focusing on a single instance in a bag makes the model less robust to outliers or multi-modal scenarios, where a single bag contains a diverse set of positive instances. We propose a general GP mixture framework that simultaneously considers multiple instances through a latent mixture model. By adding a top-k constraint, the framework is equivalent to choosing the top-k most positive instances, making it more robust to outliers and multimodal scenarios. We further introduce a Distributionally Robust Optimization (DRO) constraint that removes the limitation of specifying a fix k value. To ensure the prediction power over high-dimensional data (e.g., videos and images) that are common in MIL, we augment the GP kernel with fixed basis functions by using a deep neural network to learn adaptive basis functions so that the covariance structure of high-dimensional data can be accurately captured. Experiments are conducted on highly challenging real-world video anomaly detection tasks to demonstrate the effectiveness of the proposed model.
A Deep Learning Approach for Predicting Two-dimensional Soil Consolidation Using Physics-Informed Neural Networks (PINN)
Authors: Yue Lu, Gang Mei, Francesco Piccialli
Date: 2022-04-09 00:00:00

Soil consolidation is closely related to seepage, stability, and settlement of geotechnical buildings and foundations, and directly affects the use and safety of superstructures. Nowadays, the unidirectional consolidation theory of soils is widely used in certain conditions and approximate calculations. The multi-directional theory of soil consolidation is more reasonable than the unidirectional theory in practical applications, but it is much more complicated in terms of index determination and solution. To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation. In the proposed method, (1) a fully connected neural network is constructed, (2) the computational domain, partial differential equation (PDE), and constraints are defined to generate data for model training, and (3) the PDE of two-dimensional soil consolidation and the model of the neural network is connected to reduce the loss of the model. The effectiveness of the proposed method is verified by comparison with the numerical solution of PDE for two-dimensional consolidation. Using this method, the excess pore water pressure could be predicted simply and efficiently. In addition, the method was applied to predict the soil excess pore water pressure in the foundation in a real case at Tianjin port, China. The proposed deep learning approach can be used to investigate the large and complex multi-directional soil consolidation.
Graph Fourier transform based on singular value decomposition of directed Laplacian
Authors: Yang Chen, Cheng Cheng, Qiyu Sun
Date: 2022-05-12 00:00:00

Graph Fourier transform (GFT) is a fundamental concept in graph signal processing. In this paper, based on singular value decomposition of Laplacian, we introduce a novel definition of GFT on directed graphs, and use singular values of Laplacian to carry the notion of graph frequencies. % of the proposed GFT. The proposed GFT is consistent with the conventional GFT in the undirected graph setting, and on directed circulant graphs, the proposed GFT is the classical discrete Fourier transform, up to some rotation, permutation and phase adjustment. We show that frequencies and frequency components of the proposed GFT can be evaluated by solving some constrained minimization problems with low computational cost. Numerical demonstrations indicate that the proposed GFT could represent graph signals with different modes of variation efficiently.
Training Strategies for Own Voice Reconstruction in Hearing Protection Devices using an In-ear Microphone
Authors: Mattes Ohlenbusch, Christian Rollwage, Simon Doclo
Date: 2022-05-12 00:00:00
Tasks: Bandwidth Extension

In-ear microphones in hearing protection devices can be utilized to capture the own voice speech of the person wearing the devices in noisy environments. Since in-ear recordings of the own voice are typically band-limited, an own voice reconstruction system is required to recover clean broadband speech from the in-ear signals. However, the availability of speech data for this scenario is typically limited due to device-specific transfer characteristics and the need to collect data from in-situ measurements. In this paper, we apply a deep learning-based bandwidth-extension system to the own voice reconstruction task and investigate different training strategies in order to overcome the limited availability of training data. Experimental results indicate that the use of simulated training data based on recordings of several talkers in combination with a fine-tuning approach using real data is advantageous compared to directly training on a small real dataset.
Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model
Authors: Jean-Marc Valin, Ahmed Mustafa, Christopher Montgomery, Timothy B. Terriberry, Michael Klingbeil, Paris Smaragdis, Arvindh Krishnaswamy
Date: 2022-05-11 00:00:00
Tasks: Packet Loss Concealment, Speech Enhancement, Speech Synthesis

As deep speech enhancement algorithms have recently demonstrated capabilities greatly surpassing their traditional counterparts for suppressing noise, reverberation and echo, attention is turning to the problem of packet loss concealment (PLC). PLC is a challenging task because it not only involves real-time speech synthesis, but also frequent transitions between the received audio and the synthesized concealment. We propose a hybrid neural PLC architecture where the missing speech is synthesized using a generative model conditioned using a predictive model. The resulting algorithm achieves natural concealment that surpasses the quality of existing conventional PLC algorithms and ranked second in the Interspeech 2022 PLC Challenge. We show that our solution not only works for uncompressed audio, but is also applicable to a modern speech codec.
High-Frequency Tunable Grounded & Floating Incremental-Decremental Meminductor Emulator and Application
Authors: Garima Shukla, Pratik Kumar, Sajal K. Paul
Date: 2022-05-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper proposes a new design for realizing grounded and floating meminductor emulators built with two operational transconductance amplifiers (OTAs) and two second-generation current conveyors. The proposed grounded and floating emulators claim that the circuits are much simpler in design and can be utilized in incremental and decremental topologies. The proposed circuits' performance has been verified with Cadence Virtuoso Spectre using standard CMOS 180nm technology. Furthermore, the layout of the proposed circuits has been designed, and post-layout simulations have been performed. The non-ideal and Monte Carlo analyses have been carried out in detail. This paper also proposes the application of a meminductor as an Amplitude Modulator (AM). Moreover, the experimental results are presented to verify the theoretical and simulation analyses of proposed meminductor emulator circuits.
NeuralTree: A 256-Channel 0.227-$μ$J/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC
Authors: Uisub Shin, Cong Ding, Bingzhao Zhu, Yashwanth Vyza, Alix Trouillet, Emilie C. M. Revol, Stéphanie P. Lacour, Mahsa Shoaran
Date: 2022-05-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Specificity

Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel counts and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy tradeoff. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65-nm CMOS and achieved a 0.227-$\mu$J/class energy efficiency in a compact area of 0.014mm$^2$/channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In vivo neural recordings using soft $\mu$ECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease (PD) from human local field potentials (LFPs) was demonstrated.
Ultra-Reliable Low-Latency Communication for Aerial Vehicles via Multi-Connectivity
Authors: Fateme Salehi, Mustafa Ozger, Naaser Neda, Cicek Cavdar
Date: 2022-05-12 00:00:00

Aerial vehicles (AVs) such as electric vertical take-off and landing (eVTOL) make aerial passenger transportation a reality in urban environments. However, their communication connectivity is still under research to realize their safe and full-scale operation, which requires stringent end-to-end (E2E) reliability and delay. In this paper, we evaluate reliability and delay for the downlink communication of AVs, i.e., remote piloting, control/telemetry traffic of AVs. We investigate direct air-to-ground (DA2G) and air-to-air (A2A) communication technologies, along with high altitude platforms (HAPs) to explore the conditions of how multi-connectivity (MC) options satisfy the demanding E2E connectivity requirements under backhaul link bottleneck. Our considered use case is ultra-reliable low-latency communication (URLLC) under the finite blocklength (FBL) regime due to the nature of downlink control communication to AVs. In our numerical study, we find that providing requirements by single connectivity to AVs is very challenging due to the line-of-sight (LoS) interference and reduced gains of downtilt ground base station (BS) antenna. We also find that even with very efficient interference mitigation, existing cellular networks designed for terrestrial users are not capable of meeting the URLLC requirements calling for MC solutions.
NLOS Error Mitigation Using Weighted Least Squares and Kalman Filter in UWB Positioning
Authors: Ruixin Fan, Xin Du
Date: 2022-05-12 00:00:00

In wireless positioning systems, non-line-of-sight (NLOS) is a challenging problem. NLOS causes great ranging bias and location error, so NLOS mitigation is essential for high accuracy positioning. In this letter, we propose the Weighted-Least-Squares Robust Kalman Filter (WLS-RKF) for NLOS identification and mitigation. WLS-RKF employs a hypothesis test based on Mahalanobis distance for NLOS identification, and updates the corresponding Kalman filter using the WLS solution. It requires no prior knowledge about NLOS distribution or signal features. We perform simulations and experiments for ultra-wideband (UWB) positioning in various scenarios. The results confirm that WLS-RKF effectively mitigates NLOS error and achieves 5cm positioning accuracy.
Observer-Based Consensus of Nonlinear Positive Multi-Agent Systems with Saturated Control Input
Authors: Amirreza Zaman, Wolfgang Birk, Khalid Tourkey Atta
Date: 2022-05-12 00:00:00

This paper presents the distributed pinning consensus solution for nonlinear positive multi-agent systems with nonlinear control input by applying observer-based control protocols. The network topology is considered as a directed and fully connected structure. By considering sector input nonlinearities and various forms of topologies, two kinds of state observers involving standard observer and distributed pinning observer are presented for each regarded nonlinear agent by applying a novel analysis directly dealing with the nonlinear input and nonlinear system dynamics. The measured local output detail outlines the first observer, and the other observer is achieved via the corresponding output detail of its adjacent agents. Based on further observed state details, a distributed pinning observer-based strategy is derived for the leader-follower non-negative global consensus of the nonlinear positive multi-agent system. Additionally, two multi-step algorithms are proposed to set up the observer gains and each protocol criterion. Performance evaluations are provided to confirm the proposed control method and illustrate the effectiveness of the derived non-negative consensus observer-based protocols.
Multivariate ordered discrete response models
Authors: Tatiana Komarova, William Matcham
Date: 2022-05-11 00:00:00
Tasks: Decision Making

We introduce multivariate ordered discrete response models with general rectangular structures. From the perspective of behavioral economics, these non-lattice models correspond to broad bracketing in decision making, whereas lattice models, which researchers typically estimate in practice, correspond to narrow bracketing. In these models, we specify latent processes as a sum of an index of covariates and an unobserved error, with unobservables for different latent processes potentially correlated. We provide conditions that are sufficient for identification under the independence of errors and covariates and outline an estimation approach. We present simulations and empirical examples, with a particular focus on probit specifications.
Beyond Barker: Infant Mortality at Birth and Ischaemic Heart Disease in Older Age
Authors: Samuel Baker, Pietro Biroli, Hans van Kippersluis, Stephanie von Hinke
Date: 2022-05-12 00:00:00

Adverse conditions in early life can have consequential impacts on individuals' health in older age. In one of the first papers on this topic, Barker and Osmond 1986 show a strong positive relationship between infant mortality rates in the 1920s and ischaemic heart disease in the 1970s. We go 'beyond Barker', first by showing that this relationship is robust to the inclusion of local geographic area fixed effects, but not family fixed effects. Second, we explore whether the average effects conceal underlying heterogeneity: we examine if the infant mortality effect offsets or reinforces one's genetic predisposition for heart disease. We find considerable heterogeneity that is robust to within-area as well as within-family analyses. Our findings show that the effects of one's early life environments mainly affect individuals with the highest genetic risk for developing heart disease. Put differently, in areas with the lowest infant mortality rates, the effect of one's genetic predisposition effectively vanishes. These findings suggest that advantageous environments can cushion one's genetic risk of developing heart disease.
Welfare compensation in international transmission expansion planning under uncertainty
Authors: E. Ruben van Beesten, Ole Kristian Ådnanes, Håkon Morken Linde, Paolo Pisciella, Asgeir Tomasgard
Date: 2022-05-12 00:00:00

In transmission expansion planning, situations can arise in which an expansion plan that is optimal for the system as a whole is detrimental to a specific country in terms of its expected economic welfare. If this country is one of the countries hosting the planned capacity expansion, it has the power to veto the plan and thus, undermine the system-wide social optimum. To solve this issue, welfare compensation mechanisms may be constructed that compensate suffering countries and make them willing to participate in the expansion plan. In the literature, welfare compensation mechanisms have been developed that work in expectation. However, in a stochastic setting, even if the welfare effect after compensation is positive in expectation, countries might still be hesitant to accept the risk that the actual, realized welfare effect may be negative in some scenarios. In this paper we analyze welfare compensation mechanisms in a stochastic setting. We consider two existing mechanisms, lump-sum payments and purchase power agreements, and we develop two novel mechanisms, based on the flow through the new transmission line and its economic value. Using a case study of the Northern European power market, we investigate how well these mechanisms succeed in mitigating risk for the countries involved. Using a theoretically ideal model-based mechanism, we show that there is a significant potential for mitigating risk through welfare compensation mechanisms. Out of the four practical mechanisms we consider, our results indicate that a mechanism based on the economic value of the new transmission line is most promising.
Recent Contributions to Theories of Discrimination
Authors: Paula Onuchic
Date: 2022-05-12 00:00:00
Tasks: Decision Making

This paper surveys the literature on theories of discrimination, focusing mainly on new contributions. Recent theories expand on the traditional taste-based and statistical discrimination frameworks by considering specific features of learning and signaling environments, often using novel information- and mechanism-design language; analyzing learning and decision making by algorithms; and introducing agents with behavioral biases and misspecified beliefs. This survey also attempts to narrow the gap between the economic perspective on ``theories of discrimination'' and the broader study of discrimination in the social science literature. In that respect, I first contribute by identifying a class of models of discriminatory institutions, made up of theories of discriminatory social norms and discriminatory institutional design. Second, I discuss issues relating to the measurement of discrimination, and the classification of discrimination as bias or statistical, direct or systemic, and accurate or inaccurate.