pwc-archive/papers-with-abstracts huggingface dataset:
Bootstrap inference for fixed-effect models
Authors: Ayden Higgins, Koen Jochmans
Date: 2022-01-26 00:00:00

The maximum-likelihood estimator of nonlinear panel data models with fixed effects is consistent but asymptotically-biased under rectangular-array asymptotics. The literature has thus far concentrated its effort on devising methods to correct the maximum-likelihood estimator for its bias as a means to salvage standard inferential procedures. Instead, we show that the parametric bootstrap replicates the distribution of the (uncorrected) maximum-likelihood estimator in large samples. This justifies the use of confidence sets constructed via standard bootstrap percentile methods. No adjustment for the presence of bias needs to be made.
Democratising Risk: In Search of a Methodology to Study Existential Risk
Authors: Carla Zoe Cremer, Luke Kemp
Date: 2021-12-27 00:00:00
Tasks: Ethics

Studying potential global catastrophes is vital. The high stakes of existential risk studies (ERS) necessitate serious scrutiny and self-reflection. We argue that existing approaches to studying existential risk are not yet fit for purpose, and perhaps even run the risk of increasing harm. We highlight general challenges in ERS: accommodating value pluralism, crafting precise definitions, developing comprehensive tools for risk assessment, dealing with uncertainty, and accounting for the dangers associated with taking exceptional actions to mitigate or prevent catastrophes. The most influential framework for ERS, the 'techno-utopian approach' (TUA), struggles with these issues and has a unique set of additional problems: it unnecessarily combines the study of longtermism and longtermist ethics with the study of extinction, relies on a non-representative moral worldview, uses ambiguous and inadequate definitions, fails to incorporate insights from risk assessment in relevant fields, chooses arbitrary categorisations of risk, and advocates for dangerous mitigation strategies. Its moral and empirical assumptions might be particularly vulnerable to securitisation and misuse. We suggest several key improvements: separating the study of extinction ethics (ethical implications of extinction) and existential ethics (the ethical implications of different societal forms), from the analysis of human extinction and global catastrophe; drawing on the latest developments in risk assessment literature; diversifying the field, and; democratising its policy recommendations.
Enabling Radio Sensing for Multimodal Intelligent Transportation Systems: From Virtual Testing to Immersive Testbeds
Authors: Paul Schwarzbach, Jonas Ninnemann, Oliver Michler
Date: 2022-01-27 00:00:00

In this paper, the necessity for application-oriented development and evaluation of Joint Communication and Sensing (JC&S) applications, especially in transportation, is addressed. More specifically, an integrative evaluation chain for immersively testing JC&S location capabilities, reaching from early-stage testing, over model- and scenario-enabled ray tracing simulation, to real-world evaluation (laboratory and field testing) is presented. This includes a discussion of both challenges and requirements for location-aware applications in Intelligent Transportation Systems. Within this scope, a reproducible methodology for testing sensing and localization capabilities is derived and application scenarios are presented. This includes a proposal of a scenario-based sensing evaluation using radio propagation simulation. The paper empirically discusses a proof-of-concept of the developed method given a smart parking scenario, in which a passive occupancy detection of vehicles is performed. The conducted findings underline the need for scenario-based JC&S evaluation in both virtual and real-world environments and proposes consecutive research work.
Low-Complexity Linear Diversity-Combining Detector for MIMO-OTFS
Authors: Tharaj Thaj, Emanuele Viterbo
Date: 2022-01-27 00:00:00
Tasks: Diversity

This paper presents a low complexity detector for multiple-input multiple-output (MIMO) systems based on the recently proposed orthogonal time frequency space (OTFS) modulation. In the proposed detector, the copies of the transmitted symbol-vectors received through the different diversity branches (propagation paths and receive antennas) are linearly combined using the maximum ratio combining (MRC) technique to iteratively improve the signal to interference plus noise ratio (SINR) at the output of the combiner. To alleviate the performance degradation due to spatial correlation at the receiver antennas, we present a sample-based method to estimate such correlation and find the optimized combining weights for MRC from the estimated correlation matrix. The detector performance and complexity improve over the linear minimum mean square error (LMMSE) and message passing (MP) detectors proposed in the literature for MIMO-OTFS.
Time-varying microwave photonic filter for arbitrary waveform signal-to-noise ratio improvement
Authors: Dong Ma, Yang Chen
Date: 2022-01-27 00:00:00

A time-varying microwave photonic filter (TV-MPF) based on stimulated Brillouin scattering (SBS) is proposed and utilized to suppress the in-band noise of broadband arbitrary microwave waveforms, thereby improving the signal-to-noise ratio (SNR). The filter-controlling signal is designed according to the signal to be filtered and drives the TV-MPF so that the passband of the filter is always aligned with the frequencies of the signal to be filtered. By continuously tracking the signal spectral component, the TV-MPF only retains the spectral components of the signal and filters out the noise other than the spectral component of the signal at the current time, so as to improve the in-band SNR of the signal to be filtered. An experiment is performed. A variety of signals with different formats and in-band SNRs are used to test the noise suppression capability of the TV-MPF, and the waveform mean-square error is calculated to quantify the improvement of the signal, demonstrating the excellent adaptability of the proposed TV-MPF to different kinds of signals.
Stochastic Identification-based Active Sensing Acousto-Ultrasound SHM Using Stationary Time Series Models
Authors: Shabbir Ahmed, Fotis Kopsaftopoulos
Date: 2022-01-26 00:00:00
Tasks: Time Series, Time Series Analysis

In this work, a probabilistic damage detection and identification scheme using stochastic time series models in the context of acousto-ultrasound guided wave-based SHM is proposed, and its performance is assessed experimentally. In order to simplify the damage detection and identification process, model parameters are modified based on the singular value decomposition (SVD) as well as the principal component analysis (PCA)-based truncation approach. The modified model parameters are then used to estimate a statistical characteristic quantity that follows a chi-squared distribution. A probabilistic threshold is used instead of a user-defined margin to facilitate automatic damage detection. The method's effectiveness is assessed via multiple experiments using both metallic and composite coupons and under various damage scenarios using damage intersecting and damage non-intersecting paths. The results of the study confirm the high potential and effectiveness of the stochastic time series methods for guided wave-based damage detection and identification in a potentially automated way.
Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling
Authors: Zhenyu Wei, Raymond K. W. Wong, Thomas C. M. Lee
Date: 2022-01-26 00:00:00
Tasks: Additive models, Denoising, regression, Variable Selection

In the signal processing and statistics literature, the minimum description length (MDL) principle is a popular tool for choosing model complexity. Successful examples include signal denoising and variable selection in linear regression, for which the corresponding MDL solutions often enjoy consistent properties and produce very promising empirical results. This paper demonstrates that MDL can be extended naturally to the high-dimensional setting, where the number of predictors $p$ is larger than the number of observations $n$. It first considers the case of linear regression, then allows for outliers in the data, and lastly extends to the robust fitting of nonparametric additive models. Results from numerical experiments are presented to demonstrate the efficiency and effectiveness of the MDL approach.
Predicting Succinylation Sites in Proteins with Improved Deep Learning Architecture
Authors: Olusola Odeyomi, Gergely Zaruba
Date: 2021-12-27 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Translation

Post-translational modifications (PTMs) in proteins occur after the process of translation. PTMs account for many cellular processes such as deoxyribonucleic acid (DNA) repair, cell signaling and cell death. One of the recent PTMs is succinylation. Succinylation modifies lysine residue from $-1$ to $+1$. Locating succinylation sites using experimental methods, such as mass spectrometry is very laborious. Hence, computational methods are favored using machine learning techniques. This paper proposes a deep learning architecture to predict succinylation sites. The performance of the proposed architecture is compared to the state-of-the-art deep learning architecture and other traditional machine learning techniques for succinylation. It is shown from the performance metrics that the proposed architecture provides a good trade-off between speed of computation and classification accuracy.
Focal cortical dysplasia as a cause of epilepsy: the current evidence of associated genes and future therapeutic treatments
Authors: Garrett Garner, Daniel Streetman, Joshua Fricker, Neal Patel, Nolan Brown, Shane Shahrestani, Julian Gendreau
Date: 2022-01-26 00:00:00

Focal cortical dysplasias (FCDs) are the most common cause of treatment resistant epilepsy affecting the pediatric population. Most individuals with FCD have seizure onset during the first five years of life and the majority will have seizures by the age of sixteen. Many cases of FCD are postulated to be the result of abnormal brain development in utero by germline or somatic gene mutations regulating neuronal growth and migration during corticogenesis. Other cases of FCD are thought to be related to infections during brain development, or even other causes still unable to be fully determined. Typical anti-seizure medications are oftentimes ineffective in FCD as well as surgery is unable to be successfully performed due to the involvement of eloquent areas of the brain or insufficient resection of the epileptogenic focus, posing a challenge for physicians. The genetic nature of FCD provides an avenue for drug development with several genetic and molecular targets undergoing study over the last two decades.
Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learning
Authors: Cristiano Capone, Cosimo Lupo, Paolo Muratore, Pier Stanislao Paolucci
Date: 2022-01-27 00:00:00
Tasks: Decision Making, Imitation Learning

The brain can learn to solve a wide range of tasks with high temporal and energetic efficiency. However, most biological models are composed of simple single compartment neurons and cannot achieve the state-of-art performances of artificial intelligence. We propose a multi-compartment model of pyramidal neuron, in which bursts and dendritic input segregation give the possibility to plausibly support a biological target-based learning. In target-based learning, the internal solution of a problem (a spatio temporal pattern of bursts in our case) is suggested to the network, bypassing the problems of error backpropagation and credit assignment. Finally, we show that this neuronal architecture naturally support the orchestration of hierarchical imitation learning, enabling the decomposition of challenging long-horizon decision-making tasks into simpler subtasks.
Chronic iEEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states
Authors: Gabrielle M. Schroeder, Philippa J. Karoly, Matias Maturana, Mariella Panagiotopoulou, Peter N. Taylor, Mark J. Cook, Yujiang Wang
Date: 2022-01-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Background and Objectives: Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear. Methods: Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterised the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states (SNSs). We then compared SNS occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate. Results: In most patients, the occurrence or duration of at least one SNS was associated with the time since implantation. Some patients had one or more SNSs that were associated with phases of circadian and/or multidien spike rate cycles. A given SNS's occurrence and duration were usually not associated with the same timescale. Discussion: Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a SNS's occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures, and similar principles likely apply to other neurological conditions.
Multimodal neural networks better explain multivoxel patterns in the hippocampus
Authors: Bhavin Choksi, Milad Mozafari, Rufin VanRullen, Leila Reddy
Date: 2021-12-11 00:00:00
Proceeding: NeurIPS Workshop SVRHM 2021 12
Tasks: Hippocampus

The human hippocampus possesses "concept cells", neurons that fire when presented with stimuli belonging to a specific concept, regardless of the modality. Recently, similar concept cells were discovered in a multimodal network called CLIP (Radford et at., 2021). Here, we ask whether CLIP can explain the fMRI activity of the human hippocampus better than a purely visual (or linguistic) model. We extend our analysis to a range of publicly available uni- and multi-modal models. We demonstrate that "multimodality" stands out as a key component when assessing the ability of a network to explain the multivoxel activity in the hippocampus.
A mathematical model of COVID-19 with an underlying health condition using fraction order derivative
Authors: Samuel Okyere, Joseph Ackora-Prah, Ebenezer Bonyah, Mary Osei Fokuo
Date: 2022-01-26 00:00:00

Studies have shown that some people with underlying conditions such as cancer, heart failure, diabetes and hypertension are more likely to get COVID-19 and have worse outcomes. In this paper, a fractional-order derivative is proposed to study the transmission dynamics of COVID-19 taking into consideration population having an underlying condition. The fractional derivative is defined in the Atangana Beleanu and Caputo (ABC) sense. For the proposed model, we find the basic reproductive number, the equilibrium points and determine the stability of these equilibrium points. The existence and the uniqueness of the solution are established along with Hyers Ulam Stability. The numerical scheme for the operator was carried out to obtain a numerical simulation to support the analytical results. COVID-19 cases from March to June 2020 of Ghana were used to validate the model. The numerical simulation revealed a decline in infections as the fractional operator was increased from 0.6 within the 120 days. Time-dependent optimal control was incorporated into the model. The numerical simulation of the optimal control revealed, vaccination reduces the number of individuals susceptible to the COVID-19, exposed to the COVID-19 and Covid-19 patients with and without an underlying health condition.
Modelling the effect of vascular status on tumour evolution and outcome after thermal therapy
Authors: Jesús J. Bosque, Gabriel F. Calvo, María Cruz Navarro
Date: 2022-01-10 00:00:00

Microscale oxygenation plays a prominent role in tumour progression. Spatiotemporal variability of oxygen distribution in the tumour microenvironment contributes to cellular heterogeneity and to the emergence of normoxic and hypoxic populations. Local levels of oxygen strongly affect the response of tumours to the administration of different therapeutic modalities and, more generally, to the phenomenon of resistance to treatments. Several interventions have been proposed to improve tumour oxygenation, being the elevation of the local temperature (hyperthermia) an important one. While other factors such as the metabolic activity have to be considered, the proficiency of the tumour vascular system is a key factor both for the tissue oxygenation and for its temperature maps. Consequently, the interplay of these factors has attracted considerable attention from the mathematical modelling perspective. Here we put forward a transport-based system of partial differential equations aimed at describing the dynamics of healthy and tumour cell subpopulations at the microscale in a region placed between two blood vessels. By using this model with diverse flow conditions, we analyse the oxygen and temperature profiles that arise in different scenarios of vascular status, both during free progression and under thermal therapy. We find that low oxygen levels are associated to elevations of temperature in locations preferentially populated by hypoxic cells, and hyperthermia-induced cell death, being strongly dependent on blood flow, would only appear under highly disrupted conditions of the local vasculature. This results in a noticeable effect of heat on hypoxic cells. Additionally, when pronounced cell death occurs, it is followed by a significant increase in the oxygen levels.
Stochastic Local Volatility models and the Wei-Norman factorization method
Authors: Julio Guerrero, Giuseppe Orlando
Date: 2022-01-27 00:00:00

In this paper, we show that a time-dependent local stochastic volatility (SLV) model can be reduced to a system of autonomous PDEs that can be solved using the Heat kernel, by means of the Wei-Norman factorization method and Lie algebraic techniques. Then, we compare the results of traditional Monte Carlo simulations with the explicit solutions obtained by said techniques. This approach is new in the literature and, in addition to reducing a non-autonomous problem into an autonomous one, allows for reduced time in numerical computations.
3D Object Detection and Instance Segmentation from 3D Range and 2D Color Images
Authors: Xiaoke Shen 1, Ioannis Stamos
Date: 2021-02-09 00:00:00
Proceeding: Sensors 2021 2
Tasks: 3D Instance Segmentation, 3D Object Detection, Instance Segmentation, Object, object-detection, Object Detection, Segmentation, Semantic Segmentation

Instance segmentation and object detection are significant problems in the fields of computer vision and robotics. We address those problems by proposing a novel object segmentation and detection system. First, we detect 2D objects based on RGB, depth only, or RGB-D images. A 3D convolutional-based system, named Frustum VoxNet, is proposed. This system generates frustums from 2D detection results, proposes 3D candidate voxelized images for each frustum, and uses a 3D convolutional neural network (CNN) based on these candidates voxelized images to perform the 3D instance segmentation and object detection. Results on the SUN RGB-D dataset show that our RGB-D-based system’s 3D inference is much faster than state-of-the-art methods, without a significant loss of accuracy. At the same time, we can provide segmentation and detection results using depth only images, with accuracy comparable to RGB-D-based systems. This is important since our methods can also work well in low lighting conditions, or with sensors that do not acquire RGB images. Finally, the use of segmentation as part of our pipeline increases detection accuracy, while providing at the same time 3D instance segmentation.
moolib: A Platform for Distributed RL
Authors: Vegard Mella, Eric Hambro, Danielle Rothermel, Heinrich Küttler
Date: 2022-01-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present moolib, a library that enables the implementation of distributed reinforcement
learning and other machine learning codebases. Our implementation aims to be both simple
and scalable, targeting researchers with a wide range of available computing resources, e.g., from
individual to hundreds of GPUs. moolib is build around efficient remote procedure calls (RPCs)
for both tensor and non-tensor data. Together with the moolib library, we present example user
code which shows how moolib’s components can be used to implement common reinforcement
learning agents as a simple but scalable distributed network of homogeneous peers. Together with
this whitepaper, moolib and its examples are provided as open source code at our repository at
github.com/facebookresearch/moolib.
Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement
Authors: YijunYan, JinchangRen, GenyunSun, HuiminZhao, JunweiHan, XuelongLi, StephenMarshall, JinZhan
Date: 2018-02-14 00:00:00
Proceeding: Pattern Recognition 2018 2
Tasks: object-detection, Object Detection, Saliency Detection, Salient Object Detection

Visual attention is a kind of fundamental cognitive capability that allows human beings to focus on the region of interests (ROIs) under complex natural environments. What kind of ROIs that we pay attention to mainly depends on two distinct types of attentional mechanisms. The bottom-up mechanism can guide our detection of the salient objects and regions by externally driven factors, i.e. color and location, whilst the top-down mechanism controls our biasing attention based on prior knowledge and cognitive strategies being provided by visual cortex. However, how to practically use and fuse both attentional mechanisms for salient object detection has not been sufficiently explored. To the end, we propose in this paper an integrated framework consisting of bottom-up and top-down attention mechanisms that enable attention to be computed at the level of salient objects and/or regions. Within our framework, the model of a bottom-up mechanism is guided by the gestalt-laws of perception. We interpreted gestalt-laws of homogeneity, similarity, proximity and figure and ground in link with color, spatial contrast at the level of regions and objects to produce feature contrast map. The model of top-down mechanism aims to use a formal computational model to describe the background connectivity of the attention and produce the priority map. Integrating both mechanisms and applying to salient object detection, our results have demonstrated that the proposed method consistently outperforms a number of existing unsupervised approaches on five challenging and complicated datasets in terms of higher precision and recall rates, AP (average precision) and AUC (area under curve) values.