pwc-archive/papers-with-abstracts huggingface dataset:
Prognostic classification based on random convolutional kernel
Authors: Zekun Wu, Kaiwei Wu
Date: 2022-04-09 00:00:00
Tasks: Classification, Management, Multi-class Classification, Time Series, Time Series Analysis

Assessing the health status (HS) of system/component has long been a challenging task in the prognostic and health management (PHM) study. Differed from other regression based prognostic task such as predicting the remaining useful life, the HS assessment is essentially a multi class classificatIon problem. To address this issue, we introduced the random convolutional kernel-based approach, the RandOm Convolutional KErnel Transforms (ROCKET) and its latest variant MiniROCKET, in the paper. We implement ROCKET and MiniROCKET on the NASA's CMPASS dataset and assess the turbine fan engine's HS with the multi-sensor time-series data. Both methods show great accuracy when tackling the HS assessment task. More importantly, they demonstrate considerably efficiency especially compare with the deep learning-based method. We further reveal that the feature generated by random convolutional kernel can be combined with other classifiers such as support vector machine (SVM) and linear discriminant analysis (LDA). The newly constructed method maintains the high efficiency and outperforms all the other deop neutal network models in classification accuracy.
Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform
Authors: Mohammad Reza Yousefi, Saina Golnejad, Melika Mohammad Hosseini, Amin Dehghani
Date: 2022-04-09 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Epilepsy is one of the common neurological disorders characterized by recurrent and uncontrollable seizures, which seriously affect the life of patients. In many cases, electroencephalograms signal can provide important physiological information about the activity of the human brain which can be used to diagnose epilepsy. However, visual inspection of a large number of electroencephalogram signals is very time-consuming and can often lead to inconsistencies in physicians' diagnoses. Quantification of abnormalities in brain signals can indicate brain conditions and pathology so the electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. In this article, an attempt has been made to create a single instruction for diagnosing epilepsy, which consists of two steps. In the first step, a low-pass filter was used to preprocess the data and three separate mid-pass filters for different frequency bands and a multilayer neural network were designed. In the second step, the wavelet transform technique was used to process data. In particular, this paper proposes a multilayer perceptron neural network classifier for the diagnosis of epilepsy, that requires normal data and epilepsy data for education, but this classifier can recognize normal disorders, epilepsy, and even other disorders taught in educational examples. Also, the value of using electroencephalogram signal has been evaluated in two ways: using wavelet transform and non-using wavelet transform. Finally, the evaluation results indicate a relatively uniform impact factor on the use or non-use of wavelet transform on the improvement of epilepsy data functions, but in the end, it was shown that the use of perceptron multilayer neural network can provide a higher accuracy coefficient for experts.
A Probabilistic Model-Based Robust Waveform Design for MIMO Radar Detection
Authors: Xuyang Wang, Bo Tang, Ming Zhang
Date: 2022-04-09 00:00:00

This paper addresses robust waveform design for multiple-input-multiple-output (MIMO) radar detection. A probabilistic model is proposed to describe the target uncertainty. Considering that waveform design based on maximizing the probability of detection is intractable, the relative entropy between the distributions of the observations under two hypotheses (viz., the target is present/absent) is employed as the design metric. To tackle the resulting non-convex optimization problem, an efficient algorithm based on minorization-maximization (MM) is derived. Numerical results demonstrate that the waveform synthesized by the proposed algorithm is more robust to model mismatches.
Reaction Network Analysis of Metabolic Insulin Signaling
Authors: Patrick Vincent N. Lubenia, Eduardo R. Mendoza, Angelyn R. Lao
Date: 2022-04-10 00:00:00
Tasks: Novel Concepts

Absolute concentration robustness (ACR) and concordance are novel concepts in the theory of robustness and stability within Chemical Reaction Network Theory. In this paper, we have extended Shinar and Feinberg's reaction network analysis approach to the insulin signaling system based on recent advances in decomposing reaction networks. We have shown that the network with 20 species, 35 complexes, and 35 reactions is concordant, implying at most one positive equilibrium in each of its stoichiometric compatibility class. We have obtained the system's finest independent decomposition consisting of 10 subnetworks, a coarsening of which reveals three subnetworks which are not only functionally but also structurally important. Utilizing the network's deficiency-oriented coarsening, we have developed a method to determine positive equilibria for the entire network. Our analysis has also shown that the system has ACR in 8 species all coming from a deficiency zero subnetwork. Interestingly, we have shown that, for a set of rate constants, the insulin-regulated glucose transporter GLUT4 (important in glucose energy metabolism), has stable ACR.
Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC Algorithm
Authors: Rahul Biswas, Eli Shlizerman
Date: 2022-04-11 00:00:00
Tasks: Causal Inference, counterfactual, Functional Connectivity, Time Series, Time Series Analysis

The representation of the flow of information between neurons in the brain based on their activity is termed the causal functional connectome. Such representation incorporates the dynamic nature of neuronal activity and causal interactions between them. In contrast to connectome, the causal functional connectome is not directly observed and needs to be inferred from neural time series. A popular statistical framework for inferring causal connectivity from observations is the directed probabilistic graphical modeling. Its common formulation is not suitable for neural time series since was developed for variables with independent and identically distributed static samples. In this work, we propose to model and estimate the causal functional connectivity from neural time series using a novel approach that adapts directed probabilistic graphical modeling to the time series scenario. In particular, we develop the Time-Aware PC (TPC) algorithm for estimating the causal functional connectivity, which adapts the PC algorithm a state-of-the-art method for statistical causal inference. We show that the model outcome of TPC has the properties of reflecting causality of neural interactions such as being non-parametric, exhibits the directed Markov property in a time-series setting, and is predictive of the consequence of counterfactual interventions on the time series. We demonstrate the utility of the methodology to obtain the causal functional connectome for several datasets including simulations, benchmark datasets, and recent multi-array electro-physiological recordings from the mouse visual cortex.
3D-printed stand, timing interface, and coil localization tools for concurrent TMS-fMRI experiments
Authors: Samuel Goldstein, Farshad Rafiei, Dobromir Rahnev
Date: 2022-04-10 00:00:00
Tasks: Position

Concurrent TMS-fMRI involves administrating TMS while subjects are inside an MRI scanner and allows the study of the effects of neurostimulation on simultaneous brain activity. Despite its high promise, the technique has proven challenging to implement for at least three reasons. First, it is difficult to position and stabilize the TMS coil inside the MRI scanner in a way that precisely targets a pre-specified brain region. Second, standard task-presentation software suffers from imprecise timing, which can lead to TMS causing large image artifacts. Third, it is difficult to verify the exact TMS coil position during scanning. In this paper, we describe solutions to all three of these challenges. First, we develop a 3D-printed TMS stand that is fully adjustable and can reach most areas of the scalp. The stand is compatible with various MR coils and features an adjustable mirror holder. Second, we create an interface that can precisely time the TMS pulses with respect to the fMRI image acquisition with a variance of under 1 ms. Third, we develop software for precisely determining the TMS coil position inside the MRI scanner and computing the location of maximal stimulation. All three tools are either free or inexpensive. We provide detailed instructions for building and implementing these tools to facilitate an efficient and reliable concurrent TMS-fMRI setup.
NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding
Authors: Gabriel Kalweit, Maria Kalweit, Mansour Alyahyay, Zoe Jaeckel, Florian Steenbergen, Stefanie Hardung, Thomas Brox, Ilka Diester, Joschka Boedecker
Date: 2022-04-10 00:00:00
Tasks: Form, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Current neural decoding methods typically aim at explaining behavior based on neural activity via supervised learning. However, since generally there is a strong connection between learning of subjects and their expectations on long-term rewards, we propose NeuRL, an inverse reinforcement learning approach that (1) extracts an intrinsic reward function from collected trajectories of a subject in closed form, (2) maps neural signals to this intrinsic reward to account for long-term dependencies in the behavior and (3) predicts the simulated behavior for unseen neural signals by extracting Q-values and the corresponding Boltzmann policy based on the intrinsic reward values for these unseen neural signals. We show that NeuRL leads to better generalization and improved decoding performance compared to supervised approaches. We study the behavior of rats in a response-preparation task and evaluate the performance of NeuRL within simulated inhibition and per-trial behavior prediction. By assigning clear functional roles to defined neuronal populations our approach offers a new interpretation tool for complex neuronal data with testable predictions. In per-trial behavior prediction, our approach furthermore improves accuracy by up to 15% compared to traditional methods.
A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data
Authors: Shengjie Zheng, Wenyi Li, Lang Qian, Chenggang He, Xiaojian Li
Date: 2022-03-26 00:00:00
Tasks: Brain Computer Interface, Data Augmentation, Decoder

Brain-computer interfaces (BCIs), transform neural signals in the brain into in-structions to control external devices. However, obtaining sufficient training data is difficult as well as limited. With the advent of advanced machine learning methods, the capability of brain-computer interfaces has been enhanced like never before, however, these methods require a large amount of data for training and thus require data augmentation of the limited data available. Here, we use spiking neural networks (SNN) as data generators. It is touted as the next-generation neu-ral network and is considered as one of the algorithms oriented to general artifi-cial intelligence because it borrows the neural information processing from bio-logical neurons. We use the SNN to generate neural spike information that is bio-interpretable and conforms to the intrinsic patterns in the original neural data. Ex-periments show that the model can directly synthesize new spike trains, which in turn improves the generalization ability of the BCI decoder. Both the input and output of the spiking neural model are spike information, which is a brain-inspired intelligence approach that can be better integrated with BCI in the future.
Coalescent dynamics of planktonic communities
Authors: Paula Villa Martin, Anzhelika Koldaeva, Simone Pigolotti
Date: 2022-04-10 00:00:00
Tasks: Diversity

Planktonic communities are extremely diverse and include a vast number of rare species. The dynamics of these rare species is best described by individual-based models. However, individual-based approaches to planktonic diversity face substantial difficulties, due to the large number of individuals required to make realistic predictions. In this paper, we study diversity of planktonic communities by means of a spatial coalescence model, that incorporates transport by oceanic currents. As a main advantage, our approach requires simulating a number of individuals equal to the size of the sample one is interested in, rather than the size of the entire community. By theoretical analysis and simulations, we explore the conditions upon which our coalescence model is equivalent to individual-based dynamics. As an application, we use our model to predict the impact of chaotic advection by oceanic currents on biodiversity. We conclude that the coalescent approach permits to simulate marine microbial communities much more efficiently than with individual-based models.
Meteorological indicators of dengue epidemics in non-endemic Northwest Argentina
Authors: Javier Armando Gutierrez, Karina Laneri, Juan Pablo Aparicio, Gustavo Javier Sibona
Date: 2022-04-10 00:00:00
Tasks: Time Series Analysis

In the last two decades dengue cases increased significantly throughout the world. In several regions dengue re-emerged, particularly in Latin America, where dengue cases not only increased but also occurred more frequently. It is therefore necessary to understand the mechanisms that drive epidemic outbreaks in non-endemic regions, to help in the design of control strategies. We develop a stochastic model that includes climate variables, social structure, and mobility between a non-endemic city and an endemic area. We choose as a case study the non-endemic city of San Ram{\'o}n de la Nueva Or{\'a}n, located in Northwest Argentina. Human mobility is intense through the border with Bolivia, where dengue transmission is sustained during the whole year. City population was modelled as a meta-population taking into account households and population data for each patch. Climate variability was considered by including rainfall, relative humidity and temperature time series into the models. Those climatic variables were input of a mosquito population ecological model, which in turn is coupled to an epidemiological model. Different hypotheses regarding people's mobility between an endemic and non-endemic area are tested, taking into account the local climatic variation, typical of the non-endemic city. Simulations are qualitatively consistent with weekly clinical data reported from 2009 to 2016. Our model results allow to explain the observed pattern of outbreaks, that alternates large dengue epidemics and several years with smaller outbreaks. We found that the number of vectors per host and an effective reproductive number are proxies for large epidemics, both related with climate variability such as rainfall and temperature, opening the possibility to test these meteorological variables for forecast purposes.
Automatic Adjoint Differentiation for special functions involving expectations
Authors: José Brito, Andrei Goloubentsev, Evgeny Goncharov
Date: 2022-04-11 00:00:00

We explain how to compute gradients of functions of the form $G = \frac{1}{2} \sum_{i=1}^{m} (E y_i - C_i)^2$, which often appear in the calibration of stochastic models, using Automatic Adjoint Differentiation and parallelization. We expand on the work of arXiv:1901.04200 and give faster and easier to implement approaches. We also provide an implementation of our methods and apply the technique to calibrate European options.
The short-term effect of COVID-19 pandemic on China's crude oil futures market: A study based on multifractal analysis
Authors: Shao Ying-Hui, Liu Ying-Lin, Yang Yan-Hong
Date: 2022-04-11 00:00:00

The ongoing COVID-19 shocked financial markets globally, including China's crude oil future market, which is the third most traded crude oil futures after WTI and Brent. As China's first crude oil futures accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil futures is an important issue for investors and policy makers. Therefore this paper studies the short-term influence of COVID-19 pandemic on SC via multifractal analysis. We compare market efficiency of SC before and during the pandemic with the multifractal detrended fluctuation analysis and other commonly-used random walk tests. Then we generate shuffled and surrogate data to investigate the components of multifractal nature in SC. And we examine cross-correlations between SC returns and other financial assets returns as well as SC trading volume changes by the multifractal detrended cross-correlation analysis. The results show that market efficiency of SC and its cross-correlations with other assets increase significantly after the outbreak of COVID-19. Besides that, the sources of its multifractal nature have changed since the pandemic. The findings provide evidence for the short-term impacts of COVID-19 on SC. The results may have important implications for assets allocation, investment strategies and risk monitoring.
Willingness to pay, surplus and Insurance policy under dual theory
Authors: Neji Saidi
Date: 2022-04-10 00:00:00

In this paper, we aims to state some proprieties of willingness to pay (WTP) for partial risk reduction and links with insurance within the dual theory of decision. In the case of partial reduction, we get as Langlais (2005) that a risk-averse decision maker (DM) can have a willingness to pay small than this of a neutral one. By decomposition the WTP as Courbage and al (2008), we get that a strong averse DM is willing to give more for a reduction of a high probability portion rather than a low probability one. The main result is that in the dual theory, reducing probability of risk and supply insurance can be complementary if the surplus is increasing in risk reduction.
Worldwide city transport typology prediction with sentence-BERT based supervised learning via Wikipedia
Authors: Srushti Rath, Joseph Y. J. Chow
Date: 2022-03-29 00:00:00
Tasks: Sentence

An overwhelming majority of the world's human population lives in urban areas and cities. Understanding a city's transportation typology is immensely valuable for planners and policy makers whose decisions can potentially impact millions of city residents. Despite the value of understanding a city's typology, labeled data (city and it's typology) is scarce, and spans at most a few hundred cities in the current transportation literature. To break this barrier, we propose a supervised machine learning approach to predict a city's typology given the information in its Wikipedia page. Our method leverages recent breakthroughs in natural language processing, namely sentence-BERT, and shows how the text-based information from Wikipedia can be effectively used as a data source for city typology prediction tasks that can be applied to over 2000 cities worldwide. We propose a novel method for low-dimensional city representation using a city's Wikipedia page, which makes supervised learning of city typology labels tractable even with a few hundred labeled samples. These features are used with labeled city samples to train binary classifiers (logistic regression) for four different city typologies: (i) congestion, (ii) auto-heavy, (iii) transit-heavy, and (iv) bike-friendly cities resulting in reasonably high AUC scores of 0.87, 0.86, 0.61 and 0.94 respectively. Our approach provides sufficient flexibility for incorporating additional variables in the city typology models and can be applied to study other city typologies as well. Our findings can assist a diverse group of stakeholders in transportation and urban planning fields, and opens up new opportunities for using text-based information from Wikipedia (or similar platforms) as data sources in such fields.
Deep Normed Embeddings for Patient Representation
Authors: Thesath Nanayakkara, Gilles Clermont, Christopher James Langmead, David Swigon
Date: 2022-04-12 00:00:00
Tasks: Representation Learning, Time Series, Time Series Analysis

We introduce a novel contrastive representation learning objective and a training scheme for clinical time series. Specifically, we project high dimensional EHR. data to a closed unit ball of low dimension, encoding geometric priors so that the origin represents an idealized perfect health state and the Euclidean norm is associated with the patient's mortality risk. Moreover, using septic patients as an example, we show how we could learn to associate the angle between two vectors with the different organ system failures, thereby, learning a compact representation which is indicative of both mortality risk and specific organ failure. We show how the learned embedding can be used for online patient monitoring, can supplement clinicians and improve performance of downstream machine learning tasks. This work was partially motivated from the desire and the need to introduce a systematic way of defining intermediate rewards for Reinforcement Learning in critical care medicine. Hence, we also show how such a design in terms of the learned embedding can result in qualitatively different policies and value distributions, as compared with using only terminal rewards.
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation
Authors: Wenqiang Zhang, Zilong Huang, Guozhong Luo, Tao Chen, Xinggang Wang, Wenyu Liu, Gang Yu, Chunhua Shen
Date: 2022-04-12 00:00:00
Proceeding: CVPR 2022 1
Tasks: Segmentation, Semantic Segmentation

Although vision transformers (ViTs) have achieved great success in computer vision, the heavy computational cost hampers their applications to dense prediction tasks such as semantic segmentation on mobile devices. In this paper, we present a mobile-friendly architecture named \textbf{To}ken \textbf{P}yramid Vision Trans\textbf{former} (\textbf{TopFormer}). The proposed \textbf{TopFormer} takes Tokens from various scales as input to produce scale-aware semantic features, which are then injected into the corresponding tokens to augment the representation. Experimental results demonstrate that our method significantly outperforms CNN- and ViT-based networks across several semantic segmentation datasets and achieves a good trade-off between accuracy and latency. On the ADE20K dataset, TopFormer achieves 5\% higher accuracy in mIoU than MobileNetV3 with lower latency on an ARM-based mobile device. Furthermore, the tiny version of TopFormer achieves real-time inference on an ARM-based mobile device with competitive results. The code and models are available at: https://github.com/hustvl/TopFormer