pwc-archive/papers-with-abstracts huggingface dataset:
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Authors: Delaram Sadeghi, Afshin Shoeibi, Navid Ghassemi, Parisa Moridian, Ali Khadem, Roohallah Alizadehsani, Mohammad Teshnehlab, Juan M. Gorriz, Fahime Khozeimeh, Yu-Dong Zhang, Saeid Nahavandi, U Rajendra Acharya
Date: 2021-02-24 00:00:00
Tasks: Hippocampus

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.
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Authors: 中国外卖行业维权群
Date: 2021-03-02 00:00:00

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Authors: 工号51电报群
Date: 2021-03-01 00:00:00

https://t.me/gonghao51
工号51电报群公告：
各位朋友，近期舆论封锁疯狂，继公众号平台文章屡屡被封后，今天编辑小何的微信号也因为声援外卖江湖盟主被抓遭遇毒手。为此，我们建了电报群，用于保持联系和长期保存文章。
现在，我们需要大家帮忙做三件事：
1.帮51的电报群做做宣传，帮认识的51读者安装电报，把他们拉到这里；
2.积极跟进事情进展，联系知情人士，在群里分享最新消息；
3. 用实际行动支持盟主，利用一切可能的方式和途径向身边的骑手和工友们扩散消息！
https://t.me/gonghao51
大家有对51说的话或想参加实际工作的，可以私聊@JuemurenBot！
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Authors: 外卖骑手联盟盟主陈天河（熊焰）
Date: 2021-03-02 00:00:00

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Super-Resolution DOA Estimation for Wideband Signals using Arbitrary Linear Arrays without Focusing Matrices
Authors: Milad Javadzadeh Jirhandeh, Mohammad Hossein Kahae
Date: 2021-03-04 00:00:00
Tasks: Super-Resolution

We focus on developing an effective Direction Of Arrival (DOA) estimation method for wideband sources based on the gridless sparse concept. Previous coherent methods have been designed by dividing wideband frequencies into a few subbands which are transferred to a reference subband using focusing matrices. In this work, as opposed to the previous techniques, we propose a convex optimization problem that leads to an accurate wideband DOA estimation method with no need for any focusing matrix. Moreover, in this method, no initial DOA estimates are required and it can be used for any arbitrary linear arrays. Numerical simulations show that in comparison to some well-known techniques, the proposed method generates outstanding accuracy and better robustness to noise. The effectiveness of the method is also verified in presence of close adjacent sources.
High-dimensional estimation of quadratic variation based on penalized realized variance
Authors: Kim Christensen, Mikkel Slot Nielsen, Mark Podolskij
Date: 2021-03-04 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we develop a penalized realized variance (PRV) estimator of the quadratic variation (QV) of a high-dimensional continuous It\^{o} semimartingale. We adapt the principle idea of regularization from linear regression to covariance estimation in a continuous-time high-frequency setting. We show that under a nuclear norm penalization, the PRV is computed by soft-thresholding the eigenvalues of realized variance (RV). It therefore encourages sparsity of singular values or, equivalently, low rank of the solution. We prove our estimator is minimax optimal up to a logarithmic factor. We derive a concentration inequality, which reveals that the rank of PRV is -- with a high probability -- the number of non-negligible eigenvalues of the QV. Moreover, we also provide the associated non-asymptotic analysis for the spot variance. We suggest an intuitive data-driven bootstrap procedure to select the shrinkage parameter. Our theory is supplemented by a simulation study and an empirical application. The PRV detects about three-five factors in the equity market, with a notable rank decrease during times of distress in financial markets. This is consistent with most standard asset pricing models, where a limited amount of systematic factors driving the cross-section of stock returns are perturbed by idiosyncratic errors, rendering the QV -- and also RV -- of full rank.
Front-end Diarization for Percussion Separation in Taniavartanam of Carnatic Music Concerts
Authors: Nauman Dawalatabad, Jilt Sebastian, Jom Kuriakose, C. Chandra Sekhar, Shrikanth Narayanan, Hema A. Murthy
Date: 2021-03-04 00:00:00

Instrument separation in an ensemble is a challenging task. In this work, we address the problem of separating the percussive voices in the taniavartanam segments of Carnatic music. In taniavartanam, a number of percussive instruments play together or in tandem. Separation of instruments in regions where only one percussion is present leads to interference and artifacts at the output, as source separation algorithms assume the presence of multiple percussive voices throughout the audio segment. We prevent this by first subjecting the taniavartanam to diarization. This process results in homogeneous clusters consisting of segments of either a single voice or multiple voices. A cluster of segments with multiple voices is identified using the Gaussian mixture model (GMM), which is then subjected to source separation. A deep recurrent neural network (DRNN) based approach is used to separate the multiple instrument segments. The effectiveness of the proposed system is evaluated on a standard Carnatic music dataset. The proposed approach provides close-to-oracle performance for non-overlapping segments and a significant improvement over traditional separation schemes.
State and parameter estimation for model-based retinal laser treatment
Authors: Viktoria Kleyman, Manuel Schaller, Mitsuru Wilson, Mario Mordmüller, Ralf Brinkmann, Karl Worthmann, Matthias A. Müller
Date: 2021-03-04 00:00:00
Tasks: parameter estimation, State Estimation

We present an approach for state and parameter estimation in retinal laser treatment by a novel setup where both measurement and heating is performed by a single laser. In this medical application, the temperature that is induced by the laser in the patient's eye is critical for a successful and safe treatment. To this end, we pursue a model-based approach using a model given by a heat diffusion equation on a cylindrical domain, where the source term is given by the absorbed laser power. The model is parametric in the sense that it involves an absorption coefficient, which depends on the treatment spot and plays a central role in the input-output behavior of the system. After discretization, we apply a particularly suited parametric model order reduction to ensure real-time tractability while retaining parameter dependence. We augment known state estimation techniques, i.e., extended Kalman filtering and moving horizon estimation, with parameter estimation to estimate the absorption coefficient and the current state of the system. Eventually, we show first results for simulated and experimental data from porcine eyes. We find that, regarding convergence speed, the moving horizon estimation slightly outperforms the extended Kalman filter on measurement data in terms of parameter and state estimation, however, on simulated data the results are very similar.
The Effect of Prosumer Duality on Power Market: Evidence from the Cournot Model
Authors: Eve Tsybina, Justin Burkett, Santiago Grijalva
Date: 2021-03-04 00:00:00

Distributed energy resources behind the meter and automation systems enable traditional electricity consumers to become prosumers (producers/consumers) that can participate in peer-to-peer exchange of electricity and in retail electricity markets. Emerging prosumers can provide benefits to the system by exchanging energy and energy-related services. More importantly, they can do so in a more honest and more competitive way than the traditional producer/consumer systems. We extend the traditional Cournot model to show that the dual nature of prosumers can lead to more competitive behavior under a game theoretic scenario. We show that best response supply quantities of a prosumer are usually closer to the competitive level compared to those of a producer.
Serial Interference Cancellation for Improving uplink in LoRa-like Networks
Authors: Angesom Ataklity Tesfay, Eric Pierre Simon, Guillaume Ferré, Laurent Clavier
Date: 2021-03-04 00:00:00

In this paper, we present a new receiver design, which significantly improves performance in the Internet of Things networks such as LoRa, i.e., having a chirp spread spectrum modulation. The proposed receiver is able to demodulate multiple users simultaneously transmitted over the same frequency channel with the same spreading factor. From a non-orthogonal multiple access point of view, it is based on the power domain and uses serial interference cancellation. Simulation results show that the receiver allows a significant increase in the number of connected devices in the network.
Event-Based Dynamic Banking Network Exploration for Economic Anomaly Detection
Authors: Andry Alamsyah, Dian Puteri Ramadhani, Farida Titik Kristanti
Date: 2021-03-04 00:00:00
Tasks: Anomaly Detection

The instability of financial system issues might trigger a bank failure, evoke spillovers, and generate contagion effects which negatively impacted the financial system, ultimately on the economy. This phenomenon is the result of the highly interconnected banking transaction. The banking transactions network is considered as a financial architecture backbone. The strong interconnectedness between banks escalates contagion disruption spreading over the banking network and trigger the entire system collapse. This far, the financial instability is generally detected using macro approach mainly the uncontrolled transaction deficits amount and unpaid foreign debt. This research proposes financial instability detection in another point of view, through the macro view where the banking network structure are explored globally and micro view where focuses on the detailed network patterns called motif. Network triadic motif patterns used as a denomination to detect financial instability. The most related network triadic motif changes related to the instability period are determined as a detector. We explore the banking network behavior under financial instability phenomenon along with the major religious event in Indonesia, Eid al-Fitr. We discover one motif pattern as the financial instability underlying detector. This research helps to support the financial system stability supervision.
Sales Prediction Model Using Classification Decision Tree Approach For Small Medium Enterprise Based on Indonesian E-Commerce Data
Authors: Raden Johannes, Andry Alamsyah
Date: 2021-03-04 00:00:00

The growth of internet users in Indonesia gives an impact on many aspects of daily life, including commerce. Indonesian small-medium enterprises took this advantage of new media to derive their activity by the meaning of online commerce. Until now, there is no known practical implementation of how to predict their sales and revenue using their historical transaction. In this paper, we build a sales prediction model on the Indonesian footwear industry using real-life data crawled on Tokopedia, one of the biggest e-commerce providers in Indonesia. Data mining is a discipline that can be used to gather information by processing the data. By using the method of classification in data mining, this research will describe patterns of the market and predict the potential of the region in the national market commodities. Our approach is based on the classification decision tree. We managed to determine predicted the number of items sold by the viewers, price, and type of shoes.
Towards Designing Computer Vision-based Explainable-AI Solution: A Use Case of Livestock Mart Industry
Authors: Devam Dave, Het Naik, Smiti Singhal, Rudresh Dwivedi, Pankesh Patel
Date: 2021-02-08 00:00:00

The objective of an online Mart is to match buyers and sellers, to weigh animals and to oversee their sale. A reliable pricing method can be developed by ML models that can read through historical sales data. However, when AI models suggest or recommend a price, that in itself does not reveal too much (i.e., it acts like a black box) about the qualities and the abilities of an animal. An interested buyer would like to know more about the salient features of an animal before making the right choice based on his requirements. A model capable of explaining the different factors that impact the price point is essential for the needs of the market. It can also inspire confidence in buyers and sellers about the price point offered. To achieve these objectives, we have been working with the team at MartEye, a startup based in Portershed in Galway City, Ireland. Through this paper, we report our work-in-progress research towards building a smart video analytic platform, leveraging Explainable AI techniques.
Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics
Authors: Alberto Racca, Luca Magri
Date: 2021-02-09 00:00:00
Tasks: Bayesian Optimization, Robust Design, Time Series Analysis

An approach to the time-accurate prediction of chaotic solutions is by learning temporal patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing, can accurately predict the chaotic dynamics well beyond the predictability time. Existing studies, however, also showed that small changes in the hyperparameters may markedly affect the network's performance. The aim of this paper is to assess and improve the robustness of Echo State Networks for the time-accurate prediction of chaotic solutions. The goal is three-fold. First, we investigate the robustness of routinely used validation strategies. Second, we propose the Recycle Validation, and the chaotic versions of existing validation strategies, to specifically tackle the forecasting of chaotic systems. Third, we compare Bayesian optimization with the traditional Grid Search for optimal hyperparameter selection. Numerical tests are performed on two prototypical nonlinear systems that have both chaotic and quasiperiodic solutions. Both model-free and model-informed Echo State Networks are analysed. By comparing the network's robustness in learning chaotic versus quasiperiodic solutions, we highlight fundamental challenges in learning chaotic solutions. The proposed validation strategies, which are based on the dynamical systems properties of chaotic time series, are shown to outperform the state-of-the-art validation strategies. Because the strategies are principled-they are based on chaos theory such as the Lyapunov time-they can be applied to other Recurrent Neural Networks architectures with little modification. This work opens up new possibilities for the robust design and application of Echo State Networks, and Recurrent Neural Networks, to the time-accurate prediction of chaotic systems.
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release
Authors: Liam Fowl, Ping-Yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja, Tom Goldstein
Date: 2021-02-16 00:00:00
Tasks: Data Poisoning

Large organizations such as social media companies continually release data, for example user images. At the same time, these organizations leverage their massive corpora of released data to train proprietary models that give them an edge over their competitors. These two behaviors can be in conflict as an organization wants to prevent competitors from using their own data to replicate the performance of their proprietary models. We solve this problem by developing a data poisoning method by which publicly released data can be minimally modified to prevent others from train-ing models on it. Moreover, our method can be used in an online fashion so that companies can protect their data in real time as they release it.We demonstrate the success of our approach onImageNet classification and on facial recognition.
Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking
Authors: Victoria Huang, Gang Chen, Qiang Fu
Date: 2021-02-06 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Recently, distributed controller architectures have been quickly gaining popularity in Software-Defined Networking (SDN). However, the use of distributed controllers introduces a new and important Request Dispatching (RD) problem with the goal for every SDN switch to properly dispatch their requests among all controllers so as to optimize network performance. This goal can be fulfilled by designing an RD policy to guide distribution of requests at each switch. In this paper, we propose a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach to automatically design RD policies with high adaptability and performance. This is achieved through a new problem formulation in the form of a Multi-Agent Markov Decision Process (MA-MDP), a new adaptive RD policy design and a new MA-DRL algorithm called MA-PPO. Extensive simulation studies show that our MA-DRL technique can effectively train RD policies to significantly outperform man-made policies, model-based policies, as well as RD policies learned via single-agent DRL algorithms.
Indian Economy and Nighttime Lights
Authors: Jeet Agnihotri, Subhankar Mishra
Date: 2021-01-27 00:00:00
Tasks: Time Series, Time Series Analysis

Forecasting economic growth of India has been traditionally an uncertain exercise. The indicators and factors affecting economic structures and the variables required to model that captures the situation correctly is point of concern. Although the forecast should be specific to the country we are looking at however countries do have interlinkages among them. As the time series can be more volatile and sometimes certain variables are unavailable it is harder to predict for the developing economies as compared to stable and developed nations. However it is very important to have accurate forecasts for economic growth for successful policy formations. One of the hypothesized indicators is the nighttime lights. Here we aim to look for a relationship between GDP and Nighttime lights. Specifically we look at the DMSP and VIIRS dataset. We are finding relationship between various measures of economy.
Effective Deep Learning for Semantic Segmentation Based Bleeding Zone Detection in Capsule Endoscopy Images
Authors: Tonmoy Ghosh, Linfeng Li, Jacob Chakareski
Date: 2018-09-06 00:00:00
Tasks: Decision Making, Semantic Segmentation

Capsule endoscopy (CE) is a non-invasive way to detect small intestinal abnormalities such as bleeding. It provides a direct vision of the patients entire gastrointestinal (GI) tract. However, a manual inspection of the huge number of images produced thereby is tedious and lengthy, and thus prone to human errors. This makes automated computer assisted decision- making appealing in this context. This paper introduces a novel deep-learning based semantic segmentation approach for bleeding zone detection in CE images. A bleeding image features three regions labeled as bleeding, non-bleeding, and background. Thus, a convolutional neural network (CNN) is trained using SegNet layers with three classes. A given CE image is segmented using our training network and the detected bleeding zones are marked. The proposed network architecture is tested on different color planes and best performance is achieved using the hue saturation and value (HSV) color space. Experimental performance evaluation is carried out on a publicly available clinical dataset, on which our framework achieves 94.42% global accuracy and 90.69% weighted intersection over union (IoU), two state-of-the-art classification metrics. Performance gains are demonstrated over several recent state-of-art competiting meth- ods in terms of all performance measures we examined, including mean accuracy, mean IoU, global accuracy and weighted IoU.
Index Terms—bleeding detection, capsule endoscopy, deep learning, SegNet, convolutional neural network.
Frontiers in Scalable Distributed Control: SLS, MPC, and Beyond
Authors: Jing Shuang Li, Carmen Amo Alonso, John C. Doyle
Date: 2020-10-03 00:00:00
Tasks: Model Predictive Control

The System Level Synthesis (SLS) approach facilitates distributed control of large cyberphysical networks in an easy-to-understand, computationally scalable way. We present an overview of the SLS approach and its associated extensions in nonlinear control, MPC, adaptive control, and learning for control. To illustrate the effectiveness of SLS-based methods, we present a case study motivated by the power grid, with communication constraints, actuator saturation, disturbances, and changing setpoints. This simple but challenging case study necessitates the use of model predictive control (MPC); however, standard MPC techniques often scales poorly to large systems and incurs heavy computational burden. To address this challenge, we combine two SLS-based controllers to form a layered MPC-like controller. Our controller has constant computational complexity with respect to the system size, gives a 20-fold reduction in online computation requirements, and still achieves performance that is within 3% of the centralized MPC controller.
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data
Authors: Jushan Bai, Serena Ng
Date: 2019-10-15 00:00:00
Tasks: Imputation, Matrix Completion, Missing Values

This paper proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Assuming that a strong factor structure holds for the full panel of data and its sub-blocks, it is shown that the common component can be consistently estimated at four different rates of convergence without requiring regularization or iteration. An asymptotic analysis of the estimation error is obtained. An application of our analysis is estimation of counterfactuals when potential outcomes have a factor structure. We study the estimation of average and individual treatment effects on the treated and establish a normal distribution theory that can be useful for hypothesis testing.