pwc-archive/papers-with-abstracts huggingface dataset:
PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
Authors: Jiacheng Zhu, Gregory Darnell, Agni Kumar, Ding Zhao, Bo Li, XuanLong Nguyen, Shirley You Ren
Date: 2022-03-19 00:00:00
Tasks: counterfactual, Heart Rate Variability, regression, Rhythm

Heart rate variability (HRV) is a practical and noninvasive measure of autonomic nervous system activity, which plays an essential role in cardiovascular health. However, using HRV to assess physiology status is challenging. Even in clinical settings, HRV is sensitive to acute stressors such as physical activity, mental stress, hydration, alcohol, and sleep. Wearable devices provide convenient HRV measurements, but the irregularity of measurements and uncaptured stressors can bias conventional analytical methods. To better interpret HRV measurements for downstream healthcare applications, we learn a personalized diurnal rhythm as an accurate physiological indicator for each individual. We develop Physiological Multitask-Learning (PhysioMTL) by harnessing Optimal Transport theory within a Multitask-learning (MTL) framework. The proposed method learns an individual-specific predictive model from heterogeneous observations, and enables estimation of an optimal transport map that yields a push forward operation onto the demographic features for each task. Our model outperforms competing MTL methodologies on unobserved predictive tasks for synthetic and two real-world datasets. Specifically, our method provides remarkable prediction results on unseen held-out subjects given only $20\%$ of the subjects in real-world observational studies. Furthermore, our model enables a counterfactual engine that generates the effect of acute stressors and chronic conditions on HRV rhythms.
Multi-agent Searching System for Medical Information
Authors: Mariya Evtimova-Gardair
Date: 2022-03-23 00:00:00

In the paper is proposed a model of multi-agent security system for searching a medical information in Internet. The advantages when using mobile agent are described, so that to perform searching in Internet. Nowadays, multi-agent systems found their application into distribution of decisions. For modeling the proposed multi-agent medical system is used JADE. Finally, the results when using mobile agent are generated that could reflect performance when working with BIG DATA. The proposed system is having also relatively high precision 96% .
Multiple-Input Multiple-Output Fusion Network For Generalized Zero-Shot Learning
Authors: Fangming Zhong∗, Guangze Wang, Zhikui Chen, Xu Yuan, Feng Xia
Date: 2021-05-13 00:00:00
Proceeding: IEEE 2021 5
Tasks: Generalized Zero-Shot Learning, Zero-Shot Learning

Generalized zero-shot learning (GZSL) has attracted consid-
erable attention recently, which trains models with data from
seen classes and tests on data from both seen and unseen
classes. Most of the existing methods attempt to find a map-
ping from visual space to semantic space, such mapping can
easily result in the domain shift problem. To address this
issue, we propose a Multiple-Input Multiple-Output Fusion
Network to GZSL. It can generate similar common seman-
tic representation to paired inputs even with only the class
semantic embeddings. This makes it possible to synthesize
pseudo samples from attributes of unseen classes. Extensive
experiments carried out on three benchmark datasets show the
effectiveness of the proposed model.
CA-PMG: Channel attention and progressive multi-granularity training network for fine-grained visual classification
Authors: Peipei Zhao, Qiguang Miao, Hang Yao, Xiangzeng Liu, Ruyi Liu, Maoguo Gong
Date: 2021-04-21 00:00:00
Proceeding: IET Image Processing 2021 4
Tasks: Fine-Grained Image Classification, Fine-Grained Visual Recognition

Fine-grained visual classification is challenging due to the inherently subtle intra-class object variations. To solve this issue, a novel framework named channel attention and progressive multi-granularity training network, is proposed. It first exploits meaningful feature maps through the channel attention module and captures multi-granularity features by the progressive multi-granularity training module. For each feature map, the channel attention module is proposed to explore channel-wise correlation. This allows themodel to re-weight the channels of the feature map according to the impact of their semantic information on performance. Furthermore, the progressivemulti-granularity trainingmodule is introduced to fuse features cross multi-granularity. And the fused features pay more attention to the subtle differences between images. The model can be trained efficiently in an end-to-end manner without bounding box or part annotations. Finally, comprehensive experiments are conducted to show that the method achieves state-of-the-art performances on the CUB-200-2011, Stanford Cars, and FGVC-Aircraft datasets. Ablation studies demonstrate the effectiveness of each part in our module.
Fuzzy c-ordered-means clustering
Authors: Jacek M. Leski
Date: 2014-12-07 00:00:00
Proceeding: ScienceDirect 2014 12
Tasks: Clustering

Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its
sensitivity to the presence of noise and outliers in data. This paper introduces a new robust fuzzy clustering method named Fuzzy
C-Ordered-Means (FCOM) clustering. This method uses both the Huber’s M-estimators and the Yager’s OWA operators to obtain
its robustness. The proposed method is compared to many other ones, e.g.: the Fuzzy C-Means (FCM), the Possibilistic Clustering (PC), the fuzzy Noise Clustering Method (NCM), the Lp norm clustering (Lp FCM) (0 <p< 1), the L1 norm clustering
(L1 FCM), the Fuzzy Clustering with Polynomial Fuzzifier (PFCM) and the ε-insensitive Fuzzy C-Means (βFCM). To this end
experiments on synthetic data with outliers have been performed as well as on data with heavy-tailed and overlapping groups of
points in background noise.
Self-supervised Change Detection in Multi-view Remote Sensing Images
Authors: Yuxing Chen, Lorenzo Bruzzone
Date: 2021-03-10 00:00:00
Tasks: Change Detection, Earth Observation

The vast amount of unlabeled multi-temporal and multi-sensor remote sensing data acquired by the many Earth Observation satellites present a challenge for change detection. Recently, many generative model-based methods have been proposed for remote sensing image change detection on such unlabeled data. However, the high diversities in the learned features weaken the discrimination of the relevant change indicators in unsupervised change detection tasks. Moreover, these methods lack research on massive archived images. In this work, a self-supervised change detection approach based on an unlabeled multi-view setting is proposed to overcome this limitation. This is achieved by the use of a multi-view contrastive loss and an implicit contrastive strategy in the feature alignment between multi-view images. In this approach, a pseudo-Siamese network is trained to regress the output between its two branches pre-trained in a contrastive way on a large dataset of multi-temporal homogeneous or heterogeneous image patches. Finally, the feature distance between the outputs of the two branches is used to define a change measure, which can be analyzed by thresholding to get the final binary change map. Experiments are carried out on five homogeneous and heterogeneous remote sensing image datasets. The proposed SSL approach is compared with other supervised and unsupervised state-of-the-art change detection methods. Results demonstrate both improvements over state-of-the-art unsupervised methods and that the proposed SSL approach narrows the gap between unsupervised and supervised change detection.
Efficient Face Detection in the Fisheye Image Domain
Authors: Cheng-Yun Yang, Homer H. Chen
Date: 2021-06-14 00:00:00
Proceeding: IEEE Transactions on Image Processing 2021 6
Tasks: CPU, Face Detection

Significant progress has been made for face detection from normal images in recent years; however, accurate and fast face detection from fisheye images remains a challenging issue because of serious fisheye distortion in the peripheral region of the image. To improve face detection accuracy, we propose a light-weight location-aware network to distinguish the peripheral region from the central region in the feature learning stage. To match the face detector, the shape and scale of the anchor (bounding box) is made location dependent. The overall face detection system performs directly in the fisheye image domain without rectification and calibration and hence is agnostic of the fisheye projection parameters. Experiments on Wider-360 and real-world fisheye images using a single CPU core indeed show that our method is superior to the state-of-the-art real-time face detector RFB Net.
An Exploratory Study of Stock Price Movements from Earnings Calls
Authors: Sourav Medya, Mohammad Rasoolinejad, Yang Yang, Brian Uzzi
Date: 2022-01-31 00:00:00
Tasks: Graph Neural Network

Financial market analysis has focused primarily on extracting signals from accounting, stock price, and other numerical hard data reported in P&L statements or earnings per share reports. Yet, it is well-known that the decision-makers routinely use soft text-based documents that interpret the hard data they narrate. Recent advances in computational methods for analyzing unstructured and soft text-based data at scale offer possibilities for understanding financial market behavior that could improve investments and market equity. A critical and ubiquitous form of soft data are earnings calls. Earnings calls are periodic (often quarterly) statements usually by CEOs who attempt to influence investors' expectations of a company's past and future performance. Here, we study the statistical relationship between earnings calls, company sales, stock performance, and analysts' recommendations. Our study covers a decade of observations with approximately 100,000 transcripts of earnings calls from 6,300 public companies from January 2010 to December 2019. In this study, we report three novel findings. First, the buy, sell and hold recommendations from professional analysts made prior to the earnings have low correlation with stock price movements after the earnings call. Second, using our graph neural network based method that processes the semantic features of earnings calls, we reliably and accurately predict stock price movements in five major areas of the economy. Third, the semantic features of transcripts are more predictive of stock price movements than sales and earnings per share, i.e., traditional hard data in most of the cases.
Bubble Prediction of Non-Fungible Tokens (NFTs): An Empirical Investigation
Authors: Kensuke Ito, Kyohei Shibano, Gento Mogi
Date: 2022-03-22 00:00:00
Tasks: Time Series, Time Series Analysis

Our study empirically predicts the bubble of non-fungible tokens (NFTs): transferable and unique digital assets on public blockchains. This topic is important because, despite their strong market growth in 2021, NFTs on a project basis have not been investigated in terms of bubble prediction. Specifically, we applied the logarithmic periodic power law (LPPL) model to time-series price data associated with four major NFT projects. The results indicate that, as of December 20, 2021, (i) NFTs, in general, are in a small bubble (a price decline is predicted), (ii) the Decentraland project is in a medium bubble (a price decline is predicted), and (iii) the Ethereum Name Service and ArtBlocks projects are in a small negative bubble (a price increase is predicted). A future work will involve a prediction refinement considering the heterogeneity of NFTs, comparison with other methods, and the use of more enriched data.
Standing Forest Coin (SFC)
Authors: Marcelo de A. Borges, Guido L. de S. Filho, Cicero Inacio da Silva, Anderson M. P. Barros, Raul V. B. J. Britto, Nivaldo M. de C. Junior, Daniel F. L. de Souza
Date: 2022-03-04 00:00:00

This article describes a proposal to create a digital currency that allows the decentralized collection of resources directed to initiatives and activities that aim to protect the Brazilian Amazon ecosystem by using blockchain and digital contracts. In addition to the digital currency, the goal is to design a smart contract based in oracles to ensure credibility and security for investors and donors of financial resources invested in projects within the Standing Forest Coin (SFC - standingforest.org).
Single Carrier Frequency Domain Detectors for Internet of Underwater Things
Authors: Amer Aljanabi, Osama Alluhaibi, Qasim Z. Ahmed, Fahd A. Khan, Waqas-Bin-Abbas, Pavlos I. Lazaridis
Date: 2022-03-18 00:00:00

This paper proposes low complexity detection for internet of underwater things (IoUT)s communication. The signal is transmitted from the source to the destination using several sensors. To simplify the computational operations at the transmitter and the sensory nodes, a single carrier frequency domain equalizer (SC-FDE) is proposed and amplify-and-forward (AF) protocols are employed. Fast Fourier transform (FFT) and use of cyclic prefix (CP) are also proposed to simplify these algorithms when compared to time-domain equalization. As precise channel data is difficult to capture in underwater communications, the adaptive implementation of FDE is proposed as a solution that can be employed when the channel experiences a fast doppler shift. The two adaptive detectors are based on the least mean-square (LMS) and recursive least square (RLS) principles. Numerical simulations show that the performance of the bit error rate (BER) performance of the proposed detectors is close to that of the ideal minimum mean square error (MMSE).
Optical Network Design for 4G LTE
Authors: Firdaus, Rifa Atul Izza Asyari, Eka Indarto
Date: 2022-03-23 00:00:00

The number of mobile users is increasing rapidly, 3GPP initiated a new technology 4G Long Term Evolution (LTE). LTE is an enabling technology as a solution to the problem of network capacity and quality in areas that have a high demand like Sleman. The quality of LTE networks are supported by backbone and distribution network. This paper describes the designing of fiber optic network as the LTE backbone network in Sleman. The backbone network requires G-652 optical cable along 85 km with a ring topology and WDM-STM64 technology. The distribution network uses GPON technology and the type of G984 optical cable along 61.35 km. The minimum of power received at the end-point in the optisystem simulation is -25 dBm and -26 dBm through the calculation. This value is acceptable, since it is above the minimum of power received (Receiver Sensitivity) -28 dBm which refers to the standard parameters of G.984.2. The average value of rise time is 69 ps, this value is still below the maximum allowable value of rise time 70 ps. Average BER of backbone link is 5x10-4.
Modelling and Analysis of Car Following Algorithms for Fuel Economy Improvement in Connected and Autonomous Vehicles (CAVs)
Authors: Ozgenur Kavas-Torris, Levent Guvenc
Date: 2022-03-22 00:00:00
Tasks: Autonomous Vehicles, Decision Making

Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration, with each other. Using sensors such as cameras, radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected, as well as the relative speed. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, 3 car following algorithms for fuel economy of CAVs are presented. An Adaptive Cruise Control (ACC) algorithm was designed as the benchmark model for comparison. A Cooperative Adaptive Cruise Control (CACC) was designed, which uses lead vehicle acceleration received through V2V in car following. an Ecological Cooperative Adaptive Cruise Control (Eco-CACC) model was developed that takes the erratic lead vehicle acceleration as a disturbance to be attenuated. A High Level (HL) controller was designed for decision making when the lead vehicle was an erratic driver. Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) simulations were run to test these car following algorithms for fuel economy performance. The results show that the HL controller was able to attain a smooth speed profile that consumed less fuel through using CACC and Eco-CACC than its ACC counterpart when the lead vehicle was erratic.
Distributionally Robust Model Predictive Control with Total Variation Distance
Authors: Anushri Dixit, Mohamadreza Ahmadi, Joel W. Burdick
Date: 2022-03-22 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

This paper studies the problem of distributionally robust model predictive control (MPC) using total variation distance ambiguity sets. For a discrete-time linear system with additive disturbances, we provide a conditional value-at-risk reformulation of the MPC optimization problem that is distributionally robust in the expected cost and chance constraints. The distributionally robust chance constraint is over-approximated as a simpler, tightened chance constraint that reduces the computational burden. Numerical experiments support our results on probabilistic guarantees and computational efficiency.
A Scalable Model Specialization Framework for Training and Inference using Submodels and its Application to Speech Model Personalization
Authors: Fadi Biadsy, Youzheng Chen, Xia Zhang, Oleg Rybakov, Andrew Rosenberg, Pedro J. Moreno
Date: 2022-03-23 00:00:00
Tasks: model

Model fine-tuning and adaptation have become a common approach for model specialization for downstream tasks or domains. Fine-tuning the entire model or a subset of the parameters using light-weight adaptation has shown considerable success across different specialization tasks. Fine-tuning a model for a large number of domains typically requires starting a new training job for every domain posing scaling limitations. Once these models are trained, deploying them also poses significant scalability challenges for inference for real-time applications. In this paper, building upon prior light-weight adaptation techniques, we propose a modular framework that enables us to substantially improve scalability for model training and inference. We introduce Submodels that can be quickly and dynamically loaded for on-the-fly inference. We also propose multiple approaches for training those Submodels in parallel using an embedding space in the same training job. We test our framework on an extreme use-case which is speech model personalization for atypical speech, requiring a Submodel for each user. We obtain 128x Submodel throughput with a fixed computation budget without a loss of accuracy. We also show that learning a speaker-embedding space can scale further and reduce the amount of personalization training data required per speaker.
Upmixing via style transfer: a variational autoencoder for disentangling spatial images and musical content
Authors: Haici Yang, Sanna Wager, Spencer Russell, Mike Luo, Minje Kim, Wontak Kim
Date: 2022-03-22 00:00:00
Tasks: Style Transfer

In the stereo-to-multichannel upmixing problem for music, one of the main tasks is to set the directionality of the instrument sources in the multichannel rendering results. In this paper, we propose a modified variational autoencoder model that learns a latent space to describe the spatial images in multichannel music. We seek to disentangle the spatial images and music content, so the learned latent variables are invariant to the music. At test time, we use the latent variables to control the panning of sources. We propose two upmixing use cases: transferring the spatial images from one song to another and blind panning based on the generative model. We report objective and subjective evaluation results to empirically show that our model captures spatial images separately from music content and achieves transfer-based interactive panning.
Deep Learning based Intelligent Coin-tap Test for Defect Recognition
Authors: Hongyu Li, Peng Jiang, Tiejun Wang
Date: 2022-03-20 00:00:00
Tasks: Deep Learning, Domain Adaptation, Pseudo Label, Transfer Learning

The coin-tap test is a convenient and primary method for non-destructive testing, while its manual on-site operation is tough and costly. With the help of the latest intelligent signal processing method, convolutional neural networks (CNN), we achieve an intelligent coin-tap test which exhibited superior performance in recognizing the defects. However, this success of CNNs relies on plenty of well-labeled data from the identical scenario, which could be difficult to get for many real industrial practices. This paper further develops transfer learning strategies for this issue, that is, to transfer the model trained on data of one scenario to another. In experiments, the result presents a notable improvement by using domain adaptation and pseudo label learning strategies. Hence, it becomes possible to apply the model into scenarios with none or little (less than 10\%) labeled data adopting the transfer learning strategies proposed herein. In addition, we used a benchmark dataset constructed ourselves throughout this study. This benchmark dataset for the coin-tap test containing around 100,000 sound signals is published at https://github.com/PPhub-hy/torch-tapnet.
Geometry of finite-time thermodynamic cycles with anisotropic thermal fluctuations
Authors: Olga Movilla Miangolarra, Amirhossein Taghvaei, Yongxin Chen, Tryphon T. Georgiou
Date: 2022-03-23 00:00:00

In contrast to the classical concept of a Carnot engine that alternates contact between heat baths of different temperatures, naturally occurring processes usually harvest energy from anisotropy, being exposed simultaneously to chemical and thermal fluctuations of different intensities. In these cases, the enabling mechanism responsible for transduction of energy is typically the presence of a non-equilibrium steady state (NESS). A suitable stochastic model for such a phenomenon is the Brownian gyrator -- a two-degree of freedom stochastically driven system that exchanges energy and heat with the environment. In the context of such a model we present, from a stochastic control perspective, a geometric view of the energy harvesting mechanism that entails a forced periodic trajectory of the system state on the thermodynamic manifold. Dissipation and work output are expressed accordingly as path integrals of a controlled process, and fundamental limitations on power and efficiency are expressed in geometric terms via a relationship to an isoperimetric problem. The theory is presented for high-order systems far from equilibrium and beyond the linear response regime.