pwc-archive/papers-with-abstracts huggingface dataset:
A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy
Authors: Ali Jalalifar, Hany Soliman, Arjun Sahgal, and Ali Sadeghi-Naini
Date: 2020-08-27 00:00:00
Proceeding: 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2020 8
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Radiation therapy is a major treatment option for brain metastasis. For radiation treatment planning and outcome evaluation, magnetic resonance (MR) images areacquired before and at multiple sessions after the treatment. Accurate segmentation of brain tumors on MR images is crucial for treatment planning, response evaluation, anddeveloping data-driven models for outcome prediction. Due to the high volume of imaging data acquired from each patient at multiple follow-up sessions, manual tumor segmentation is resource- and time-consuming in clinic, hence developing an automatic segmentation framework is highly desirable. In this work, we proposed a cascaded 2D-3D Unet framework to segment brain tumors automatically on contrast-enhanced T1-weighted images acquired before and at multiple scan sessions after radiotherapy. 2D Unet is a well-known structure for medical image segmentation. 3D Unet is an extension of 2D Unet with a volumetric input image to provide richer spatial information. The limitation of 3D Unet is that it is memory consuming and cannot process large volumetric images. To address this limitation, a large volumetric input of 3D Unet is often patched to smaller volumes which leads to loss of context. To overcome this problem, we proposed using two cascaded 2D Unets to crop the input volume around the tumor area and reduce the input size of the 3D Unet, obviating the need to patch the input images. The framework was trained using images acquired from 96 patients before radiation therapy and tested using images acquired from 10 patients before and at four follow-up scans after radiotherapy. The segmentation results for the images of independent test set demonstrated that the cascaded framework outperformed the 2D and 3D Unets alone, with an average Dice score of 0.9 versus 0.86 and 0.88 for the baseline, and 0.87 versus 0.83 and 0.84 for the first followup. Similar results were obtained for the other follow-up scans.
Deep Bidirectional Transformers for SoC Flow Specification Mining
Authors: Md Rubel Ahmed, Hao Zheng
Date: 2022-03-09 00:00:00

High-quality system-level message flow specifications can lead to comprehensive validation of system-on-chip (SoC) designs. We propose a disruptive method that utilizes an attention mechanism to produce accurate flow specifications from SoC IP communication traces. The proposed method can overcome the inherent complexity of SoC traces induced by the concurrency and parallelism of multicore designs that existing flow specification mining tools often find extremely challenging. Experiments on highly interleaved traces show promising flow reconstruction compared to several tools dedicated to the flow specification mining problem.
Decouple-and-Sample: Protecting sensitive information in task agnostic data release
Authors: Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
Date: 2022-03-17 00:00:00

We propose sanitizer, a framework for secure and task-agnostic data release. While releasing datasets continues to make a big impact in various applications of computer vision, its impact is mostly realized when data sharing is not inhibited by privacy concerns. We alleviate these concerns by sanitizing datasets in a two-stage process. First, we introduce a global decoupling stage for decomposing raw data into sensitive and non-sensitive latent representations. Secondly, we design a local sampling stage to synthetically generate sensitive information with differential privacy and merge it with non-sensitive latent features to create a useful representation while preserving the privacy. This newly formed latent information is a task-agnostic representation of the original dataset with anonymized sensitive information. While most algorithms sanitize data in a task-dependent manner, a few task-agnostic sanitization techniques sanitize data by censoring sensitive information. In this work, we show that a better privacy-utility trade-off is achieved if sensitive information can be synthesized privately. We validate the effectiveness of the sanitizer by outperforming state-of-the-art baselines on the existing benchmark tasks and demonstrating tasks that are not possible using existing techniques.
Mobile Wireless Rechargeable UAV Networks: Challenges and Solutions
Authors: Yuntao Wang, Zhou Su, Ning Zhang, Ruidong Li
Date: 2022-03-24 00:00:00
Tasks: energy trading, Privacy Preserving, Scheduling

Unmanned aerial vehicles (UAVs) can help facilitate cost-effective and flexible service provisioning in future smart cities. Nevertheless, UAV applications generally suffer severe flight time limitations due to constrained onboard battery capacity, causing a necessity of frequent battery recharging or replacement when performing persistent missions. Utilizing wireless mobile chargers, such as vehicles with wireless charging equipment for on-demand self-recharging has been envisioned as a promising solution to address this issue. In this article, we present a comprehensive study of \underline{v}ehicle-assisted \underline{w}ireless rechargeable \underline{U}AV \underline{n}etworks (VWUNs) to promote on-demand, secure, and efficient UAV recharging services. Specifically, we first discuss the opportunities and challenges of deploying VWUNs and review state-of-the-art solutions in this field. We then propose a secure and privacy-preserving VWUN framework for UAVs and ground vehicles based on differential privacy (DP). Within this framework, an online double auction mechanism is developed for optimal charging scheduling, and a two-phase DP algorithm is devised to preserve the sensitive bidding and energy trading information of participants. Experimental results demonstrate that the proposed framework can effectively enhance charging efficiency and security. Finally, we outline promising directions for future research in this emerging field.
Two-timescale Resource Allocation for Automated Networks in IIoT
Authors: Yanhua He, Yun Ren, Zhenyu Zhou, Shahid Mumtaz, Saba Al-Rubaye, Antonios Tsourdos, Octavia A. Dobre
Date: 2022-03-24 00:00:00
Tasks: channel selection, energy management, Management, Stochastic Optimization, Vocal Bursts Valence Prediction

The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the two-timescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a low-complexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations.
A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data
Authors: Hongda Wu, Ali Nasehzadeh, Ping Wang
Date: 2022-03-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), valid

The Internet of Things (IoT) has been continuously rising in the past few years, and its potentials are now more apparent. However, transient data generation and limited energy resources are the major bottlenecks of these networks. Besides, minimum delay and other conventional quality of service measurements are still valid requirements to meet. An efficient caching policy can help meet the standard quality of service requirements while bypassing IoT networks' specific limitations. Adopting deep reinforcement learning (DRL) algorithms enables us to develop an effective caching scheme without the need for any prior knowledge or contextual information. In this work, we propose a DRL-based caching scheme that improves the cache hit rate and reduces energy consumption of the IoT networks, in the meanwhile, taking data freshness and limited lifetime of IoT data into account. To better capture the regional-different popularity distribution, we propose a hierarchical architecture to deploy edge caching nodes in IoT networks. The results of comprehensive experiments show that our proposed method outperforms the well-known conventional caching policies and an existing DRL-based solution in terms of cache hit rate and energy consumption of the IoT networks by considerable margins.
Bidding and Scheduling in Energy Markets: Which Probabilistic Forecast Do We Need?
Authors: Mario Beykirch, Tim Janke, Florian Steinke
Date: 2022-03-24 00:00:00
Tasks: Scheduling

Probabilistic forecasting in combination with stochastic programming is a key tool for handling the growing uncertainties in future energy systems. Derived from a general stochastic programming formulation for the optimal scheduling and bidding in energy markets we examine several common special instances containing uncertain loads, energy prices, and variable renewable energies. We analyze for each setup whether only an expected value forecast, marginal or bivariate predictive distributions, or the full joint predictive distribution is required. For market schedule optimization, we find that expected price forecasts are sufficient in almost all cases, while the marginal distributions of renewable energy production and demand are often required. For bidding curve optimization, pairwise or full joint distributions are necessary except for specific cases. This work helps practitioners choose the simplest type of forecast that can still achieve the best theoretically possible result for their problem and researchers to focus on the most relevant instances.
Tuning rules for passivity-based integral control for a class of mechanical systems
Authors: Carmen Chan-Zheng, Mauricio Muñoz-Arias, Jacquelien M. A. Scherpen
Date: 2022-03-24 00:00:00

This manuscript introduces a passivity-based integral control approach for fully-actuated mechanical systems. The novelty of our methodology is that we exploit the gyroscopic forces of the mechanical systems to exponentially stabilize the mechanical system at the desired equilibrium even in the presence of matched disturbances; additionally, we show that our approach is robust against unmatched disturbances. Furthermore, we provide tuning rules to prescribe the performance of the closed-loop system. We conclude this manuscript with experimental results obtained from a robotic arm.
Self-Triggered Coordination Control of Connected Automated Vehicles in Traffic Networks
Authors: Nader Meskin, Ehsan Sabouni, Wei Xiao, Christos G. Cassandras
Date: 2022-03-24 00:00:00

In this paper, a self-triggered scheme is proposed to optimally control the traffic flow of Connected and Automated Vehicles (CAVs) at conflict areas of a traffic network with the main aim of reducing the data exchange among CAVs in the control zone and at the same to minimize the travel time and energy consumption. The safety constraints and the vehicle limitations are considered using the Control Barrier Function (CBF) framework and a self-triggered scheme is proposed using the CBF constraints. Moreover, modified CBF constraints are developed to ensure a minimum inter-event interval for the proposed self-triggered schemes. Finally, it is shown through a simulation study that the number of data exchanges among CAVs is significantly reduced using the proposed self-triggered schemes in comparison with the standard time-triggered framework.
A New Virtual Oscillator based Grid-forming Controller with Decoupled Control Over Individual Phases and Improved Performance of Unbalanced Fault Ride-through
Authors: Ritwik Ghosh, Narsa Reddy Tummuru, Bharat Singh Rajpuohit
Date: 2022-03-24 00:00:00

Virtual Oscillator (VO) control is the latest and promising control technique for grid-forming and grid-supporting inverters. VO Controllers (VOCs) provide time-domain synchronization with a connected electrical network. At the same time, a VOC can incorporate additional nested control loops to meet the system-level requirements such as fault ride-through capability. However, existing VOCs have two limitations. Firstly, the existing VOCs do not have decoupled control over individual phases. As a result, the performance of the existing VOCs is not satisfactory in presence of unbalanced grid voltages. Secondly, the fault ride-through performance of the existing VOCs under unbalanced faults is not satisfactory. The power operation at a healthy phase is badly affected by a faulty phase. This paper has introduced a modified VO based system-level grid-forming controller to overcome the limitations mentioned above. Systematic development of the proposed control architecture with analytical reasoning is presented. Simulation studies and hardware experiments are conducted for validation.
Local Measurement Based Robust Voltage Stability Index & Identification of Voltage Collapse Onset
Authors: Kishan Prudhvi Guddanti, Amarsagar Matavalam, Yang Weng
Date: 2022-03-24 00:00:00
Tasks: parameter estimation

This paper addresses the problem of real-time monitoring of long-term voltage instability (LTVI) by using local field measurements. Existing local measurement-based methods use Thevenin equivalent parameter estimation that is sensitive to the noise in measurements. For solving this issue, we avoid the Thevenin approach by projecting the power flow equations as circles to develop local static-voltage stability indicator (LS-VSI) that is robust to measurement noises. The proposed method is an attractive option for practical implementation because of its decentralized nature, robustness to noise, and realistic modeling. Next, we utilize LS-VSI to propose a new local dynamic - VSI (LD-VSI) to identify the LTVI triggered by large disturbances and load tap changer dynamics. LD-VSI can identify not only the onset of instability with an alarm but also the voltage collapse point (VCP). Extensive numerical validation comparing LS-VSI with existing methods is presented on IEEE 30-bus and larger test systems like 2000-bus Texas synthetic grid to validate the robustness, accuracy, and situational awareness feature. We also verify the LD-VSI behavior on the Nordic power grid using PSSE dynamic simulation. When compared to existing methods, we observe that LD-VSI is not only more robust to measurement noise but also can identify VCP.
Disturbance Observer-based Robust Control Barrier Functions
Authors: Yujie Wang, Xiangru Xu
Date: 2022-03-24 00:00:00

This work presents a safe control design approach that integrates the disturbance observer (DOB) and the control barrier function (CBF) for systems with external disturbances. Different from existing robust CBF results that consider the "worst case" of disturbances, this work utilizes a DOB to estimate and compensate for the disturbances. DOB-CBF-based controllers are constructed with provably safe guarantees by solving convex quadratic programs online, to achieve a better tradeoff between safety and performance. Two types of systems are considered individually depending on the magnitude of the input and disturbance relative degrees. The effectiveness of the proposed methods is illustrated via numerical simulations.
High Gain Array Antenna With FSS for Vital Sign Monitoring Through the Wall
Authors: Rifa Atul Izza Asyari, Rezki El Arif, Wei-Chih Su, Tzyy-Sheng Horng
Date: 2022-03-23 00:00:00

In this research, a 2.4 GHz planar array antenna 2X2 combined with frequency selective surface (FSS) is proposed to obtain a high gain and high efficiency for improving the performance of vital sign monitoring through the wall using Self-injection-locked radar systems. The FSS is designed on a double-layer with unit cells of 9X7 for each layer. Modified Jerusalem cross-shaped of FSS is proposed to enhance the gain. The antenna array and additional FSS can obtain a gain of 15.2 dB and 82% of efficiency. the vital sign monitoring of the subject behind the wall has been successfully carried out.
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection
Authors: Mert Nakıp, Cüneyt Güzeliş, Osman Yildiz
Date: 2021-06-10 00:00:00
Proceeding: IEEE Access 2021 6
Tasks: Fire Detection, Multivariate Time Series Forecasting, Time Series, Time Series Analysis, Time Series Regression

We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level prediction for the time series of each sensor reading and captures trends on multivariate time series data produced by multi-sensor detector. We compare the performance of the rTPNN model with that of each of the Linear Regression (LR), Nonlinear Perceptron (NP), Multi-Layer Perceptron (MLP), Kendall- τ combined with MLP, Probabilistic Bayesian Neural Network (PBNN), Long-Short Term Memory (LSTM), and Support Vector Machine (SVM) on a publicly available fire data set. Our results show that rTPNN model significantly outperforms all of the other models (with 96% accuracy) while it is the only model that achieves high True Positive and True Negative rates (both above 92%) at the same time. rTPNN also triggers an alarm in only 11 s from the start of the fire, where this duration is 22 s for the second-best model. Moreover, we present that the execution time of rTPNN is acceptable for real-time applications.
Dynamical properties of neuromorphic Josephson junctions
Authors: Dimitrios Chalkiadakis, Johanne Hizanidis
Date: 2022-01-28 00:00:00

Neuromorphic computing exploits the dynamical analogy between many physical systems and neuron biophysics. Superconductor systems, in particular, are excellent candidates for neuromorphic devices due to their capacity to operate in great speeds and with low energy dissipation compared to their silicon counterparts. In this study we revisit a prior work on Josephson Junction-based "neurons" in order to identify the exact dynamical mechanisms underlying the system's neuron-like properties and reveal new complex behaviors which are relevant for neurocomputation and the design of superconducting neuromorphic devices. Our work lies at the intersection of superconducting physics and theoretical neuroscience, both viewed under a common framework, that of nonlinear dynamics theory.
The application of techniques derived from artificial intelligence to the prediction of the solvency of bank customers: case of the application of the cart type decision tree (dt)
Authors: Karim Amzile, Rajaa Amzile
Date: 2022-03-24 00:00:00
Tasks: Missing Values

In this study we applied the CART-type Decision Tree (DT-CART) method derived from artificial intelligence technique to the prediction of the solvency of bank customers, for this we used historical data of bank customers. However we have adopted the process of Data Mining techniques, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values as well as rows with empty columns, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our CART decision tree method using the SPSS tool. After completing our process of building our model (AD-CART), we started the process of evaluating and testing the performance of our model, by which we found that the accuracy and precision of our model is 71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to conclude that our model at a fairly good level in terms of precision, predictability and very precisely in predicting the solvency of our banking customers.
Correcting Attrition Bias using Changes-in-Changes
Authors: Dalia Ghanem, Sarojini Hirshleifer, Désiré Kédagni, Karen Ortiz-Becerra
Date: 2022-03-23 00:00:00

Attrition is a common and potentially important threat to internal validity in treatment effect studies. We extend the changes-in-changes approach to identify the average treatment effect for respondents and the entire study population in the presence of attrition. Our method, which exploits baseline outcome data, can be applied to randomized experiments as well as quasi-experimental difference-in-difference designs. A formal comparison highlights that while widely used corrections typically impose restrictions on whether or how response depends on treatment, our proposed attrition correction exploits restrictions on the outcome model. We further show that the conditions required for our correction can accommodate a broad class of response models that depend on treatment in an arbitrary way. We illustrate the implementation of the proposed corrections in an application to a large-scale randomized experiment.
The Impact of Acquisitions in the Biotechnology Sector on R&D Productivity
Authors: Luca Verginer, Federica Parisi, Jeroen van Lidth de Jeude, Massimo Riccaboni
Date: 2022-03-24 00:00:00

This study examines the effects of acquisitions on the retention and R&D productivity of inventors in the biotech sector, using data from 15,318 inventors involved in 1,375 acquisitions between 1990 and 2010. We employ a staggered difference-in-differences approach and find that acquisitions lead to a 13.5% decrease in inventor retention and a 35% drop in citation-weighted patent productivity post-acquisition. The productivity decline is more severe for inventors who remain with the acquiring firm, particularly for those whose expertise is closely tied to the target company. However, older inventors and those whose expertise aligns with the acquiring company's existing R&D portfolio tend to retain higher productivity levels after the acquisition.
The differences in SAPA Needs by Route, Traffic Volume and after COVID-19
Authors: Katsunobu Okamoto, Takuji Takemoto, Yoshimi Kawamoto, Sachiyo Kamimura
Date: 2022-03-24 00:00:00

This study aims to identify the differences in SAPA user interest due to each route, traffic volumes, and after COVID-19 by the daily feedback from them and to help develop SAPA plans. Food was the most common opinion. However, for the route, some showed interest in other options. For the traffic volume, the difference of interest was also shown in some heavy traffic areas, On the other hand, the changes in customer needs after the COVID-19 disaster were less changed.
Financial statements of companies in Norway
Authors: Ranik Raaen Wahlstrøm
Date: 2022-03-24 00:00:00
Tasks: All

This document details a dataset that contains all unconsolidated annual financial statements of the universe of Norwegian private and public limited liability companies. It also includes all financial statements of other company types reported to the Norwegian authorities.