pwc-archive/papers-with-abstracts huggingface dataset:
A Framework for the Joint Optimization of Assignment and Pricing in Mobility-on-Demand Systems with Shared Rides
Authors: Yang Liu, Qi Luo, Raga Gopalakrishnan, Samitha Samaranayake
Date: 2021-12-28 00:00:00

Mobility-on-Demand (MoD) systems have become a fixture in urban transportation networks, with the rapid growth of ride-hailing services such as Uber and Lyft. Ride-hailing is typically complemented with ridepooling options, which can reduce the negative externalities associated with ride-hailing services and increase the utilization of vehicles. Determining optimal policies for vehicle dispatching and pricing, two key components that enable MoD services, are challenging due to their massive scale and online nature. The challenge is amplified when the MoD platform offers exclusive (conventional ride-hailing) and shared services, and customers have the option to select between them. The pricing and dispatching problems are coupled because the realized demand depends on the quality of service (i.e., whom to share rides with) and the prices for each service type. We propose an integrated and computationally efficient method for solving the joint pricing and dispatching problem -- both when the problem is solved one request at a time or in batches (a common strategy in the industry). The main results of this research include showing that: (i) the sequential pricing problem has a closed-form solution under a multinomial logit (MNL) choice model, and (ii) the batched pricing problem is jointly concave in the expected demand distributions. To account for the spatial evolution of supply and demand, we introduce so-called retrospective costs to retain a tractable framework. Our numerical experiments demonstrate how this framework yields significant profit increases using taxicab data in Manhattan, New York City, compared to dynamic dispatching with static pricing policies.
CDRec: Cayley-Dickson Recommender
Authors: Anchen Li, Bo Yang, Huan Huo, Farookh Hussain
Date: 2021-12-16 00:00:00

In this paper, we propose a recommendation framework named Cayley-Dickson Recommender. We introduce Cayley-Dickson construction which uses a recursive process to define hypercomplex algebras and their mathematical operations. We also design a graph convolution operator to learn representations in the hypercomplex space. To the best of our knowledge, it is the first time that Cayley-Dickson construction and graph convolution techniques have been used in hypercomplex recommendation. Compared with the state-of-the-art recommendation methods, our method achieves superior performance on real-world datasets.
An Empirical Study on Transfer Learning for Privilege Review
Authors: Haozhen Zhao, Shi Ye, Jingchao Yang
Date: 2021-12-16 00:00:00
Tasks: BIG-bench Machine Learning, Document Classification, Transfer Learning

Protecting privileged communications and data from inadvertent disclosure is a paramount task in the US legal practice. Traditionally counsels rely on keyword searching and manual review to identify privileged documents in cases. As data volumes increase, this approach becomes less and less defensible in costs. Machine learning methods have been used in identifying privilege documents. Given the generalizable nature of privilege in legal cases, we hypothesize that transfer learning can capitalize knowledge learned from existing labeled data to identify privilege documents without requiring labeling new training data. In this paper, we study both traditional machine learning models and deep learning models based on BERT for privilege document classification tasks in legal document review, and we examine the effectiveness of transfer learning in privilege model on three real world datasets with privilege labels. Our results show that BERT model outperforms the industry standard logistic regression algorithm and transfer learning models can achieve decent performance on datasets in same or close domains.
A Model-Based Architecture for Automatic Anesthesia Infusion
Authors: Dayang Liu
Date: 2021-12-28 00:00:00
Tasks: model

This paper presents a model-based control architecture. Based on the Medical Cyber-physical Systems (MCPS) concept, we construct a safe and reliable automatic anesthesia control closed-loop system. The control architecture uses the patient's propofol pharmacokinetic/pharmacodynamic model, using the calculated results as a feedback signal to correct the model's uncertainties. During anesthesia, the bispectral index (BIS) level is used as a control variable to control the depth of anesthesia. The proposed control architecture can automatically adjust the infusion rate of anesthesia drugs without concussion or overshoot. The system adaptively adjusts the drug infusion rate to maintain the BIS level at a stable target value. We utilized MATLAB-Simulink to verify the effectiveness of this method, and the robustness test was carried out by adding noise blocks. At last, we analyze the simulation result of the average patient. In general, the proposed method achieves a stable and fast induction period during the maintenance phase of anesthesia with a good disturbance rejection.
LSH methods for data deduplication in a Wikipedia artificial dataset
Authors: Juan Ciro, Daniel Galvez, Tim Schlippe, David Kanter
Date: 2021-12-10 00:00:00
Tasks: Articles

This paper illustrates locality sensitive hasing (LSH) models for the identification and removal of nearly redundant data in a text dataset. To evaluate the different models, we create an artificial dataset for data deduplication using English Wikipedia articles. Area-Under-Curve (AUC) over 0.9 were observed for most models, with the best model reaching 0.96. Deduplication enables more effective model training by preventing the model from learning a distribution that differs from the real one as a result of the repeated data.
Analytical voltage estimation in power packet dispatching network
Authors: Shinji Katayama, Takashi Hikihara
Date: 2021-12-28 00:00:00

This paper proposes an analytical voltage estimation method for the power packet dispatching network. A unit power packet consists of signals and DC pulsed voltage waveform. In the network, power packets are transmitted among power packet routers (routers) by time-division multiplexing. Routers have capacitors as the storage of power during the power transmission. The power flows depending on the voltage gradient among the storage. The power flow in the network has been studied with numerical approaches. However, the existing studies have considered transmission losses but ignored transient responses. Considering transient responses, the power transmission analysis generally requires circuit simulation and takes considerable time. This paper focuses on a one-to-one connection between routers and underdamped conditions. They allow the estimation of storage voltages considering the transient responses without circuit simulation. First, theoretical analysis and experimental verification are performed on the conditions. Then, an analytical voltage estimation method is proposed by expanding the analysis of the one-to-one connection to the network. Comparison with circuit simulation reveals that the estimation method is accurate enough to analyze power transmission in the network.
Real-Time Power System Dynamic Simulation using Windowing based Waveform Relaxation Method
Authors: M Al Mamun, Sumit Paudyal, Sukumar Kamalasadan
Date: 2021-12-28 00:00:00

Power system dynamic modeling involves nonlinear differential and algebraic equations (DAEs). Solving DAEs for large power grid networks by direct implicit numerical methods could be inefficient in terms of solution time; thus, such methods are not preferred when real-time or faster than real-time performance is sought. Hence, this paper revisits Waveform Relaxation (WR) algorithm, as a distributed computational technique to solve power system dynamic simulations. Case studies performed on the IEEE NE 10-generator 39-bus system demonstrate that, for a certain simulation time window, the solve time for WR method is larger than the length of the simulation window; thus, WR lacks the performance needed for real-time simulators, even for a small power network. To achieve real-time performance, then a Windowing technique is applied on top of the WR, for which the solve time was obtained less than the length of a simulation window, that shows the effectiveness of the proposed method for real-time dynamic simulation of power systems.
Automated Drug-Related Information Extraction from French Clinical Documents: ReLyfe Approach
Authors: Azzam Alwan, Maayane Attias, Larry Rubin, Adnan El Bakri
Date: 2021-11-29 00:00:00
Tasks: Management

Structuring medical data in France remains a challenge mainly because of the lack of medical data due to privacy concerns and the lack of methods and approaches on processing the French language. One of these challenges is structuring drug-related information in French clinical documents. To our knowledge, over the last decade, there are less than five relevant papers that study French prescriptions. This paper proposes a new approach for extracting drug-related information from French clinical scanned documents while preserving patients' privacy. In addition, we deployed our method in a health data management platform where it is used to structure drug medical data and help patients organize their drug schedules. It can be implemented on any web or mobile platform. This work closes the gap between theoretical and practical work by creating an application adapted to real production problems. It is a combination of a rule-based phase and a Deep Learning approach. Finally, numerical results show the outperformance and relevance of the proposed methodology.
Towards Transactive Energy: An Analysis of Information-related Practical Issues
Authors: Yue Chen, Yu Yang, Xiaoyuan Xu
Date: 2021-12-28 00:00:00
Tasks: Privacy Preserving

The development of distributed energy resources, such as rooftop photovoltaic (PV) panels, batteries, and electric vehicles (EVs), has decentralized our power system operation, where transactive energy markets empower local energy exchanges. Transactive energy contributes to building a low-carbon energy system by better matching the distributed renewable sources and demand. Effective market mechanisms are a key part of transactive energy market design. Despite fruitful research on related topics, some practical challenges must be addressed. This review surveys three practical issues related to information exchange in transactive energy markets: asynchronous computing, truthful reporting, and privacy preservation. We summarize the state-of-the-art results and introduce relevant multidisciplinary theories. Based on these findings, we suggest several potential research directions that could provide insights for future studies.
Scheduling HVAC loads to promote renewable generation integration with a learning-based joint chance-constrained approach
Authors: Ge Chen, Hongcai Zhang, Hongxun Hui, Yonghua Song
Date: 2021-12-18 00:00:00
Tasks: Computational Efficiency, Scheduling

The integration of distributed renewable generation (DRG) in distribution networks can be effectively promoted by scheduling flexible resources such as heating, ventilation, and air conditioning (HVAC) loads. However, finding the optimal scheduling for them is nontrivial because DRG outputs are highly uncertain. To address this issue, this paper proposes a learning-based joint chance-constrained approach to coordinate HVAC loads with DRG. Unlike cutting-edge works adopting individual chance constraints to manage uncertainties, this paper controls the violation probability of all critical constraints with joint chance constraints (JCCs). This joint manner can explicitly guarantee the operational security of the entire system based on operators' preferences. To overcome the intractability of JCCs, we first prove that JCCs can be safely approximated by robust constraints with proper uncertainty sets. A famous machine learning algorithm, one-class support vector clustering, is then introduced to construct a small enough polyhedron uncertainty set for these robust constraints. A linear robust counterpart is further developed based on the strong duality to ensure computational efficiency. Numerical results based on various distributed uncertainties confirm the advantages of the proposed method in optimality and feasibility.
Design, Dynamics, and Dissipation of a Torsional-Magnetic Spring Mechanism
Authors: Ali Kanj, Rhinithaa P. Thanalakshme, Chengzhang Li, John Kulikowski, Gaurav Bahl, Sameh Tawfick
Date: 2021-12-27 00:00:00
Tasks: Friction

We present an analytical and experimental study of torsional magnetic mechanism where the restoring torque is due to magnetic field interactions between rotating and fixed permanent magnets. The oscillator consists of a ball bearing-supported permanent magnet, called the rotor, placed between two fixed permanent magnets called the stators. Perturbing the rotor from its equilibrium angle induces a restoring magnetic torque whose effect is modeled as a torsional spring. This restoring effect is accompanied by dissipation mechanisms arising from structural viscoelasticity, air and electromagnetic damping, as well as friction in the ball bearings. To investigate the system dynamics, we constructed an experimental setup capable of mechanical, electrical and magnetic measurements. For various rotor-stator gaps in this setup, we validated an analytical model that assumes viscous and dry (Coulomb) damping during the rotor free response. Moreover, we forced the rotor by a neighboring electromagnetic coil into high amplitude oscillations. We observed unusual resonator nonlinearity: at large rotor-stator gaps, the oscillations are softening; at reduced gaps, the oscillations stiffen-then-soften. The developed reduced-order models capture the nonlinear effects of the rotor-to-stator and the rotor-to-coil distances. These magnetic oscillators are promising in low-frequency electromagnetic signal transmission and in designing magneto-elastic metamaterials with tailorable nonlinearity.
Smart Grid Architecture with High Proportion of Energy Utilization
Authors: Javeria Noor
Date: 2021-12-27 00:00:00
Tasks: Management, Vocal Bursts Intensity Prediction

DC power generation is an emerging trend and has been preferred due to its low cost and low in system power losses within local distribution grid. The theme of this paper is the indigenous design of a DC standalone micro grid which will stabilize the fluctuating power generated from hybrid energy resources and manage its power distribution. The power is distributed through a DC distribution line and converted to the required AC or DC form by converters placed near loads. This project includes power generation, distribution and management strategies for a sustainable micro grid primarily powered by wind and solar energy. DC transmittable power can increase the system efficiency up to 10% as compared to AC.
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
Authors: Wenbo Chen, Seonho Park, Mathieu Tanneau, Pascal Van Hentenryck
Date: 2021-12-27 00:00:00

The Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO) to clear real-time energy markets while ensuring reliable operations of power grids. In a context of growing operational uncertainty, due to increased penetration of renewable generators and distributed energy resources, operators must continuously monitor risk in real-time, i.e., they must quickly assess the system's behavior under various changes in load and renewable production. Unfortunately, systematically solving an optimization problem for each such scenario is not practical given the tight constraints of real-time operations. To overcome this limitation, this paper proposes to learn an optimization proxy for SCED, i.e., a Machine Learning (ML) model that can predict an optimal solution for SCED in milliseconds. Motivated by a principled analysis of the market-clearing optimizations of MISO, the paper proposes a novel ML pipeline that addresses the main challenges of learning SCED solutions, i.e., the variability in load, renewable output and production costs, as well as the combinatorial structure of commitment decisions. A novel Classification-Then-Regression architecture is also proposed, to further capture the behavior of SCED solutions. Numerical experiments are reported on the French transmission system, and demonstrate the approach's ability to produce, within a time frame that is compatible with real-time operations, accurate optimization proxies that produce relative errors below $0.6\%$.
Two-layer nonlinear control of DC-DC buck converters with meshed network topology
Authors: Pablo R. Baldivieso-Monasterios, Mahdieh Sadabadi, George C. Konstantopoulos
Date: 2021-12-26 00:00:00

In this paper, we analyse the behaviour of a buck converter network that contains arbitrary, up to mild regularity assumptions, loads. Our analysis of the network begins with the study of the current dynamics; we propose a novel Lyapunov function for the current in closed-loop with a bounded integrator. We leverage on these results to analyse the interaction properties between voltages and bounded currents as well as between node voltages and to propose a two-layer optimal controller that keeps network voltages within a compact neighbourhood of the nominal operational voltage. We analyse the stability of the closed loop system in two ways: one considering the interconnection properties which yields a weaker ISS type property and a second that contemplates the network in closed loop with a distributed optimal controller. For the latter, we propose a novel distributed way of controlling a Laplacian network using neighbouring information which results in asymptotic stability. We demonstrate our results in a meshed topology network containing 6 power converters, each converter feeding an individual constant power load with values chaning arbitrarily within a pre-specified range.
Fitting nonlinear models to continuous oxygen data with oscillatory signal variations via a loss based on DynamicTime Warping
Authors: Judit Aizpuru, Annina Karolin Kemmer, Jong Woo Kim, Stefan Born, Peter Neubauer, Mariano N. Cruz Bournazou, Tilman Barz
Date: 2021-12-25 00:00:00
Tasks: Dynamic Time Warping

High throughput experimental systems play an important role in bioprocess development, as they provide an efficient way of analysing different experimental conditions and perform strain discrimination in previous phases to the industrial scale production. In the millilitre scale, these systems are combinations of parallel mini-bioreactors, liquid handling robots and automated workflows for data handling and model based operation. For successfully monitoring cultivation conditions and improving the overall process quality by model-based approaches, a proper model identification is crucial. However, the quality and amount of measurements makes this task challenging considering the complexity of the bio-processes. TheDissolved Oxygen Tension is often the only measurement which is available online, and therefore, a good understanding of the errors in this signal is important for performing a robust estimation.Some of the expected errors will provoke uncertainties in the time-domain of the measurement, and in those cases, the common Weighted Least Squares estimation procedure can fail providing good results. Moreover, these errors will have even a larger effect in the fed-batch phase where bolus feeding is applied, as this generates fast dynamic responses in the signal. In the present work, an insilico study of the performance of Weighted Least Squares estimator is analysed when the expected time-uncertainties are present in the oxygen signal. As an alternative, a loss based on the Dynamic Time Warping measure is proposed. The results show how this latter procedure outperforms the former reconstructing the oxygen signal, and in addition, returns less biased parameter estimates.
A Comprehensive Review of Myoelectric Prosthesis Control
Authors: Mohammad Reza Mohebbian, Marjan Nosouhi, Farzaneh Fazilati, Zahra Nasr Esfahani, Golnaz Amiri, Negar Malekifar, Fatemeh Yusefi, Mohsen Rastegari, Hamid Reza Marateb
Date: 2021-12-25 00:00:00

Prosthetic hands can be used to support upper-body amputees. Myoelectric prosthesis, one of the externally-powered active prosthesis categories, requires proper processing units in addition to recording electrodes and instrumentation amplifiers. In this paper, the following myoelectric prosthesis control methods were discussed in detail: On-off and finite-state, proportional, direct, and posture, simultaneous, classification and regression-based control, and deep learning methods. Myoelectric control performance indices, such as completion time and rate, throughput, lag, and path length, were reviewed. The advantages and disadvantages of the control methods were also discussed. Some of myoelectric prosthesis control's significant challenges are comfort, durability, cost, the application of under-sampled signals, and electrode shift. Moreover, the proposed algorithms must be usually tuned after each don and doff, which is not comfortable for the users. Real-time simultaneous and proportional myoelectric control, resampling human's arm, has brought much attention. However, increasing the degree of freedom reduces the overall performance. Applying a 3D printed prosthesis arm and under-sampled electromyographic signals could reduce the fabrication cost and improve the application of such methods in practice. There are many technological and clinical challenges in this area to reduce the prosthesis rejection rate.
Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning
Authors: Hanfeng Zhai, Timothy Sands
Date: 2021-12-25 00:00:00

Controlling nonlinear dynamics is a long-standing problem in engineering. Harnessing known physical information to accelerate or constrain stochastic learning pursues a new paradigm of scientific machine learning. By linearizing nonlinear systems, traditional control methods cannot learn nonlinear features from chaotic data for use in control. Here, we introduce Physics-Informed Deep Operator Control (PIDOC), and by encoding the control signal and initial position into the losses of a physics-informed neural network (PINN), the nonlinear system is forced to exhibit the desired trajectory given the control signal. PIDOC receives signals as physics commands and learns from the chaotic data output from the nonlinear van der Pol system, where the output of the PINN is the control. Applied to a benchmark problem, PIDOC successfully implements control with higher stochasticity for higher-order terms. PIDOC has also been proven to be capable of converging to different desired trajectories based on case studies. Initial positions slightly affect the control accuracy at the beginning stage yet do not change the overall control quality. For highly nonlinear systems, PIDOC is not able to execute control with high accuracy compared with the benchmark problem. The depth and width of the neural network structure do not greatly change the convergence of PIDOC based on case studies of van der Pol systems with low and high nonlinearities. Surprisingly, enlarging the control signal does not help to improve the control quality. The proposed framework can potentially be applied to many nonlinear systems for nonlinear controls.