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Predicting Taxi and Uber Demand in Cities: Approaching the Limit of Predictability

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第6期33卷 2723-2736页

作者： Zhao, Kai Khryashchev, Denis Vo, Huy Georgia State Univ Robinson Coll Business Atlanta GA 30302 USA CUNY Grad Ctr New York NY 10016 USA CUNY City Coll New York NY 10031 USA NYU Ctr Urban Sci & Progress New York NY 10003 USA

Time series prediction has wide applications ranging from stock price prediction, product demand estimation to economic forecasting. In this article, we treat the taxi and Uber demand in each location as a time series, and reduce the taxi and Uber demand prediction problem to a time series prediction problem. We answer two key questions in this area. First, time series have different temporal regularity. Some are easy to be predicted and others are not. Given a predictive algorithm such as LSTM (deep learning) or ARIMA (time series), what is the maximum prediction accuracy that it can reach if it captures all the temporal patterns of that time series? Second, given the maximum predictability, which algorithm could approach the upper bound in terms of prediction accuracy? To answer these two question, we use temporal-correlated entropy to measure the time series regularity and obtain the maximum predictability. Testing with 14 million data samples, we find that the deep learning algorithm is not always the best algorithm for prediction. When the time series has a high predictability a simple Markov prediction algorithm (training time 0.5s) could outperform a deep learning algorithm (training time 6 hours). The predictability can help determine which predictor to use in terms of the accuracy and computational costs. We also find that the Uber demand is easier to be predicted compared the taxi demand due to different cruising strategies as the former is demand driven with higher temporal regularity.

关键词： Three-dimensional displays Shape Measurement Detectors Feature extraction Object recognition Clutter Sharing economy deep learning predictive algorithm predictability of time-series data mining

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A review on machine learning algorithms to predict daylighting inside buildings

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SOLAR ENERGY 2020年 202卷 249-275页

作者： Ayoub, Mohammed Arab Acad Sci Technol & Maritime Transport Architectural Engn & Environm Design Dept Alexandria Egypt

Steep increases in air temperatures and CO2 emissions have been associated with the global demand for energy. This is coupled with population growth and improved living standards that encourages the reliance on mechanical acclimatization. Lighting energy alone is responsible for a large portion of total energy consumption in office buildings;and the demand for artificial light is expected to grow in the next years. One of sustainable approaches to enhance energy-efficiency is to incorporate daylighting strategies, which entail the controlled use of daylight inside buildings. Daylight simulation is an active area of research that offers accurate estimations, yet requires a complex set of inputs. Even with today's computers, simulations are computationally expensive and time-consuming, hindering to acquire accelerated preliminary approximations in acceptable timeframes, especially for the iterative design alternatives. Alternatively, predictive models that build on machine learning algorithms have granted much interest from the building design community due to their ability to handle such complex non-linear problems, acting as proxies to heavy simulations. This research presents a review on the growing directions that exploit machine learning to rapidly predict daylighting performance inside buildings, putting a particular focus on scopes of prediction, used algorithms, data sources and sizes, besides evaluation metrics. This work should improve architects decision-making and increase the applicability to predict daylighting. Another implication is to point towards knowledge gaps and missing opportunities in the related research domain, revealing future trends that allow for such innovative approaches to be exploited more commonly in Architectural practice.

关键词： Daylighting Illuminance Prediction Estimation Machine learning predictive algorithm

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Beam Tracking for Interference Alignment in Slowly Fading MIMO Interference Channels: A Perturbations Approach Under a Linear Framework

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2012年第4期60卷 1910-1926页

作者： Yu, Heejung Sung, Youngchul Kim, Haksoo Lee, Yong H. Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

In this paper, the beam design for signal-space interference alignment in slowly fading multiuser multiple-input multiple-output (MIMO) interference channels is considered. Based on a linear formulation for interference alignment, a predictive beam tracking algorithm is proposed using matrix perturbation theory. The proposed algorithm, based on a mixture of iteration and update, computes interference-aligning beamforming vectors at the current time by updating the previous beam vectors based on the channel difference between the two time steps during the predictively updating phase, and yields significant reduction in computational complexity compared with existing methods recalculating beams at each time step. The tracking performance of the algorithm is analyzed in terms of mean square error and sum rate loss between the predictively updating approach and the recalculating approach, and the impact of imperfect channel knowledge is also investigated under the state-space channel model. Numerical results show that the proposed algorithm has almost the same performance as non-predictive methods in sum rate. Thus, the proposed algorithm provides a very efficient way to realize interference alignment in a realistic slowly fading MIMO channel environment.

关键词： Interference alignment least squares null space tracking perturbation theory predictive algorithm slowly fading channels

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Fixed-Time Practical Consensus Tracking of Multi-Agent Systems With Communication Delay

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第3期9卷 1319-1334页

作者： Ni, Junkang Zhao, Yu Cao, Jinde Li, Weilin Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

This paper addresses the fixed-time consensus tracking problem for second-order multi-agent systems (MASs) subjected to communication delays under directed communication network. First, a predictive algorithm is designed to predict the neighbors' states at current time by using the delayed information. Next, based on the predicted neighbor's states, a fixed-time distributed observer is developed to estimate the leader's states. Finally, a predefined-time nonsingular terminal sliding mode control is proposed to stabilize the tracking error system and we can determine the controller gain as soon as the convergence time bound is prescribed. It is rigorously proved that the presented scheme achieves fixed-time consensus tracking of the studied MASs. This study contributes to providing a solution to fixed-time consensus tracking problem of second-order MASs subjected to communication delays under directed communication network, which is a challenging and important issue that has not been studied. The effectiveness of the presented strategy is validated through simulation results.

关键词： Delays Communication networks Observers Consensus protocol Upper bound Task analysis Switches Fixed-time consensus tracking multi-agent system communication delay distributed observer predictive algorithm

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Data-Driven-Based Event-Triggered Resilient Control for Cigarette Weight Control System Under Denial of Service Attacks and False Data Injection Attacks

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IEEE ACCESS 2024年 12卷 53633-53645页

作者： Ye, Guoqian Shen, Lixiang Feng, Yi Fan, Lifeng Zhang, Chi Li, Yuliang China Tobacco Zhejiang Ind Co Ltd ZJTI Zhejiang Hangzhou Cigarette Factory Hangzhou 310024 Peoples R China

This article proposes an event-triggered resilient control algorithm for cigarette weight control systems to defend against denial of service attacks (DoS) attacks and false data injection (FDI) attacks. First, the mathematical model of the system is derived by considering its physical and electromagnetic characteristics. Then, an attack detection mechanism is designed to identify potential FDI attacks. A predictive compensation algorithm is also developed to mitigate the influence of these attacks, incorporating an observer to estimate unknown compensation signals. Due to limited network resources, we employ an event-based model-free adaptive control (MFAC) framework, where data is transmitted only when specific conditions are violated. Throughout the entire control process, only the input and output data of the system are used. Finally, simulation comparisons are provided to demonstrate the effectiveness and superiority of our method.

关键词： Weight control systems DoS attacks FDI attacks model free adaptive control (MFAC) predictive algorithm

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Baseline Values of the Compensatory Reserve Index in a Healthy Pediatric Population

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PEDIATRIC CARDIOLOGY 2022年第2期43卷 344-349页

作者： Rodriguez, Salvador A. Morgan, Gareth J. Lara, Claudia Zablah, Jenny E. Childrens Hosp Colorado Heart Inst Box 10013123 East 16th Ave Aurora CO 80045 USA Univ Colorado Sch Med Anschutz Med Campus Aurora CO 80045 USA Univ Colorado Hosp Dept Cardiol Aurora CO USA Univ Dr Jose Matias Delgado Dept Pediat Antiguo Cuscatlan El Salvador

The objective of this study is to describe the compensatory reserve index (CRI) baseline values in a healthy cohort of healthy pediatric patients and evaluate the existing correlation with other physiological parameters that influence compensatory hemodynamic mechanisms. CRI is a computational algorithm that has been broadly applied to non-invasively estimate hemodynamic vascular adaptations during acute blood loss. So far, there is a lack of baseline values from healthy individuals, which complicates accurately estimating the severity of the hemodynamic compromise. Additionally, the application of this technology in pediatric populations is limited to a few reports, highlighting a marked variability by age, weight, and other physiological parameters. The CRI of 205 healthy subjects from 0 to 60 years of age were prospectively evaluated from January to February 2020 at several public outpatient clinics in El Salvador;vital signs and sociodemographic data were also collected during this period. After data collection, baseline values were classified for each age group. Multiple correlation models were tested between the CRI and the other physiological parameters. CRI value varies significantly for each age group, finding for patients under 18 years old a mean value lower than 0.6, which is currently considered the lower normal limit for adults. CRI presents strong correlations with other physiological variables such as age, weight, estimated blood volume, and heart rate (R > 0.8, R2 > 0.6, p < 0.0001). There is significant variability in the CRI normal values observed in healthy patients based on age, weight, estimated blood volume, and heart rate.

关键词： Compensatory reserve index Machine learning Normal values predictive algorithm

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The application of a neural network to predict hypotension and vasopressor requirements non-invasively in obstetric patients having spinal anesthesia for elective cesarean section (C/S)

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BMC ANESTHESIOLOGY 2020年第1期20卷 98-98页

作者： Gratz, Irwin Baruch, Martin Takla, Magdy Seaman, Julia Allen, Isabel McEniry, Brian Deal, Edward Cooper Univ Hosp 1 Cooper Plaza Camden NJ 08103 USA Caretaker Med Charlottesville VA USA Quahog Res Grp Oakland CA USA Univ Calif San Francisco San Francisco CA 94143 USA

Background Neural networks are increasingly used to assess physiological processes or pathologies, as well as to predict the increased likelihood of an impending medical crisis, such as hypotension. Method We compared the capabilities of a single hidden layer neural network of 12 nodes to those of a discrete-feature discrimination approach with the goal being to predict the likelihood of a given patient developing significant hypotension under spinal anesthesia when undergoing a Cesarean section (C/S). Physiological input information was derived from a non-invasive blood pressure device (Caretaker [CT]) that utilizes a finger cuff to measure blood pressure and other hemodynamic parameters via pulse contour analysis. Receiver-operator-curve/area-under-curve analyses were used to compare performance. Results The results presented here suggest that a neural network approach (Area Under Curve [AUC] = 0.89 [p < 0.001]), at least at the implementation level of a clinically relevant prediction algorithm, may be superior to a discrete feature quantification approach (AUC = 0.87 [p < 0.001]), providing implicit access to a plurality of features and combinations thereof. In addition, the expansion of the approach to include the submission of other physiological data signals, such as heart rate variability, to the network can be readily envisioned. Conclusion This pilot study has demonstrated that increased coherence in Arterial Stiffness (AS) variability obtained from the pulse wave analysis of a continuous non-invasive blood pressure device appears to be an effective predictor of hypotension after spinal anesthesia in the obstetrics population undergoing C/S. This allowed us to predict specific dosing thresholds of phenylephrine required to maintain systolic blood pressure above 90 mmHg.

关键词： Arterial stiffness Cesarean section Finger cuff Hypotension Neural network Non-invasive predictive algorithm

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CADD score has limited clinical validity for the identification of pathogenic variants in noncoding regions in a hereditary cancer panel

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GENETICS IN MEDICINE 2016年第12期18卷 1269-1275页

作者： Mather, Cheryl A. Mooney, Sean D. Salipante, Stephen J. Scroggins, Sheena Wu, David Pritchard, Colin C. Shirts, Brian H. Univ Washington Dept Lab Med Seattle WA 98195 USA Univ Washington Dept Biomed Informat & Med Educ Seattle WA 98195 USA Univ Calif San Francisco Dept Pathol San Francisco CA 94140 USA

Purpose: Several in silico tools have been shown to have reasonable research sehsitivity and specificity for classifying sequence variants in coding regions. The recently developed combined annotation dependent depletion (CADD) method generates predictive scores for single-nucleotide variants (SNVs) in all areas of the genome, including noncoding regions. We sought for non-coding variants to determine the clinical validity of common CADD scores. Methods: We evaluated 12,391 unique SNVs in 624 patient samples submitted for germ-line mutation testing in a cancer-related gene panel. Stratifying by genomic region, we compared the distributions of CADD scores of rare SNVs, SNVs common in our patient population, and the null distribution of all possible SNVs. Results: The median CADD scores of intronic and nonsynonymous variants were significantly different between rare and common SNVs (P < 0.0001). Despite these different distributions, no individual variants could be identified as plausibly causative among the rare intronic variants with the highest scores. The receiver-operating characteristics (ROC) area under the curve (AUC) for noncoding variants is modest, and the positive predictive value of CADD for intronic variants in panel testing was found to be 0.088. Conclusion: Focused in silico scoring systems with much higher predictive value will be necessary for clinical genomic applications.

关键词： combined annotation-dependent depletion CADD score in silico predictor noncoding sequences predictive algorithm

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Biophysical Characterization Platform Informs Protein Scaffold Evolvability

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ACS COMBINATORIAL SCIENCE 2019年第4期21卷 323-335页

作者： Golinski, Alexander W. Holec, Patrick V. Mischler, Katelynn M. Hackel, Benjamin J. Univ Minnesota Twin Cities Dept Chem Engn & Mat Sci 421 Washington Ave Southeast356 Amundson Hall Minneapolis MN 55455 USA

Evolving specific molecular recognition function of proteins requires strategic navigation of a complex mutational landscape. Protein scaffolds aid evolution via a conserved platform on which a modular paratope can be evolved to alter binding specificity. Although numerous protein scaffolds have been discovered, the underlying properties that permit binding evolution remain unknown. We present an algorithm to predict a protein scaffold's ability to evolve novel binding function based upon computationally calculated biophysical parameters. The ability of 17 small proteins to evolve binding functionality across seven discovery campaigns was determined via magnetic activated cell sorting of 10(10) yeast-displayed protein variants. Twenty topological and biophysical properties were calculated for 787 small protein scaffolds and reduced into independent components. Regularization deduced which extracted features best predicted binding functionality, providing a 4/6 true positive rate, a 9/11 negative predictive value, and a 4/6 positive predictive value. Model analysis suggests a large, disconnected paratope will permit evolved binding function. Previous protein engineering endeavors have suggested that starting with a highly developable (high producibility, stability, solubility) protein will offer greater mutational tolerance. Our results support this connection between developability and evolvability by demonstrating a relationship between protein production in the soluble fraction of Escherichia coli and the ability to evolve binding function upon mutation. We further explain the necessity for initial developability by observing a decrease in proteolytic stability of protein mutants that possess binding functionality over nonfunctional mutants. Future iterations of protein scaffold discovery and evolution will benefit from a combination of computational prediction and knowledge of initial developability properties.

关键词： protein scaffolds predictive algorithm protein evolvability

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The Compensatory Reserve Index Responds to Acute Hemodynamic Changes in Patients with Congenital Heart Disease: A Proof of Concept Study

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PEDIATRIC CARDIOLOGY 2020年第6期41卷 1190-1198页

作者： Ehrmann, Daniel E. Leopold, David K. Phillips, Ryan Shahi, Niti Campbell, Kristen Ross, Michael Zablah, Jenny E. Moulton, Steven L. Morgan, Gareth Kim, John S. Univ Colorado Div Cardiol Dept Pediat Childrens Hosp ColoradoSch Med 13123 East 16th AveB100 Aurora CO 80045 USA Univ Colorado Sch Med Dept Anesthesia Aurora CO USA Univ Colorado Dept Surg Div Pediat Surg Sch Med Aurora CO USA Univ Colorado Dept Pediat Sch Med Aurora CO USA Univ N Carolina Div Pediat Cardiol Chapel Hill NC 27515 USA

Patients with congenital heart disease (CHD) who undergo cardiac procedures may become hemodynamically unstable. predictive algorithms that utilize dense physiologic data may be useful. The compensatory reserve index (CRI) trends beat-to-beat progression from normovolemia (CRI = 1) to decompensation (CRI = 0) in hemorrhagic shock by continuously analyzing unique sets of features in the changing pulse photoplethysmogram (PPG) waveform. We sought to understand if the CRI accurately reflects changing hemodynamics during and after a cardiac procedure for patients with CHD. A transcatheter pulmonary valve replacement (TcPVR) model was used because left ventricular stroke volume decreases upon sizing balloon occlusion of the right ventricular outflow tract (RVOT) and increases after successful valve placement. A single-center, prospective cohort study was performed. The CRI was continuously measured to determine the change in CRI before and after RVOT occlusion and successful TcPVR. Twenty-six subjects were enrolled with a median age of 19 (interquartile range (IQR) 13-29) years. The mean (+/- standard deviation) CRI decreased from 0.66 +/- 0.15 1-min before balloon inflation to 0.53 +/- 0.16 (p = 0.03) 1-min after balloon deflation. The mean CRI increased from a pre-valve mean CRI of 0.63 [95% confidence interval (CI) 0.56-0.70] to 0.77 (95% CI 0.71-0.83) after successful TcPVR. In this study, the CRI accurately reflected acute hemodynamic changes associated with TcPVR. Further research is justified to determine if the CRI can be useful as an early warning tool in patients with CHD at risk for decompensation during and after cardiac procedures.

关键词： Compensatory reserve index predictive algorithm Transcatheter pulmonary valve replacement Congenital heart disease

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