Scholarly Works, Computer Science

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2nd Workshop on Uncertainty Reasoning and Quantification in Decision Making
Zhao, Xujiang; Zhao, Chen; Chen, Feng; Cho, Jin-Hee; Chen, Haifeng (ACM, 2023-08-06)
Uncertainty reasoning and quantification play a critical role in decision making across various domains, prompting increased attention from both academia and industry. As real-world applications become more complex and data-driven, effectively handling uncertainty becomes paramount for accurate and reliable decision making. This workshop focuses on the critical topics of uncertainty reasoning and quantification in decision making. It provides a platform for experts and researchers from diverse backgrounds to exchange ideas on cutting-edge techniques and challenges in this field. The interdisciplinary nature of uncertainty reasoning and quantification, spanning artificial intelligence, machine learning, statistics, risk analysis, and decision science, will be explored. The workshop aims to address the need for robust and interpretable methods for modeling and quantifying uncertainty, fostering reasoning decision-making in various domains. Participants will have the opportunity to share research findings and practical experiences, promoting collaboration and advancing decision-making practices under uncertainty.
The Abridgment and Relaxation Time for a Linear Multi-Scale Model Based on Multiple Site Phosphorylation
Wang, Shuo; Cao, Yang (PLOS, 2015-08-11)
Random effect in cellular systems is an important topic in systems biology and often simulated with Gillespie’s stochastic simulation algorithm (SSA). Abridgment refers to model reduction that approximates a group of reactions by a smaller group with fewer species and reactions. This paper presents a theoretical analysis, based on comparison of the first exit time, for the abridgment on a linear chain reaction model motivated by systems with multiple phosphorylation sites. The analysis shows that if the relaxation time of the fast subsystem is much smaller than the mean firing time of the slow reactions, the abridgment can be applied with little error. This analysis is further verified with numerical experiments for models of bistable switch and oscillations in which linear chain system plays a critical role.
Accepted Tutorials at The Web Conference 2022
Tommasini, Riccardo; Basu Roy, Senjuti; Wang, Xuan; Wang, Hongwei; Ji, Heng; Han, Jiawei; Nakov, Preslav; Da San Martino, Giovanni; Alam, Firoj; Schedl, Markus; Lex, Elisabeth; Bharadwaj, Akash; Cormode, Graham; Dojchinovski, Milan; Forberg, Jan; Frey, Johannes; Bonte, Pieter; Balduini, Marco; Belcao, Matteo; Della Valle, Emanuele; Yu, Junliang; Yin, Hongzhi; Chen, Tong; Liu, Haochen; Wang, Yiqi; Fan, Wenqi; Liu, Xiaorui; Dacon, Jamell; Lye, Lingjuan; Tang, Jiliang; Gionis, Aristides; Neumann, Stefan; Ordozgoiti, Bruno; Razniewski, Simon; Arnaout, Hiba; Ghosh, Shrestha; Suchanek, Fabian; Wu, Lingfei; Chen, Yu; Li, Yunyao; Liu, Bang; Ilievski, Filip; Garijo, Daniel; Chalupsky, Hans; Szekely, Pedro; Kanellos, Ilias; Sacharidis, Dimitris; Vergoulis, Thanasis; Choudhary, Nurendra; Rao, Nikhil; Subbian, Karthik; Sengamedu, Srinivasan; Reddy, Chandan; Victor, Friedhelm; Haslhofer, Bernhard; Katsogiannis- Meimarakis, George; Koutrika, Georgia; Jin, Shengmin; Koutra, Danai; Zafarani, Reza; Tsvetkov, Yulia; Balachandran, Vidhisha; Kumar, Sachin; Zhao, Xiangyu; Chen, Bo; Guo, Huifeng; Wang, Yejing; Tang, Ruiming; Zhang, Yang; Wang, Wenjie; Wu, Peng; Feng, Fuli; He, Xiangnan (ACM, 2022-04-25)
This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% of these are hands on.
Access to Autism Spectrum Disorder Services for Rural Appalachian Citizens
Scarpa, Angela; Jensen, Laura S.; Gracanin, Denis; Ramey, Sharon L.; Dahiya, Angela V.; Ingram, L. Maria; Albright, Jordan; Gatto, Alyssa J.; Scott, Jen Pollard; Ruble, Lisa (2020-01)
Background: Low-resource rural communities face significant challenges regarding availability and adequacy of evidence-based services. Purposes: With respect to accessing evidence-based services for Autism Spectrum Disorder (ASD), this brief report summarizes needs of rural citizens in the South-Central Appalachian region, an area notable for persistent health disparities. Methods: A mixed-methods approach was used to collect quantitative and qualitative data during focus groups with 33 service providers and 15 caregivers of children with ASD in rural southwest Virginia. Results: Results supported the barriers of availability and affordability of ASD services in this region, especially relating to the need for more ASD-trained providers, better coordination and navigation of services, and addition of programs to assist with family financial and emotional stressors. Results also suggested cultural attitudes related to autonomy and trust towards outside professionals that may prevent families from engaging in treatment. Implications: Relevant policy recommendations are discussed related to provider incentives, insurance coverage, and telehealth. Integration of autism services into already existing systems and multicultural sensitivity of providers are also implicated.
Accuracy Improvement of Vehicle Recognition by Using Smart Device Sensors
Pias, Tanmoy Sarkar; Eisenberg, David; Fresneda Fernandez, Jorge (MDPI, 2022-06-10)
This paper explores the utilization of smart device sensors for the purpose of vehicle recognition. Currently a ubiquitous aspect of people’s lives, smart devices can conveniently record details about walking, biking, jogging, and stepping, including physiological data, via often built-in phone activity recognition processes. This paper examines research on intelligent transportation systems to uncover how smart device sensor data may be used for vehicle recognition research, and fit within its growing body of literature. Here, we use the accelerometer and gyroscope, which can be commonly found in a smart phone, to detect the class of a vehicle. We collected data from cars, buses, trains, and bikes using a smartphone, and we designed a 1D CNN model leveraging the residual connection for vehicle recognition. The model achieved more than 98% accuracy in prediction. Moreover, we also provide future research directions based on our study.
Accurate and Efficient Gene Function Prediction using a Multi-Bacterial Network
Law, Jeffrey N.; Kale, Shiv D.; Murali, T. M. (2019-05-24)
The rapid rise in newly sequenced genomes requires the development of computational methods to supplement experimental functional annotations. The challenge that arises is to develop methods for gene function prediction that integrate information for multiple species while also operating on a genomewide scale. We develop a label propagation algorithm called FastSinkSource and apply it to a sequence similarity network integrated with species-specific heterogeneous data for 19 pathogenic bacterial species. By using mathematically-provable bounds on the rate of progress of FastSinkSource during power iteration, we decrease the running time by a factor of 100 or more without sacrificing prediction accuracy. To demonstrate scalability, we expand to a 73-million edge network across 200 bacterial species while maintaining accuracy and efficiency improvements. Our results point to the feasibility and promise of multi-species, genomewide gene function prediction, especially as more experimental data and annotations become available for a diverse variety of organisms.
Acoustic differences between healthy and depressed people: a cross-situation study
Wang, Jingying; Zhang, Lei; Liu, Tianli; Pan, Wei; Hu, Bin; Zhu, Tingshao (2019-10-15)
Background Abnormalities in vocal expression during a depressed episode have frequently been reported in people with depression, but less is known about if these abnormalities only exist in special situations. In addition, the impacts of irrelevant demographic variables on voice were uncontrolled in previous studies. Therefore, this study compares the vocal differences between depressed and healthy people under various situations with irrelevant variables being regarded as covariates. Methods To examine whether the vocal abnormalities in people with depression only exist in special situations, this study compared the vocal differences between healthy people and patients with unipolar depression in 12 situations (speech scenarios). Positive, negative and neutral voice expressions between depressed and healthy people were compared in four tasks. Multiple analysis of covariance (MANCOVA) was used for evaluating the main effects of variable group (depressed vs. healthy) on acoustic features. The significances of acoustic features were evaluated by both statistical significance and magnitude of effect size. Results The results of multivariate analysis of covariance showed that significant differences between the two groups were observed in all 12 speech scenarios. Although significant acoustic features were not the same in different scenarios, we found that three acoustic features (loudness, MFCC5 and MFCC7) were consistently different between people with and without depression with large effect magnitude. Conclusions Vocal differences between depressed and healthy people exist in 12 scenarios. Acoustic features including loudness, MFCC5 and MFCC7 have potentials to be indicators for identifying depression via voice analysis. These findings support that depressed people’s voices include both situation-specific and cross-situational patterns of acoustic features.
Active Learning for Microarray based Leukemia Classification
Zhu, Kecheng (ACM, 2021-11-12)
In machine learning, data labeling is assumed to be easy and cheap. However, in real word cases especially clinical field, data sets are rare and expensive to obtain. Active learning is an approach that can query the most informative data for the training. This leads to an alternative to deal with the concern mentioned above. The Sampling method is one of the key parts in active learning because it minimizes the training cost of the classifier. By different query method, models with considerable difference could be produced. The difference in model could lead to significant difference in training cost and final accuracy outcome. The approaches that were used to in this experiment is uncertainty sampling, diversity sampling and query by committee. In the experiment, active learning is applied on the microarray data with improving results. The classification on two types leukemia (acute myeloid leukemia and acute lymophoblastic leukemia) indicates a boost in accuracy with the same number of samples compared to passive machine learning. The experiments leads to the conclusion that with small number of samples with randomness in the field of leukemia classification, active learning produce an more active model. Additionally, active learning with query by committee finds the most informative sample with fewest trials.
Adaptive graph convolutional imputation network for environmental sensor data recovery
Chen, Fanglan; Wang, Dongjie; Lei, Shuo; He, Jianfeng; Fu, Yanjie; Lu, Chang-Tien (Frontiers, 2022-11)
Environmental sensors are essential for tracking weather conditions and changing trends, thus preventing adverse effects on species and environment. Missing values are inevitable in sensor recordings due to equipment malfunctions and measurement errors. Recent representation learning methods attempt to reconstruct missing values by capturing the temporal dependencies of sensor signals as handling time series data. However, existing approaches fall short of simultaneously capturing spatio-temporal dependencies in the network and fail to explicitly model sensor relations in a data-driven manner. In this work, we propose a novel Adaptive Graph Convolutional Imputation Network for missing value imputation in environmental sensor networks. A bidirectional graph convolutional gated recurrent unit module is introduced to extract spatio-temporal features which takes full advantage of the available observations from the target sensor and its neighboring sensors to recover the missing values. In addition, we design an adaptive graph learning layer that learns a sensor network topology in an end-to-end framework, in which no prior network information is needed for capturing spatial dependencies. Extensive experiments on three real-world environmental sensor datasets (solar radiation, air quality, relative humidity) in both in-sample and out-of-sample settings demonstrate the superior performance of the proposed framework for completing missing values in the environmental sensor network, which could potentially support environmental monitoring and assessment.
ADOC: Automatically Harmonizing Dataflow Between Components in Log-Structured Key-Value Stores for Improved Performance
Yu, Jinghuan; Noh, Sam H.; Choi, Young-ri R.; Xue, Chun Jason (Usenix Association, 2023)
Log-Structure Merge-tree (LSM) based Key-Value (KV) systems are widely deployed. A widely acknowledged problem with LSM-KVs is write stalls, which refers to sudden performance drops under heavy write pressure. Prior studies have attributed write stalls to a particular cause such as a resource shortage or a scheduling issue. In this paper, we conduct a systematic study on the causes of write stalls by evaluating RocksDB with a variety of storage devices and show that the conclusions that focus on the individual aspects, though valid, are not generally applicable. Through a thorough review and further experiments with RocksDB, we show that data overflow, which refers to the rapid expansion of one or more components in an LSM-KV system due to a surge in data flow into one of the components, is able to explain the formation of write stalls. We contend that by balancing and harmonizing data flow among components, we will be able to reduce data overflow and thus, write stalls. As evidence, we propose a tuning framework called ADOC (Automatic Data Overflow Control) that automatically adjusts the system configurations, specifically, the number of threads and the batch size, to minimize data overflow in RocksDB. Our extensive experimental evaluations with RocksDB show that ADOC reduces the duration of write stalls by as much as 87.9% and improves performance by as much as 322.8% compared with the auto-tuned RocksDB. Compared to the manually optimized state-of-the-art SILK, ADOC achieves up to 66% higher throughput for the synthetic write-intensive workload that we used, while achieving comparable performance for the real-world YCSB workloads. However, SILK has to use over 20% more DRAM on average.
Advocating for Key-Value Stores with Workload Pattern Aware Dynamic Compaction
Yoon, Heejin; Yang, Jin; Bang, Juyoung; Noh, Sam H.; Choi, Young-ri (ACM, 2024-07-08)
In real life, the ratio of write and read operations of key-value (KV) store workloads usually changes over time. In this paper, we present a Dynamic wOrkload Pattern Aware LSM-based KV store (DOPA-DB), which supports dynamic compaction strategies depending on the workload pattern. In particular, DOPA-DB is a tiered LSM-based KV store with multiple key ranges, which enables varying compaction sizes. For write-intensive workloads, DOPA-DB can minimize write stalls while minimizing compaction overhead, and for readintensive workloads, it can aggressively perform compaction to reduce the number of file accesses. Our preliminary experimental results show the potential benefits of dynamic compaction and provide insight into research directions for dynamic compaction strategies.
AgroSeek: a system for computational analysis of environmental metagenomic data and associated metadata
Liang, Xiao; Akers, Kyle; Keenum, Ishi M.; Wind, Lauren L.; Gupta, Suraj; Chen, Chaoqi; Aldaihani, Reem; Pruden, Amy; Zhang, Liqing; Knowlton, Katharine F.; Xia, Kang; Heath, Lenwood S. (2021-03-10)
Background Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance. Results Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis. Conclusion AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/ .
AI in and for K-12 Informatics Education. Life after Generative AI.
Barendsen, Erik; Lonati, Violetta; Quille, Keith; Altin, Rukiye; Divitini, Monica; Hooshangi, Sara; Karnalim, Oscar; Kiesler, Natalie; Melton, Madison; Suero Montero, Calkin; Morpurgo, Anna (ACM, 2024-12-05)
The use and adoption of Generative AI (GenAI) has revolutionised various sectors, including computing education. However, this narrow focus comes at a cost to the wider AI in and for educational research. This working group aims to explore current trends and explore multiple sources of information to identify areas of AI research in K-12 informatics education that are being underserved but needed in the post-GenAI AI era. Our research focuses on three areas: curriculum, teacher-professional learning and policy. The denouement of this aims to identify trends and shortfalls for AI in and for K-12 informatics education. We will systematically review the current literature to identify themes and emerging trends in AI education at K-12. This will be done under two facets, curricula and teacher-professional learning. In addition, we will conduct interviews and surveys with educators and AI experts. Next, we will examine the current policy (such as the European AI Act, and European Commission guidelines on the use of AI and data in education and training as well as international counterparts). Policies are often developed by both educators and experts in the domain, thus providing a source of topics or areas that may be added to our findings. Finally, by synthesising insights from educators, AI experts, and policymakers, as well as the literature and policy, our working group seeks to highlight possible future trends and shortfalls.
Ajna: A Wearable Shared Perception System for Extreme Sensemaking
Wilchek, Matthew; Luther, Kurt; Batarseh, Feras A. (ACM, 2024)
This paper introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of depth sensors and cameras. These limitations confine object detection to close proximity and hinder perception beyond immediate surroundings, through obstructions, or across different structural levels, impacting collaborative use. It harnesses the Inertial Measurement Unit (IMU) in AR devices to measure users? relative distances from a set physical point, enabling object detection sharing among multiple users across obstacles like walls and over distances. We tested Ajna's effectiveness in a controlled study with 15 participants simulating emergency situations in a multi-story building. We found that Ajna improved object detection, location awareness, and situational awareness, and reduced search times by 15%. Ajna's performance in simulated environments highlights the potential of artificial intelligence (AI) to enhance sensemaking in critical situations, offering insights for law enforcement, search and rescue, and infrastructure management.
ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
Wang, Shengkun; Bai, Yangxiao; Fu, Kaiqun; Wang, Linhan; Lu, Chang-Tien; Ji, Taoran (ACM, 2023-11-06)
For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentiment analysis, macroeconomic indicators, search engine data, and historical prices within a multi-attention deep learning model, masterfully decoding the complex patterns inherent in the data. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.
Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems
Chang, Tyler; Watson, Layne T.; Larson, Jeffrey; Neveu, Nicole; Thacker, William; Deshpande, Shubhangi; Lux, Thomas (ACM, 2022-09-10)
VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of Deshpande et al. [2016], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.
Analysis and remedy of negativity problem in hybrid stochastic simulation algorithm and its application
Chen, Minghan; Cao, Yang (2019-06-20)
Background The hybrid stochastic simulation algorithm, proposed by Haseltine and Rawlings (HR), is a combination of differential equations for traditional deterministic models and Gillespie’s algorithm (SSA) for stochastic models. The HR hybrid method can significantly improve the efficiency of stochastic simulations for multiscale biochemical networks. Previous studies on the accuracy analysis for a linear chain reaction system showed that the HR hybrid method is accurate if the scale difference between fast and slow reactions is above a certain threshold, regardless of population scales. However, the population of some reactant species might be driven negative if they are involved in both deterministic and stochastic systems. Results This work investigates the negativity problem of the HR hybrid method, analyzes and tests it with several models including a linear chain system, a nonlinear reaction system, and a realistic biological cell cycle system. As a benchmark, the second slow reaction firing time is used to measure the effect of negative populations on the accuracy of the HR hybrid method. Our analysis demonstrates that usually the error caused by negative populations is negligible compared with approximation errors of the HR hybrid method itself, and sometimes negativity phenomena may even improve the accuracy. But for systems where negative species are involved in nonlinear reactions or some species are highly sensitive to negative species, the system stability will be influenced and may lead to system failure when using the HR hybrid method. In those circumstances, three remedies are studied for the negativity problem. Conclusions The results of different models and examples suggest that the Zero-Reaction rule is a good remedy for nonlinear and sensitive systems considering its efficiency and simplicity.
ANTHEM: Attentive Hyperbolic Entity Model for Product Search
Choudhary, Nurendra; Rao, Nikhil; Katariya, Sumeet; Subbian, Karthik; Reddy, Chandan K. (ACM, 2022-02-11)
Product search is a fundamentally challenging problem due to the large-size of product catalogues and the complexity of extracting semantic information from products. In addition to this, the blackbox nature of most search systems also hamper a smooth customer experience. Current approaches in this area utilize lexical and semantic product information to match user queries against products. However, these models lack (i) a hierarchical query representation, (ii) a mechanism to detect and capture inter-entity relationships within a query, and (iii) a query composition method specific to e-commerce domain. To address these challenges, in this paper, we propose an AtteNTive Hyperbolic Entity Model (ANTHEM), a novel attention-based product search framework that models query entities as two-vector hyperboloids, learns inter-entity intersections and utilizes attention to unionize individual entities and inter-entity intersections to predict product matches from the search space. ANTHEM utilizes the first and second vector of hyperboloids to determine the query’s semantic position and to tune its surrounding search volume, respectively. The attention networks capture the significance of intra-entity and inter-entity intersections to the final query space. Additionally, we provide a mechanism to comprehend ANTHEM and understand the significance of query entities towards the final resultant products. We evaluate the performance of our model on real data collected from popular e-commerce sites. Our experimental study on the offline data demonstrates compelling evidence of ANTHEM’s superior performance over state-of-the-art product search methods with an improvement of more than 10% on various metrics. We also demonstrate the quality of ANTHEM’s query encoder using a query matching task.
Apigenin Impacts the Growth of the Gut Microbiota and Alters the Gene Expression of Enterococcus
Wang, Minqian; Firrman, Jenni; Zhang, Liqing; Arango-Argoty, Gustavo; Tomasula, Peggy; Liu, Lin Shu; Xiao, Weidong; Yam, Kit (MDPI, 2017-08-03)
Apigenin is a major dietary flavonoid with many bioactivities, widely distributed in plants. Apigenin reaches the colon region intact and interacts there with the human gut microbiota, however there is little research on how apigenin affects the gut bacteria. This study investigated the effect of pure apigenin on human gut bacteria, at both the single strain and community levels. The effect of apigenin on the single gut bacteria strains Bacteroides galacturonicus, Bifidobacterium catenulatum, Lactobacillus rhamnosus GG, and Enterococcus caccae, was examined by measuring their anaerobic growth profiles. The effect of apigenin on a gut microbiota community was studied by culturing a fecal inoculum under in vitro conditions simulating the human ascending colon. 16S rRNA gene sequencing and GC-MS analysis quantified changes in the community structure. Single molecule RNA sequencing was used to reveal the response of Enterococcus caccae to apigenin. Enterococcus caccae was effectively inhibited by apigenin when cultured alone, however, the genus Enterococcus was enhanced when tested in a community setting. Single molecule RNA sequencing found that Enterococcus caccae responded to apigenin by up-regulating genes involved in DNA repair, stress response, cell wall synthesis, and protein folding. Taken together, these results demonstrate that apigenin affects both the growth and gene expression of Enterococcus caccae.
Application and Evaluation of Surrogate Models for Radiation Source Search
Cook, Jared A.; Smith, Ralph C.; Hite, Jason M.; Stefanescu, Razvan; Mattingly, John (MDPI, 2019-12-12)
Surrogate models are increasingly required for applications in which first-principles simulation models are prohibitively expensive to employ for uncertainty analysis, design, or control. They can also be used to approximate models whose discontinuous derivatives preclude the use of gradient-based optimization or data assimilation algorithms. We consider the problem of inferring the 2D location and intensity of a radiation source in an urban environment using a ray-tracing model based on Boltzmann transport theory. Whereas the code implementing this model is relatively efficient, extension to 3D Monte Carlo transport simulations precludes subsequent Bayesian inference to infer source locations, which typically requires thousands to millions of simulations. Additionally, the resulting likelihood exhibits discontinuous derivatives due to the presence of buildings. To address these issues, we discuss the construction of surrogate models for optimization, Bayesian inference, and uncertainty propagation. Specifically, we consider surrogate models based on Legendre polynomials, multivariate adaptive regression splines, radial basis functions, Gaussian processes, and neural networks. We detail strategies for computing training points and discuss the merits and deficits of each method.

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