Impact Of Digital Twins And Metaverse On Cities: History, Current Situation, And Application Perspectives,
2022
Uppsala Universitet
Impact Of Digital Twins And Metaverse On Cities: History, Current Situation, And Application Perspectives, Zhihan Lv, Wen Long Shang, Mohsen Guizani
Machine Learning Faculty Publications
To promote the expansion and adoption of Digital Twins (DTs) in Smart Cities (SCs), a detailed review of the impact of DTs and digitalization on cities is made to assess the progression of cities and standardization of their management mode. Combined with the technical elements of DTs, the coupling effect of DTs technology and urban construction and the internal logic of DTs technology embedded in urban construction are discussed. Relevant literature covering the full range of DTs technologies and their applications is collected, evaluated, and collated, relevant studies are concatenated, and relevant accepted conclusions are summarized by modules. First, the …
Resel: N-Ary Relation Extraction From Scientific Text And Tables By Learning To Retrieve And Select,
2022
Georgia Institute of Technology
Resel: N-Ary Relation Extraction From Scientific Text And Tables By Learning To Retrieve And Select, Yuchen Zhuang, Yinghao Li, Jerry Junyang Cheung, Yue Yu, Yingjun Mou, Xiang Chen, Le Song, Chao Zhang
Machine Learning Faculty Publications
We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and modalities of the document. Our proposed method RESEL decomposes this task into a two-stage procedure that first retrieves the most relevant paragraph/table and then selects the target entity from the retrieved component. For the high-level retrieval stage, RESEL designs a simple and effective feature set, which captures multilevel lexical and semantic similarities between the query and components. For the low-level selection stage, RESEL designs a cross-modal entity correlation graph along with a multi-view …
Efficient (Soft) Q-Learning For Text Generation With Limited Good Data,
2022
Carnegie Mellon University
Efficient (Soft) Q-Learning For Text Generation With Limited Good Data, Han Guo, Bowen Tan, Zhengzhong Liu, Eric P. Xing, Zhiting Hu
Machine Learning Faculty Publications
Maximum likelihood estimation (MLE) is the predominant algorithm for training text generation models. This paradigm relies on direct supervision examples, which is not applicable to many emerging applications, such as generating adversarial attacks or generating prompts to control language models. Reinforcement learning (RL) on the other hand offers a more flexible solution by allowing users to plug in arbitrary task metrics as reward. Yet previous RL algorithms for text generation, such as policy gradient (on-policy RL) and Q-learning (off-policy RL), are often notoriously inefficient or unstable to train due to the large sequence space and the sparse reward received only …
Amp: Automatically Finding Model Parallel Strategies With Heterogeneity Awareness,
2022
Carnegie Mellon University
Amp: Automatically Finding Model Parallel Strategies With Heterogeneity Awareness, Dacheng Li, Hongyi Wang, Eric Xing, Hao Zhang
Machine Learning Faculty Publications
Scaling up model sizes can lead to fundamentally new capabilities in many machine learning (ML) tasks. However, training big models requires strong distributed system expertise to carefully design model-parallel execution strategies that suit the model architectures and cluster setups. In this paper, we develop AMP, a framework that automatically derives such strategies. AMP identifies a valid space of model parallelism strategies and efficiently searches the space for high-performed strategies, by leveraging a cost model designed to capture the heterogeneity of the model and cluster specifications. Unlike existing methods, AMP is specifically tailored to support complex models composed of uneven layers …
Unpaired Image-To-Image Translation With Density Changing Regularization,
2022
Carnegie Mellon University
Unpaired Image-To-Image Translation With Density Changing Regularization, Shaoan Xie, Qirong Ho, Kun Zhang
Machine Learning Faculty Publications
Unpaired image-to-image translation aims to translate an input image to another domain such that the output image looks like an image from another domain while important semantic information are preserved. Inferring the optimal mapping with unpaired data is impossible without making any assumptions. In this paper, we make a density changing assumption where image patches of high probability density should be mapped to patches of high probability density in another domain. Then we propose an efficient way to enforce this assumption: we train the flows as density estimators and penalize the variance of density changes. Despite its simplicity, our method …
On Pac Learning Halfspaces In Non-Interactive Local Privacy Model With Public Unlabeled Data,
2022
Mohamed Bin Zayed University of Artificial Intelligence
On Pac Learning Halfspaces In Non-Interactive Local Privacy Model With Public Unlabeled Data, Jinyan Su, Jinhui Xu, Di Wang
Machine Learning Faculty Publications
In this paper, we study the problem of PAC learning halfspaces in the non-interactive local differential privacy model (NLDP). To breach the barrier of exponential sample complexity, previous results studied a relaxed setting where the server has access to some additional public but unlabeled data. We continue in this direction. Specifically, we consider the problem under the standard setting instead of the large margin setting studied before. Under different mild assumptions on the underlying data distribution, we propose two approaches that are based on the Massart noise model and self-supervised learning and show that it is possible to achieve sample …
A Damped Newton Method Achieves Global O(1/K2) And Local Quadratic Convergence Rate,
2022
Mohamed Bin Zayed University of Artificial Intelligence
A Damped Newton Method Achieves Global O(1/K2) And Local Quadratic Convergence Rate, Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk, Alexander Gasnikov, Peter Richtárik, Martin Takáč
Machine Learning Faculty Publications
In this paper, we present the first stepsize schedule for Newton method resulting in fast global and local convergence guarantees. In particular, a) we prove an O (1/k2) global rate, which matches the state-of-the-art global rate of cubically regularized Newton method of Polyak and Nesterov (2006) and of regularized Newton method of Mishchenko (2021) and Doikov and Nesterov (2021), b) we prove a local quadratic rate, which matches the best-known local rate of second-order methods, and c) our stepsize formula is simple, explicit, and does not require solving any subproblem. Our convergence proofs hold under affine-invariance assumptions closely related to …
Automs: Automatic Model Selection For Novelty Detection With Error Rate Control,
2022
Nankai University
Automs: Automatic Model Selection For Novelty Detection With Error Rate Control, Yifan Zhang, Haiyan Jiang, Haojie Ren, Changliang Zou, Dejing Dou
Machine Learning Faculty Publications
Given an unsupervised novelty detection task on a new dataset, how can we automatically select a “best” detection model while simultaneously controlling the error rate of the best model? For novelty detection analysis, numerous detectors have been proposed to detect outliers on a new unseen dataset based on a score function trained on available clean data. However, due to the absence of labeled anomalous data for model evaluation and comparison, there is a lack of systematic approaches that are able to select the “best” model/detector (i.e., the algorithm as well as its hyperparameters) and achieve certain error rate control simultaneously. …
Factored Adaptation For Non-Stationary Reinforcement Learning,
2022
City University of Hong Kong
Factored Adaptation For Non-Stationary Reinforcement Learning, Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane
Machine Learning Faculty Publications
Dealing with non-stationarity in environments (e.g., in the transition dynamics) and objectives (e.g., in the reward functions) is a challenging problem that is crucial in real-world applications of reinforcement learning (RL). While most current approaches model the changes as a single shared embedding vector, we leverage insights from the recent causality literature to model non-stationarity in terms of individual latent change factors, and causal graphs across different environments. In particular, we propose Factored Adaptation for Non-Stationary RL (FANS-RL), a factored adaption approach that learns jointly both the causal structure in terms of a factored MDP, and a factored representation of …
Eureka: Euphemism Recognition Enhanced Through Knn-Based Methods And Augmentation,
2022
Carnegie Mellon University
Eureka: Euphemism Recognition Enhanced Through Knn-Based Methods And Augmentation, Sedrick Scott Keh, Rohit Bharadwaj, Emmy Liu, Simone Tedeschi, Varun Gangal, Roberto Navigli
Computer Vision Faculty Publications
We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection. We (1) identify and correct potentially mislabelled rows in the dataset, (2) curate an expanded corpus called EuphAug, (3) leverage model representations of Potentially Euphemistic Terms (PETs), and (4) explore using representations of semantically close sentences to aid in classification. Using our augmented dataset and kNN-based methods, EUREKA was able to achieve state-of-the-art results on the public leaderboard of the Euphemism Detection Shared Task, ranking first with a macro F1 score of 0.881.
Rare Gems: Finding Lottery Tickets At Initialization,
2022
University of Wisconsin-Madison
Rare Gems: Finding Lottery Tickets At Initialization, Kartik Sreenivasan, Jy Yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric Xing, Kangwook Lee, Dimitris Papailiopoulos
Machine Learning Faculty Publications
Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming “train, prune, re-train” approach. Frankle & Carbin [9] conjecture that we can avoid this by training lottery tickets, i.e., special sparse subnetworks found at initialization, that can be trained to high accuracy. However, a subsequent line of work [11, 41] presents concrete evidence that current algorithms for finding trainable networks at initialization, fail simple baseline comparisons, e.g., against training random sparse subnetworks. Finding lottery tickets that train to better accuracy compared to simple baselines remains an open …
Independence Testing-Based Approach To Causal Discovery Under Measurement Error And Linear Non-Gaussian Models,
2022
Carnegie Mellon University
Independence Testing-Based Approach To Causal Discovery Under Measurement Error And Linear Non-Gaussian Models, Haoyue Dai, Peter Spirtes, Kun Zhang
Machine Learning Faculty Publications
Causal discovery aims to recover causal structures generating the observational data. Despite its success in certain problems, in many real-world scenarios the observed variables are not the target variables of interest, but the imperfect measures of the target variables. Causal discovery under measurement error aims to recover the causal graph among unobserved target variables from observations made with measurement error. We consider a specific formulation of the problem, where the unobserved target variables follow a linear non-Gaussian acyclic model, and the measurement process follows the random measurement error model. Existing methods on this formulation rely on non-scalable over-complete independent component …
An Ai-Based Framework For Studying Visual Diversity Of Urban Neighborhoods And Its Relationship With Socio-Demographic Variables,
2022
West Chester University of Pennsylvania
An Ai-Based Framework For Studying Visual Diversity Of Urban Neighborhoods And Its Relationship With Socio-Demographic Variables, Md Amiruzzaman, Ye Zhao, Stefanie Amiruzzaman, Aryn C. Karpinski, Tsung Heng Wu
Computer Science Faculty Publications
This study presents a framework to study quantitatively geographical visual diversities of urban neighborhood from a large collection of street-view images using an Artificial Intelligence (AI)-based image segmentation technique. A variety of diversity indices are computed from the extracted visual semantics. They are utilized to discover the relationships between urban visual appearance and socio-demographic variables. This study also validates the reliability of the method with human evaluators. The methodology and results obtained from this study can potentially be used to study urban features, locate houses, establish services, and better operate municipalities.
Synthetic Data Generation For Intelligent Inspection Of Structural Environments,
2022
University of Texas at El Paso
Synthetic Data Generation For Intelligent Inspection Of Structural Environments, Noshin Habib
Open Access Theses & Dissertations
Automated detection of cracks and corrosion in pavements and industrial settings is essential to a cost-effective approach to maintenance. Deep learning has paved the path for vast levels of improvement in the area. Such models require a plethora of data with accurate ground truth and enough variation for the model to generalize to the data, which is notwidely available. There has been recent progress in computer graphics being used for the creation of synthetic data to address the issue of deficient data availability, but it is limited to specific objects, such as cars and human beings. Textures and deformities within …
Identity Term Sampling For Measuring Gender Bias In Training Data,
2022
Technological University Dublin
Identity Term Sampling For Measuring Gender Bias In Training Data, Nasim Sobhani, Sarah Jane Delany
Conference Papers
Predictions from machine learning models can reflect biases in the data on which they are trained. Gender bias has been identified in natural language processing systems such as those used for recruitment. The development of approaches to mitigate gender bias in training data typically need to be able to isolate the effect of gender on the output to see the impact of gender. While it is possible to isolate and identify gender for some types of training data, e.g. CVs in recruitment, for most textual corpora there is no obvious gender label. This paper proposes a general approach to measure …
Motion Planning Under Uncertainties,
2022
University at Albany, State University of New York
Motion Planning Under Uncertainties, Sourav Dutta
Legacy Theses & Dissertations (2009 - 2024)
A robot is an agent that can bring some changes to the environment around it. Motion planning is the problem of carrying out specialized tasks by a robot by either moving itself or some other object (usually called \textit{payload}) from one place to another. In a real-world scenario, a robot is faced with constraints such as momentum, friction, sensor inaccuracies, etc., that can affect its decision-making while performing specialized tasks. These constraints are identified as uncertainties, and successful planning involves making provisions for such uncertainties. In this work, we present methods like stochastic processes, sequential inference, and pattern recognition to …
Development Of Nucleic Acid Diagnostics For Targeted And Non-Targeted Biosensing,
2022
University at Albany, State University of New York
Development Of Nucleic Acid Diagnostics For Targeted And Non-Targeted Biosensing, Christopher William Smith
Legacy Theses & Dissertations (2009 - 2024)
The field of nucleic acid technology is rapidly expanding with new impactful discoveriesbeing made each year. Starting from the discovery of the double-helix structure, cloning, gene editing, polymerase chain reaction (PCR), CRISPR technology, and even the late mRNA vaccines; nucleic acid technology is at the forefront of improving medicine. Nucleic acid technology is extremely versatile due to its easy programmability, automated cheap synthesis, and even its catalog for numerous chemical modifications that can be used to alter structure stability. For example, the number of permutations that can be made with DNA just by altering the code for adenine (A), cytosine …
Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest,
2022
University at Albany, State University of New York
Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak
Legacy Theses & Dissertations (2009 - 2024)
Operational forecasters face a plethora of challenges when making a forecast; they must consider multiple data sources ranging from radar and satellites to surface and upper air observations, to numerical weather prediction output. Forecasts must be done in a limited window of time, which adds an additional layer of difficulty to the task. These challenges are exacerbated by winter mixed precipitation events where slight differences in thermodynamic profiles or changes in terrain create different precipitation types across small areas. In addition to being difficult to forecast, mixed precipitation events can have large-scale impacts on our society.
Wildfire Spread Prediction Using Attention Mechanisms In U-Net,
2022
Calpoly
Wildfire Spread Prediction Using Attention Mechanisms In U-Net, Kamen Haresh Shah, Kamen Haresh Shah
Master's Theses
An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression …
The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics.,
2022
University of Louisville
The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah
Electronic Theses and Dissertations
Computational technologies can contribute to the modeling and simulation of the biological environments and activities towards achieving better interpretations, analysis, and understanding. With the emergence of digital pathology, we can observe an increasing demand for more innovative, effective, and efficient computational models. Under the umbrella of artificial intelligence, deep learning mimics the brain’s way in learn complex relationships through data and experiences. In the field of bioimage analysis, models usually comprise discriminative approaches such as classification and segmentation tasks. In this thesis, we study how we can use generative AI models to improve bioimage analysis tasks using Generative Adversarial Networks …
