arXiv subjects

Maryam Hosseini

Publications and source records attributed to Maryam Hosseini.

29 records · Page 2Linked to original sources

arXiv2022

Urban Rhapsody: Large-scale exploration of urban soundscapes

Noise is one of the primary quality-of-life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low-cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state-of-the-art audio representation, machine learning, and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high-precision annotated database of urban sound recordings. We demonstrate the tool's utility through case studies performed by domain experts using data generated over the five-year deployment of a one-of-a-kind sensor network in New York City.

cs.CY↗

arXiv2022

Tail redundancy and its characterization of compression of memoryless sources

We formalize the tail redundancy of a collection of distributions over a countably infinite alphabet, and show that this fundamental quantity characterizes the asymptotic per-symbol redundancy of universally compressing sequences generated iid from a collection $\mathcal P$ of distributions over a countably infinite alphabet. Contrary to the worst case formulations of universal compression, finite single letter (average case) redundancy of $\mathcal P$ does not automatically imply that the expected redundancy of describing length-$n$ strings sampled iid from $\mathcal P$ grows sublinearly with $n$. Instead, we prove that universal compression of length-$n$ \iid sequences from $\mathcal P$ is characterized by how well the tails of distributions in $\mathcal P$ can be universally described, showing that the asymptotic per-symbol redundancy of iid strings is equal to the tail redundancy.

cs.IT↗

arXiv2022

CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials

While designing sustainable and resilient urban built environment is increasingly promoted around the world, significant data gaps have made research on pressing sustainability issues challenging to carry out. Pavements are known to have strong economic and environmental impacts; however, most cities lack a spatial catalog of their surfaces due to the cost-prohibitive and time-consuming nature of data collection. Recent advancements in computer vision, together with the availability of street-level images, provide new opportunities for cities to extract large-scale built environment data with lower implementation costs and higher accuracy. In this paper, we propose CitySurfaces, an active learning-based framework that leverages computer vision techniques for classifying sidewalk materials using widely available street-level images. We trained the framework on images from New York City and Boston and the evaluation results show a 90.5% mIoU score. Furthermore, we evaluated the framework using images from six different cities, demonstrating that it can be applied to regions with distinct urban fabrics, even outside the domain of the training data. CitySurfaces can provide researchers and city agencies with a low-cost, accurate, and extensible method to collect sidewalk material data which plays a critical role in addressing major sustainability issues, including climate change and surface water management.

cs.CV↗

arXiv2021

Sidewalk Measurements from Satellite Images: Preliminary Findings

Large-scale analysis of pedestrian infrastructures, particularly sidewalks, is critical to human-centric urban planning and design. Benefiting from the rich data set of planimetric features and high-resolution orthoimages provided through the New York City Open Data portal, we train a computer vision model to detect sidewalks, roads, and buildings from remote-sensing imagery and achieve 83% mIoU over held-out test set. We apply shape analysis techniques to study different attributes of the extracted sidewalks. More specifically, we do a tile-wise analysis of the width, angle, and curvature of sidewalks, which aside from their general impacts on walkability and accessibility of urban areas, are known to have significant roles in the mobility of wheelchair users. The preliminary results are promising, glimpsing the potential of the proposed approach to be adopted in different cities, enabling researchers and practitioners to have a more vivid picture of the pedestrian realm.

cs.CV↗

arXiv2021

A Visual Analytics System for Profiling Urban Land Use Evolution

The growth of cities calls for regulations on how urban space is used and zoning resolutions define how and for what purpose each piece of land is going to be used. Tracking land use and zoning evolution can reveal a wealth of information about urban development. For that matter, cities have been releasing data sets describing the historical evolution of both the shape and the attributes of land units. The complex nature of zoning code and land-use data, however, makes the analysis of such data quite challenging and often time-consuming. We address these challenges by introducing Urban Chronicles, an open-source web-based visual analytics system that enables interactive exploration of changes in land use patterns. Using New York City's Primary Land Use Tax Lot Output (PLUTO) as an example, we show the capabilities of the system by exploring the data over several years at different scales. Urban Chronicles supports on-the-fly aggregation and filtering operations by using a tree-based data structure that leverages the hierarchical nature of the data set to index the shape and attributes of geographical regions that change over time. We demonstrate the utility of our system through a set of case studies that analyze the impact of Hurricane Sandy on land use attributes, as well as the effects of proposed rezoning plans in Downtown Brooklyn.

cs.CY↗

arXiv2020

Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data

Urban planning is increasingly data driven, yet the challenge of designing with data at a city scale and remaining sensitive to the impact at a human scale is as important today as it was for Jane Jacobs. We address this challenge with Urban Mosaic,a tool for exploring the urban fabric through a spatially and temporally dense data set of 7.7 million street-level images from New York City, captured over the period of a year. Working in collaboration with professional practitioners, we use Urban Mosaic to investigate questions of accessibility and mobility, and preservation and retrofitting. In doing so, we demonstrate how tools such as this might provide a bridge between the city and the street, by supporting activities such as visual comparison of geographically distant neighborhoods,and temporal analysis of unfolding urban development.

cs.HC↗

arXiv2020

On Topological Rank of Factors of Cantor Minimal Systems

A Cantor minimal system is of finite topological rank if it has a Bratteli-Vershik representation whose number of vertices per level is uniformly bounded. We prove that if the topological rank of a minimal dynamical system on a Cantor set is finite then all its minimal Cantor factors have finite topological rank as well. This gives an affirmative answer to a question posed by Donoso, Durand, Maass, and Petite.

math.DS↗

arXiv2018

Redundancy of unbounded memory Markov classes with continuity conditions

We study the redundancy of universally compressing strings $X_1,\dots, X_n$ generated by a binary Markov source $p$ without any bound on the memory. To better understand the connection between compression and estimation in the Markov regime, we consider a class of Markov sources restricted by a continuity condition. In the absence of an upper bound on memory, the continuity condition implies that $p(X_0|X^{-1}_{-m})$ gets closer to the true probability $p(X_0|X_{-\infty}^{-1})$ as $m$ increases, rather than vary around arbitrarily. For such sources, we prove asymptotically matching upper and lower bounds on the redundancy. In the process, we identify what sources in the class matter the most from a redundancy perspective.

cs.IT↗

arXiv2017

Orbit equivalence of Cantor minimal systems and their continuous spectra

To any continuous eigenvalue of a Cantor minimal system $(X,\,T)$, we associate an element of the dimension group $K^0(X,\,T)$ associated to $(X,\,T)$. We introduce and study the concept of irrational miscibility of a dimension group. The main property of these dimension groups is the absence of irrational values in the additive group of continuous spectrum of their realizations by Cantor minimal systems. The strong orbit equivalence (respectively orbit equivalence) class of a Cantor minimal system associated to an irrationally miscible dimension group $(G,\,u)$ (resp. with trivial infinitesimal subgroup) with trivial rational subgroup, have no non-trivial continuous eigenvalues.

math.DS↗

arXiv2014

On redundancy of memoryless sources over countable alphabets

The minimum average number of bits need to describe a random variable is its entropy, assuming knowledge of the underlying statistics On the other hand, universal compression supposes that the distribution of the random variable, while unknown, belongs to a known set $\cal P$ of distributions. Such universal descriptions for the random variable are agnostic to the identity of the distribution in $\cal P$. But because they are not matched exactly to the underlying distribution of the random variable, the average number of bits they use is higher, and the excess over the entropy used is the "redundancy". This formulation is fundamental to problems not just in compression, but also estimation and prediction and has a wide variety of applications from language modeling to insurance. In this paper, we study the redundancy of universal encodings of strings generated by independent identically distributed (iid) sampling from a set $\cal P$ of distributions over a countable support. We first show that if describing a single sample from $\cal P$ incurs finite redundancy, then $\cal P$ is tight but that the converse does not always hold. If a single sample can be described with finite worst-case-regret (a more stringent formulation than redundancy above), then it is known that length-$n$ iid samples only incurs a diminishing (in $n$) redundancy per symbol as $n$ increases. However, we show it is possible that a collection $\cal P$ incurs finite redundancy, yet description of length-$n$ iid samples incurs a constant redundancy per symbol encoded. We then show a sufficient condition on $\cal P$ such that length-$n$ iid samples will incur diminishing redundancy per symbol encoded.

cs.IT↗

arXiv2012

IP-rigidity and eigenvalue groups

We examine the class of increasing sequences of natural numbers which are IP-rigidity sequences for some weakly mixing probability preserving transformation. This property is closely related to the uncountability of the eigenvalue group of a corresponding non-singular transformation. We give examples, including a super-lacunary sequence which is not IP-rigid.

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