arXiv subjects

Mohammad Hashemi

Publications and source records attributed to Mohammad Hashemi.

14 recordsLinked to original sources

arXiv2026

Synthetic Wastewater Epidemiology Data Generation using Patterns-of-Life Simulation

Wastewater contains rich biological signals that can be used to monitor population health, track infectious diseases, and detect emerging outbreaks. Pathogens and other biomarkers in sewage provide a unique, noninvasive view of disease prevalence at the community level. However, extracting these signals requires extensive field sampling, laboratory analysis, and expert interpretation. Consequently, wastewater-based epidemiology (WBE) datasets are scarce, geographically fragmented, and rarely released as open data. Even when available, existing datasets typically cover short time periods and limited geographic regions, restricting their usefulness for method development, benchmarking, and large-scale modeling. To address this gap, we present an application of an existing patterns-of-life simulation framework for generating synthetic infectious disease and wastewater pathogen datasets for wastewater-based epidemiology. Our approach extends a patterns-of-life simulation framework by using it as a model of human mobility and behavior while incorporating disease transmission and pathogen shedding dynamics. The resulting framework generates high-resolution spatial and temporal datasets capturing infection dynamics, mobility behavior, and wastewater-associated pathogen signals. We release a fully simulated dataset containing check-in records, social network links, infection states, pathogen loads, and ground-truth disease transmission information. These data support controlled experimentation for outbreak detection, source localization, resource allocation, surveillance strategy design, mobility-aware wastewater analysis, and targeted public health interventions. We provide datasets for Fulton County and demonstrate that the framework generalizes to other regions by generating data for any city with available OpenStreetMap information.

q-bio.PE↗

arXiv2026

Staypoint Detection from Noisy Trajectory Data [Experiment Paper]

Detecting staypoints from raw trajectory data is fundamental to numerous spatial computing applications. This process transforms raw numeric sequences of geolocations into semantically meaningful locations, such as homes, workplaces, or restaurants. Despite its importance for semantic trajectory analysis, staypoint detection lacks standard benchmarks, and existing algorithms have never been systematically evaluated. This gap persists because no publicly available datasets provide both raw individual trajectories and ground-truth staypoint annotations. This benchmark paper addresses this limitation with two key contributions: (1) we introduce 16 large-scale simulated datasets capturing thousands of agents with annotated staypoints across varying trajectory noise levels, and (2) we evaluate nine staypoint detection algorithms-including both state-of-the-art and novel methods-to analyze their robustness to noise. Our evaluation reveals that existing state-of-the-art algorithms perform poorly under realistic noise conditions. Conversely, our proposed unsupervised methods yield substantial improvements, while supervised approaches drastically outperform existing baselines. While these results are very promising, these datasets and methods are only meant as starting points for future research in staypoint detection.

cs.LG↗

arXiv2025

World-POI: Global Point-of-Interest Data Enriched from Foursquare and OpenStreetMap as Tabular and Graph Data

Recently, Foursquare released a global dataset with more than 100 million points of interest (POIs), each representing a real-world business on its platform. However, many entries lack complete metadata such as addresses or categories, and some correspond to non-existent or fictional locations. In contrast, OpenStreetMap (OSM) offers a rich, user-contributed POI dataset with detailed and frequently updated metadata, though it does not formally verify whether a POI represents an actual business. In this data paper, we present a methodology that integrates the strengths of both datasets: Foursquare as a comprehensive baseline of commercial POIs and OSM as a source of enriched metadata. The combined dataset totals approximately 1 TB. While this full version is not publicly released, we provide filtered releases with adjustable thresholds that reduce storage needs and make the data practical to download and use across domains. We also provide step-by-step instructions to reproduce the full 631 GB build. Record linkage is achieved by computing name similarity scores and spatial distances between Foursquare and OSM POIs. These measures identify and retain high-confidence matches that correspond to real businesses in Foursquare, have representations in OSM, and show strong name similarity. Finally, we use this filtered dataset to construct a graph-based representation of POIs enriched with attributes from both sources, enabling advanced spatial analyses and a range of downstream applications.

cs.DB↗

arXiv2025

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data

Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries. Existing geospatial representation learning approaches typically aggregate Points of Interest (POIs) into pre-defined administrative regions such as census units or ZIP code areas, assigning a single embedding to each region. However, POIs often form semantically meaningful groups that extend across, within, or beyond these boundaries, defining places that better reflect human activity and urban function. To address this limitation, we propose PlaceRep, a geospatial representation learning method that constructs place-level representations by clustering spatially and semantically related POIs. PlaceRep summarizes large-scale POI graphs from U.S. Foursquare data to produce general-purpose urban region embeddings while automatically identifying places across multiple spatial scales. By eliminating model pre-training, PlaceRep provides a scalable and efficient solution for multi-granular geospatial analysis. Experiments using the tasks of population density estimation and housing price prediction as downstream tasks show that PlaceRep outperforms most state-of-the-art graph-based geospatial representation learning methods and achieves up to a x100 speedup in generating region-level representations on large-scale POI graphs. The implementation of PlaceRep is available at https://github.com/mohammadhashemii/PlaceRep.

cs.LG↗

arXiv2025

From Points to Places: Towards Human Mobility-Driven Spatiotemporal Foundation Models via Understanding Places

Capturing human mobility is essential for modeling how people interact with and move through physical spaces, reflecting social behavior, access to resources, and dynamic spatial patterns. To support scalable and transferable analysis across diverse geographies and contexts, there is a need for a generalizable foundation model for spatiotemporal data. While foundation models have transformed language and vision, they remain limited in handling the unique challenges posed by the spatial, temporal, and semantic complexity of mobility data. This vision paper advocates for a new class of spatial foundation models that integrate geolocation semantics with human mobility across multiple scales. Central to our vision is a shift from modeling discrete points of interest to understanding places: dynamic, context-rich regions shaped by human behavior and mobility that may comprise many places of interest. We identify key gaps in adaptability, scalability, and multi-granular reasoning, and propose research directions focused on modeling places and enabling efficient learning. Our goal is to guide the development of scalable, context-aware models for next-generation geospatial intelligence. These models unlock powerful applications ranging from personalized place discovery and logistics optimization to urban planning, ultimately enabling smarter and more responsive spatial decision-making.

cs.AI↗

arXiv2025

Scalable Graph Condensation with Evolving Capabilities

The rapid growth of graph data creates significant scalability challenges as most graph algorithms scale quadratically with size. To mitigate these issues, Graph Condensation (GC) methods have been proposed to learn a small graph from a larger one, accelerating downstream tasks. However, existing approaches critically assume a static training set, which conflicts with the inherently dynamic and evolving nature of real-world graph data. This work introduces a novel framework for continual graph condensation, enabling efficient updates to the distilled graph that handle data streams without requiring costly retraining. This limitation leads to inefficiencies when condensing growing training sets. In this paper, we introduce GECC (\underline{G}raph \underline{E}volving \underline{C}lustering \underline{C}ondensation), a scalable graph condensation method designed to handle large-scale and evolving graph data. GECC employs a traceable and efficient approach by performing class-wise clustering on aggregated features. Furthermore, it can inherit previous condensation results as clustering centroids when the condensed graph expands, thereby attaining an evolving capability. This methodology is supported by robust theoretical foundations and demonstrates superior empirical performance. Comprehensive experiments including real world scenario show that GECC achieves better performance than most state-of-the-art graph condensation methods while delivering an around 1000$\times$ speedup on large datasets.

cs.LG↗

arXiv2024

High-precision chemical quantum sensing in flowing monodisperse microdroplets

We report on a novel flow-based method for high-precision chemical detection that integrates quantum sensing with droplet microfluidics. We deploy nanodiamond particles hosting fluorescent nitrogen vacancy defects as quantum sensors in flowing, monodisperse, picoliter-volume microdroplets containing analyte molecules. ND motion within these microcompartments facilitates close sensor-analyte interaction and mitigates particle heterogeneity. Microdroplet flow rates are rapid (upto 4cm/s) and with minimal drift. Pairing this controlled flow with microwave control of NV electronic spins, we introduce a new noise-suppressed mode of Optically Detected Magnetic Resonance that is sensitive to chemical analytes while resilient against experimental variations, achieving detection of analyte-induced signals at an unprecedented level of a few hundredths of a percent of the ND fluorescence. We demonstrate its application to detecting paramagnetic ions in droplets with simultaneously low limit-of-detection and low analyte volumes, in a manner significantly better than existing technologies. This is combined with exceptional measurement stability over >103s and across hundreds of thousands of droplets, while utilizing minimal sensor volumes and incurring low ND costs (<$0.70 for an hour of operation). Additionally, we demonstrate using these droplets as micro-confinement chambers by co-encapsulating ND quantum sensors with analytes, including single cells. This versatility suggests wide-ranging applications, like single-cell metabolomics and real-time intracellular measurements in bioreactors. Our work paves the way for portable, high-sensitivity, amplification-free, chemical assays with high throughput; introduces a new chemical imaging tool for probing chemical reactions in microenvironments; and establishes the foundation for developing movable, arrayed quantum sensors through droplet microfluidics.

quant-ph↗

arXiv2024

A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation

Many real-world datasets can be naturally represented as graphs, spanning a wide range of domains. However, the increasing complexity and size of graph datasets present significant challenges for analysis and computation. In response, graph reduction, or graph summarization, has gained prominence for simplifying large graphs while preserving essential properties. In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation. Specifically, we establish a unified definition for these methods and introduce a hierarchical taxonomy to categorize the challenges they address. Our survey then systematically reviews the technical details of these methods and emphasizes their practical applications across diverse scenarios. Furthermore, we outline critical research directions to ensure the continued effectiveness of graph reduction techniques, as well as provide a comprehensive paper list at \url{https://github.com/Emory-Melody/awesome-graph-reduction}. We hope this survey will bridge literature gaps and propel the advancement of this promising field.

cs.SI↗

arXiv2022

HWGN2: Side-channel Protected Neural Networks through Secure and Private Function Evaluation

Recent work has highlighted the risks of intellectual property (IP) piracy of deep learning (DL) models from the side-channel leakage of DL hardware accelerators. In response, to provide side-channel leakage resiliency to DL hardware accelerators, several approaches have been proposed, mainly borrowed from the methodologies devised for cryptographic implementations. Therefore, as expected, the same challenges posed by the complex design of such countermeasures should be dealt with. This is despite the fact that fundamental cryptographic approaches, specifically secure and private function evaluation, could potentially improve the robustness against side-channel leakage. To examine this and weigh the costs and benefits, we introduce hardware garbled NN (HWGN2), a DL hardware accelerator implemented on FPGA. HWGN2 also provides NN designers with the flexibility to protect their IP in real-time applications, where hardware resources are heavily constrained, through a hardware-communication cost trade-off. Concretely, we apply garbled circuits, implemented using a MIPS architecture that achieves up to 62.5x fewer logical and 66x less memory utilization than the state-of-the-art approaches at the price of communication overhead. Further, the side-channel resiliency of HWGN2 is demonstrated by employing the test vector leakage assessment (TVLA) test against both power and electromagnetic side-channels. This is in addition to the inherent feature of HWGN2: it ensures the privacy of users' input, including the architecture of NNs. We also demonstrate a natural extension to the malicious security modeljust as a by-product of our implementation.

cs.CR↗

arXiv2022

Garbled EDA: Privacy Preserving Electronic Design Automation

The complexity of modern integrated circuits (ICs) necessitates collaboration between multiple distrusting parties, including thirdparty intellectual property (3PIP) vendors, design houses, CAD/EDA tool vendors, and foundries, which jeopardizes confidentiality and integrity of each party's IP. IP protection standards and the existing techniques proposed by researchers are ad hoc and vulnerable to numerous structural, functional, and/or side-channel attacks. Our framework, Garbled EDA, proposes an alternative direction through formulating the problem in a secure multi-party computation setting, where the privacy of IPs, CAD tools, and process design kits (PDKs) is maintained. As a proof-of-concept, Garbled EDA is evaluated in the context of simulation, where multiple IP description formats (Verilog, C, S) are supported. Our results demonstrate a reasonable logical-resource cost and negligible memory overhead. To further reduce the overhead, we present another efficient implementation methodology, feasible when the resource utilization is a bottleneck, but the communication between two parties is not restricted. Interestingly, this implementation is private and secure even in the presence of malicious adversaries attempting to, e.g., gain access to PDKs or in-house IPs of the CAD tool providers.

cs.CR↗

arXiv2022

Fully Automated Assessment of Cardiac Coverage in Cine Cardiovascular Magnetic Resonance Images using an Explainable Deep Visual Salient Region Detection Model

Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential basic quality controls of CMR images is to investigate the complete cardiac coverage, which is necessary for the volumetric and functional assessment. This study examines the full cardiac coverage using a 3D convolutional model and then reduces the number of false predictions using an innovative salient region detection model. Salient regions are extracted from the short-axis cine CMR stacks using a three-step proposed algorithm. Combining the 3D CNN baseline model with the proposed salient region detection model provides a cascade detector that can reduce the number of false negatives of the baseline model. The results obtained on the images of over 6,200 participants of the UK Biobank population cohort study show the superiority of the proposed model over the previous state-of-the-art studies. The dataset is the largest regarding the number of participants to control the cardiac coverage. The accuracy of the baseline model in identifying the presence/absence of basal/apical slices is 96.25\% and 94.51\%, respectively, which increases to 96.88\% and 95.72\% after improving using the proposed salient region detection model. Using the salient region detection model by forcing the baseline model to focus on the most informative areas of the images can help the model correct misclassified samples' predictions. The proposed fully automated model's performance indicates that this model can be used in image quality control in population cohort datasets and also real-time post-imaging quality assessments.

eess.IV↗

arXiv2021

Parkinson's Disease Diagnosis based on Gait Cycle Analysis Through an Interpretable Interval Type-2 Neuro-Fuzzy System

In this paper, an interpretable classifier using an interval type-2 fuzzy neural network for detecting patients suffering from Parkinson's Disease (PD) based on analyzing the gait cycle is presented. The proposed method utilizes clinical features extracted from the vertical Ground Reaction Force (vGRF), measured by 16 wearable sensors placed in the soles of subjects' shoes and learns interpretable fuzzy rules. Therefore, experts can verify the decision made by the proposed method based on investigating the firing strength of interpretable fuzzy rules. Moreover, experts can utilize the extracted fuzzy rules for patient diagnosing or adjust them based on their knowledge. To improve the robustness of the proposed method against uncertainty and noisy sensor measurements, Interval Type-2 Fuzzy Logic is applied. To learn fuzzy rules, two paradigms are proposed: 1- A batch learning approach based on clustering available samples is applied to extract initial fuzzy rules, 2- A complementary online learning is proposed to improve the rule base encountering new labeled samples. The performance of the method is evaluated for classifying patients and healthy subjects in different conditions including the presence of noise or observing new instances. Moreover, the performance of the model is compared to some previous supervised and unsupervised machine learning approaches. The final Accuracy, Precision, Recall, and F1 Score of the proposed method are 88.74%, 89.41%, 95.10%, and 92.16%. Finally, the extracted fuzzy sets for each feature are reported.

cs.LG↗

arXiv2021

FPGA-based Implementation of a New Data Frame Correction System for Merging Units

With today's increasing demand for digital devices in Substation Automation Systems (SAS) based on the IEC61850 standard, the measured data error due to the synchronization problem should be considered as a significant problem in digitalized SAS. Although time tagging and mathematical methods have been proposed to alleviate this problem, they require a massive amount of calculations and elaborations. To develop a solution for both problems of the data error and the massive computation, in this paper, we propose a data frame correction (DFC) system with a new method of data shift computation as a data correction method implemented as a hardware accelerator on FPGA. Compared to the state-of-the-art DFC systems, the results show that the proposed DFC system can achieve data correction with up to 99.6% fewer hardware resources utilization and fulfills 9x calculation speed while maintaining IEC61850 required accuracy in 2.1ms.

eess.SY↗

arXiv2018

Stochastic Substitute Training: A Gray-box Approach to Craft Adversarial Examples Against Gradient Obfuscation Defenses

It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input. These adversarial examples are crafted, for example, by calculating gradients of a carefully defined loss function with respect to the input. As a countermeasure, some researchers have tried to design robust models by blocking or obfuscating gradients, even in white-box settings. Another line of research proposes introducing a separate detector to attempt to detect adversarial examples. This approach also makes use of gradient obfuscation techniques, for example, to prevent the adversary from trying to fool the detector. In this paper, we introduce stochastic substitute training, a gray-box approach that can craft adversarial examples for defenses which obfuscate gradients. For those defenses that have tried to make models more robust, with our technique, an adversary can craft adversarial examples with no knowledge of the defense. For defenses that attempt to detect the adversarial examples, with our technique, an adversary only needs very limited information about the defense to craft adversarial examples. We demonstrate our technique by applying it against two defenses which make models more robust and two defenses which detect adversarial examples.

cs.LG↗