Abstract

A wide variety of information pertaining to human behaviour, ranging from connections on social networks to accounts of discrimination and abuse, is being produced on the internet. Predictive models built using this data can be valuable for various domains including computational social science, personalization, recommendation, and marketing. Creating such predictive models can be challenging owing to the fact that the data available on online media relevant to human behaviour may be unstructured, inadequate and/or highly imbalanced. In this thesis, we consider two contrasting instances of human behaviour: trust and discrimination, especially gender-based discrimination, i.e., sexism. We formulate a novel approach based on matrix factorization for predicting user-user trust by capitalizing on community memberships and devising a new way to model the homophily theory. With regard to sexism, we introduce the problem of multi-label categorization of accounts describing sexism of any kind(s) and propose neural network based solutions for it. The prediction of trust relations between users of social networks is critical for finding credible information. Inferring trust is challenging, since user-specified trust relations are highly sparse and power-law distributed. In this thesis, we explore utilizing community memberships for trust prediction in a principled manner. We also propose a novel method to model homophily that complements existing work and a way of capitalizing on helpfulness scores of users’ item ratings. To the best of our knowledge, this is the first work that mathematically formulates a hypothesis pertaining to community memberships for unsupervised trust prediction. We propose and model the hypothesis that a user is more (or as) likely to develop a trust relation within the user’s community than (or as) outside it. Unlike existing work, our approach for encoding homophily directly links user-user similarities with the pair-wise trust model. Our formulation for tapping rating helpfulness scores relates to the notion that users receiving low rating helpfulness scores should not receive high levels of trust on average. We derive mathematical factors that model our hypothesis relating community memberships to trust relations, the homophily effect, and the premise involving helpfulness scores. Along with an existing method, they are combined into chTrust, the proposed multi-faceted optimization framework. Our experiments on the standard Ciao and Epinions datasets show that in the majority of the settings involving the fraction of the trust relations used for training, whether all user pairs or only the low-degree ones are considered in the evaluation, and the dataset, the proposed framework outperforms all but one of the unsupervised trust prediction baselines considered; no proposed or baseline method performs the best across all settings.We also develop a supervised method for trust prediction that avoids any dependence on auxiliary information such as ratings or reviews. We propose combining representation learning with the inference of trust relations. Using only a small amount of binary user-user trust relations, our approach simultaneously learns embeddings for the users and a trust prediction model. Another human behaviour that we endeavour to analyze through predictive models is discrimination, especially the form of discrimination that is perpetrated on the basis of sex. Sexism, an injustice that gives rise to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in studying and thereby countering sexism. The existing work on sexism classification has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We also consider the related task of the classification of misogyny. While sexism classification is performed on textual accounts describing sexism suffered or observed, misogyny classification is carried out on tweets perpetrating misogyny. We devise a novel neural framework for classifying sexism and misogyny that can combine text representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. In order to evaluate the versatility of our neural approach for tasks pertaining to sexism and misogyny, we experiment with adapting it for misogyny identification. For categorizing sexism, we investigate m

Abstract

Computer Aided Diagnosis (CAD) systems are developed to aid doctors and clinicians in diagnosis after interpreting and examining a medical image. CAD systems aids in performing the task more consistently. With the arrival of data-driven deep learning paradigm and availability of large amount of data in the medical domain, CAD systems are being developed to diagnose a large variety of diseases ranging from different types of cancers, heart and brain diseases, Alzheimer’s disease, and diabetic retinopathy, etc. These systems are highly competent in performing the task on which they are trained. Although such systems perform at par with the trained clinicians, they suffer from a limitation in that they are completely black box in nature and are trained only on image-level class labels. This poses a problem in deploying CAD systems as stand alone solutions for disease diagnosis. This is because decisions in the medical domain are about health of a patient and are well reasoned and backed by evidence, sometimes from multiple modalities. Hence, there is a critical need for CAD system’s decisions to be explainable. Restricting our focus to only image modality, a solution to design explainable CAD systems could be to train the system using both class labels and local annotations and derive the explanation in a fully supervised manner. However, getting these local annotations is very expensive, time-consuming, and infeasible in most circumstances. In this thesis we address this explainability and data scarcity problem and propose two different approaches towards the development of weakly supervised explainable CAD systems. Firstly, we aim to explain the classification decision by providing heatmaps denoting important regions of interest in the image, which helped the model make the prediction. In order to generate anatomically accurate heatmaps, we provide a mixed set of annotations to our model - class labels for the entire training set of images and rough localization of suspect regions for a smaller subset of images in the training set. The proposed approach is illustrated on two different disease classification tasks based on disparate image modalities - Diabetic macular edema (DME) classification from OCT slices and Breast Cancer detection from mammographic images. Good classification results are shown on public datasets, supplemented by explanations in the form of suspect regions; these are derived using just a third of images with local annotations, emphasizing the potential for generalisability of the proposed solution.

Abstract

Quantum states that possess negative conditional von Neumann entropy (quantum conditional entropy) provide quantum advantage in several information-theoretic protocols including superdense coding, state merging, distributed private randomness distillation and one-way entanglement distillation. While entanglement is an important resource, only a subset of entangled states have negative conditional von Neumann entropy. This thesis deals with the characterization, study and detection of these resourceful states. The first part of the thesis deals with the detection of negative conditional entropy states. We characterize the class of density matrices having non-negative conditional von Neumann entropy as convex and compact. This allows us to prove the existence of a Hermitian operator (a witness) for the detection of states having negative conditional entropy for bipartite systems in arbitrary dimensions. We show two constructions of such witnesses. For one of the constructions, the expectation value of the witness in a state is an upper bound to the conditional entropy of the state. We pose the problem of obtaining a tight upper bound to the set of conditional entropies of states in which an operator gives the same expectation value. We solve this convex optimization problem numerically for a two qubit case and find that this enhances the usefulness of our witnesses. We also find that for a particular witness, the estimated tight upper bound matches the value of conditional entropy for Werner states. We explicate the utility of the methods proposed in the detection of useful states in several protocols. In the second part of the thesis, we introduce the resource-theory framework, and give pertinent directions for the study of conditional entropy under the resource-theoretic framework.

Abstract

Water is an essential resource for sustaining human life. The ever-increasing demand for clean water from the growing human population and other anthropogenic needs have pushed several regions into highly water- stressed areas. Diminishing freshwater resources and high amounts of water contamination are increasing the threat of severe water scarcity even further. Since the total available freshwater resources cannot be modified, the options are to maintain and/or improve water quality in water bodies and increase the reusability of the water. The former can be achieved by continuous monitoring of the water quality in the water bodies and identifying and regulating the source of contaminations that pollute these water bodies. So, water quality in rivers and large inland water bodies are being measured occasionally through in-situ approaches. While these do provide valuable inputs, they are not sufficient to understand the underlying causes for such changes in water quality, including the seasonal and other drivers of contamination. Hence, there is a need to monitor water bodies, especially the lakes and reservoirs, regularly. Constant inflows and long storage time of water with limited or no proper outflow make these water bodies much more susceptible to the adverse effects of contaminants than rivers. This study takes a three-pronged approach to study and understand the water quality in inland water bodies – first, by developing and evaluating a methodology for monitoring nutrient contamination and its changes in inland water bodies using remote sensing satellite data; second, to hypothesize and study the probable source of the contamination as land use and its practices; and three, to understand, analyze and model the interactions between the land use changes in the contributing watershed and the water quality to help improve the regional- scale decision making capabilities. The case study area is Nagarjuna Sagar (NS) reservoir in the Krishna River basin, one of the largest inland water bodies in India. NS is a multipurpose dam and inland water body (reservoir) with a spatial spread of 285 Km2 and a catchment area of 215,000 Km 2 . In addition to Irrigation and Power generation, it is also a primary source of drinking water to Hyderabad, a large metropolis in India with nearly 10million population. All these make it essential to study and maintain the water quality of the NS water body. While the literature suggests that the remote sensing approach can help monitor the nutrient content in the water body, most studies and analyses have been done for oceans, seas, and enormous water bodies. Their application to inland water bodies is still a challenge due to the spatial and spectral limitations of the remote sensing sensors. Hence, the study explored the effectiveness of the remote sensing data in studying the inland water body nutrient contamination. Here, the presence and spatial spread of Chl-a, a remote sensing detectable agent, present in the photosynthetic organisms such as algae, phytoplankton, and cyanobacteria (AP) was used as a proxy for monitoring nutrient contamination in water bodies. The unique spectral signature of Chl-a was used to detect and identify its spatial spread across the water body. For this process, a decision-tree-based classification technique was developed that exploits the spectral response peaks within the region of 705nm to 860nm for each pixel to detect and classify them into No to Low contamination, Moderate and High Chl-a levels. The spatial spread of moderate and high Chl-a areas was used to understand the severity of the contamination in the water body. Further, through meta-analysis and comparative study, it was found that MODIS and Sentinel satellite data are best suited for the detection of Chl-a content in the water body using the spectral signature-based method. While various works have well-studied MODIS data, Sentinel data is relatively recent and needs to be tested for its sensitivity towards Chl-a across various concentrations of Chl-a in water bodies situated in different environments. Since the NS water quality data is not available, it is prudent to use the water quality data from earlier published studies to evaluate the method. As part of this, Lake Taihu (TL, with an area of 2250 Km 2 ) and Lake Bebe (LB, with an area of 24 Km 2 ) with different concentrations of Chl-a were used to test the sensitivity of the method. The Manasarovar Lake (ML) (area of 411 Km 2 ) was used as a zero baseline case as it is free of any Chl-a contamination to check if the method was working without any false positives. The results from TL showed that more than 50% of the water body indicates the presence of Chl-a content which matched with the data given in the literature. Similarly, the method could not detect any Chl-a content in the ML, which agrees with the literature but showed some limitations in the case of LB. On applying

Abstract

With rapid advancements in technology, the volume of data generated keeps increasing at an incredible pace. Data mining is the process of discovering novel and potentially useful information from large amounts of data. Currently, data mining techniques are being employed by organizations to improve performance by extracting useful knowledge from large datasets. Pattern mining is one of the important data mining tasks. The pattern mining techniques are being applied in applications such as social media, e-commerce, advertising and so on. In the literature, models and algorithms have been proposed for mining different kinds of patterns such as frequent patterns, correlated patterns, utility patterns, weighted frequent patterns and coverage patterns. There is an ongoing research to develop efficient parallel algorithms to extract patterns from large datasets by employing parallel processing frameworks such as shared memory, shared nothing, GPUs, Grid computing and MapReduce. In this thesis, we proposed parallel algorithms by employing MapReduce framework to extract correlated patterns and weighted frequent patterns. Notably, MapReduce framework is a programming model that enables distributed processing of huge amounts of data on a very large number of commodity machines in geographically distributed environments in a reliable and fault-tolerant manner. Once an algorithm is developed on a MapReduce framework, it can be easily scaled to process large datasets by employing a large number of machines in a shared-nothing environment. The correlated pattern mining involves the extraction of patterns from transactional databases subject to the user specified minimum support and minimum all-confidence constraints. Typically, for any parallel algorithm that runs on multiple machines, the data is segmented into multiple partitions and distributed among the machines. This segmentation influences the data shuffle among the machines and runtime of the algorithm. We introduce two novel data segmentation techniques - database segmentation and transaction segmentation to discover the correlated patterns efficiently. The former technique involves segmenting the database into multiple sub-databases such that each sub-database can be mined independently. The latter technique involves segmenting a transaction into multiple sub-transactions such that each sub-transaction can be processed as an individual transaction. We employ these data segmentation techniques and leverage MapReduce framework to propose Parallel Correlated Patterngrowth algorithm.The proposed segmentation techniques are algorithm agnostic, and therefore, can be incorporated into any parallel correlated pattern mining algorithm. The process of weighted frequent itemset (WFI) mining involves the extraction of itemsets from a given transactional database subject to the user defined minimum support, minimum weight and min imum weighted support constraints. It has the following two challenges: (i) finding WFIs is a computationally expensive process as these itemsets do not satisfy the downward closure property and (ii) there are only few computationally efficient algorithms to find WFIs in very large databases. Firstly, we propose Sequential Weighted Frequent Pattern-growth algorithm to discover WFIs efficiently. It employs three novel pruning techniques to reduce the computational cost effectively. We also leverage the MapReduce framework to achieve further computational optimization and propose Parallel Weighted Frequent Pattern-growth algorithm. Overall, we propose MapReduce based approaches to extract correlated patterns and weighted frequent patterns from large transactional databases. We demonstrate that the proposed approaches are highly scalable, memory and runtime efficient through results of experiments conducted on both realworld and synthetic datasets.

Abstract

Speech produced in practical environments is affected by several sources of degradations, which includenoises, reflections, reverberation and sounds from other sources such as background speech, vehicles, etc. Thesedegradations reduce the performance of several speech-based applications, such as speech and speaker recogni-tion systems. It is difficult to characterize these degradations, as they are not known in advance, and also theyare time varying. Hence, it is necessary to explore and extract speech-specific and speaker-specific informationin the collected speech signal to improve the performance of speech-based systems. The objective of the studiesproposed in this thesis is to explore methods based on single frequency filtering (SFF) analysis of speech signalsand artificial neural network (ANN) models for some speech-specific and speaker-specific tasks. The SFF analy-sis gives flexibility in the representation of speech information at different spectral and temporal resolutions. TheANN models are used for capturing the implicit features, specific to a given application. The SFF analysis can beused to provide a representation of the excitation source and the vocal tract system characteristics of the dynamicspeech production mechanism. The ANN models help in extracting the discriminative and descriptive featuresas needed for a given task. The speech-specific tasks explored in this thesis include speech/nonspeech detectionfrom degraded speech, discrimination of natural and synthetic speech. The speaker-specific tasks examined inthis thesis include speaker separation in two speaker conversation data, mimicked voice detection and speakerverification.The speech/nonspeech detection task is addressed using approaches based on signal processing and on ANNmodels. The signal processing approach exploits speech-specific characteristics in the degraded signal. The re-sults of speech/nonspeech detection are given for the speech data from TIMIT corpus, corrupted by syntheticallyadding noises of different types at different levels. The signal processing approach is also used for detectingthe speech regions, when the speech is corrupted by unknown sources of degradations in a practical informationaccess situation. The performance of speech/nonspeech discrimination using ANN models is examined for syn-thetically added degradations to speech signals, mainly to study the effectiveness of different types of SFF-basedrepresentations of the signal. These models have been successfully used for voice activity detection (VAD) taskon the naturalistic Apollo corpus. The effectiveness of SFF-based representations and the discriminative charac-teristics of ANN models are examined for several speech-specific tasks such as discrimination of close vs distantspeech, live vs recorded speech, and natural vs synthetic speech.Speaker-specific tasks such as study of speaker separation, mimicked voice detection and speaker verificationare explored using discriminative and descriptive models of ANN. Speaker separation task is successfully demon-strated on several cases of 2-speaker conversation data. The proposed approach for speaker verification task isdemonstrated only for a limited number of speakers, as the speaker verification task should not be dependenton the number of speakers. Overall, this study examined the effectiveness of different SFF-based representa-tions for some speech-specific and speaker-specific tasks using ANN models. It also highlighted deficiencies inrepresentation for some other tasks.

Abstract

Steer-by-Wire (SBW) systems are being adapted widely in semi-autonomous and fully autonomous vehicles. The main control challenge in a SBW system is to follow the steering commands in the face of parametric uncertainties, external disturbances and actuator delay;crucially, perturbations in inertial parameters and damping forces give rise to state-dependent uncertainties, which cannot be bounded a priori by a constant. However, the state-of-the-art control methods of SBW system rely on a priori bounded uncertainties, and thus, become inapplicable when state-dependent dynamics become unknown. In addition, ensuring tracking accuracy under actuator delay is always a challenging task. This work proposes two control frameworks to overcome these challenges. Firstly, an adaptive controller is proposed to tackle the state-dependent uncertainties and external disturbances in a typical SBW system without any a priori knowledge of their structures and of their bounds. The stability of the closedloop system is studied analytically via uniformly ultimately bounded notion and the effectiveness of the proposed solution is verified via simulations against the state-of-the-art solution. While this proposed controller handles the uncertainties and external perturbations, it does not consider the actuator delay which sometimes result in decreased accuracy. Therefore, a new adaptive-robust control framework is devised to tackle the same control problem of an SBW system under the influence of time-varying input delay. In comparison to the existing strategies, the proposed framework removes the conservative assumption of a priori bounded uncertainty and, in addition, the Razumikhin theorem based stability analysis allows the proposed scheme to deal with arbitrary variation in input delay. The effectiveness of the both controllers is proved using comparative simulation studies.

Abstract

Future’s technological advancements will foresee robotics as a leading cause of change. Since 1960, robots are predominantly used in manufacturing, where the automotive industries were the main customers. Today, its applications are spread across various fields, starting from utilisation in factories, storage facilities, transportation, household, underwater and even in outer space to perform multiple tasks. Even though these tasks are combinations of basic operations like inspection, grabbing, lifting, catching, carrying etc., complex and unstructured environments and diverse robot structures introduce multiple challenges. Autonomous operation requires addressing these challenges reliably, accurately and efficiently for successful task completion. Sensing plays a crucial role in achieving autonomy. Among various sensor modalities, vision is the most vital and can provide rich information about the environment that a robot can perceive. The introduction of visual feedback with perceived images provides powerful means for a robot to perform accurate positioning tasks. Vision-based autonomous control of robotic systems has been a field of interest in robotics for many years. Image-Based Visual Servoing (IBVS) is one such method that gives autonomy in reaching the desired pose to provide a robust grasp compared to conventional controls. Robots with diverse architectures bring in different challenges while performing visual servoing and requires attention. These robotic systems include industrial robots, mobile robots, underactuated systems like quadrotors and redundant systems like humanoids and multi-arm robots. Many application scenarios require dealing with tasks that cannot be executed by a single-arm robot and demand coordinated control strategies. Using a multi-arm robotic system with enhanced control strategies is inevitable for such tasks beyond the capabilities of a single-arm. Hence, multi-arm robotic systems are getting more attention to perform multiple tasks and dextrous operations, making it our primary focus of study. Even though various solutions have been proposed for vision based-control, significant challenges exist for multi-arm robotic systems working in complex environments. Driven by this motivation, an attempt has been made in this work to develop a generic framework for visual servoing of a multi-arm robotic system in the presence of task constraints. An analytical framework for kinematics of fixed and free-floating multi-arm robotic system has been proposed for executing IBVS. It is worth mentioning that, while designing IBVS for multi-arm robotic systems, a novel concept for reactionless visual servoing is introduced, which builds on the system’s redundancy to performs multiple tasks. One of the primary concerns in any IBVS scheme is the uncertainty in acquiring precise knowledge of the environment due to unreliable sensor inputs, noise, varying lighting conditions, and occlusions. The existing solutions depend upon filtering, feature extraction, matching and real-time tracking for alleviating these concerns. However, incorporating such multiple methods and their additional computations become extra overheads for visual servoing. In many computer vision applications, probabilistic techniques help obtain desirable performance under similar situations. Therefore, an effort has been made to investigate visual servoing through this paradigm. A novel approach to visual servoing is proposed using student’s t-distribution based mixture models that consider the whole image a probabilistic function. The introduced probabilistic model-based approach provides efficient servoing operation and satisfactory performance than many commonly used dense visual servoing methodologies reported in the literature. Another contribution is in the development of an approach for servoing towards a noncooperative tumbling object. Although strategies for capturing fixed and moving objects are well studied, existing methods for capturing tumbling objects use complex reconstruction and motion estimation techniques. On the other hand, visual servoing is less complicated and provides efficient autonomous motion for a robot towards a fixed object. These benefits motivated the choice of IBVS for the capture of tumbling objects. Unlike standard approaches, the proposed technique does not require estimation of inertia, centroid, angular velocities or orientation of the unknown tumbling object. The elliptical feature motion exhibited by features on a tumbling object is utilised to develop an enhanced IBVS framework. The approach is generic enough to be extended for any robot servoing towards a tumbling object, and space debris capture is one of the potential areas of application. The proposed Visual servoing framework is demonstrated using both manipulator and mobile robotic systems. Task constraints, like, image plane limits, joint limits, kinodynamic limits of motors, kinema

Abstract

Ion channels are proteins present across the lipid bilayer membrane possessing a pore that selectively allows certain ions to pass through. They control various cellular processes and mechanisms in living organisms that are vital for the functioning of cells. Synthetic ion channels are a promising technology in the medical and materials sciences because of their ability to conduct ions. Synthetic ion channels based on cyclodextrin, a cyclic oligomer of glucose, are of particular interest because of their non-toxicity and bio-compatibility for use in real-life pharmaceutical applications. This study uses the topology from the experimental findings for the construction of the cyclodextrin channels and investigates the permeability and selectivity of each of the three ions proposed. This work employs umbrella samplingbased molecular dynamics simulations in an explicit lipid bilayer environment to probe ion transport through the different kinds of cyclodextrin-based channels and identifies the channel types that are best suited to serve as synthetic ion channels. Free energy profiles show that the connectivity in the channel determines whether the channel is more selective towards cations or anions. The energy barrier for ion transport is governed by the number of glucose molecules constituting the cyclodextrin units of the channel. Furthermore, channels constructed from cyclodextrin units with the highest number of glucose molecules pose the least energy barrier to ion transport, since they facilitate the retention of a large number of coordinating water molecules during the transport process. A detailed mechanism is proposed for ion transport through these channels. An overall picture of how the permeation of the ions is influenced by the different variants of cyclodextrin channels and how the ions respond to different regions of the channels is discussed in this study. Findings from this study will aid in designing cyclodextrin-based channels that could be either cation-selective or anion-selective, by modifying the linkages of the channel or the number of glucose molecules in the cyclodextrin rings.

Abstract

The phrase coined by Clive Humby in 2006 has proven to be true in the research community where state-of-the-art has been progressively achieved with advent of sophisticated models as a result of new datasets. Current state-of-the-art models like Transformer-based BERT requires a lot of data to train but gives higher benchmark results for several tasks. This is good when the problem at hand already has a dataset defined. It becomes challenging when we are addressing a problem for which there exists no dataset. These problems mostly lie in a specific domain. In this thesis, we explore two such domainspecific problems and propose approach to address the problems even when less or no data exists for those problems. First problem we look at is Automatic Summarization of Financial Annual reports. Financial reports filed by various companies discuss compliance, risks, and future plans, such as goals and new projects, which directly impact their stock price. Quick consumption of such information is critical for financial analysts and investors to make stock buy/sell decisions and for equity evaluations. Hence, we study the problem of extractive summarization of 10-K reports. Recently, Transformer-based summarization models have become very popular. However, lack of in-domain labeled summarization data is a major roadblock to train such finance-specific summarization models. We also show that zero-shot inference on such pre-trained models is not as effective either. We address this challenge by modeling 10-K report summarization using a goal-directed setting where we leverage summaries with labeled goal-related data for the stock buy/sell classification goal. Further, we provide improvements by considering a multi-task learning method with an industry classification auxiliary task. We also show how idiosyncratic factors within a company can be incorporated into the summaries by using the proposed framework. Intrinsic evaluation as well as extrinsic evaluation for the stock buy/sell classification and portfolio construction tasks shows that our proposed method significantly outperforms strong baselines. Second problem we look at is the Automatic Question Generation from technical text. Asking questions about a subject has been an integral part to test candidates for subject knowledge and understanding. These questions generally require higher order reasoning and thinking. The answers or reasoning of such questions can span across paragraphs or even documents. Generating such questions has mostly been ignored in current research. We propose fully automated answer unaware question generation for technical text used for evaluating candidate’s understanding. It follows unsupervised learning paradigm and does not need labeled training data. It constructs a concept graph (CG) by automatically extract ing concepts and inter-concept relations from input text. It then exploits the concept graph to generate questions. Concept graph is used in order to address the issue of inter-sentence question generation. We evaluate the question acceptability using the well-established criteria laid down by Heilman et. al. [23] and show that the proposed approach consistently outperforms the classic linguistics based method as well as state-of-the-art Deep Learning and Transformer based question generation systems. We also propose a ranking mechanism for questions which further increases the number of acceptable questions. To summarize, we make use of other existing data which is related to the problem but not addresses the exact problem to approach the problem in a novel way by still leveraging the complexity of stateto-the-art data hungry models. We also make use of linguistic clues and other pre-trained models to address the data scarcity issue.