Quantum computing has become a promising computing approach because of its capability to solve certain problems, exponentially faster than classical computers. A n -qubit quantum system is capable of providing 2 n computational space to a quantum algorithm. However, quantum computers are prone to errors. Quantum circuits that can reliably run on today’s Noisy Intermediate-Scale Quantum (NISQ) devices are not only limited by their qubit counts but also by their noisy gate operations. In this article, we have introduced i -QER, a scalable machine learning-based approach to evaluate errors in a quantum circuit and reduce these without using any additional quantum resources. The i -QER predicts possible errors in a given quantum circuit using supervised learning models. If the predicted error is above a pre-specified threshold, it cuts the large quantum circuit into two smaller sub-circuits using an error-influenced fragmentation strategy for the first time to the best of our knowledge. The proposed fragmentation process is iterated until the predicted error reaches below the threshold for each sub-circuit. The sub-circuits are then executed on a quantum device. Classical reconstruction of the outputs obtained from the sub-circuits can generate the output of the complete circuit. Thus, i -QER also provides classical control over a scalable hybrid computing approach, which is a combination of quantum and classical computers. The i -QER tool is available at https://github.com/SaikatBasu90/i-QER .

OpenStreetMap (OSM) is a collaborative good quality crowd-sourced geospatial database (GDB). The quality of OSM is generally very good, it lacks good coverage in many parts of the world. A natural approach for extending its coverage is to conflate missing spatial features from other GDBs into OSM, but this is laborious and time-consuming. We propose a system MAYUR solving road network conflation between two vector GDBs, representing the GDBs as a graph of road intersections (vertices) and road segments (edges). MAYUR is based on a novel map matching framework that adapts the classic Rank Join in databases, where each edge of the reference GDB is modeled as a relation. Our algorithm finds the best matching between a reference and target GDB, respecting the connectivity of the road network. While classic Rank Join in databases gets quickly inefficient on instances with more than 10 relations, MAYUR's enhanced Rank Join incorporates three optimizations that boost the algorithm's efficiency, making it scale to our problem setting featuring hundreds to thousands of relations. Our manual evaluation of MAYUR conflation results on sidewalks in OSM and Boston Open Data shows an impressive 98.65% precision and 99.55% recall.

Road network extraction from satellite images often produce fragmented road segments leading to road maps unfit for real applications. Pixel-wise classification fails to predict topologically correct and connected road masks due to the absence of connectivity supervision and difficulty in enforcing topological constraints. In this paper, we propose a connectivity task called Orientation Learning, motivated by the human behavior of annotating roads by tracing it at a specific orientation. We also develop a stacked multi-branch convolutional module to effectively utilize the mutual information between orientation learning and segmentation tasks. These contributions ensure that the model predicts topologically correct and connected road masks. We also propose Connectivity Refinement approach to further enhance the estimated road networks. The refinement model is pre-trained to connect and refine the corrupted ground-truth masks and later fine-tuned to enhance the predicted road masks. We demonstrate the advantages of our approach on two diverse road extraction datasets SpaceNet and DeepGlobe. Our approach improves over the state-of-the-art techniques by 9% and 7.5% in road topology metric on SpaceNet and DeepGlobe, respectively.

Classification techniques for images of handwritten characters are susceptible to noise. Quadtrees can be an efficient representation for learning from sparse features. In this paper, we improve the effectiveness of probabilistic quadtrees by using a pixel level classifier to extract the character pixels and remove noise from handwritten character images. The pixel level denoiser (a deep belief network) uses the map responses obtained from a pretrained CNN as features for reconstructing the characters eliminating noise. We experimentally demonstrate the effectiveness of our approach by reconstructing and classifying a noisy version of handwritten Bangla Numeral and Basic Character datasets.

Users of distributed datastores that employ quorum-based replication are burdened with the choice of a suitable client-centric consistency setting for each storage operation. The above matching choice is difficult to reason about as it requires deliberating about the tradeoff between the latency and staleness, i.e., how stale (old) the result is. The latency and staleness for a given operation depend on the client-centric consistency setting applied, as well as dynamic parameters such as the current workload and network condition. We present OptCon, a machine learning-based predictive framework, that can automate the choice of client-centric consistency setting under user-specified latency and staleness thresholds given in the service level agreement (SLA). Under a given SLA, OptCon predicts a client-centric consistency setting that is matching, i.e., it is weak enough to satisfy the latency threshold, while being strong enough to satisfy the staleness threshold. While manually tuned consistency settings remain fixed unless explicitly reconfigured, OptCon tunes consistency settings on a per-operation basis with respect to changing workload and network state. Using decision tree learning, OptCon yields 0.14 cross validation error in predicting matching consistency settings under latency and staleness thresholds given in the SLA. We demonstrate experimentally that OptCon is at least as effective as any manually chosen consistency settings in adapting to the SLA thresholds for different use cases. We also demonstrate that OptCon adapts to variations in workload, whereas a given manually chosen fixed consistency setting satisfies the SLA only for a characteristic workload.

Users of distributed datastores that employquorum-based replication are burdened with the choice of asuitable client-centric consistency setting for each storage operation. The above matching choice is difficult to reason about asit requires deliberating about the tradeoff between the latencyand staleness, i.e., how stale (old) the result is. The latencyand staleness for a given operation depend on the client-centricconsistency setting applied, as well as dynamic parameters such asthe current workload and network condition. We present OptCon, a novel machine learning-based predictive framework, that canautomate the choice of client-centric consistency setting underuser-specified latency and staleness thresholds given in the servicelevel agreement (SLA). Under a given SLA, OptCon predictsa client-centric consistency setting that is matching, i.e., it isweak enough to satisfy the latency threshold, while being strongenough to satisfy the staleness threshold. While manually tunedconsistency settings remain fixed unless explicitly reconfigured, OptCon tunes consistency settings on a per-operation basis withrespect to changing workload and network state. Using decisiontree learning, OptCon yields 0.14 cross validation error in predictingmatching consistency settings under latency and stalenessthresholds given in the SLA. We demonstrate experimentally thatOptCon is at least as effective as any manually chosen consistencysettings in adapting to the SLA thresholds for different usecases. We also demonstrate that OptCon adapts to variationsin workload, whereas a given manually chosen fixed consistencysetting satisfies the SLA only for a characteristic workload.

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity.

While the role of ternary reversible and quantum computation has been growing, synthesis methodologies for such logic, have been addressed in only a few works. A reversible ternary logic function can be expressed as minterms by using projection operators. In this paper, a novel realization of the projection operators using a minimum number of permutative ternary Muthukrishnan-Stroud (M-S) gates is presented. Next, an efficient method for logic simplification for ternary reversible logic is proposed. This method along with the new construction of projection operators yields significantly lower gate cost of approximately 31% less than that obtained by earlier methodologies, for the synthesis of ternary benchmark circuits.

Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification approaches are not suitable for handling satellite datasets. The progress of satellite image analytics has also been inhibited by the lack of a single labeled high-resolution dataset with multiple class labels. The contributions of this paper are twofold -- (1) first, we present two new satellite datasets called SAT-4 and SAT-6, and (2) then, we propose a classification framework that extracts features from an input image, normalizes them and feeds the normalized feature vectors to a Deep Belief Network for classification. On the SAT-4 dataset, our best network produces a classification accuracy of 97.95% and outperforms three state-of-the-art object recognition algorithms, namely - Deep Belief Networks, Convolutional Neural Networks and Stacked Denoising Autoencoders by ~11%. On SAT-6, it produces a classification accuracy of 93.9% and outperforms the other algorithms by ~15%. Comparative studies with a Random Forest classifier show the advantage of an unsupervised learning approach over traditional supervised learning techniques. A statistical analysis based on Distribution Separability Criterion and Intrinsic Dimensionality Estimation substantiates the effectiveness of our approach in learning better representations for satellite imagery.

Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps.