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Duchenne Muscular Disease

Abhishek Dubey, Gaurav Dubey, Sambodhan Dhawane, Rishikesh Sharma
2018 American Journal of PharmTech Research  
Dubey et. al., Am. J.  ...  The fact that you picked spades in Dubey et. al., Am. J.  ... 
doi:10.46624/ajptr.2018.v8.i5.003 fatcat:3rdwght6pnaojlsere63xdglr4

Data-Driven Optimization of Public Transit Schedule [article]

Sanchita Basak, Fangzhou Sun, Saptarshi Sengupta, Abhishek Dubey
2019 arXiv   pre-print
Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints
more » ... ith delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these,this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.
arXiv:1912.02574v1 fatcat:g2klvsd3h5gqljlybswdkfl6ry

Congenital Sideroblastic AnaemiaClassic Presentation

Abhishek Dubey
2016 Journal of Clinical and Diagnostic Research  
doi:10.7860/jcdr/2016/16883.8509 pmid:27790505 pmcid:PMC5072005 fatcat:s72wgbjofjh5jisesob3wtp6ha

Speaker Recognition using Deep Belief Networks [article]

Adrish Banerjee, Akash Dubey, Abhishek Menon, Shubham Nanda, Gora Chand Nandi
2018 arXiv   pre-print
Short time spectral features such as mel frequency cepstral coefficients(MFCCs) have been previously deployed in state of the art speaker recognition systems, however lesser heed has been paid to short term spectral features that can be learned by generative learning models from speech signals. Higher dimensional encoders such as deep belief networks (DBNs) could improve performance in speaker recognition tasks by better modelling the statistical structure of sound waves. In this paper, we use
more » ... hort term spectral features learnt from the DBN augmented with MFCC features to perform the task of speaker recognition. Using our features, we achieved a recognition accuracy of 0.95 as compared to 0.90 when using standalone MFCC features on the ELSDSR dataset.
arXiv:1805.08865v1 fatcat:ts2h6qtybnczrmeqhn3hsgramu

NASIB: Neural Architecture Search withIn Budget [article]

Abhishek Singh, Anubhav Garg, Jinan Zhou, Shiv Ram Dubey, Debo Dutta
2019 arXiv   pre-print
Neural Architecture Search (NAS) represents a class of methods to generate the optimal neural network architecture and typically iterate over candidate architectures till convergence over some particular metric like validation loss. They are constrained by the available computation resources, especially in enterprise environments. In this paper, we propose a new approach for NAS, called NASIB, which adapts and attunes to the computation resources (budget) available by varying the exploration
more » ... exploitation trade-off. We reduce the expert bias by searching over an augmented search space induced by Superkernels. The proposed method can provide the architecture search useful for different computation resources and different domains beyond image classification of natural images where we lack bespoke architecture motifs and domain expertise. We show, on CIFAR10, that itis possible to search over a space that comprises of 12x more candidate operations than the traditional prior art in just 1.5 GPU days, while reaching close to state of the art accuracy. While our method searches over an exponentially larger search space, it could lead to novel architectures that require lesser domain expertise, compared to the majority of the existing methods.
arXiv:1910.08665v1 fatcat:5s3solxtjjhddo32up665fnmrm

Generative Anomaly Detection for Time Series Datasets [article]

Zhuangwei Kang, Ayan Mukhopadhyay, Aniruddha Gokhale, Shijie Wen, Abhishek Dubey
2022 arXiv   pre-print
Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic conditions in the area of interest and to locate road segments under abnormal congestion states. Modeling congestion patterns can achieve these goals for citywide roadways, which amounts to learning the distribution of multivariate time series (MTS). However, existing works are either not scalable or unable to capture
more » ... e spatial-temporal information in MTS simultaneously. To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach first clusters segments in the feature space and then uses conditional normalizing flow to identify anomalous temporal snapshots at the cluster level in an unsupervised setting. Then, we identify anomalies at the segment level by using a kernel density estimator on the anomalous cluster. Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score. We also use the generative model to sample labeled data, which can train classifiers in a supervised setting, alleviating the lack of labeled data for anomaly detection in sparse settings.
arXiv:2206.14597v1 fatcat:pcwusjljfrdd5lnyplffejieiy

A Methodology for Automating Assurance Case Generation [article]

Shreyas Ramakrishna, Charles Hartsell, Abhishek Dubey, Partha Pal, Gabor Karsai
2020 arXiv   pre-print
Safety Case has become an integral component for safety-certification in various Cyber Physical System domains including automotive, aviation, medical devices, and military. The certification processes for these systems are stringent and require robust safety assurance arguments and substantial evidence backing. Despite the strict requirements, current practices still rely on manual methods that are brittle, do not have a systematic approach or thorough consideration of sound arguments. In
more » ... ion, stringent certification requirements and ever-increasing system complexity make ad-hoc, manual assurance case generation (ACG) inefficient, time consuming, and expensive. To improve the current state of practice, we introduce a structured ACG tool which uses system design artifacts, accumulated evidence, and developer expertise to construct a safety case and evaluate it in an automated manner. We also illustrate the applicability of the ACG tool on a remote-control car testbed case study.
arXiv:2003.05388v1 fatcat:hdrbapttfnghnir23fhp63uaxy

Chronic Primary Tinnitus: A Management Dilemma

Annanya Soni, Abhishek Dubey
2020 Audiology Research  
Tinnitus often described as sound in the ear in absence of any external stimulus. It poses a challenge to the psychological and mental wellbeing of the patient and professional unsatisfaction to the clinician. The patient often an old aged individual usually approaches the outpatient department with various sounds in the ear, making him feel ill or unable to have a sound sleep. The middle-aged patient often complains of professional incapability and lack of concentration due to tinnitus.
more » ... vast academic research and advances, the efficiency of available treatment is debatable, often compelling the clinician to convey the message that "you may have to learn to live with it". In the present overview of reviews, we tend to look into the management of tinnitus and present a comprehensive outlook of various evidence-based reviews from Cochrane and augmented with various studies from PubMed.
doi:10.3390/audiolres10020010 pmid:33255533 fatcat:slpyafwt3vcflm4tzms2fsur54

BIG DATA

Abhishek Dubey
2020 International Journal of Engineering Technologies and Management Research  
[Dubey *, Vol.5 (Iss.2: SE): February, 2018] ISSN: 2454-1907 [Communication, Integrated Networks & Signal Processing-CINSP 2018] DOI: 10.5281/zenodo.1195071 Http://www.ijetmr.com©International Journal  ...  10] Communication, Integrated Networks & Signal Processing-CINSP 2018] DOI: 10.5281/zenodo.1195071 Http://www.ijetmr.com©International Journal of Engineering Technologies and Management Research [Dubey  ... 
doi:10.29121/ijetmr.v5.i2.2018.606 fatcat:cy7ddk3srrfcrdwmjroqcpafge

Software health management

Abhishek Dubey, Gabor Karsai
2013 Innovations in Systems and Software Engineering  
A Dubey (B) Research Scientist Institute for Software-Integrated Systems, Vanderbilt University, Nashville, USA e-mail: dabhishe@isis.vanderbilt.edu G.  ...  Finally, the paper by Mahadevan, Dubey, Balasubramanian, and Karsai shows how software fault mitigation can be implemented using a declarative specifications and general search algorithms.  ... 
doi:10.1007/s11334-013-0226-7 fatcat:kezi7aybm5elbidbiqajeahv3q

Towards a Socially Optimal Multi-Modal Routing Platform [article]

Chinmaya Samal, Liyuan Zheng, Fangzhou Sun, Lillian J. Ratliff, Abhishek Dubey
2018 arXiv   pre-print
The increasing rate of urbanization has added pressure on the already constrained transportation networks in our communities. Ride-sharing platforms such as Uber and Lyft are becoming a more commonplace, particularly in urban environments. While such services may be deemed more convenient than riding public transit due to their on-demand nature, reports show that they do not necessarily decrease the congestion in major cities. One of the key problems is that typically mobility decision support
more » ... ystems focus on individual utility and react only after congestion appears. In this paper, we propose socially considerate multi-modal routing algorithms that are proactive and consider, via predictions, the shared effect of riders on the overall efficacy of mobility services. We have adapted the MATSim simulator framework to incorporate the proposed algorithms present a simulation analysis of a case study in Nashville, Tennessee that assesses the effects of our routing models on the traffic congestion for different levels of penetration and adoption of socially considerate routes. Our results indicate that even at a low penetration (social ratio), we are able to achieve an improvement in system-level performance.
arXiv:1802.10140v1 fatcat:tx2wka5sxzdkrp5ii37nmccw3e

DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion [article]

Fangzhou sun and Abhishek Dubey and Jules White
2018 arXiv   pre-print
Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional
more » ... pproaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately.
arXiv:1802.00002v1 fatcat:tc27t3aamjfodhlmqq6ta3dzjy

Metabolic reprograming of tumor-associated macrophages

Abhishek Puthenveetil, Shweta Dubey
2020 Annals of Translational Medicine  
A large body of scientific evidence corroborated by clinical and animal model experiments indicates that tumor-associated macrophages (TAMs) play a crucial role in tumor development and progression. TAMs are a key immune cell type present in tumor microenvironment (TME) and associated with poor prognosis, drug resistance, enhanced angiogenesis and metastasis in cancer. TAMs are a phenotypically diverse population of myeloid cells which display tremendous plasticity and dynamic metabolic nature.
more » ... A complete interpretation of pro-tumoral and anti-tumoral metabolic switch in TAMs is essential to understand immune evasion mechanisms in cancer. Recent studies have also implicated epigenetic mechanisms as significantly regulators of TAM functions. In this review we provide an overview of metabolic circuitry in TAMs, its impact on immune effector cells and interventions aimed at rewiring the metabolic circuits in TAMs. Mechanisms responsible for TAM polarization in cancer are also discussed.
doi:10.21037/atm-20-2037 pmid:32953830 pmcid:PMC7475460 fatcat:mird4yrsivczvmcuqracecwdqa

Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion [article]

Yasas Senarath, Saideep Nannapaneni, Hemant Purohit, Abhishek Dubey
2020 arXiv   pre-print
The number of emergencies have increased over the years with the growth in urbanization. This pattern has overwhelmed the emergency services with limited resources and demands the optimization of response processes. It is partly due to traditional 'reactive' approach of emergency services to collect data about incidents, where a source initiates a call to the emergency number (e.g., 911 in U.S.), delaying and limiting the potentially optimal response. Crowdsourcing platforms such as Waze
more » ... s an opportunity to develop a rapid, 'proactive' approach to collect data about incidents through crowd-generated observational reports. However, the reliability of reporting sources and spatio-temporal uncertainty of the reported incidents challenge the design of such a proactive approach. Thus, this paper presents a novel method for emergency incident detection using noisy crowdsourced Waze data. We propose a principled computational framework based on Bayesian theory to model the uncertainty in the reliability of crowd-generated reports and their integration across space and time to detect incidents. Extensive experiments using data collected from Waze and the official reported incidents in Nashville, Tenessee in the U.S. show our method can outperform strong baselines for both F1-score and AUC. The application of this work provides an extensible framework to incorporate different noisy data sources for proactive incident detection to improve and optimize emergency response operations in our communities.
arXiv:2011.05440v1 fatcat:ksntuaufr5gsxcrhb42ijy5fra

Green Chemistry in Agricultural Pest Management Programmes

Abhishek Kumar Dwivedy, Manoj Kumar, Neha Upadhyay, N K Dubey
2015 Medicinal Chemistry  
The article deals with recommendation of plant products as eco-friendly alternative of synthetic pesticides in agricultural pest management programme and emphasizes plant based green pesticides as important component in achieving ever green revolution in view of their high efficacy and favourable safety profile.
doi:10.4172/2161-0444.1000005 fatcat:tjvqi6dkincjjj55z3nk2ltx7e
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