arXiv subjects

Rohit Verma

Publications and source records attributed to Rohit Verma.

At least 19 recordsLinked to original sources

arXiv2024

Evaluating tDCS Intervention Effectiveness via Functional Connectivity Network on Resting-State EEG Data in Major Depressive Disorder

Transcranial direct current stimulation (tDCS) has emerged as a promising non-invasive therapeutic intervention for major depressive disorder (MDD), yet its effects on neural mechanisms remain incompletely understood. This study investigates the impact of tDCS in individuals with MDD using resting-state EEG data and network neuroscience to analyze functional connectivity. We examined power spectral density (PSD) changes and functional connectivity (FC) patterns across theta, alpha, and beta bands before and after tDCS intervention. A notable aspect of this research involves the modification of the binarizing threshold algorithm to assess functional connectivity networks, facilitating a meaningful comparison at the group level. Our analysis using optimal threshold binarization techniques revealed significant modifications in network topology, particularly evident in the beta band, indicative of reduced randomization or enhanced small-worldness after tDCS. Furthermore, the hubness analysis identified specific brain regions, notably the dorsolateral prefrontal cortex (DLPFC) regions across all frequency bands, exhibiting increased functional connectivity, suggesting their involvement in the antidepressant effects of tDCS. Notably, tDCS intervention transformed the dispersed high connectivity into localized connectivity and increased left-sided asymmetry across all frequency bands. Overall, this study provides valuable insights into the effects of tDCS on neural mechanisms in MDD, offering a potential direction for further research and therapeutic development in the field of neuromodulation for mental health disorders.

q-bio.QM↗

arXiv2024

Towards a Flexible Scale-out Framework for Efficient Visual Data Query Processing

There is growing interest in visual data management systems that support queries with specialized operations ranging from resizing an image to running complex machine learning models. With a plethora of such operations, the basic need to receive query responses in minimal time takes a hit, especially when the client desires to run multiple such operations in a single query. Existing systems provide an ad-hoc approach where different solutions are clubbed together to provide an end-to-end visual data management system. Unlike such solutions, the Visual Data Management System (VDMS) natively executes queries with multiple operations, thus providing an end-to-end solution. However, a fixed subset of native operations and a synchronous threading architecture limit its generality and scalability. In this paper, we develop VDMS-Async that adds the capability to run user-defined operations with VDMS and execute operations within a query on a remote server. VDMS-Async utilizes an event-driven architecture to create an efficient pipeline for executing operations within a query. Our experiments have shown that VDMS-Async reduces the query execution time by 2-3X compared to existing state-of-the-art systems. Further, remote operations coupled with an event-driven architecture enables VDMS-Async to scale query execution time linearly with the addition of every new remote server. We demonstrate a 64X reduction in query execution time when adding 64 remote servers.

cs.DB↗

arXiv2022

CDBB West Cambridge Digital Twin: Lessons Learned

The report describes the digital architecture developed for the West Cambridge Digital Twin, particularly focussed on real-time sensor data collection and analysis with a privacy framework allowing occupants of the buildings to be first-class participants in the system. The implementation has some notable characteristics. In particular 'push' technology is used throughout such that information streams from the incoming asynchronous individual sensor events through to the end-user web pages with the absolute minimum latency, including real-time generated simple and complex events derived from the the underlying sensor data and the updating of visualisations such as an in-building heatmap. We believe the ability of the entire system to respond in the timescale of individual sensor messages to be unique. JSON structures are used to represent all data types including sensor readings, sensor types, building objects, organisations and people, with the idea that JSON-LD may represent a more suitable way than XML/RDF for managing relations between those objects (such as the 'occupies' relationship of people to offices, or the 'type' relationship of sensors to sensor types).

eess.SY↗

arXiv2021

FedFm: Towards a Robust Federated Learning Approach For Fault Mitigation at the Edge Nodes

Federated Learning deviates from the norm of "send data to model" to "send model to data". When used in an edge ecosystem, numerous heterogeneous edge devices collecting data through different means and connected through different network channels get involved in the training process. Failure of edge devices in such an ecosystem due to device fault or network issues is highly likely. In this paper, we first analyse the impact of the number of edge devices on an FL model and provide a strategy to select an optimal number of devices that would contribute to the model. We observe how the edge ecosystem behaves when the selected devices fail and provide a mitigation strategy to ensure a robust Federated Learning technique.

cs.LG↗

arXiv2021

SmartSplit: Latency-Energy-Memory Optimisation for CNN Splitting on Smartphone Environment

Artificial Intelligence has now taken centre stage in the smartphone industry owing to the need of bringing all processing close to the user and addressing privacy concerns. Convolution Neural Networks (CNNs), which are used by several AI applications, are highly resource and computation intensive. Although new generation smartphones come with AI-enabled chips, minimal memory and energy utilisation is essential as many applications are run concurrently on a smartphone. In light of this, optimising the workload on the smartphone by offloading a part of the processing to a cloud server is an important direction of research. In this paper, we analyse the feasibility of splitting CNNs between smartphones and cloud server by formulating a multi-objective optimisation problem that optimises the end-to-end latency, memory utilisation, and energy consumption. We design SmartSplit, a Genetic Algorithm with decision analysis based approach to solve the optimisation problem. Our experiments run with multiple CNN models show that splitting a CNN between a smartphone and a cloud server is feasible. The proposed approach, SmartSplit fares better when compared to other state-of-the-art approaches.

cs.LG↗

arXiv2021

Impact of Driving Behavior on Commuter's Comfort during Cab Rides: Towards a New Perspective of Driver Rating

Commuter comfort in cab rides affects driver rating as well as the reputation of ride-hailing firms like Uber/Lyft. Existing research has revealed that commuter comfort not only varies at a personalized level but also is perceived differently on different trips for the same commuter. Furthermore, there are several factors, including driving behavior and driving environment, affecting the perception of comfort. Automatically extracting the perceived comfort level of a commuter due to the impact of the driving behavior is crucial for a timely feedback to the drivers, which can help them to meet the commuter's satisfaction. In light of this, we surveyed around 200 commuters who usually take such cab rides and obtained a set of features that impact comfort during cab rides. Following this, we develop a system Ridergo which collects smartphone sensor data from a commuter, extracts the spatial time series feature from the data, and then computes the level of commuter comfort on a five-point scale with respect to the driving. Ridergo uses a Hierarchical Temporal Memory model-based approach to observe anomalies in the feature distribution and then trains a Multi-task learning-based neural network model to obtain the comfort level of the commuter at a personalized level. The model also intelligently queries the commuter to add new data points to the available dataset and, in turn, improve itself over periodic training. Evaluation of Ridergo on 30 participants shows that the system could provide efficient comfort score with high accuracy when the driving impacts the perceived comfort.

cs.HC↗

arXiv2021

Latency-Memory Optimized Splitting of Convolution Neural Networks for Resource Constrained Edge Devices

With the increasing reliance of users on smart devices, bringing essential computation at the edge has become a crucial requirement for any type of business. Many such computations utilize Convolution Neural Networks (CNNs) to perform AI tasks, having high resource and computation requirements, that are infeasible for edge devices. Splitting the CNN architecture to perform part of the computation on edge and remaining on the cloud is an area of research that has seen increasing interest in the field. In this paper, we assert that running CNNs between an edge device and the cloud is synonymous to solving a resource-constrained optimization problem that minimizes the latency and maximizes resource utilization at the edge. We formulate a multi-objective optimization problem and propose the LMOS algorithm to achieve a Pareto efficient solution. Experiments done on real-world edge devices show that, LMOS ensures feasible execution of different CNN models at the edge and also improves upon existing state-of-the-art approaches.

cs.LG↗

arXiv2021

Certain Homological Invariants of Bipartite Kneser Graphs

In this paper, we obtain a combinatorial formula for computing the Betti numbers in the linear strand of edge ideals of bipartite Kneser graphs. We deduce lower and upper bounds for regularity of powers of edge ideals of these graphs in terms of associated combinatorial data and show that the lower bound is attained in some cases. Also, we obtain bounds on the projective dimension of edge ideals of these graphs in terms of combinatorial data.

math.AC↗

arXiv2021

SenseRT: A Streaming Architecture for Smart Building Sensors

Building Management Systems (BMSs) have evolved in recent years, in ways that require changes to existing network architectures that follow the store-then-analyse approach. The primary cause is the increasing deployment of a diverse range of cost-effective sensors and actuators in smart buildings that generate real-time streaming data. Any in-building system with a large number of sensors needs a framework for real-time data collection and concurrent stream processing from sensors connected using a range of networks. We present SenseRT, a system for managing and analysing in-building real-time streams of sensor data. SenseRT collects streams of real-time data from sensors connected using a range of network protocols. It supports concurrent modules simultaneously performing stream processing over real-time data, asynchronously and non-blocking, with results made available with minimal latency. We describe a prototype implementation deployed in two University department buildings, demonstrating its effectiveness.

cs.NI↗

arXiv2021

Data Management for Building Information Modelling in a Real-Time Adaptive City Platform

Legacy Building Information Modelling (BIM) systems are not designed to process the high-volume, high-velocity data emitted by in-building Internet-of-Things (IoT) sensors. Historical lack of consideration for the real-time nature of such data means that outputs from such BIM systems typically lack the timeliness necessary for enacting decisions as a result of patterns emerging in the sensor data. Similarly, as sensors are increasingly deployed in buildings, antiquated Building Management Systems (BMSs) struggle to maintain functionality as interoperability challenges increase. In combination these motivate the need to fill an important gap in smart buildings research, to enable faster adoption of these technologies, by combining BIM, BMS and sensor data. This paper describes the data architecture of the Adaptive City Platform, designed to address these combined requirements by enabling integrated BIM and real-time sensor data analysis across both time and space.

cs.HC↗

arXiv2020

Feature Engineering Combined with 1 D Convolutional Neural Network for Improved Mortality Prediction

The intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of Electronic Health Record (EHR). This data allows for the development of a prediction tool with perfect knowledge backing. We aimed to build a mortality prediction model on 2012 Physionet Challenge mortality prediction database of 4000 patients admitted in ICU. The challenges in the dataset, such as high dimensionality, imbalanced distribution, and missing values were tackled with analytical methods and tools via feature engineering and new variable construction. The objective of the research is to utilize the relations among the clinical variables and construct new variables which would establish the effectiveness of 1-Dimensional Convolutional Neural Network (1- D CNN) with constructed features. Its performance with the traditional machine learning algorithms like XGBoost classifier, Support Vector Machine (SVM), K-Neighbours Classifier (K-NN), and Random Forest Classifier (RF) is compared for Area Under Curve (AUC). The investigation reveals the best AUC of 0.848 using 1-D CNN model.

cs.LG↗

arXiv2017

Exploring the predictability of symmetric texture zeros in quark mass matrices

The predictability of hierarchical quark mass matrices with symmetrically placed texture zeros is explored in the light of current precision measurements on quark masses and mixing data and interesting phenomenological relations among the quark mass ratios and flavor mixing angles along with CP asymmetry angle $β$ are deduced for different predictive textures within the framework of the Standard Model. It is shown that a single non-trivial phase in these mass matrices is sufficient enough to account for the observed CP-violation in the quark sector. In particular, it is observed that the Cabibbo angle is predominantly determined by the ratio $\sqrt{m_{d}/m_{s}}$ and corrections may also result from $\sqrt{m_{u}/m_{c}}$. Furthermore, $V_{cb}\cong \sqrt{m_d/m_b}$ or $V_{cb}\cong \sqrt{m_c/2m_t}$ along with $V_{ub}\simeq \sqrt{m_u/m_t}$ provide excellent agreements with the current mixing data and $β=-Arg\lbrace V_{td}\rbrace$ may provide a rigorous test at the B-factories for some of these texture zero structures.

hep-ph↗

arXiv2017

Quark Mass Matrices, textures and CKM precision measurements

In the light of several recent analyses pointing towards texture 4-zero Fritzsch-like quark mass matrices as the only viable structures for quark mass matrices, this work adopts a model independent approach to reconstruct an alternate and simplified structure of texture specific quark mass matrices in a generalized 'u-diagonal' basis within the Standard Model framework using the Unitarity of CKM matrix and the observed hierarchies in quark mass spectra and mixing angles. It is observed that the measured 1$σ$ values of the three physical parameters namely $m_{\rm{u}}$, $m_{\rm{d}}$ and $s_{12}$ naturally lead to the vanishing of (11) element in the down type quark mass matrix and that the single measurable CP violating phase $δ_{13}$ in the CKM matrix is sufficient enough in $M^{\prime}_{\rm{d}}$ to explain the observed mixing pattern in a suitable basis. The leading order as well as exact analytic phenomenological solutions are addressed for the modest pattern of quark mass matrices derived from CKM matrix and precision measurements of mixing parameters.

hep-ph↗

arXiv2016

Implications of $δ^{CP}_l\sim 270^\circ$ and $θ_{23}\gtrsim 45^\circ$ for texture specific lepton mass matrices and $0νββ$ decay

We study the phenomenological consequences of recent results from atmospheric and accelerator neutrino experiments, favoring normal neutrino mass ordering $m_1 < m_2 < m_3$, a near maximal lepton Dirac CP phase $δ_{l}\sim 270^\circ$ along with $θ_{23}\gtrsim 45^\circ$, for possible realization of natural structure in the lepton mass matrices characterized by ${({M_{ij}})} \sim O(\sqrt {{m_i}{m_j}}) $ for $i,j=1,2,3$. It is observed that deviations from parallel texture structures for $M_{l}$ and $M_ν$ are essential for realizing such structures. In particular, such hierarchical neutrino mass matrices are not supportive for a vanishing neutrino mass $m_{ν1}\rightarrow 0$ characterized by Det$M_ν\ne 0$ and predict ${m_{ν1}} \simeq (0.1 - 8.0)$ $meV$ , ${m_{ν2}} \simeq (8.0 - 13.0)$ $meV$, ${m_{ν3}} \simeq (47.0 - 52.0)$ $meV$, $Σ\simeq (56.0 - 71.0)$ $meV$ and $\left\langle {m_{ee}} \right\rangle \simeq (0.01 - 10.0)$ $meV$, respectively, indicating that the task of observing a $0νββ$ decay may be rather challenging for near future experiments.

hep-ph↗

arXiv2016

A Dynamic Web Service Registry Framework for Mobile Environments

Advancements in technology have transformed mobile devices from being mere communication widgets to versatile computing devices. Proliferation of these hand held devices has made them a common means to access and process digital information. Most web based applications are today available in a form that can conveniently be accessed over mobile devices. However, webservices (applications meant for consumption by other applications rather than humans) are not as commonly provided and consumed over mobile devices. Facilitating this and in effect realizing a service-oriented system over mobile devices has the potential to further enhance the potential of mobile devices. One of the major challenges in this integration is the lack of an efficient service registry system that caters to issues associated with the dynamic and volatile mobile environments. Existing service registry technologies designed for traditional systems fall short of accommodating such issues. In this paper, we propose a novel approach to manage service registry systems provided 'solely' over mobile devices, and thus realising an SOA without the need for high-end computing systems. The approach manages a dynamic service registry system in the form of light weight and distributed registries. We assess the feasibility of our approach by engineering and deploying a working prototype of the proposed registry system over actual mobile devices. A comparative study of the proposed approach and the traditional UDDI (Universal Description, Discovery, and Integration) registry is also included. The evaluation of our framework has shown propitious results in terms of battery cost, scalability, hindrance with native applications.

cs.DC↗

arXiv2016

A Dynamic Service Description for Mobile Environments

With the increasing processing capability of mobile platforms and advancements in Internet of Things, modern mobile devices have shown a favorable prospect for on-the-go service provisioning. However, there is much to be done to realize this. A detailed, dynamic, and lightweight service description is an important requirement for automatic and efficient discovery, selection, and subsequent provisioning of services over mobile devices. Traditional approaches for service description are usually not directly adaptable to mobile environments owing to the latter's dynamic and distinct nature. In this paper, we propose a dynamic, lightweight, extensible, and detailed service description especially designed for mobile environments, considering crucial aspects such as isolated data source, collaborator partners, and hardware aspects along with the functional, non-functional, business, and contextual aspects. The description has been partitioned along these lines and various parts of the description are distributed between service registries and the mobile service providers. An up-to-date and light weight description has been achieved by this, without compromising on the overall consistency of the description. A prototype of the proposed system has been implemented with the intent of validating the feasibility of the approach. Further, the proposed approach is suitable for a heterogeneous environment comprising both wired and wireless systems.

cs.SE↗

arXiv2016

Quark Flavor Mixings from Hierarchical Mass Matrices

In this paper, we extend the Fritzsch ansatz of quark mass matrices while retaining their hierarchical structures and show that the main features of the Cabibbo-Kobayashi-Maskawa (CKM) matrix $V$, including $|V^{}_{us}| \simeq |V^{}_{cd}|$, $|V^{}_{cb}| \simeq |V^{}_{ts}|$ and $|V^{}_{ub}|/|V^{}_{cb}| < |V^{}_{td}|/|V^{}_{ts}|$, can be well understood. This agreement is observed especially when the mass matrices have non-vanishing $(1,3)$ and $(3,1)$ off-diagonal elements. The phenomenological consequences of these for the allowed texture content and gross structural features of `hierarchical' quark mass matrices are addressed from a model independent prospective under the assumption of factorizable phases in these. The approximate and analytical expressions of the CKM matrix elements are derived, and a detailed analysis reveals that such structures are in good agreement with the observed quark flavor mixing angles and the CP-violating phase at the $1σ$ level and call upon a further investigation of the realization of these structures from a top-down prospective.

hep-ph↗

arXiv2014

Lepton textures and neutrino oscillations

Systematic analyses of the textures arising in lepton mass matrices have been carried out using unitary transformations and condition of naturalness for the Dirac and Majorana neutrino possibilities. It is observed that the recent three neutrino oscillation data together with the effective mass in neutrinoless double beta decay provide vital clues in predicting the general structures of these lepton mass matrices.

hep-ph↗