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Network science provides very powerful tools for extracting information from interacting data. Although recently the unsupervised detection of phases of matter using machine learning has raised significant interest, the full prediction power of network science has not yet been systematically explored in this context. Here we fill this gap by providing an in-depth statistical, combinatorial, geometrical, and topological characterization of 2D Ising snapshot networks (IsingNets) extracted from Monte Carlo simulations of the 2D Ising model at different temperatures, going across the phase transition. Our analysis reveals the complex organization properties of IsingNets in both the ferromagnetic and paramagnetic phases and demonstrates the significant deviations of the IsingNets with respect to randomized null models. In particular percolation properties of the IsingNets reflect the existence of the symmetry between configurations with opposite magnetization below the critical temperature and the very compact nature of the two emerging giant clusters revealed by our persistent homology analysis of the IsingNets. Moreover, the IsingNets display a very broad degree distribution and significant degree-degree correlations and weight-degree correlations demonstrating that they encode relevant information present in the configuration space of the 2D Ising model. The geometrical organization of the critical IsingNets is reflected in their spectral properties deviating from the one of the null model. This work reveals the important insights that network science can bring to the characterization of phases of matter. The set of tools described hereby can be applied as well to numerical and experimental data., QC 20240522

Network science provides very powerful tools for extracting information from interacting data. Although recently the unsupervised detection of phases of matter using machine learning has raised significant interest, the full prediction power of network science has not yet been systematically explored in this context. Here we fill this gap by providing an in-depth statistical, combinatorial, geometrical, and topological characterization of 2D Ising snapshot networks (IsingNets) extracted from Monte Carlo simulations of the 2⁢D Ising model at different temperatures, going across the phase transition. Our analysis reveals the complex organization properties of IsingNets in both the ferromagnetic and paramagnetic phases and demonstrates the significant deviations of the IsingNets with respect to randomized null models. In particular percolation properties of the IsingNets reflect the existence of the symmetry between configurations with opposite magnetization below the critical temperature and the very compact nature of the two emerging giant clusters revealed by our persistent homology analysis of the IsingNets. Moreover, the IsingNets display a very broad degree distribution and significant degree-degree correlations and weight-degree correlations demonstrating that they encode relevant information present in the configuration space of the 2⁢D Ising model. The geometrical organization of the critical IsingNets is reflected in their spectral properties deviating from the one of the null model. This work reveals the important insights that network science can bring to the characterization of phases of matter. The set of tools described hereby can be applied as well to numerical and experimental data.

Robot-assisted feeding can greatly enhance the lives of those with mobility limitations. Modern feeding systems can pick up and position food in front of a care recipient's mouth for a bite. However, many with severe mobility constraints cannot lean forward and need direct inside-mouth food placement. This demands precision, especially for those with restricted mouth openings, and appropriately reacting to various physical interactions - incidental contacts as the utensil moves inside, impulsive contacts due to sudden muscle spasms, deliberate tongue maneuvers by the person being fed to guide the utensil, and intentional bites. In this paper, we propose an inside-mouth bite transfer system that addresses these challenges with two key components: a multi-view mouth perception pipeline robust to tool occlusion, and a control mechanism that employs multimodal time-series classification to discern and react to different physical interactions. We demonstrate the efficacy of these individual components through two ablation studies. In a full system evaluation, our system successfully fed 13 care recipients with diverse mobility challenges. Participants consistently emphasized the comfort and safety of our inside-mouth bite transfer system, and gave it high technology acceptance ratings - underscoring its transformative potential in real-world scenarios. Supplementary materials and videos can be found at http://emprise.cs.cornell.edu/bitetransfer/ ., Comment: HRI 2024

We demonstrate a robot-assisted feeding system that enables people with mobility impairments to feed themselves. Our system design embodies Safety, Portability, and User Control, with comprehensive full-stack safety checks, the ability to be mounted on and powered by any powered wheelchair, and a custom web-app allowing care-recipients to leverage their own assistive devices for robot control. For bite acquisition, we leverage multi-modal online learning to tractably adapt to unseen food types. For bite transfer, we leverage real-time mouth perception and interaction-aware control. Co-designed with community researchers, our system has been validated through multiple end-user studies., Comment: HRI 2024 Demo; Corrected inaccurate author ordering in ACM DL which occurred due to formatting issues

Robot-assisted bite acquisition involves picking up food items that vary in their shape, compliance, size, and texture. A fully autonomous strategy for bite acquisition is unlikely to efficiently generalize to this wide variety of food items. We propose to leverage the presence of the care recipient to provide feedback when the system encounters novel food items. However, repeatedly asking for help imposes cognitive workload on the user. In this work, we formulate human-in-the-loop bite acquisition within a contextual bandit framework and propose a novel method, LinUCB-QG, that selectively asks for help. This method leverages a predictive model of cognitive workload in response to different types and timings of queries, learned using data from 89 participants collected in an online user study. We demonstrate that this method enhances the balance between task performance and cognitive workload compared to autonomous and querying baselines, through experiments in a food dataset-based simulator and a user study with 18 participants without mobility limitations., Comment: Under submission to IROS 2024. The second and third authors contributed equally. The last two authors advised equally

In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational efficiency but often lack precision. Applying conventional super-resolution to these simulations poses a significant challenge due to the fundamental contrast between downsampling high-resolution images and authentically emulating low-resolution physics. The former method conserves more of the underlying physics, surpassing the usual constraints of real-world scenarios. We propose a novel definition of super-resolution tailored for PDE-based problems. Instead of simply downsampling from a high-resolution dataset, we use coarse-grid simulated data as our input and predict fine-grid simulated outcomes. Employing a physics-infused UNet upscaling method, we demonstrate its efficacy across various 2D-CFD problems such as discontinuity detection in Burger's equation, Methane combustion, and fouling in Industrial heat exchangers. Our method enables the generation of fine-mesh solutions bypassing traditional simulation, ensuring considerable computational saving and fidelity to the original ground truth outcomes. Through diverse boundary conditions during training, we further establish the robustness of our method, paving the way for its broad applications in engineering and scientific CFD solvers., Comment: Accepted at Machine Learning and the Physical Sciences Workshop, NeurIPS 2023

Network science provides very powerful tools for extracting information from interacting data. Although recently the unsupervised detection of phases of matter using machine learning has raised significant interest, the full prediction power of network science has not yet been systematically explored in this context. Here we fill this gap by providing an in-depth statistical, combinatorial, geometrical and topological characterization of 2D Ising snapshot networks (IsingNets) extracted from Monte Carlo simulations of the $2$D Ising model at different temperatures, going across the phase transition. Our analysis reveals the complex organization properties of IsingNets in both the ferromagnetic and paramagnetic phases and demonstrates the significant deviations of the IsingNets with respect to randomized null models. In particular percolation properties of the IsingNets reflect the existence of the symmetry between configurations with opposite magnetization below the critical temperature and the very compact nature of the two emerging giant clusters revealed by our persistent homology analysis of the IsingNets. Moreover, the IsingNets display a very broad degree distribution and significant degree-degree correlations and weight-degree correlations demonstrating that they encode relevant information present in the configuration space of the $2$D Ising model. The geometrical organization of the critical IsingNets is reflected in their spectral properties deviating from the one of the null model. This work reveals the important insights that network science can bring to the characterization of phases of matter. The set of tools described hereby can be applied as well to numerical and experimental data., Comment: 17 pages, 18 figures

Clove oil-based Nano-emulsions (NE) were prepared ultrasonically using Tween 80, The developed NEs were characterized for various parameters (particle size, polydispersity index, zeta potential, morphology, viscosity, colour, turbidity and pH) and the comparative effect of both the surfactants at variable levels. It was found that the type of surfactant and oil to surfactant ratio significantly affected particle size and stability of NEs. NEs prepared using tween 80 were found to be more viscous than those prepared with soy lecithin. The prepared clove oil NEs has important implication to be used as a natural delivery system to increase the shelf life of food products. Access to a healthy diet plays a more fundamental role for several reasons, such as environmental changes and oxidative stress factors. As a result, researchers focus on developing the functionality and beneficial effect of foods by natural components within a large health benefits scale. In particular, plant-derived compounds have rising importance in food processing for the reason of functional properties.

Clove oil-based Nano-emulsions (NE) were prepared ultrasonically using Tween 80, The developed NEs were characterized for various parameters (particle size, polydispersity index, zeta potential, morphology, viscosity, colour, turbidity and pH) and the comparative effect of both the surfactants at variable levels. It was found that the type of surfactant and oil to surfactant ratio significantly affected particle size and stability of NEs. NEs prepared using tween 80 were found to be more viscous than those prepared with soy lecithin. The prepared clove oil NEs has important implication to be used as a natural delivery system to increase the shelf life of food products. Access to a healthy diet plays a more fundamental role for several reasons, such as environmental changes and oxidative stress factors. As a result, researchers focus on developing the functionality and beneficial effect of foods by natural components within a large health benefits scale. In particular, plant-derived compounds have rising importance in food processing for the reason of functional properties.

Clove oil-based Nano-emulsions (NE) were prepared ultrasonically using Tween 80, The developed NEs were characterized for various parameters (particle size, polydispersity index, zeta potential, morphology, viscosity, colour, turbidity and pH) and the comparative effect of both the surfactants at variable levels. It was found that the type of surfactant and oil to surfactant ratio significantly affected particle size and stability of NEs. NEs prepared using tween 80 were found to be more viscous than those prepared with soy lecithin. The prepared clove oil NEs has important implication to be used as a natural delivery system to increase the shelf life of food products. Access to a healthy diet plays a more fundamental role for several reasons, such as environmental changes and oxidative stress factors. As a result, researchers focus on developing the functionality and beneficial effect of foods by natural components within a large health benefits scale. In particular, plant-derived compounds have rising importance in food processing for the reason of functional properties.

Clove oil-based Nano-emulsions (NE) were prepared ultrasonically using Tween 80, The developed NEs were characterized for various parameters (particle size, polydispersity index, zeta potential, morphology, viscosity, colour, turbidity and pH) and the comparative effect of both the surfactants at variable levels. It was found that the type of surfactant and oil to surfactant ratio significantly affected particle size and stability of NEs. NEs prepared using tween 80 were found to be more viscous than those prepared with soy lecithin. The prepared clove oil NEs has important implication to be used as a natural delivery system to increase the shelf life of food products. Access to a healthy diet plays a more fundamental role for several reasons, such as environmental changes and oxidative stress factors. As a result, researchers focus on developing the functionality and beneficial effect of foods by natural components within a large health benefits scale. In particular, plant-derived compounds have rising importance in food processing for the reason of functional properties.

We present RCareWorld, a human-centric simulation world for physical and social robotic caregiving designed with inputs from stakeholders, including care recipients, caregivers, occupational therapists, and roboticists. RCareWorld has realistic human models of care recipients with mobility limitations and caregivers, home environments with multiple levels of accessibility and assistive devices, and robots commonly used for caregiving. It interfaces with various physics engines to model diverse material types necessary for simulating caregiving scenarios, and provides the capability to plan, control, and learn both human and robot control policies by integrating with state-of-the-art external planning and learning libraries, and VR devices. We propose a set of realistic caregiving tasks in RCareWorld as a benchmark for physical robotic caregiving and provide baseline control policies for them. We illustrate the high-fidelity simulation capabilities of RCareWorld by demonstrating the execution of a policy learnt in simulation for one of these tasks on a real-world setup. Additionally, we perform a real-world social robotic caregiving experiment using behaviors modeled in RCareWorld. Robotic caregiving, though potentially impactful towards enhancing the quality of life of care recipients and caregivers, is a field with many barriers to entry due to its interdisciplinary facets. RCareWorld takes the first step towards building a realistic simulation world for robotic caregiving that would enable researchers worldwide to contribute to this impactful field. Demo videos and supplementary materials can be found at: https://emprise.cs.cornell.edu/rcareworld/.

Existing work in physical robot caregiving is limited in its ability to provide long-term assistance. This is majorly due to (i) lack of well-defined problems, (ii) diversity of tasks, and (iii) limited access to stakeholders from the caregiving community. We propose Structuring Physically Assistive Robotics for Caregiving with Stakeholders-in-the-loop (SPARCS) to address these challenges. SPARCS is a framework for physical robot caregiving comprising (i) Building Blocks, models that define physical robot caregiving scenarios, (ii) Structured Workflows, hierarchical workflows that enable us to answer the Whats and Hows of physical robot caregiving, and (iii) SPARCS-Box, a web-based platform to facilitate dialogue between all stakeholders. We collect clinical data for six care recipients with varying disabilities and demonstrate the use of SPARCS in designing well-defined caregiving scenarios and identifying their care requirements. All the data and workflows are available on SPARCS-Box. We demonstrate the utility of SPARCS in building a robot-assisted feeding system for one of the care recipients. We also perform experiments to show the adaptability of this system to different caregiving scenarios. Finally, we identify open challenges in physical robot caregiving by consulting care recipients and caregivers. Supplementary material can be found at https://emprise.cs.cornell.edu/sparcs/., Comment: 8 pages, 9 figures, IEEE International Conference on Intelligent Robots and Systems (IROS) 2022

Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents from their start locations to end locations. We consider an extension to this problem, Precedence Constrained Multi-Agent Path Finding (PC-MAPF), wherein agents are assigned a sequence of planning tasks that contain precedence constraints between them. PC-MAPF has various applications, for example in multi-agent pickup and delivery problems where some objects might require multiple agents to collaboratively pickup and move them in unison. Precedence constraints also arise in warehouse assembly problems where before a manufacturing task can begin, its input resources must be manufactured and delivered. We propose a novel algorithm, Precedence Constrained Conflict Based Search (PC-CBS), which finds makespan-optimal solutions for this class of problems. PC-CBS utilizes a Precedence-Constrained Task-Graph to define valid intervals for each planning task and updates them when precedence conflicts are encountered. We benchmark the performance of this algorithm over various warehouse assembly, and multi-agent pickup and delivery tasks, and use it to evaluate the sub-optimality of a recently proposed efficient baseline.

In this work, we propose a two-stage autoencoder based compressor-decompressor framework for compressing malaria RBC cell image patches. We know that the medical images used for disease diagnosis are around multiple gigabytes size, which is quite huge. The proposed residual-based dual autoencoder network is trained to extract the unique features which are then used to reconstruct the original image through the decompressor module. The two latent space representations (first for the original image and second for the residual image) are used to rebuild the final original image. Color-SSIM has been exclusively used to check the quality of the chrominance part of the cell images after decompression. The empirical results indicate that the proposed work outperformed other neural network related compression technique for medical images by approximately 35%, 10% and 5% in PSNR, Color SSIM and MS-SSIM respectively. The algorithm exhibits a significant improvement in bit savings of 76%, 78%, 75% & 74% over JPEG-LS, JP2K-LM, CALIC and recent neural network approach respectively, making it a good compression-decompression technique.

We propose a learning-based compression scheme that envelopes a standard codec between pre and post-processing deep CNNs. Specifically, we demonstrate improvements over prior approaches utilizing a compression-decompression network by introducing: (a) an edge-aware loss function to prevent blurring that is commonly occurred in prior works & (b) a super-resolution convolutional neural network (CNN) for post-processing along with a corresponding pre-processing network for improved rate-distortion performance in the low rate regime. The algorithm is assessed on a variety of datasets varying from low to high resolution namely Set 5, Set 7, Classic 5, Set 14, Live 1, Kodak, General 100, CLIC 2019. When compared to JPEG, JPEG2000, BPG, and recent CNN approach, the proposed algorithm contributes significant improvement in PSNR with an approximate gain of 20.75%, 8.47%, 3.22%, 3.23% and 24.59%, 14.46%, 10.14%, 8.57% at low and high bit-rates respectively. Similarly, this improvement in MS-SSIM is approximately 71.43%, 50%, 36.36%, 23.08%, 64.70% and 64.47%, 61.29%, 47.06%, 51.52%, 16.28% at low and high bit-rates respectively. With CLIC 2019 dataset, PSNR is found to be superior with approximately 16.67%, 10.53%, 6.78%, and 24.62%, 17.39%, 14.08% at low and high bit-rates respectively, over JPEG2000, BPG, and recent CNN approach. Similarly, the MS-SSIM is found to be superior with approximately 72%, 45.45%, 39.13%, 18.52%, and 71.43%, 50%, 41.18%, 17.07% at low and high bit-rates respectively, compared to the same approaches. A similar type of improvement is achieved with other datasets also., Comment: 13 pages, 9 figures, 16 tables

The long spin-diffusion length, spin-lifetime and excellent optical absorption coefficient of graphene provide an excellent platform for building opto-electronic devices and spin-based logic in a nanometer regime. In this study, by using density functional theory and its time-dependent version, we provide a detailed analysis of how the size and shape of graphene nanoflakes can be used to alter their magnetic structures and optical properties. As the edges of zigzag graphene nanoribbons are known to align anti-ferromagnetically and armchair nanoribbons are typically non-magnetic, a combination of both in a nanoflake geometry can be used to optimize the ground-state magnetic structure and tailor the exchange coupling decisive for ferro- or anti-ferromagnetic edge magnetism, thereby offering the possibility to optimize the external fields needed to switch magnetic ordering. Most importantly, we show that the magnetic state alters the optical response of the flake leading to the possibility of opto-spintronic applications.

We experimentally investigate the shape oscillations of an initially nonspherical water droplet falling in air using high-speed imaging. We design a customized experimental setup that allows us to study the freely falling droplets of initially oblate/prolate/tilted configurations. The setup uses a pneumatic piston-cylinder arrangement and a superhydrophobically coated plate to propel a droplet upwards in air whose motion is then recorded using a high-speed camera. Due to the propulsive force imparted to the droplet, it undergoes oblate-prolate oscillations and eventually comes to rest at a maximum height, at which time the droplet has a zero vertical velocity and a nonspherical shape with an inclination to the horizontal. We study the effect of the initial aspect ratio and size of the droplet on its shape oscillations during its downward motion.

We experimentally investigate the shape oscillations of an initially nonspherical water droplet falling in air using high-speed imaging. We design a customized experimental setup that allows us to study the freely falling droplets of initially oblate/prolate/tilted configurations. The setup uses a pneumatic piston-cylinder arrangement and a superhydrophobically coated plate to propel a droplet upwards in air whose motion is then recorded using a high-speed camera. Due to the propulsive force imparted to the droplet, it undergoes oblate-prolate oscillations and eventually comes to rest at a maximum height, at which time the droplet has a zero vertical velocity and a nonspherical shape with an inclination to the horizontal. We study the effect of the initial aspect ratio and size of the droplet on its shape oscillations during its downward motion.

We experimentally investigate the shape oscillations of an initially nonspherical water droplet falling in air using high-speed imaging. We design a customized experimental setup that allows us to study the freely falling droplets of initially oblate/prolate/tilted configurations. The setup uses a pneumatic piston-cylinder arrangement and a superhydrophobically coated plate to propel a droplet upwards in air whose motion is then recorded using a high-speed camera. Due to the propulsive force imparted to the droplet, it undergoes oblate-prolate oscillations and eventually comes to rest at a maximum height, at which time the droplet has a zero vertical velocity and a nonspherical shape with an inclination to the horizontal. We study the effect of the initial aspect ratio and size of the droplet on its shape oscillations during its downward motion.

We experimentally investigate the shape oscillations of an initially nonspherical water droplet falling in air using high-speed imaging. We design a customized experimental setup that allows us to study the freely falling droplets of initially oblate/prolate/tilted configurations. The setup uses a pneumatic piston-cylinder arrangement and a superhydrophobically coated plate to propel a droplet upwards in air whose motion is then recorded using a high-speed camera. Due to the propulsive force imparted to the droplet, it undergoes oblate-prolate oscillations and eventually comes to rest at a maximum height, at which time the droplet has a zero vertical velocity and a nonspherical shape with an inclination to the horizontal. We study the effect of the initial aspect ratio and size of the droplet on its shape oscillations during its downward motion.

We experimentally investigate the shape oscillations of an initially nonspherical water droplet falling in air using high-speed imaging. We design a customized experimental setup that allows us to study the freely falling droplets of initially oblate/prolate/tilted configurations. The setup uses a pneumatic piston-cylinder arrangement and a superhydrophobically coated plate to propel a droplet upwards in air whose motion is then recorded using a high-speed camera. Due to the propulsive force imparted to the droplet, it undergoes oblate-prolate oscillations and eventually comes to rest at a maximum height, at which time the droplet has a zero vertical velocity and a nonspherical shape with an inclination to the horizontal. We study the effect of the initial aspect ratio and size of the droplet on its shape oscillations during its downward motion.

We experimentally investigate the shape oscillations of an initially nonspherical water droplet falling in air using high-speed imaging. We design a customized experimental setup that allows us to study the freely falling droplets of initially oblate/prolate/tilted configurations. The setup uses a pneumatic piston-cylinder arrangement and a superhydrophobically coated plate to propel a droplet upwards in air whose motion is then recorded using a high-speed camera. Due to the propulsive force imparted to the droplet, it undergoes oblate-prolate oscillations and eventually comes to rest at a maximum height, at which time the droplet has a zero vertical velocity and a nonspherical shape with an inclination to the horizontal. We study the effect of the initial aspect ratio and size of the droplet on its shape oscillations during its downward motion.

We experimentally investigate the shape oscillations of an initially nonspherical water droplet falling in air using high-speed imaging. We design a customized experimental setup that allows us to study the freely falling droplets of initially oblate/prolate/tilted configurations. The setup uses a pneumatic piston-cylinder arrangement and a superhydrophobically coated plate to propel a droplet upwards in air whose motion is then recorded using a high-speed camera. Due to the propulsive force imparted to the droplet, it undergoes oblate-prolate oscillations and eventually comes to rest at a maximum height, at which time the droplet has a zero vertical velocity and a nonspherical shape with an inclination to the horizontal. We study the effect of the initial aspect ratio and size of the droplet on its shape oscillations during its downward motion.

We experimentally investigate the shape oscillations of an initially nonspherical water droplet falling in air using high-speed imaging. We design a customized experimental setup that allows us to study the freely falling droplets of initially oblate/prolate/tilted configurations. The setup uses a pneumatic piston-cylinder arrangement and a superhydrophobically coated plate to propel a droplet upwards in air whose motion is then recorded using a high-speed camera. Due to the propulsive force imparted to the droplet, it undergoes oblate-prolate oscillations and eventually comes to rest at a maximum height, at which time the droplet has a zero vertical velocity and a nonspherical shape with an inclination to the horizontal. We study the effect of the initial aspect ratio and size of the droplet on its shape oscillations during its downward motion.

We experimentally investigate the shape oscillations of an initially nonspherical water droplet falling in air using high-speed imaging. We design a customized experimental setup that allows us to study the freely falling droplets of initially oblate/prolate/tilted configurations. The setup uses a pneumatic piston-cylinder arrangement and a superhydrophobically coated plate to propel a droplet upwards in air whose motion is then recorded using a high-speed camera. Due to the propulsive force imparted to the droplet, it undergoes oblate-prolate oscillations and eventually comes to rest at a maximum height, at which time the droplet has a zero vertical velocity and a nonspherical shape with an inclination to the horizontal. We study the effect of the initial aspect ratio and size of the droplet on its shape oscillations during its downward motion.

We experimentally investigate the shape oscillations of an initially nonspherical water droplet falling in air using high-speed imaging. We design a customized experimental setup that allows us to study the freely falling droplets of initially oblate/prolate/tilted configurations. The setup uses a pneumatic piston-cylinder arrangement and a superhydrophobically coated plate to propel a droplet upwards in air whose motion is then recorded using a high-speed camera. Due to the propulsive force imparted to the droplet, it undergoes oblate-prolate oscillations and eventually comes to rest at a maximum height, at which time the droplet has a zero vertical velocity and a nonspherical shape with an inclination to the horizontal. We study the effect of the initial aspect ratio and size of the droplet on its shape oscillations during its downward motion.

Digital contact tracing plays a crucial role in alleviating an outbreak, and designing multilevel digital contact tracing for a country is an open problem due to the analysis of large volumes of temporal contact data. We develop a multilevel digital contact tracing framework that constructs dynamic contact graphs from the proximity contact data. Prominently, we introduce the edge label of the contact graph as a binary circular contact queue, which holds the temporal social interactions during the incubation period. After that, our algorithm prepares the direct and indirect (multilevel) contact list for a given set of infected persons from the contact graph. Finally, the algorithm constructs the infection pathways for the trace list. We implement the framework and validate the contact tracing process with synthetic and real-world data sets. In addition, analysis reveals that for COVID-19 close contact parameters, the framework takes reasonable space and time to create the infection pathways. Our framework can apply to any epidemic spreading by changing the algorithm's parameters., Comment: 21 Pages, 14 Figures with Appendix

Sampling based methods are widely used for robotic motion planning. Traditionally, these samples are drawn from probabilistic ( or deterministic ) distributions to cover the state space uniformly. Despite being probabilistically complete, they fail to find a feasible path in a reasonable amount of time in constrained environments where it is essential to go through narrow passages (bottleneck regions). Current state of the art techniques train a learning model (learner) to predict samples selectively on these bottleneck regions. However, these algorithms depend completely on samples generated by this learner to navigate through the bottleneck regions. As the complexity of the planning problem increases, the amount of data and time required to make this learner robust to fine variations in the structure of the workspace becomes computationally intractable. In this work, we present (1) an efficient and robust method to use a learner to locate the bottleneck regions and (2) two algorithms that use local sampling methods to leverage the location of these bottleneck regions for efficient motion planning while maintaining probabilistic completeness. We test our algorithms on 2 dimensional planning problems and 7 dimensional robotic arm planning, and report significant gains over heuristics as well as learned baselines., Comment: Accepted at Fourth Machine Learning in Planning and Control of Robot Motion Workshop, 2020 IEEE International Conference on Robotics and Automation

This thesis involves the study of the dynamics of upward propelled droplets using the experimental method. An experimental setup was designed consisting of a pneumatic cylinder, an acrylic glass plate, a compressor, control valves, and tubing. A droplet is placed on an acrylic plate which is actuated to move up and down using a pressurized pneumatic cylinder. When the pressure is applied, the plate undergoes a sudden linear upward motion and launches the droplet into air whose behaviour is then recorded using a high-speed camera. We developed a MATLAB® code for image processing and using the code, we try to understand the behaviour of the droplet in terms of shape oscillation, aspect ratio, etc. by varying the size of the droplet and propulsive force. The results of this experimental setup agree well with earlier simulation results within our research group.

This thesis involves the study of the dynamics of upward propelled droplets using the experimental method. An experimental setup was designed consisting of a pneumatic cylinder, an acrylic glass plate, a compressor, control valves, and tubing. A droplet is placed on an acrylic plate which is actuated to move up and down using a pressurized pneumatic cylinder. When the pressure is applied, the plate undergoes a sudden linear upward motion and launches the droplet into air whose behaviour is then recorded using a high-speed camera. We developed a MATLAB® code for image processing and using the code, we try to understand the behaviour of the droplet in terms of shape oscillation, aspect ratio, etc. by varying the size of the droplet and propulsive force. The results of this experimental setup agree well with earlier simulation results within our research group.

The dynamics of a freely falling liquid drop in air was examined experimentally using a high speed camera. To obtain the freely falling drop we placed a drop on a plate and propelled the drop by using a pneumatic cylinder arrangement. This drop then reaches a terminal height before it starts to freely fall and then the drop dynamics are recorded. We investigate how the drop deforms initially and during free fall as a function of the drop velocity, size of droplet and liquid properties such as viscosity. In addition, the influence of the surface such as hydrophobic surface is an important parameter for initial drop contact angle. This work was motivated to experimentally validate the dynamics of a non-spherical liquid droplet falling in air which had been presented using simulations by Agrawal et al.

The dynamics of a freely falling liquid drop in air was examined experimentally using a high speed camera. To obtain the freely falling drop we placed a drop on a plate and propelled the drop by using a pneumatic cylinder arrangement. This drop then reaches a terminal height before it starts to freely fall and then the drop dynamics are recorded. We investigate how the drop deforms initially and during free fall as a function of the drop velocity, size of droplet and liquid properties such as viscosity. In addition, the influence of the surface such as hydrophobic surface is an important parameter for initial drop contact angle. This work was motivated to experimentally validate the dynamics of a non-spherical liquid droplet falling in air which had been presented using simulations by Agrawal et al.

The dynamics of a freely falling liquid drop in air was examined experimentally using a high speed camera. To obtain the freely falling drop we placed a drop on a plate and propelled the drop by using a pneumatic cylinder arrangement. This drop then reaches a terminal height before it starts to freely fall and then the drop dynamics are recorded. We investigate how the drop deforms initially and during free fall as a function of the drop velocity, size of droplet and liquid properties such as viscosity. In addition, the influence of the surface such as hydrophobic surface is an important parameter for initial drop contact angle. This work was motivated to experimentally validate the dynamics of a non-spherical liquid droplet falling in air which had been presented using simulations by Agrawal et al.

Deep learning has recently become one of the most popular sub-fields of machine learning owing to its distributed data representation with multiple levels of abstraction. A diverse range of deep learning algorithms are being employed to solve conventional artificial intelligence problems. This paper gives an overview of some of the most widely used deep learning algorithms applied in the field of computer vision. It first inspects the various approaches of deep learning algorithms, followed by a description of their applications in image classification, object identification, image extraction and semantic segmentation in the presence of noise. The paper concludes with the discussion of the future scope and challenges for construction and training of deep neural networks., Comment: Accepted in 2017 International Conference on Intelligent Computing and Control (I2C2)

Background: Fine needle aspiration cytology (FNAC) breast is generally considered as a rapid, safe and reliable diagnostic tool to distinguish benign from malignant breast lesions. Masood's Scoring Index has been proposed to categorize the breast lesions so as to help in surgical management. Objectives : To find out the usefulness of Modified Masoods scoring Index (MMSI) in proliferative breast diseases. Materials and Methods: This is a prospective study, done over period of one year 2012 to 2013, which includes fifty cases of palpable breast lesions with cytological diagnosis, followed by histological confirmation on biopsy. MMSI was applied on corresponding FNAC aspirates. Breast lesions were categorized into four groups, non proliferative breast disease, proliferative breast disease (PBD) without atypia, PBD with atypia and carcinoma breast. Results: Out of total 50 cases, age group ranged from 17-64 years. Modification of Masoods scoring system by shifting of score 9 and 10 from category I to category II, was found to be easily reproducible scoring method of breast lesions as it has improved the diagnostic accuracy of PBD. Conclusion: FNAC of breast is highly sensitive and specific modality for distinguishing benign and malignant lesions but its role in diagnosing proliferative breast lesions is debatable. MMSI can be applied on FNAC aspirates in stepwise manner after cytomorphological assessment to improve diagnostic accuracy of PBD without atypia.

Background: Fine needle aspiration cytology (FNAC) breast is generally considered as a rapid, safe and reliable diagnostic tool to distinguish benign from malignant breast lesions. Masood's Scoring Index has been proposed to categorize the breast lesions so as to help in surgical management. Objectives : To find out the usefulness of Modified Masoods scoring Index (MMSI) in proliferative breast diseases. Materials and Methods: This is a prospective study, done over period of one year 2012 to 2013, which includes fifty cases of palpable breast lesions with cytological diagnosis, followed by histological confirmation on biopsy. MMSI was applied on corresponding FNAC aspirates. Breast lesions were categorized into four groups, non proliferative breast disease, proliferative breast disease (PBD) without atypia, PBD with atypia and carcinoma breast. Results: Out of total 50 cases, age group ranged from 17-64 years. Modification of Masoods scoring system by shifting of score 9 and 10 from category I to category II, was found to be easily reproducible scoring method of breast lesions as it has improved the diagnostic accuracy of PBD. Conclusion: FNAC of breast is highly sensitive and specific modality for distinguishing benign and malignant lesions but its role in diagnosing proliferative breast lesions is debatable. MMSI can be applied on FNAC aspirates in stepwise manner after cytomorphological assessment to improve diagnostic accuracy of PBD without atypia.