Abstract

Numerical simulations of condensed matter systems, for example biomolecular systems, are notorious for slow dynamics and consequent incomplete sampling lead to incorrect inferences on system thermodynamics and kinetics. In this study, we propose a protocol for efficient exploration of relevant phase space using machine learning techniques. The suggested protocol explicitly performs machine learning in two concurrent parallel stages: (i) map different metastable states into different classes by a classification model with assumption that large jumps that result in anomalous data (to the current class) are indicative of transitions between metastable states and (ii) construct a non-linear map within individual free energy minima (here, a class) by use of regression model that can learn local information. We apply the protocol on alanine tetrapeptide and show that a significant increase in sampling in all relevant phase space of the system; specifically, in comparison to the benchmark study using metadynamics using a well designed collective variable. We report sampling of all highly probable metastable states, and a significant increase in the sampling of relevant low probability metastable states, along with the concomitant increase in the sampling of the transitions between all such metastable states. The protocol proposed in the current study will be useful in construction of complete metastable states connectivity graph, thus enabling computation of thermodynamic and kinetic process of systems with very large degrees of freedom.

Abstract

Centrality measures on graphs have applications in many domains, including modeling the spread of an infection/disease, social network analysis, and transportation networks. As a result, parallel algorithms for computing various centrality metrics on graphs are gaining significant research attention in recent years. Also, since the networks of importance are extremely large, and the exact computation of the metric requires super-quadratic time, finding efficient algorithms to approximate the measure is essential. We study parallel algorithms for the percolation centrality (PC) measure, which extends the betweenness centrality measure by incorporating a time-dependent state variable with every node. We present parallel algorithms that compute the source-based and source-destination variants of the percolation centrality values of nodes in a network. Our algorithms extend the algorithm of Brandes and introduce optimizations aimed at exploiting the structural properties of graphs. Experimental studies of our algorithms on an Intel Xeon(R) Silver 4116 CPU indicate that our algorithmic techniques offer a significant speedup. We also present a new randomized approximation algorithm, called Dual-Brandes, for the sourcedestination variant of PC for undirected graphs. Our algorithm is inspired by the source-sampling technique developed by Brandes and Pich [5]. We show that the estimates produced by our algorithm are equal to PC in expectation. We also experimentally evaluate our algorithm against baselines based on existing ideas on a collection of 12 real-world graphs. Our algorithm outperforms baselines in accuracy and other metrics. On a fixed sample size of 10% nodes, our algorithm achieves a mean squared error 61% lower than the nearest baseline

Abstract

The weather observation and forecasting systems have become vital to every country for improving the efficiency of several systems that deal with life and property. Over the years, the weather information and forewarning systems are gradually becoming powerful, domain-specific, and location-specific. Weather-based decision support systems (DSSs) are being built to improve the efficiency of healthcare delivery, agricultural production systems, transportation sector, governance systems, and so on. In weather based DSS, given a weather situation, the domain experts prepare the appropriate suggestions to improve the efficiency of the stakeholders for carrying out day-to-day operations. Once the domain experts prepare a suggestion for a given weather situation, there is a scope to reuse the same suggestion for the similar weather situations in the future. As a result, the performance of the weather-based DSS could be improved. Notably, the notion of reuse is widely applied to improve the performance of DSSs in multiple domains. For example, the notion of reuse is widely applied to reduce the software development cost in the software domain. Notably, in the literature, the concept of reuse is not being explored to improve the performance of weather-based DSS. In this thesis, we introduced the problem context and proposed two frameworks to improve reuse in weather-based DSSs. As a part of the problem context, we defined two notions, viz. weather condition (WC) and coupled WC, to capture two types of weather situations. Moreover, we have defined the notions of cycle, period, temporal reuse, spatial reuse and coverage percentage metric. As the first approach, we proposed a framework to improve reuse by proposing the notion of Categorybased WC (CWC). The basic idea is that it is possible to improve the performance of weather-based DSS by exploiting the weather-based categories of the given domain. By considering the weather categories provided by India Meteorological Department (IMD), we have analyzed the extent of temporal reuse among the daily and five-day CWCs by conducting extensive experiments on 30 years of weather data collected at Rajendranagar, Hyderabad, Telangana, India. By varying the number of weather variables in CWC from one to five, we have computed the extent of temporal reuse among one-day and five-day CWCs for the following period types: year, season, and phenophases (i.e., growth stages) of the Rice crop. The results show that it is possible to improve reuse significantly with the proposed framework. The results also show that by preparing agro advisories for the first two years, there is a scope to achieve about 80 percent reuse in the third year for all period types. We have also conducted experiments to analyze both temporal reuse and spatial reuse among the CWCs by considering weather data of 12 locations (blocks) in the Telangana state. The results show that

Abstract

The number of people consuming social media and the amount of time they dedicate to it increases every day. With it, cases of mental illness and suicide risk are escalating on social media platforms. The lack of mental health detection mechanisms capable of proactively reaching out to people in need is becoming increasingly evident. The space of automatic mental health detection is ripe with potential, as previous work on mental health has predominantly been theoretical and on a small scale. Work done to detect mental illness on a more expansive scale is few and far between. However, it is riddled with many practical and ethical challenges. We must consider the degree or severity of a case of mental illness in order to estimate whether a person requires intervention. Identifying these intervention points can help us contact the user and provide them with guidance and resources before their condition escalates to suicidal ideation. However, research efforts are limited due to the social stigma and discrimination around accessing mental health care. This has often kept users from disclosing their personal problems not only to public social media forums but to mental health professionals as well. In addition to this, there are other barriers to accessing the appropriate help when necessary, such as financial concerns, apprehensions about privacy and confidentiality, and long waiting periods to see a mental health professional. Anonymous social media platforms such as mental health blogs or Reddit forums have therefore become increasingly popular as they can share their personal stories without judgment. They also generate a shared sense of community as they no longer have to suffer alone. People who face similar issues can share their experiences, give advice, motivate and persuade them to seek counsel from professionals. Therefore, social media has become a valuable source of linguistic cues to help us identify mental health problems from textual data. As researchers, it is imperative that we maintain user privacy and do not disclose personal identities or any other information associated with users. Therefore, anonymous subreddits and mental health forums make for excellent data sources. We additionally preprocess author usernames, as they could contain sensitive information related to the names or locations of the user. Our work takes steps towards automated identification, classification, and analysis of mental health on large-scale social media platforms. Research has indicated that approximately two-thirds of people who die by suicide were dealing with depression or other mental illnesses at the time of death. Hence, we first turn our efforts toward the identification of depression. Our novel architecture considers various issues that arise from dealing with large social media datasets. A majority of these challenges stem from the inherent size and composition of the internet. Social media is quite vast, and the amount of content on depression is nearly negligible when comparing the number and variety of topics being discussed on these platforms. This can often lead to skewed results and can be incredibly damaging to our purpose due to the sensitivity and severity of our problem. We attempt to simulate and consequently adapt our system to these conditions. We also strive to decrease the load on time and computational resources. The resulting system executes in a quicker, more efficient manner, making it suitable for deployment. We also experimented with different deep learning methods to analyze and categorize mental health issues from social media forums. While our user-level depression detection system is at its crux, a binary classification problem, we now focus on tackling the challenges that come when dealing with including multiple labels or categories of mental health issues. The dataset used contains information pertaining to 15 specific mental health support groups collected from related subreddits. We explore different approaches and class imbalance techniques to develop more effective learning models and counteract the bias in any given dataset. While our work as computational linguists usually revolves around finding linguistic intricacies from textual data, we also attempt to evaluate sentiment from pictorial data. We must not assume that social media users would limit themselves to sharing their mental health issues on predominantly textual platforms such as Twitter and Reddit. With the rise of meme culture and the popularity of sites such as Instagram, pictorial formats are being utilized to a greater extent. We take our knowledge of textual deep learning methods to experiment with incorporating this mode of data, which includes a short amount of text in their images, as seen in memes. This would allow us to detect positive or negative sentiments from these images and is a step towards detecting mental health sufferers on multi-modal social med

Abstract

Depression has always been an exciting topic of research due to its high prevalence and complex underlying mechanisms. Cognitive impairment, alterations in mood, emotion dysregulation, aversion to activity are just a few of the consequences of depression. Despite there being an ocean of literature present there, attempting to understand depression, we have just scratched the surface. There is still a lot to explore about the intricacies of this perplexing subject. This thesis is yet another attempt to understand and evaluate how depression changes how we understand and represent information (or concepts). In this work, our primary focus is on emotional concepts. In a nutshell, we are trying to look for answers to the question, ’Whether or not there exist any differences in the way people exhibiting moderate to severe depressive symptoms encode emotional concepts, compared to people with no or minimal depressive symptoms?’. To address this question, we first conducted a pilot study involving 48 participants. A corpus of 50 emotional words of varying hedonic and arousing content was given to the participants in this study. They were instructed to rate their affective experience of valence and arousal for each word on a 9-point self-assessment manikin (SAM) scale. This study aimed to record the Indian affective experience of emotional words and construct a comprehensive affective word database for the following main study. Fifteen words were selected from the corpus, which according to the participants’ responses, were capable of accurately capturing the required affect and arousal. These 15 words were clustered on the arousal-valence bi-dimensional model into five groups (3 belonging to each) and used as stimuli for the second study. An additional objective of the study was to compare and contrast the affective responses of the Indian population with the data collected by other studies on the western people to see if there existed any cultural differences between them. Following the pilot study, we conducted a detailed psychological study involving 107 participants and instructed them to generate properties for 15 emotional words (from the pilot study) presented to them as stimuli. Using state-of-the-art computational models, we evaluated the data collected to efficaciously shed some light on the linguistic, semantic, and sentiment differences in the properties generated by the two groups of samples (participants exhibiting no or mild symptoms and participants showing moderate to severe symptoms). These two groups were created after assessing the predisposition emotional state of the entire sample individually by using Beck Depression Inventory-II (BDI). We found an overall increased negative sentiment and decreased positive sentiment in the properties generated by the sample showing depressive symptoms. Emotion analysis of the data reflected that the sample with

Abstract

Augmented Reality and Virtual Reality(AR/VR) applications can become far more widespread if they can photo-realistically capture our surroundings and modify them in different ways. It could include editing the scene’s lighting, changing the objects’ material, or augmenting virtual objects onto the scene. There has been a significant amount of work done in this domain. However, most of these works capture the data in a controlled setting consisting of expensive setups such as light stages. These methods are impractical and cannot scale. Thus, we must design solutions that capture scenes casually from offthe-shelf devices commonly available to the public. Further, the user should be able to interact with the captured scenes and modify these scenes in exciting directions, such as editing the material or augmenting new objects into the scene. In this thesis, we study how we can produce novel views of a casually captured scene and modify them in interesting ways. First, we present a neural rendering framework for simultaneous novel view synthesis and appearance editing of a casually captured scene using off-the-shelf smartphone cameras under known illumination. Existing approaches cannot perform novel view synthesis and edit the materials of the scene objects. We propose a method to explicitly disentangle appearance from lighting while estimating radiance and learn an independent lighting estimation of the scene. This allows us to generalize arbitrary changes in the scene’s materials while performing novel view synthesis. We demonstrate our results on synthetic and real scenes. Next, we present PanoHDR-NeRF, a neural representation of an indoor scene’s high dynamic range (HDR) radiance field that can be captured casually without elaborate setups or complex capture protocols. First, a user captures a low dynamic range (LDR) omnidirectional video of the scene by freely waving an off-the-shelf camera around the scene. Then, an LDR2HDR network converts the captured LDR frames to HDR, which are subsequently used to train a modified NeRF++ model. The resulting PanoHDR-NeRF representation can synthesize full HDR panoramas from any location in the scene. We also show that the HDR images produced by PanoHDR-NeRF can synthesize correct lighting effects, enabling the augmentation of indoor scenes with synthetic objects that are lit correctly. Through these works, we demonstrate how we can casually capture scenes for AR/VR applications that the user can further edit.

Abstract

Human action recognition, with its irrefutable and varied use cases across fields of surveillance, robotics, human object interaction analysis and many more, has gained critical importance and attention in the field of compute vision. Traditionally entirely based on RGB sequences, action recognition domain has shifted focus towards using skeleton sequences due to the easy availability of skeleton data capturing apparatus and the release of large scale datasets, in recent years. Skeleton based human action recognition, having superiority in terms of privacy, robustness and computational efficiency over traditional RGB based action recognition, is the primary focus of this thesis. Ever since the release of large scale skeleton action datasets namely NTURGB+D and NTURGB+D 120, the community has solely focused on developing complex approaches, ranging from CNNs to complex GCNs and more recently transformers, to achieve the best classification accuracy for these datasets. However, in this rat race for state of the art performance, the community turned a blind eye to a major drawback at the data level which bottlenecks even the most sophisticated approaches. This drawback is where we start our explorations in this thesis. The pose tree provided in the NTURGB+D datasets contains only 25 joints, out of which only 6 joints (3 for each hand) are finger joints. This is a major drawback since only 3 finger level joints are not sufficient enough to distinguish between action categories such as ”Thumbs up” and ”Thumbs down” or ”Make ok sign” and ”Make victory sign”. To specifically address this bottleneck, we introduce two new pose based human action datasets - NTU60-X and NTU120-X. Our datasets extend the largest existing action recognition dataset, NTU-RGBD. In addition to the 25 body joints for each skeleton as in NTURGBD, NTU60-X and NTU120-X dataset include finger and facial joints, enabling a richer skeleton representation. We appropriately modify the state of the art approaches to enable training using the introduced datasets. Our results demonstrate the effectiveness of these NTU-X datasets in overcoming the aforementioned bottleneck and improving the state of the art performance, overall and on previously worst performing action categories. Pose-based action recognition is predominantly tackled by approaches that treat the input skeleton in a monolithic fashion, i.e. joints in the pose tree are processed as a whole. However, such approaches ignore the fact that action categories are often characterized by localized action dynamics involving only small subsets of part joint groups involving hands (e.g. ‘Thumbs up’) or legs (e.g. ‘Kicking’). Although part-grouping based approaches exist, each part group is not considered within the global pose frame, causing such methods to fall short. Further, conventional approaches employ independent modality streams (e.g. joint, bone, joint velocity, bone velocity) and train their network multiple times on these streams, which massively increases the number of training parameters. To address these issues, we introduce PSUMNet, a novel approach for scalable and efficient pose-based action recognition. At the representation level, we propose a global frame based part stream approach as opposed to conventional modality based streams. Within each part stream, the associated data from multiple modalities is unified and consumed by the processing pipeline. Experimentally, PSUMNet achieves the state of the art performance on the widely used NTURGB+D 60/120 dataset and dense joint skeleton dataset NTU 60-X/120-X. PSUMNet is highly efficient and outperforms competing methods which use 100%-400% more parameters. PSUMNet also generalizes to the SHREC hand gesture dataset with competitive performance. Overall, PSUMNet’s scalability, performance and efficiency make it an attractive choice for action recognition and for deployment on compute-restricted embedded and edge devices. Finally, we conclude this thesis by exploring new and more challenging frontiers under the umbrella of skeleton action recognition namely ”in the wild” skeleton action recognition and ”non-contextual” skeleton action recognition. We introduce Skeletics-152, a curated and 3D pose dataset derived from the RGB videos included in the larger Kinetics-700 dataset to explore in the wild skeleton action recognition. We further introduce, Skeleton-mimetics, a 3D pose dataset derived from recently introduced non-contextual action dataset-Mimetics. By benchmarking and analysing various approaches on these two new dataset we lay the ground for future exploration in these two challenging problems within skeleton action recognition. Overall in this thesis, we draw attention to prevailing drawbacks in the existing skeleton action datasets and introduce extensions of these datasets to counter their shortcomings. We also introduce a novel, efficient and hi

Abstract

Unmanned traffic management (UTM) has been gaining popularity these days because we are switching to autonomous navigation for drones in a real-world scenario. Unmanned Aerial Systems (UAS) Traffic Management (UTM) provides a reliable holistic solution for multi-UAV missions including air space design, motion planning, communication, control, geo-fencing, contingency management, etc. Motion planning is one of the key elements involved in UTM. Motion planning includes generating collision-free trajectories from the starting position to the goal position for the UAV in an uncertain and dynamic 3D environment. These trajectories can be planned for many kinds of UAVs, they include holonomic UAVs like a quadrotor or non-holonomic UAVs like fixed-winged aircraft (FW A). Traditionally these collision-free trajectories are planned using methods like velocity obstacles and their variants to avoid obstacles in a 3D space. These obstacles can be static or dynamic whereas the aerial vehicles can also be of various kinds like quadrotor drones or fixed-winged aircraft. So, we start with discussing an improvement on the basic velocity obstacle scheme for a single agent like a quadrotor and an FWA in an uncertain 3D environment. This improvement on the basic velocity obstacle to handle measurement and control noise (uncertainty in the environment) is done by extending it to a probabilistic version called probabilistic versions of Velocity Obstacles (PVO). We explore Probabilistic Inverse Velocity Obstacle (PIVO) as an alternative to PVO for free-flying quadrotor systems. Inverse Velocity Obstacles compute effective controls from a sequence of observations on other agents without the need to access ego state information. As a direct consequence of this, the ego state noise is not entailed in probabilistic formulations bringing in verifiable advantages in the form of reduced path lengths, less conservative maneuvers, and reduced occurrences of stopping/hovering to let others pass. These advantages are vividly tabulated in this thesis, showcasing the efficacy of PIVO as an alternative to probabilistic versions of Velocity Obstacles. In particular, we show the benefits of PIVO over PVO in sample complexity as well as overall trajectory lengths. We also show the efficacy of our probabilistic formulation in handling non-parametric and often multimodal noise distributions. Afterward, we extend our navigation and collision avoidance scheme for multiple drones of the same kind navigating in the same 3D space. Here we propose a predictive optimal control-based autonomous navigation and collision avoidance for multiple fixed-wing aircraft (FWA) or for multiple quadrotor drones that also takes into account the physical constraints of the aircraft. The proposed method is implemented in a cooperative framework, analogous to the Reciprocal Velocity Obstacle (RVO) framework for autonomous navigation and collision avoidance for FWA swarm moving in a three-dimensional (3D) space. As a result of predictive optimal control and cooperation, we get less conservative maneuvers, reduced path lengths, and less deviation from the shortest path when compared to the conventional method without prediction and cooperation. Also, the change in performance with respect to varying prediction horizons is analyzed through numerical simulations. Simulation results are provided, highlighting the advantages of the proposed method when compared to a popular method (Optimal reciprocal collision avoidance) for collision avoidance in 3D for FWA swarm and also against the FGA algorithm (Fast geometric avoidance algorithm). Finally, in a typical UTM scenario, we can encounter multiple vehicles of different kinds (heterogeneous agents) trying to navigate and avoid collisions in a 3D space. So, we try to formalize a navigation and collision avoidance scheme for this multi-agent heterogeneous system of UAVs.

Abstract

The use of cell phones is embedded in our lives. We use it for various purposes by finding answers to our curious questions, ordering services for our homes and self, social media and many more. So when we are asked to stowaway our mobile phones or use Airplane Mode when travelling by air, it feels like a violation of our freedom to use our devices. Such a policy exists because aviation authorities such as Federal Aviation Authority (FAA), Internation Civil Aviation Authority (ICAO) etc. found that these devices could pose a safety risk to airplane systems and operations. Such devices are referred to as Personal Electronic Devices (PED). There are multiple malfunctions of aircraft systems that could be caused by usage of PEDs inside the aircraft which pose a safety risk to the flight and its passengers. Airplane Mode of cell phones was introduced to help aviation industry enforce the policy of no PED usage when in flight. In this thesis we focus on studying the existing policies for usage of cell phones inside an aircraft and provide a prototype solution where we could enable cell phone’s Airplane Mode automatically without any human interaction. We studied policies enforced by different aviation authorities in different countries, how they differ from each other and their common aspects. We found most of the aviation authorities have a policy of either stowing away PEDs or using Airplane Mode during Takeoff and Landing. We also studied reports presenting technical data on why such a policy is required. We also went through reports submitted by flight crew and cabin crew related to some instances where they observed disturbances during flight due to usage of PEDs inside the aircraft. We have also included individual experiences reported by passengers while travelling on how people react and behave when asked to turn on Airplane Mode or switch off their electronic devices. After establishing the problem, we discussed solutions which could be used to either switch off cell phones in flight or enable Airplane Mode automatically without any human interaction. Three solutions proposed were, use of PICO cells inside the aircraft, use of mobile phone jammer and develop a cell phone application to enable Airplane Mode automatically based on a trigger. We discussed the pros and cons of each solution proposed. We decided to develop a cell phone application to enable or disable Airplane Mode based on a trigger. In this thesis, we also discussed technical challenges which we faced to develop such an application. We also studied social challenges which already exist due to existing policy as well as potential social challenges once our auto Airplane Mode system is used. In this thesis we also present data on how we created this application and its limitations. We have also identified some other scenarios and places where such an application can be used. At the end, we conclude our work and list out some future scope activities

Abstract

Environmental sounds convey a large amount of information regarding day-to-day activities in nature.These sounds also provide powerful means of communication. The acoustic scene of environmentalsounds contains a set of audio events, which occur over a certain duration. Human hearing enablesus recognition of specific sounds and allows the processing of these sounds continuously without anyeffort. Therefore, even in the absence of visual cues, humans can identify most of the times, events, andsounds with acoustic cues. In this regard, it is necessary to develop an Acoustic Scene Classification(ASC) system which makes the machine to behave like human beings in the context of soundscapes. AnASC task mainly focuses on portrayal of the acoustic surroundings from an audio track by choosing atextual tag for it. The motivation for this work is to make some of the electronic gadgets to be moreintelligent. This is done by incorporating the acoustic scene knowledge in context-awareness devices,listening robots, hearing aids, automatic data tagging, and so on. The main objectives of this researchwork are to address: (a) To extract desired information that characterizes the environmental sounds in anacoustic signal using known standard acoustic features and unknown features, (b) To effectively classifythe features using the standard DNN model, (c) To develop an ASC system for the dataset containingboth the clean and channel mismatch conditions, and (d) To improve the ASC system performance bycombining evidence from multiple sources.In this regard, this research work aims to address some of the issues in the development of a sys-tem that analyses and classifies acoustic scenes. We have investigated the effect of the standard featuresnamely Mel-Frequency Cepstral Coefficients (MFCC), Log-Mel Band Energies (LOGMEL), Linear Pre-diction Cepstral Coefficients (LPCC), and All-Pole Group Delay (APGD) for the representation of ASC.We have also proposed new features namely Inverted Mel-Frequency Cepstral Coefficients (IMFCC),Spectral Centriod Magnitude Coefficients (SCMC), Spectral Subband Flux Coefficients (SSFC), andSingle Frequency Filtering Cepstral Coefficients (SFFCC). The effect of these features on the acousticscene classification of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 and2018 datasets has been studied using DNN classification modelsFrom our studies, it has been observed that, no single system can perform better for all the acousticscenes. Therefore, to reduce the confusion, we have combined the evidences for the acoustic scenesby using a late fusion mechanism. From our studies on DCASE 2017 and 2018 datasets, it is observedthat combining evidence suitably from the pair of classifiers has improved the performance of the ASCsystem.When the number of acoustic scenes are more it may be difficult for a single classifier to discriminatethe classes better. In this regard, we have proposed two-level hierarchical classification approach, inwhich initially identifying the meta category of acoustic scene is followed by fine-grained classificationbelonging to a particular meta category.Keywords:Acoustic Scene Classification, Mel-Frequency Cepstral Coefficients, Inverted Mel-FrequencyCepstral Coefficients, Spectral Centroid Magnitude Coefficients, Subband Spectral Flux Coefficients,Single Frequency Filtering Cepstral Coefficients, Deep Neural Networks, Late Fusion Mechanism, Hier-archical Classification, Detection and Classification of Acoustic Scenes and Events.