Research community has witnessed substantial growth in the detection of mental health issues and their associated reasons from analysis of social media. We introduce a new dataset for Causal Analysis of Mental health issues in Social media posts (CAMS). Our contributions for causal analysis are two-fold: causal interpretation and causal categorization. We introduce an annotation schema for this task of causal analysis. We demonstrate the efficacy of our schema on two different datasets: (i) crawling and annotating 3155 Reddit posts and (ii) re-annotating the publicly available SDCNL dataset of 1896 instances for interpretable causal analysis. We further combine these into the CAMS dataset and make this resource publicly available along with associated source code: this https URL. We present experimental results of models learned from CAMS dataset and demonstrate that a classic Logistic Regression model outperforms the next best (CNN-LSTM) model by 4.9\% accuracy.

Climate change has become one of the biggest global problems increasingly compromising the Earth's habitability. Recent developments such as the extraordinary heat waves in California & Canada, and the devastating floods in Germany point to the role of climate change in the ever-increasing frequency of extreme weather. Numerical modelling of the weather and climate have seen tremendous improvements in the last five decades, yet stringent limitations remain to be overcome. Spatially and temporally localized forecasting is the need of the hour for effective adaptation measures towards minimizing the loss of life and property. Artificial Intelligence-based methods are demonstrating promising results in improving predictions, but are still limited by the availability of requisite hardware and software required to process the vast deluge of data at a scale of the planet Earth. Quantum computing is an emerging paradigm that has found potential applicability in several fields. In this opinion piece, we argue that new developments in Artificial Intelligence algorithms designed for quantum computers - also known as Quantum Artificial Intelligence (QAI) - may provide the key breakthroughs necessary to furthering the science of climate change. The resultant improvements in weather and climate forecasts are expected to cascade to numerous societal benefits.

This paper considers smooth convex optimization problems with many functional constraints. To solve this general class of problems we propose a new stochastic perturbed augmented Lagrangian method, called SGDPA, where a perturbation is introduced in the augmented Lagrangian function by multiplying the dual variables with a subunitary parameter. Essentially, we linearize the objective and one randomly chosen functional constraint within the perturbed augmented Lagrangian at the current iterate and add a quadratic regularization that leads to a stochastic gradient descent update for the primal variables, followed by a perturbed random coordinate ascent step to update the dual variables. We provide a convergence analysis in both optimality and feasibility criteria for the iterates of SGDPA algorithm using basic assumptions on the problem. In particular, when the dual updates are assumed to be bounded, we prove sublinear rates of convergence for the iterates of algorithm SGDPA of order $\mathcal{O} (k^{-1/2})$ when the objective is convex and of order $\mathcal{O} (k^{-1})$ when the objective is strongly convex, where $k$ is the iteration counter. Under some additional assumptions, we prove that the dual iterates are bounded and in this case we obtain convergence rates of order $\mathcal{O} (k^{-1/4})$ and $\mathcal{O} (k^{-1/2})$ when the objective is convex and strongly convex, respectively. Preliminary numerical experiments on problems with many quadratic constraints demonstrate the viability and performance of our method when compared to some existing state-of-the-art optimization methods and software.

Astrophysical and cosmological observations strongly suggest the existence of Dark Matter\,(DM). Experiments at the Large Hadron Collider\,(LHC) have the potential to probe the particle nature of the DM. In the present work, we investigate the potential of the mono-Higgs plus Missing Transverse Energy signature at the LHC to search for a relatively light fermionic dark matter candidate using the framework of Effective Field Theory. In our study, the DM interacts with the Standard Model\,(SM) via dimension-6 and dimension-7 effective operators involving the Higgs and the gauge bosons. Although, our analysis is independent of any Ultra Violet complete dynamics of DM, such interactions can be realized in an extension of the SM where the gauge group is extended minimally by adding an extra $U(1)$. Both cut-based and Boosted Decision Tree\,(BDT) discriminators are used to estimate and optimize the signal sensitivity over the SM backgrounds, assuming an integrated luminosity of $3000~fb^{-1}$ at $\sqrt{s}=14$ TeV at the High Luminosity phase of the LHC\,(HL-LHC). It can be seen that in the best scenario, atleast $4\sigma$ significance can be achieved for relic masses upto 200 GeV, showcasing the prospects of this search at the HL-LHC. This study provides a foundation for future explorations in this direction.

Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. In our research, we present an avant-garde methodology that synergistically integrates ECG readings and retinal fundus images to facilitate the early disease tagging as well as triaging of the CVDs in the order of disease priority. Recognizing the intricate vascular network of the retina as a reflection of the cardiovascular system, alongwith the dynamic cardiac insights from ECG, we sought to provide a holistic diagnostic perspective. Initially, a Fast Fourier Transform (FFT) was applied to both the ECG and fundus images, transforming the data into the frequency domain. Subsequently, the Earth Mover's Distance (EMD) was computed for the frequency-domain features of both modalities. These EMD values were then concatenated, forming a comprehensive feature set that was fed into a Neural Network classifier. This approach, leveraging the FFT's spectral insights and EMD's capability to capture nuanced data differences, offers a robust representation for CVD classification. Preliminary tests yielded a commendable accuracy of 84 percent, underscoring the potential of this combined diagnostic strategy. As we continue our research, we anticipate refining and validating the model further to enhance its clinical applicability in resource limited healthcare ecosystems prevalent across the Indian sub-continent and also the world at large.

Wildlife corridors are components of landscapes, which facilitate the movement of organisms and processes between intact habitat areas, and thus provide connectivity between the habitats within the landscapes. Corridors are thus regions within a given landscape that connect fragmented habitat patches within the landscape. The major concern of designing corridors as a conservation strategy is primarily to counter, and to the extent possible, mitigate the effects of habitat fragmentation and loss on the biodiversity of the landscape, as well as support continuance of land use for essential local and global economic activities in the region of reference. In this paper, we use game theory, graph theory, membership functions and chain code algorithm to model and design a set of wildlife corridors with tiger (Panthera tigris tigris) as the focal species. We identify the parameters which would affect the tiger population in a landscape complex and using the presence of these identified parameters construct a graph using the habitat patches supporting tiger presence in the landscape complex as vertices and the possible paths between them as edges. The passage of tigers through the possible paths have been modelled as an Assurance game, with tigers as an individual player. The game is played recursively as the tiger passes through each grid considered for the model. The iteration causes the tiger to choose the most suitable path signifying the emergence of adaptability. As a formal explanation of the game, we model this interaction of tiger with the parameters as deterministic finite automata, whose transition function is obtained by the game payoff.

Real time outdoor navigation in highly dynamic environments is an crucial problem. The recent literature on real time static SLAM don't scale up to dynamic outdoor environments. Most of these methods assume moving objects as outliers or discard the information provided by them. We propose an algorithm to jointly infer the camera trajectory and the moving object trajectory simultaneously. In this paper, we perform a sparse scene flow based motion segmentation using a stereo camera. The segmented objects motion models are used for accurate localization of the camera trajectory as well as the moving objects. We exploit the relationship between moving objects for improving the accuracy of the poses. We formulate the poses as a factor graph incorporating all the constraints. We achieve exact incremental solution by solving a full nonlinear optimization problem in real time. The evaluation is performed on the challenging KITTI dataset with multiple moving cars.Our method outperforms the previous baselines in outdoor navigation.

The concept of simplicial complex from Algebraic Topology is applied to understand and model the flow of genetic information, processes and organisms between the areas of unimpaired habitats to design a network of wildlife corridors for Tigers (Panthera Tigris Tigris) in Central India Eastern Ghats landscape complex. The work extends and improves on a previous work that has made use of the concept of minimum spanning tree obtained from the weighted graph in the focal landscape, which suggested a viable corridor network for the tiger population of the Protected Areas (PAs) in the landscape complex. Centralities of the network identify the habitat patches and the critical parameters that are central to the process of tiger movement across the network. We extend the concept of vertex centrality to that of the simplicial centrality yielding inter-vertices adjacency and connection. As a result, the ecological information propagates expeditiously and even on a local scale in these networks representing a well-integrated and self-explanatory model as a community structure. A simplicial complex network based on the network centralities calculated in the landscape matrix presents a tiger corridor network in the landscape complex that is proposed to correspond better to reality than the previously proposed model. Because of the aforementioned functional and structural properties of the network, the work proposes an ecological network of corridors for the most tenable usage by the tiger populations both in the PAs and outside the PAs in the focal landscape.

The expressibility of an ansatz used in a variational quantum algorithm is defined as the uniformity with which it can explore the space of unitary matrices, i.e., its covering number. The expressibility of a particular ansatz has a well-defined upper bound [1]. In this work, we show that the expressibility also has a well-defined lower bound in the hypothesis space. We provide an analytical expression for the lower bound of the covering number, which is directly related to expressibility. Further, we provide numerical analysis to support our claim. By calculating the bond length of hydrogen molecule ($H_2$) using different ansatzes in a variational quantum eigensolver (VQE) setting, we study the variation of equilibrium energy error with circuit depths. We show that in each ansatz template, a plateau exists for a range of circuit depths, which we call the set of acceptable points, and the corresponding expressibility as the best expressive region. We report that the width of this best expressive region in the hypothesis space is inversely proportional to the average error. Our analysis reveals that alongside trainability, the lower bound of expressibility also plays a crucial role in selecting variational quantum ansatzes

This paper presents a demonstration of the developed prototype showcasing a way to preserve the Intangible Cultural Heritage of Uttarakhand, India. Aipan is a traditional art form practiced in the Kumaon region in the state of Uttarakhand. It is typically used to decorate floors and walls at places of worship or entrances of homes and is considered auspicious to begin any work or event. This art is associated with a great degree of social, cultural as well as religious significance and is passed from generation to generation. However, in the present era of modernization and technological advancements, this art form now stands on the verge of depletion. This study presents a humble attempt to preserve this vanishing art form through the use of Virtual Reality (VR). Ethnographic studies were conducted in Almora, Nainital, and Haldwani regions of Uttarakhand to trace the origins as well as to gain a deeper understanding of this art form. A total of ten (N =10) Aipan designers were interviewed. Several interesting insights are revealed through these studies that show the potential to be incorporated as a VR experience.

The structural, electronic, and dielectric (optical) properties of graphene-like 2D MgO monolayer have been explored through first-principles calculations under bi-axial tensile and compressive mechanical strain within a range of -10% to +10%. Our findings revealed that the pristine MgO monolayer is an indirect band gap semiconducting material and the semiconducting mature of MgO monolayer remains consistent under both compressive and tensile mechanical strain. This nature of MgO is confirmed through partial density of states (PDOS) as well as electronic band structure. PDOS exhibits the contribution of different atomic orbitals in bond formation and nature of bond, while band structure provides insight into electron transitions between energy levels of valance and conduction bands. All optical parameters (dielectric function, reflectivity, energy loss, refractive index, extinction coefficient and absorption) are plotted in an energy range 0-15 eV. Within this energy interval, MgO possesses the highest value of the refractive index (2.13) at 3.12 eV energy. Also, a detailed analysis of changes in the geometrical structure of MgO monolayer is provided.

Transformers have evolved with great success in various artificial intelligence tasks. Thanks to our recent prevalence of self-attention mechanisms, which capture long-term dependency, phenomenal outcomes in speech processing and recognition tasks have been produced. The paper presents a comprehensive survey of transformer techniques oriented in speech modality. The main contents of this survey include (1) background of traditional ASR, end-to-end transformer ecosystem, and speech transformers (2) foundational models in a speech via lingualism paradigm, i.e., monolingual, bilingual, multilingual, and cross-lingual (3) dataset and languages, acoustic features, architecture, decoding, and evaluation metric from a specific topological lingualism perspective (4) popular speech transformer toolkit for building end-to-end ASR systems. Finally, highlight the discussion of open challenges and potential research directions for the community to conduct further research in this domain.

Understanding the interfaces of layered nanostructures is key to optimizing their structural and magnetic properties for the desired functionality. In the present work, the two interfaces of a few nm thick Fe layer in Ag-57Fe-Ag trilayers are studied with a depth resolution of a fraction of a nanometer using x-ray standing waves (XSWs) generated by an underlying [W-Si]x10 multilayer (MLT) at an x-ray incident angle around the Bragg peak of the MLT. Interface selectivity in Ag-57Fe-Ag trilayers was achieved by moving XSW antinodes across the interfaces by optimizing suitable incident angles and performing depth-resolved nuclear resonance scattering (NRS) and X-ray fluorescence (XRF) measurements for magnetic and structural properties. The combined analysis revealed that the rms roughness of 57Fe-on-Ag and Ag-on-57Fe interfaces are not equal. The roughness of the 57Fe-on-Ag interface is 10 Angstrom, while that of the Ag-on-57Fe interface is 6 Angstrom. 57Fe isotope sensitive NRS revealed that hyperfine field (HFF) at both interfaces of 57Fe-on-Ag and Ag-on-57Fe interfaces are distinct, which is consistent with the difference in interface roughnesses measured as root mean square (RMS) roughness. Thermal annealing induces 57Fe diffusion into the Ag layer, and annealing at 325 C transforms the sample into a paramagnetic state. This behavior is attributed to forming 57Fe nanoparticles within the Ag matrix, exhibiting a paramagnetic nature. These findings provide deep insights into interface properties crucial for developing advanced nanostructures and spintronic devices.

The dynamics of the second order rational difference equation $\displaystyle{z_{n+1}=\frac{\alpha + z_{n-1}}{\beta z_n + z_{n-1}}}$ with the real parameter $\alpha$, $\beta$ and arbitrary non-negative real initial conditions is investigated a decade ago. In the present manuscript, the same has been revisited considering the parameters $\alpha$ and $\beta$ as complex numbers and the initial values as arbitrary complex numbers. It is found that some of the results which are valid in real line but does not valid in complex plane. The chaotic solutions of the difference equation with complex parameters are achieved, however there does not exists such solutions in the case of real parameters.

Slow magnetoacoustic waves with a 3 minute period are upward-propagating waves traveling through the density-stratified umbral atmosphere. The decreasing density causes their amplitude to increase, developing into nonlinear waves through steepening and eventually forming shocks. To investigate the vertical evolution of this wave nonlinearity, we utilized multi-wavelength data from the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), covering from the photosphere to the lower corona across 20 active regions. The steepening of the wave profile leads to the generation of higher harmonics. We quantify this using a nonlinearity index (NI), defined as the ratio of the amplitude of 2nd harmonic to the fundamental obtained using wavelet analysis. We find a characteristic pattern: nonlinearity increases from the photosphere through the lower chromosphere, peaking near the AIA 1700 Å formation height, and decreases at higher altitudes, notably in the AIA 304 Å channel. This trend indicates progressive wave steepening and subsequent energy dissipation before reaching the formation of AIA 304 Å, consistent with shock formation in the lower atmosphere. An additional rise in NI is observed at the AIA 131 Å channel, followed by a decline in AIA 171 Å, suggesting a 2nd phase of wave nonlinearity evolution in the lower corona. Based on the NI profile and the formation heights of these channels, we conjecture that nonlinear wave processes are most prominent between the AIA 1700 Å and AIA 304 Å formation layers and again between AIA 131 Å and AIA 171 Å.

This thesis is divided into two parts:Part I: Analysis of Fruits, Vegetables, Cheese and Fish based on Image Processing using Computer Vision and Deep Learning: A Review. It consists of a comprehensive review of image processing, computer vision and deep learning techniques applied to carry out analysis of fruits, vegetables, cheese and fish.This part also serves as a literature review for Part II.Part II: GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer. This part introduces to an end-to-end deep neural network architecture that can predict the degree of acceptability by the consumer for a guava based on sensory evaluation.

In order to provide the agricultural industry with the infrastructure it needs to take advantage of advanced technology, such as big data, the cloud, and the internet of things (IoT); smart farming is a management concept that focuses on providing the infrastructure necessary to track, monitor, automate, and analyse operations. To represent the knowledge extracted from the primary data collected is of utmost importance. An agricultural ontology framework for smart agriculture systems is presented in this study. The knowledge graph is represented as a lattice to capture and perform reasoning on spatio-temporal agricultural data.

Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This paper proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.