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Following the first clear evidence of the presence of intrinsic charm contribution in the proton, the axial-vector charges of the light and charmed baryons are investigated in the framework of $SU(4)$ chiral constituent quark model after including the explicit contributions from the $u\bar u $, $d\bar d $, $s\bar s $ and $c\bar c $ fluctuations. The axial-vector charges having physical significance correspond to the generators of the $SU(4)$ group with flavor singlet $\lambda^0$, flavor isovector $\lambda^3$, flavor hypercharge $\lambda^8$ and flavor charmed $\lambda^{15}$ combinations of axial-vector current at zero momentum transfer. In contemplation to further understand the $Q^2$ dependence of these charges, we have used the conventionally established dipole form of parametrization. The baryons considered here are the spin $\frac{1}{2}^+$ and spin $\frac{3}{2}^+$ multiplets decomposed further depending on the charm content of baryons., Comment: 37 pages, 6 figures. To appear in The European Physical Journal Plus

We compute the light antiquark flavor asymmetry in the proton using the Chiral Quark Model ($\chi_{\rm QM}$). The distribution functions for the light antiquarks $\bar{d}(x)$ and $\bar{u}(x)$ have been extracted with the help of experimental data from NuSea/E866 and HERMES for the Bjorken$-x$ range $0.015 < x < 0.35$ as well from the most recent SeaQuest data for an extended $x$ range $0.13 < x < 0.45$. Our results on the $\bar{d}(x)-\bar{u}(x)$, $\frac{\bar{d}(x)}{\bar{u}(x)}$ and Gottfried Integral $I_G$ are in agreement with the experimental data and confirm the presence of enhanced $\bar{d}$ sea whose origin is purely non-perturbative based on chiral symmetry breaking in QCD., Comment: 16 pages, 4 figures

In this study, the T-even sub-leading twist transverse momentum dependent distributions (TMDs) of proton in the light-front quark-diquark model (LFQDM) have been investigated. We have derived the overlap form of the light-front wave functions (LFWFs) for the sub-leading twist proton TMDs by detangling the un-integrated quark-quark correlator for the semi-inclusive deep inelastic scattering (SIDIS). We have obtained the explicit expressions of TMDs for both the cases of the diquark being a scalar or a vector and analysed their relationships with leading twist TMDs within the same model. Average transverse momenta and average square transverse momenta for the TMDs have been tabulated and compared with the results from the light-front bag model and the light-front constituent quark model (LFCQM). In addition to this, we have also compared our results for the PDF $e(x)$ with the recent CLAS collaboration results., Comment: 37 Pages, 12 Figures, 6 Tables, accepted for publication in Nuclear Physics B, arXiv admin note: text overlap with arXiv:2301.09536

We have dealt with the twist-4 T-even transverse momentum-dependent parton distributions (TMDs) for the case of proton in the light-front quark-diquark model (LFQDM). By decoding the unintegrated quark-quark correlator for the semi-inclusive deep inelastic scattering (SIDIS), we have specifically obtained the overlap form for the unpolarized \bigg($f_{3}^{\nu}(x, {\bf p_\perp^2})$\bigg), longitudinally polarized \bigg($ g_{3L}^{\nu}(x, {\bf p_\perp^2}),~h_{3L}^{\perp\nu}(x, {\bf p_\perp^2})$\bigg) and transversely polarized \bigg( ${g}^{\nu }_{3T}(x, {\bf p_\perp^2}),~{h}^{\nu }_{3T}(x, {\bf p_\perp^2})$ and ${h}^{\nu\perp}_{3T}(x, {\bf p_\perp^2})$\bigg) proton TMDs. We have provided the explicit expressions for both the cases of the diquark being a scalar or a vector. Average transverse momenta and the average square transverse momenta for the TMDs have been calculated and the results have been tabulated with corresponding leading twist TMDs. In addition, the value of average transverse momentum and average square transverse momentum for TMD ${f}^{\nu }_3(x, {\bf p_\perp^2})$ has been compared with the available light-front constituent quark model (LFCQM) results. From TMDs, we have also obtained and discussed the transverse momentum-dependent parton distribution functions (TMDPDFs). The model relations of the twist-4 T-even TMDs with the available leading twist T-even TMDs have also been obtained., Comment: Accepted in International Journal of Modern Physics A

Indian e-commerce industry has evolved over the last decade and is expected to grow over the next few years. The focus has now shifted to turnaround time (TAT) due to the emergence of many third-party logistics providers and higher customer expectations. The key consideration for delivery providers is to balance their overall operating costs while meeting the promised TAT to their customers. E-commerce delivery partners operate through a network of facilities whose strategic locations help to run the operations efficiently. In this work, we identify the locations of hubs throughout the country and their corresponding mapping with the distribution centers. The objective is to minimize the total network costs with TAT adherence. We use Genetic Algorithm and leverage business constraints to reduce the solution search space and hence the solution time. The results indicate an improvement of 9.73% in TAT compliance compared with the current scenario.

This article presents a survey of literature in the area of Human-Robot Interaction (HRI), specifically on systems containing more than two agents (i.e., having multiple humans and/or multiple robots). We identify three core aspects of ``Multi-agent" HRI systems that are useful for understanding how these systems differ from dyadic systems and from one another. These are the Team structure, Interaction style among agents, and the system's Computational characteristics. Under these core aspects, we present five attributes of HRI systems, namely Team size, Team composition, Interaction model, Communication modalities, and Robot control. These attributes are used to characterize and distinguish one system from another. We populate resulting categories with examples from recent literature along with a brief discussion of their applications and analyze how these attributes differ from the case of dyadic human-robot systems. We summarize key observations from the current literature, and identify challenges and promising areas for future research in this domain. In order to realize the vision of robots being part of the society and interacting seamlessly with humans, there is a need to expand research on multi-human -- multi-robot systems. Not only do these systems require coordination among several agents, they also involve multi-agent and indirect interactions which are absent from dyadic HRI systems. Adding multiple agents in HRI systems requires advanced interaction schemes, behavior understanding and control methods to allow natural interactions among humans and robots. In addition, research on human behavioral understanding in mixed human-robot teams also requires more attention. This will help formulate and implement effective robot control policies in HRI systems with large numbers of heterogeneous robots and humans; a team composition reflecting many real-world scenarios., Comment: 23 pages, 7 figures

We revisit the epoch of cosmic speed-up characterized by the redshift of transition from a decelerated to an accelerated phase. This redshift is termed the transition redshift ($z_t$). We use the spatially Flat and Non-Flat variants of the most common $\Lambda$CDM and XCDM models to put constraints on the transition redshift along with the other model parameters. The data for this analysis comes from the recent and updated Pantheon+ Supernova dataset and the Hubble parameter measurements obtained from Cosmic Chronometers. We consider both datasets with their respective covariance matrices incorporating all kinds of statistical and systematic uncertainties. We observe that using the combined datasets of H(z) and SNe, the best fit value of transition redshift lies in the range $0.61 < z_t < 0.82$ for all four dark energy models. Incidentally, we observe a positive curvature for the Non-Flat models and correlations between several model parameters., Comment: 16 pages, 4 Figures, 3 Tables, Accepted for publication in Research in Astronomy & Astrophysics

In the decision tree computation model for Boolean functions, the depth corresponds to query complexity, and size corresponds to storage space. The depth measure is the most well-studied one, and is known to be polynomially related to several non-computational complexity measures of functions such as certificate complexity. The size measure is also studied, but to a lesser extent. Another decision tree measure that has received very little attention is the minimal rank of the decision tree, first introduced by Ehrenfeucht and Haussler in 1989. This measure is closely related to the logarithm of the size, but is not polynomially related to depth, and hence it can reveal additional information about the complexity of a function. It is characterised by the value of a Prover-Delayer game first proposed by Pudl\'ak and Impagliazzo in the context of tree-like resolution proofs. In this paper we study this measure further. We obtain an upper bound on depth in terms of rank and Fourier sparsity. We obtain upper and lower bounds on rank in terms of (variants of) certificate complexity. We also obtain upper and lower bounds on the rank for composed functions in terms of the depth of the outer function and the rank of the inner function. This allow us to easily recover known asympotical lower bounds on logarithm of the size for Iterated AND-OR and Iterated 3-bit Majority. We compute the rank exactly for several natural functions and use them to show that all the bounds we have obtained are tight. We also show that rank in the simple decision tree model can be used to bound query complexity, or depth, in the more general conjunctive decision tree model. Finally, we improve upon the known size lower bound for the Tribes function and conclude that in the size-rank relationship for decision trees, obtained by Ehrenfeucht and Haussler, the upper bound for Tribes is asymptotically tight.

In this paper, we consider the problem of allocating human operator assistance in a system with multiple autonomous robots. Each robot is required to complete independent missions, each defined as a sequence of tasks. While executing a task, a robot can either operate autonomously or be teleoperated by the human operator to complete the task at a faster rate. We show that the problem of creating a teleoperation schedule that minimizes makespan of the system is NP-Hard. We formulate our problem as a Mixed Integer Linear Program, which can be used to optimally solve small to moderate sized problem instances. We also develop an anytime algorithm that makes use of the problem structure to provide a fast and high-quality solution of the operator scheduling problem, even for larger problem instances. Our key insight is to identify blocking tasks in greedily-created schedules and iteratively remove those blocks to improve the quality of the solution. Through numerical simulations, we demonstrate the benefits of the proposed algorithm as an efficient and scalable approach that outperforms other greedy methods.

Indoor thermal comfort in smart buildings has a significant impact on the health and performance of occupants. Consequently, machine learning (ML) is increasingly used to solve challenges related to indoor thermal comfort. Temporal variability of thermal comfort perception is an important problem that regulates occupant well-being and energy consumption. However, in most ML-based thermal comfort studies, temporal aspects such as the time of day, circadian rhythm, and outdoor temperature are not considered. This work addresses these problems. It investigates the impact of circadian rhythm and outdoor temperature on the prediction accuracy and classification performance of ML models. The data is gathered through month-long field experiments carried out in 14 classrooms of 5 schools, involving 512 primary school students. Four thermal comfort metrics are considered as the outputs of Deep Neural Networks and Support Vector Machine models for the dataset. The effect of temporal variability on school children's comfort is shown through a "time of day" analysis. Temporal variability in prediction accuracy is demonstrated (up to 80%). Furthermore, we show that outdoor temperature (varying over time) positively impacts the prediction performance of thermal comfort models by up to 30%. The importance of spatio-temporal context is demonstrated by contrasting micro-level (location specific) and macro-level (6 locations across a city) performance. The most important finding of this work is that a definitive improvement in prediction accuracy is shown with an increase in the time of day and sky illuminance, for multiple thermal comfort metrics., Comment: Accepted for publication in IEEE SMC 2022

Aspect based sentiment analysis (ABSA) deals with the identification of the sentiment polarity of a review sentence towards a given aspect. Deep Learning sequential models like RNN, LSTM, and GRU are current state-of-the-art methods for inferring the sentiment polarity. These methods work well to capture the contextual relationship between the words of a review sentence. However, these methods are insignificant in capturing long-term dependencies. Attention mechanism plays a significant role by focusing only on the most crucial part of the sentence. In the case of ABSA, aspect position plays a vital role. Words near to aspect contribute more while determining the sentiment towards the aspect. Therefore, we propose a method that captures the position based information using dependency parsing tree and helps attention mechanism. Using this type of position information over a simple word-distance-based position enhances the deep learning model's performance. We performed the experiments on SemEval'14 dataset to demonstrate the effect of dependency parsing relation-based attention for ABSA.