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The groundbreaking invention of ChatGPT has triggered enormous discussion among users across all fields and domains. Among celebration around its various advantages, questions have been raised with regards to its correctness and ethics of its use. Efforts are already underway towards capturing user sentiments around it. But it begs the question as to how the research community is analyzing ChatGPT with regards to various aspects of its usage. It is this sentiment of the researchers that we analyze in our work. Since Aspect-Based Sentiment Analysis has usually only been applied on a few datasets, it gives limited success and that too only on short text data. We propose a methodology that uses Explainable AI to facilitate such analysis on research data. Our technique presents valuable insights into extending the state of the art of Aspect-Based Sentiment Analysis on newer datasets, where such analysis is not hampered by the length of the text data.

PR, IFPRI3; 2 Promoting Healthy Diets and Nutrition for all; ISI, PHND, Evidence on strategies to improve infant and young child feeding in India, a country that carries the world's largest burden of undernutrition, is limited. In the context of a programme evaluation in two districts in Uttar Pradesh, we sought to understand the multiple influences on breastfeeding practices and to model potential programme influence on improving breastfeeding. A cross‐sectional survey was conducted among 1,838 recently delivered women, 1,194 husbands, and 1,353 mothers/mothers‐in‐law. We used bivariate and multivariable logistic regression models to examine the association between key determinants (maternal, household, community, and health services) and breastfeeding outcomes [early initiation of breastfeeding (EIBF)], prelacteal feed, and exclusive breastfeeding (EBF). We used population attributable risk analysis to estimate potential improvement in breastfeeding practices. Breastfeeding practices were suboptimal: EIBF (26.3%), EBF (54%), and prelacteal feeding (33%). EIBF was positively associated with maternal knowledge, counselling during pregnancy/delivery, and vaginal delivery at a health facility. Prelacteal feeds were less likely to be given when mothers had higher knowledge, beliefs and self‐efficacy, delivered at health facility, and mothers/mothers‐in‐law had attended school. EBF was positively associated with maternal knowledge, beliefs and self‐efficacy, parity, and socio‐economic status. High maternal stress and domestic violence contributed to lower EBF. Under optimal programme implementation, we estimate EIBF can be improved by 25%, prelacteal feeding can be reduced by 25%, and EBF can be increased by 23%. A multifactorial approach, including maternal‐, health service‐, family‐, and community‐level interventions has the potential to lead to significant improvements in breastfeeding practices in Uttar Pradesh.

Orthogonal frequency division multiplexing (OFDM) with dynamic scheduling and resource allocation is a key component of most emerging broadband wireless access networks such as WiMAX and LTE (long term evolution) for 3GPP. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (i) the discrete nature of subchannel assignments, and (ii) the heterogeneity of the users' subchannel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem and solved using a dual decomposition approach. This optimal solution has prohibitively high computational complexity but reveals guiding principles that we use to generate lower complexity sub-optimal algorithms. We analyze the complexity and compare the performance of these algorithms via extensive simulations.

—We consider scheduling and resource allocation for the downlink of a cellular OFDM system, with various practical considerations including integer tone allocations, different subchannelization schemes, maximum SNR constraint per tone, and "self-noise" due to channel estimation errors and phase noise. During each time-slot a subset of users must be scheduled, and the available tones and transmission power must be allocated among them. Employing a gradient-based scheduling scheme presented in earlier papers reduces this to an optimization problem to be solved in each time-slot. Using a dual formulation, we give an optimal algorithm for this problem when multiple users can time-share each tone. We then give several low complexity heuristics that enforce integer tone allocations. Simulations are used to compare the performance of different algorithms.

Orthogonal frequency division multiplexing (OFDM) with dynamic scheduling and resource allocation is a key component of most emerging broadband wireless access networks such as WiMAX and LTE (long term evolution) for 3GPP. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (i) the discrete nature of subchannel assignments, and (ii) the heterogeneity of the users' subchannel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem and solved using a dual decomposition approach. This optimal solution has prohibitively high computational complexity but reveals guiding principles that we use to generate lower complexity sub-optimal algorithms. We analyze the complexity and compare the performance of these algorithms via extensive simulations.

—We consider scheduling and resource allocation for the downlink of a cellular OFDM system, with various practical considerations including integer tone allocations, different subchannelization schemes, maximum SNR constraint per tone, and "self-noise" due to channel estimation errors and phase noise. During each time-slot a subset of users must be scheduled, and the available tones and transmission power must be allocated among them. Employing a gradient-based scheduling scheme presented in earlier papers reduces this to an optimization problem to be solved in each time-slot. Using a dual formulation, we give an optimal algorithm for this problem when multiple users can time-share each tone. We then give several low complexity heuristics that enforce integer tone allocations. Simulations are used to compare the performance of different algorithms.

Orthogonal Frequency Division Multiplexing (OFDM) with dynamic scheduling and resource allocation is widely considered to be a key component of 4G cellular networks. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (1) the discrete nature of channel assignments, and (2) the heterogeneity of the users' channel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework presented in previous work. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem. We present an optimal solution using a dual decomposition. This solution has prohibitively high computational complexity but reveals guiding principles that we use to generate a family of lower complexity sub-optimal algorithms. We compare the performance of these algorithms via a realistic OFDM simulator.

Orthogonal Frequency Division Multiplexing (OFDM) with dynamic scheduling and resource allocation is widely considered to be a key component of 4G cellular networks. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (1) the discrete nature of channel assignments, and (2) the heterogeneity of the users' channel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework presented in previous work. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem. We present an optimal solution using a dual decomposition. This solution has prohibitively high computational complexity but reveals guiding principles that we use to generate a family of lower complexity sub-optimal algorithms. We compare the performance of these algorithms via a realistic OFDM simulator.

Orthogonal Frequency Division Multiplexing (OFDM) with dynamic scheduling and resource allocation is widely considered to be a key component of 4G cellular networks. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (1) the discrete nature of channel assignments, and (2) the heterogeneity of the users' channel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework presented in previous work. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem. We present an optimal solution using a dual decomposition. This solution has prohibitively high computational complexity but reveals guiding principles that we use to generate a family of lower complexity sub-optimal algorithms. We compare the performance of these algorithms via a realistic OFDM simulator.

We consider a scheduling and resource allocation problem for the downlink of an OFDMA-based wireless network, where the channel estimation error is modeled by a self-noise term in the decoding process. During each time-slot this involves selecting a subset of users for transmission, determining the assignment of available subcarriers to selected users, and for each subcarrier determining the transmission power and the coding and modulation scheme used. We address this in the context of a utility-based scheduling scheme presented in earlier papers. This results in an optimization problem, which is convex for a reasonable model of the feasible rates. By exploiting the structure of this problem, we develop optimal and sub-optimal algorithms for its solution. We provide simulation results comparing different algorithms and parameter settings.

We consider a scheduling and resource allocation problem for the downlink of an OFDMA-based wireless network, where the channel estimation error is modeled by a self-noise term in the decoding process. During each time-slot this involves selecting a subset of users for transmission, determining the assignment of available subcarriers to selected users, and for each subcarrier determining the transmission power and the coding and modulation scheme used. We address this in the context of a utility-based scheduling scheme presented in earlier papers. This results in an optimization problem, which is convex for a reasonable model of the feasible rates. By exploiting the structure of this problem, we develop optimal and sub-optimal algorithms for its solution. We provide simulation results comparing different algorithms and parameter settings.

We consider a scheduling and resource allocation problem for the downlink of an OFDMA-based wireless network, where the channel estimation error is modeled by a self-noise term in the decoding process. During each time-slot this involves selecting a subset of users for transmission, determining the assignment of available subcarriers to selected users, and for each subcarrier determining the transmission power and the coding and modulation scheme used. We address this in the context of a utility-based scheduling scheme presented in earlier papers. This results in an optimization problem, which is convex for a reasonable model of the feasible rates. By exploiting the structure of this problem, we develop optimal and sub-optimal algorithms for its solution. We provide simulation results comparing different algorithms and parameter settings.

Consider a controlled Markov chain whose transition probabilities and initial distribution are parametrized by an unknown parameter Theta belonging to some known parameter space Theta. There is a one-step reward associated with each pair of control and the following state of the process. The objective is to maximize the expected value of the sum of one step rewards over an infinite horizon. By introducing the Loss associated with a control scheme, the problem is equivalent to minimizing this Loss. Define a uniformly good adaptive control schemes and restrict attention to these schemes. A lower bound is developed on the Loss associated with any uniformly good control scheme. Finally, an adaptive control scheme is constructed whose Loss equals the lower bound, and is therefore optimal. Keywords: Adaptive control scheme; Controlled Markov chain; Asymptotically optimal., Prepared in cooperation with Cornell Univ., Ithaca, NY. School of Electrical Engineering.