Mapping the Intellectual Structure of Social Entrepreneurship Research: A Citation/Co-citation Analysis

In this paper, we employ bibliometric analysis to empirically analyse the research on social entrepreneurship published between 1996 and 2017. By employing methods of citation analysis, document co-citation analysis, and social network analysis, we analyse 1296 papers containing 74,237 cited references and uncover the structure, or intellectual base, of research on social entrepreneurship. We identify nine distinct clusters of social entrepreneurship research that depict the intellectual structure of the field. The results provide an overall perspective of the social entrepreneurship field, identifying its influential works and analysing scholarly communication between these works. The results further aid in clarifying the overall centrality features of the social entrepreneurship research network. We also examine the integration of ethics into social entrepreneurship literature. We conclude with a discussion on the structure and evolution of the social entrepreneurship field

Large-scale magnetic fields from inflation due to a CPTCPT-even Chern-Simons-like term with Kalb-Ramond and scalar fields

We investigate the generation of large-scale magnetic fields due to the breaking of the conformal invariance in the electromagnetic field through the $CPT$-even dimension-six Chern-Simons-like effective interaction with a fermion current by taking account of the dynamical Kalb-Ramond and scalar fields in inflationary cosmology. It is explicitly demonstrated that the magnetic fields on 1Mpc scale with the field strength of $\sim 10^{-9}$G at the present time can be induced.Comment: 18 pages, 6 figures, version accepted for publication in Eur. Phys. J.

Study on accessory abductor digiti minimi

Background: Abductor digiti minimi (ADM) along with other hypothenar muscles, is prone to lot of variations. Its close relation to the ulnar neurovascular bundle makes it an important muscle. The most common being the presence of accessory slips (accessory ADM [AADM]). The embryological basis for such slips, their morphology and their relation to ulnar neurovascular bundle were studied in detail. Materials and Methods: A total of 100 upper limbs (80 males and 20 females) were studied for the presence or absence of AADM and its relation to ulnar nerve (UN) and vessels were studied in detail. Results: Of these 100, four limbs had AADM, in which three were superfi cial to the ulnar artery and nerve and hence compressing them. Conclusion: Presence of AADM can result in hypothenar hammer syndrome of ulnar artery and/or in compression neuropathy of UN and cause undue problems during any fl ap surgeries and other procedures of the hand

SARC: Soft Actor Retrospective Critic

The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but since the critic learns faster than the actor, it ensures eventual consistency between the two. Various strategies have been introduced in literature to learn better gradient estimates to help achieve better convergence. Since gradient estimates depend upon the critic, we posit that improving the critic can provide a better gradient estimate for the actor at each time. Utilizing this, we propose Soft Actor Retrospective Critic (SARC), where we augment the SAC critic loss with another loss term - retrospective loss - leading to faster critic convergence and consequently, better policy gradient estimates for the actor. An existing implementation of SAC can be easily adapted to SARC with minimal modifications. Through extensive experimentation and analysis, we show that SARC provides consistent improvement over SAC on benchmark environments. We plan to open-source the code and all experiment data at: https://github.com/sukritiverma1996/SARC.Comment: Accepted at RLDM 202

Explaining RL Decisions with Trajectories

Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature, the explanation is often provided by saliency attribution to the features of the RL agent's state. In this work, we propose a complementary approach to these explanations, particularly for offline RL, where we attribute the policy decisions of a trained RL agent to the trajectories encountered by it during training. To do so, we encode trajectories in offline training data individually as well as collectively (encoding a set of trajectories). We then attribute policy decisions to a set of trajectories in this encoded space by estimating the sensitivity of the decision with respect to that set. Further, we demonstrate the effectiveness of the proposed approach in terms of quality of attributions as well as practical scalability in diverse environments that involve both discrete and continuous state and action spaces such as grid-worlds, video games (Atari) and continuous control (MuJoCo). We also conduct a human study on a simple navigation task to observe how their understanding of the task compares with data attributed for a trained RL policy. Keywords -- Explainable AI, Verifiability of AI Decisions, Explainable RL.Comment: Published at International Conference on Learning Representations (ICLR), 202

Effect of whey protein beverages on surface roughness of a packable resin composite

Objective: This study aimed to analyze the effect of whey protein beverages on the surface roughness of a packable resin composite restorative material.Methods: Twenty disc-shaped specimens were prepared from a resin composite (Ivoclar Te-Econom Plus). The specimens were divided into two equal groups (n=10) and subjected to different immersion media, as follows: Group 1: Whey-1 solution (BigMuscles Essential Whey) and Group 2: Whey-2 solution (Muscletech Nitro Tech 100% Whey Gold). Each whey solution was prepared by mixing 25 g of the corresponding whey powder into 250 ml of water. The samples were immersed in the respective solutions for 10 minutes daily for 14 days. The surface roughness of each composite specimen was analyzed pre- and post-immersion using a stylus profilometer. The average surface roughness value (Ra) of each specimen was recorded in µm at each interval. The data were subjected to statistical analysis using SPSS software 26.0. Wilcoxon signed-rank test and Mann-Whitney U test were used for statistical analysis. P-values less than 0.05 were considered significant.Results: Both groups exhibited a significant increase in surface roughness values after immersion in whey protein drinks (P= 0.045 for group 1 and P=0.038 for group 2). The mean post-immersion Ra value was significantly greater in group 2 (0.402 ± 0.328) compared to group 1 (0.302 ± 0.167) (P=0.048). Conclusions: Both types of whey protein beverages significantly increased the surface roughness of a packable resin composite. These results imply that exposure to whey protein beverages may adversely affect the surface roughness of composite restorations

Behavior Optimized Image Generation

The last few years have witnessed great success on image generation, which has crossed the acceptance thresholds of aesthetics, making it directly applicable to personal and commercial applications. However, images, especially in marketing and advertising applications, are often created as a means to an end as opposed to just aesthetic concerns. The goal can be increasing sales, getting more clicks, likes, or image sales (in the case of stock businesses). Therefore, the generated images need to perform well on these key performance indicators (KPIs), in addition to being aesthetically good. In this paper, we make the first endeavor to answer the question of "How can one infuse the knowledge of the end-goal within the image generation process itself to create not just better-looking images but also "better-performing'' images?''. We propose BoigLLM, an LLM that understands both image content and user behavior. BoigLLM knows how an image should look to get a certain required KPI. We show that BoigLLM outperforms 13x larger models such as GPT-3.5 and GPT-4 in this task, demonstrating that while these state-of-the-art models can understand images, they lack information on how these images perform in the real world. To generate actual pixels of behavior-conditioned images, we train a diffusion-based model (BoigSD) to align with a proposed BoigLLM-defined reward. We show the performance of the overall pipeline on two datasets covering two different behaviors: a stock dataset with the number of forward actions as the KPI and a dataset containing tweets with the total likes as the KPI, denoted as BoigBench. To advance research in the direction of utility-driven image generation and understanding, we release BoigBench, a benchmark dataset containing 168 million enterprise tweets with their media, brand account names, time of post, and total likes