Common Representation Learning Using Step-based Correlation Multi-Modal CNN

Deep learning techniques have been successfully used in learning a common representation for multi-view data, wherein the different modalities are projected onto a common subspace. In a broader perspective, the techniques used to investigate common representation learning falls under the categories of canonical correlation-based approaches and autoencoder based approaches. In this paper, we investigate the performance of deep autoencoder based methods on multi-view data. We propose a novel step-based correlation multi-modal CNN (CorrMCNN) which reconstructs one view of the data given the other while increasing the interaction between the representations at each hidden layer or every intermediate step. Finally, we evaluate the performance of the proposed model on two benchmark datasets - MNIST and XRMB. Through extensive experiments, we find that the proposed model achieves better performance than the current state-of-the-art techniques on joint common representation learning and transfer learning tasks.Comment: Accepted in Asian Conference of Pattern Recognition (ACPR-2017

Factors Influencing Customer Satisfaction in Retail Malls in Hyderabad: A Study

The study looks at the growing retail sector in India and tries to find out the various factors which influence the customer satisfaction in retail stores and shopping mall in the city of Hyderabad in India. First, focus group interviews were held and the existing literature was examined to bring out the variables affecting the customer satisfaction in a retail mall. Further focus group discussion was carried out to ascertain that these factors were actually applicable in the Indian context. Primary data collection was used and the responses were gathered via the mall intercept method from people who had shopped in malls in Hyderabad. Regression analysis was conducted and it was observed that the impact of customer orientation and ambience was more important compared to the other factors. The behavior of the sales person was also a very integral element influencing customer satisfaction. Necessitating adequate sales training. This study highlights the importance of the quality of service rendered

A multicentric retrospective study for the treatment of humerus bone fracture following humerus plate fixation with screws

Background: The goal of this study was to investigate the performance of the humerus bone fixation with screws while treating humerus bone fracture.Methods: The 34 patients’ retrospective data was collected with 1 year of follow up. Humerus fractures were treated by humerus plate fixation in different hospitals and countries, including 26 males and 8 females, with the age range of 32 -74 years (mean 47.4 years). Clinical and radiological follow-ups were conducted at 1 month, 3 months, 6 months and 1 year after surgery to check the bone union and implant-related complications. Ten different plates were used for the treatment of fracture as per the fracture type. The patient's health status was evaluated by the American society of anesthesiologists grade and the visual analogue score (VAS) was also obtained.Results: The progressive decline in the VAS score showed positive results related to pain management. All patients receive continuous physiotherapy under the supervision of physiotherapists, which aids in faster recovery and mobilization. No biomechanical issue related to implant plate and screw loosening, corrosion, bend, or other factors was detected in our 34 patients. Out of 34 patients 91% were satisfied with no pain and the remaining 9% were unsatisfied due to pain. About 85% of patients were happy with aesthetic appearance and the rest 14% of patients were unhappy related to aesthetic appearance.Conclusions: Humerus plate fixation is feasible for the treatment of humerus fracture. The clinical outcomes and prognosis of patients are dependent on the accuracy of intraoperative reduction and surgical expertise

Clinical performance of tibia bone plate system for fixation of tibia fracture

Background: In this study, we aimed to investigate the performance of the tibia plates system while treating the tibia fracture fixation. The objective of this study was to reduce the post-operative complications of proximal and distal tibia fracture by using indigenously manufactured implants (plates and screws).Methods: In this retrospective study, we studied the results of the tibia plate system in treatment of tibia fracture. A total of 34 consecutive patients were included in this study (24 males, 10 females and average age 48.6 years). Fracture type was classified as per the Muller AO classification of fracture. According to the AO classification, proximal and distal tibia fractures 41-A, 41-B and 41-C was observed with one year follow up period followed by physical exercises after one month of the surgery. The fractures were treated with wise-lock proximal and distal tibia plates.Results: The outcomes of clinical treatment were obtained in our study; no pain (88.2%), mild pain (11.7%) after 1 year follow up. The follow up of patients was taken on 1 month, 6 months and 1 year according to visual analog scale (VAS) score. No implant related problem have been found like loosening, bending and corrosion. X-ray was used to check the union, non-union. Functional outcomes were assessed with VAS.Conclusions: Treatment of tibia fracture with wise-lock proximal and distal tibia plate shows good outcomes with less complications

GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts

Model predictive control (MPC) is a popular approach for trajectory optimization in practical robotics applications. MPC policies can optimize trajectory parameters under kinodynamic and safety constraints and provide guarantees on safety, optimality, generalizability, interpretability, and explainability. However, some behaviors are complex and it is difficult to hand-craft an MPC objective function. A special class of MPC policies called Learnable-MPC addresses this difficulty using imitation learning from expert demonstrations. However, they require the demonstrator and the imitator agents to be identical which is hard to satisfy in many real world applications of robotics. In this paper, we address the practical problem of training Learnable-MPC policies when the demonstrator and the imitator do not share the same dynamics and their state spaces may have a partial overlap. We propose a novel approach that uses a generative adversarial network (GAN) to minimize the Jensen-Shannon divergence between the state-trajectory distributions of the demonstrator and the imitator. We evaluate our approach on a variety of simulated robotics tasks of DeepMind Control suite and demonstrate the efficacy of our approach at learning the demonstrator's behavior without having to copy their actions.Comment: 16 page