OmniVec: Learning robust representations with cross modal sharing

Majority of research in learning based methods has been towards designing and training networks for specific tasks. However, many of the learning based tasks, across modalities, share commonalities and could be potentially tackled in a joint framework. We present an approach in such direction, to learn multiple tasks, in multiple modalities, with a unified architecture. The proposed network is composed of task specific encoders, a common trunk in the middle, followed by task specific prediction heads. We first pre-train it by self-supervised masked training, followed by sequential training for the different tasks. We train the network on all major modalities, e.g.\ visual, audio, text and 3D, and report results on $22$ diverse and challenging public benchmarks. We demonstrate empirically that, using a joint network to train across modalities leads to meaningful information sharing and this allows us to achieve state-of-the-art results on most of the benchmarks. We also show generalization of the trained network on cross-modal tasks as well as unseen datasets and tasks.Comment: Accepted to WACV 202

Information Technology Capability and Firm Performance: The Role of Strategic Orientation

A key strand of IT business value research is concerned with the measurement of IT capability of firms and the impact of IT capability on the accounting-based measures of firm performance. Previous empirical studies examining this relationship in the context of developed economies have reported mixed results, and there is a dearth of studies in the context of emerging economies. In this study, we seek to employ archival data from the emerging economy context of India, and replicate the findings from the earlier studies, in order to examine the existing theory. We also propose to extend the existing theory by incorporating the role of strategic orientation (indicated by the Miles and Snow strategy types) of the firms while examining the impact of IT capability on firm performance. Thus, the results of this study offer possibilities of both theoretical and practical implications

The business value of social media: A dynamic managerial capabilities perspective

In the last decade and a half, social media usage has become ubiquitous in the workplace. Prior research has noted both the benefits as well as the potential pitfalls of allowing employees to use social media during work hours. In this research-in-progress paper, we propose a conceptual model that shows how the use of social media may help in the enablement of dynamic managerial capabilities by enhancing the managerial social capital. Thus, this paper adds to the literature on the business value of social media. This paper also shows how two distinct types of social media (i.e., public social media and enterprise social media) play complementary roles in the enhancement of managerial social capital, and consequently, in the enablement of dynamic managerial capabilities. Managerial implications are also discussed

Scattering Properties of Paramagnetic Ground States in the Three-Dimensional Random-Field Ising Model

We study the ground-state (T = 0) morphologies in the d = 3 random-field Ising model (RFIM) using a computationally efficient graph-cut method. We focus on paramagnetic states which arise for disorder strengths \Delta > \Delta c, where \Delta c is the critical disorder strength at T = 0. These paramagnetic states consist of correlated "domains" of up and down spins which are separated by rough, fractal interfaces. They show novel scattering properties with a cusp singularity in the correlation function at short distances.Comment: 10 pages, 6 figures, Accepted for publication in Europhysics Letter

Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits

To navigate in an environment safely and autonomously, robots must accurately estimate where obstacles are and how they move. Instead of using expensive traditional 3D sensors, we explore the use of a much cheaper, faster, and higher resolution alternative: programmable light curtains. Light curtains are a controllable depth sensor that sense only along a surface that the user selects. We adapt a probabilistic method based on particle filters and occupancy grids to explicitly estimate the position and velocity of 3D points in the scene using partial measurements made by light curtains. The central challenge is to decide where to place the light curtain to accurately perform this task. We propose multiple curtain placement strategies guided by maximizing information gain and verifying predicted object locations. Then, we combine these strategies using an online learning framework. We propose a novel self-supervised reward function that evaluates the accuracy of current velocity estimates using future light curtain placements. We use a multi-armed bandit framework to intelligently switch between placement policies in real time, outperforming fixed policies. We develop a full-stack navigation system that uses position and velocity estimates from light curtains for downstream tasks such as localization, mapping, path-planning, and obstacle avoidance. This work paves the way for controllable light curtains to accurately, efficiently, and purposefully perceive and navigate complex and dynamic environments. Project website: https://siddancha.github.io/projects/active-velocity-estimation/Comment: 9 pages (main paper), 3 pages (references), 9 pages (appendix

Learned Lock-free Search Data Structures

Non-blocking search data structures offer scalability with a progress guarantee on high-performance multi-core architectures. In the recent past, "learned queries" have gained remarkable attention. It refers to predicting the rank of a key computed by machine learning models trained to infer the cumulative distribution function of an ordered dataset. A line of works exhibits the superiority of learned queries over classical query algorithms. Yet, to our knowledge, no existing non-blocking search data structure employs them. In this paper, we introduce \textbf{Kanva}, a framework for learned non-blocking search. Kanva has an intuitive yet non-trivial design: traverse down a shallow hierarchy of lightweight linear models to reach the "non-blocking bins," which are dynamic ordered search structures. The proposed approach significantly outperforms the current state-of-the-art -- non-blocking interpolation search trees and elimination (a,b) trees -- in many workload and data distributions. Kanva is provably linearizable