70 research outputs found
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 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
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