skip to main content

Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 11:00 PM ET on Thursday, February 13 until 2:00 AM ET on Friday, February 14 due to maintenance. We apologize for the inconvenience.


Search for: All records

Creators/Authors contains: "Sharma, Abhishek"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Arpaci-Dusseau, Andrea ; Keeton, Kimberly (Ed.)
    Just-in-time (JIT) compilers make JavaScript run efficiently by replacing slow JavaScript interpreter code with fast machine code. However, this efficiency comes at a cost: bugs in JIT compilers can completely subvert all language-based (memory) safety guarantees, and thereby introduce catastrophic exploitable vulnerabilities. We present Icarus: a new framework for implementing JIT compilers that are automatically, formally verified to be safe, and which can then be converted to C++ that can be linked into browser runtimes. Crucially, we show how to build a JIT with Icarus such that verifying the JIT implementation statically ensures the security of all possible programs that the JIT could ever generate at run-time, via a novel technique called symbolic meta-execution that encodes the behaviors of all possible JIT-generated programs as a single Boogie meta-program which can be efficiently verified by SMT solvers. We evaluate Icarus by using it to re-implement components of Firefox's JavaScript JIT. We show that Icarus can scale up to expressing complex JITs, quickly detects real-world JIT bugs and verifies fixed versions, and yields C++ code that is as fast as hand-written code. 
    more » « less
    Free, publicly-accessible full text available November 4, 2025
  2. Free, publicly-accessible full text available September 29, 2025
  3. Rich variations in gait are generated according to several attributes of the individual and environment, such as age, athleticism, terrain, speed, personal “style”, mood, etc. The effects of these attributes can be hard to quantify explicitly, but relatively straightforward to sample. We seek to generate gait that expresses these attributes, creating synthetic gait samples that exemplify a custom mix of attributes. This is difficult to perform manually, and generally restricted to simple, human-interpretable and handcrafted rules. In this manuscript, we present neural network architectures to learn representations of hard to quantify attributes from data, and generate gait trajectories by composing multiple desirable attributes. We demonstrate this method for the two most commonly desired attribute classes: individual style and walking speed. We show that two methods, cost function design and latent space regularization, can be used individually or combined. We also show two uses of machine learning classifiers that recognize individuals and speeds. Firstly, they can be used as quantitative measures of success; if a synthetic gait fools a classifier, then it is considered to be a good example of that class. Secondly, we show that classifiers can be used in the latent space regularizations and cost functions to improve training beyond a typical squared-error cost. 
    more » « less
  4. Monte Carlo simulations were used to study the influence of particle aspect ratio on the kinetics and phase behavior of hard gyrobifastigia (GBF). First, the formation of a highly anisotropic nucleus shape in the isotropic-to-crystal transition in regular GBF is explained by the differences in interfacial free energies of various crystal planes and the nucleus geometry predicted by the Wulff construction. GBF-related shapes with various aspect ratios were then studied, mapping their equations of state, determining phase coexistence conditions via interfacial pinning, and computing nucleation free-energy barriers via umbrella sampling using suitable order parameters. Our simulations reveal a reduction of the kinetic barrier for isotropic–crystal transition upon an increase in aspect ratio, and that for highly oblate and prolate aspect ratios, an intermediate nematic phase is stabilized. Our results and observations also support two conjectures for the formation of the crystalline state from the isotropic phase: that low phase free energies at the ordering phase transition correlate with low transition barriers and that the emergence of a mesophase provides a steppingstone that expedites crystallization. 
    more » « less
  5. Gait complexity is widely used to understand risk factors for injury, rehabilitation, the performance of assistive devices, and other matters of clinical interest. We analyze the complexity of out-of-the-lab locomotion activities via measures that have previously been used in gait analysis literature, as well as measures from other domains of data analysis. We categorize these broadly as quantifying either the intrinsic dimensionality, the variability, or the regularity, periodicity, or self-similarity of the data from a nonlinear dynamical systems perspective. We perform this analysis on a novel full-body motion capture dataset collected in out-of-the-lab conditions for a variety of indoor environments. This is a unique dataset with a large amount (over 24 h total) of data from participants behaving without low-level instructions in out-of-the-lab indoor environments. We show that reasonable complexity measures can yield surprising, and even profoundly contradictory, results. We suggest that future complexity analysis can use these guidelines to be more specific and intentional about what aspect of complexity a quantitative measure expresses. This will become more important as wearable motion capture technology increasingly allows for comparison of ecologically relevant behavior with lab-based measurements. 
    more » « less
  6. Abstract

    In this manuscript, we describe a unique dataset of human locomotion captured in a variety of out-of-the-laboratory environments captured using Inertial Measurement Unit (IMU) based wearable motion capture. The data contain full-body kinematics for walking, with and without stops, stair ambulation, obstacle course navigation, dynamic movements intended to test agility, and negotiating common obstacles in public spaces such as chairs. The dataset contains 24.2 total hours of movement data from a college student population with an approximately equal split of males to females. In addition, for one of the activities, we captured the egocentric field of view and gaze of the subjects using an eye tracker. Finally, we provide some examples of applications using the dataset and discuss how it might open possibilities for new studies in human gait analysis.

     
    more » « less
  7. Topic models are some of the most popular ways to represent textual data in an interpret- able manner. Recently, advances in deep gen- erative models, specifically auto-encoding vari- ational Bayes (AEVB), have led to the intro- duction of unsupervised neural topic models, which leverage deep generative models as op- posed to traditional statistics-based topic mod- els. We extend upon these neural topic models by introducing the Label-Indexed Neural Topic Model (LI-NTM), which is, to the extent of our knowledge, the first effective upstream semi- supervised neural topic model. We find that LI- NTM outperforms existing neural topic models in document reconstruction benchmarks, with the most notable results in low labeled data regimes and for data-sets with informative la- bels; furthermore, our jointly learned classi- fier outperforms baseline classifiers in ablation studies. 
    more » « less