arXiv subjects

Deepak Kumar

Publications and source records attributed to Deepak Kumar.

At least 91 records · Page 5Linked to original sources

arXiv2024

Motion-Driven Neural Optimizer for Prophylactic Braces Made by Distributed Microstructures

Joint injuries, and their long-term consequences, present a substantial global health burden. Wearable prophylactic braces are an attractive potential solution to reduce the incidence of joint injuries by limiting joint movements that are related to injury risk. Given human motion and ground reaction forces, we present a computational framework that enables the design of personalized braces by optimizing the distribution of microstructures and elasticity. As varied brace designs yield different reaction forces that influence kinematics and kinetics analysis outcomes, the optimization process is formulated as a differentiable end-to-end pipeline in which the design domain of microstructure distribution is parameterized onto a neural network. The optimized distribution of microstructures is obtained via a self-learning process to determine the network coefficients according to a carefully designed set of losses and the integrated biomechanical and physical analyses. Since knees and ankles are the most commonly injured joints, we demonstrate the effectiveness of our pipeline by designing, fabricating, and testing prophylactic braces for the knee and ankle to prevent potentially harmful joint movements.

physics.med-ph↗

arXiv2024

PressProtect: Helping Journalists Navigate Social Media in the Face of Online Harassment

Social media has become a critical tool for journalists to disseminate their work, engage with their audience, and connect with sources. Unfortunately, journalists also regularly endure significant online harassment on social media platforms, ranging from personal attacks to doxxing to threats of physical harm. In this paper, we seek to understand how to make social media usable for journalists who face constant digital harassment. To begin, we conduct a set of need-finding interviews with Asian American and Pacific Islander journalists to understand where existing platform tools and newsroom resources fall short in adequately protecting journalists, especially those of marginalized identities. We map journalists' unmet needs to concrete design goals, which we use to build PressProtect, an interface that provides journalists greater agency when engaging with readers on Twitter/X. Through user testing with eight journalists, we evaluate PressProtect and find that participants felt it effectively protected them against harassment and could also generalize to serve other visible and vulnerable groups. We conclude with a discussion of our findings and recommendations for social platforms hoping to build defensive defaults for journalists facing online harassment.

cs.CY↗

arXiv2024

Comparing Femtosecond Optical Tweezers with Conventional CW Optical Tweezers

In this work, we present a comparative study between continuous-wave (CW) and pulsed optical tweezers for 250 nm, 500 nm and 1-micron radius polystyrene beads at 5 different laser powers. We have used a Ti:Sapphire (MIRA 900F) laser that can be easily switched from CW to pulsed mode of operation, so there is no change in the experimental conditions in the two cases. We have measured the difference in the trap strength in both cases by fitting the power spectrum curve with Lorentzian. As it turns out, trapping with pulsed tweezers seems to be more effective for the smaller particles and as the particle size is increased both CW and pulsed tweezers appear to be equally effective at lower average laser powers but as the power is increased pulsed tweezers do a better job at stable trapping.

physics.optics↗

arXiv2024

A framework to identify supercritical and subcritical Turing bifurcations: Case study of a system sustaining cubic and quadratic autocatalysis

In this work, we focus on an autocatalytic reaction-diffusion model and carry out multiple scale weakly nonlinear analysis. A cubic and a quadratic autocatalytic reaction system is analysed. We develop a framework to identify the critical surfaces in parameter space across which the nature of the Turing bifurcation changes from supercritical to subcritical. These are verified by direct numerical simulations of the system. Using weakly nonlinear analysis, we derive equations up to the fifth order that governs the amplitude of the spatial patterns. The limit point of the bifurcating solution is captured accurately by extending the analysis to the fifth order for the case of subcritical bifurcation. The numerical solutions are in good agreement with the predictions of the weakly nonlinear analysis for supercritical bifurcations. We show that when multiple steady states arise Turing patterns can coexist with another spatially uniform steady states. Furthermore, we show that our framework can be extended to get different patterns like squares and hexagons in a two-dimensional domain. We show that the shape of Turing patterns is influenced by the domain size. This shows that the geometry can influence the kind of patterns formed in natural systems. This study will aid the experimentalist identify operating conditions where Turing patterns can be obtained.

nlin.AO↗

arXiv2024

Calderón-Zygmund theory of nonlocal parabolic equations with discontinuous coefficients

We prove Calderón-Zygmund type estimates of weak solutions to non-homogeneous nonlocal parabolic equations under a minimal regularity requirement on kernel coefficients. In particular, the right-hand side is presented by a sum of fractional Laplacian type data and a non-divergence type data. Interestingly, even though the kernel coefficients are discontinuous, we obtain a significant increment of fractional differentiability for the solutions, which is not observed in the corresponding local parabolic equations.

math.AP↗

arXiv2024

End-to-End Waveform and Beamforming Optimization for RF Wireless Power Transfer

Radio frequency (RF) wireless power transfer (WPT) is a key technology for future low-power wireless systems. However, the inherently low end-to-end power transfer efficiency (PTE) is challenging for practical applications. The main factors contributing to it are the channel losses, transceivers' power consumption, and losses related, e.g., to the digital-to-analog converter (DAC), high-power amplifier, and rectenna. Optimizing PTE requires careful consideration of these factors, motivating the current work. Herein, we consider an analog multi-antenna power transmitter that aims to charge a single energy harvester. We first provide a mathematical framework to calculate the harvested power from multi-tone signal transmissions and the system power consumption. Then, we formulate the joint waveform and analog beamforming design problem to minimize power consumption and meet the charging requirements. Finally, we propose an optimization approach relying on swarm intelligence to solve the specified problem. Simulation results quantify the power consumption reduction as the DAC, phase shifters resolution, and antenna length are increased, while it is seen that increasing system frequency results in higher power consumption.

eess.SP↗

arXiv2024

Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers

To detect infected wounds in Diabetic Foot Ulcers (DFUs) from photographs, preventing severe complications and amputations. Methods: This paper proposes the Guided Conditional Diffusion Classifier (ConDiff), a novel deep-learning infection detection model that combines guided image synthesis with a denoising diffusion model and distance-based classification. The process involves (1) generating guided conditional synthetic images by injecting Gaussian noise to a guide image, followed by denoising the noise-perturbed image through a reverse diffusion process, conditioned on infection status and (2) classifying infections based on the minimum Euclidean distance between synthesized images and the original guide image in embedding space. Results: ConDiff demonstrated superior performance with an accuracy of 83% and an F1-score of 0.858, outperforming state-of-the-art models by at least 3%. The use of a triplet loss function reduces overfitting in the distance-based classifier. Conclusions: ConDiff not only enhances diagnostic accuracy for DFU infections but also pioneers the use of generative discriminative models for detailed medical image analysis, offering a promising approach for improving patient outcomes.

cs.CV↗

arXiv2024

Geometry-induced friction at a soft interface

Soft and biological matter come in a variety of shapes and geometries. When soft surfaces that do not fit into each other due to a mismatch in Gaussian curvatures form an interface, beautiful geometry-induced patterns emerge. In this paper, we study the effect of geometry on the dynamical response of soft surfaces moving relative to each other. Using a novel experimental scheme, we measure friction between a highly bendable thin polymer sheet and a hydrogel substrate. At this soft and low-friction interface, we find a strong dependence of friction on the relative geometry of the two surfaces - a flat sheet experiences significantly larger friction on a spherical substrate than on planar or cylindrical substrate. We show that the stress developed in the sheet due to its geometrically incompatible confinement is responsible for the enhanced friction. This mechanism also leads to a transition in the nature of friction as the sheet radius is increased beyond a critical value. Our finding reveals a hitherto unnoticed non-specific mechanism of purely geometrical origin that may influence friction significantly in soft, biological, and nano-scale systems. In particular, it provokes us to re-examine our understanding of phenomena such as the curvature dependence of biological cell mobility.

cond-mat.soft↗

arXiv2024

Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale

Misinformation, propaganda, and outright lies proliferate on the web, with some narratives having dangerous real-world consequences on public health, elections, and individual safety. However, despite the impact of misinformation, the research community largely lacks automated and programmatic approaches for tracking news narratives across online platforms. In this work, utilizing daily scrapes of 1,334 unreliable news websites, the large-language model MPNet, and DP-Means clustering, we introduce a system to automatically identify and track the narratives spread within online ecosystems. Identifying 52,036 narratives on these 1,334 websites, we describe the most prevalent narratives spread in 2022 and identify the most influential websites that originate and amplify narratives. Finally, we show how our system can be utilized to detect new narratives originating from unreliable news websites and to aid fact-checkers in more quickly addressing misinformation. We release code and data at https://github.com/hanshanley/specious-sites.

cs.SI↗

arXiv2024

Discrete symmetries tested at 10$^{-4}$ precision using linear polarization of photons from positronium annihilations

Discrete symmetries play an important role in particle physics with violation of CP connected to the matter-antimatter imbalance in the Universe. We report the most precise test of P, T and CP invariance in decays of ortho-positronium, performed with methodology involving polarization of photons from these decays. Positronium, the simplest bound state of an electron and positron, is of recent interest with discrepancies reported between measured hyperfine energy structure and theory at the level of $10^{-4}$ signaling a need for better understanding of the positronium system at this level. We test discrete symmetries using photon polarizations determined via Compton scattering in the dedicated J-PET tomograph on an event-by-event basis and without the need to control the spin of the positronium with an external magnetic field, in contrast to previous experiments. Our result is consistent with QED expectations at the level of 0.0007 and one standard deviation.

hep-ph↗

arXiv2024

Watch Your Language: Investigating Content Moderation with Large Language Models

Large language models (LLMs) have exploded in popularity due to their ability to perform a wide array of natural language tasks. Text-based content moderation is one LLM use case that has received recent enthusiasm, however, there is little research investigating how LLMs perform in content moderation settings. In this work, we evaluate a suite of commodity LLMs on two common content moderation tasks: rule-based community moderation and toxic content detection. For rule-based community moderation, we instantiate 95 subcommunity specific LLMs by prompting GPT-3.5 with rules from 95 Reddit subcommunities. We find that GPT-3.5 is effective at rule-based moderation for many communities, achieving a median accuracy of 64% and a median precision of 83%. For toxicity detection, we evaluate a suite of commodity LLMs (GPT-3, GPT-3.5, GPT-4, Gemini Pro, LLAMA 2) and show that LLMs significantly outperform currently widespread toxicity classifiers. However, recent increases in model size add only marginal benefit to toxicity detection, suggesting a potential performance plateau for LLMs on toxicity detection tasks. We conclude by outlining avenues for future work in studying LLMs and content moderation.

cs.HC↗

arXiv2023

Do Users Write More Insecure Code with AI Assistants?

We conduct the first large-scale user study examining how users interact with an AI Code assistant to solve a variety of security related tasks across different programming languages. Overall, we find that participants who had access to an AI assistant based on OpenAI's codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities. Finally, in order to better inform the design of future AI-based Code assistants, we provide an in-depth analysis of participants' language and interaction behavior, as well as release our user interface as an instrument to conduct similar studies in the future.

cs.CR↗

arXiv2023

A Golden Age: Conspiracy Theories' Relationship with Misinformation Outlets, News Media, and the Wider Internet

Do we live in a "Golden Age of Conspiracy Theories?" In the last few decades, conspiracy theories have proliferated on the Internet with some having dangerous real-world consequences. A large contingent of those who participated in the January 6th attack on the US Capitol fervently believed in the QAnon conspiracy theory. In this work, we study the relationships amongst five prominent conspiracy theories (QAnon, COVID, UFO/Aliens, 9/11, and Flat-Earth) and each of their respective relationships to the news media, both authentic news and misinformation. Identifying and publishing a set of 755 different conspiracy theory websites dedicated to our five conspiracy theories, we find that each set often hyperlinks to the same external domains, with COVID and QAnon conspiracy theory websites having the largest amount of shared connections. Examining the role of news media, we further find that not only do outlets known for spreading misinformation hyperlink to our set of conspiracy theory websites more often than authentic news websites but also that this hyperlinking increased dramatically between 2018 and 2021, with the advent of QAnon and the start of COVID-19 pandemic. Using partial Granger-causality, we uncover several positive correlative relationships between the hyperlinks from misinformation websites and the popularity of conspiracy theory websites, suggesting the prominent role that misinformation news outlets play in popularizing many conspiracy theories.

cs.CY↗

arXiv2023

GroundLink: A Dataset Unifying Human Body Movement and Ground Reaction Dynamics

The physical plausibility of human motions is vital to various applications in fields including but not limited to graphics, animation, robotics, vision, biomechanics, and sports science. While fully simulating human motions with physics is an extreme challenge, we hypothesize that we can treat this complexity as a black box in a data-driven manner if we focus on the ground contact, and have sufficient observations of physics and human activities in the real world. To prove our hypothesis, we present GroundLink, a unified dataset comprised of captured ground reaction force (GRF) and center of pressure (CoP) synchronized to standard kinematic motion captures. GRF and CoP of GroundLink are not simulated but captured at high temporal resolution using force platforms embedded in the ground for uncompromising measurement accuracy. This dataset contains 368 processed motion trials (~1.59M recorded frames) with 19 different movements including locomotion and weight-shifting actions such as tennis swings to signify the importance of capturing physics paired with kinematics. GroundLinkNet, our benchmark neural network model trained with GroundLink, supports our hypothesis by predicting GRFs and CoPs accurately and plausibly on unseen motions from various sources. The dataset, code, and benchmark models are made public for further research on various downstream tasks leveraging the rich physics information at https://csr.bu.edu/groundlink/.

cs.GR↗

arXiv2023

The role of molecular structure on the microscopic thermodynamics: unveiling with Femtosecond Optical Tweezers

Microscopic thermodynamic studies can elucidate specific molecular interactions. In this work, we report the microscopic thermodynamics in binary liquid mixtures, which elucidate the role of molecular structure in nonlinear solvent response using femtosecond optical tweezers (FOT). We obtain the excess thermodynamics property of mixing in various Newtonian liquid mixtures by analyzing Microrheology data from FOT. Using our noninvasive 780 nm pulse laser we have trapped micron-sized particles to show how excess viscosity and residual Gibbs free energy change due to mixing. Furthermore, we establish from this study that hydrocarbon chain length and branching can modulate microscopic thermodynamics through intermolecular interaction. This work sheds light on the relationship between thermodynamic properties and viscosity, which is of immense importance for predicting transport properties, mixing, and chemical reactions.

physics.chem-ph↗

arXiv2023

Parameter-efficient Modularised Bias Mitigation via AdapterFusion

Large pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial training) impose debiasing by updating a model's parameters, effectively transferring the model to a new, irreversible debiased state. In this work, we propose a novel approach to develop stand-alone debiasing functionalities separate from the model, which can be integrated into the model on-demand, while keeping the core model untouched. Drawing from the concept of AdapterFusion in multi-task learning, we introduce DAM (Debiasing with Adapter Modules) - a debiasing approach to first encapsulate arbitrary bias mitigation functionalities into separate adapters, and then add them to the model on-demand in order to deliver fairness qualities. We conduct a large set of experiments on three classification tasks with gender, race, and age as protected attributes. Our results show that DAM improves or maintains the effectiveness of bias mitigation, avoids catastrophic forgetting in a multi-attribute scenario, and maintains on-par task performance, while granting parameter-efficiency and easy switching between the original and debiased models.

cs.CL↗

arXiv2023

Robust universal relations in neutron star asteroseismology

The non-radial oscillations of the neutron stars (NSs) have been suggested as a useful tool to probe the composition of neutron star matter (NSM). With this scope in mind, we consider a large number of equations of states (EOSs) that are consistent with nuclear matter properties and pure neutron matter EOS based on a chiral effective field theory (chEFT) calculation for the low densities and perturbative QCD EOS at very high densities. This ensemble of EOSs is also consistent with astronomical observations, gravitational waves in GW170817, mass and radius measurements from Neutron star Interior Composition ExploreR (NICER). We analyze the robustness of known universal relations (URs) among the quadrupolar $f$ mode frequencies, masses and radii with such a large number of EOSs and we find a new UR that results from a strong correlation between the $f$ mode frequencies and the radii of NSs. Such a correlation is very useful in accurately determining the radius from a measurement of $f$ mode frequencies in the near future. We also show that the quadrupolar $f$ mode frequencies of NS of masses 2.0 M$_\odot$ and above lie in the range $\sim$ 2-3 kHz in this ensemble of physically realistic EOSs. A NS of mass 2M$_{\odot}$ with a low $f$ mode frequency may indicate the existence of non-nucleonic degrees of freedom.

nucl-th↗

arXiv2023

Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks

Societal biases are reflected in large pre-trained language models and their fine-tuned versions on downstream tasks. Common in-processing bias mitigation approaches, such as adversarial training and mutual information removal, introduce additional optimization criteria, and update the model to reach a new debiased state. However, in practice, end-users and practitioners might prefer to switch back to the original model, or apply debiasing only on a specific subset of protected attributes. To enable this, we propose a novel modular bias mitigation approach, consisting of stand-alone highly sparse debiasing subnetworks, where each debiasing module can be integrated into the core model on-demand at inference time. Our approach draws from the concept of \emph{diff} pruning, and proposes a novel training regime adaptable to various representation disentanglement optimizations. We conduct experiments on three classification tasks with gender, race, and age as protected attributes. The results show that our modular approach, while maintaining task performance, improves (or at least remains on-par with) the effectiveness of bias mitigation in comparison with baseline finetuning. Particularly on a two-attribute dataset, our approach with separately learned debiasing subnetworks shows effective utilization of either or both the subnetworks for selective bias mitigation.

cs.LG↗