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Vinay Damodaran

Publications and source records attributed to Vinay Damodaran.

5 recordsLinked to original sources

LLM-Based Insight Extraction for Contact Center Analytics and Cost-Efficient Deployment

Large Language Models have transformed the Contact Center industry, manifesting in enhanced self-service tools, streamlined administrative processes, and augmented agent productivity. This paper delineates our system that automates call driver generation, which serves as the foundation for tasks such as topic modeling, incoming call classification, trend detection, and FAQ generation, delivering actionable insights for contact center agents and administrators to consume. We present a cost-efficient LLM system design, with 1) a comprehensive evaluation of proprietary, open-weight, and fine-tuned models and 2) cost-efficient strategies, and 3) the corresponding cost analysis when deployed in production environments.

cs.CL

Mitigating Gender Bias in Contextual Word Embeddings

Word embeddings have been shown to produce remarkable results in tackling a vast majority of NLP related tasks. Unfortunately, word embeddings also capture the stereotypical biases that are prevalent in society, affecting the predictive performance of the embeddings when used in downstream tasks. While various techniques have been proposed \cite{bolukbasi2016man, zhao2018learning} and criticized\cite{gonen2019lipstick} for static embeddings, very little work has focused on mitigating bias in contextual embeddings. In this paper, we propose a novel objective function for MLM(Masked-Language Modeling) which largely mitigates the gender bias in contextual embeddings and also preserves the performance for downstream tasks. Since previous works on measuring bias in contextual embeddings lack in normative reasoning, we also propose novel evaluation metrics that are straight-forward and aligned with our motivations in debiasing. We also propose new methods for debiasing static embeddings and provide empirical proof via extensive analysis and experiments, as to why the main source of bias in static embeddings stems from the presence of stereotypical names rather than gendered words themselves. All experiments and embeddings studied are in English, unless otherwise specified.\citep{bender2011achieving}.

cs.CL

Multilayered Recoverable Sandwich Composite Structures with Architected Core

In this paper, we propose a novel design and fabrication strategy to produce architected core structures for use as the core in composite sandwich structures. A traditional foam core or honeycomb structure is lightweight and stiff, but susceptible to permanent deformation when subjected to excessive loading. Here we propose the use of an architected structure composed of arrays of hollow truncated cone unit cells that dissipate energy and exhibit structural recovery. These structures printed with a viscoelastic material rely on buckling of their sidewalls to dissipate energy and snap-back to prevent permanent deformation. We explore the mechanical response of these conical unit cells in terms of their buckling strength and post-buckling stability condition, and develop design maps for the same, by relating them to non-dimensional geometric parameters $α$, $β$, $γ$, where $α$ represents the slenderness of the curved sidewalls, $β$ is the angle of the sidewall to the base, and $γ$ represents the curvature of the sidewall. A validated finite element model is developed and used to investigate the effect of these parameters. We see that the peak buckling load is directly proportional to both $α$ & $β$ and is not dependent on $γ$ when the load is normalized by the volume of material in the curved sidewall. Interestingly, the post-buckling stability is influenced by $γ$, or the initial curvature of the sidewall, where a larger radius of curvature makes the structure less susceptible to exhibit structural bistability. The structures presented here are printed using a viscoelastic material, that causes them to exhibit pseudo-bistability, or a time-delayed recovery. This allows the structures to buckle and dissipate energy, and then recover to their original configurations without the need for external stimuli or energy.

physics.app-ph

Understanding the Role of Scene Graphs in Visual Question Answering

Visual Question Answering (VQA) is of tremendous interest to the research community with important applications such as aiding visually impaired users and image-based search. In this work, we explore the use of scene graphs for solving the VQA task. We conduct experiments on the GQA dataset which presents a challenging set of questions requiring counting, compositionality and advanced reasoning capability, and provides scene graphs for a large number of images. We adopt image + question architectures for use with scene graphs, evaluate various scene graph generation techniques for unseen images, propose a training curriculum to leverage human-annotated and auto-generated scene graphs, and build late fusion architectures to learn from multiple image representations. We present a multi-faceted study into the use of scene graphs for VQA, making this work the first of its kind.

cs.CV

Flexural Fatigue Life of Woven Carbon/Vinyl Ester Composites under Sea Water Saturation

The adverse effects of sea water environment on the fatigue life of woven carbon fiber/vinyl ester composites are established at room temperature in view of long-term survivability of offshore structures. It is observed that the influence of sea water saturation on the fatigue life is more pronounced when the maximum cyclic displacement approaches maximum quasi-static deflection, that is, the reduction in the number of cycles to failure are comparable between dry and sea water saturated samples at lower strain ranges (~37% at 0.46% strain), but are drastically different at higher strain ranges (~90% at 0.62% strain). Key damage modes that manifest during the fatigue loading is also identified, and a non-linear model is established for predicting low cycle fatigue life of these composites in dry and sea water saturated conditions.

physics.app-ph