Maximum-Likelihood Sequence Detector for Dynamic Mode High Density Probe Storage

There is an increasing need for high density data storage devices driven by the increased demand of consumer electronics. In this work, we consider a data storage system that operates by encoding information as topographic profiles on a polymer medium. A cantilever probe with a sharp tip (few nm radius) is used to create and sense the presence of topographic profiles, resulting in a density of few Tb per in.2. The prevalent mode of using the cantilever probe is the static mode that is harsh on the probe and the media. In this article, the high quality factor dynamic mode operation, that is less harsh on the media and the probe, is analyzed. The read operation is modeled as a communication channel which incorporates system memory due to inter-symbol interference and the cantilever state. We demonstrate an appropriate level of abstraction of this complex nanoscale system that obviates the need for an involved physical model. Next, a solution to the maximum likelihood sequence detection problem based on the Viterbi algorithm is devised. Experimental and simulation results demonstrate that the performance of this detector is several orders of magnitude better than the performance of other existing schemes.Comment: This paper is published in IEEE Trans. on communicatio

Virtual Build to order environment: - Predicting inventory behaviour in an open pipeline

In the following thesis an attempt has been made to predict inventory behaviour in a Virtual Build to Order Environment (VBTO). Simulations runs have been undertaken based on the model developed under the FULSIM project to analyze specific stock related metrics originating in a production system. It focuses on a comparative evaluation of the VBTO and the conventional system by changing three-core simulation run parameters i.e. Product Variants Number, Batch Size and Feedback Process. Data obtained from the run has been analyzed using the spreadsheet application Microsoft Excel. Further study focuses on co-relating the traits observed with basic inventory theory like the Newsboy Model, identify pitfalls, and suggest areas for further research

3D Searching

As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people. Unfortunately, traditional text-based search techniques are not always effective for 3D data. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. We also present a web-based search engine system that supports multimodel queries which include both text query and sketch query. This results in faster retrieval of the result and the percentage efficiency also increases. The net result is a growing interactive index of 3D models available on the Web (i.e., a Google for 3D models)

Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning models

Image captioning models have been able to generate grammatically correct and human understandable sentences. However most of the captions convey limited information as the model used is trained on datasets that do not caption all possible objects existing in everyday life. Due to this lack of prior information most of the captions are biased to only a few objects present in the scene, hence limiting their usage in daily life. In this paper, we attempt to show the biased nature of the currently existing image captioning models and present a new image captioning dataset, Egoshots, consisting of 978 real life images with no captions. We further exploit the state of the art pre-trained image captioning and object recognition networks to annotate our images and show the limitations of existing works. Furthermore, in order to evaluate the quality of the generated captions, we propose a new image captioning metric, object based Semantic Fidelity (SF). Existing image captioning metrics can evaluate a caption only in the presence of their corresponding annotations; however, SF allows evaluating captions generated for images without annotations, making it highly useful for real life generated captions.Comment: 15 pages, 25 figures, Accepted at Machine Learning in Real Life (ML-IRL) ICLR 2020 Worksho

Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning models

Virtual Conference, Formerly Addis AbabaInternational audienceImage captioning models have been able to generate grammatically correct and human understandable sentences. However most of the captions convey limited information as the model used is trained on datasets that do not caption all possible objects existing in everyday life. Due to this lack of prior information most of the captions are biased to only a few objects present in the scene, hence limiting their usage in daily life. In this paper, we attempt to show the biased nature of the currently existing image captioning models and present a new image captioning dataset, Egoshots, consisting of 978 real life images with no captions. We further exploit the state of the art pre-trained image captioning and object recognition networks to annotate our images and show the limitations of existing works. Furthermore , in order to evaluate the quality of the generated captions, we propose a new image captioning metric, object based Semantic Fidelity (SF). Existing image cap-tioning metrics can evaluate a caption only in the presence of their corresponding annotations; however, SF allows evaluating captions generated for images without annotations, making it highly useful for real life generated captions

Blockchain and the World of Data

This is a video recording of a Workshop presented during the AIS 2022 Student Chapter Leadership Conference (SCLC) Blockchain and the World of Dat

Queer In AI: A Case Study in Community-Led Participatory AI

Queerness and queer people face an uncertain future in the face of ever more widely deployed and invasive artificial intelligence (AI). These technologies have caused numerous harms to queer people, including privacy violations, censoring and downranking queer content, exposing queer people and spaces to harassment by making them hypervisible, deadnaming and outing queer people. More broadly, they have violated core tenets of queerness by classifying and controlling queer identities. In response to this, the queer community in AI has organized Queer in AI, a global, decentralized, volunteer-run grassroots organization that employs intersectional and community-led participatory design to build an inclusive and equitable AI future. In this paper, we present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community’s programs over the years. We discuss different challenges that emerged in the process, look at ways this organization has fallen short of operationalizing participatory and intersectional principles, and then assess the organization’s impact. Queer in AI provides important lessons and insights for practitioners and theorists of participatory methods broadly through its rejection of hierarchy in favor of decentralization, success at building aid and programs by and for the queer community, and effort to change actors and institutions outside of the queer community. Finally, we theorize how communities like Queer in AI contribute to the participatory design in AI more broadly by fostering cultures of participation in AI, welcoming and empowering marginalized participants, critiquing poor or exploitative participatory practices, and bringing participation to institutions outside of individual research projects. Queer in AI’s work serves as a case study of grassroots activism and participatory methods within AI, demonstrating the potential of community-led participatory methods and intersectional praxis, while also providing challenges, case studies, and nuanced insights to researchers developing and using participatory methods