A Cluster-Matching-Based Method for Video Face Recognition

Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluster the faces present in both the dataset and targeted videos selected for face recognition. Moreover, we design a cluster matching heuristic to associate clusters in both sets that is also capable of identifying when a face belongs to a non-registered person. Our method has achieved a recall of 99.435% and a precision of 99.131% in the task of video face recognition. Besides performing face recognition, it can also be used to determine the video segments where each person is present.Comment: 13 page

Saturn Platform: Foundation Model Operations and Generative AI for Financial Services

Saturn is an innovative platform that assists Foundation Model (FM) building and its integration with IT operations (Ops). It is custom-made to meet the requirements of data scientists, enabling them to effectively create and implement FMs while enhancing collaboration within their technical domain. By offering a wide range of tools and features, Saturn streamlines and automates different stages of FM development, making it an invaluable asset for data science teams. This white paper introduces prospective applications of generative AI models derived from FMs in the financial sector

Hierarchical Classification of Financial Transactions Through Context-Fusion of Transformer-based Embeddings and Taxonomy-aware Attention Layer

This work proposes the Two-headed DragoNet, a Transformer-based model for hierarchical multi-label classification of financial transactions. Our model is based on a stack of Transformers encoder layers that generate contextual embeddings from two short textual descriptors (merchant name and business activity), followed by a Context Fusion layer and two output heads that classify transactions according to a hierarchical two-level taxonomy (macro and micro categories). Finally, our proposed Taxonomy-aware Attention Layer corrects predictions that break categorical hierarchy rules defined in the given taxonomy. Our proposal outperforms classical machine learning methods in experiments of macro-category classification by achieving an F1-score of 93\% on a card dataset and 95% on a current account dataset

CMS physics technical design report : Addendum on high density QCD with heavy ions

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Search for the production of dark matter in association with top-quark pairs in the single-lepton final state in proton-proton collisions at √s=8 TeV

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Search for vector-like T quarks decaying to top quarks and Higgs bosons in the all-hadronic channel using jet substructure

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Angular analysis of the decay B-0 -> K*(0)mu(+)mu(-) from pp collisions at root s=8 TeV

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Search for standard model production of four top quarks in the lepton + jets channel in pp collisions at √s = 8 TeV

Open Access, Copyright CERN, for the benefit of the CMS Collaboration. Article funded by SCOAP3.Abstract: A search is presented for standard model (SM) production of four top quarks (Formula presented.) in pp collisions in the lepton + jets channel. The data correspond to an integrated luminosity of 19.6 fb−1 recorded at a centre-of-mass energy of 8 TeV with the CMS detector at the CERN LHC. The expected cross section for SM (Formula presented.) production is (Formula presented.). A combination of kinematic reconstruction and multivariate techniques is used to distinguish between the small signal and large background. The data are consistent with expectations of the SM, and an upper limit of 32 fb is set at a 95% confidence level on the cross section for producing four top quarks in the SM, where a limit of 32 ± 17 fb is expected

Search for a charged Higgs boson in pp collisions at root s=8 TeV

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