SearcharxivSearch

arXiv subjects

Amitabha Dey

Publications and source records attributed to Amitabha Dey.

2 recordsLinked to original sources

Constraining the heavy leptophilic neutral gauge bosons through the $Z\to\ell^+\ell^-$, $W^\pm\to\ell^\pm\nu_\ell$, and $h\to\ell^+\ell^-$ decays

We consider the hypothetical possibility of neutral gauge bosons ($Z^\prime$) with flavor-specific leptophilic couplings. For such New Physics (NP) interactions, the current experimental constraints are much relaxed in the heavy mass regime, particularly for masses $\geq \mathcal{O}(1)$ TeV. However, in the presence of a leptophilic $Z^\prime$, leptonic decay modes of the electroweak gauge bosons and Higgs can be corrected at the loop level. Using the existing upper bounds on the corresponding decay widths, we find that one can impose stronger exclusion limits on the interactions of a heavy $Z^\prime$. Future updates on the aforesaid decay channels can be used in complementarity with the proposed lepton colliders to probe even weaker leptophilic NP interactions at the TeV scale and beyond.

hep-ph

LDEB -- Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues

Emotion recognition in conversations (ERC) is vital to the advancements of conversational AI and its applications. Therefore, the development of an automated ERC model using the concepts of machine learning (ML) would be beneficial. However, the conversational dialogues present a unique problem where each dialogue depicts nested emotions that entangle the association between the emotional feature descriptors and emotion type (or label). This entanglement that can be multiplied with the presence of data paucity is an obstacle for a ML model. To overcome this problem, we proposed a novel approach called Label Digitization with Emotion Binarization (LDEB) that disentangles the twists by utilizing the text normalization and 7-bit digital encoding techniques and constructs a meaningful feature space for a ML model to be trained. We also utilized the publicly available dataset called the FETA-DailyDialog dataset for feature learning and developed a hierarchical ERC model using random forest (RF) and artificial neural network (ANN) classifiers. Simulations showed that the ANN-based ERC model was able to predict emotion with the best accuracy and precision scores of about 74% and 76%, respectively. Simulations also showed that the ANN-model could reach a training accuracy score of about 98% with 60 epochs. On the other hand, the RF-based ERC model was able to predict emotions with the best accuracy and precision scores of about 78% and 75%, respectively.

cs.CL