Clockworked VEVs and Neutrino Mass

In this paper we present an augmented version of the Abelian scalar clockwork model to generate geometrically suppressed vacuum expectation values (vev) of the pseudo Nambu-Goldstone bosons, that we call the clockworked vevs. We briefly comment on generalization of the setup and possible 5D UV realizations. We demonstrate how tiny neutrino mass can be generated by clockworking a weak scale vev.Comment: 13 pages, 2 captioned figures, 1 table, further clarifications added in text, references updated, matches version published in JHE

Awn Reduction and the Domestication of Asian Rice: A Syndrome or Crop Improvement Trait?

International audienc

Utilization of Agro industrial Food Processing Wastes and Pollutants for Manufacture of Products of Industrial Value A review

Rapid industrialization as a consequence of the population explosion has led to the expansion of the agricultureand food processing sector to feed every mouth and to meet rapidly growing market demand. Extensive harvesting and processing of crops and raw agricultural harvests, and production of secondary and tertiary wastes from industrial manufacturing operations associated with agricultural and food products have impacted the environment in adverse ways, which is causing irreparable damages. To minimize the carbon load on earth, several sustainable technologies have been developed, which can save the environment as well as generate some useful and industrially important products. This review work focuses on the current scenario of these wastes, and their harmful effects on nature in general, and on the environment in particular. It also suggests that sustainable techniques can minimize these harmful impacts, and can instead manufacture some valuable products like antibiotics, enzymes, organic acid, organic chemicals, biomass, pigment, flavors, solid fuel, and bioalcohol. Thus, this is a comprehensive and extensive account of the utilization of agricultural and food processing wastes to derive valuable, useful products

Probing composite Higgs boson substructure at the HL-LHC

The Higgs boson may well be a composite scalar with a finite extension in space. Owing to the momentum dependence of its couplings, the imprints of such a composite pseudo Goldstone Higgs may show up in the tails of various kinematic distributions at the LHC, distinguishing it from an elementary state. From the bottom up, we construct the momentum-dependent form factors to capture the interactions of the composite Higgs with the weak gauge bosons. We demonstrate their impact in the differential distributions of various kinematic parameters for the pp -> Z*H -> l+l-bb over bar channel. We show that this channel can provide an important handle to probe the Higgs\u27 substructure at the HL-LHC

Generating Dialogue Responses from a Semantic Latent Space

Existing open-domain dialogue generation models are usually trained to mimic the gold response in the training set using cross-entropy loss on the vocabulary. However, a good response does not need to resemble the gold response, since there are multiple possible responses to a given prompt. In this work, we hypothesize that the current models are unable to integrate information from multiple semantically similar valid responses of a prompt, resulting in the generation of generic and uninformative responses. To address this issue, we propose an alternative to the end-to-end classification on vocabulary. We learn the pair relationship between the prompts and responses as a regression task on a latent space instead. In our novel dialog generation model, the representations of semantically related sentences are close to each other on the latent space. Human evaluation showed that learning the task on a continuous space can generate responses that are both relevant and informative.Comment: EMNLP 202

Code-Switched Text Synthesis in Unseen Language Pairs

Existing efforts on text synthesis for code-switching mostly require training on code-switched texts in the target language pairs, limiting the deployment of the models to cases lacking code-switched data. In this work, we study the problem of synthesizing code-switched texts for language pairs absent from the training data. We introduce GLOSS, a model built on top of a pre-trained multilingual machine translation model (PMMTM) with an additional code-switching module. This module, either an adapter or extra prefixes, learns code-switching patterns from code-switched data during training, while the primary component of GLOSS, i.e., the PMMTM, is frozen. The design of only adjusting the code-switching module prevents our model from overfitting to the constrained training data for code-switching. Hence, GLOSS exhibits the ability to generalize and synthesize code-switched texts across a broader spectrum of language pairs. Additionally, we develop a self-training algorithm on target language pairs further to enhance the reliability of GLOSS. Automatic evaluations on four language pairs show that GLOSS achieves at least 55% relative BLEU and METEOR scores improvements compared to strong baselines. Human evaluations on two language pairs further validate the success of GLOSS.Comment: Paper accepted by ACL2023 as a Finding pape