Abstract

We determine the strange quark mass ( � � ) and quark mixing element | � � � | , and their joint determination from the Cabibbo suppressed hadronic � decays in various perturbative schemes. We improve this analysis compared to the previous analysis based on the optimal renormalization or the renormalization group summed perturbation theory (RGSPT) scheme by replacing the theoretical longitudinal contributions with phenomenological parametrization; the RGSPT coefficients are used for the dimension-4 Adler functions. The improved analysis results in the extraction of � � ( 2     GeV ) = 98 ± 19   MeV and | � � � | = 0.2191 ± 0.0043 from the RGSPT scheme

Abstract

Ketones are the key functional group that recurs in chemistry and biology, and accessing them through simple and economic ways is highly desirable. Herein, we report the synthesis of unsymmetrical ketones from abundant toluene and alkyl esters, where volatile alcohols are the sole byproduct. This protocol applies to a repertoire of substrates bearing electron‐donating, electron‐withdrawing, and neutral substituents. Most importantly, the organometallic ferrocenyl ester underwent aroylation with ease. This method is the first example to furnish diketones from methyl arenes and diesters. Furthermore, cyclic imide was synthesized by this protocol utilizing KN(SiMe3)2 as a ′nitrogen′ source. Density functional theory studies provide insight into deprotonation of toluene by K+‐π interaction by increasing its acidity, and this being the rate‐determining step.

Abstract

We investigate the possible sizes of all the CP-violating asymmetries offered by the angular distribution of rare decay Λb → Λ(→ pπ−)` +` − in the Standard Model and new physics scenarios motivated by the recent b → s`+` − anomalies. We work in a model-independent effective theory framework and discuss the sensitivity of CP asymmetries to new O9,10 operators and their chirality flipped counterparts. We find that the size of many of the CP asymmetries can be at the level of few percent in new physics scenarios consistent with current b → s`+` − data at level of 1σ. We emphasize that measurements of these CP asymmetries can be used to discriminate different new physics scenarios in b → s`+` −.

Abstract

Recent development in deep learning-based recognizers needs a large annotated corpus for creating the model. Manually annotating a large corpus is time-consuming, costly, and tedious. In this work, we propose a framework for automatic annotation at the word level for given handwritten data and corresponding text sequences (or corpora). The proposed framework consists of five modules (i) pre-processing,(ii) word detection,(iii) word recognition,(iv) alignment, and (v) manual correction and verification. The preprocessing module cleans the image and crops the text region from an image. Word detection and recognition modules localize and recognize words. It is necessary to align words in the sequence with the word images during detection and recognition because of errors in writing. The alignment module aligns words in text sequence to the word images. The human annotator will correct the errors in the automatic annotation process and verify the document. Finally, we created an annotated dataset containing word images and their corresponding ground truth transcriptions. In this work, we demonstrate the proposed tool for annotating 14 sets corresponding to 13 Indic languages and English. Each set contains 15000 handwritten document images. On an extensive collection of handwritten document images in 14 languages, 80% of words are correctly annotated by the automatic annotation tool, while the remaining 20% are corrected manually.

Abstract

The presence of a new decay mode relaxes the current mass exclusion limits on vectorlike quarks considerably. We consider the case of a weak-singlet vectorlike B quark that can decay to a singlet scalar or pseudoscalar Φ. In an earlier paper~[A. Bhardwaj et al., Roadmap to explore vectorlike quarks decaying to a new scalar or pseudoscalar, Phys. Rev. D, 106 (2022) 075024 [arXiv:2203.13753]], we mapped the possibilities to explore such setups at the LHC. We showed that it is possible for a B quark to decay into Φ and the Φ to dominantly decay to a pair of gluons or b quark(s) without fine-tuning the parameters. In this paper, we present a collider search strategy to look for the pair production of singlet B quarks. If both B's decay into bΦ pairs, the final state is fully hadronic: BB→(bΦ)(bΦ)→(bgg)(bgg)/(bbb)(bbb), which is very challenging to probe. Therefore, we consider a relatively simpler mixed decay specific to the singlet B case, BB→(bΦ)(tW) with a lepton in the final state, to achieve the best sensitivity at the high-luminosity LHC. We use a Deep Neural Network with a weighted cross-entropy loss to separate the signal from the huge SM background. We see that large areas of the MB−MΦ parameter space are discoverable through this signature. We also show how the discovery and exclusion regions scale with the branching ratio in the new decay mode.

Abstract

We investigate a SO(10) grand unification scenario where the complex 10-dimensional scalar multiplet, containing the Standard Model (SM) Higgs boson, resides at the TeV scale altogether. The resulting low-energy model is a 2-Higgs-doublet model augmented with two S1-type leptoquarks. The gauge-coupling unification is achieved with only one intermediate scale at which the Pati-Salam gauge group is broken down to the SM. A U(1)PQ global symmetry is also introduced, with the corresponding axion providing a solution to the strong CP problem while also being an important dark matter candidate. The proton-destabilising couplings of the light leptoquarks are forbidden by a discrete symmetry. We discuss how the simultaneous explanation for the RK(∗) , RD(∗) , and aµ anomalies comes about in the model, and investigate its phenomenological implications. Keywords: B-decay anomalies, scalar leptoquark, SO(10) grand unification, LHC, 2HDM, axions, PecceiQuinn symmetry, strong CP problem, dark matte

Abstract

Localizing objects in 3D scenes based on natural language requires understanding and reasoning about spatial relations. In particular, it is often crucial to distinguish similar objects referred by the text, such as "the left most chair" and "a chair next to the window". In this work we propose a language-conditioned transformer model for grounding 3D objects and their spatial relations. To this end, we design a spatial self-attention layer that accounts for relative distances and orientations between objects in input 3D point clouds. Training such a layer with visual and language inputs enables to disambiguate spatial relations and to localize objects referred by the text. To facilitate the cross-modal learning of relations, we further propose a teacher-student approach where the teacher model is first trained using ground-truth object labels, and then helps to train a student model using point cloud inputs. We perform ablation studies showing advantages of our approach. We also demonstrate our model to significantly outperform the state of the art on the challenging Nr3D, Sr3D and ScanRefer 3D object grounding datasets.

Abstract

Chest radiograph (or Chest X-Ray, CXR) is a popular medical imaging modality that is used by radiologists across the world to diagnose heart or lung conditions. Over the last decade, Convolutional Neural Networks (CNN), have seen success in identifying pathologies in CXR images. Typically, these CNNs are pretrained on the standard ImageNet classification task, but this assumes availability of large-scale annotated datasets. In this work, we analyze the utility of pretraining on unlabeled ImageNet or Chest X-Ray (CXR) datasets using various algorithms and in multiple settings. Some findings of our work include: (i) supervised training with labeled ImageNet learns strong representations that are hard to beat; (ii) self-supervised pretraining on ImageNet (∼1M images) shows performance similar to self-supervised pretraining on a CXR dataset (∼100K images); and (iii) the CNN trained on supervised ImageNet can be trained further with self-supervised CXR images leading to improvements, especially when the downstream dataset is on the order of a few thousand images. Keywords: Chest X-Ray, SelfSupervised Pretraining

Abstract

In human environments, robots are expected to accomplish a variety of manipulation tasks given simple natural language instructions. Yet, robotic manipulation is extremely challenging as it requires fine-grained motor control, long-term memory as well as generalization to previously unseen tasks and environments. To address these challenges, we propose a unified transformer-based approach that takes into account multiple inputs. In particular, our transformer architecture integrates (i) natural language instructions and (ii) multi-view scene observations while (iii) keeping track of the full history of observations and actions. Such an approach enables learning dependencies between history and instructions and improves manipulation precision using multiple views. We evaluate our method on the challenging RLBench benchmark and on a real-world robot. Notably, our approach scales to 74 diverse RLBench tasks and outperforms the state of the art. We also address instruction-conditioned tasks and demonstrate excellent generalization to previously unseen variations.

Abstract

We aim to teach robots to perform simple object manipulation tasks by watching a single video demonstration. Towards this goal, we propose an optimization approach that outputs a coarse and temporally evolving 3D scene to mimic the action demonstrated in the input video. Similar to previous work, a differentiable renderer ensures perceptual fidelity between the 3D scene and the 2D video. Our key novelty lies in the inclusion of a differentiable approach to solve a set of Ordinary Differential Equations (ODEs) that allows us to approximately model laws of physics such as gravity, friction, and hand-object or object-object interactions. This not only enables us to dramatically improve the quality of estimated hand and object states, but also produces physically admissible trajectories that can be directly translated to a robot without the need for costly reinforcement learning. We evaluate our approach on a 3D reconstruction task that consists of 54 video demonstrations sourced from 9 actions such as pull something from right to left or put something in front of something. Our approach improves over previous state-of-the-art by almost 30%, demonstrating superior quality on especially challenging actions involving physical interactions of two objects such as put something onto something. Finally, we showcase the learned skills on a Franka Emika Panda robot.