Abstract

Table-top Rearrangement and Planning is a challenging problem that relies heavily on an excellent perception stack. The perception stack involves observing and registering the 3D scene on the table, detecting what objects are on the table, and how to manipulate them. Consequently, it greatly influences the system’s task-planning and motion-planning stacks that follow. We present a comprehensive overview and discuss different challenges associated with the perception module. This work is a result of our extensive involvement in the ICRA 2022 Open Cloud Robot Table Organization Challenge, in which we currently stand first on the leaderboard (as of 24th of April 2022, the final week of the challenge).

Abstract

We embark on a hitherto unreported problem of an autonomous robot (self-driving car) navigating in dynamic scenes in a manner that reduces its localization error and eventual cumulative drift or Absolute Trajectory Error, which is pronounced in such dynamic scenes. With the hugely popular Velodyne-16 3D LIDAR as the main sensing modality, and the accurate LIDAR-based Localization and Mapping algorithm, LOAM, as the state estimation framework, we show that in the absence of a navigation policy, drift rapidly accumulates in the presence of moving objects. To overcome this, we learn actions that lead to drift-minimized navigation through a suitable set of reward and penalty functions. We use Proximal Policy Optimization, a class of Deep Reinforcement Learning methods, to learn the actions that result in drift-minimized trajectories. We show by extensive comparisons on a variety of synthetic, yet photo-realistic scenes made available through the CARLA Simulator the superior performance of the proposed framework vis-a-vis methods that do not adopt such policies.

Abstract

In addition to the three long-standing anomalies seen in the muon g − 2, RK(∗), and RD(∗) observables by various independent experiments, the CDF collaboration has found another significant one in the W boson mass. These anomalies might be intertwined at a fundamental level and have a single new physics explanation. In this paper, we investigate the possibility of whether these anomalies can be explained in a model of a weak-singlet S1 and a weak-triplet S3 scalar leptoquarks. We find that with an appropriate flavour ansatz and mass mixing between the S1 and S3 leptoquarks through a Higgs portal, the model can explain the four anomalies simultaneously while being consistent with potential spoilsport observables like R νν K(∗) .

Abstract

The U(1) extensions of the Standard Model contain a heavy neutral gauge boson Z 0 . If leptophobic, the boson can evade the stringent bounds from the dilepton resonance searches. In these extensions, righthanded neutrinos NR are also well-motivated by the gauge-anomaly cancellation and light-neutrino mass generation via seesaw mechanisms. The coexistence of a leptophobic Z 0 and the right-handed neutrinos opens up a new possibility of searching for these particles simultaneously through the production of Z 0 at the LHC and its decay to a NR pair. We study this process in an inverse seesaw set-up where the heavy neutrinos are pseudo-Dirac in nature. Hence, in our case, the NR pair produce only opposite-sign lepton pairs, as opposed to the Majorana-type neutrinos, which can produce both the same and the oppositesign lepton pairs. In particular, the signature we study involves a same-flavour-opposite-sign lepton pair plus hadronically-decaying boosted W bosons. Our analysis shows that the High Luminosity LHC can discover a TeV-scale leptophobic Z 0 decaying via NR pair for a wide range of available parameters in our model. Considerable parameter regions beyond the reach of the future dijet resonance searches can be probed through our channel.

Abstract

TeV-scale vector-like quarks (VLQs) are an essential ingredient of many new physics models. Because of their vectorlike nature, they do not contribute to the gauge anomalies and are less restricted than their chiral counterparts by the current experimental data. Ones that couple with the thirdgeneration quarks (top and bottom partners, we shall collectively refer to them as top partners) appear in composite Higgs models with a partially-composite top quark [1– 6], extra-dimensional models [7–11], Little-Higgs models [12–15], etc. However, in the last few years, an extensive direct search program at the LHC has gradually tightened the mass bounds on these quarks. For top partners like the T quark (with electromagnetic charge 2/3) or the B quark (with charge −1/3), the current exclusion limit stands as high as ∼ 1.5 TeV [16–20]. The direct VLQ searches generally assume that they decay exclusively to Standard Model (SM) particles, i.e., to a third-generation quark and a heavy vector-boson or a Higgs. However, this assumption need not hold in general, especially if one looks beyond the minimal models where the top partners can have new decay modes. For example, a top partner can decay to another heavy quark or a new boson [11, 21–43]. A possibility that has attracted some interest in the current literature is that a vector-like top partner decays to a new spinless state singlet under the SM gauge group [i.e., (1, 1, 0) under SU(3)c×SU(2)L ×U(1)Y ] and a third-generation quark. The singlet state could be

Abstract

Ankyrin is one of the most abundant protein repeat families found across all forms of life. It is found in a variety of multi-domain and single domain proteins in humans with diverse number of repeating units. They are observed to occur in several functionally diverse proteins, such as transcriptional initiators, cell cycle regulators, cytoskeletal organizers, ion transporters, signal transducers, developmental regulators, and toxins, and, consequently, defects in ankyrin repeat proteins have been associated with a number of human diseases. In this study, we have classified the human ankyrin proteins into clusters based on the sequence similarity in their ankyrin repeat domains. We analyzed the amino acid compositional bias and consensus ankyrin motif sequence of the clusters to understand the diversity of the human ankyrin proteins. We carried out network-based structural analysis of human ankyrin proteins across different clusters and showed the association of conserved residues with topologically important residues identified by network centrality measures. The analysis of conserved and structurally important residues helps in understanding their role in structural stability and function of these proteins. In this paper, we also discuss the significance of these conserved residues in disease association across the human ankyrin protein clusters.

Abstract

Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion.

Abstract

Existing Math Word Problem (MWP) solvers have achieved high accuracy on benchmark datasets. However, prior works have shown that such solvers do not generalize well and rely on superficial cues to achieve high performance. In this paper, we first conduct experiments to showcase that this behaviour is mainly associated with the limited size and diversity present in existing MWP datasets. Next, we propose several data augmentation techniques broadly categorized into Substitution and Paraphrasing based methods. By deploying these methods we increase the size of existing datasets by five folds. Extensive experiments on two benchmark datasets across three state-of-the-art MWP solvers show that proposed methods increase the generalization and robustness of existing solvers. On average, proposed methods significantly increase the state-of-the-art results by over five percentage points on benchmark datasets. Further, the solvers trained on the augmented dataset perform comparatively better on the challenge test set. We also show the effectiveness of proposed techniques through ablation studies and verify the quality of augmented samples through human evaluation.

Abstract

We investigate the task of complex NER for the English language. The task is non-trivial due to the semantic ambiguity of the textual structure and the rarity of occurrence of such entities in the prevalent literature. Using pretrained language models such as BERT, we obtain a competitive performance on this task. We qualitatively analyze the performance of multiple architectures for this task. All our models are able to outperform the baseline by a significant margin. Our best performing model beats the baseline F1-score by over 9%.

Abstract

The Securities and Exchange Board of India is the regulatory body for securities and commodity market in India. A growing number of SEBI documents ranging from government regulations to legal case files are now available in the digital form. Advances in natural language processing and machine learning provide opportunities for extracting semantic insights from these documents. We present here a system that performs semantic processing of SEBI documents using state-of-the-art language models to produce enriched regulations containing timelines of amendments and cross references to legal case files.