Abstract

This paper presents a fast and efficient optimization engine with multi-directional, multi-objective algorithms based on a robust transistor sizing approach to improve digital circuit performance. However, such optimization processes are highly simulator-dependent and computationally expensive tasks. Therefore, we propose developing machine learning-based reliable models considering process and operating variations to speed up the optimization procedure by running them on developed Residual Neural Network (ResNN) models instead of running expensive circuit simulations. Results on 22nm Metal Gate HighK digital cells show a reduction in delay and leakage up to 36.7% and 18.8%, respectively improving computational efficiency by several orders.

Abstract

Information Security in the cyber world is a major cause for concern, with significant increase in the number of attack surfaces. Existing information on vulnerabilities, attacks, controls, and advisories available on web provides an opportunity to represent knowledge and perform security analytics to mitigate some of the concerns. Representing security knowledge in the form of ontology facilitates anomaly detection, threat intelligence, reasoning and relevance attribution of attacks, and many more. This necessitates dynamic and automated enrichment of information security ontologies. However, existing ontology enrichment algorithms based on natural language processing and ML models have issues with contextual extraction of concepts in words, phrases and sentences. This motivates the need for sequential Deep Learning architectures that traverse through dependency paths in text and extract embedded vulnerabilities, threats, controls, products and other security related concepts and instances from learned path representations. In the proposed approach, Bidirectional LSTMs trained on a large DBpedia dataset and Wikipedia corpus of 2.8 GB along with Universal Sentence Encoder is deployed to enrich ISO 27001 [1] based information security ontology. The model is trained and tested on an high performance computing (HPC) environment to handle Wiki text dimensionality. The approach yielded a test accuracy of over 80% when tested with knocked out concepts from ontology and web page instances to validate the robustness. Index Terms—Ontology Enrichment, Information Security, Bidirectional LSTM, Universal Sentence Encoder

Abstract

Agricultural industry is one of the most vital industries that has a major contribution to the economy due to its share in the Gross Domestic Product (GDP) and as a source of employment. The past few decades have seen immense change in the operation of agricultural sector with the introduction of precision farming in conjunction with Internet of Things (IoT). The application of such advancements is highly based on exchange of messages between various devices in the farming. This paper aims to study the security scenarios applicable in husbandry through the analysis of possible attacks and threats. The testbeds available for agriculture based on IoT have been studied. An architecture for smart farming is proposed which is independent of the underlying technologies that may be used and the requirements of security have been laid out based on the proposed architecture. A literature survey of security protocols …

Abstract

Quadrotors have become extremely popular in various application scenarios which include disaster response, maintenance etc. A crucial challenge in terms of control design surfaces when a quadrotor has to operate under space constraints (e.g. pipeline inspection from inside, payload delivery at a precise location) under parametric perturbations and environmental disturbances (e.g. wind, gust). To deal with such scenarios, a suitable controller should simultaneously ensure tracking accuracy within predefined bounds to avoid constraint violation and tackle uncertainties. However, the state-of-the-art designs are either inapplicable for quadrotor system which is under-actuated in nature, or are unable to tackle system parametric uncertainties while being under full state (i.e. position, attitude angle, linear velocity and angular velocity) constraints. To address such issues in literature, a robust controller is introduced in …

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

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.