Abstract

Protein-ligand complex structures have been utilised to design benchmark machine learning methods that perform important tasks related to drug design such as receptor binding site detection, small molecule docking and binding affinity prediction. However, these methods are usually trained on only ligand bound (or holo) conformations of the protein and therefore are not guaranteed to perform well when the protein structure is in its native unbound conformation (or apo), which is usually the conformation available for a newly identified receptor. A primary reason for this is that the local structure of the binding site usually changes upon ligand binding. To facilitate solutions for this problem, we propose a dataset called APObind that aims to provide apo conformations of proteins present in the PDBbind dataset, a popular dataset used in drug design. Furthermore, we explore the performance of methods specific to three use cases on this dataset, through which, the importance of validating them on the APObind dataset is demonstrated.

Abstract

Images in the medical domain are fundamentally different from the general domain images. Consequently, it is infeasible to directly employ general domain Visual Question Answering (VQA) models for the medical domain. Additionally, medical image annotation is a costly and time-consuming process. To overcome these limitations, we propose a solution inspired by self-supervised pretraining of Transformer-style architectures for NLP, Vision, and Language tasks. Our method involves learning richer medical image and text semantic representations using Masked Vision-Language Modeling as the pretext task on a large medical image+caption dataset. The proposed solution achieves new state-of-the-art performance on two VQA datasets for radiology images – VQA-Med 2019 and VQA-RAD, outperforming even the ensemble models of previous best solutions. Moreover, our solution provides attention maps which help in model interpretability.

Abstract

We present CCO-VOXEL: the very first chance-constrained optimization (CCO) algorithm that can compute trajectory plans with probabilistic safety guarantees in real-time directly on the voxel-grid representation of the world. CCO-VOXEL maps the distribution over the distance to the closest obstacle to a distribution over collision-constraint violation and computes an optimal trajectory that minimizes the violation probability. Importantly, unlike existing works, we never assume the nature of the sensor uncertainty or the probability distribution of the resulting collision-constraint violations. We leverage the notion of Hilbert Space embedding of distributions and Maximum Mean Discrepancy (MMD) to compute a tractable surrogate for the original chance-constrained optimization problem and employ a combination of A* based graph-search and Cross-Entropy Method for obtaining its minimum. We show tangible performance gain in terms of collision avoidance and trajectory smoothness as a consequence of our probabilistic formulation vis a vis state-of-the-art planning methods that do not account for such nonparametric noise. Finally, we also show how a combination of low-dimensional feature embedding and pre-caching of Kernel Matrices of MMD allows us to achieve real-time performance in simulations as well as in implementations on on-board commodity hardware that controls the quadrotor flight

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

Synaptic integration is a prominent aspect of neuronal information processing. The detailed mechanisms that modulate synaptic inputs determine the computational properties of any given neuron. We study a simple model for the summation of excitatory inputs from synapses and illustrate its use by characterizing some functional properties of postsynaptic neurons. In this regard, we study the response of postsynaptic neurons as defined by the model to two well known noise driven processes: stochastic and coherence resonance. The model requires a small number of parameters and is especially useful to isolate the role of integration mechanisms that rely on summation of inputs with little dendritic processing.

Abstract

Surface codes are quantum error correcting codes normally defined on 2D arrays of qubits. In this paper, we introduce a surface code design based on the fact that the severity of bit flip and phase flip errors in the physical quantum systems is asymmetric. For our proposed surface code design for asymmetric error channels, we present pseudo-threshold and threshold values in the presence of various degrees of asymmetry of Pauli Xˆ, Yˆ , and Zˆ errors in a depolarization channel. We show that, compared to symmetric surface codes, our asymmetric surface codes can provide almost double the pseudo-threshold rates while requiring less than half the number of physical qubits in the presence of increasing asymmetry in the error channel. We also demonstrate that as the asymmetry of the surface code increases, the advantage in the pseudo-threshold rates begins to saturate for any degree of asymmetry in the channel. Keywords: Surface Codes, Quantum Error Correction, Quantum Computing

Abstract

In this paper, we develop a Kinetic Monte Carlo (KMC) based model to simulate the atomistic growth behavior of metallic nanoparticle in the solution of its ions and understand the growth pattern. KMC is used as it can model the growth of nanoparticle to the timescale comparable with that actual experiments. Energy minimas where metal atom can adsorb or surface diffuse have been found using Shrake–Rupley algorithm and conjugate gradient energy minimization algorithm. The rate of adsorption, desorption, and surface diffusion was artificially accelerated, and decelerated to observe different shapes. We demonstrate the model by a case study on growth of gold nanoparticles and find that shapes like the truncated octahedron, cuboctahedron, truncated cube, cube, rhombic dodecahedron, and sphere are seen to form by the model during the growth of NP. This KMC model provides a simplistic understanding of the mechanism and progression of shapes that may be seen during the growth of nanoparticle; these in turn may provide clues to synthesize NP of specific shapes.

Abstract

In recent times, quadrotors have become immensely applicable in scenarios such as relief operations, infrastructure maintenance, search-and-rescue missions etc. A key control design challenge arises in these applications when the quadrotor has to manoeuvre through constrained spaces such as narrow windows, pipelines in the presence of external disturbances and parametric uncertainties: such conditions necessitate the controller to guarantee predefined tracking accuracy so as to not violate the constraints and simultaneously tackle uncertainties. However, state-of-the-art controllers dealing with constrained system motion are not applicable either for an underactuated system like quadrotor or for an uncertain system dynamics. This work proposes a robust controller that enables the quadrotor to follow a trajectory with predefined tracking accuracy in constrained space as well as to tackle uncertainties stemming from imprecise system modelling and external disturbances. The closed-loop system stability is analysed via the Barrier Lyapunov approach and the effectiveness of the proposed controller is validated via simulation with state of the art.

Abstract

Recently leptoquarks (LQs) are receiving a lot of attention in the literature. Since they connect the quark and the lepton sectors of the Standard Model (SM), their phenomenology is interesting in general. However, the recent attention is mainly due to their ability to explain the long-standing anomalies observed in B-meson decays and the muon anomalous magnetic moment measurements (see, e.g., [1–34] for a few recent studies). Generally, the models explaining the anomalies contain LQs that couple with the third-generation quarks and leptons. This gives additional motivation to search for such LQs (commonly known as the third-generation LQs in the literature) at the Large Hadron Collider (LHC). Various strategies have already been applied in the searches for third-generation LQs at the LHC [

Abstract

The use of cell phones inside an aircraft is prohibited because cell phones pose a safety risk to aircraft systems and operations. Personal Electronic Device (PED) policies exist to avoid radio interference caused by the operation of cell phones in flight. The PED policies require passengers and crew members to stow away or use Airplane Mode when traveling by air. This paper focuses on studying the existing PED policies for the usage of cell phones inside an aircraft. We also study reports presenting technical data on why such a policy is required. We have also included individual experiences reported by passengers while travelling on how people react and behave when asked to turn on Airplane Mode or switch off their electronic devices. After establishing the problem, we discuss solutions that might switch off cell phones in flight or enable Airplane Mode automatically without any human intervention. We deliberate on three potential solutions (a) Mobile phone jammer, (b) Cellular Pico-cell onboard, (c) an Automatic Airplane Mode system. We propose creating an Automatic Airplane Mode System. We discuss various aspects of the proposed system, technical development choices and social features considerations. We discuss system development details and architecture along with the proposed uses of the system proposed. In the end, we conclude our work and list out some future scope activities.