Abstract

Afghanistan is situated close to the area where the Indian, Arabian, and Eurasian tectonic plates converge. The consequent seismic hazard causes the region to be repeatedly struck by major earthquakes. However, there is a lack of seismic instrumentation in the region, and only limited historical ground motions are available thus presenting a challenge for structural designers. Due to the absence of information and a database of ground motions, buildings in the region are often built without guidance from seismic codes. Consequently, the earthquakes in the region continue to cause enormous destruction to the built environment of the region. On 22 June 2022, the southeastern region of the country was struck by a major Mw 6.2 earthquake having a maximum modified Mercalli intensity of IX. In this study, synthetic

Abstract

Drug design involves the process of identifying and designing molecules that bind well to a given receptor. A vital computational component of this process is the protein–ligand interaction scoring functions that evaluate the binding ability of various molecules or ligands with a given protein receptor binding pocket reasonably accurately. With the publicly available protein–ligand binding affinity data sets in both sequential and structural forms, machine learning methods have gained traction as a top choice for developing such scoring functions. While the performance shown by these models is optimistic, there are several hidden biases present in these data sets themselves that affect the utility of such models for practical purposes such as virtual screening. In this work, we use published methods to systematically investigate several such factors or biases present in these data sets. In our analysis, we highlight the importance of considering sequence, protein–ligand interaction, and pocket structure similarity while constructing data splits and provide an explanation for good protein-only and ligand-only performances in some data sets. Through this study, we provide to the community several pointers for the design of binding affinity predictors and data sets for reliable applicability.

Abstract

Manipulating a target object using non-prehensile actions presents a complex problem that requires addressing multiple constraints, such as finding collision-free trajectories, modeling the object dynamics, and speed of execution. Previous approaches have relied on contact-rich manipulation, where the object moves predictably while attached to the manipulator, thereby avoiding the need to model the object’s dynamics. However, this assumption may not always hold, and different end-effectors may be necessary depending on the use case. In place of contact-rich manipulation, we implement the pushingby-striking method (without tactile feedback) by explicitly modeling the object dynamics. Our novel framework disentangles planning and control enabling us to operate in a contextfree manner. Our method consists of two components: an A* planner and a low-level RL controller. The low-level RL controller feeds on purely object-centric representations and has an object-centric action space, thus making it agnostic of the scene context. On the other hand, the planning module operates idependently of the low-level control and only takes scene context into account, making the approach quick to adapt and implement. We demonstrate the performance of our algorithm against a global RL policy which is computationally

Abstract

This paper presents a fully integrated ultra- low power trim-free current reference for wide-temperature applications. The proposed circuit doesn’t incorporate resistors, amplifiers, and start-up circuitry. A sub-threshold current-based curvature compensation is utilized to extend the temperature range. A process calibration technique is included to calibrate against process variations. Designed in a 55nm CMOS process for a temperature range of -55◦C to 150◦C, the current reference occupies an active area of 0.00458mm2. It achieves a line sensitivity of 0.06%/V for a supply range of 1.2-3.6V. The current produced is 250pA, with a temperature coefficient of 70ppm/oC at the typical corner.

Abstract

We present UrbanFly: an uncertainty-aware real-time planning framework for quadrotor navigation in urban high-rise environments. A core aspect of UrbanFly is its ability to robustly plan directly on the sparse point clouds generated by a Monocular Visual Inertial SLAM (VINS) backend. It achieves this by using the sparse point clouds to build an uncertainty-integrated cuboid representation of the environment through a data-driven monocular plane segmentation network. Our chosen world model provides faster distance queries than the more common voxel-grid representation, and UrbanFly leverages this capability in two different ways leading to two trajectory optimizers. The first optimizer uses a gradient-free cross-entropy method to compute trajectories that minimize collision probability and smoothness cost. Our second optimizer is a simplified version of the first and uses a sequential convex programming optimizer initialized based on probabilistic safety estimates on a set of randomly drawn trajectories. Both our trajectory optimizers are made computationally tractable and independent of the nature of underlying uncertainty by embedding the distribution of collision violations in Reproducing Kernel Hilbert Space. Empowered by the algorithmic innovation, UrbanFly outperforms competing baselines in metrics such as collision rate, trajectory length, etc., on a high-fidelity AirSim simulator augmented with synthetic and real-world dataset scenes.

Abstract

In this paper we present a novel framework for unsupervised topological clustering resulting in improved loop detection and closure for SLAM. A navigating mobile robot clusters its traversal into visually similar topologies where each cluster (topology) contains a set of similar looking images typically observed from spatially adjacent locations. Each such set of spatially adjacent and visually similar grouping of images constitutes a topology obtained without any supervision. We formulate a hierarchical loop discovery strategy that first detects loops at the level of topologies and subsequently at the level of images between the looped topologies. We show over a number of traversals across different Habitat environments that such a hierarchical pipeline significantly improves SOTA image based loop detection and closure methods. Further, as a consequence of improved loop detection, we enhance the loop closure and backend SLAM performance. Such a rendering of a traversal into topological segments is beneficial for downstream tasks such as navigation that can now build a topological graph where spatially adjacent topological clusters are connected by an edge and navigate over such topological graphs.

Abstract

Recovering full 3D shapes from partial observations is a challenging task that has been extensively addressed in the computer vision community. Many deep learning methods tackle this problem by training 3D shape generation networks to learn a prior over the full 3D shapes. In this training regime, the methods expect the inputs to be in a fixed canonical form, without which they fail to learn a valid prior over the 3D shapes. We propose SCARP, a model that performs Shape C ompletion in ARbitrary Poses. Given a partial pointcloud of an object, SCARP learns a disentangled feature representation of pose and shape by relying on rotationally equivariant pose features and geometric shape features trained using a multi-tasking objective. Unlike existing methods that depend on an external canonicalization method, SCARP performs canonicalization, pose estimation, and shape completion in a single network, improving the performance by 45% over the existing baselines. In this work, we use SCARP for improving grasp proposals on tabletop objects. By completing partial tabletop objects directly in their observed poses, SCARP enables a SOTA grasp proposal network improve their proposals by 71.2% on partial shapes. Project page: https://bipashasen.github.io/scarp

Abstract

The paper presents a novel nW range gate-leakage-based Sub-Bandgap Voltage Reference (sub-BGR) for low-power and high-temperature range IoT applications. It generates a reference voltage of 336mV without incorporating any resistors and operating for a high-temperature range of −40°C to 150°C and a supply range of 0.7V-4V. In the above temperature and supply ranges, the proposed circuit's power consumption only goes up by 30x and 1.025x times, respectively. Designed in a 65nm CMOS process, the proposed architecture achieves an accuracy of 94ppm/°C. It achieves a line sensitivity of 0.0066%/V for a supply range of 0.7V to 4V and a PSRR of 89dB at DC and 1V supply. The proposed circuit shows a±3σ -inaccuracy of 4.295% without additional trimming circuits. It occupies only 0.0851mm2 area while consuming only 2.3nW at 27°C and 21.74nW at 150°C for a 0.7V supply.

Abstract

Recent developments in mmWave radar technologies have enabled the truly non-contact heart-rate (HR) and breath-rate (BR) measurement approaches, which provides a great ease in patient monitoring. Additionally, these technologies also provide opportunities to simultaneously detect HR and BR of multiple patients, which has become increasingly important for efficient mass monitoring scenarios. In this work, a frequency modulated continuous wave (FMCW) mmWave radar based truly non-contact multiple patient HR and BR monitoring system has been presented. Furthermore, a novel approach is also proposed, which combines multiple processing methods using a least squares solution to improve measurement accuracy, generalization, and handle measurement error. The proposed system has been developed using Texas Instruments’ FMCW radar and experimental results with multiple subjects are also presented, which show >97% and >93% accuracy in the measured BR and HR values, respectively.

Abstract

This paper focuses on the Multi-Object Navigation (MultiON) task, which requires a robot to localize an instance (each) of multiple object classes. This is a fundamental task for an assistive robot in a home or a factory. Existing methods for this task have viewed this as a direct extension of Object Navigation (ON), the task of localising a single instance of an object class, and are pre-sequenced, i.e., the sequence in which the object classes are to be explored is provided in advance. This is a strong limitation in practical applications characterized by dynamic changes. On the other hand, our deep reinforcement learning framework for sequence-agnostic MultiON is based on an actor-critic architecture and a reward specification. It exploits past experiences and seeks to reward progress towards individual as well as multiple target object classes. We use photo-realistic scenes from Gibson benchmark dataset in the AI Habitat 3D simulation environment to experimentally show that our method performs better than a pre-sequenced approach and a state of the art ON method extended to MultiON.