Abstract

Weak measurements introduced by Aharonov, Albert and Vaidman (AAV) can provide informations about the system with minimal back action. Weak values of product observables (commuting) or higher moments of an observable are informationally important in the sense that they are useful to resolve some paradoxes, realize strange quantum effects, reconstruct density matrices, etc. In this work, we show that it is possible to access the higher moment weak values of an observable using weak values of that observable with pairwise orthogonal post-selections. Although the higher moment weak values of an observable are inaccessible with Gaussian pointer states, our method allows any pointer state. We have calculated product weak values in a bipartite system for any given pure and mixed pre selected states. Such product weak values can be obtained using only the measurements of local weak values (which are defined as single system weak values in a multi- partite system). As an application, we use higher moment weak values and product weak values to reconstruct unknown quantum states of single and bipartite systems, respectively. Further, we give a necessary separability criteria for finite dimensional systems using product weak values and certain class of entangled states violate this inequality by cleverly choosing the product observables and the post selections. By such choices, positive partial transpose (PPT) criteria can be achieved for these classes of entangled states. Robustness of our method which occurs due to inappropriate choices of quantum observables and noisy post-selections is also discussed here. Our method can easily be generalized to the multi-partite systems

Abstract

Internet of Vehicles (IoV) is a wireless network, which employs well-established Internet protocols and networks to share information between vehicles, people, and infrastructure by utilising Vehicular Adhoc Networks (VANETs). IoV aims to increase road user safety and lower accident rates. It is one of the main components of an intelligent transportation system (ITS). ITS appears as the most crucial component since the cities in any country are evolving into digital civilisations as a result of the smart city concept, regardless of whether people are travelling within the city, to work, in school, or for other purposes. It can also be used to improve infrastructure utilisation and road safety (i.e., reduction in road accidents), in addition to enhancing traffic management and easing congestion. However, IoV-driven ITS is susceptible to several forms of attacks because of the insecure communication among the participating entities. We, therefore, propose a secure authentication protocol for IoV-driven ITS to stop these attacks from happening (in short, SAKP-ITS). Its resistance to a number of potential attacks is confirmed by the security analysis of the proposed SAKP-ITS. Additionally, SAKP-ITS is juxtaposed with other comparable competing methods put out in an IoV environment. Additionally, it has been found that SAKP-ITS outperforms competing schemes in terms of critical metrics like communication costs, computation costs, and security and functionality attributes. To test the effect of the proposed SAKP-ITS on the critical performance attributes, a practical implementation of SAKP-ITS

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

We present a modular and multi-level framework for the differential diagnosis of malignant melanoma. Our framework integrates contextual information and evidence at the lesion, patient, and population levels, enabling decision-making at each level. We introduce an anatomic-site aware masked transformer, which effectively models the patient context by considering all lesions in a patient, which can be variable in count, and their site of incidence. Additionally, we incorporate patient metadata via learnable demographics embeddings to capture population statistics. Through extensive experiments, we explore the influence of specific information on the decision-making process and examine the tradeoff in metrics when considering different types of information. Validation results using the SIIM-ISIC 2020 dataset indicate including the lesion context with location and metadata improves specificity by 17.15% and 7.14%, respectively, while enhancing balanced accuracy. The code is available at https://github.com/narenakash/meldd. Keywords: Melanoma Diagnosis · Differential Recognition · Ugly Duckling Context · Patient Demographics · Evidence-Based Medicine.

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