Abstract

Coordinate-based implicit neural networks, or neural fields, have emerged as useful representations of shape and appearance in 3D computer vision. Despite advances, however, it remains challenging to build neural fields for categories of objects without datasets like ShapeNet that provide “canonicalized” object instances that are consistently aligned for their 3D position and orientation (pose).We present Canonical Field Network (CaFi-Net), a self-supervised method to canonicalize the 3D pose of instances from an object category represented as neural fields, specifically neural radiance fields (NeRFs). CaFi-Net directly learns from continuous and noisy radiance fields using a Siamese network architecture that is designed to extract equivariant field features for category-level canonicalization. During inference, our method takes pre-trained neural radiance fields of novel object instances at arbitrary 3D pose and estimates a canonical field with consistent 3D pose across the entire category. Extensive experiments on a new dataset of 1300 NeRF models across 13 object categories show that our method matches or exceeds the performance of 3D point cloud-based methods.

Abstract

The Internet of Things (IoT) allows better solutions for managing challenges such as reliability and power quality in a smart grid environment. It also assists in adopting smart grid measures in smart city development. A smart meter in the smart grid environment must securely access services from a service provider via public channels. An adversary can pose several security vulnerabilities as the communication occurs through a public channel. Various researchers recently conducted several research types to determine the most challenging problems with the smart grid. The security features like integrity, authentication, and confidentiality are indispensable requirements of the smart grid environment. On the other hand, different objects like smart meters in the smart grid environment are resource-constrained, posing in designing lightweight security protocols. Hence, we propose a lightweight mutual authentication

Abstract

The cloud computing technology is a novel storage and computing paradigm that enables individuals and organizations to store data, share data with intended group of users and retrieve data when require. It greatly improves peoples’ data storage and sharing, and data retrieval capabilities by providing flexible, less expensive and quality services. For data security and privacy concerns, secure and authenticated data storage, fine-grained access control of encrypted data, secure search for the outsourced data and search results verification are of critical importance. However, achieving the aforementioned functionalities simultaneously is quite challenging. In this paper, for the first time, we propose a secure lightweight Attribute-Based verifiable Data Storage and data Retrieval Scheme (ABDSRS) for cloud environments that attains the following features: (i) lightweight design, (ii) provably secure, (iii) fine-grained …

Abstract

G-protein coupled receptors (GPCRs) are the most prominent family of membrane proteins that serve as major targets for one-third of the drugs produced. A detailed understanding of the molecular mechanism of drug-induced activation and inhibition of GPCRs is crucial for the rational design of novel therapeutics. The binding of the neurotransmitter adrenaline to the β2-adrenergic receptor (β2AR) is known to induce a flight or fight cellular response, but much remains to be understood about binding-induced dynamical changes in β2AR and adrenaline. In this article, we examine the potential of mean force (PMF) for the unbinding of adrenaline from the orthosteric binding site of β2AR and the associated dynamics using umbrella sampling and molecular dynamics (MD) simulations. The calculated PMF reveals a global energy minimum, which corresponds to the crystal structure of β2AR−adrenaline complex, and a meta-stable state in which the adrenaline is moved slightly deeper into the binding pocket with a different orientation compared to that in the crystal structure. The orientational and conformational changes in adrenaline during the transition between these two states and the underlying driving forces of this transition are also explored. Based on the clustering of MD configurations and machine learning-based statistical analyses of time series of relevant collective variables, the structures and stabilizing interactions of these two states of the β2AR−adrenaline complex are also investigated.

Abstract

A method for real-time detection of nerves together with a needle and its trajectory in ultrasound videos to autonomously guide the needle towards nerve block target. We also publicly release needle annotations for further research.

Abstract

Peripheral nerve blocks are crucial to treatment of post-surgical pain and are associated with reduction in perioperative opioid use and hospital stay. Accurate interpretation of sono-anatomy is critical for the success of ultrasound (US) guided peripheral nerve blocks and can be challenging to the new operators. This prospective study enrolled 227 subjects who were systematically scanned for supraclavicular and interscalene brachial plexus in various settings using three different US machines to create a dataset of 227 unique videos. In total, 41,000 video frames were annotated by experienced anaesthesiologists using partial automation with object tracking and active contour algorithms. Four baseline neural network models were trained on the dataset and their performance was evaluated for object detection and segmentation tasks. Generalizability of the best suited model was then tested on the datasets constructed

Abstract

Lack of encyclopedic text contributors, especially on Wikipedia, makes automated text generation for low resource (LR) languages a critical problem. Existing work on Wikipedia text generation has focused on English only where English reference articles are sum- marized to generate English Wikipedia pages. But, for low-resource languages, the scarcity of reference articles makes monolingual summarization inefective in solving this problem. Hence, in this work, we propose XWikiGen, which is the task of cross-lingual multi-document summarization of text from multiple reference ar- ticles, written in various languages, to generate Wikipedia-style text. Accordingly, we contribute a benchmark dataset, XWikiRef, spanning ∼69K Wikipedia articles covering fve domains and eight languages. We harness this dataset to train a two-stage system where the input is a set of citations and a section title and the output is a section-specifc LR summary. The proposed system is based on a novel idea of neural unsupervised extractive summariza- tion to coarsely identify salient information followed by a neural abstractive model to generate the section-specifc text. Extensive experiments show that multi-domain training is better than the multi-lingual setup on average. We make our code and dataset publicly availableLack of encyclopedic text contributors, especially on Wikipedia, makes automated text generation for low resource (LR) languages a critical problem. Existing work on Wikipedia text generation has focused on English only where English reference articles are sum- marized to generate English Wikipedia pages. But, for low-resource languages, the scarcity of reference articles makes monolingual summarization inefective in solving this problem. Hence, in this work, we propose XWikiGen, which is the task of cross-lingual multi-document summarization of text from multiple reference ar- ticles, written in various languages, to generate Wikipedia-style text. Accordingly, we contribute a benchmark dataset, XWikiRef, spanning ∼69K Wikipedia articles covering fve domains and eight languages. We harness this dataset to train a two-stage system where the input is a set of citations and a section title and the output is a section-specifc LR summary. The proposed system is based on a novel idea of neural unsupervised extractive summariza- tion to coarsely identify salient information followed by a neural abstractive model to generate the section-specifc text. Extensive experiments show that multi-domain training is better than the multi-lingual setup on average. We make our code and dataset publicly available

Abstract

Location-based services (LBSs) in a mobile network environment that provide personalized and timely information to users entail privacy concerns due to the leakage of user locations to adversaries. In the literature, several cloaking-based privacy preservation approaches have been proposed by considering the P2P environment. However, existing spatial cloaking approaches form a large cluster of mobile users to cloak the user query location. Maintaining such structures in a highly dynamic mobile- P2P network is challenging. In addition to user’s location, the user’s intent is also an important concern and needs to be preserved from an adversary. This paper proposes a location privacy and intent privacy preservation scheme, especially for spatial range queries. The key contributions of our work are three-fold. First, we introduce the concept of ijk-anonymity to achieve improved location privacy. Second, we propose a location- based privacy-preservation approach, which we designate as ijkCloak, for spatial range queries in a mobile network environ- ment. The proposed approach preserves user location and intent information from the LBS provider and nearby peers. Third, we conduct theoretical analysis and experiments on resistance to attacks. We show that ijkCloak effectively facilitates improved user location privacy and intent privacy by employing fewer peers w.r.t. existing approaches. Index Terms—Mobile computing, location-based services, loca- tion privacy, spatial cloaking, spatial queries, peer-to-peer, spatial range queries

Abstract

Cocaine use disorder (CUD) is described as a compulsive urge to seek and consume cocaine despite the inimical consequences. MRI studies from different modalities have shown that CUD patients exhibit structural and/or functional connectivity pathology among several brain regions. Nevertheless, both connectivities are commonly studied and analyzed separately, which may potentially obscure its relationship between them, and with the clinical pathology. Here, we compare and contrast structural and functional brain networks in CUD patients and healthy controls (HC) using multimodal fusion. The sample consisted of 63 (8 females) CUD patients and 42 (9 females) healthy controls (HC), recruited as part of the SUDMEX CONN database. For this, we computed a battery of graph-based measures from multi-shell diffusion-weighted imaging and resting state fc-fMRI to quantify local and global connectivity. Then we used multimodal canonical component analysis plus joint independent component analysis (mCCA+jICA) to compare between techniques, and evaluate group differences and its association with clinical alteration. Unimodal results showed a striatal decrease in the participation coefficient, but applied supervised data fusion revealed other regions with cocaine-related alterations in joint functional communication. When performing multimodal fusion analysis, we observed a higher centrality of the interrelationship and a lower participation coefficient in patients with CUD. In contrast to the unimodal approach, the multimodal fusion method was able to reveal latent information about brain regions involved in impairment due to cocaine abuse. The present results could help in understanding the pathology of CUD in order to develop better pre-treatment/post-treatment intervention designs. Keywords: Cocaine use disorder; Imaging; Graph theory; sMRI; fMRI; Multimodal fusion; mCCA + jICA

Abstract

A large number of computational and scientific methods commonly require decomposing a sparse matrix into triangular factors as LU decomposition. A common problem faced during this decomposition is that even though the given matrix may be very sparse, the decompo- sition may lead to a denser triangular factors due to fill-in. A significant fill-in may lead to prohibitively larger computational costs and memory requirement during decomposition as well as during the solve phase. To this end, several heuristic sparse matrix reordering methods have been proposed to reduce fill-in before the decomposition. However, finding an optimal reordering algorithm that leads to minimal fill-in during such de- composition is known to be a NP-hard problem. A reinforcement learning based approach is proposed for this problem. The sparse matrix reorder- ing problem is formulated as a single player game. More specifically, Monte-Carlo tree search in combination with neural network is used as a decision making algorithm to search for the best move in our game. The proposed method, Alpha Elimination is found to produce significantly lesser non-zeros in the LU decomposition as compared to existing state- of-the-art heuristic algorithms with little to no increase in overall running time of the algorithm. The code for the project is publicly available 1. Keywords: Reinforcement Learning · Sparse Matrices · Deep Learning · LU · MCTS