Abstract

ccurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers

Abstract

Extensive searches to probe the particle nature of dark matter (DM) have been going on for some decades now but, so far, no conclusive evidence has been found. Among various options, the Weakly Interacting Massive Particles (WIMP) remains one of the prime possibilities as candidates for DM near the TeV scale. Taking a phenomenological view, such null results may be explained for a generic WIMP in a Higgs-portal scenario if we allow the light-quark Yukawa couplings to assume non-Standard Model (non-SM)-like values. This follows from a cancellation among different terms in the DM-nucleon scattering which can, in turn, lead to a vanishingly small direct-detection cross section. It might also lead to isospin violation in the DM-nucleon scattering. Such non-SM values of light-quark Yukawa couplings may be probed in the high luminosity run of the LHC.

Abstract

The problem of data exchange between multiple nodes with storage and communication capabilities models several current multi-user communication problems like Coded Caching, Data Shuffling, Coded Computing, etc. The goal in such problems is to design communication schemes which accomplish the desired data exchange between the nodes with the optimal (minimum) amount of communication load. In this work, we present a converse to such a general data exchange problem. The expression of the converse depends only on the number of bits to be moved between different subsets of nodes, and does not assume anything further specific about the parameters in the problem. Specific problem formulations, such as those in Coded Caching, Coded Data Shuffling, and Coded Distributed Computing, can be seen as instances of this generic data exchange problem. Applying our generic converse, we can efficiently recover known important converses in these formulations. Further, for a generic coded caching problem with heterogeneous cache sizes at the clients with or without a central server, we obtain a new general converse, which subsumes some existing results. Finally we relate a “centralized” version of our bound to the known generalized independence number bound in index coding and discuss our bound’s tightness in this context.

Abstract

This paper analyses patch antennas operating at 16 GHz on flexible Kapton polyimide substrate in various shapes, namely rectangular, circular, pentagon, hexagon, heptagon, and octagon. Their applications in medical areas, more particularly in wearable devices for E-health, are targeted. The bending effects of these antennas are studied, more specifically on gain, return loss, radiation characteristics, bandwidth, and beamwidth. A detailed comparison results showed that the rectangular patch had better performance even under the bending when the diameter of the surface is varied from 40mm to 120mm and maintains a gain of 4.8 dB at the given frequency. Under such extreme bending, the antennas operate satisfactorily with an efficiency of 43.424% to 47.41%

Abstract

Localization of multiple speakers using microphone arrays remains a challenging problem, especially in the presence of noise and reverberation. State-of-the-art localization algorithms generally exploit the sparsity of speech in some representation for this purpose. Whereas the broadband approaches exploit time-domain sparsity for multi-speaker localization, narrowband approaches can additionally exploit sparsity and disjointness in the time-frequency representation. Broadband approaches are robust to spatial aliasing but do not optimally exploit the frequency domain sparsity, leading to poor localization performance for arrays with short inter-microphone distances. Narrowband approaches, on the other hand, are vulnerable to spatial aliasing, making them unsuitable for arrays with large inter-microphone spacing. Proposed here is an approach that decomposes a signal spectrum into a weighted sum of broadband spectral components (atoms) and then exploits signal sparsity in the time-atom representation for simultaneous multiple source localization. The decomposition into atoms is performed in situ using non-negative

Abstract

Sexism, a permeate form of oppression, causes profound sufering through various manifestations. Given the increasing number of experiences of sexism shared online, categorizing these recollections automatically can support the battle against sexism, since it can promote successful evaluations by gender studies researchers and government representatives engaged in policy making. In this paper, we examine the fne-grained, multi-label classifcation of accounts (reports) of sexism. To the best of our knowledge, we consider substantially more categories of sexism than any related prior work through our 23-class problem formulation. Moreover, we present the frst semi-supervised work for the multi-label classifcation of accounts describing any type(s) of sexism. We devise self-training-based techniques tailor-made for the multi-label nature of the problem to utilize unlabeled samples for augmenting the labeled set. We identify high textual diversity with respect to the existing labeled set as a desirable quality for candidate unlabeled instances and develop methods for incorporating it into our approach. We also explore ways of infusing class imbalance alleviation for multi-label classifcation into our semisupervised learning, independently and in conjunction with the method involving diversity. In addition to data augmentation methods, we develop a neural model which combines biLSTM and attention with a domain-adapted BERT model in an endto-end trainable manner. Further, we formulate a multi-level training approach in which models are sequentially trained using

Abstract

Residential electricity consumption (REC) in India has tripled in the past two decades accounting for 24% of the overall electricity consumption during 2018-19. Residential air conditioning (AC) usage is responsible for about 20%-40% of REC in India. This paper investigates the relationship of residential AC use with indoor temperature and relative humidity (RH) using concurrent time-series monitoring data gathered in eight dwellings during summer and monsoon seasons. Contextual data about the dwelling (physical) and household characteristics were gathered using face-to-face interview based surveys. The dwellings were located in Hyderabad representing the composite climate of India. The mean daily electricity consumption was found to be higher in summer (11.5kWh) possibly due to the higher usage of AC (because of higher ambient conditions) as compared to 6.5kWh/day during monsoon season. Binary logistic regression identified the trigger indoor temperature and RH at which AC was likely to be switched on in the summer as 29 °C - 31.9 °C for indoor temperature and 36%-38.9% RH. In the monsoon season AC was predicted to come on sooner at 26°C-28.9°C but at higher RH range of 59%-61.9%. These empirical findings can be used to reduce residential cooling energy demand through smart management of ACs in Indian dwellings

Abstract

A smart city is a collection of a variety of smart devices representing several disparate infrastructural verticals. The data gathered by these smart devices helps bring a strong integration of human, collective, and artificial intelligence within the city operation. With the advent and proliferation of electric vehicles (EVs), the EV charge point is a relatively new addition to this plethora of smart devices. We believe that the EV charge point can acquire enhanced functionality if it can be seamlessly integrated with the rest of the smart city infrastructure. For example, charging services can be stopped if smart grid or smart fire alarms sense an electrical overload or fire hazard in the vicinity. However, the interaction of an EV charge point with the smart city infrastructure requires seamless information exchange across various verticals. This horizontal flow of information is enabled by making the charge point complaint with the oneM2M platform. To this end, we present the design and fabrication of an EV charge point based on the OCPP communication standard and complaint with the oneM2M platform. Further, we discuss various use cases showing an increased functional capability of the EV charger due to access to data from other IoT devices.

Abstract

Natural Language Understanding has made recent advancements where context-aware token representation and word disambiguation have become possible to a large extent. In this scenario, comprehension of phrasal semantics particularly in the context of multi word expressions (MWE) and idioms, is the subsequent task to be addressed. Word level metaphor detection is unable to handle phrases or MWE(s) which occur in both literal and idiomatic context. State of the art transformer architectures can be useful in this context, but the absence of a large comprehensive dataset is a bottleneck. In this paper, we present a labelled EPIE dataset containing 3136 occurrences for 358 formal idioms. To prove the efficacy of our dataset, we also train a sequence classification model effectively and perform cross-dataset evaluation on three independent datasets. Our method achieves good results on all datasets with F1 score of 96% on our test data, and 82%, 74% and 76% F1 score on SemEval All Words, SemEval Lex Sample, and PIE Corpus datasets respectively.

Abstract

Conventional approaches for addressing road safety rely on manual interventions or immobile CCTV infras-tructure. Such methods are expensive in enforcing compliance to traffic rules and do not scale to large road networks. This paper proposes a simple mobile imaging setup to address several common problems in road safety at scale. We use recent computer vision techniques to identify possible irregularities on roads, the absence of street lights, and defective traffic signs using videos from a moving camera-mounted vehicle. Beyond the inspection of static road infrastructure, we also demonstrate the mobile imaging solution’s applicability to spot traffic violations. Before deploying our system in the real-world, we investigate the strengths and shortcomings of computer vi- sion techniques on thirteen condition-based hierarchical labels. These conditions include different timings, road type, traffic density, and state of road damage. Our demonstrations are then carried out on 2000 km of unconstrained road scenes, captured across an entire city. Through this, we quantitatively measure the overall safety of roads in the city through carefully constructed metrics. We also show an interactive dashboard for visually inspecting and initiating action in a time, labor and cost-efficient manner. Code, models, and datasets used in this work will be publicly released