Abstract

long-term meditation intervention. MCI or mild AD patients underwent detailed clinical and neuropsychological assessment and were assigned into meditation or non-meditation groups. High resolution T1-weighted magnetic resonance images (MRI) were acquired at baseline and after 6 months. Longitudinal symmetrized percentage changes (SPC) in cortical thickness and gray matter volume were estimated. Left caudal middle frontal, left rostral middle frontal, left superior parietal, right lateral orbitofrontal, and right superior frontal cortices showed changes in both cortical thickness and gray matter volume; the left paracentral cortex showed changes in cortical thickness; the left lateral occipital, left superior frontal, left banks of the superior temporal sulcus (bankssts), and left medial orbitofrontal cortices showed changes in gray matter volume. All these areas exhibited significantly higher SPC values in meditators as compared to non-meditators. Conversely, the left lateral occipital, and right posterior cingulate cortices showed significantly lower SPC values for cortical thickness in the meditators. In hippocampal subfields analysis, we observed significantly higher SPC in gray matter volume of the left CA1, molecular layer HP, and CA3 with a trend for increased gray matter volume in most other areas. No significant changes were found for the hippocampal

Abstract

Experiences from past earthquake disasters clearly shows that the ground motion was responsible for majority of property and life loss. Among the collapsed structures during the 1964 Niigata earthquake, the 1995 Kobe earthquake, the 1999 Kocaeli (Izmit) earthquake, the 2001 Bhuj earthquake and the 2004 Sumatra earthquake, excessive damage had occurred to pile supported bridges, towers, chimneys, high rise structures, etc. In view of this there is a need to study the complex behaviour of soil-pile-structure interaction problems. However, clause 6.1.5 IS 1893-2016, says that soil-structure-interaction (SSI) is not needed for structures founded on rock at shallow depth, but not enough details are given for SSI.

Abstract

Learning linguistic generalizations from only a few examples is a challenging task. Recent work has shown that program synthesis – a method to learn rules from data in the form of programs in a domain-specific language – can be used to learn phonological rules in highly data-constrained settings. In this paper, we use the problem of phonological stress placement as a case to study how the design of the domain-specific language influences the generalization ability when using the same learning algorithm. We find that encoding the distinction between consonants and vowels results in much better performance, and providing syllable-level information further improves generalization. Program synthesis, thus, provides a way to investigate how access to explicit linguistic information influences what can be learnt from a small number of examples.

Abstract

In recent years, given the exponential increase in social media content also led to an increase in online hate speech. We need automatic hate speech detection methods due to the volume of data on the web. Various approaches have been proposed to address hate speech and offensive content on social media. This paper surveys how neural-based models have rapidly evolved to address hate speech on multilingual code-mixed data. We discuss the current state of the research in hate speech and offensive language detection on code-mixed Indian datasets.

Abstract

This paper addresses the problem of multiclass classification with corrupted or noisy bandit feedback. In this setting, the learner may not receive true feedback. Instead, it receives feedback that has been flipped with some non-zero probability. We propose a novel approach to deal with noisy bandit feedback based on the unbiased estimator technique. We further offer a method that can efficiently estimate the noise rates, thus providing an end-to-end framework. The proposed algorithm enjoys a mistake bound of the order of O(√T ) in the high noise case and of the order of O(T 2/3) in the worst case. We show our approach’s effectiveness using extensive experiments on several benchmark datasets.

Abstract

We present a neural rendering framework for simultane- ous view synthesis and appearance editing of a scene from multi-view images captured under known environment il- lumination. Existing approaches either achieve view syn- thesis alone or view synthesis along with relighting, with- out direct control over the scene’s appearance. Our ap- proach explicitly disentangles the appearance and learns a lighting representation that is independent of it. Specif- ically, we independently estimate the BRDF and use it to learn a lighting-only representation of the scene. Such dis- entanglement allows our approach to generalize to arbi- trary changes in appearance while performing view synthe- sis. We show results of editing the appearance of a real scene, demonstrating that our approach produces plausible appearance editing. The performance of our view synthesis approach is demonstrated to be at par with state-of-the-art approaches on both real and synthetic data

Abstract

e present a neural rendering framework for simultaneous view synthesis and appearance editing of a scene with known envi- ronmental illumination captured using a mobile camera. Existing approaches either achieve view synthesis alone or view synthesis along with relighting, without control over the scene’s appearance. Our approach explicitly disentangles the appearance and learns a lighting representation that is independent of it. Specifically, we jointly learn the scene appearance and a lighting-only represen- tation of the scene. Such disentanglement allows our approach to generalize to arbitrary changes in appearance while performing view synthesis. We show results of editing the appearance of real scenes in interesting and non-trivial ways. The performance of our view synthesis approach is on par with state-of-the-art approaches on both real and synthetic data.

Abstract

Aspect extraction is not a well-explored topic in Hindi, with only one corpus having been developed for the task. In this paper, we dis- cuss the merits of the existing corpus in terms of quality, size, sparsity, and performance in aspect extraction tasks using established mod- els. To provide a better baseline corpus for aspect extraction, we translate the SemEval 2014 aspect-based sentiment analysis dataset and annotate the aspects in that data. We provide rigorous guidelines and a replicable methodology for this task. We quantitatively evaluate the translations and annotations us- ing inter-annotator agreement scores. We also evaluate our dataset using state-of-the-art neu- ral aspect extraction models in both monolin- gual and multilingual settings and show that the models perform far better on our corpus than on the existing Hindi dataset. With this, we establish our corpus as the gold-standard aspect extraction dataset in Hindi

Abstract

In this paper, we describe our submissions for LoResMT Shared Task @MT Summit 2021 Con- ference. We built statistical translation systems in each direction for English ⇐⇒ Marathi lan- guage pair. This paper outlines initial baseline experiments with various tokenization schemes to train models. Using optimal tokenization scheme we create synthetic data and further train augmented dataset to create more statistical models. Also, we reorder English to match Marathi syntax to further train another set of baseline and data augmented models using various tok- enization schemes. We report configuration of the submitted systems and results produced by them

Abstract

Modeling person interactions with their surroundings has proven to be effective for recognizing and localizing human actions in videos. While most recent works focus on learning short term interactions, in this work, we consider long-term person interactions and jointly localize actions of multiple actors over an entire video shot. We construct a graph with nodes that correspond to keyframe actor instances and connect them with two edge types. Spatial edges connect actors within a keyframe, and temporal edges connect multiple instances of the same actor over a video shot. We propose a Graph Neural Network that explicitly models spatial and temporal states for each person instance and learns to effectively combine information from both modalities to make predictions at the same time. We conduct experiments on the AVA dataset and show that our graph-based model provides consistent improvements over several video descriptors, achieving state-of-the-art performance without any fine-tuning.