Abstract

We present our work in using enhanced sampling method of metadynamics in combination with machine learning techniques to speed-up the exploration of all relevantphase space of biomolecular systems that keeps the two related tasks separate: (a) collective variable should cluster configurations into correct states, that is, classify states accurately and (b) collective variable should improve the local sampling in the ‘current free energy basin’, that is, shape the collective variable in line with the data collected (regression). We demonstrate the method on the alanine tetrapeptide system in explicit solvent. We use Elliptic Envelope (EE) classifier to identify outliers from a particular class, and each class will use a different fully-connected autoencoder (ANC) whose bottleneck layer (latent variable) will be used as collective variable in Well-Tempred Metadynamics simulation to generate more data. Comparison against benchmark indicate that the proposed scheme can traverse all relevant phase space

Abstract

Sexism, a prejudice that causes enormous suffering, manifests in blatant as well as subtle ways. As sexist content towards women is increasingly spread on social networks, the automatic detection and categorization of these tweets/posts can help social scientists and policymakers in research, thereby combating sexism. In this paper, we explore the problem of detecting whether a Twitter/Gab post is sexist or not. We further discriminate the detected sexist post into one of the fine-grained sexism categories. We propose a neural model for this sexism detection and classification that can combine representations obtained using RoBERTa model and linguistic features such as Empath, Hurtlex, and Perspective API by involving recurrent components. We also leverage the unlabeled sexism data to infuse the domain-specific transformer model into our framework. Our proposed framework also features a knowledge module comprised of emoticon and hashtag representations to infuse the external knowledge-specific features into the learning process. Several proposed methods outperform various baselines across several standard metrics.

Abstract

Machine learning (ML) methods enable computers to address problems by learning from existing data. Such applications are becoming commonplace in molecular sciences. Interest in applying ML techniques across chemical compound space, from predicting properties to designing molecules and materials is in the surge. Especially, ML models have started to accelerate computational chemistry, and are often as accurate as state‐of‐the‐art electronic/atomistic models. Being an integral part of the ML architecture, representation of a molecular entity, uniquely encoded, plays a crucial role to what extent an ML model would be accurately predicting the desired property. This review aims to demonstrate a hierarchy of representations which has been introduced, to capture all degrees of freedom of a molecule or an atom the best, to map the quantum mechanical properties. We discuss their diverse applications how …

Abstract

This article describes an experiment to evaluate the impact of different types of ellipses discussed in theoretical linguistics on Neural Machine Translation (NMT), using English to Hindi/Telugu as source and target languages. Evaluation with manual methods shows that most of the errors made by Google NMT are located in the clause containing the ellipsis, the frequency of such errors is slightly more in Telugu than Hindi, and the translation adequacy shows improvement when ellipses are reconstructed with their antecedents. These findings not only confirm the importance of ellipses and their resolution for MT, but also hint toward a possible correlation between the translation of discourse devices like ellipses with the morphological incongruity of the source and target. We also observe that not all ellipses are translated poorly and benefit from reconstruction, advocating for a disparate treatment of different ellipses in MT research.

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