arXiv subjects

Aditya Sharma

Publications and source records attributed to Aditya Sharma.

50 records · Page 3Linked to original sources

arXiv2023

Galilean fermions: Classical and quantum aspects

We study the classical and quantum "properties" of Galilean fermions in 3+1 dimensions. We have taken the case of massless Galilean fermions minimally coupled to the scalar field. At the classical level, the Lagrangian is obtained by null reducing the relativistic theory in one higher dimension. The resulting theory is found to be invariant under infinite Galilean conformal symmetries. Using Noether's procedure, we construct the corresponding infinite conserved charges. Path integral techniques are then employed to probe the quantum "properties" of the theory. The theory is found to be renormalizable. A novel feature of the theory is the emergence of mass scale at the first order of quantum correction. The conformal symmetry of the theory breaks at the quantum level. We confirm this by constructing the beta function of the theory.

hep-th↗

arXiv2022

WikiWhy: Answering and Explaining Cause-and-Effect Questions

As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging. Recent question answering (QA) benchmarks that attempt to assess reasoning are often limited by a narrow scope of covered situations and subject matters. We introduce WikiWhy, a QA dataset built around a novel auxiliary task: explaining why an answer is true in natural language. WikiWhy contains over 9,000 "why" question-answer-rationale triples, grounded on Wikipedia facts across a diverse set of topics. Each rationale is a set of supporting statements connecting the question to the answer. WikiWhy serves as a benchmark for the reasoning capabilities of LLMs because it demands rigorous explicit rationales for each answer to demonstrate the acquisition of implicit commonsense knowledge, which is unlikely to be easily memorized. GPT-3 baselines achieve only 38.7% human-evaluated correctness in the end-to-end answer & explain condition, leaving significant room for future improvements.

cs.CL↗

arXiv2022

Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces

Analogical Reasoning problems challenge both connectionist and symbolic AI systems as these entail a combination of background knowledge, reasoning and pattern recognition. While symbolic systems ingest explicit domain knowledge and perform deductive reasoning, they are sensitive to noise and require inputs be mapped to preset symbolic features. Connectionist systems on the other hand can directly ingest rich input spaces such as images, text or speech and recognize pattern even with noisy inputs. However, connectionist models struggle to include explicit domain knowledge for deductive reasoning. In this paper, we propose a framework that combines the pattern recognition abilities of neural networks with symbolic reasoning and background knowledge for solving a class of Analogical Reasoning problems where the set of attributes and possible relations across them are known apriori. We take inspiration from the 'neural algorithmic reasoning' approach [DeepMind 2020] and use problem-specific background knowledge by (i) learning a distributed representation based on a symbolic model of the problem (ii) training neural-network transformations reflective of the relations involved in the problem and finally (iii) training a neural network encoder from images to the distributed representation in (i). These three elements enable us to perform search-based reasoning using neural networks as elementary functions manipulating distributed representations. We test this on visual analogy problems in RAVENs Progressive Matrices, and achieve accuracy competitive with human performance and, in certain cases, superior to initial end-to-end neural-network based approaches. While recent neural models trained at scale yield SOTA, our novel neuro-symbolic reasoning approach is a promising direction for this problem, and is arguably more general, especially for problems where domain knowledge is available.

cs.AI↗

arXiv2022

de Sitterization via Kerr-Schild

Spatially homogeneous cosmological spacetimes, evolving in the presence of a positive cosmological constant and matter satisfying some reasonable energy conditions, typically approach the de Sitter geometry asymptotically (at least locally). In this work, we propose an alternate way to characterize this phenomena. We focus on a subset of such models admitting a generalized Kerr-Schild representation. We argue that the functions which define such a representation can be chosen, such that, their asymptotic behavior make the evolution towards the de Sitter spacetime manifest through the representation. We verify our claim for the Kantowski-Sachs family of cosmological spacetimes.

gr-qc↗

arXiv2022

Quantization of Interacting Galilean Field theories

We present the quantum field description of Galilean electrodynamics minimally coupled to massless Galilean fermion in (3 + 1) dimensions. At the classical level, the Lagrangian is obtained as a null reduction of a relativistic theory in one higher dimension. We use functional techniques to develop the quantum field description of the theory. Quantum corrections to the propagators and vertex are obtained upto first order and the theory is found to be renormalizable to this order. The beta function of the theory is found to grow linearly; the theory is not asymptotically free.

hep-th↗

arXiv2022

Multi-Modal Model Predictive Control through Batch Non-Holonomic Trajectory Optimization: Application to Highway Driving

Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only a local search to solve a complex non-convex optimization problem. As a result, they cannot capture the multi-modal characteristic of human driving. A global optimizer can be a potential solution but is computationally intractable in a real-time setting. In this paper, we present a real-time MPC capable of searching over different driving modalities. Our basic idea is simple: we run several goal-directed parallel trajectory optimizations and score the resulting trajectories based on user-defined meta cost functions. This allows us to perform a global search over several locally optimal motion plans. Although conceptually straightforward, realizing this idea in real-time with existing optimizers is highly challenging from technical and computational standpoints. With this motivation, we present a novel batch non-holonomic trajectory optimization whose underlying matrix algebra is easily parallelizable across problem instances and reduces to computing large batch matrix-vector products. This structure, in turn, is achieved by deriving a linearization-free multi-convex reformulation of the non-holonomic kinematics and collision avoidance constraints. We extensively validate our approach using both synthetic and real data sets (NGSIM) of traffic scenarios. We highlight how our algorithm automatically takes lane-change and overtaking decisions based on the defined meta cost function. Our batch optimizer achieves trajectories with lower meta cost, up to 6x faster than competing baselines.

cs.RO↗

arXiv2022

The BTZ black hole spectrum and partition function

In this article, we revisit the spectrum of the Lorentzian BTZ black hole conformal field theory. Building on a detailed analysis of geodesics, we identify a complete set of states for the harmonic analysis. We then demonstrate that the CFT spectrum is, plausibly, that of $AdS_3$ by rewriting the partition function of the latter CFT.

hep-th↗

arXiv2021

Multilevel profiling of situation and dialogue-based deep networks for movie genre classification using movie trailers

Automated movie genre classification has emerged as an active and essential area of research and exploration. Short duration movie trailers provide useful insights about the movie as video content consists of the cognitive and the affective level features. Previous approaches were focused upon either cognitive or affective content analysis. In this paper, we propose a novel multi-modality: situation, dialogue, and metadata-based movie genre classification framework that takes both cognition and affect-based features into consideration. A pre-features fusion-based framework that takes into account: situation-based features from a regular snapshot of a trailer that includes nouns and verbs providing the useful affect-based mapping with the corresponding genres, dialogue (speech) based feature from audio, metadata which together provides the relevant information for cognitive and affect based video analysis. We also develop the English movie trailer dataset (EMTD), which contains 2000 Hollywood movie trailers belonging to five popular genres: Action, Romance, Comedy, Horror, and Science Fiction, and perform cross-validation on the standard LMTD-9 dataset for validating the proposed framework. The results demonstrate that the proposed methodology for movie genre classification has performed excellently as depicted by the F1 scores, precision, recall, and area under the precision-recall curves.

cs.CL↗

arXiv2021

"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to an SS labeled as either supportive, informative, or similar (sharing experiences) using the principles of natural language inference. Evaluation by 21 domain experts indicates the efficacy of incorporated knowledge and shows the efficacy the matching system.

cs.IR↗

arXiv2021

Interacting Conformal Carrollian Theories: Cues from Electrodynamics

We construct the free Lagrangian of the magnetic sector of Carrollian electrodynamics. The construction relies on Helmholtz integrability condition for differential equations in a self consistent algorithm, working hand in hand with imposing invariance under infinite dimensional Conformal Carroll algebra. It requires inclusion of new fields in the dynamics and the system is free of gauge redundancies. We next add interaction (quartic) terms to the free Lagrangian, strictly constrained by conformal invariance and Carrollian symmetry. The dynamical realization of the non-semi simple infinite dimensional symmetry algebra at the level of charge algebra is exact and free from central terms.

hep-th↗

arXiv2020

Explainable Knowledge Tracing Models for Big Data: Is Ensembling an Answer?

In this paper, we describe our Knowledge Tracing model for the 2020 NeurIPS Education Challenge. We used a combination of 22 models to predict whether the students will answer a given question correctly or not. Our combination of different approaches allowed us to get an accuracy higher than any of the individual models, and the variation of our model types gave our solution better explainability, more alignment with learning science theories, and high predictive power.

cs.LG↗

arXiv2020

Tweets Sentiment Analysis via Word Embeddings and Machine Learning Techniques

Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about peoples sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social media tools to provide various services and interact with customers. Sentiment Analysis (SA) classifies the polarity of given tweets to positive and negative tweets in order to understand the sentiments of the public. This paper aims to perform sentiment analysis of real-time 2019 election twitter data using the feature selection model word2vec and the machine learning algorithm random forest for sentiment classification. Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF. Word2vec improves the quality of features by considering contextual semantics of words in a text hence improving the accuracy of machine learning and sentiment analysis.

cs.CL↗

arXiv2018

Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images

Increasing demand for high field magnetic resonance (MR) scanner indicates the need for high-quality MR images for accurate medical diagnosis. However, cost constraints, instead, motivate a need for algorithms to enhance images from low field scanners. We propose an approach to process the given low field (3T) MR image slices to reconstruct the corresponding high field (7T-like) slices. Our framework involves a novel architecture of a merged convolutional autoencoder with a single encoder and multiple decoders. Specifically, we employ three decoders with random initializations, and the proposed training approach involves selection of a particular decoder in each weight-update iteration for back propagation. We demonstrate that the proposed algorithm outperforms some related contemporary methods in terms of performance and reconstruction time.

cs.CV↗

arXiv2017

The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning

How much can pruning algorithms teach us about the fundamentals of learning representations in neural networks? And how much can these fundamentals help while devising new pruning techniques? A lot, it turns out. Neural network pruning has become a topic of great interest in recent years, and many different techniques have been proposed to address this problem. The decision of what to prune and when to prune necessarily forces us to confront our assumptions about how neural networks actually learn to represent patterns in data. In this work, we set out to test several long-held hypotheses about neural network learning representations, approaches to pruning and the relevance of one in the context of the other. To accomplish this, we argue in favor of pruning whole neurons as opposed to the traditional method of pruning weights from optimally trained networks. We first review the historical literature, point out some common assumptions it makes, and propose methods to demonstrate the inherent flaws in these assumptions. We then propose our novel approach to pruning and set about analyzing the quality of the decisions it makes. Our analysis led us to question the validity of many widely-held assumptions behind pruning algorithms and the trade-offs we often make in the interest of reducing computational complexity. We discovered that there is a straightforward way, however expensive, to serially prune 40-70% of the neurons in a trained network with minimal effect on the learning representation and without any re-training. It is to be noted here that the motivation behind this work is not to propose an algorithm that would outperform all existing methods, but to shed light on what some inherent flaws in these methods can teach us about learning representations and how this can lead us to superior pruning techniques.

cs.NE↗