Surface Structural Disordering in Graphite upon Lithium Intercalation/Deintercalation

We report on the origin of the surface structural disordering in graphite anodes induced by lithium intercalation and deintercalation processes. Average Raman spectra of graphitic anodes reveal that cycling at potentials that correspond to low lithium concentrations in LixC (0 \leq x < 0.16) is responsible for most of the structural damage observed at the graphite surface. The extent of surface structural disorder in graphite is significantly reduced for the anodes that were cycled at potentials where stage-1 and stage-2 compounds (x > 0.33) are present. Electrochemical impedance spectra show larger interfacial impedance for the electrodes that were fully delithiated during cycling as compared to electrodes that were cycled at lower potentials (U < 0.15 V vs. Li/Li+). Steep Li+ surface-bulk concentration gradients at the surface of graphite during early stages of intercalation processes, and the inherent increase of the LixC d-spacing tend to induce local stresses at the edges of graphene layers, and lead to the breakage of C-C bonds. The exposed graphite edge sites react with the electrolyte to (re)form the SEI layer, which leads to gradual degradation of the graphite anode, and causes reversible capacity loss in a lithium-ion battery.Comment: 12 pages, 5 figure

Projective toric varieties of codimension 2 with maximal Castelnuovo--Mumford regularity

The Eisenbud--Goto conjecture states that $\operatorname{reg} X\le\operatorname{deg} X -\operatorname{codim} X+1$ for a nondegenerate irreducible projective variety $X$ over an algebraically closed field. While this conjecture is known to be false in general, it has been proven in several special cases, including when $X$ is a projective toric variety of codimension $2$. We classify the projective toric varieties of codimension $2$ having maximal regularity, that is, for which equality holds in the Eisenbud--Goto bound. We also give combinatorial characterizations of the arithmetically Cohen--Macaulay toric varieties of maximal regularity in characteristic $0$.Comment: 26 page

Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond

Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this area have been heavily focusing on improving accuracy using various techniques. Explainability is undoubtedly an important aspect for deep learning-based models including joint NLU models. Without explainability, their decisions are opaque to the outside world and hence, have tendency to lack user trust. Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy. Further, as we enable the full joint NLU model explainable, we show that our extension can be successfully used in other general classification tasks. We demonstrate this using sentiment analysis and named entity recognition.Comment: Accepted at CIKM 202

Brain Inspired Enhanced Learning Mechanism Based on Spike Timing Dependent Plasticity (STDP) for Efficient Pattern Recognition in Spiking Neural Networks

Artificial neural networks, that try to mimic the brain, are a very active area of research today. Such networks can potentially solve difficult problems such as image recognition, video analytics, lot more energy efficiently than when implemented in standard von-Neumann computing machines. New algorithms for neural computing with high bio-fidelity are being developed today to solve hard machine learning problems. In this work, we used a spiking network model, and implemented a self-learning technique using a Spike Timing Dependent Plasticity (STDP) algorithm, that closely mimics the neural activity of the brain. The basic STDP algorithm modulates the synaptic weights interconnecting the neurons based on pairs of pre- and post-synaptic spikes. This ignores the timing information embedded in the frequency of the post-synaptic spikes. We calculated the average of the membrane potential of each column of neurons to give an idea of how it behaved and spiked for the particular output neuron for a particular image in the past .The update of the weights or the synapses are done on the basis of the frequency obtained. The resultant synaptic updates are less frequent and made wisely making the learning process better. With the present algorithm, we are able to achieve an accuracy of 79% for classifying images from the MNIST data set for a network of 400 output neurons. So the model was able to identify 79% of the total images correctly which is greater than the original STDP signifying that slow and sensible updates are definitely having a better impact on the learning process

Efficient Web Service Discovery and Selection Model

Selection of an optimal web service is a challenging task due to the uncertainty of Quality of Service, which is the deciding factor to identify the accurate web service. Several discovery mechanisms have proposed but most of the research work does not consider the non-functional characteristics called Quality of service. The proposed model for web service selection combines two techniques. First, with Skyline method reduce the search space by filtering the redundant service and secondly to calculate the Relevancy function to normalize the skyline services. The experimental results show that the proposed technique outperforms the existing method

Implementation of molecular collection theory

Hayes, in his Naive Physics Manifesto, identified two alternate ontologies for reasoning about liquids, an ontology based on the notion of a contained substance and one based on the notion of a molecular collection. Qualitative Process theory, proposed by Forbus, lends itself easily to encoding the contained substance ontology. It does not, however, provide any mechanism to perform molecular collection reasoning. The primary objective of this research is to implement a mechanism for supporting molecular collection reasoning and evaluate its usefulness in various domains

ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs

Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy users’ needs. If realized, such a system has far-reaching benefits in the real world; for example, a CIS system can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query. Firstly, ISEEQ uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the knowledge-enriched query to retrieve relevant context passages to ask coherent ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep generative adversarial reinforcement learning-based approach for generating ISQs. We show that ISEEQ can generate high-quality ISQs to promote the development of CIS agents. ISEEQ significantly outperforms comparable baselines on five ISQ evaluation metrics across four datasets having user queries from diverse domains. Further, we argue that ISEEQ is transferable across domains for generating ISQs, as it shows the acceptable performance when trained and tested on different pairs of domains. The qualitative human evaluation confirms ISEEQ-generated ISQs are comparable in quality to human-generated questions and outperform the best comparable baseline