15 research outputs found
Disruption of cell wall fatty acid biosynthesis in Mycobacterium tuberculosis using a graph theoretic approach
Fatty acid biosynthesis of Mycobacterium tuberculosis was analyzed using graph theory and influential (impacting) proteins were identified. The graphs (digraphs) representing this biological network provide information concerning the connectivity of each protein or metabolite in a given pathway, providing an insight into the importance of various components in the pathway, and this can be quantitatively analyzed. Using a graph theoretic algorithm, the most influential set of proteins (sets of {1, 2, 3}, etc.), which when eliminated could cause a significant impact on the biosynthetic pathway, were identified. This set of proteins could serve as drug targets. In the present study, the metabolic network of Mycobacterium tuberculosis was constructed and the fatty acid biosynthesis pathway was analyzed for potential drug targeting. The metabolic network was constructed using the KEGG LIGAND database and subjected to graph theoretical analysis. The nearness index of a protein was used to determine the influence of the said protein on other components in the network, allowing the proteins in a pathway to be ordered according to their nearness indices. A method for identifying the most strategic nodes to target for disrupting the metabolic networks is proposed, aiding the development of new drugs to combat this deadly disease
Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis
Representational Similarity Analysis is a method from cognitive neuroscience,
which helps in comparing representations from two different sources of data. In
this paper, we propose using Representational Similarity Analysis to probe the
semantic grounding in language models of code. We probe representations from
the CodeBERT model for semantic grounding by using the data from the IBM
CodeNet dataset. Through our experiments, we show that current pre-training
methods do not induce semantic grounding in language models of code, and
instead focus on optimizing form-based patterns. We also show that even a
little amount of fine-tuning on semantically relevant tasks increases the
semantic grounding in CodeBERT significantly. Our ablations with the input
modality to the CodeBERT model show that using bimodal inputs (code and natural
language) over unimodal inputs (only code) gives better semantic grounding and
sample efficiency during semantic fine-tuning. Finally, our experiments with
semantic perturbations in code reveal that CodeBERT is able to robustly
distinguish between semantically correct and incorrect code.Comment: Under review at ADMA 202
Home Automation Using SSVEP & Eye-Blink Detection Based Brain-Computer Interface
In this paper, we present a novel brain computer interface based home
automation system using two responses - Steady State Visually Evoked Potential
(SSVEP) and the eye-blink artifact, which is augmented by a Bluetooth based
indoor localization system, to greatly increase the number of controllable
devices. The hardware implementation of this system to control a table lamp and
table fan using brain signals has also been discussed and state-of-the-art
results have been achieved.Comment: 2 pages, 1 table, published at IEEE SMC 201
Continuous Time Continuous Space Homeostatic Reinforcement Learning (CTCS-HRRL) : Towards Biological Self-Autonomous Agent
Homeostasis is a biological process by which living beings maintain their
internal balance. Previous research suggests that homeostasis is a learned
behaviour. Recently introduced Homeostatic Regulated Reinforcement Learning
(HRRL) framework attempts to explain this learned homeostatic behavior by
linking Drive Reduction Theory and Reinforcement Learning. This linkage has
been proven in the discrete time-space, but not in the continuous time-space.
In this work, we advance the HRRL framework to a continuous time-space
environment and validate the CTCS-HRRL (Continuous Time Continuous Space HRRL)
framework. We achieve this by designing a model that mimics the homeostatic
mechanisms in a real-world biological agent. This model uses the
Hamilton-Jacobian Bellman Equation, and function approximation based on neural
networks and Reinforcement Learning. Through a simulation-based experiment we
demonstrate the efficacy of this model and uncover the evidence linked to the
agent's ability to dynamically choose policies that favor homeostasis in a
continuously changing internal-state milieu. Results of our experiments
demonstrate that agent learns homeostatic behaviour in a CTCS environment,
making CTCS-HRRL a promising framework for modellng animal dynamics and
decision-making.Comment: This work is a result of the ongoing collaboration between Cognitive
Neuroscience Lab, BITS Pilani K K Birla Goa Campus and Ecole Normale
Superieure, Paris France. This work is jointly supervised by Prof. Boris
Gutkin and Prof. Veeky Baths. arXiv admin note: substantial text overlap with
arXiv:2109.0658
Long-Term Memorability On Advertisements
Marketers spend billions of dollars on advertisements but to what end? At the
purchase time, if customers cannot recognize a brand for which they saw an ad,
the money spent on the ad is essentially wasted. Despite its importance in
marketing, until now, there has been no study on the memorability of ads in the
ML literature. Most studies have been conducted on short-term recall (<5 mins)
on specific content types like object and action videos. On the other hand, the
advertising industry only cares about long-term memorability (a few hours or
longer), and advertisements are almost always highly multimodal, depicting a
story through its different modalities (text, images, and videos). With this
motivation, we conduct the first large scale memorability study consisting of
1203 participants and 2205 ads covering 276 brands. Running statistical tests
over different participant subpopulations and ad-types, we find many
interesting insights into what makes an ad memorable - both content and human
factors. For example, we find that brands which use commercials with fast
moving scenes are more memorable than those with slower scenes (p=8e-10) and
that people who use ad-blockers remember lower number of ads than those who
don't (p=5e-3). Further, with the motivation of simulating the memorability of
marketing materials for a particular audience, ultimately helping create one,
we present a novel model, Sharingan, trained to leverage real-world knowledge
of LLMs and visual knowledge of visual encoders to predict the memorability of
a content. We test our model on all the prominent memorability datasets in
literature (both images and videos) and achieve state of the art across all of
them. We conduct extensive ablation studies across memory types, modality,
brand, and architectural choices to find insights into what drives memory
Mental health assessment in undergraduate students using DASS21 and Visual Working Memory Task
Substantial adolescence is spent in an academic environment where the student can experience varying intensities of depression, stress, and anxiety, which can be fatal. To address this concern, we utilized the Depression Anxiety and Stress Survey (DASS) 21 and Modified Sternberg working memory, thereby assessing the emotional states and assessing the impact on the cognitive ability of students (n=37, F=7) in terms of working memory. An intervention was provided (Art of Living YES+ program) for ten days. The assessment is carried out in the time window of two months before and after the intervention. F-test and T-test(pâ€0.05) on the scores and reaction time are performed for hypothesis testing. This statistical analysis reveals that both the depression category and stress category reject the null hypothesis. Among the thirty-seven, only five students took part in the post-intervention assessment, the scores in 28% of the questions had lower scores, and 19 % did not have any change; however, there was an increase in the scores in 42% of the questions. No significant changes are observed in the working memory ability of the students. Based on reaction time analysis: 11.62%, 16.27%, and 25.58% are outliers for each type of question, respectively. Two participants showed significantly lower reaction times, indicating a faster reading ability than the rest. This study shows that the intervention can positively impact emotional states-depression, stress, and affect working memory abilities