With the increasing use of digital cameras, people come around a variety of images in their daily life. Some of the images are of good quality while a few images we encounter with which are poor in quality. This noise may be caused by low light conditions or other intensity problems. To denoise an image, i.e., to reduce the noise in an image, there are various approaches used. It has been a hot topic of research for a long time and is still under experimentation by researchers.

The existential threat of Covid-19 has highlighted an acute need to develop working therapeutics against emerging health concerns. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds -- so long as we can keep up with the viral threat, and access the right data. As with all new medical maladies, oftentimes the data need time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Jameel Clinic for Machine Learning in Health to ask: How can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2? Typically, data scientists use deep learning to pick out drug combinations with large existing datasets for things like cancer and cardiovascular disease, but, understandably, they can't be used for new illnesses with limited data.

Conversational context means knowing what a conversation is about by picking up cues from previous lines of dialogue or through environmental factors and humans have the ability to understand conversational context right from their childhood. If you ask a child, "Candy?", their reply will most probably be "Yes!". The understanding of the child here is that you are offering them some candy and their choices for a response are either "Yes", "No" or total silence. However, if you ask a chatbot the same question, it wouldn't know what to make out of it since chatbots do not have the innate ability to understand conversational context like humans do. So, in this article, we will discuss how you can add an understanding of the conversational context in Chatbots to make them more human-like.

In theory, RNNs (Recurrent Neural Networks) should extract features (hidden states) from long sequential data. In reality, researchers had a hard time training the basic RNNs using BPTT (Back-Propagation Through Time).

Artificial Intelligence is shaping the future of stock trading; while humans remain a big part of the trading equation, AI plays a critical role. One of the markets that benefit the most from Artificial Intelligence and Machine Learning is forex trading. The Forex industry is worth $1.93 quadrillion. Stats emphasize that 90% of successful forex trades made are using AI. Artificial Intelligence helps to minimize human error in trading, is much faster and more accurate.

FAST will not perform well where detection of multiple features has to be performed in same region of an image. For this Non-Maximum Suppression is used. In Non-Maximum Suppression a score function is computed, V for all the detected feature points. In a nut shell, FAST is faster than many existing feature detectors but performs poorly in presence of high level of noise. Mainly because the pixel values will be altered because of high-level of noise. Opencv documentation mentions two feature matching methods.

Although our computational techniques and hardware resources have advanced greatly these past few decades, given the rise of large language models which have applications in multiple sectors, the environmental impact of training and developing NLP models, particularly on a large scale, could have detrimental consequences on the environment. This is due to the energy usage (whether carbon neutral or not) [1, 2] possibly contributing directly or indirectly to the effects of climate change. With experiments on total time expected for models such as Transformer, BERT, and GPT-2 to train and the subsequent cost of training, Strubell et al. [2] provides substantial evidence that researchers need to increasingly prioritize computationally efficient hardware and algorithms. There has been research to suggest that large language models could be outperformed by their less computationally intensive counterparts on multiple tasks with the help of fine-tuning [3] and techniques such as using random search for hyperparameter search [1, 4-6] or pruning [7, 8]. Additionally, as performance across different tasks tends to vary based on the languages used, data availability, model architectures among other factors, it is likely that training models to a certain performance level for some languages are less carbon-intensive than others. This is speculation is substantiated by the correlation found between morphological ambiguity of languages and the performance of language models on European languages [9]. The primary objective of our work is to measure the differences in carbon emissions released between multiple language pairs and assess the contributions of various components, within the two architectures we've used, to the carbon We are grateful to the Research Society MIT, Manipal for supporting this work, and we attribute equal contribution to all the authors of this paper.

Quantum Machine Intelligence manuscript No. (will be inserted by the editor) Abstract The Large Hadron Collider (LHC) at the Keywords Quantum Graph Neural Networks Quantum European Organisation for Nuclear Research (CERN) Machine Learning Particle Track Reconstruction will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity 1 Introduction will significantly increase the number of particles interacting with the detector. The interaction of Particle accelerator experiments aim to understand the particles with a detector is referred to as "hit". The nature of particles by colliding groups of particles at HL-LHC will yield many more detector hits, which will high energies and try to observe creation of particles pose a combinatorial challenge by using reconstruction and their decays, e.g. to validate theories. The Large algorithms to determine particle trajectories from those Hadron Collider (LHC) at the European Organisation hits. This work explores the possibility of converting for Nuclear Research (CERN) provides proton-proton a novel Graph Neural Network model, that can optimally collisions to four main experiments as well as other take into account the sparse nature of the tracking small experiments and fixed-target experiments. In order detector data and their complex geometry, to a Hybrid to achieve a high sensitivity, these experiments use Quantum-Classical Graph Neural Network that advanced software and hardware.

This paper presents a hybrid approach between scale-space theory and deep learning, where a deep learning architecture is constructed by coupling parameterized scale-space operations in cascade. By sharing the learnt parameters between multiple scale channels, and by using the transformation properties of the scale-space primitives under scaling transformations, the resulting network becomes provably scale covariant. By in addition performing max pooling over the multiple scale channels, a resulting network architecture for image classification also becomes provably scale invariant. We investigate the performance of such networks on the MNISTLargeScale dataset, which contains rescaled images from original MNIST over a factor of 4 concerning training data and over a factor of 16 concerning testing data. It is demonstrated that the resulting approach allows for scale generalization, enabling good performance for classifying patterns at scales not present in the training data.

Machine Learning (ML) has emerged as a core technology to provide learning models to perform complex tasks. Boosted by Machine Learning as a Service (MLaaS), the number of applications relying on ML capabilities is ever increasing. However, ML models are the source of different privacy violations through passive or active attacks from different entities. In this paper, we present MixNN a proxy-based privacy-preserving system for federated learning to protect the privacy of participants against a curious or malicious aggregation server trying to infer sensitive attributes. MixNN receives the model updates from participants and mixes layers between participants before sending the mixed updates to the aggregation server. This mixing strategy drastically reduces privacy without any trade-off with utility. Indeed, mixing the updates of the model has no impact on the result of the aggregation of the updates computed by the server. We experimentally evaluate MixNN and design a new attribute inference attack, Sim, exploiting the privacy vulnerability of SGD algorithm to quantify privacy leakage in different settings (i.e., the aggregation server can conduct a passive or an active attack). We show that MixNN significantly limits the attribute inference compared to a baseline using noisy gradient (well known to damage the utility) while keeping the same level of utility as classic federated learning.