Reviewerly/exHarmony huggingface dataset:
Constructing High-Order Signed Distance Maps from Computed Tomography Data with Application to Bone Morphometry

An algorithm is presented for constructing high-order signed distance fields for two phase materials imaged with computed tomography. The signed distance field is high-order in that it is free of the quantization artifact associated with the distance transform of sampled signals. The narrowband is solved using a closest point algorithm extended for implicit embeddings that are not a signed distance field. The high-order fast sweeping algorithm is used to extend the narrowband to the remainder of the domain. The order of accuracy of the narrowband and extension methods are verified on ideal implicit surfaces. The method is applied to ten excised cubes of bovine trabecular bone. Localization of the surface, estimation of phase densities, and local morphometry is validated with these subjects. Since the embedding is high-order, gradients and thus curvatures can be accurately estimated locally in the image data.
Broadening Language Learner Perceptions of "Actual, Proper Study" to be More Inclusive of Smartphones in Irish Secondary Schools: ‘‘For Like Actual Like Proper Study, and Schoolwork, I Wouldn’t Use My Phone at All Really’’

New digital and mobile technologies are appearing at an ever-increasing rate, and there are potentially valuable educational applications of many of these smartphone-mediated resources. In Ireland, although the Department of Education and Skills (DES) has long advocated for a role for technology into the classroom, an educational focus on integrating these new mobile resources into the daily practices of language learners remains the exception rather than the norm. This paper aims to describe the findings of a study conducted with third-level learners of modern languages at an Irish university, which involved surveys, case studies, and a group interview, and which revealed the limited and tangential role that smartphones play in the learning habits of the participants. The paper will also describe how interpretation of the data identified a clear perception among learners of a narrow range of resources and practices that constitute "actual, proper study", a perception in which there is little space for the kind of spontaneous, multimodal, autonomous learning afforded by smartphones, with learners instead displaying a clear preference for more traditional practices such as writing out lists of verbs, and traditional materials such as grammar books. This paper will argue that firstly, the teaching practices and the lack of focus on smartphones that exist at secondary school levels play a key role in learners’ developing these attitudes and perceptions towards language learning, and secondly, that there is a clear need to foster a broader perception of what constitutes language learning that encompasses a balanced approach to smartphone-enhanced language learning. This is important not only to help the students as language learners, but also to develop the digital literacy skills which are increasingly important across all aspects of Irish and global society. The paper concludes by providing a series of steps which teachers can take which will help both to broaden perceptions of what constitutes language learning, and to allow for supervised, structured use of smartphones in the classroom to allow learners develop their learning-related smartphone literacy and wider digital literacy.
Numerical Approximation in CFD Problems Using Physics Informed Machine Learning

The thesis focuses on various techniques to find an alternate approximation method that could be universally used for a wide range of CFD problems but with low computational cost and low runtime. Various techniques have been explored within the field of machine learning to gauge the utility in fulfilling the core ambition. Steady advection diffusion problem has been used as the test case to understand the level of complexity up to which a method can provide solution. Ultimately, the focus stays over physics informed machine learning techniques where solving differential equations is possible without any training with computed data. The prevalent methods by I.E. Lagaris et.al. and M. Raissi et.al are explored thoroughly. The prevalent methods cannot solve advection dominant problems. A physics informed method, called as Distributed Physics Informed Neural Network (DPINN), is proposed to solve advection dominant problems. It increases the lexibility and capability of older methods by splitting the domain and introducing other physics-based constraints as mean squared loss terms. Various experiments are done to explore the end to end possibilities with the method. Parametric study is also done to understand the behavior of the method to different tunable parameters. The method is tested over steady advection-diffusion problems and unsteady square pulse problems. Very accurate results are recorded. Extreme learning machine (ELM) is a very fast neural network algorithm at the cost of tunable parameters. The ELM based variant of the proposed model is tested over the advection-diffusion problem. ELM makes the complex optimization simpler and Since the method is non-iterative, the solution is recorded in a single shot. The ELM based variant seems to work better than the simple DPINN method. Simultaneously scope for various development in future are hinted throughout the thesis.
Quantum optimisation via maximally amplified states

This paper presents the Maximum Amplification Optimisation Algorithm (MAOA), a novel quantum algorithm designed for combinatorial optimisation in the restricted circuit depth context of near-term quantum computing. The MAOA first produces a quantum state in which the optimal solutions to a problem are amplified to the maximum extent possible subject to a given restricted circuit depth. Subsequent repeated preparation and measurement of this maximally amplified state produces solutions of the highest quality as efficiently as possible. The MAOA performs considerably better than other near-term quantum algorithms, such as the Quantum Approximate Optimisation Algorithm (QAOA), as it amplifies optimal solutions significantly more and does so without the computationally demanding variational procedure required by these other algorithms. Additionally, a restricted circuit depth modification of the existing Grover adaptive search is introduced. This modified algorithm is referred to as the restricted Grover adaptive search (RGAS), and provides a useful comparison to the MAOA. The MAOA and RGAS are simulated on a practical vehicle routing problem, a computationally demanding portfolio optimisation problem, and an arbitrarily large problem with normally distributed solution qualities. In all cases, the MAOA and RGAS are shown to provide substantial speedup over classical random sampling in finding optimal solutions, while the MAOA consistently outperforms the RGAS. The speedup provided by the MAOA is quantified by demonstrating numerical convergence to a theoretically derived upper bound.
Sign-to-Speech Model for Sign Language Understanding: A Case Study of  Nigerian Sign Language

Through this paper, we seek to reduce the communication barrier between the hearing-impaired community and the larger society who are usually not familiar with sign language in the sub-Saharan region of Africa with the largest occurrences of hearing disability cases, while using Nigeria as a case study. The dataset is a pioneer dataset for the Nigerian Sign Language and was created in collaboration with relevant stakeholders. We pre-processed the data in readiness for two different object detection models and a classification model and employed diverse evaluation metrics to gauge model performance on sign-language to text conversion tasks. Finally, we convert the predicted sign texts to speech and deploy the best performing model in a lightweight application that works in real-time and achieves impressive results converting sign words/phrases to text and subsequently, into speech.
Muon $g-2$ and Screened Modified Gravity

We show how light scalar fields can account for the discrepancy between the theoretical and observed values of the anomalous magnetic moment of the (anti)muon. When coupled to both matter and photons, light scalar fields induce a change of the anomalous magnetic moment of charged particles. This arises from two concurrent effects. Classically, light scalars induce a change of the cyclotron frequency, complementing the electromagnetic effects coming from the magnetic and electric fields used experimentally. Light scalars also contribute to the anomalous magnetic moment quantum mechanically at the one-loop level. For unscreened scalar fields coupling with a Yukawa interaction to matter, these contributions are negligible after applying the Cassini bound on deviations from Newtonian gravity. On the other hand, screened scalars such as chameleons or symmetrons can couple strongly to matter in the laboratory and decouple in the Solar System. This allows us to probe branches of their parameter spaces where the recently measured anomalous magnetic moment of the (anti)muon can be accounted for in the chameleon and symmetron cases. This might be a hint that modified gravity is at play in the laboratory.
Sequence Transduction with Graph-based Supervision

The recurrent neural network transducer (RNN-T) objective plays a major role in building today's best automatic speech recognition (ASR) systems for production. Similarly to the connectionist temporal classification (CTC) objective, the RNN-T loss uses specific rules that define how a set of alignments is generated to form a lattice for the full-sum training. However, it is yet largely unknown if these rules are optimal and do lead to the best possible ASR results. In this work, we present a new transducer objective function that generalizes the RNN-T loss to accept a graph representation of the labels, thus providing a flexible and efficient framework to manipulate training lattices, e.g., for studying different transition rules, implementing different transducer losses, or restricting alignments. We demonstrate that transducer-based ASR with CTC-like lattice achieves better results compared to standard RNN-T, while also ensuring a strictly monotonic alignment, which will allow better optimization of the decoding procedure. For example, the proposed CTC-like transducer achieves an improvement of 4.8% on the test-other condition of LibriSpeech relative to an equivalent RNN-T based system.
FREGAN : an application of generative adversarial networks in enhancing the frame rate of videos

A digital video is a collection of individual frames, while streaming the video the scene utilized the time slice for each frame. High refresh rate and high frame rate is the demand of all high technology applications. The action tracking in videos becomes easier and motion becomes smoother in gaming applications due to the high refresh rate. It provides a faster response because of less time in between each frame that is displayed on the screen. FREGAN (Frame Rate Enhancement Generative Adversarial Network) model has been proposed, which predicts future frames of a video sequence based on a sequence of past frames. In this paper, we investigated the GAN model and proposed FREGAN for the enhancement of frame rate in videos. We have utilized Huber loss as a loss function in the proposed FREGAN. It provided excellent results in super-resolution and we have tried to reciprocate that performance in the application of frame rate enhancement. We have validated the effectiveness of the proposed model on the standard datasets (UCF101 and RFree500). The experimental outcomes illustrate that the proposed model has a Peak signal-to-noise ratio (PSNR) of 34.94 and a Structural Similarity Index (SSIM) of 0.95.
Intervention Efficient Algorithm for Two-Stage Causal MDPs

We study Markov Decision Processes (MDP) wherein states correspond to causal graphs that stochastically generate rewards. In this setup, the learner's goal is to identify atomic interventions that lead to high rewards by intervening on variables at each state. Generalizing the recent causal-bandit framework, the current work develops (simple) regret minimization guarantees for two-stage causal MDPs, with parallel causal graph at each state. We propose an algorithm that achieves an instance dependent regret bound. A key feature of our algorithm is that it utilizes convex optimization to address the exploration problem. We identify classes of instances wherein our regret guarantee is essentially tight, and experimentally validate our theoretical results.
Unsupervised Discovery of Unaccusative and Unergative Verbs

We present an unsupervised method to detect English unergative and unaccusative verbs. These categories allow us to identify verbs participating in the causative-inchoative alternation without knowing the semantic roles of the verb. The method is based on the generation of intransitive sentence variants of candidate verbs and probing a language model. We obtained results on par with similar approaches, with the added benefit of not relying on annotated resources.
STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

In this work we investigate stochastic non-convex optimization problems where the objective is an expectation over smooth loss functions, and the goal is to find an approximate stationary point. The most popular approach to handling such problems is variance reduction techniques, which are also known to obtain tight convergence rates, matching the lower bounds in this case. Nevertheless, these techniques require a careful maintenance of anchor points in conjunction with appropriately selected "mega-batchsizes". This leads to a challenging hyperparameter tuning problem, that weakens their practicality. Recently, [Cutkosky and Orabona, 2019] have shown that one can employ recursive momentum in order to avoid the use of anchor points and large batchsizes, and still obtain the optimal rate for this setting. Yet, their method called STORM crucially relies on the knowledge of the smoothness, as well a bound on the gradient norms. In this work we propose STORM+, a new method that is completely parameter-free, does not require large batch-sizes, and obtains the optimal $O(1/T^{1/3})$ rate for finding an approximate stationary point. Our work builds on the STORM algorithm, in conjunction with a novel approach to adaptively set the learning rate and momentum parameters.
Local simultaneous state discrimination

Quantum state discrimination is one of the most fundamental problems studied in quantum information theory. Applications range from channel coding to metrology and cryptography. In this work, we introduce a new variant of this task: Local Simultaneous State Discrimination (LSSD). While previous distributed variants of the discrimination problem always allowed some communication between the parties to come up with a joint answer, the parties in LSSD cannot communicate and have to simultaneously answer correctly. This simultaneity implies, e.g., that for classical states, the problem does not trivialize to a non-distributed distinguishing task. While interesting in its own right, this problem also arises in quantum cryptography. After introducing the problem, we give a number of characterization results. We give examples showing that i) the optimal strategy for local discrimination need not coincide with the optimal strategy for LSSD, even for classical states, ii) an additional entangled resource can increase the optimal success probability in LSSD, and iii) stronger-than-quantum non-signalling resources can allow for a higher success probability in some cases, compared to strategies using entanglement. Finally, we show that finding the optimal strategy in (classical) 3-party LSSD is NP-hard.
Dropout in Training Neural Networks: Flatness of Solution and Noise Structure

It is important to understand how the popular regularization method dropout helps the neural network training find a good generalization solution. In this work, we show that the training with dropout finds the neural network with a flatter minimum compared with standard gradient descent training. We further find that the variance of a noise induced by the dropout is larger at the sharper direction of the loss landscape and the Hessian of the loss landscape at the found minima aligns with the noise covariance matrix by experiments on various datasets, i.e., MNIST, CIFAR-10, CIFAR-100 and Multi30k, and various structures, i.e., fully-connected networks, large residual convolutional networks and transformer. For networks with piece-wise linear activation function and the dropout is only at the last hidden layer, we then theoretically derive the Hessian and the covariance of dropout randomness, where these two quantities are very similar. This similarity may be a key reason accounting for the goodness of dropout.
Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions

People affected by machine learning model decisions may benefit greatly from access to recourses, i.e. suggestions about what features they could change to receive a more favorable decision from the model. Current approaches try to optimize for the cost incurred by users when adopting a recourse, but they assume that all users share the same cost function. This is an unrealistic assumption because users might have diverse preferences about their willingness to change certain features. In this work, we introduce a new method for identifying recourse sets for users which does not assume that users' preferences are known in advance. We propose an objective function, Expected Minimum Cost (EMC), based on two key ideas: (1) when presenting a set of options to a user, there only needs to be one low-cost solution that the user could adopt; (2) when we do not know the user's true cost function, we can approximately optimize for user satisfaction by first sampling plausible cost functions from a distribution, then finding a recourse set that achieves a good cost for these samples. We optimize EMC with a novel discrete optimization algorithm, Cost Optimized Local Search (COLS), which is guaranteed to improve the recourse set quality over iterations. Experimental evaluation on popular real-world datasets with simulated users demonstrates that our method satisfies up to 25.89 percentage points more users compared to strong baseline methods, while, the human evaluation shows that our recourses are preferred more than twice as often as the strongest baseline recourses. Finally, using standard fairness metrics we show that our method can provide more fair solutions across demographic groups than baselines. We provide our source code at: https://github.com/prateeky2806/EMC-COLS-recourse
Revisiting joint decoding based multi-talker speech recognition with DNN acoustic model

In typical multi-talker speech recognition systems, a neural network-based acoustic model predicts senone state posteriors for each speaker. These are later used by a single-talker decoder which is applied on each speaker-specific output stream separately. In this work, we argue that such a scheme is sub-optimal and propose a principled solution that decodes all speakers jointly. We modify the acoustic model to predict joint state posteriors for all speakers, enabling the network to express uncertainty about the attribution of parts of the speech signal to the speakers. We employ a joint decoder that can make use of this uncertainty together with higher-level language information. For this, we revisit decoding algorithms used in factorial generative models in early multi-talker speech recognition systems. In contrast with these early works, we replace the GMM acoustic model with DNN, which provides greater modeling power and simplifies part of the inference. We demonstrate the advantage of joint decoding in proof of concept experiments on a mixed-TIDIGITS dataset.
Integer complexity: Stability and self-similarity

Define $||n||$ to be the complexity of $n$, the smallest number of ones needed to write $n$ using an arbitrary combination of addition and multiplication. The set $\mathscr{D}$ of defects, differences $\delta(n):=||n||-3\log_3 n$, is known to be a well-ordered subset of $[0,\infty)$, with order type $\omega^\omega$. This is proved by showing that, for any $r$, there is a finite set $\mathcal{S}_s$ of certain multilinear polynomials, called low-defect polynomials, such that $\delta(n)\le s$ if and only if one can write $n = f(3^{k_1},\ldots,3^{k_r})3^{k_{r+1}}$. In this paper we show that, in addition to it being true that $\mathscr{D}$ (and thus $\overline{\mathscr{D}}$) has order type $\omega^\omega$, this set satisifies a sort of self-similarity property with $\overline{\mathscr{D}}' = \overline{\mathscr{D}} + 1$. This is proven by restricting attention to substantial low-defect polynomials, ones that can be themselves written efficiently in a certain sense, and showing that in a certain sense the values of these polynomials at powers of $3$ have complexity equal to the na\"ive upper bound most of the time. As a result, we also prove that, under appropriate conditions on $a$ and $b$, numbers of the form $b(a3^k+1)3^\ell$ will, for all sufficiently large $k$, have complexity equal to the na\"ive upper bound. These results resolve various earlier conjectures of the second author.
Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units

Language models (LMs) for text data have been studied extensively for their usefulness in language generation and other downstream tasks. However, language modelling purely in the speech domain is still a relatively unexplored topic, with traditional speech LMs often depending on auxiliary text LMs for learning distributional aspects of the language. For the English language, these LMs treat words as atomic units, which presents inherent challenges to language modelling in the speech domain. In this paper, we propose a novel LSTM-based generative speech LM that is inspired by the CBOW model and built on linguistic units including syllables and phonemes. This offers better acoustic consistency across utterances in the dataset, as opposed to single melspectrogram frames, or whole words. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features. Through our experiments, we also highlight some well known, but poorly documented challenges in training generative speech LMs, including the mismatch between the supervised learning objective with which these models are trained such as Mean Squared Error (MSE), and the true objective, which is speech quality. Our experiments provide an early indication that while validation loss and Mel Cepstral Distortion (MCD) are not strongly correlated with generated speech quality, traditional text language modelling metrics like perplexity and next-token-prediction accuracy might be.
PIE: Pseudo-Invertible Encoder

We consider the problem of information compression from high dimensional data. Where many studies consider the problem of compression by non-invertible transformations, we emphasize the importance of invertible compression. We introduce new class of likelihood-based autoencoders with pseudo bijective architecture, which we call Pseudo Invertible Encoders. We provide the theoretical explanation of their principles. We evaluate Gaussian Pseudo Invertible Encoder on MNIST, where our model outperforms WAE and VAE in sharpness of the generated images.