Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategies

Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world and the third leading cause of mortality worldwide. It is often underdiagnosed or not diagnosed until later in the disease course. Spirometry tests are the gold standard for diagnosing COPD but can be difficult to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may serve as a screening tool to identify patients with COPD who should undergo further testing. Currently, no research applies deep learning (DL) algorithms that use large multi-site and multi-modal data to detect COPD patients and evaluate fairness across demographic groups. We use three CXR datasets in our study, CheXpert to pre-train models, MIMIC-CXR to develop, and Emory-CXR to validate our models. The CXRs from patients in the early stage of COPD and not on mechanical ventilation are selected for model training and validation. We visualize the Grad-CAM heatmaps of the true positive cases on the base model for both MIMIC-CXR and Emory-CXR test datasets. We further propose two fusion schemes, (1) model-level fusion, including bagging and stacking methods using MIMIC-CXR, and (2) data-level fusion, including multi-site data using MIMIC-CXR and Emory-CXR, and multi-modal using MIMIC-CXRs and MIMIC-IV EHR, to improve the overall model performance. Fairness analysis is performed to evaluate if the fusion schemes have a discrepancy in the performance among different demographic groups. The results demonstrate that DL models can detect COPD using CXRs, which can facilitate early screening, especially in low-resource regions where CXRs are more accessible than spirometry. The multi-site data fusion scheme could improve the model generalizability on the Emory-CXR test data. Further studies on using CXR or other modalities to predict COPD ought to be in future work.
ECE496Y Final Report: Edge Machine Learning for Detecting Freezing of Gait in Parkinson's Patients

Parkinson's disease is a common neurological disease, entailing a multitude of motor deficiency symptoms. In this project, we developed a device with an uploaded edge machine learning algorithm that can detect the onset of freezing of gait symptoms in a Parkinson's patient. The algorithm achieved an accuracy of 83.7% in a validation using data from ten patients. The model was deployed in a microcontroller Arduino Nano 33 BLE Sense Board model and validated in real-time operation with data streamed to the microcontroller from a computer.
Algorithmic proof for the completeness of the two-dimensional Ising model

We show that the two-dimensional (2D) Ising model is complete, in the sense that the partition function of any lattice model on any graph is equal to the partition function of the 2D Ising model with complex coupling. The latter model has all its spin-spin coupling equal to $i\frac{\ensuremath{\pi}}{4}$ and all parameters of the original model are contained in the local magnetic fields of the Ising model. This result has already been derived by using techniques from quantum information theory and by exploiting the universality of cluster states. Here we do not use the quantum formalism and hence make the completeness result accessible to a wide audience. Furthermore, our method has the advantage of being algorithmic in nature so that, by following a set of simple graphical transformations, one is able to transform any discrete lattice model to an Ising model defined on a (polynomially) larger 2D lattice.
AutoDRIVE Simulator - Technical Report

This work is an outcome of research internship focused on the research topic entitled “Development of a Simulation System using Robot Operating System” supervised by Assoc. Prof. Dr. Ming Xie at the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. This report was submitted to the India Connect @ NTU (IC@N) Office as a part of the India Connect @ NTU (IC@N) Research Internship Program, 2020. ABSTRACT AutoDRIVE is envisioned to be a comprehensive research platform for scaled autonomous vehicles. This work is a stepping-stone towards the greater goal of realizing such a research platform. Particularly, this work proposes a pseudo-realistic simulator for scaled autonomous vehicles, which is targeted towards simplicity, modularity and flexibility. The AutoDRIVE Simulator not only mimics realistic system dynamics but also simulates a comprehensive sensor suite and realistic actuator response. The simulator also features a communication bridge in order to interface externally developed autonomous driving software stack, which allows users to design and develop their algorithms conveniently and have them tested on our simulator. Presently, the bridge is compatible with Robot Operating System (ROS) and can be interfaced directly with the Python and C++ scripts developed as a part of this project. The bridge supports local as well as distributed computing.
Leptogenesis with Lepton-Number-Violating Dirac Neutrinos

Dirac neutrinos with lepton-number-violating interactions can give rise to a new leptogenesis mechanism. In its simplest renormalizable realization, based on a gauged B-L symmetry spontaneously broken by four units, the decay of a new scalar creates an asymmetry in the right-handed neutrinos. A neutrinophilic two-Higgs-doublet model converts this asymmetry to the baryons, provides a natural explanation of the small neutrino masses, and can lead to an effective number of relativistic degrees of freedom of Neff≃3.29 due to the entropy-suppressed contribution of the right-handed neutrinos.
The edgewise subdivision criterion for 2-Segal objects

<p>We show that the edgewise subdivision of a <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="2"> <mml:semantics> <mml:mn>2</mml:mn> <mml:annotation encoding="application/x-tex">2</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-Segal object is always a Segal object, and furthermore that this property characterizes <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="2"> <mml:semantics> <mml:mn>2</mml:mn> <mml:annotation encoding="application/x-tex">2</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-Segal objects. </p>
Shared Loss between Generators of GANs

Generative adversarial networks are generative models that are capable of replicating the implicit probability distribution of the input data with high accuracy. Traditionally, GANs consist of a Generator and a Discriminator which interact with each other to produce highly realistic artificial data. Traditional GANs fall prey to the mode collapse problem, which means that they are unable to generate the different variations of data present in the input dataset. Recently, multiple generators have been used to produce more realistic output by mitigating the mode collapse problem. We use this multiple generator framework. The novelty in this paper lies in making the generators compete against each other while interacting with the discriminator simultaneously. We show that this causes a dramatic reduction in the training time for GANs without affecting its performance.
Offline Estimation of Controlled Markov Chains: Minimax Nonparametric Estimators and Sample Efficiency

Abstract Controlled Markov chains (CMCs) have wide applications in engineering and machine learning, forming a key component in many reinforcement learning problems. In this work, we consider the estimation of the transition probabilities of a finite-state finite-control CMC, and develop a minimax sample complexity bounds for nonparametric estimation of these transition probability matrices. Unlike most studies that have been done in the online setup, we consider offline MDPs. Our results are quite general, since we do not assume anything specific about the logging policy. Instead, the dependence of our statistical bounds on the logging policy comes in the form of a natural mixing coefficient. We demonstrate an interesting trade-off between stronger assumptions on mixing versus requiring more samples to achieve a particular PAC-bound. We demonstrate the validity of our results under various examples, like ergodic Markov chains, weakly ergodic inhomogenous Markov chains, and controlled Markov chains with non-stationary Markov, episodic, and greedy controls. Lastly, we use the properties of the estimated transition matrix to perform estimate the value function when the controls are stationary and Markov.
Evaluating CNN with Oscillatory Activation Function

The reason behind CNNs capability to learn high-dimensional complex features from the images is the non-linearity introduced by the activation function. Several advanced activation functions have been discovered to improve the training process of neural networks, as choosing an activation function is a crucial step in the modeling. Recent research has proposed using an oscillating activation function to solve classification problems inspired by the human brain cortex. This paper explores the performance of one of the CNN architecture ALexNet on MNIST and CIFAR10 datasets using oscillatory activation function (GCU) and some other commonly used activation functions like ReLu, PReLu, and Mish.
SportsTrack: An Innovative Method for Tracking Athletes in Sports Scenes

The SportsMOT dataset aims to solve multiple object tracking of athletes in different sports scenes such as basketball or soccer. The dataset is challenging because of the unstable camera view, athletes' complex trajectory, and complicated background. Previous MOT methods can not match enough high-quality tracks of athletes. To pursue higher performance of MOT in sports scenes, we introduce an innovative tracker named SportsTrack, we utilize tracking by detection as our detection paradigm. Then we will introduce a three-stage matching process to solve the motion blur and body overlapping in sports scenes. Meanwhile, we present another innovation point: one-to-many correspondence between detection bboxes and crowded tracks to handle the overlap of athletes' bodies during sports competitions. Compared to other trackers such as BOT-SORT and ByteTrack, We carefully restored edge-lost tracks that were ignored by other trackers. Finally, we reached the SOTA result in the SportsMOT dataset.
Semiclassical analysis of Bose–Hubbard dynamics

In this work the two-site Bose–Hubbard model is studied analytically in the limit of weak coupling u and large number of particles N. In particular, the difference in the occupation between the two sites, where initially all particles are at one site, was calculated analytically. This quantity exhibits collapses and revivals that superimpose rapid oscillations. Excellent agreement with the exact numerical solution was found. The semiclassical approximation where 1 N ? > plays the role of Planck's constant was used and perturbation theory to order u 2 ? > was applied. The occupation difference was calculated also for the case where initially both sites are occupied provided that the difference in occupation is sufficiently large. This work provides an analytical description of results that were so far found only numerically. Similar behavior and analysis are expected for a large variety of physical situations.
Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction

Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications such as coverage extension, enhanced security monitoring, and disaster management. However, these drones need to deploy large antenna arrays and use narrow directive beams to maintain a sufficient link budget. The large beam training overhead associated with these arrays makes adjusting these narrow beams challenging for highly-mobile drones. To address these challenges, this paper proposes a vision-aided machine learning-based approach that leverages visual data collected from cameras installed on the drones to enable fast and accurate beam prediction. Further, to facilitate the evaluation of the proposed solution, we build a synthetic drone communication dataset consisting of co-existing wireless and visual data. The proposed vision-aided solution achieves a top-1 beam prediction accuracy of ≈ 91 % and close to 100% top-3 accuracy. These results highlight the efficacy of the proposed solution towards enabling highly mobile mmWave/THz drone communication.
mOKB6: A Multilingual Open Knowledge Base Completion Benchmark

Automated completion of open knowledge bases (Open KBs), which are constructed from triples of the form (subject phrase, relation phrase, object phrase), obtained via open information extraction (Open IE) system, are useful for discovering novel facts that may not be directly present in the text. However, research in Open KB completion (Open KBC) has so far been limited to resource-rich languages like English. Using the latest advances in multilingual Open IE, we construct the first multilingual Open KBC dataset, called mOKB6, containing facts from Wikipedia in six languages (including English). Improvingthe previous Open KB construction pipeline by doing multilingual coreference resolution andkeeping only entity-linked triples, we create a dense Open KB. We experiment with several models for the task and observe a consistent benefit of combining languages with the help of shared embedding space as well as translations of facts. We also observe that current multilingual models struggle to remember facts seen in languages of different scripts.
Distributed and secure linear algebra - Master Thesis

Cryptography is the discipline that allows securing of the exchange of information. In this internship, we will focus on a certain branch of this discipline, secure computation in a network. The main goal of this internship, illustrated in this report, is to adapt a roster of protocols intended to do linear algebra. We want to adapt them to do algebra for matrices with polynomial coefficients. We then wish to make a complete analysis of the different complexities of these protocols.
Modeling Human Exploration Through Resource-Rational Reinforcement Learning

Equipping artificial agents with useful exploration mechanisms remains a challenge to this day. Humans, on the other hand, seem to manage the trade-off between exploration and exploitation effortlessly. In the present article, we put forward the hypothesis that they accomplish this by making optimal use of limited computational resources. We study this hypothesis by meta-learning reinforcement learning algorithms that sacrifice performance for a shorter description length (defined as the number of bits required to implement the given algorithm). The emerging class of models captures human exploration behavior better than previously considered approaches, such as Boltzmann exploration, upper confidence bound algorithms, and Thompson sampling. We additionally demonstrate that changing the description length in our class of models produces the intended effects: reducing description length captures the behavior of brain-lesioned patients while increasing it mirrors cognitive development during adolescence.
Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation

Code generation focuses on the automatic conversion of natural language (NL) utterances into code snippets. The sequence-to-tree (Seq2Tree) approaches are proposed for code generation, with the guarantee of the grammatical correctness of the generated code, which generate the subsequent Abstract Syntax Tree (AST) node relying on antecedent predictions of AST nodes. Existing Seq2Tree methods tend to treat both antecedent predictions and subsequent predictions equally. However, under the AST constraints, it is difficult for Seq2Tree models to produce the correct subsequent prediction based on incorrect antecedent predictions. Thus, antecedent predictions ought to receive more attention than subsequent predictions. To this end, in this paper, we propose an effective method, named Antecedent Prioritized (AP) Loss, that helps the model attach importance to antecedent predictions by exploiting the position information of the generated AST nodes. We design an AST-to-Vector (AST2Vec) method, that maps AST node positions to two-dimensional vectors, to model the position information of AST nodes. To evaluate the effectiveness of our proposed loss, we implement and train an Antecedent Prioritized Tree-based code generation model called APT. With better antecedent predictions and accompanying subsequent predictions, APT significantly improves the performance. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate the superiority and generality of our proposed method.
WASHINGTON PHOTOMETRY OF THE GLOBULAR CLUSTERS IN THE VIRGO GIANT ELLIPTICAL GALAXY M86

We present a photometric study of the globular clusters (GCs) in the Virgo giant elliptical galaxy M86 based on Washington CT1 images. The colors of the GCs in M86 show a bimodal distribution with a blue peak at (C − T1) = 1:30 and a red peak at (C − T1) = 1:72. The spatial distribution of the red GCs is elongated similar to that of the stellar halo, while that of the blue GCs is roughly circular. The radial number density profile of the blue GCs is more extended than that of the red GCs. The radial number density profile of the red GCs is consistent with the surface brightness profile of the M86 stellar halo. The GC system has a negative radial color gradient, which is mainly due to the number ratio of the blue GCs to the red GCs increasing as galactocentric radius increases. The bright blue GCs in the outer region of M86 show a blue tilt: the brighter they are, the redder their mean colors get. These results are discussed in comparison with other Virgo giant elliptical galaxies.
Robust Markov decision processes under parametric transition distributions

This paper considers robust Markov decision processes under parametric transition distributions. We assume that the true transition distribution is uniquely specified by some parametric distribution, and explicitly enforce that the worst-case distribution from the model is uniquely specified by a distribution in the same parametric family. After formulating the parametric robust model, we focus on developing algorithms for carrying out the robust Bellman updates required to complete robust value iteration. We first formulate the update as a linear program by discretising the ambiguity set. Since this model scales poorly with problem size and requires large amounts of pre-computation, we develop two additional algorithms for solving the robust Bellman update. Firstly, we present a cutting surface algorithm for solving this linear program in a shorter time. This algorithm requires the same pre-computation, but only ever solves the linear program over small subsets of the ambiguity set. Secondly, we present a novel projection-based bisection search algorithm that completely eliminates the need for discretisation and does not require any pre-computation. We test our algorithms extensively on a dynamic multi-period newsvendor problem under binomial and Poisson demands. In addition, we compare our methods with the non-parametric phi-divergence based methods from the literature. We show that our projection-based algorithm completes robust value iteration significantly faster than our other two parametric algorithms, and also faster than its non-parametric equivalent.
Spectral evolution and invariance in linear-width neural networks

We investigate the spectral properties of linear-width feed-forward neural networks, where the sample size is asymptotically proportional to network width. Empirically, we show that the weight spectra in this high dimensional regime are invariant when trained by gradient descent for small constant learning rates and the changes in both operator and Frobenius norm are $\Theta(1)$ in the limit. This implies the bulk spectra for both the conjugate and neural tangent kernels are also invariant. We demonstrate similar characteristics for models trained with mini-batch (stochastic) gradient descent with small learning rates and provide a theoretical justification for this special scenario. When the learning rate is large, we show empirically that an outlier emerges with its corresponding eigenvector aligned to the training data structure. We also show that after adaptive gradient training, where we have a lower test error and feature learning emerges, both the weight and kernel matrices exhibit heavy tail behavior. Different spectral properties such as invariant bulk, spike, and heavy-tailed distribution correlate to how far the kernels deviate from initialization. To understand this phenomenon better, we focus on a toy model, a two-layer network on synthetic data, which exhibits different spectral properties for different training strategies. Analogous phenomena also appear when we train conventional neural networks with real-world data. Our results show that monitoring the evolution of the spectra during training is an important step toward understanding the training dynamics and feature learning.
Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles

Rare genetic disorders affect more than 6% of the global population. Reaching a diagnosis is challenging because rare disorders are very diverse. Many disorders have recognizable facial features that are hints for clinicians to diagnose patients. Previous work, such as GestaltMatcher, utilized representation vectors produced by a DCNN similar to AlexNet to match patients in high-dimensional feature space to support "unseen" ultra-rare disorders. However, the architecture and dataset used for transfer learning in GestaltMatcher have become outdated. Moreover, a way to train the model for generating better representation vectors for unseen ultra-rare disorders has not yet been studied. Because of the overall scarcity of patients with ultra-rare disorders, it is infeasible to directly train a model on them. Therefore, we first analyzed the influence of replacing GestaltMatcher DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Additionally, we experimented with different face recognition datasets for transfer learning. Furthermore, we proposed test-time augmentation, and model ensembles that mix general face verification models and models specific for verifying disorders to improve the disorder verification accuracy of unseen ultra-rare disorders. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders. Code is available at github.com/igsb/GestaltMatcher-Arc.