Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate MLEs of the parameters of location-scale models under type II censoring

Log-location-scale distributions are widely used parametric models that have fundamental importance in both parametric and semiparametric frameworks. The likelihood equations based on a Type II censored sample from location-scale distributions do not provide explicit solutions for the para-meters. Statistical software is widely available and is based on iterative methods (such as, Newton Raphson Algorithm, EM algorithm etc.), which require starting values near the global maximum. There are also many situations that the specialized software does not handle. This paper provides a method for determining explicit estimators for the location and scale parameters by approximating the likelihood function, where the method does not require any starting values. The performance of the proposed approximate method for the Weibull distribution and Log-Logistic distributions is compared with those based on iterative methods through the use of simulation studies for a wide range of sample size and Type II censoring schemes. Here we also examine the probability coverages of the pivotal quantities based on asymptotic normality. In addition, two examples are given.
Investigating the Engagement of Mature Students with Mathematics Learning Support.

The Maths Learning Support Centre (MLSC) in the Dublin Institute of Technology (DIT) provides free mathematical support to all DIT students. This support is primarily delivered through a drop-in service, where students can receive one-to-one tuition, without an appointment, in any area of mathematics. In the first semester of the 2013/14 academic year, a significant proportion of students that availed of this drop-in service were mature students enrolled in Engineering programmes. This is of particular interest as mature students constitute a relatively small proportion of the total student body, motivating a deeper study of the reasons for the high levels of engagement in this cohort. To this end two focus groups were conducted, consisting of those who do and do not attend the MLSC. Particular interest was paid to the motivations of students who attend and the reasons given by those who do not. The motivations of mature students were found to be multifaceted whereas the reasons for non-engagement were mostly in line with the literature. In additon, some quantitative analysis was carried out to determine what effect the MLSC had on students' academic performance.
Comptonization and Reprocessing Processes in Accretion Disks: Applications to the Seyfert 1 Galaxies NGC 5548 and NGC 4051

Simultaneous multi-wavelength observations have revealed complex variability in AGNs. To explain the variability we considered a theoretical model consisting of an inner hot comptonizing corona and an outer thin accretion disk, with interactions between the two components in the form of comptonization and reprocessing. We found that the variability of AGNs is strongly affected by the parameters of the model, namely, the truncated disk radius rmin, the corona radius rs, the temperature KTe and the optical depth τ0 of the corona. We applied this model to the two best observed Seyfert 1 galaxies, NGC 5548 and NGC 4051. Our model can reproduce satisfactory the observed SEDs. Our fits indicate that NGC 5548 may have experienced dramatic changes in physical parameters between 1989–1990 and 1998, and that NGC 4051 has a much larger truncated disk radius (700 Schwarzschild radii) than NGC 5548 (several tens of Schwarzschild radii). Since we adopted a more refined treatment of the comptonization process rather than simply assuming a cut-off power law, our results should be more reasonable than the previous ones.
An absolute bound on limit cycles due to roundoff errors in digital filters

An absolute bound on limit cycle oscillations in fixed-point digital filter implementations due to roundoff errors is presented. Periodicity of the limit cycles is assumed in the derivation. Useful design results are explicitly given for the case of second-order filter sections. In addition it is shown that this bound is equal to the rms bound of Sandberg and Kaiser for real roots, and is never more than a factor of two greater than the rms bound for complex roots for second-order filters.
Generation of helical Ince-Gaussian beams: beam-shaping with a liquid crystal display

We review the three types of laser beams - Hermite-Gaussian (HG), Laguerre-Gaussian (LG) and the newly discovered Ince-Gaussian (IG) beams. We discuss the helical forms of the LG and IG beams that consist of linear combinations of the even and odd solutions and form a number of vortices that are useful for optical trapping applications. We discuss how to generate these beams by encoding the desired amplitude and phase onto a single parallel-aligned liquid crystal display (LCD). We introduce a novel interference technique where we generate both the object and reference beams using a single LCD and show the vortex interference patterns.
Silicon-proven ASIC Design for the Polynomial Operations of Fully Homomorphic Encryption

—The proliferation of cloud computing has led to heightened concerns regarding the security and privacy of sensitive data, as their need to be decrypted before processing, renders them susceptible to potential breaches. Fully Homomorphic Encryption (FHE) serves as a countermeasure to this issue by enabling computation to be executed directly on encrypted data. Nevertheless, the execution of FHE is orders of magnitude slower compared to unencrypted computation, thereby impeding its practicality and adoption. Therefore, enhancing the performance of FHE is crucial for its implementation in real-world scenarios. In this study, we elaborate on our endeavors to design, implement, fabricate, and post-silicon validate a co-processor for FHE, named CoFHEE. With a compact design area of 12 mm 2 , CoFHEE features ASIC implementations of fundamental polynomial operations, including polynomial addition and subtraction, Hadamard product, and Number Theoretic Transform, which underlie all higher-level FHE primitives. CoFHEE is capable of natively supporting polynomial degrees of up to n = 2 14 with a coefﬁcient size of 128 bits, and has been fabricated and silicon-veriﬁed using 55nm CMOS technology. To evaluate it, we conduct performance and power experiments on our chip, and compare it to state-of-the-art software implementations and other ASIC designs.
Passive three-colour submillimetre-wave video camera

Stand-off detection for concealed weapons is one of the applications for passive submillimetre-wave imaging. The operating frequency (neglecting technology limitations) is often a compromise between the diffraction-limited angular resolution for a fixed maximum aperture diameter, and the extinction of the signal in obscurant layers: At high frequencies towards the 1 THz mark, excellent angular resolution is readily achievable with modest aperture diameters, while scattering and attenuation by clothing is high which creates potentially more clutter rather than improving detection capability. At lower frequencies towards 100 GHz, attenuation and scattering by clothing is much less pronounced, albeit at significantly reduced spatial definition thanks to increased diffraction. In order to avoid the above-mentioned compromise, we have constructed a three-band passive imaging system operating at effective centre frequencies of 250 GHz, 450 GHz and 720 GHz. Aspects of the system will be presented.
Collisionless dynamics of general non-Fermi liquids from hydrodynamics of emergent conserved quantities

Given the considerable theoretical challenges in understanding strongly coupled metals and non-Fermi liquids, it is valuable to have a framework to understand properties of metals that are universal, in the sense that they must hold in any metal. It has previously been argued that an infinite-dimensional emergent symmetry group is such a property, at least for clean, compressible metals. In this paper, we will show that such an emergent symmetry group has very strong implications for the dynamics of the metal. Specifically, we show that consideration of the hydrodynamics of the associated infinitely many emergent conserved quantities automatically recovers the collisionless Boltzmann equation that governs the dynamics of a Fermi liquid. Therefore, the hydrodynamic prediction is that in the low-temperature, collisionless regime where the emergent conservation laws hold, the dynamics and response to external fields of a general spinless metal will be identical to a Fermi liquid. We discuss some potential limitations to this general statement, including the possibility of non-hydrodynamic modes. We also report some interesting differences in the case of spinful metals.
Unraveling the effects of inter-site Hubbard interactions in spinel Li-ion cathode materials.

Accurate first-principles predictions of the structural, electronic, magnetic, and electrochemical properties of cathode materials can be key in the design of novel efficient Li-ion batteries. Spinel-type cathode materials LixMn2O4 and LixMn1.5Ni0. 5O4 are promising candidates for Li-ion battery technologies, but they present serious challenges when it comes to their first-principles modeling. Here, we use density-functional theory with extended Hubbard functionals-DFT+U+V with on-site U and inter-site V Hubbard interactions-to study the properties of these transition-metal oxides. The Hubbard parameters are computed from first-principles using density-functional perturbation theory. We show that while U is crucial to obtain the right trends in properties of these materials, V is essential for a quantitative description of the structural and electronic properties, as well as the Li-intercalation voltages. This work paves the way for reliable first-principles studies of other families of cathode materials without relying on empirical fitting or calibration procedures.
A Golden Age: Conspiracy Theories' Relationship with Misinformation Outlets, News Media, and the Wider Internet

Do we live in a "Golden Age of Conspiracy Theories? " In the last few decades, conspiracy theories have proliferated on the Internet with some having dangerous real-world consequences. A large contingent of those who participated in the January 6th attack on the US Capitol believed fervently in the QAnon conspiracy theory. In this work, we study the relationships amongst five prominent conspiracy theories (QAnon, COVID, UFO/Aliens, 9/11, and Flat-Earth) and each of their respective relationships to the news media, both mainstream and fringe. Identifying and publishing a set of 755 different conspiracy theory websites dedicated to our five conspiracy theories, we find that each of them often hyperlinks to the same external domains, with COVID and QAnon conspiracy theory websites having the strongest connections. Examining the role of news media, we further find that not only do outlets known for spreading misinformation hyperlink to our set of conspiracy theory websites more often than mainstream websites but this hyperlinking has increased dramatically between 2018 and 2021, with the advent of QAnon and the start of COVID-19 pandemic. Using partial Granger-causality, we uncover several positive correlative relationships between the hyperlinks from misinformation websites and the popularity of conspiracy theory websites, suggesting the prominent role that misinformation news outlets play in popularizing many conspiracy theories.
Towards a semantic approach in GLAM Labs: the case of the Data Foundry at the National Library of Scotland

GLAM organisations have been exploring the benefits of publishing their digital collections in a wide variety of forms since the 2000s. Many institutions, and in particular libraries, have applied the Semantic Web and Linked Data to their main catalogues. Recent advances in technology and innovative approaches concerning the reuse of the digital collections by means of computational access have paved the way for the creation of laboratories within GLAM organisations. In this work, we present a framework that works in several steps to transform into linked open data (LOD), assess and enrich with external repositories the data sets made available by GLAM organisations under open licences. The framework has been applied to three metadata data sets made available by the Data Foundry at the National Library of Scotland obtaining as a result a collection of LOD repositories, a data quality assessment and examples of exploration. The results of this work are publicly available and can be applied to other domains such as digital humanities and data science.
Stability Analysis in Terms of Two Measures for Impulsive Differential Equations

Through the use of the so‐called variational Lyapunov method, which is developed by combining the method of variation of parameters and the Lyapunov second method, stability and instability properties in terms of two measures for impulsive differential equations with variable moments of impulsive effects are discussed. Some stability and instability criteria are established. These theorems, together with an example, show that perturbation and impulsive effects may make a stable system uniformly asymptotically stable or unstable. These results significantly generalize the known ones.
Optimal Decision Making in Robotic Assembly and Other Trial-and-Error Tasks

Uncertainty in perception, actuation, and the environment often require multiple attempts for a robotic task to be successful. We study a class of problems providing (1) low-entropy indicators of terminal success / failure, and (2) unreliable (high-entropy) data to predict the final outcome of an ongoing task. Examples include a robot trying to connect with a charging station, parallel parking, or assembling a tightly-fitting part. The ability to restart after predicting a failure early, versus simply running to failure, can significantly decrease the makespan, that is, the total time to completion, with the drawback of potentially short-cutting an otherwise successful operation. Assuming task running times to be Poisson distributed, and using a Markov Jump Process to capture the dynamics of the underlying Markov Decision Process, we derive a closed-form solution that predicts makespan based on the confusion matrix of the failure predictor. This allows the robot to learn failure prediction in a production environment, and only adopt a preemptive policy when it actually saves time. We demonstrate this approach using a robotic peg-in-hole assembly problem. Failures are predicted by a dilated convolutional network based on force-torque data, showing an average makespan reduction from 101s to 81s ($\mathrm{N}=120,\ \mathrm{p} < 0.05$). We posit that the proposed algorithm generalizes to any robotic behavior with an unambiguous terminal reward, with wide ranging applications on how robots can learn and improve their behaviors in the wild.
Re-embedding data to strengthen recovery guarantees of clustering

We propose a clustering method that involves chaining four known techniques into a pipeline yielding an algorithm with stronger recovery guarantees than any of the four components separately. Given $n$ points in $\mathbb R^d$, the first component of our pipeline, which we call leapfrog distances, is reminiscent of density-based clustering, yielding an $n\times n$ distance matrix. The leapfrog distances are then translated to new embeddings using multidimensional scaling and spectral methods, two other known techniques, yielding new embeddings of the $n$ points in $\mathbb R^{d'}$, where $d'$ satisfies $d'\ll d$ in general. Finally, sum-of-norms (SON) clustering is applied to the re-embedded points. Although the fourth step (SON clustering) can in principle be replaced by any other clustering method, our focus is on provable guarantees of recovery of underlying structure. Therefore, we establish that the re-embedding improves recovery SON clustering, since SON clustering is a well-studied method that already has provable guarantees.
A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization With MGARCH and Small Transaction Costs

We analyze a fixed-point algorithm for reinforcement learning (RL) of optimal portfolio mean-variance preferences in the setting of multivariate generalized autoregressive conditional-heteroskedasticity (MGARCH) with a small penalty on trading. A numerical solution is obtained using a neural network (NN) architecture within a recursive RL loop. A fixed-point theorem proves that NN approximation error has a big-oh bound that we can reduce by increasing the number of NN parameters. The functional form of the trading penalty has a parameter <inline-formula> <tex-math notation="LaTeX">$\epsilon >0$ </tex-math></inline-formula> that controls the magnitude of transaction costs. When <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> is small, we can implement an NN algorithm based on the expansion of the solution in powers of <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>. This expansion has a base term equal to a myopic solution with an explicit form, and a first-order correction term that we compute in the RL loop. Our expansion-based algorithm is stable, allows for fast computation, and outputs a solution that shows positive testing performance.
Uncovering Optimal Attached Eddies in Wall-bounded Turbulence

Townsend's attached eddy hypothesis decomposes the logarithmic layer of high Reynolds number turbulent boundary layers as a field of randomly distributed self-similar eddies that are assumed to be attached to the wall and obey a scaling relationship with the wall-distance. The attached eddy model has emerged as an elegant and successful theory to explain the physics, structure and statistics of the logarithmic layer in wall turbulence. Building on the statistical framework of Woodcock and Marusic (2015), the present work details quantitative results on the structure of the attached eddies and their impact on velocity moment predictions. An inverse problem is posed to infer the ideal eddy contribution function that yields precise first and second order velocity moments obtained from direct numerical simulations. This ideal function is further simplified and is used to guide the proposal of a hairpin-type prototypical eddy which is shown to yield reasonable predictions of the velocity moments. This hairpin-type structure is improved upon by a) solving a constrained optimization problem to infer an idealized eddy shape, and b) inferring the circulation distribution of a hairpin packet. The associated forward and inverse modeling procedure is general enough to serve as a foundation to model the flow beyond the log layer and the codes are open sourced to the community to aid further research.
Tutorial on the Executable ASM Specification of the AB Protocol and Comparison with TLA+

The main aim of this report is to provide an introductory tutorial on the Abstract State Machines (ASM) specification method for software engineering to an audience already familiar with the Temporal Logic of Actions (TLA) method. The report asks to what extent the ASM and TLA methods are complementary in checking specifications against stated requirements and proposes some answers. A second aim is to provide a comparison between different executable frameworks that have been developed for the same specification languages. Thus, the ASM discussion is complemented by executable Corinthian ASM (CASM) and CoreASM models. Similarly, the two TLA specifications presented, which rely on the TLC and Apalache model checkers, respectively, are complemented by a Quint specification, a new language developed by Informal Systems to serve as a user-friendly syntax layer for TLA. For the basis of comparison we use the specification of the Alternating Bit (AB) protocol because it is a simple and well-understood protocol already extensively analysed in the literature. While the models reported here and developed with the two methods are semantically equivalent, ASMs and Quint are better suited for top-down specification from abstract requirements by iterative refinement. TLA seems to be more easily used bottom-up, to build abstractions on top of verified components in spite of the fact that it, too, emphasizes iterative refinement. In the final section, the report begins to scope out the possibility of a homomorphism between the specification of the AB protocol and its finite-state machine (FSM) through state space visualizations, motivated by a search for a formal decomposition method. 1 ar X iv :2 30 1. 10 87 5v 2 [ cs . S E ] 3 1 Ja n 20 23
Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations

In the United States, primary open-angle glaucoma (POAG) is the leading cause of blindness, especially among African American and Hispanic individuals. Deep learning has been widely used to detect POAG using fundus images as its performance is comparable to or even surpasses diagnosis by clinicians. However, human bias in clinical diagnosis may be reflected and amplified in the widely-used deep learning models, thus impacting their performance. Biases may cause (1) underdiagnosis, increasing the risks of delayed or inadequate treatment, and (2) overdiagnosis, which may increase individuals' stress, fear, well-being, and unnecessary/costly treatment. In this study, we examined the underdiagnosis and overdiagnosis when applying deep learning in POAG detection based on the Ocular Hypertension Treatment Study (OHTS) from 22 centers across 16 states in the United States. Our results show that the widely-used deep learning model can underdiagnose or overdiagnose under-served populations. The most underdiagnosed group is female younger (< 60 yrs) group, and the most overdiagnosed group is Black older (≥ 60 yrs) group. Biased diagnosis through traditional deep learning methods may delay disease detection, treatment and create burdens among under-served populations, thereby, raising ethical concerns about using deep learning models in ophthalmology clinics.
AlignGraph: A Group of Generative Models for Graphs

It is challenging for generative models to learn a distribution over graphs because of the lack of permutation invariance: nodes may be ordered arbitrarily across graphs, and standard graph alignment is combinatorial and notoriously expensive. We propose AlignGraph, a group of generative models that combine fast and efficient graph alignment methods with a family of deep generative models that are invariant to node permutations. Our experiments demonstrate that our framework successfully learns graph distributions, outperforming competitors by 25% -560% in relevant performance scores.