Miracle-dz/UniFAR-FTUs huggingface dataset:
On Information Gain and Regret Bounds in Gaussian Process Bandits

Consider the sequential optimization of an expensive to evaluate and possibly non-convex objective function $f$ from noisy feedback, that can be considered as a continuum-armed bandit problem. Upper bounds on the regret performance of several learning algorithms (GP-UCB, GP-TS, and their variants) are known under both a Bayesian (when $f$ is a sample from a Gaussian process (GP)) and a frequentist (when $f$ lives in a reproducing kernel Hilbert space) setting. The regret bounds often rely on the maximal information gain $\gamma_T$ between $T$ observations and the underlying GP (surrogate) model. We provide general bounds on $\gamma_T$ based on the decay rate of the eigenvalues of the GP kernel, whose specialisation for commonly used kernels, improves the existing bounds on $\gamma_T$, and consequently the regret bounds relying on $\gamma_T$ under numerous settings. For the Matern family of kernels, where the lower bounds on $\gamma_T$, and regret under the frequentist setting, are known, our results close a huge polynomial in $T$ gap between the upper and lower bounds (up to logarithmic in $T$ factors).
Detecting Subhalos in Strong Gravitational Lens Images with Image Segmentation

We develop a machine learning model to detect dark substructure (subhalos) within simulated images of strongly lensed galaxies. Using the technique of image segmentation, we turn the task of identifying subhalos into a classification problem where we label each pixel in an image as coming from the main lens, a subhalo within a binned mass range, or neither. Our network is only trained on images with a single smooth lens and either zero or one subhalo near the Einstein ring. On a test set of noiseless simulated images with a single subhalo, the network is able to locate subhalos with a mass of $10^{8} M_{\odot}$ and place them in the correct or adjacent mass bin, effectively detecting them 97% of the time. For this test set, the network detects subhalos down to masses of $10^{6} M_{\odot}$ at 61% accuracy. However, noise limits the sensitivity to light subhalo masses. With 1% noise (with this level of noise, the distribution of signal-to-noise in the image pixels approximates that of images from the Hubble Space Telescope for sources with magnitude $< 20$), a subhalo with mass $10^{8.5}M_{\odot}$ is detected 86% of the time, while subhalos with masses of $10^{8}M_{\odot}$ are only detected 38% of the time. Furthermore, the model is able to generalize to new contexts it has not been trained on, such as locating multiple subhalos with varying masses, subhalos far from the Einstein ring, or more than one large smooth lens.
Certification of quantum measurements

In this report we study certification of quantum measurements, which can be viewed as the extension of quantum hypotheses testing. This extensions involves the study of the input state and the measurement procedure. Here, we will be interested in two-point (binary) certification scheme in which the null alternative hypotheses are single element sets. Our goal is to minimize the probability of the type II error given some fixed statistical significance. In this report, we begin with studying the two-point certification of pure quantum states and unitary channels to later use them to prove our main result, which is the certification of von Neumann measurements in single-shot and parallel scenarios. From our main result follow the conditions when two pure states, unitary operations and von Neumann measurements cannot be distinguished perfectly but still can be certified with a given statistical significance. Moreover, we show the connection between the certification of quantum channels and the notion of $q$-numerical range.
Preparation of alginate hydrogel microparticles using droplet-based microfluidics: a review of methods

This review focuses on a microparticle-producing technique widely used for its efficacy in controlling physicochemical properties: droplet-based microfluidics. The review describes the various strategies applied under this technique, and the size, shape, concentration, stability and mechanical properties of the alginate hydrogel microparticles obtained.
Variational principles of nonlinear magnetoelastostatics and their correspondences

We derive the equations of nonlinear magnetoelastostatics using several variational formulations involving the mechanical deformation and an independent field representing the magnetic component. An equivalence is also discussed, modulo certain boundary integrals or constant integrals, between these formulations using the Legendre transform and properties of Maxwell’s equations. Bifurcation equations based on the second variation are stated for the incremental fields as well for all five variational principles. When the total potential energy is defined over the infinite space surrounding the body, we find that the inclusion of certain terms in the energy principle, associated with the externally applied magnetic field, leads to slight changes in the Maxwell stress tensor and associated boundary conditions. Conversely, when the energy contained in the magnetic field is restricted to finite volumes, we find that there is a correspondence between the discussed formulations and associated expressions of physical entities. In view of a diverse set of boundary data and the nature of externally applied controls in the problems studied in the literature, along with an equally diverse list of variational principles employed in modelling, our analysis emphasises care in the choice of variational principle and unknown fields so that consistency with other choices is also satisfied.
Armandia sampadae, a new species of polychaete (Opheliidae) from Andaman Sea, Northern Indian Ocean

A new species of polychaete, Armandia sampadae was collected from shallow sandy sediments off Rutland Island (57 m depth) and off North Andaman (52 m depth) during a benthic survey along the continental shelf of Andaman and Nicobar Islands. The major character which distinguishes this species from other members of the genus Armandia is the presence of a pair of large, flattened, rounded, stalked, pigmented, leaf-shaped ventral papillae at the ventral base of the ringed anal funnel and the occurrence of a pair of sub-triangular black pigmented spots in the basal portion of the ventral papillae.
Choked accretion onto a Kerr black hole

The choked accretion model consists of a purely hydrodynamical mechanism in which, by setting an equatorial to polar density contrast, a spherically symmetric accretion flow transitions to an inflow-outflow configuration. This scenario has been studied in the case of a (non-rotating) Schwarzschild black hole as central accretor, as well as in the non-relativistic limit. In this article, we generalize these previous works by studying the accretion of a perfect fluid onto a (rotating) Kerr black hole. We first describe the mechanism by using a steady-state, irrotational analytic solution of an ultrarelativistic perfect fluid, using a stiff equation of state. We then use hydrodynamical numerical simulations in order to explore a more general equation of state. Analyzing the effects of the black hole's rotation on the flow, we find in particular that the choked accretion inflow-outflow morphology prevails for all possible values of the black hole's spin parameter, showing the robustness of the model.
On NOMA-Based mmWave Communications

Non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) communication are two promising techniques to increase the system capacity in the fifth-generation (5G) mobile network. The former can achieve high spectral efficiency by modulating the information in power domain and the latter can provide extremely large spectrum resources. Fluctuating two-ray (FTR) channel model has already been proved to accurately agree with the small-scale fading effects in mmWave communications in experiments. In this paper, the performance of NOMA-based communications over FTR channels in mmWave communication systems is investigated in terms of outage probability (OP) and ergodic capacity (EC). Specifically, we consider the scenario that one base station (BS) transmits signals to two users simultaneously under NOMA scheme. The BS and users are all equipped with a single antenna. Two power allocation strategies are considered: the first one is a general (fixed) power allocation scheme under which we derive the OP and EC of NOMA users in closed form; the other one is an optimal power allocation scheme that can achieve the maximum sum rate for the whole system. Under the second scheme, not only the closed-form OP and EC but also the upper and lower bounds of EC are derived. Furthermore, we also derive the asymptotic expression for the OP in high average SNR region to investigate the diversity order under these two schemes. Finally, we show the correctness and accuracy of our derived expressions by Monte-Carlo simulation.
Sustained Online Amplification of COVID-19 Elites in the United States

In the absence of clear, consistent guidelines about the COVID-19 pandemic in the United States, many people use social media to learn about the virus, public health directives, vaccine distribution, and other health information. As people individually sift through a flood of information online, they collectively curate a small set of accounts, known as crowdsourced elites, that receive disproportionate attention for their COVID-19 content. However, these elites are not all created equal: not all accounts have received the same attention during the pandemic, and various demographic and ideological groups have crowdsourced their own elites. Using a mixed-methods approach with a panel of Twitter users in the United States over the first year of the COVID-19 pandemic, we identify COVID-19 crowdsourced elites. We distinguish sustained amplification from episodic amplification and demonstrate that crowdsourced elites vary across demographics with respect to race, geography, and political alignment. Specifically, we show that different subpopulations preferentially amplify elites that are demographically similar to them, and that they crowdsource different types of elite accounts, such as journalists, elected officials, and medical professionals, in different proportions. In light of this variation, we discuss the potential for using the disproportionate online voice of crowdsourced COVID-19 elites to equitably promote public health information and mitigate misinformation across networked publics.
DUMA: Reading Comprehension With Transposition Thinking

Multi-choice Machine Reading Comprehension (MRC) requires models to decide the correct answer from a set of answer options when given a passage and a question. Thus, in addition to a powerful Pre-trained Language Model (PrLM) as an encoder, multi-choice MRC especially relies on a matching network design that is supposed to effectively capture the relationships among the triplet of passage, question, and answers. While the newer and more powerful PrLMs have shown their strengths even without the support from a matching network, we propose a new DUal Multi-head Co-Attention (DUMA) model. It is inspired by the human transposition thinking process solving the multi-choice MRC problem by considering each other’s focus from the standpoint of passage and question. The proposed DUMA has been shown to be effective and is capable of generally promoting PrLMs. Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM, and RACE. Our results show that in terms of powerful PrLMs, DUMA can further boost the models to obtain higher performance.
Role of trophic models and indicators in current marine fisheries management

The previous decade has witnessed a flourishing of studies on how fisheries and marine food webs interact, and how trophic models and indicators can be used for assessment and management purposes. Acknowledging the importance of complex interactions among species, fishermen and the environment has led to a shift from single species to an ecosystem-wide approach in the science supporting fisheries management (e.g. Johannesburg Declaration, Magnuson-Stevens Act). Moreover, fisheries managers today acknowledge that fishing activities are linked to a range of societal benefits and services, and their work is necessarily a multi-objective practice (i.e. ecosystem-based management). We argue that the knowledge accumulated thus far points to tropho-dynamic models and indicators as key tools for such multi-dimensional assessments. Nevertheless, trophodynamic approaches are still underutilised in fisheries management. More specifically, most management decisions continue to rely on single species and sector-specific models. Here we review examples of applications of trophodynamic indicators within fisheries assessments in well-studied ecosystems, and discuss progress made (as well as lack thereof) towards increased integration of these metrics into marine resource management. Having clarified how trophic indicators fit within current policy and management contexts, we propose ways forward to increase their use in view of future management challenges.
Optical Gaze Tracking with Spatially-Sparse Single-Pixel Detectors

Gaze tracking is an essential component of next generation displays for virtual reality and augmented reality applications. Traditional camera-based gaze trackers used in next generation displays are known to be lacking in one or multiple of the following metrics: power consumption, cost, computational complexity, estimation accuracy, latency, and form-factor. We propose the use of discrete photodiodes and light-emitting diodes (LEDs) as an alternative to traditional camera-based gaze tracking approaches while taking all of these metrics into consideration. We begin by developing a rendering-based simulation framework for understanding the relationship between light sources and a virtual model eyeball. Findings from this framework are used for the placement of LEDs and photodiodes. Our first prototype uses a neural network to obtain an average error rate of $2. 67^{\circ}$ at 400 Hz while demanding only 16 mW. By simplifying the implementation to using only LEDs, duplexed as light transceivers, and more minimal machine learning model, namely a light-weight supervised Gaussian process regression algorithm, we show that our second prototype is capable of an average error rate of $1. 57^{\circ}$ at 250 Hz using 800 mW.
Distributed Model Predicted Control of Multi-agent Systems with Applications to Multi-vehicle Cooperation

This paper proposes a distributed model predicted control (DMPC) approach for consensus control of multi-agent systems (MASs) with linear agent dynamics and bounded control input constraints. Within the proposed DMPC framework, each agent exchanges assumed state trajectories with neighbors and solves a local open-loop optimization problem to obtain the optimal control input. In the optimization problem, a discrete-time consensus protocol is introduced into update law design for assumed terminal states, with which asymptotic consensus of assumed terminal states and recursive feasibility are rigorously proved. Together with the optimal cost function, an infinite series of cost-to-go functions is introduced into the design of a Lyapunov function, with which closed-loop asymptotic consensus is finally proved. Two applications including cooperation of autonomous underwater vehicles (AUVs) and connected and automated vehicles (CAVs) are used to validate the effectiveness of the proposed DMPC approach.
Analysis of Random Access in NB-IoT Networks With Three Coverage Enhancement Groups: A Stochastic Geometry Approach

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values and transmission resources. To characterize the RACH performance of the NB-IoT network with three CE groups, this paper develops a novel traffic-aware spatio-temporal model to analyze the RACH success probability, where both the preamble transmission outage and the collision events of each CE group jointly determine the traffic evolution and the RACH success probability. Based on this analytical model, we derive the analytical expression for the RACH success probability of a randomly chosen IoT device in each CE group over multiple time slots with different RACH schemes, including baseline, back-off (BO), access class barring (ACB), and hybrid ACB and BO schemes (ACB&BO). Our results have shown that the RACH success probabilities of the devices in three CE groups outperform that of a single CE group network but not for all the groups, which is affected by the choice of the categorizing parameters.This mathematical model and analytical framework can be applied to evaluate the performance of multiple group users of other networks with spatial separations.
Molecular characterisation of Afghan pistachio accessions by amplified fragment length polymorphisms (AFLPs)

Summary In Afghanistan, pistachio (Pistacia vera L.) is found mainly in the wild in natural forests. In this study, 17 wild Afghan pistachio accessions and three cultivated genotypes were characterised using amplified fragment length polymorphisms (AFLPs). Material was sampled mainly from the Kunduz and Takhar regions. A total of 288 AFLP fragments were generated using eight AFLP primer combinations. The number of amplified fragments varied from 18 – 48 per primer combination, and 136 bands were polymorphic, with an average of 17 polymorphic bands per primer combination. The percentages of polymorphic bands ranged from 26.2 – 79.2 per primer pair. According to UPGMA clustering of the Nei and Li distance matrix, the 17 wild accessions were grouped according to their region of origin and were distinct from the three cultivars. The AFLP technique was found to be effective for characterising wild P. vera material, which was genetically the closest to cultivated pistachio.
Impact of Leakage for Electricity Generation by Pyroelectric Converter

Pyroelectric energy converter is a functional capacitor using pyroelectric material as the dielectric layer. Utilizing the first-order phase transformation of the material, the pyroelectric device can generate adequate electricity within small temperature fluctuations. However, most pyroelectric capacitors are leaking during energy conversion. In this paper, we analyze the thermodynamics of pyroelectric energy conversion with consideration of the electric leakage. Our thermodynamic model is verified by experiments using three phase-transforming ferroelectric materials with different pyroelectric properties and leakage behaviors. We demonstrate that the impact of leakage for electric generation is prominent, and sometimes may be confused with the actual power generation by pyroelectricity. We discover an ideal material candidate, (Ba,Ca)(Ti,Zr,Ce)O$_3$, which exhibits large pyroelectric current and extremely low leakage current. The pyroelectric converter made of this material generates 1.95 $\mu$A/cm$^2$ pyroelectric current density and 0.2 J/cm$^3$ pyroelectric work density even after 1389 thermodynamic conversion cycles.
Image Based Artificial Intelligence in Wound Assessment: A Systematic Review

SIGNIFICANCE Accurately predicting wound healing trajectories is difficult for wound care clinicians due to the complex and dynamic processes involved in wound healing. Wound care teams capture images of wounds during clinical visits generating big data sets over time. Developing novel artificial intelligence (AI) systems can help clinicians diagnose, assess the effectiveness of therapy, and predict healing outcomes. Recent Advances: Rapid developments in computer processing have enabled the development of AI-based systems that can improve the diagnosis and effectiveness of therapy in various clinical specializations. In the past decade, we have witnessed AI revolutionizing all types of medical imaging like X-ray, ultrasound, CT, MRI, etc., but AI-based systems remain to be developed clinically and computationally for high-quality wound care that can result in better patient outcomes. CRITICAL ISSUES In the current standard of care, collecting wound images on every clinical visit, interpreting and archiving the data are cumbersome and time-consuming. Commercial platforms are developed to capture images, perform wound measurements, and provide clinicians with a workflow for diagnosis, but AI-based systems are still in their infancy. This systematic review summarizes the breadth and depth of the most recent and relevant work in intelligent image-based data analysis and system developments for wound assessment. FUTURE DIRECTIONS With increasing availabilities of massive data (wound images, wound-specific electronic health records, etc.) as well as powerful computing resources, AI-based digital platforms will play a significant role in delivering data-driven care to people suffering from debilitating chronic wounds.
Microsoft Word-GBE160647rev.complete.docx

Complete mitochondrion-related organelle (MRO) genomes of several subtypes (STs) of the unicellular stramenopile Blastocystis are presented. Complete conservation of gene content and synteny in gene order is observed across all MRO genomes, comprising 27 protein coding genes, 2 ribosomal RNA genes and 16 transfer RNA (tRNA) genes. Despite the synteny, differences in the degree of overlap between genes was observed between subtypes and also between isolates within the same subtype. Other notable features include unusual base-pairing mismatches in the predicted secondary structures of some tRNAs. Intriguingly, the rps4 gene in some MRO genomes is missing a start codon and, based on phylogenetic relationships among STs, this loss has happened twice independently. One unidentified open reading frame (orf160) is present in all MRO genomes. However, with the exception of ST4 where the feature has been lost secondarily, orf160 contains variously one or two in-frame stop codons. The overall evidence suggests that both the orf160 and rps4 genes are functional in all STs, but how they are expressed remains unclear.
Everyday Examples in Linear Algebra: Individual and Collective Creativity

This paper investigates creativity in students’ constructions of everyday examples about basis in Linear Algebra. We analyze semi-structured interview data with 18 students from the United States and Germany with diverse academic and social backgrounds. Our analysis of creativity in students’ everyday examples is organized into two parts. First, we analyze the range of students’ creative products by investigating the mathematical variability in the more commonly mentioned examples. Second, we unpack some of the collective processes in the construction of students’ examples. We examine how creativity was distributed through the interactions among the student, the interviewers, and other artifacts and ideas. Thus, in addition to contributing to the process vs. product discussion of creativity, our work also adds to the few existing studies that focus on collective mathematical creativity. The paper closes with connections to anti-deficit perspectives in mathematics education and some recommendations for individual and collective creativity in the classroom.